Acta Energetica Electrical Power Engineering Quarterly no. 01/2010

01/2010 number 3/year 2

nergeticaact Electrical Power Engineering Quarterly

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CONTROL OF A DOUBLE-FED MACHINE OPERATING AS A GENERATOR IN A WIND POWER PLANT IN THE EVENT OF VOLTAGE DIPSKrzysztof Blecharz

NATIONAL POWER SYSTEM (KSE) BALANCING BY MEANS OF THE PRIMARY REGULATION PROCESS FOLLOWING THE DISTRIBUTED GENERATION SOURCES REMOVAL; PART I: ALLOCATED OPERATION OF THE NATIONAL POWER SYSTEM (KSE)Krzysztof Dobrzyński

NATIONAL POWER SYSTEM (KSE) BALANCING BY MEANS OF THE PRIMARY REGULATION PROCESS FOLLOWING THE DISPERSED GENERATION SOURCES REMOVAL; PART II: NATIONAL POWER SYSTEM SYNCHRONOUS OPERATION WITH UCTEKrzysztof Dobrzyński

CONTRIBUTION OF WIND FARMS TO VOLTAGE CONTROL IN A DISTRIBUTION GRIDJacek Klucznik

DISTRIBUTED GENERATION IN DEVELOPMENT SCENARIOS OF THE POLISH POWER INDUSTRY BY 2020Henryk Kocot

ROAD TO THE SMART GRIDRobert Masiąg

SUPERCAPACITORS AS ENERGY STORAGE DEVICESAnna Lisowska-OleksiakAndrzej NowakMonika Wilamowska

MODERN PHOTOVOLTAIC POWER STATIONS WITH ENERGY STORAGE SYSTEMS CONNECTED TO POWER GRIDSAntoni DmowskiKamil KompaŁukasz RosłaniecBernard Szymański

We live in a world described with slogans and expressions which are, as a rule, the synonyms for products or actions which do not define them in an accurate way. This also applies to the power industry and power engi-neering. For example, the European Union programmes include the campaign called Intelligent Energy for Eu-rope. Power networks called Smart grids are built. Programmes called Smart metering are implemented. Words like smart, intelligent and also prefixes like bio-, or especially eco-, in different configurations are the identifiersfor the new technologies and products or, which is to say, identifiers or promises regarding safety, decrease incosts and, furthermore, a happy and better future.

Sometimes, expressions like intelligent energy have no real physical meaning. This results from the fact that there is no intelligent energy in the field engineering (physics), as there is no intelligent power, mass, speedor distance.

Despite their common use, these terms are sometimes somewhat vaguely defined. This applies to the termSmart grids where the commonly accepted definition is still to be found. As an example, let us have a look atone of the definitions provided by the European Technology Platform SmartGrids: The smart grids are “electric-ity networks that can intelligently integrate the behaviour and actions of all users connected to it – generators, consumers and those that do both – in order to efficiently deliver sustainable, economic and secure electricitysupplies”. One should pay closer attention to the omnipresent term “intelligent”.

Such terms as Smart metering or Smart grids sometimes promise more, in their popular meaning, than the described systems can really deliver. For example, Smart metering in the currently implemented range means only, or perhaps as much as, a system comprising energy meters ensuring remote communication with a settlement centre and measurement data processing system. In its developed form, i.e. with two-way commu-nication with energy meters, it may also mean a system enabling the system operator to control the power and energy drawn by the recipients. Does it really deserve to be called smart or intelligent? From the technical point of view, the answer is no. For clarity’s sake, one should mention the fact that the term intelligence has not been defined in a commonly accepted and explicit manner, yet. Obviously, the Turing test of an intelligent machine(intelligent system) is not applicable here.

These terms, however, are the compressed essence of modernity that have an extensive social impact, which is apparently their creators’ goal. This is, beyond any doubt, their advantage and strength. However, there is still the real risk of over-interpreting these terms by their recipients, which, as a consequence, may result in disappointment.

As, like goods, all terms and expressions have their expiry dates, one might wonder how such systems will be called in the future when words such as smart and intelligent lose their impact. However, we should put our faith in people in this case, too.

Regardless of the terminology used and our approach to these terms (the digressions provided above must not be treated as a negation but as an observation), power systems have been and will be developed. This development undoubtedly aims at creating distributed systems; however, large energy sources will still be maintained in a relatively long period of time. Some of the numerous contributions to this development are the articles presented in issue 3 of . Enjoy reading.

prof. dr hab. inż. Zbigniew Lubośny Acta Energetica Editor-in-Chief

4

Authors / Biographies

Krzysztof BlecharzGdańsk / Poland

He graduated from the Faculty of Electrical Engineering at the Częstochowa University of Technology (2002). He obtained his PhD degree at the Faculty of Electrical and Control Engineering at the Gdańsk University of Technology (2008). He is currently working as a lecturer at the Depart-ment of High Voltage and Electrical Apparatus at the Gdańsk University of Technology. His main research fields of interest include: electricdrive control, generator units with doubly-fed ma-chines, modern wind power plants, and high-power power electronics converters.

Krzysztof Blecharz / Gdańsk University of Technology

5Control of a Doubly-fed Induction Machine Operating as a Generator

in a Wind Power Plant in the Event of Voltage Dips

Summary

The paper presents the problems related to the me-thods of regulating the power of a doubly-fed induction machine operating as a generator in a wind power plant. It touches upon the problem of generator operation when voltage interferences appear on the power system side in

the form of a voltage dip. The paper presents power regu-lation systems based on asynchronous machine multi-sca-lar models enabling to extend the range of the generator’s continuous operation in the event of a voltage dip on the wind power plant terminal connecting it to the system.

CONTROL OF A DOUBLY-FED INDUCTION MACHINE OPERATING AS A GENERATOR IN A WIND POWER PLANT IN THE EVENT OF VOLTAGE DIPS


1. INTRODUCTION

Modern generator units used currently in wind power plants make it possible to supply power to the power grid in a wide wind turbine changes range. The operation of a machine in a wide range of generator shaft rotatio-nal speed facilitates a decrease in mechanical stresses both on the shaft and mechanical transmission gear and an increase in wind turbine operating efficiency. In high-power wind power plants, slip-ring induction machines are used as generators where the rotor is powered by a power electronics converter facilitating a two-way flow of energy; however, the stator is connected directly to the power grid (fig. 1). In the literature, such solutions are referred to as generator units with a doubly-fed induction machine.

Fig. 1. Wind power plant with a doubly-fed induction machine – propagation of energy with over-synchronous speed

The main advantage differentiating such solutions from others is the power of the converter supplying the rotor being approximately 30% of the nominal power of the whole generator. This is crucial with regard to the costs of constructing the converter in the rotor circumference, taking into account the constantly increasing installed unit power in wind power plants.

The sensitivity to voltage interferences on the stator side is a significant disadvantage of generator units with a doubly-fed induction machine.

The rotor and stator are magnetically coupled, which results in voltage interferences originating in the power system being directly transformed to the rotor side. The voltage interferences have a significant influence on the operation of the converter supplying the generator’s rotor and may result in its being damaged.

�

ir

Pm

Ps

Pr

Pss

Psr

udc

i s

Machineconverter

~- ~

-

ips

Power grid

Grid converter

6

A short-circuit is the most common disturbance in the operation of a power system. It directly results in voltage drops on the transmission grid elements and voltage dips in the system nodes.

The characteristic feature of a doubly-fed induction machine is the poorly attenuated flux oscillation re-sulting from the voltage dip on the stator side. Flux oscillations result directly in the oscillation of power trans-mitted to the system. This is a disadvantageous phenomenon.

The goal of a generator control system in standard operating conditions is to be able to independently regulate the active and reactive power constantly maintaining the generated energy quality parameters [1].

If there is voltage interference on the power grid side, the generator control system should function properly within the range of the construction limits of a converter in the rotor circumference and attenuate the oscillations of the output power transmitted to the system. This facilitates the active operation of a wind power plant in the direction of voltage stabilisation on the terminal connecting it to the power grid through supplying the reactive power.

The regulations provided by transmission grid operators in different countries [2, 3] include guidelines for continuous wind power plant operation when a voltage dip is present.

The fulfilment of conditions imposed by regulations provided by individual operators makes it possibleto maintain a relatively large number of wind turbines in the system. Thus, the risk of generating an additional disturbance or system destabilisation is lower.

The power regulation unit structures presented in the literature may be divided according to the type of control methods used. The largest group of regulation systems includes the solutions based on the Field Orien-ted Control (FOC) technique and systems using the direct torque control (DTC) method.

The smaller group of regulation systems comprises solutions using the non-linear control technique with doubly-fed induction machine multi-scalar models developed at the Gdańsk University of Technology [4].

2. MATHEMATICAL MODEL OF A WIND POWER PLANT SYSTEM

In order to examine the dynamics doubly-fed induction machine control system operation and the genera-tor unit reaction to symmetrical voltage dips on the power system side, a mathematical model of a wind power plant has been developed.

Fig. 2 Wind power plant model diagram

The mathematical model comprises several elements: a simplified model of the power grid, filter model,functional model of a two-way converter in the rotor circumference and the model of a doubly-fed induction machine. In order to describe the dynamics of a doubly-fed induction machine model, vector equations for a mono-harmonic machine have been used – see below [4]:

(1)

�

DFIMG

RgLgRnetLnet

Cf

Cd

is

igi net

idridg

unet

Pgsc Pr

DFIM model two-wayconverter model

model of the power grid and filteron the wind power plant output

� SS SS S a

du R i jd


7

(2)

(3)

(4)

(5)

where: �

S R, – stator and rotor voltage space vectors, � S Ru , u – stator and rotor current space vectors current, �

S Ru , u – stator and rotor voltage space vectors, RS, RR – stator and rotor windings resistance, τ – relative time; ωm – rotor angular velocity; ωu – angular speed of reference system rotationsJ – rotor moment of inertia; m0 – driving torque on the machine shaft.A detailed description of the remaining elements of the mathematical model has been presented, in the

form of differential equations, in paper [5].

3. DOUBLY-FED INDUCTION MACHINE MULTI-SCALAR MODEL

For the purposes of a generator power control system, it is beneficial to use the slip-ring asynchronousmachine multi-scalar model described in paper [6].

The type “z” multi-scalar model of a doubly-fed induction machine is obtained as a result of adopting the va-riables of state depending on the values of the stator stream and rotor current vectors and the angle between these vectors; the variables of state are, however, independent from the co-ordinate system. The variables look as follows:

(6)

(7)

(8)

(9)

By determining the multi-scalar variable derivatives using the equations of the machine vector mathematical model equations (1)-(5), a non-linear differential equations system of the multi-scalar model is obtained [6]:

(10)

(11)

(12)

(13)

where:

(14)

� rR rR R a m

du R i j( )d

� *mS S o

dJ Im i md

� S RS S mL i L i

� R SR R mL i L i

�11 rz

� 12 sx ry sy rxz i i

� 221 Sz

� 22 sx rx sy ryz i i

� 11 m12 0

S

dz L 1z md JL J

� 12 m s m12 11 22 11 21 r1 sf1 si1

V

dz 1 L L Lz z z z z u u ud T w w w

� 21 S S m21 22 sf 2

S S

dz R R L2 z 2 z 2ud L L

� 2 222 S m S m 12 22 S m

22 21 11 12 r2 sf 2 si2V S S 21

dz 1 R L R L z z L Lz z z z u u ud T L w L z w w

� r1 ry sx rx syu u u

Control of a Doubly-fed Induction Machine Operating as a Generator in a Wind Power Plant in the Event of Voltage Dips

8

(15)

(16)

(17)

(18)

(19)

(20)

where:

(21)

(22)

The active and reactive power of a doubly-fed induction machine on the stator side in the generator’s fixedcondition may be expressed as follows by means of adopted multi-scalar variables [6]:

4. DOUBLY-FED INDUCTION MACHINE POWER REGULATION SYSTEMS

For the purposes of control system synthesis, the type “z” multi-scalar model of a doubly-fed induction machine has been used. Different types of controllers may be used in the generator power regulation system. In paper [4], the active and reactive power control has been achieved by means of four cascade-connected PI type controllers, two of which operate the active and reactive power control lane. If PI type controllers are used, it is necessary to ensure the linearization of the machine equations by using the decoupling block [4]. Unfortunately, this control system does not ensure attenuation of the oscillation of power transmitted to the system caused by the voltage dip on the power grid side [5].

In order to improve the control system operation, it has been suggested to provide, on the control loop, a non-linear sliding controller based on the sliding control technique. The control structure diagram is shown in fig. 3 [7]. Using the sliding controller results in low-amplitude and high-frequency oscillations in the regulatedvalues ranges and it is likely that there will be a constant average error value present. This is a characteristic feature of systems featuring sliding controllers caused by the effect of rapid switchovers within the controller structure.

One of the solutions making it possible to limit this phenomenon is to force a sliding movement in an ad-ditional auxiliary feedback loop whose operating rage includes the controlled variables observer. The generator power control system structure based on this algorithmic approach has been presented in fig. 4. The mathema-tical description of the multi-scalar variables dynamics observer has been presented in paper [9].

The synthesis method and the sliding controller internal structure have been shown in paper [7]. In both suggested control systems, the generator shaft speed has been estimated on the basis of the rotor current me-asurement in the rotor co-ordinate system, and next on the basis of the calculations related to the same current in the stator co-ordinate system [4].

� r2 rx sx ry syu u u

� sf1 sy sx sx syu u u

� sf 2 sx sx sy syu u u

� si1 sy rx sx ryu u i u i

� si2 sx rx sy ryu u i u i

� SV 2 2

S r m S S

L wTL R L R w R

�S 12

m

s

LP zL

�S 22

m

s s

L1Q zL L


9

�

Vector controllerTransformation

Grid control system

TransformationSliding controller

Estimation of: rotor angle location, stator flux, multi-scalar model variables, power P and Q

Correction

Correction

ur1

ur2

power grid

DFIM

is��S

us��S

ir��R

z11, z12, z21, z22

usf1, usf2, usi1, usi2

�s��S

�RSest

�RSestK

ur�S ur�S

ur�R

ur�R

Pzad

Qzad

ps

qs

(+)

(+) (-)

(-)

(+)(+)

�

Vector controller

Power grid inverter regulation system

Transformation, correctionSliding controller

Estimation of: rotor angle location, stator flux, multi-scalar model variables, power P and Q

ur1

ur2

power grid

DFIM

is��S

us��S

ir��R

z11, z12, z21, z22 usf1, usf2, usi1, usi2

ur�S ur�S

ur�R

ur�R

Pzad

Qzad

ps

qs

Multi-scalar variables observer

z12

z22

(-)

(-)(+)

(+)�s��S

Transformation, correction

Fig. 3. Diagram of the power control system structure for a doubly-fed induction machine with a sliding controller based on type “z” multi--scalar model dependencies

Fig. 4. Diagram of the power regulation system structure for a doubly-fed induction machine with a sliding controller and multi-scalar variables observer based on type “z” multi-scalar model dependencies


10

5. DFIM ACTIVE AND REACTIVE POWER CONTROL

In the standard condition of a power grid operation where all its parameters are maintained within per-missible ranges [10], the active power in a wind power plant which is transferred to the system is determined on the output of a supervisory power regulation system.

The value of this power depends on the wind power and wind turbine parameters. From the point of view of wind power plant operation efficiency, maximisation of the power obtained from wind is significantly relevant.This issue is discussed in numerous publications [12, 13].

In order to ensure correct and stable operation of the power system, the transmission system operators (in the case of large generating units) require forecasting the active power value which may be transferred from a wind power plant to the system [3].

The Grid Code [10] contains also guidelines referring to the active power change speed on the power plant terminal. The Polish transmission system operator demands that the average active power change gradient during 1 minute does not exceed 30% of the wind farm nominal power and the regulation systems of individual generator units must ensure the active power decreases to the level of at least 20% of the nominal power in less than 2 seconds. As far as smaller generating units are concerned, these requirements are provided individually in connection agreements.

The reactive power regulation on the stator side in the nominal power grid condition may be achieved using two different strategies.

The first strategy entails the machine being magnetised by the rotor current magnetising component andthe reactive power being generated by a machine inverter. A machine supplied in this way does not draw the inductive reactive power from the power grid. The generator operates with a power factor equal to unity. The reactive power value set in the regulation system is zero.

The second strategy requires that the generator can operate by any power factor that is possible to obtain. The reactive power value on the wind power plant generator output is defined by the wind farm operator takinginto account the present value of generated active power and the required voltage level at the point of connec-ting the wind power plant to the system, according to the following dependency [11]:

(23)

where: ΔQPCC means the reactive power value on the generator terminal connecting it to the system in order to ensure the required voltage level. However, QS

max is the maximum reactive power value taking into account the nominal apparent power of the generator and the active power supplied to the system. It is deter-mined as follows:

(24)

According to the transmission power grid operators’ requirements, in standard operating conditions, the wind power plant generator connected to the power system must be able to operate with a power factor in the range from 0.975 of the inductive type to 0.975 of the capacitive type [10], within the full load range.

The change of active and reactive power on the generator output in the broad range of the cos(φ) power factor is related with changes of the generator’s rotor supply voltage. The voltage generated by the converter supplying the machine rotor is the function of active and reactive power set values and the generator shaft slip. The value of this voltage may be expressed by the dependency determined in the vector equation below:

(25)

The dependency (25) has been obtained on the basis of a generator’s vector equations (1) - (5) in the steady state for the synchronously rotating co-ordinate system. Fig. 5 and 6 show the rotor voltage amplitude value depending on the shaft rotational speed and generator’s operating point. The presented diagrams have been obtained on the basis of dependencies and expressions for active and reactive power on the stator side

� zad maxS S PCCQ min Q , Q

� 2 2max max zadS MDZ SQ S P

� 2R R S S mR R

SR S

m m

R jsL R jL sLR jsLu u ijL jL


11

while properly parametrizing the active and reactive power values set in the power control system. The active power value Ps = –0,5 is equal to the generator’s nominal power. However, the reactive power value Qs = 0,7 is equal to the inductive reactive power drawn by the generator’s stator in the condition when the machine rotor is not powered.

Fig. 5. Amplitude of rotor voltage in the function of shaft rotatio-nal speed and active power Ps with the constant reactive power value Qs

Fig. 6. Amplitude of rotor voltage in the function of shaft rotatio-nal speed and reactive power Qs with the constant active power value Ps

The diagrams in fig. 5 and 6 show that it is possible to estimate the reserve of the rotor supply voltagevalue that can be generated by the machine converter depending on the generator operation point. This is par-ticularly relevant taking into account the possibility of continuing constant generator operation during a power grid voltage dip.

Reactive power control on the stator side is important taking into account the method of operation of a doubly-fed induction machine with power grid voltage fluctuations and in case a voltage dip on the power plantterminal occurs. Together with the decrease in the power grid voltage value, the area of the active and reactive power to be generated by the generator [8] also decreases.

6. RESULTS OF SIMULATIONS AND EXPERIMENTS

In order to check the correct operation of suggested control systems and feasibility of the assumed ma-thematical model of a wind power plant system, simulations and experiments have been carried out.

Simulations for operation of individual control systems and generator’s reaction to the symmetrical vol-tage dip on the power grid side have been carried out by a computer program written in C++, in the Borland C++ 4.5 programming package. In order to solve the differential equation, the fourth-order Runge–Kutta me-thod has been used. The simulation program has taken into account the digital character of the control systems operation and the impulse width modulation algorithm, both on the machine and power grid inverter side. All values have been expressed in relative units [4].

The experiments on the wind power plant model have been carried out in a laboratory station whose structure is shown in fig. 7. The examinations included a 2 kW doubly-fed induction machine and a synchronousgenerator (apparent power 20 kVA). The power grid voltage dips have been forced by short-term activation of a symmetrical 3-phase rectifier little R3 resistance. This solution allowed achieving voltage dips in a broad rangeof depth and duration. During the tests, the constancy of the wind power plant shaft rotational speed was as-sumed.

�

0.7 0.8 0.9 1 1.1 1.2 1.30

0.1

0.2

0.3

0.4

0.5

|ur|

��

Qs=-0.3

Qs=0.7

Ps= -0.5

�

0.7 0.8 0.9 1 1.1 1.2 1.30

0.1

0.2

0.3

0.4

0.5

|ur|

��

Ps= -0.2

Ps= -0.5Qs=0.7


12

The operation in the system shown in fig. 7 where the energy is exchanged between the doubly-fed induc-tion machine transmitting the energy obtained on the shaft to a synchronous generator is not favourable, taking into account high power oscillations in the system. Thus, the synchronous generator was loaded with an external R1 3-phase receiver with resistance characteristics. This enabled equalizing the power balance in the system.

The tests for the operation dynamics of the presented regulation systems consisted in forcing step chan-ges of values set in active and reactive power control sloops. The influence of the operation of individual controlsloops on each other and the speed of reaction to power spikes was also assessed. In order to compare the dynamic properties of the tested control systems, all systems underwent the same sequence of changes of set-points. The simulation results are shown in fig. 8 and 9. The short duration of the active and reactive power stepchange sequence resulted from the small time constants of the generator.

The examination of the reactor system reaction to a voltage dip consisted in forcing symmetrical voltage dips (of different depth and duration) on the machine stator side. The ability of the following control systems to attenuate oscillations of the power transmitted to the system and the range of the regulation system correct operation was assessed.

The results of the experiments for dips lasting 200 ms are shown in fig. 10 and 11.

�MDFIMG

thyristor controller

R3R1

R2

M

operator’s console Fig. 7. Laboratory station

structure

a) simulation b)

�

-0.5

0ps

-1

0qs

0

2z11

1.0

1.5

0

1

z21

z22

0 50 100 time [ms]

-0.5

0z12

-0.5

0ps

-1

0qs

0

2z11

1.0

1.5

0

1

z21

z22

-0.5

0z12

0 50 100 time [ms]

Fig. 8. Active power ps and reactive power qs runs on the DFIM stator side and multi-scalar variable runs for a controller model based on type “z” model dependencies with a sliding controller a) and a sliding control and observer b)


13

Fig. 10. DFIM reaction to a voltage dip (duration 200 ms and depth 70%) for a control system based on the type “z” model dependencies with a sliding controller (EXPERIMENT)

�

0 50 100 time [ms]

0

2

0

1

z21

z22

-0.5

0z12

Fig. 9. Runs of multi-scalar variables recreated in the observer; runs as for the event shown in fig. 8b

�

-0.50

0.5ps

-101

qs

0

1ωr

-0.50

0.5z12

-202uab

isc

irb

0 200 400 time [ms]

ubcuca

-0.50

0.5isa

isb

0-202ira

irc


14

Fig. 11. DFIM reaction to a voltage dip (duration 200 ms and depth 60%) for a control system based on the type “z” model dependencies with a sliding controller and observer (EXPERIMENT)

7. SUMMARY

On the basis of the conducted tests, it may be concluded that the developed regulation systems enable independent active and reactive power control on the doubly-fed induction machine stator side. The control systems are have high operation dynamics and the reaction of the control systems to step changes of the set power values, in individual control loop, is very fast. In the event of network voltage dips occurring, the control system ensures the generator’s continuous operation. The range of the generator’s correct operation depends on the maximum permissible voltage on the rotor side which can be generated by the machine converter. Among the examined control systems, the system with a sliding controller and observer has the best properties related to attenuation of active power oscillations.

�

-101

ps

-101

qs

-101

z12

0

2

-202uab

isc

irb

0 200 400 time [ms]

ubcuca

-101isa

isb

0-202ira

irc

ωr


15

REFERENCES

1. Standard: EN 61400-21: 2001 Wind turbine generator systems. Part 21: measurement and assessment of power quality characteristics of network connected wind turbines.

2. Jauch C, Sorensen P , Bak-Jensen B., International Review of Network Connection Requirements for Wind Turbines. Proc. of Nordic Wind Power Conference, 2004.

3. Matevosyan J., Ackermann T, Bolik S., Soder L, Comparison of International Regulations for Connection of Wind Turbines to the Network. Proc. of Nordic Wind Power Conference, 2004.

4. Krzemiński Z., Cyfrowe sterowanie maszynami asynchronicznymi. Wydawnictwo Politechniki Gdańskiej, 2001.5. Blecharz K., Krzemiński Z., Kulesza K., Problemy dostosowana układu sterowania maszyną dwustronnie zasilaną do

nowych wymagań. Modelowanie i Symulacja, Kościelisko, 2004.6. Krzemiński Z., Sensorless Multiscalar Control of Double Fed Machine for Wind Power Generators. Proc. of PCC, Osaka

2002.7. Blecharz K.: Sterowanie ślizgowe maszyną dwustronnie zasilaną. Materiały konferencyjne SENE, Łódź 2005.8. Blecharz K.: Sterowanie maszyną dwustronnie zasilaną pracującą jako generator w elektrowni wiatrowej przy zmia-

nach napięcia sieci zasilającej. PhD dissertation. Gdańsk, 2008.9. Blecharz K., Krzemiński Z., Bogalecka E., Control of a Doubly-Fed Induction Generator in Wind Park during and after

Line-Voltage Distortion. Proc. of Electromotion 2009, Lille.10. PSE Operator SA (2006): Instrukcja ruchu i eksploatacji sieci przesyłowej.11. Ko H.S., Jatskevitch J.: Increase of Fault Ride-Trough Capability for the Network-Connected Wind Farms. Power

Engineering Society General Meeting, 2006.12. Abo-Khalil A., Lee D.-C, Seok J.-K., Variable Speed Wind Power Generation System Based on Fuzzy Logic Control for

Maximum Output Power Tracking. Proc. of 35th Annual IEEE Power Electronics Specialists Conference, Germany, 2004.13. Koutroulis E., Kalaitzakis K., Design of a Maximum Power Tracking System for Wind-Energy-Conversion Applica-

tions, IEEE Transaction on Industrial Electronics, vol. 53, no. 2, 2006.


16


Krzysztof DobrzyńskiGdańsk / Poland

He graduated from the Faculty of Electrical Engineering

at the Warsaw University of Technology in 1999. Since

2005, he has been working as a doctoral student in

the Faculty of Electrical and Control Engineering at the

Gdańsk University of Technology. His areas of interest

include the cooperation of distributed generation sources

with a power system, mathematical modelling, power

system control and intelligent installations in buildings.

Krzysztof Dobrzyński / Gdańsk University of Technology

17National Power System (KSE) Balancing by Means of the Primary Regulation Process

Following the Distributed Generation Sources Removal; Part I: Allocated Operation of the National Power System (KSE)

Abstract

The paper discusses the problem of active power balancing in the National Power System after removing the distributed generation sources. The high saturation with the wind power generation is also assumed, i.e. energy sources most likely to be developed. The paper analyses the active power balancing process by means of primary regulation after removing a given number of distributed energy sources.

NATIONAL POWER SYSTEM (KSE) BALANCING BY MEANS OF THE PRIMARY REGULATION PROCESS FOLLOWING THE DISTRIBUTED GENERATION SOURCES REMOVAL; PART I: ALLOCATED OPERATION OF THE NATIONAL POWER SYSTEM (KSE)


Article developed on the basis of the study within the framework of the Ordered Research ProjectNo. PBZ-MEiN-1/2/2006 “Bezpieczeństwo Elektroenergetyczne Kraju” (National Power Security)

1. INTRODUCTION

In the National Power System (KSE) over 90% of electric energy is generated in conventional power plants. However, an intense interest of investors in distributed generation sources has been observed lately. This applies mostly to wind power generation. It is visible in the number of applications for defining the range of the expert opinion regarding the influence of the connected source to the system submitted to the national transmission operator.

Taking into account the high interest in the wind power generation sector and, consequently, the real de-velopment of these sources, this paper provides the results of a calculation using wind farms (FW) as distributed generation sources.

The power system is subjected to constant variations of power drawn by recipients which, in order to maintain a frequency value close to the nominal value, is balanced by generators operating in the system. De-pending on the deviation of the power drawn in the system and its duration, the following stages of active power balancing in the system are used: primary regulation, secondary regulation and tertiary regulation. The analyses presented below use only the first power balancing stage, namely the primary regulation.

2. NATIONAL POWER SYSTEM (KSE) MODEL AND LOCATIONS OF DISTRIBUTED GENERATION SOURCES

The contribution of a generating unit to the primary regulation means that the unit operates according to the static characteristic of a set slope. Active power regulation, depending on the frequency changes in the sys-tem, is done in the ±5% band. As the frequency in the system constantly fluctuates, the additional characteristic parameter is the regulator’s operation dead band, usually equal to ±20 mHz (frequency variations in this band are treated as standard system operation).

The analyses of the system behaviour following the removal of distributed generation systems have been carried out on the Polish system excluded from synchronous operation with UCTE. The analyses required using correctly modelled generating units participating in the primary regulation process. To this end, the generator model uses a turbine model enabling the generating unit operation within the framework of the primary regula-tion. This model is described in study [3].

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It has been assumed that conventional system power plants participate in the primary regulation process and in the case of selected ones the dead band is 0 mHz, and for the others it is equa ±20 mHz. Fig. 1 shows the power plants participating in the primary regulation. For each power plant the statism value and the regulator dead band value has been assumed.

Fig. 1. Statism values (the first value) and dead band [Hz] (the second value) assumed in individual system power plants1. The map of the transmission network in Poland downloaded downloaded from www.pse-operator.pl

Assuming the defined ±5% band of power available for a given generator within the primary regulationmeans that the system has the defined power that can be used in this process. Having taken into account theabove-mentioned assumptions, in the National Power System model, this power is approximately ±1000 MW. This means that the active power deviation of this value should be regulated in the primary regulation process.

As mentioned before, the analyses use wind farms as distributed generation sources. Thus it has been as-sumed that there are 130 wind farms operating in the National Power System, they have various nominal power and their locations overlap the general investors’ interests. Taking into account the best wind conditions, the largest area of interest is located along the northern coast line where most wind farms operate. The geographi-cally oriented location of individual wind farms has been presented in fig. 2 with the installed power value ina given node2. This figure shows areas where large numbers of wind farms are present. The total farm powervalues in these areas have also been indicated. The figure shows that the installed power in the north part of thesystem exceeds 5000 MW and constitutes over 77% of the power installed in all wind farms (6593 MW).

Moreover, apart from wind farm operation areas, fig. 3 shows the areas where the significant power gener-ated in system power plants is present. This figure shows that the main part of the power generated by systempower plants is located in central and southern Poland.

1 In the Pątnów power plant, one unit operates with statism equal to 0.06 and the remaining three units operate with statism equal to 0.04.2 One should remember that more than one wind farm may be connected to a node. In such a case, the power indicated for the node will be the sum of the wind farms power.


19

Fig. 2. Location and nominal power of wind farms operating in the National Power System 3

Fig. 3. Division of the National Power System into concentrated generation areas

3 The power marked in red shows the connection of a farm (farms) to the 220 kV or 400 kV network..

National Power System (KSE) Balancing by Means of the Primary Regulation Process Following the Distributed Generation Sources Removal;

Part I: Allocated Operation of the National Power System (KSE)

20

3. ASSUMED VARIANTS OF THE DISTRIBUTED GENERATION SOURCES

In Poland, the average annual wind farm operation is at the level of 10-20% of the installed power. Thus it has been assumed that the maximum share of the power generated in wind farms does not exceed 20% of the power generated in the entire system. The analyses take into account four different variants of the level of power generated by wind farms:

• W20 – power generated in wind farms is 20% of the power generated in the system and equals 5226 MW4

• W15 – power generated in wind farms is 15% of the power generated in the system and equals 3920 MW• W10 – power generated in wind farms is 10% of the power generated in the system and equals 2613 MW• W5 – power generated in wind farms is 5% of the power generated in the system and equals 1307 MWIt has also been assumed that the power generated in all wind farms is equal.Connection of a given value of generated power to the system in the form of wind farms means that in

order to maintain the power balance, the power generated in system power plants must be decreased. The W5 and W10 variants provide power generated in selected generating units of system power plants lowered to the level of 80%. However, apart from lowering the power, in variants W15 and W20, selected units have been deac-tivated (the units which are supposed to be deactivated in the next few years).

Excluding a wind farm from operation may be caused by a malfunction (in the farm or in the system) or weather conditions (e.g. wind speed too high or too low). Simultaneous deactivation of all wind farms operating in the system is not very likely. Thus it has been assumed that 25%, 50% or 75% of operating farms are deactivated simultaneously. Additionally, three different sets of deactivated farms have been randomly defined (sets: A, Band C).

As a result of the above assumptions, 36 National Power System balancing variants after sudden deactiva-tion of distributed energy sources have been analysed. Tab. 1 shows total power values deactivated in individual variants.

Tab. 1. Power deactivated in wind farms in individual calculation variants [MW]

Wind farm deactivation variants

25% 50% 75%

A B C A B C A B C

W5 344 273 330 610 698 596 907 959 926

W10 688 546 660 1220 1396 1192 1814 1918 1852

W15 1031 820 991 1829 2095 1788 2720 2877 2778

W20 1375 1093 1321 2439 2793 2384 3627 3836 3704

4. RESULTS OF THE ANALYSES PERFORMED

4.1. POWER VARIATION IN CONVENTIONAL POWER PLANTS PARTICIPATING IN THE PRIMARY REGULATION PROCESS AFTER DEACTIVATING THE DISTRIBUTED GENERATION SOURCES

Deactivation of a selected part of operating wind farms results in the deficit of a reactive power in thesystem equal to the total power of deactivated farms (excluding the power loss change resulting from the change of propagation in the system). During the first few minutes after the interference, the deficit is coveredwithin the primary regulation. The simulation time after the deactivation was 720 seconds, which was sufficientfor reaching the steady state in the system and, at the same time, it was similar to the real time of the primary regulation process operation.

Tab. 2 shows the results obtained for individual variants of wind farm deactivation. The following values are presented in the tab.:

ΣPFW – sum of active power deactivated in wind farms in a given variant;

4 Power generated in the National Power System before connecting the wind farms is 27,072 MW.


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ΣΔPGS – sum of active power additionally generated by conventional power plants participating in the primary regulation process after deactivating selected wind farms of a total value equal to ΣPFW;

Δƒ – frequency deviation in the system after the interference and reaching the steady state.

Tab. 2. Presentation of the power generated within the primary regulation (ΣΔPGS) in system power plants and the frequency deviation in the system (Δƒ) as a response to deactivation of given power in wind farms (ΣPFW) in individual variants

25% 50% 75%

A B C A B C A B C

W5

ΣPFW[MW]

344 273 330 610 698 596 907 959 926

ΣΔPGS[MW]

329.3 248.9 312 596.5 684.8 581.0 915.1 960.3 924.9

Δf[Hz]

-0.060 -0.048 -0.058 -0.101 -0.115 -0.099 -0.155 -0.165 -0.158

W10

ΣPFW[MW]

688 546 660 1220 1396 1192 1814 1918 1852

ΣΔPGS[MW]

638.2 488 605.6 977.1 978 979.9 974.7 979.8 956.9

Δf[Hz]

-0.106 -0.083 -0.101 -0. 472 -0.690 -0.290 -1.860 -2.060 -1.903

W15

ΣPFW[MW]

1031 820 991 1829 2094 1788 2721 2877 2778

ΣΔPGS[MW]

829.6 702,4 827.8 822.1 – 835.5 – – –

Δf[Hz]

-0.296 -0.137 -0.227 -1.934 – -1.766 – – –

W20

ΣPFW[MW]

1375 1093 1321 2440 2792 2384 3628 3836 3704

ΣΔPGS[MW]

841.2 842.1 841.9 – – – – – –

Δf[Hz]

-0.665 -0.209 -0.559 – – – – – –

In selected analysed variants, the wind farm deactivation causes system instability. This applies to 10 out of 36 analysed variants (tab. 2). All these cases are connected with large power deactivation (in relation to the power generated in the system).

The sum of power obtained within primary regulation depends on the non-balanced value (i.e. the value of power deactivated in wind farms) and the number of operational units, i.e. available power.

The units operating within the primary regulation have a limited possibility of balancing the active power shortage in the system. Thus, if the value of deactivated power exceeds the power available in the primary regulation, after the unsteady state the frequency deviation remains and its value depends on the value of the power to be still balanced. In the analysed variants, the frequency deviation value reaches approximately 2 Hz for deactivated power at the level exceeding 1800 MW.

In the case of the systems operating in UCTE, the sudden power shift to 3000 MW is treated as a standard situation. In the case of an independently operating National Power System, the maximum power value ensuring system stability after its deactivation is approximately 1900 MW.



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4.2. CHANGE OF NETWORK ELEMENTS LOAD FOLLOWING THE DISTRIBUTED GENERATIONS SOURCES DEACTIVATION

In Poland, 110 kV networks are managed by Electricity Boards (EB) belonging to power concerns. The transmission among individual EBs is ensured by means of the 110 kV network lines and via the transmission network. The national Power System divides the 110 kV network into sections to achieve optimum propagation and limit the power transmission by bypassing the transmission network. Some connections on the 110 kV net-work level are often disconnected between electricity boards.

5 Location of the nodes does not always relate to their geographical locations , which mostly relates to PGE and ENR0N.

Fig. 4. Change of load (≥5%) in lines connecting individual electricity boards. Red – increased load, green – decreased load. Variant W15 50% A5

One of the analysis elements is the observation of the influence of deactivated wind farms on the loadson the lines connecting individual electricity boards. Deactivation of these farms changes the propagation in the system, which also causes a change of loads in lines connecting individual electricity boards. Fig. 4 shows the results of the changes of loads in the lines connecting individual electricity boards for a given deactivation variant. Lines with increased loads are marked in red and the lines with decreased loads are marked in green. The line load change percentage value has also been given. In addition, the deactivated lines are marked with dotted lines.

In the National Power System, the electric energy is transmitted from a power plant to distribution net-works mostly via highest-voltage transmission networks (220 kV and 400 kV). Fig. 5 shows the transmission network overlapped with the National Power System division into electricity boards.


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Fig. 5. Load change (≥5%) in transmission network lines. Increased loads are expressed in red values and decreased loads are expressed in green values. Variant W15 50% A

The main bulk of installed wind farms is located in the north of Poland. Taking into account the fact that the power generated in this region is higher than the received power, it is transmitted towards the centre of Poland. Deactivation of a large number of wind farms operating in that region results in partial removing of loads between electricity boards belonging to the ENERGA and ENEA Groups (fig. 4). The load increase in thelines connecting electricity boards is observed mainly in the vicinity of classic power plants which increase their generation as a result of their participation in the primary regulation process.

A similar situation is observed in the transmission network (fig. 5). As some system power plants are, as arule, connected to the transmission network, in this situation the deactivation of wind farms is much more visi-ble than in the 110 kV network. The load increase in the low-voltage lines is observed mainly from the Bełchatów power plant level towards the north of the country. Additionally, significant load increase in the low-voltage linesis visible in the 400 kV Dunowo - Krajnik line and 220 kV Żydowo - Piła-Krzewina line.

As a result of connecting the wind farms to the system, some transformers show the change of the power flow direction. The selected transformers for individual variants are shown in tab. 3 together with active powervalues flowing through a given transformer. The values have been defined for an initial variant (before connect-ing the wind farms) and for analysed variants (W5, W10, W15 and W20). The characteristic phenomenon is the change of flow direction in the low-voltage/110 kV/kV transformers operating in northern Poland (e.g. Krajnik[KRA]) station) which no longer supply the 110 kV network but become one of the points of receiving the power generated in the 110 kV network in the region. In addition, deactivation of a defined number of distributed gen-eration sources results in the fact that the transformer loads change towards the value in the initial variant.



24

REFERENCES

Tab. 3. Selected transformers in which, following the connection of wind farms to the National Power System, the power flow direction was changed. (-) by the power value means that the power flows from the To node towards the From node.

From To Initial variant Variant

W5Variant

W10Variant

W15Variant

W20

– – [MW] [MW] [MW] [MW] [MW]

KRA214 KRA114 38.5 -3.6 -26.2 -56.8 -72.1

KRA414 KRA224 -248.0 61.0 12.9 107.1 77.9

PEL412 PEL212 -43.4 26.2 34.1 61.3 67.5

TCN413 TCN113 -1.4 46.5 63.0 114.6 124.4

DUN425 DUN115 129.1 – -62.7 -156.0 -231.5

5. SUMMARYIn the analysed National Power System model excluded from the synchronous operation with UCTE, one

may simultaneously deactivate the distributed generation sources of the total power equal to approximately 1900 MW. Assuming this level, the frequency deviation is approximately 2 Hz. Such a significant frequency change results from insufficient power in the primary regulation. In a real system, this power should be regu-lated within the primary and secondary regulation process.

The performed analyses forecast sudden (simultaneous) deactivation of a defined number of wind farms. This situation is highly unlikely as it relates to a very large area. In reality, within the framework of any wind farm, individual power plants (e.g. due to excessive wind speed) are deactivated within an interval lasting a few minutes. The possibility that wind at the same time will exceed the limit value for wind power plants in the whole system is highly unlikely. Thus the limitation of power that can be generated in wind farms is the abil-ity of the system to supplement the resulting generated power deficit. To a lesser extent, this is related to the primary regulation in which the limited power value is available and, to a larger extent, it is connected with the secondary regulation which should guarantee power supplementing the resulting deficit. This requires ensuring a proper spinning reserve.

The specific character of the geographical location of Poland results in the fact that investors are mainly interested in the northern coastline areas. Currently, almost 80% of the power to be installed in the wind power generation sector concentrated in a poorly developed (as far as the power network is concerned) northern part of the national Power System. This results in problems connected with transmitting power from this region, mainly consisting in overloading individual lines.

1. Lubośny Z., Elektrownie wiatrowe w systemie elektroenergetycznym, WNT, Warsaw, 2006.2. Lubośny Z., Farmy wiatrowe w systemie elektroenergetycznym, WNT, Warsaw, 2009.3. Lubośny Z., Klucznik J., Dobrzyński K., Modele turbin parowych i wodnych. Modele regulatorów turbin parowych i

wodnych, study within the framework of the Ordered Research Project no. PBZ-MEiN-1/2/2006 “National Power Security”, December 2007, Gdańsk.

4. Dobrzyński K., Badanie procesu bilansowania KSE po wypadnięciu źródeł rozproszonych. Bilansowanie KSE bez i z wykorzystaniem wymiany międzynarodowej. Etap 1: Badanie procesu bilansowania KSE po wypadnięciu źródeł rozpro-szonych. Bilansowanie KSE bez wykorzystania wymiany międzynarodowej, study within the framework of the Ordered Research Project no PBZ-MEiN-1/2/2006 “National Power Security”, September 2009, Gdańsk.

5. www.pse-operator.pl.6. Zajczyk R., Modele matematyczne systemu elektroenergetycznego do badań elektroenergetycznych stanów nieust-

alonych i procesów regulacyjnych, Wydawnictwo Politechniki Gdańskiej, Gdańsk, 2003.


26


Krzysztof DobrzyńskiGdańsk / Poland

He graduated from the Faculty of Electrical Engineering at the Warsaw University of Technol-ogy in 1999. Since 2005, he has been working as a doctoral student in the Faculty of Electrical and Control Engineering at the Gdańsk University of Technology. His areas of interest include the cooperation of distributed generation sources with a power system, mathematical modelling, power system control and intelligent installations in buildings.


27National Power System (KSE) Balancing by Means of the Primary Regulation Process

Following the Dispersed Generation Sources Removal; Part II: National Power System Synchronous Operation with UCTE

Abstract

The article is a continuation of the article of the same title but here the National Power System synchro-nous operation with UCTE is analysed. The article analyses the power balancing process after deactivating distributed generation sources within the primary regulation in which generating units from the whole UCTE participate.

NATIONAL POWER SYSTEM (KSE) BALANCING BY MEANS OF THE PRIMARY REGULATION PROCESS FOLLOWING THE DISPERSED GENERATION SOURCES REMOVAL; PART II: NATIONAL POWER SYSTEM SYNCHRONOUS OPERATION WITH UCTE


Article developed on the basis of the study within the framework of the Ordered Research ProjectNo. PBZ-MEiN-1/2/2006 “Bezpieczeństwo Elektroenergetyczne Kraju” (National Power Security)

1. INTRODUCTION

This paper is a continuation of the analysis of the active power balancing in the National Power System after deactivating distributed generation sources but in this case the National Power System operating synchro-nously with UCTE is analysed.

The connection of power systems belonging to European countries within the framework of UCTE is, on the one hand, aimed at enhancing the security of cooperating systems and, on the other hand, ensuring the com-mercial exchange of electric energy. The exchange of the National Power System generated power with related systems is defined in applicable contracts signed by operators. These contracts define the power exchange level on different cross-border connections. However, high saturation with distributed power sources, i.e. wind farms, which are largely dependent on the weather, causes certain problems. One of them is the possibility of deacti-vation, e.g due to excessive wind speed. The loss of the power generated in the system must be supplemented within the first few minutes by generating units which operate within the primary regulation framework. As for the National Power System synchronous operation with UCTE, also units operating in other systems will partici-pate in the regulation process, thus influencing the power transmitted in cross-border connections.

2. NATIONAL POWER SYSTEM (KSE) MODEL AND LOCATIONS OF DISTRIBUTED GENERATION SOURCES

The assumptions concerning the location of distributed energy sources and their power have remained unchanged in comparison with Part I of this paper. So 130 wind farms of various nominal power values (the total installed power in the farms is 6593 MW) have been connected to the National Power System and the vast majority of the sources have been connected in the coastline area of northern Poland.

The cross-border exchange is characterised with the following power import in the western (German) section:

• to the Krajnik station: 496.8 MW• to the Mikułowo station: 430.2 MWand the following export in the southern section:• from the Dobrzeń station: 383.6 MW• from the Wielopole station: 667.1 MW

28

• from the Kopanina station: 156.5 MW• from the Bujaków station: 104.7 MW• from the Krosno-Iskrzynia station: -547.4 MW.In total, the national Power System exports power equal to approximately 932 MW.Fig. 1 shows the distributed generation sources locations and areas characterised by high saturation of

these sources. In addition, the figure shows the values of power generated in individual system power plants andpower on cross-border connections (the analyses have been carried out without the power exchange by means of the DC current connection with Sweden).

Fig. 1. Power installed in wind farms, power generated in system power plants and power on cross-border connections. Initial state

3. ASSUMED VARIANTS OF THE DISTRIBUTED GENERATION SOURCES DEACTIVATION

The analyses regarding the National Power System synchronously operating with UCTE assume the same variants of the percentage share of the wind farms in the total power generation value as in Part I. Only the power values regarding individual percentage shares have been changed1:

• W20 – power generated in wind farms is 20% of the power generated in the system and is equal to 5414 MW2 (approximately 82% of the power generated in farms)

• W15 – power generated in wind farms is 15% of the power generated in the system and equals 4061 MW (approximately 62% of the power generated in farms)

• W10 – power generated in wind farms is 10% of the power generated in the system and equals 2707 MW (approximately 41% of the power generated in farms)

• W5 – power generated in wind farms is 5% of the power generated in the system and equals 1354 MW (approximately 21% of the power generated in farms).

1 The change results from the fact that in the case of the National Power System independent operation selected units have been deactivated in order to balance the power exported during the National Power System synchronous operation with UCTE.2 Power generated in the National Power System before connecting the wind farms is 27,072 MW.


29

The value of power in cross-border connections varies depending on the level of the power generated by wind farms (tab. 1). The most extensive changes are observed in the Krajnik – Vierraden connections where, for variant W20, the power change in comparison with the initial model is approximately 758 MW. This is caused by the vicinity of the connection to the area where a large number of wind farms are present. The least significantpower change is observed in the south of Poland on the two-lane Krosno-Iskrzynia – Lemieszany line.

Tab. 1. Power changes in cross-border lines in individual variants

Station in the National Power System

Station outside the Natio-nal Power System

Initial3W5 W10 W15 W20

Change by ΔP

[MW] [MW] [MW] [MW] [MW]

Bujaków Liskowiec 105 (export) -154 -39 -67 -89

Dobrzeń Albrechtice 384 (export) -35 -36 -23 -20

Kopanina Liskowiec 156 (export) -17 -50 -67 -107

Wielopole Noszowice 667 (export) -46 -132 -157 -225

Krajnik (Lane I)

Vierraden (Lane I)

248 (import) -119 -217 -264 -379

Krajnik (Lane II)

Vierraden (Lane II)

248 (import) -119 -217 -264 -379

Mikułowa (Lane I)

Hagenwerder (Lane I)

218 (import) +49 +61 +71 +133

Mikułowa (Lane II)

Hagenwerder (Lane II)

212 (import) +47 +58 +44 +128

Krosno-Iskrzynia (Lane I)

Lemieszany (Lane I)

274 (export) -6 -11 -18 -9

Krosno-Iskrzynia (Lane II)

Lemieszany (Lane II)

274 (export) -6 -11 -18 -9

In the case of the National Power System operation with UCTE, the distributed generation sources deac-tivation variants also remain unchanged. The power deactivated in the analysed variants have been presented in tab. 2.

Tab. 2. Power deactivated in wind farms in different calculation variants [MW]

Wind farm deactivation variants

25% 50% 75%

A B C A B C A B C

W5 344 273 330 610 698 596 907 959 926

W10 688 546 660 1220 1396 1192 1814 1918 1852

W15 1031 820 991 1829 2095 1788 2720 2877 2778

W20 1375 1093 1321 2439 2793 2384 3627 3836 3704

3 “Initial” means a National Power System model without wind farms.4 “(-)” means the power decrease in relation to the value obtained for the initial model (without wind farms).

National Power System (KSE) Balancing by Means of the Primary Regulation Process Following the Dispersed Generation Sources Removal;

Part II: National Power System Synchronous Operation with UCTE

30

4. RESULTS OF THE ANALYSES PERFORMED

4.1. POWER VARIATION IN CONVENTIONAL POWER PLANTS PARTICIPATING IN THE PRIMARY REGULATION PROCESS AFTER DEACTIVATING THE DISTRIBUTED GENERATION SOURCES

On the one hand, the National Power System synchronous operation with UCTE facilitates electric energy exchange among systems and, on the other hand, enhances the operational safety of connected systems. The reaction to the interference in a give system is similar for all connected systems. During the first few minutes,the resulting power deviation is eliminated by units operating within the primary regulation process. The com-mon synchronous operation of several systems significantly increases the power available within the primaryregulation process (in comparison with a single system). However, the cross-border connections provide some limitations as their number and capacity greatly influence the power value which can be regulated in a givensystem.

The analyses of the National Power System synchronous operation with UCTE include a turbine model tak-ing into account primary regulation [3] and it has been used in selected National Power System units (the same ones as in Part I) and selected generators in the remaining UCTE systems.

The simulation time after the interference equal to 5 minutes has been assumed. The length of this time is sufficient to achieve the steady state in the system. Tab. 3 shows the examples of results obtained for individualcalculation variants and, similarly to Part I, the following values have been provided here:

• PFW – sum of active power deactivated in wind farms in a given variant• PGS – sum of active power additionally generated by conventional power plants in the National Power

System participating in the primary regulation process after deactivating selected wind farms of power equal to ΣP

• ƒ – frequency deviation in the system after the interference and reaching the steady state

Tab. 3. Presentation of the power generated within the primary regulation (ΣΔPGS) in the National Power System power plants and the fre-quency deviation in the system (Δƒ) as a response to deactivation of given power in wind farms (ΣPFW) in individual variants

25% 50% 75%

A B C A B C A B C

W5

ΣPFW[MW]

344 273 330 610 698 596 907 959 926

ΣΔPGS[MW]

124 113 124 230 228 219 322 321 325

Δf[Hz]

-0.029 -0.027 -0.028 -0.043 -0.042 -0.041 -0.055 -0.055 -0.056

W10

ΣPFW[MW]

688 546 660 1220 1396 1192 1814 1918 1852

ΣΔPGS[MW]

239 209 236 477 471 456 684 692 688

Δf[Hz]

-0.044 -0.039 -0.043 -0.078 -0.077 -0.075 -0.107 -0.108 -0.108

W15

ΣPFW[MW]

1031 820 991 1829 2094 1788 2721 2877 2778

ΣΔPGS[MW]

292.21 253.29 287.55 609.66 602.68 582.77 – – –

Δf[Hz]

-0.059 -0.052 -0.058 -0.112 -0.110 -0.107 – – –

W20

ΣPFW[MW]

1375 1093 1321 2440 2792 2384 3628 3836 3704

ΣΔPGS[MW]

353.65 312.59 349.54 749.11 737.72 713.93 – – –

Δf[Hz]

-0.068 -0.061 -0.067 -0.135 -0.133 -0.129 – – –


31

The obtained data shows that in the case of 6 variants of distributed generation sources deactivation, the system loses its stability. All of them are related to deactivation of large power sources.

The UCTE operation assumptions show that sudden deactivation of 3000 MW power is not treated as a malfunction but only as an interference. Thus, deactivation of power of this value should not result in instabil-ity of connected systems. In the analysed UCTE model, the limit value of deactivated power is approximately 2700 MW. The lower value of the power results from the method of representing UCTE in a model which, taking into account the considerable area of the system, includes several simplifications (systems outside the nationalPower System are represented with equivalents).

It is worth noting that the limit value of the power possible to be deactivated is not precisely defined and,to some extent, it depends on the location and power of deactivated sources. This conclusion may be drawn from the fact that after deactivating wind farms of total power equal to 2792 MW in the W20 50% B variant, the system remains stable and in the W15 75% A and W15 75% B variants it decreases power (2721 MW and 2778 MW respectively) causing instability.

The static frequency deviation depending on the variant is approximately from -29 mHz to approximately -135 mHz. Thus, the maximum permissible frequency deviation (±180 mHz assumed in UCTE) has not been exceeded in any variant.

4.2. CHANGE OF NETWORK ELEMENTS LOAD FOLLOWING THE DISTRIBUTED GENERATIONS SOURCES DEACTIVATION

The analysis of the impact of the distributed generation sources deactivation on the power lines may be divided into voltage levels and, consequently, into lines which are managed by electricity boards (110 kV net-works) and the transmission operator (low-voltage networks). As far as the 110 kV networks are concerned, the lines connecting individual electricity boards (EB) have been analysed. Tab. 4 shows (for all variants) the number of lines connecting individual electricity boards where a load change not smaller than 10% has been noted due to the distributed generation sources deactivation. The tab. also includes the transmission network lines.

Tab. 4. Number of lines (110 kV and low-voltage) connecting electricity boards where the load changes by �±10%. In brackets – first item: loadincrease; second item: load decrease

25% 50% 75%

A B C A B C A B C

W5 0 0 02

(1, 1)5

(4, 1)6

(5, 1)11

(8, 3)7

(4, 3)10

(8, 2)

W108

(3, 5)5

(3, 2)5

(2, 3)26

(13, 13)19

(9, 10)23

(10, 13)50

(35, 15)44

(30, 14)57

(40, 17)

W1514

(4, 10)13

(7, 6)12

(2, 10)46

(26, 20)36

(16, 20)43

(23, 20)– – –

W2016

(5, 11)14

(8, 6)21

(3, 18)57

(26, 31)48

(21, 27)52

(27, 25)– – –

The results presented in tab. 4 do not explicitly show that deactivation of a given power level causes a larger number of additionally loaded lines than unloaded lines and vice versa. This results from the fact that for distributed sources utilisation their location in the system is relevant as well as the distance (in the electrical context) of the deactivated source(s) from a given line connecting individual electricity boards.

A similar analysis (as for the lines connecting individual electricity boards) has been provided for network transformers – see tab. 5. It shows that in this case there are many more transformers additionally loaded in the range of ≥10%.



32

Tab. 5. Number of network transformers where the load changes by ≥±10%. In brackets – first item: load increase; second item: load decrease

25% 50% 75%

A B C A B C A B C

W51

(1, 0)1

(1, 0)0

4(4, 0)

6(6, 0)

4(4, 0)

15(15,.0)

15(15, 0)

14(14, 0)

W104

(4, 0)2

(2, 0)2

(2, 0)17

(17, 0)16

(16, 0)14

(14, 0)28

(24, .4)23

(18, 5)27

(22, 5)

W157

(4, 3)5

(4, 1)3

(2, 1)26

(17, 9)21

(14, 7)21

(15, 6)– – –

W2012

(5, 7)8

(5, 3)11

(3, 8)33

(17, 16)33

(16, 17)31

(16, 15)– – –

Fig. 2. Change of load (≥±10%) in lines connecting individual electricity boards. Red – increased load, green – decreased load. Variant W15 50% A

The 110 kV network located in the north of Poland (coastline area), where the majority of distributed generation sources are installed, is managed by two distribution network operators: ENERGA-OPERATOR SA and ENEA Operator Sp. z o.o. These operators coordinate the operation of the 110 kV network located in the electric-ity boards areas whose division is shown in fig. 2. The connections between some electricity boards in northernPoland are sometimes open, which is indicated with a dotted lined in the figure.

In addition, fig. 2 shows the lines connecting individual electricity boards where the load changed by atleast 10%. The majority of unloaded lines (green lines) are located in northern Poland in the area highly satu-rated with distributed sources. The power is supplied (possibly exported) to this area mainly via the transmission network (fig. 3). In variants where large power values are generated in the distributed generation sources, theflow of power from northern to central Poland is observed.


33

Fig. 3. Load change (≥10%) in transmission network lines. Increased loads are expressed in red values and decreased loads are expressed in green values. Variant W15 50%A. National Power System synchronous operation with UCTE

Deactivation of the defined total value generated in distributed sources has a specific impact on transmis-sion lines. On the one hand, it unloads lines which transmit power from the area (in fig. 3, e.g. ZYD-PKW line). Onthe other hand, it provides additional load on lines transmitting the power from power plants to the area where the power loss took place (in fig. 3, e.g. PAT-JAS or KRA-DUN line). The transmission lines in the close vicinity ofsystem power plants are also additionally loaded. This results from the increase in power provided by the power plants within the framework of the primary regulation process (in fig. 3, e.g. ROG-PAB or KOZ-MIL line).

4.3. CHANGE OF CROSS-BORDER LINES LOAD FOLLOWING THE DISTRIBUTED GENERATIONS SOURCES DEACTIVATION

In the National Power System synchronous operation with UCTE, sudden deactivation of a specified num-ber of distributed generation sources results in the fact that, apart from the National Power System power plants, power plants located in UCTE also participate in the generated power loss compensation process. This results in changing the power flowing in cross-border lines where the value of this change depends on the valueof deactivated power in distributed generation sources.

The value of power in cross-border connections varies depending on the level of the power generated by wind farms (tab. 6). The most extensive changes are observed in the Krajnik – Vierraden connections where, for variant W20, the power change in comparison with the initial model is approximately 758 MW. This is caused by the vicinity of the connection to the area where a large number of distributed generation sources are present. The least significant power change is observed in the two-lane Krosno-Iskrzynia – Lemieszany line.



34

Tab. 6. Power changes in cross-border lines in individual variants


Station outside the National

Power System

Initial5W5 W10 W15 W20

Change by P

[MW] [MW] [MW] [MW] [MW]

Bujaków (BUJ213)

Liskowiec (CLIS 21)

105 (export)

-156 -39 -67 -89

Dobrzeń (DBN413)

Albrechtice (CALB_1A)384

(export)-35 -36 -23 -20

Kopanina (KOP223)

Liskowiec (CLIS 21)

156 (export)

-17 -50 -67 -107

Wielopole (WIE413)

Noszowice (CNOS_ll)

667 (export)

-46 -132 -157 -225

Krajnik (KRA214)

Vierraden (D8VIE_21)

248 (import)

-119 -217 -264 -379

Krajnik (KRA224)

Vierraden (D8VIE_21)

248 (import) -119 -217 -264 -379

Mikutowa (MIK414)

Hagenwerder (D8HGWJ1)

218 (import)

+49 +61 +71 +133

Mikutowa (MIK424)

Hagenwerder (D8HGWJ1)

212 (import)

+47 +58 +44 +128

Krosno-Iskrzynia (KRI412)

(Lemieszany) QLEME_1

274 (export)

-6 -11 -18 -9

Krosno-Iskrzynia KRI422

Lemieszany (QLEME_1)

274 (export)

-6 -11 -18 -9

Tab. 7 shows power value changes (including surge values) flowing through cross-border lines obtainedfor one of the analysed variants. The tab. shows that the most significant power change is present in the Ger-man section where, in the two-lane Krajnik-Vierraden line, it reaches the level of approximately 438 MW. One should also note that none of the analysed variants exceeds the long-term permissible load capacity for a given cross-border line.

5 “Initial” means a National Power System model without wind farms.6 “(-)” means the power decrease in relation to the value obtained for the initial model (without wind farms).


35

Tab. 7. Cross-border lines load change. Variant W15 50% A


Station outside the National Power System

Ppocz7 Pust

8 ΔP9 Pudr10 Pmin

11

[%] [%][%]

([MW])[MW] [MW]

Bujaków(BUJ213)

Liskowiec(CLIS__21)

9.2 0.2-9.0

(-37.1)39.3 0.0

Dobrzeń(DBN413)

Albrechtice(CALB__1A)

26.1 20.6-5.5

(-76.2)362.7 223.9

Kopanina(KOP223)

Liskowiec(CLIS__21)

21.4 11.1-10.3

(-42.4)90.3 6.1

Wielopole(WIE413)

Noszowice(CNOS__11)

36.8 24.1-12.7(-176)

516.1 192.3

Krajnik(KRA214)

Vierraden(D8VIE_21)

4.3 54,458.7

(218.9)292.5 2.9

Krajnik(KRA224)

Vierraden(D8VIE_21)

4.3 54.458.7

(218.9)292.5 2.9

Mikułowa(MIK414)

Hagenwerder(D8HGW_11)

20.9 36.015.1

(209.3)624.4 285.6

Mikułowa(MIK424)

Hagenwerder(D8HGW_11)

20.3 34.914.6

(202.4)605.5 276.9

Krosno-Iskrzynia(KRI412)

(Lemieszany)QLEME_1

30.6 19.2-114

(-94.7)256.8 84.9

Krosno-IskrzyniaKRI422

Lemieszany(QLEME_1)

30.6 19.2-114

(-94.7)256.8 84.9

The initial mathematical model shows the power import in the German section and power export in the southern section. The connection of a given number of distributed generation sources to the system may cause a change of the flow direction in the cross-border lines. This applies to the quoted W15 50% A variant where onecan observe the flow direction change in the Krajnik – Vierraden connection. During the operation of the con-nected wind farms, power is exported in this line, which results from the large power generation in wind farms located in northern Poland. Deactivation of the wind farms defined for the W15 50% A variant results in the factthat, after reaching the steady state in the Krajnik – Vierraden line, power is imported at the level of approxi-mately 406 MW in relation to the power export at the level of approximately 32 MW before the interference (fig. 4). Generally, the power value change is observed in all cross-border lines and deactivation of distributedgeneration sources results in the fact that, after the interference, the National Power System imports power at the level of approximately 403 MW in relation to the power export before the interference at the level of ap-proximately 968 MW. Thus, the power exchange difference is approximately 1371 MW. This difference should be balanced by means of the secondary regulation, which requires a spinning reserve of a similar value in power plants operating in the National Power System.

7 P ocz- active power load [%] on the line before the interference.8 Pust-active power load [%] on the line after the interference and entering into the steady state.9 ΔP – active power change in the line caused by deactivation of distributed generation sources calculated as the difference between the steady power and initial power.10 Pudr – maximum active power obtained in the line during the unsteady state.11 Pmin - minimum active power obtained in the line during the unsteady state.



36

Fig. 4. Power flowing in the cross-border lines before and after deactivation of the distributed generation sources in variant W15 50% A

5. SUMMARY

In the analysed UCTE model variants (including the National Power System), the limit power value which does not cause a loss of stability is approximately 2800 MW. This value causes a static frequency deviation at the level of -135 mHz which does not exceed the permissible range of ±180 mHz. In reality, sudden deactivation of (generated or drawn) power in UCTE is not treated as an emergency and should not lead to the loss of stability in related systems. The system instability caused by deactivating lower power is related with certain simplifica-tions used for modelling the system related with the National Power System.

Large power change in cross-border connections appears to be a significant issue which has a significantimpact on the exchange power. One should remember that the power values in these lines are regulated by inter-system agreements so the exchange power agreed upon with other operators must be maintained. The interference consisting in deactivation of a given number of distributed generation sources and, as a result, the loss of a certain generated power value, means that the exchange power must be regulated as soon as possible. This is achieved by means of the secondary regulation and its ability to cover the resulting imbalance depends on the available spinning reserve.

Tab. 6 shows how the power flowing in individual cross-border lines changes. The tab. shows that the mostsignificant change is present in the Krajnik – Vierraden line where in the W20 variant it reaches approximately758 MW. This shows that the connection with the German system, especially via the Krajnik – Vierraden line, is relatively weak. It is currently visible when strong winds blow in northern Germany, which cause power fluctua-tions transmitted by the above-mentioned line to the Polish system. A larger number of wind farms in the north of Poland will probably have an even more detrimental impact on this situation.


37

REFERENCES

1. Lubośny Z., Elektrownie wiatrowe w systemie elektroenergetycznym, WNT, Warsaw, 2006.2. Lubośny Z., Farmy wiatrowe w systemie elektroenergetycznym, WNT, Warsaw, 2009.3. Lubośny Z., Klucznik J., Dobrzyński K., Modele turbin parowych i wodnych. Modele regulatorów turbin parowych i

wodnych, study within the framework of the Ordered Research Project no. PBZ-MEiN-1/2/2006 “National Power Security”, December 2007, Gdańsk.

4. Dobrzyński K., Badanie procesu bilansowania KSE po wypadnięciu źródeł rozproszonych. Bilansowanie KSE bez i z wykorzystaniem wymiany międzynarodowej, study within the framework of the Ordered Research Project no. PBZ-MEiN-1/2/2006 “National Power Security”, September 2009, Gdańsk.

5 www.pse-operator.pl6. Zajczyk R., Modele matematyczne systemu elektroenergetycznego do badań elektroenergetycznych stanów niepusta

lonych i procesów regulacyjnych, Wydawnictwo Politechniki Gdańskiej, Gdańsk, 2003.



38

Authors / Biografphies

Jacek KlucznikGdańsk / Poland

Graduated from the Faculty of Electrical and Control En-

gineering, Gdańsk University of Technology with Master’s

degree in 1999. Five years later completed his doctoral

studies. Works as a Lecturer at the Chair of Electrical

Power Engineering, Gdańsk University of Technology.

Focuses his research on regulation systems of turbines

and generators, wind power and protection systems.

Jacek Klucznik / Gdańsk University of Technology

39Contribution of Wind Farms to Voltage Control in a Distribution Grid

Abstract

The paper presents a concept of utilizing wind farms in a process of voltage control in 110 kV distribution grids. It has been shown that when properly controlled, a wind farm can assist in decreasing voltage variations in a HV grid caused by daily load variations.It has been also shown that

stabilization of HV grid voltage caused by the influence of a wind farm is reflected with decreased frequency of tap changes in HV/MV transformers operating within the same grid and therefore extends lifetime of tap changers.

CONTRIBUTION OF WIND FARMS TO VOLTAGE CONTROL IN A DISTRIBUTION GRID


1. INTRODUCTION

Poland is witnessing a huge interest in the construction of new renewable power generation systems. This interest results from the influence of EU energy policy, aimed at decreasing carbon dioxide emissions, on the lo-cal energy market. Out of various power generation technologies characterized by a limited environmental foot-print, the most popular in terms of new installed capacities is wind power. Total wind power capacity installed in Poland has exceeded 400 MW and keeps rising. Development plans forecast that in quite a short time this value could reach between 4 and even 10 GW, depending on scenario. Wind turbines are grouped into installa-tions of several or several dozen units. Such systems are known as wind farms and are in most cases connected to existing 110 kV power distribution lines or – in the case of large systems – with radial transmission lines to LV/HV substations.

Legal regulations defined by the Grid Code [3] oblige an investor of a wind farm project to ensure con-trol of reactive power generated by the farm. Therefore distributed sources of reactive power with sometimes significant values appear in the power system. This paper deals with the methods of putting this high control potential, created along with wind power development in Poland, into practical use.

2. POSSIBILITY OF CONTROLLING REACTIVE POWER OF A WIND FARM

The process of generating active power in a wind power station is coupled to either generation or con-sumption of reactive power. The possibility of using a wind power plant’s reactive power depends on the type of generator used for conversion of wind energy into electricity. Solutions used in wind power applications include: asynchronous cage generators, asynchronous slip-ring generators with a convertor in rotor winding (double-fed generators) and synchronous generators with frequency converters (converter in stator winding).

Asynchronous cage generators used in smaller plants consume reactive power from the grid when operat-ing. The value of that reactive power is a function of generated active power. It is technically impossible to adjust the reactive power at fixed stator voltage, and active power resulting from current wind speed and propeller blade pitch. Consumed reactive power is in most cases locally compensated with a capacitor bank. Usually up to three control capacitance sections are used at such a bank. A power station is equipped with a controller which connects appropriate sections of the bank to compensate for reactive power consumed by the generator. For small output applications often also uncontrollable capacitor banks are used, dimensioned to compensate reactive power of an idle generator. Therefore asynchronous cage generators cannot be seen as a controllable source of reactive power within a power grid.

Currently there is a tendency to increase specific power of single wind turbines. Designs based on asyn-chronous cage generators have been practically phased out by double-fed asynchronous generators. Control-ling voltage on the rotor side allows adjusting reactive power generated in the stator winding. Modern control

40

systems enable a wide range of reactive power control, both with generation and consumption options. For ex-ample, for the Vestas V90 model the manufacturer states a control range between cosφ = 0.98 (reactive power generation) and cosφ = 0.96 (reactive power consumption) at nominal active power output. When operating at active power below nominal value, it is possible to increase reactive power generation beyond the limits speci-fied above. A scheme of control possibilities for the Vestas V90 turbine is presented in fig. 1.

Fig. 1. For example, for the Vestas V90 model the manufacturer states a control range between (reactive power generation) and cosφ = 0.96 (reactive power consumption) at nominal active power output

The third type of generator used in wind power generation is synchronous generators connected to the grid with an electronic power frequency converter. Depending on the electronic power converter design, such a system can have a potential capability allowing one to control generated reactive power.

As the double-fed generators are most popular in the European power system, this paper analyzes the control strategy for a wind farm composed of turbines with this generator type.

3. AIM OF REACTIVE POWER CONTROL AT A WIND FARM

Ability to control reactive power of a single wind turbine allows one to control reactive power output of the entire wind farm. This ability may potentially be used for accomplishing the following tasks:

• Limiting the influence of wind variability on voltage variations at a farm’s interconnection point with the local grid

• Voltage control in a farm’s neighbourhood in normal and emergency conditions• • Limiting power losses in the distribution grid to which the farm is connected• Limiting voltage variations in the distribution grid caused by load variability• Increasing voltage stability marginThis paper deals with the methodology of reactive power generation control aimed at obtaining a positive

influence on a distribution grid. The proposed strategy for reactive power generation control of a wind farm is based on the voltage criterion, where the superior wind farm regulator determines wind farm’s reactive power generation level based on a voltage measurement at the farm’s interconnection point to the grid. Fig. 2 presents the proposed characteristic curve which should be enforced by the regulator. The characteristic curve presented in fig. 2a is described by the equation:

Limitation

Limitation

Limitation

Limitation

0.98 (Generation)

0.96 (Consumption)


41

(1)

where:Qg – generated reactive power,U – voltage at interconnection point,Uz – voltage setpoint,ku – slope of the characteristic curve defined as:

(2)

A farm control curve is defined by Umax and Umin limiting values at which the farm will operate with extreme values of reactive power QmaxA and QminA. It needs to be noted that limits Qmax and Qmin are not fixed, but ratherdepend on the value of generated active power, which determines the characteristic of a wind turbine – for ex-ample like that presented in fig. 1. This means that while in some circumstances the full reactive control range�

min max,gQ Q Q will be available; in other situations it will be limited to the present minimum and maximum�

min max,g A AQ Q Q .The proposed controller is intended to keep a farm’s reactive power output close to zero as long as the

voltage value at the interconnection point is equal to the setpoint value U. When the actual voltage increase above the required value, the farm is expected to increase reactive power generation in order to increase the voltage. If the voltage drops below nominal value, the farm shifts for reactive power consumption, thus lowering the grid voltage. The required voltage value may be set at a nominal grid voltage (e.g. 110 kV, as assumed for further investigation) or any other value. It is useful to make the voltage setpoint controllable for the owner/op-erator of the grid to which the farm is connected.

The characteristic curve presented in fig. 2b is a modification of the one described above. A dead band of2ε is introduced – it causes wind farm operation without reactive power flow as long as the voltage value at theinterconnection point is close to the setpoint value.

� ( )g z uQ U U k

� max min

max min

A Au

Q QkU U

Fig. 2. Proposed wind farm control characteristics

4. CONTROL ALGORITHM VERIFICATION

This chapter presents an analysis of a wind farm regulator operating according to the proposed concept.Fig. 3 presents a schematic diagram of an example power grid used for further analysis. The system in-

cludes a 110 kV grid fed at two points via 220/110 kV transformers. Further investigation assumes that those transformers operate at constant ratio values, so the voltage in a 110 kV grid depends on power consumption at its nodes and power generation at a wind farm connected at node B14. The investigated wind farm with a rated output of 160 MW consists of 80 Vestas V90 wind turbines rated at 2 MW each, which have a reactive power control characteristic as presented in fig. 1.

�

Qg

U

QmaxAQminA QmaxQmin

Umax

Umin

Uz

�

Qg

U

QmaxAQminA QmaxQmin

Umax

Umin

Uz

2

a) b)

Contribution of Wind Farms to Voltage Control in a Distribution Grid

42

Fig. 3. Schematic diagram of the investigated grid

Generation of both active and reactive power at a wind farm WF affects the value of voltage in the 110 kV grid. Fig. 4 presents the variability of voltage at nodes B4L, B12, B14, B13 and B3L if power generation at the wind farm changes.

When analyzing the presented graphs we can notice that variations of reactive power generation strongly affect voltage values in the investigated 110 kV grid. Changes in reactive power output cause the largest devia-tions of voltage at the interconnection point of the WF (B14) – in the investigated case voltage changes exceed 15%. The smallest influence is observed at a 110 kV grid’s feeding points, i.e. nodes B3L and B4L, although alsothe influence of the wind farm is noticeable. It should be noted that variations of the wind farm’s active powerresult in changed limitations of reactive power output, and limitations of reactive power generation of individual turbines are combined with reactive power losses at the farm’s transformer. Because of those factors at full ac-tive power generation asymmetrical limitations of reactive power are observed – from -48 to 32 MVar.

FW

B14B

B14A

AC Machine

AC Machine

AC Machine


43

Fig. 4. Voltage changes at nodes of the 110 kV grid caused by variation of WF’s output

In real power systems loads on 110 kV grids vary over the course of a day along with a customer’s de-mands. Further analysis assumed that power at all nodes of the 110 kV grid varies according to the presented curve. Differences between the individual nodes are only different maximum power values; variability of the curve however is the same for all of them.

Fig. 5. Example load variability for node B14A

�

-5

0

5

10

15

20

25

30

35

40

0 5 10 15 20

P [M

W],

Q[M

var]

t [h]

Qo - B14A Po - B14A

�

0,850,9

0,951

1,05

-40 -40 -40 -32 -16 0 16 32 40 40 40

U [-

]

Qg [MVar]

Pg = 32 MW

B4L

B12

B14

B13

B3L

�

0,850,9

0,951

1,05

-80 -64 -48 -32 -16 0 16 32 48 64 80

U [-

]

Qg [MVar]

Pg = 96 MW

B4L

B12

B14

B13

B3L

�

0,850,9

0,951

1,051,1

-48 -48 -48 -32 -16 0 16 32 32 32 32

U [-

]

Qg [MVar]

Pg = 160 MW

B4L

B12

B14

B13

B3L


44

During further analysis it has been determined how the voltage values in the 110 kV grid will vary, if the wind power output is constant, when it is zero, and when the reactive power is controlled according to fig. 2. Thefollowing assumptions were made for the wind farm controller: voltage setpoint U = 1 (110 kV), limiting voltage values Umin = 0.95, Umax = 1.05.

Fig. 6 presents analysis results which show how changes of consumed power (according to fig. 5) affectvoltage level at nodes B14 and B12. It was assumed that the wind farm operates at a constant active power output of 64 MW, which is 40% of its installed capacity.

a) b)

Fig. 6. Influence of consumer power variability in the 110 kV grid on the voltage levels at nodes B14 and B12

Voltage curves at nodes B14 and B12 presented in f ig. 6a and 6b indicate that dependency between reac-tive power generated at the wind farm and voltage level results in attenuated voltage variability at nodes of the 110 kV grid. The largest variability attenuation is obtained of course at the interconnection point (node B14), but also in neighbouring nodes, e.g. B12, significant improvement is observed as well – voltage variations caused byload variability decrease considerably. Fig. 6c and 6d show voltage variability as a function of the wind farm’s reactive power, i.e. present realization of the farm’s control characteristic. Blue points present a situation when the farm participates in voltage control and its reactive power is controlled, while red ones – a situation with reactive power kept constant. These graphs allow easily evaluating voltage variability in nodes resulting from load changes in the 110 kV grid. If the farm operates at constant reactive power, voltage variations reach ap-proximately 6% for node B14 and approximately 5% for node B12. Utilizing the farm for voltage control process decreases voltage variations to 2.5% at node B14 and 2% at node B12, which is a very significant improvement.

Transformers installed at switching stations HV/MV are equipped with on-load tap changers allowing adjusting a transformer’s ratio. The tap changer is controlled by the transformer’s voltage regulator, popularly known simply as a transformer regulator. Its task is to maintain a preset voltage value on the MV busbars. Be-cause of the daily power demand variations, the voltage value in the grid varies as well. Changes of voltage in a 110 kV grid are accompanied by changes of voltage at the medium voltage side of all transformers connected to that 110 kV grid. In addition, loading each HV/MV transformer causes additional voltage losses on that transformer, influencing the value of voltage at the power supply for medium voltage grids. Therefore, in order

�

0.98

0.99

1

1.01

1.02

1.03

1.04

1.05

1.06

0 5 10 15 20

U [-

]

t [h]

B14 - Qg = var B14 - Qg = const�

0.99

1

1.01

1.02

1.03

1.04

1.05

0 5 10 15 20U

[-]

t [h]

B12 - Qg = var B12 - Qg = const

�

0.98

0.99

1

1.01

1.02

1.03

1.04

1.05

1.06

-40 -30 -20 -10 0 10 20

U [-

]

Qg [Mvar]

B14 - Qg = var B14 - Qg = const�

0.99

1

1.01

1.02

1.03

1.04

1.05

-40 -30 -20 -10 0 10 20

U [-

]

Qg [Mvar]

B12 - Qg = var B12 - Qg = const

c) d)


45

to keep the voltage at a predetermined level it is necessary to adjust the transformer ratio – this process is ac-complished with on-load tap changers. A tap changer, however, is a device with limited mechanical strength. The permissible number of tap changes for each changer is defined. After it is exceeded, it is required to overhaul thedevice. Usually the permissible frequency of tap changes is 50-60 per day. Tap changer wear is caused mainly by wear of tap contacts. Restoring the good condition of a tap changer requires an expensive maintenance procedure which also requires putting the transformer out of operation. Therefore, it is desirable to limit the frequency (number) of tap changes controlled by transformer regulators.

Voltage stabilization in 110 kV grids positively influences operation of transformer regulators at switchingstations. For example, it has been investigated how the ratio of an HV/MV transformer installed at B14/B14A node changes along with load changes in the 110 kV grid. It was assumed that the transformer regulator is sup-posed to maintain the preset voltage of 1 at MV busbars.

Fig. 7 .Influence of variation of load in the 110 kV grid on operation of the tap changer of a HV/MV transformer for wind farm operation atconstant reactive power and with reactive power control

Fig. 7 presents a comparison of a traditional system, without reactive power control, and a system where reactive power depends on the voltage value measured at the wind farm’s interconnection point. Fig. 7a indi-cates that both investigated variants basically cause similar variability of voltage at transformer MV busbars. However, analysis of curves in fig. 7b reveal that maintaining a quasi-constant voltage level at MV busbars re-quires a higher number of tap changes when the wind farm operates at a constant reactive power value. For the system with active control of reactive power there were 17 tap changes, while for a classic system there were as many as 451.This effect is caused by stabilization of voltage in the 110 kV grid by reactive power generation at the wind farm. This allows limiting the frequency of tap changes at HV/MV transformers connected to the same grid.

Coordination of wind farm regulators with transformer regulators occurs naturally without the necessity to provide any data exchange, as the transformer regulators always act with delay. It is therefore sufficient forthe wind farm reactive power control to be faster than the operation of transformer regulators, to assure that the reactive power control will always be exercised before changing transformer taps.

5. CONCLUSION

The study indicates that wind farms may form a new element of a system controlling voltage levels and re-active power controls in the national power grid. Modern wind turbine designs – particularly those with double-fed asynchronous generators – allow fluent control of generated reactive power. This advantage may and shouldbe used in order to improve quality of voltage control, mainly in 110 kV distribution grids, to which most newly constructed wind farms are connected.

1 Single tap change is understood as a change of transformer ratio by a value corresponding with a single control tap. In the investigated case that value is 1.11%.

a) b)�

0.985

0.99

0.995

1

1.005

1.01

1.015

1.02

0 5 10 15 20

U [-

]

t [h]

B14A - Qg = var B14A - Qg = const�

0.92

0.94

0.96

0.98

1

1.02

1.04

1.06

0 5 10 15 20

teta

[-]

t [h]

teta - Qg = var teta - Qg = const

�


46

It needs to be pointed out, however, that despite the fact that it is not necessary to assure communica-tion with superior control systems or other farms connected in the area, achieving defined regulation targets requires coordination of control systems of all farms and other sources of reactive power by shaping their static characteristics Q = f(U) and speed of operation.

If there are limitations in the possibility of reactive power control by wind farms of low output or equipped with old design wind turbines (asynchronous generators without converters) real results of cooperation between such farms with a power grid may be less satisfactory than those described in the paper.

REFERENCES

1. Lubośny Z., Farmy wiatrowe w systemie elektroenergetycznym, WNT, Warsaw 2009.2. Lubośny Z., Klucznik J., Dobrzyński K., Opracowanie koncepcji wykorzystania farm wiatrowych (FW) w procesie

planowania pracy i prowadzenia ruchu, przy uwzględnieniu możliwości uczestnictwa FW w regulacji parametrów pracy systemu elektroenergetycznego w stanach normalnej i zakłóceniowej pracy KSE oraz określenie sposobu integracji FW w nadrzędnych systemach sterowania i regulacji OSR PSE-Operator, 2007

3. IRiESP - Warunki korzystania, prowadzenia ruchu, eksploatacji i planowania rozwoju sieci v. 1.2. Grid code consoli-dated text effective since 5 November 2007.


48


Henryk KocotGliwice / Poland

Since graduating from the Faculty of Electrical En-

gineering, the Silesian University of Technology in

Gliwice (1989) he has worked at the Institute of Power

Systems and Control at the same university. In 2000,

after obtaining his doctoral degree, appointed Lecturer

- he has worked in this position ever since. Focuses his

research activities in the field of power grids, including

technical and economic analyses of grid operations and

development. Important area of his scientific activity is

energy market development issues, mainly the influence

of transmission fees. Key element here is issues related

to investigating national long term power supply safety

combined with environment protection targets (EU

Climate-and-Energy Package 3 × 20). Another important

field of activity, particularly between 2003 and 2007, was

regular collaboration with Poland’s largest electricity

consumers, related with their presence in the energy

market.

Henryk Kocot / Silesian University of Technology

49Distributed Generation in Development Scenarios of the Polish Power Industry by 2020

Abstract

The paper presents key problems related to devel-opment of the power generation sector in the long-term perspective. Highlighted issues include challenges related to meeting requirements of the EU Climate-and-Energy Package 3 × 20, as well as security and cost of supplying energy to end consumers.

Implementation of the EU package will result in considerable development of distributed power generation. Connecting multiple low-capacity sources to the grid on the one hand causes significant technical problems related to grid operation in a closed structure (so far they have oper-

ated as open), and on the other, due to decreasing distances between generation capacities and consumers, improves technical and economic parameters of grid operation. The paper presents a mathematical model for limitations which must be imposed on power system development scenarios. The influence of distributed generation on the total cost of grid operation and on self-sufficiency of the power system, in the case of the following the scenario with rapid devel-opment of distributed generation (innovative scenario) in comparison to the large-scale generation development scenario (continuation scenario) has also been analysed.

DISTRIBUTED GENERATION IN DEVELOPMENT SCENARIOS OF THE POLISH POWER INDUSTRY BY 2020


INTRODUCTION

Energy security, including security of power supplies, has recently become one of the key priorities of eco-nomic policy, both for Poland, and for the whole European Union. In the nearest future the Polish power industry sector will face a significant challenge. On the one hand, it must keep meeting a growing demand for electricity (despite the economic crisis it is estimated that annual growth in demand for electricity will be maintained at ap-proximately 2%; only in the first years that value might be slightly lower), but on the other most power and heat generation plants require modernization (or total decommissioning). Admittedly, recent declarations of energy utilities and the government mention very rapid and dynamic development of large power plants, both coal-fired and nuclear. On the other hand environmental issues defined by the EU Climate-and-Energy Package 3 × 20 impose significant limitations on the structure of new power generation capacity. The requirement to increase the share of energy generated at renewable sources up to 15% (total share in the electricity, heat and transport fuel markets) encourages individual utilities to become more involved in renewable generation (those projects have already been included in many utilities’ strategies). The increasing share of renewables, as well as that of combined heat and power based on natural gas, has resulted in a qualitative change in the generation structure by significantly increasing the share of distributed generation in the total installed capacity.

In these circumstances it is necessary to define further directions of national energy policy. For the new definition to be rational it is necessary to carry out an analysis of different power industry development sce-narios. Due to the significant impact of renewable sources, such an analysis should also address the demand and production of heat. The analyzed scenarios should then be subjected to identical technical and economic analy-ses, which should answer the question of the strategy for the Polish power industry during the next 10-15 years. Such scenarios are constructed by various scientific establishments [1]. Sometimes, however, they are made according to significantly different rules, which in most cases prevents any direct comparison between them.

This paper proposes the concept of a uniform approach for creating different development scenarios (with a significant share of distributed generation), taking into account various restrictions. Those restrictions result firstly from the necessity to meet the energy demand in a long term perspective (power and heat genera-tion), secondly from the necessity to meet the 3 × 20 targets, and finally from sufficiency of generation capacity, related directly to the security of energy supplies. According to those restrictions it is possible to find solutions permissible for scenarios (compliant with the limitations listed above). Another approach is an attempt to find an optimal solution according to various criteria (different target functions), bearing in mind the restrictions mentioned above. Due to the specific character of the task and close relation between the security of supplies and the energy price, it seems that the best optimization criterion is the energy cost. Nonetheless, it needs to be taken into account that the latter approach is difficult to solve, as it is a case of dynamic optimization [4].

50

PREMISES FOR CONSTRUCTION OF DEVELOPMENT SCENARIOS

In order to build a mathematical model of restrictions and optimized function it is necessary to sort the generation plants into several groups. This division may be more or less detailed, although in any case it should at least include groups like – for power generation – large-scale coal-fired thermal power plants, coal-firedcombined heat and power stations, nuclear plants, small-scale gas plants (fired with natural gas – CHPs andpeak load power plants), wind power, biogas plants (CHPs and possibly also autonomous power sources) and hydroelectric plants. For heat generation the groups should be the already mentioned CHPs and autonomous heat generation plants fired with coal, natural gas and biogas.

Demand for electricity for the investigated period may be presented as:

(1)

where:αA δA – increment of electricity demand in discrete and continuous models respectively; δA= ln (1+αA).Peak power load Pz(t) can be defined analogically as the energy demand, i.e.:

(2)

where the designation of power increments is identical to in the equation (1).The condition of meeting demand for electricity in the year t can be expressed as:

(3)

where:

� tPEi – increase of generation capacity for the i-th group of generation plants at the year (moment) t; �iT – relative equivalent operational time of the installed capacity for the i-th group of generation plants

�

Ei

str

PP

– differential of grid power losses with respect to the capacity increase for the i-th group of genera-tion plants

The condition for generation capacity sufficiency may be formulated in the following way: the probabilityof occurrence of the case where the available power generation capacity PD is lower than the system peak load PZ is lower than permissible value wdop. This condition can be expressed as:

(4)

and

(5)

where:� tPEi 0 – entire trajectory of generation capacity increase steps for the i-th group of plants from

the time 0 to t,

� iAF – availability factor for the i-th group of plants,superscripted W denotes analogical values for the plants decommissioned between the time 0 and t.

Limitations resulting from the 3 × 20 package must take into account all three target markets. After as-suming that the transport fuel market will have a constant share of renewables at 10%, it is necessary to take into account the electricity and heat markets, which are strongly interconnected. The restriction resulting from the share of renewable energy AODN may be expressed as:

� tZ

tPZZ

PePPtP �� 010

� tZ

tAZZ

AeAAtA �� 010

� t

Z

t

iiEi

Ei

strEiZ

AeA dt TtPP

PtPA �00

0

� twtPtPP dopZD

� Wi

WEiiEiDD AFtPAFtPAFPftP ,0,,0,0,0)(


51

(6)

where� tPQj – increase of installed heat generation capacity for the j-th group of plants, though subscripts i

and j in the equation (6) only refer to groups of renewable generation plants.A very important factor influencing the course of the analysis and its result is the method for distributing

targets of the 3 × 20 package over time, which in equation (t) is expressed as the form of the function A(t) [and analogically MCO2(t)]. One option is to only impose restrictions for those parameters for the final year ofthe analysis (target values of the 3 × 20 package). Another variant is to distribute the restriction in a linear or some other predetermined way (e.g. shares of renewable energy in the final electricity consumption as definedby the current legal regulations). This element of the analysis may take into account the national energy policy or – being a result of already completed analysis – propose assumptions for such a policy.

The last limitation related to construction of development scenarios is the availability of funding for gen-eration capacity increase. The funds can be of different origin: private capital, state budget assets and funds indirectly generated by the industry itself. This third group will include for example funds generated by sales of CO2 emission allowances. This restriction is directly connected with the energy policy, but also – in the wider outlook – with the economic policy of the country (one of the elements of anti-crisis measures undertaken by all EU member states as well as USA is incentives for distributed power generation, mainly from renewable sources).

DEFINITION OF COMPARABLE DEVELOPMENT SCENARIOS

A comparative analysis of two scenarios will be presented as an example application of the presented restricted model. The compared scenarios will be the innovative scenario IS (rapid development of renewables, mainly biogas plants) and the continuation scenario CS (development of large coal-fired plants). Those scenarioshave been defined e.g. in [1,7], assuming a 2% increase in electricity demand per year. Their main parametersare as follows:

Continuation scenario:1. Strengthening of the corporate character of the power generation industry.2. Partial lifting of financial effectiveness criteria for the investment projects aimed at improving security

of supplies.3. Focus on development of large generation plants and transmission grid.This scenario will be mainly investigated with respect to item 3 and focus on coal-fired generation.The CS scenario significantly limits development of renewable energy sources as well as gas-fired plants.

It assumes maintaining the share of those segments at the level resulting from ministerial regulations for 2009. In order to meet the requirements of the 3 × 20 package according to this scenario (which can be called a “ne-glect scenario” in reference to the further development of renewable power generation) the share of renewable sources in the heat market would have to amount to 25.4%.

Innovative scenario:1. Intensified usage of existing generation capacities and transmission grids achieved through market

mechanisms (with minimal investments).2. Construction of a 20-percent segment of innovative distributed generation (power generation, heat

generation, transport fuels) and development of energy agriculture.3. System for managing security of supplies based on using capabilities of power generation technologies

to respond to market signals (price increase).4. System of regulations based on reference cost of electricity supplies, assuming full internalization of

external (environmental) costs.

� tA dt TtP dtTtP

PP

tPA ODN

t

jjQj

t

iiEi

Ei

strEiODN

00

0

Distributed Generation in Development Scenarios of the Polish Power Industry by 2020

52

This scenario will be mainly analyzed with respect to items 1 and 2.For this scenario it has been assumed that the share of electricity generated by renewable technolo-

gies will amount to approximately 20% of total consumption, which in turn requires a share of renewable heat generation of 15.4% (with 10% of renewable share in transport fuels). Additionally, it has been assumed that the natural gas-based power generation (yellow energy) will increase to 5% and generation at CHP plants (red en-ergy) to 25% in 2020.

The assumed growth of yellow and red energy share by 2020 is similar to that called for by the current ministerial regulation concerning the development of those segments by 2012. As for renewable technologies, it has been assumed – according to data presented in [5] – that the share of hydroelectricity in this segment will grow only insignificantly, that the capacity installed in wind farms will reach 5000 MW in 2020, and that theenergy generation will amount to 8.5 TWh. Another significant assumption of the analysis of individual scenariosis the structure of the CHP segment. In the innovative scenario the use of renewable technologies is maximized by utilizing primarily biogas cogeneration sources. In the continuation scenario CHPs mainly use hard coal (as before).

Fig. 1 presents electricity generation for the presented scenarios, with respect to “coloured” energy forms.

Fig. 1. Electricity generation for the investigated timeframe a) for IS, b) for CS

COMPARATIVE ANALYSIS OF SCENARIOS

Sufficiency of the systemThe issue of sufficiency of a generation system has been comprehensively discussed in the literature.

A good picture of the Polish situation is presented in the study [2]. Limitation (4) is synonymous with application of the Loss of Load Probability (LOLP) indicator to evaluate sufficiency of the generation system by specificationof the limiting permissible value wdop.

Sufficiency of the generation capacities in the investigated scenarios has been analysed by a simulationfor each year of the analyzed timeframe. Fig. 2 shows the distribution of the generation capacity for 2007 (which was used as a reference year). This distribution was obtained from data for all power generation units installed in the national system and their availability factors AF. A significant feature of this distribution is that it is a normal distribution (appropriate statistical tests were run) with an average value of 27, 413 MW and standard deviation of 913 MW.

�

0.00�

50.00�

100.00�

150.00�

200.00�

250.00�

Rok�

Pozostała�Żółta�Czerwona�Zielona�

�

0.00�

50.00�

100.00�

150.00�

200.00�

250.00�

Rok�

TWh�

Pozostała�Żółta�Czerwona�Zielona�

��

TWh�

a) b)

OtherYellowRedGreen

OtherYellowRedGreen

YearYear


53

Fig. 2. Distribution of available generation capacity in reference year 2007

Generation capacity increments required to meet demand for individual years were calculated according to typical average utilization factors (equivalent operational times) for individual types of sources. The resulting increment values were used to simulate available capacity and obtain distribution of capacities for the following years and two analyzed scenarios.

Tab. 1 presents statistical parameters for the obtained distributions. Fig. 3 presents example distributions for 2020.

Tab. 1. Statistical parameters of obtained distributions for innovative and continuation scenarios in individual years

Year

Innovative scenario Continuation scenario

E(P) (MW)

σ(P) (MW) v E(P)

(MW)σ(P)

(MW) v

2007 27. 413 913 0.0333 27. 413 913 0.0333

2008 27.825 890 0.0320 27.753 925 0.0333

2009 28.446 909 0.0319 28.265 908 0.0321

2010 28.941 915 0.0316 28.700 921 0.0321

2011 29.508 899 0.0305 29.243 951 0.0325

2012 30.148 912 0.0303 29.713 959 0.0323

2013 30.781 927 0.0301 30.149 954 0.0317

2014 31.441 936 0.0298 30.565 966 0.0316

2015 32.014 946 0.0296 30.947 984 0.0318

2016 32.685 963 0.0295 31.523 1008 0.0320

2017 33.229 974 0.0293 31.927 1002 0.0314

2018 33.654 963 0.0286 32.278 1012 0.0314

2019 34.253 949 0.0277 32.943 1040 0.0316

2020 34.784 944 0.0271 33.296 1051 0.0316

�Rok 2007

0,0001 0,0001 0,00520,0159

0,0448

0,0878

0,1574

0,2196 0,2196

0,1491

0,0702

0,02510,0037 0,0014

0

0,05

0,1

0,15

0,2

0,25

(23 376 – 30 628) MW

Moc [MW]

Czę

stoś

ć

Year 2007

Capacity [MW]

Freq

uenc

y


54

Fig. 3. Distribution of available generation capacity in 2020: a) for IS, b) for CS

Fig. 4 presents the average available capacity values and total installed capacity values for individual years of the analysis and development scenarios.

If we calculate the ratio between installed capacity and the average available capacity, its value keeps growing for IS, while for CS it remains at roughly the same level. This means that in order to deliver the same amount of electricity to the grid the innovative scenario requires installation of higher capacities than CS. This is caused by the lower equivalent operational time of renewable sources which dominate in the development of the innovative scenario. It is unquestionably a disadvantage, but on the other hand it positively influences theLoss of Load Probability (LOLP) value.

�

0.0041 0.00440.0194

0.0497

0.0901

0.146

0.179 0.1818

0.1538

0.0978

0.051

0.01780.0039 0.0012

0

0.05

0.1

0.15

0.2

(31 942,5 – 37 618,5) MW

Fre

quency

�Innovative scenario 2020

0.0004 0.0016 0.00530.0176

0.0398

0.0895

0.164

0.2034 0.2081

0.1598

0.0795

0.,02520.0051 0.0007

0

0.05

0.1

0.15

0.2

0.25

(28 790 –36 858) MW

Fre

quency

Innovative scenario 2020

Capacity [MW] Capacity [MW]

a) b)

Fig. 4. Average available capacity and installed capacity

LOLP can be determined from the obtained distribution of available capacity and peak load for a given year. Assuming increments of the peak system load of 1.5% and 2% per year, LOLP values for individual years and for both scenarios have been obtained. Results are presented in tab. 2.

�

Change of installed and average available capacity

20

25

30

35

40

45

50

2007 2009 2011 2013 2015 2017 2019

Year

Installed capacity CS Installed capacity ISAverage available capacity CS Average available capacity IS

Cap

acity

[GW

]


55

Tab. 2. Loss of load probability for 1.5% and 2% increments of peak load increase

Year

Annual load increment 1.5% Annual load increment 2.0%

Peak load (MW)

IS CSPeak load

(MW)IS CS

2007 24.611 1.074-10-3 1.074·103 24.611 1.074·103 1.074·10-3

2008 24.980 6.952·10-4 1.360·103 25.103 1.113·103 2.086·10-3

2009 25.349 3.284-10-4 6.603·104 25.595 8.519·10-4 1.638·10-3

2010 25.718 2.138-10-4 6.023·104 26.088 9.103·10-4 2.284·10-3

2011 26.088 7.112-10-5 4.540·104 26.580 5.631·10-4 2.553·10-3

2012 26.457 2.592-10-5 3429·104 27.072 3.720·10-4 2.944·10-3

2013 26.826 9.930·10-6 2477·104 27.564 2.599·10-4 3.368·10-3

2014 27.195 2.862·10-6 2428·104 28.057 1.500·10-4 4.712·10-3

2015 27.564 1.275·10-6 2.930·104 28.549 1.247·10-4 7405·10-3

2016 27.933 4.016-10-7 1.844·104 29.041 7.716·10-5 6.902·10-3

2017 28.303 2.124·10-7 1492·104 29.533 7.392·10-5 8442·10-3

2018 28.672 1.149·10-7 1.832·104 30.025 8.214·10-5 1.300·10-2

2019 29.041 1.986·10-8 8.774·105 30.518 4.147·10-5 9.857·10-3

2020 29.410 6.249·10-9 1.089·104 31.010 3.196·10-5 1.500·10-2

�

1.00E-09

1.00E-08

1.00E-07

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+002007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Year

LO

LP

va

lue


Fig. 5. Loss of Load Probability values for IS and CS for individual years for the peak load increments a) 1.5%, b) 2.0%

a)

b)�

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+002007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Year

LO

LP

val

ue


It is notable that in each case LOLP values for the innovative scenario are lower than for the continuation scenario. This results primarily from the higher total installed capacity of the system, which results in lowering the probability of simultaneous unscheduled outage of multiple units.


56

When comparing the obtained probability values it is worth noting that at 1.5% load increments for both scenarios LOLPs decrease with time and their values are very low, while for 2% load increments the probability starts to grow for the continuation scenario and its value exceeds 1%, which means that the probability of power shortages in the system is very high. This means that condition (4) is not fulfilled and it is necessary to increasethe capacity installed in the system.

Impact of the distributed generation on grid operationAnalysis of the closed grid operations has been carried out by performing calculations of optimal power

flows (OFP) using MATPOWER software [9]. For winter conditions in 2008-2009 to 2014-2015 optimal powerflows were determined in a basic system (without significant distributed generation connected), and each sys-tem was modified by the connection of small power generation sources to the distribution substations (100/MV). Due to the high level of uncertainty regarding the locations, small sources were connected to the nodes in a random way. For each year 100 various source locations were simulated and the grid operational parameters were determined from statistical analysis of the obtained results. The primary indicator used to describe grid operation is the grid surplus (NS). This single value allows evaluating losses and limitations in the grid and thus compare various operational conditions of the grid [10]. The higher the NS value, the poorer the grid condition is (higher costs of losses and/or limitations). Tab. 3 presents the obtained values of specific grid surplus (in refer-ence to consumed energy) for individual years, in systems without and with distributed generation connected to the grid. Values given for systems with distributed generation are average values obtained as a result of analysis of random locations of distributed sources. Results of NS calculations are also presented in fig. 6.

Tab. 3. Specific grid surplus (PLN/MWh) in a basic grid during winter without and with distributed generation

Season Without distributed generation With distributed generation

2008–2009 8.86 6.34

2009–2010 12.61 5.90

2010–2011 11.82 4.77

2011–2012 9.75 3.63

2012–2013 11.08 5.87

2013–2014 11.48 6.18

2014–2015 15.32 5.79

It needs to be pointed out that average grid surplus values after taking into account distributed genera-tion are for each year much lower than those for cases without distributed plants. This means that small plants distributed all over the analyzed area will positively influence grid operations (decreased grid losses and limi-tations in branch flow capacity). Apart from average values, NS distribution for individual random locations ofdistributed plants is also important. Fig. 7 presents example NS values obtained for two different years. What is distinctive about those histograms is that there are some values which exceed a corresponding value obtained for the system without distributed generation. This allows drawing a conclusion that not every possible distribu-tion of small plants (not every location) positively influences grid operation. As the load grows, the shape of theNS distribution becomes more favourable, i.e. higher number of location distributions has NS lower than the ba-sic system. This means that particularly during the later years, distributed generation can significantly improvethe quality of grid operation.


57

Fig. 6. Specific grid surplus values for grid operations without and with distributed sources

�

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

��

Fig. 7. NS distribution for the system a) 2008-2009, b) 2011-2012

If we statistically analyze the grid surplus values, we can see that the values for systems without distrib-uted generation are higher than the expected value of that surplus for a system with distributed generation plus double the value of standard deviation for the season 2008-2009, and those three values in seasons 2011-2012 and 2014-2015. This means that the probability of the situation in which the distributed generation adversely affects grid operations is very low (although not zero). Apart from analysing NS it is also important to analyze grid operational limitations. The value of a single limitation (its impact on total transmission cost and grid sur-plus) is characterized by a Lagrange multiplier µ for a branch flow, resulting from OPF optimization task. For a base system in the winter 2008-2009 limitations occurred on five 110 kV lines, and Lagrange multipliers for those limitations ranged from 60.7 to 574.2 PLN/MWh. Tab. 4 presents all values of Lagrange multipliers for the lines where limitations were reached without taking into account distributed generation and after its connec-tion. In the case with distributed sources expected values E(μ) and standard deviations (μ) are given.

a) b)

Freq

uenc

y

AverageStd. Dev.

AverageStd. Dev.


58

Tab. 4. Lagrange multipliers (PLN/MWh) on lines with limitations for the system of winter 2008-2009

LineNo distrib.gen. With distributed gen.

μ E(μ) σ(μ)

Line 1 574.2 497.2 170.2

Line 2 60.7 28.7 7.4

Line 3 207.1 39.8 81.7

Line 4 371.2 266.7 109.3

Line 5 499.7 273.8 215.2

It needs to be added that in many cases after introduction of distributed generation the limitations were totally removed (μ = 0). This is another argument highlighting the importance of selecting an appropriate loca-tion at least for some distributed power generation plants. Tab. 5 shows the variability of the Lagrange multiplier for a single line, for which in base variants (without distributed sources) limitations always occurred.

Tab. 5. Lagrange multipliers (PLN/MWh) for a selected line in individual years of the analysis

SeasonNo distrib.gen. With distributed gen.

μ E(μ) σ(μ)

2008–2009 574.2 497.2 170.2

2009–2010 657.7 276.7 257.6

2010–2011 614.6 361.9 215.0

2011–2012 582.1 345.2 210.7

2012–2013 555,2 323.0 223.9

2013–2014 576.0 293.0 201.7

2014–2015 764.8 293.9 192.1

Also in this case benefits from the introduction of distributed generation keep growing with the capacityof the distributed sources. Statistically, just as in the case of the grid surplus, it can be said that the probability that values of Lagrange multipliers for a line with limitations will be higher for a system with distributed genera-tion than for a system without distributed sources, is slim.

CONCLUSION

The presented, mathematically formalized model for development of power generation capacity enables an analysis of power system development which takes into account technical and environmental limitations. This model allows one to coherently evaluate various development scenarios with respect to technical (security of supplies) and economic factors.

While defining analyzed development scenarios it has been clearly assumed that those were extremecases, which are unlikely to be fully realized, and that development of the power generation sector resulting from changes in the transmission and distribution sectors will be a combination of both those cases. This how-ever does not invalidate the key conclusion that the presented results of comparative analyses reveal a higher effectiveness of the innovative scenario.

Practical implementation of the innovative scenario depends on the introduction of changes in regulations as proposed in [3,11]. Only adoption of full internalization of external cost (or appropriate calibration of certifi-cates) along with introduction of location signals into the transmission fees will allow achieving full economic effectiveness for the proposed solution in the development of power generation capacity in the country. And the economic effectiveness is the only driver which can encourage investors to construct local power generation sources.


59

REFERENCES

The presented results of comparative analyses for two scenarios highlight the complexity of the issue and the necessity to take into account various aspects when making decisions concerning energy policy. Key aspects here are sufficiency of the system (measurement of the security of supplies) and cost parameters – investment costs (availability of funding for development) and cost of supplying energy to consumers. Another important factor is taking into account operation of the power grids, not only electricity generation costs. Valuation of loss-es and mainly limitations through grid surplus values allows one to coherently compare various grid topologies and operational conditions. It is also worth noting that grid losses [addressed in the equation (3)] significantly influence the quantitative share of renewable energy, as according to the definitions given by EU documents they are counted as consumed energy (shares of renewable energy should also be calculated for the grid losses and power plants’ own consumption). Cost of meeting requirements of the EU 3 × 20 package, which addition-ally increases interdependency of electricity, heat and transport fuel markets, becomes an important issue.

This paper is based on the results obtained during the author’s participation in the state-funded Research Project “Bezpieczeństwo Elektroenergetyczne Kraju” (“National Energy Security”) (PBZ MEiN 1/2/2006), carried out by a consortium formed by Gdańsk University of Technology, Warsaw University of Technology and Wrocław University of Technology.

1. Proceedings of the conference: Stabilizacja bezpieczeństwa energetycznego Polski w okresie 2008-2020 (z uwzględnieniem perspektywy 2050) za pomocą mechanizmów rynkowych (ekonomiki) i innowacyjnych technologii - różne scenariusze rozwojowe energetyki, Conference KPE PAN, Warsaw - Serock 16-June 2008.

2. Paska J.: Ocena niezawodności podsystemu wytwórczego systemu elektroenergetycznego. Prace Naukowe Elektryka z. 120, Oficyna Wydawnicza Politechniki Warszawskiej, Warsaw 2002.

3. Bezpieczeństwo elektroenergetyczne w społeczeństwie postprzemysłowym na przykładzie Polski. Monograph edited by J. Popczyk, Wydawnictwa Politechniki Śląskiej, Gliwice 2009.

4. Górecki H., Optymalizacja systemów dynamicznych, Biblioteka Naukowa Inżyniera, PWN 1993.5. Report from the task 1.2.1A Bezpieczeństwo strategiczne - w horyzoncie wieloletnim - związane z inwestycjami, Re-

search Grant No PBZ-MEiN 1/2/2006, “Bezpieczeństwo Elektroenergetyczne Kraju” , Gdańsk - Gliwice, January 2008.6. Kocot H., Korab R., Ceny referencyjne dla wybranych technologii elektroenergetycznych, Proceedings of the REE ’07

Conference, Kazimierz Dolny, May 2007 r.7. Kocot H., Wpływ scenariusza rozwoju elektroenergetyki na koszty dostawy energii do odbiorcy w świetle wymagań

środowiskowych do 2020 r., Przegląd Elektrotechniczny 3/ 2009, p. 164-1678. Kocot H., Bezpieczeństwo elektroenergetyczne kraju w horyzoncie wieloletnim (związane z inwestycjami), proceed-

ings of APE ’09 Conference, Jurata, June 2009 r.9. Zimmerman R., Murillo-Sánchez Carlos E., MATPOWER - a MATLAB Power System Simulation Package. Version 3.2,

Cornell University, September 200710. Kocot. H., Korab R., Siwy E., Żmuda K., Wykorzystanie krótkookresowych kosztów krańcowych w działalności op-

eratorów sieciowych na rynku energii, Przegląd Elektrotechniczny 9/2004.11. Popczyk J., Innowacyjna energetyka. Kontekst ekologiczno-energetyczny i ekonomiczno-cywilizacyjny, Acta Ener-

getica 1/2009.


60


Robert MasiągENERGA-OPERATOR SA

He studied Electrical Engineering at the Faculty of

Electrical Engineering at the Lublin University of Technol-

ogy. He graduated in the following specialties: electric

energy processing and utilisation and IT application in

engineering. Having graduated from the university, he

started working for a large telecommunications company

where he was responsible for telecommunications and

IT infrastructure management. He managed special-

ist teams with a view to implementing extensive and

complex projects. The largest project he was involved in

consisted in developing the concept of constructing and

implementing a central data collection system operating

billing records from over 10 million customers. Lately, he

has been responsible for the simultaneous development

of several IT systems in the field of billing data collection

and inter-operator settlements. He has been working

in ENERGA--OPERATOR SA as the BoM representative

for smart grid implementation and his range of duties

includes supervision over the design works related to

implementation of the AMI system constituting the basis

for constructing the Smart Grid.

Robert Masiąg / ENERGA-OPERATOR SA

61Road to the Smart Grid

Abstract

The AMI (Advanced Metering Infrastructure) sys-tem is one of the enterprises undertaken within the frame-work of the tasks resulting from the ENERGA-OPERATOR S A strategy. The main goals of the AMI system implementa-tion include energy supply quality improvement, loss ma-

nagement process enhancement and making distributed electric energy generation more common. The AMI system implementation offers new operation possibilities for in-frastructure management, metering management and cu-stomer service.

ROAD TO THE SMART GRID


Within the framework of the AMI project, industrial and municipal recipients will be equipped with mea-suring devices facilitating automatic data readouts. The above-mentioned devices will ensure two-way commu-nication. The vital elements of the project will be the construction of the central IT system and provision of the telecommunications infrastructure used for measurement data acquisition and management.

Taking into account the innovation and large scale of this undertaking, numerous technical and organisa-tional issues will have to be dealt with. Selection of correct technologies, implemented environment architec-ture, implementation model and logistics for exchanging and installing over three million meters are the main challenges to face. Another significant challenge is to effectively manage a large project team and collaborate with external contractors.

A dedicated team has been established in ENERGA-OPERATOR SA which consists of specialists having ex-tensive knowledge in project management and the required technical expertise. The project will use the experi-ences gained during similar implementation processes undertaken in other countries and pilot implementations undertaken by ENERGA-OPERATOR SA.

1. WHY WE ARE UNDERTAKING THE AMI PROJECT: ENERGA-OPERATOR SA STRATEGIC GOALS

The aim of constructing the AMI (Advanced Metering Infrastructure) system is to ensure the achievement of ENERGA-OPERATOR’s strategic goals:

• enhanced reliability and quality of the electricity supply process • customer service quality improvement• adapting the company’s business and organisational model to present and future conditions• better operating efficiency in order to provide more development possibilities

The achievement of the ENERGA-OPERATOR’s strategic goals, made possible, among others, by the AMI implementation (fig. 1) is consistent with the following Energa SA Group’s strategy:

• achieving the leading position in the field of constructing distributed, particularly, renewable, energy generation sources

• creating a model for cooperation with end users – an energy recipient may also become a co-generator and provider of services offered by the Group

62

Fig. 1. AMI system implementation goals

The necessity for the AMI project implementation also results from numerous legal regulations which, consequently, have an influence on the method of this implementation. The system must be planned and con-tracted on the basis of numerous guidelines resulting, among other things, from the Polish energy policy, Trade Metrology Act, ordinances of the Minister of Economy and the European Parliament directives.

The project will take into account conclusions resulting from the close cooperation with the Energy Regu-latory Authority and PSE Operator responsible for coordination of activities related to establishing the Indepen-dent Measuring Operator / creating the Central Data Repository.

The project implementation will result in various benefits in different areas of ENERGA-OPERATOR SA operation, including enhanced energy losses monitoring, which will allow optimising the losses. In addition, the project will limit the number of crimes, such as infrastructure and energy theft. The energy supply quality con-trol will be improved. Tariffs adapted to individual recipients’ needs and stimulating the recipients’ behaviour will be implemented, which, as a result, will make it possible to consciously control their energy demand. The meter management process will be streamlined. Thanks to making high-quality measurement data available, effective purchasing management and energy sales mechanisms will be introduced. ENERGA-OPERATOR SA will become a more competitive company.

The AMI system implementation will also be highly beneficial for energy recipients (fig. 2). Tariffs cus-tomised to customers’ individual needs will be available. Energy consumption structure awareness will grow, which may result in a reduction of energy consumption. The settlements for energy consumption will be more accurate. The vendor replacement procedures will be simplified.

To sum up, one should mention that the AMI system implementation will be beneficial both for ENERGA-OPERATOR SA and customers. However, one must also remember that the majority of these benefits are impos-sible to define today. This results from the fact that the creation of the AMI system will constitute the basis for all future applications. Construction of the AMI system is the first of the necessary steps to be taken in order to create the Smart Grids.

Metering system remote management

and metering data acquisition

Leading position in Poland in the field

of smart grid elements implementation

Better operational efficiency

Streamlining the network management

and developmentMetering system

remote management and metering data

acquisition

Mobilisation of electric energy recipients in

relation to enhancing energy efficiency and distributed generation

Facilitate equal access to the network


63

Fig. 2 Possible benefits resulting from AMI implementation

2. EXPERIENCES SO FAR: PILOT IMPLEMENTATIONS

The decision regarding commencement of the AMI project implementation has also resulted from assess-ing ENERGA-OPERATOR SA experiences related to the remote readout system implementation, particularly the pilot implementations for large and small recipients.

See below for short descriptions of selected pilot implementations:

ELBLĄG• Skome – Innsoft, 291 recipients and approximately 100 balancing meters (limit meters and meters on

transformers).• Energia 3 – Numerom, 291 meters; readout for customers’ settlements.• Linexpert – Elster, 70 meters. The whole installation constructed in the metering room. The meters

communicate with the concentrator using the PLC protocol; the concentrator transmits data using GSM. At present, the system has not been developed. However, it has shown the possibility of utilis-ing the PLC communication on low voltage. The software for remote transmission was created during operation and turned out to be unreliable.

• Routbase – APATOR, 39 recipients. A system requiring a portable reading device. It was installed in order to discipline the recipients who hindered access to the meter and were often behind with pay-ments. The system has shown the possibility of radio communication in buildings where meters are installed in flats. Increased effectiveness of debt collecting activities.

• AMR – Politech (Iskra Emeco), 76 recipients. The system includes Kraśniewo near Malbork. The system has been installed to reduce commercial losses and inspect the PLC communication to the concentra-tor and GSM / GPRS to the readout server. Serious problems with PLC communication; in four cases it was necessary to install GPRS modems in the meters. This system has not been further developed.

GDAŃSK• Energia 3 – Numerom, 545 recipients.• eSpim – Winuel, 153 limit meters, balancing meters and meters on transformer stations.• AMRSystem – APATOR, 1592 meters. The system has been operated and developed to today. It includes

1592 electricity meters and works with gas meters. The system ensures radio communication with con-

Optimum integra-tion of the distrib-uted generation

Implementation of the Prosumer concept

AMITwo-way communicationReadout on request Supply quality controlAMR Automatic readout

One-way communication

AMR Automatic readout

Manual readout

Load controlRemote deactiva-tion and activa-tionSupport for ener-gy theft detection activitiesRemote meter programming

Smart Grid

Electric car

Distributed energy storage

Integration with the Home Area Network (smart building)

Demand and supply management (DSM)

Providing possibilities to offer new products and services

???

Smart Grid — benefits:

Energy efficiency enhance-ment

Optimisation of the power system development and operation

Integration of the large-scale generation with the distributed generation

Better energy supply reliability, safety and quality

Reduction of the system’s negative environmental impact

Energy market development

Ben

efits

for t

he m

arke

t par

ticip

ants

Comprehensive solution and high level of innovation

Road to the Smart Grid

64

centrators and further by means of GSM / GPRS. In the Wejherowo unit, the readout data is input into the billing system. The system assists in detecting illegal electric energy consumption cases.

• Zol-Net – JM Tronik. The operating meters have been supplied with 600 radio modems for communica-tion with portable meter reading terminals. At present, the system has not been developed.

• Sea Tower – JM Tronik, 281 municipal meters; 3 balancing meters. The Power Engineering Department’s research project consisting in a functional check of transmission lanes to the Measurement Data Col-lector. During the implementation stage, an application for monitoring network losses in the building was created. The meters communicate with the concentrator by means of a UTP cable.

KALISZ• Skome – Innsoft, approximately 100 meters; mainly concentrators and limit meters.• Energia 3, 410 recipients; readouts for settlement purposes. • DCG 300 – Landis+Gyr, 75 ZMB meters.• AMRSystem – APATOR, 850 meters. The project has been implemented mainly to limit network losses

and streamline debt collecting activities. Data readout by means of portable terminals.

KOSZALIN• Solen – Pozyton, 230 meters.• Poligon – APATOR meters; Innsoft software; 63 meters. Data transmission by means of a cable TV in-

frastructure. At present, the system has not been developed.

OLSZTYN• Skome – Innsoft, 150 meters.• AMRSystem – APATOR, 99 meters. The pilot implementation was used to gain experience in loss balancing.

PŁOCK• Energia 3, 772 meters in recipients’ locations.• NETPAF – made by PAFAL; 370 meters. Connection between meters and the concentrator by means of

the PLC protocol. The system has not been maintained – high failure frequency. Negative evaluation of the PLC communication.

• ENERGO-CONTROL – made by Energosystems, 568 meters. Mostly seasonal recipients have been con-nected to the system. Communication with the concentrator by means of the PLC protocol; commu-nication from the concentrator to the acquisition server by means of GSM / GPRS. The system was implemented in 2009. PLC communication problems have been encountered.

• Addax made by T-Matic, 1002 meters. A system with low– and medium-voltage PLC transmission. It has been a very promising installation. A very high level of PLC readouts efficiency has been obtained.Presently, the system transmits data to the billing system. The meters are read out once a day; the hourly load profile for each recipient is read out.

SŁUPSK• Premia – Power Engineering Department, 200 recipients.

TORUŃ• eSpim – Winuel, 220 meters.• Syndis Energia – Sindis, 34 meters, mostly transformers.• Energia 3 – Numerom, 185 meters.• PcCombiBase – Kamstrup, 8 meters. It is a very small installation with the aim of inspecting different

techniques of communication between a meter and concentrator.• Zolnet – JM-Tronik, 77 meters. Radio connection between a meter and portable terminal. At present,

the system has not been developed.The pilot implementations have been a source of valuable experience regarding communication technolo-

gies. In particular, the PLC, LV and MV have been tested and the usefulness of the radio communication and GSM technology has been checked.


65

3. READOUT SYSTEM FOR INDUSTRIAL RECIPIENTS’ METERS

One of the elements of the target AMI architecture in ENERGA-OPERATOR SA is ensuring readouts for industrial recipients. As a result of numerous studies and analyses, it has been decided that this goal will be achieved by means of the Converge system made by Landis + Gyr. The introduction of this solution has resulted in decreasing readout costs and unifying the operation of recipients’ meters. The Converge will eventually re-place all presently operated remote acquisition systems for industrial recipients. Moreover, there are additional plans for connecting this system with meters for TPA recipients from the C1X tariff group.

Converge is a system supporting measuring systems operation and it works with the end user information system and the billing system. PCs are operator’s terminals in this system. Separate servers for databases, com-munication and data processing ensure optimum efficiency. All system elements are connected via an internal LAN which is connected via a dedicated bridge or router to the company’s LAN. An internet browser provides remote access to the system.

See fig. 3 for the general Converge system architecture:

Fig. 3. General Converge system architecture

By the end of 2010, 16 800 meters will be connected to the Converge system. It will be the largest instal-lation of this type in Poland. See below (fig. 4) for the number of meters connected to the Converge system by 14 May 2010.

Converge system LAN

Company LAN

Printer

Klient

Bridge / Router

Database server

Service access 1 9 2 0 2 1 2 2 2 3 2 4 1 3 1 4 1 5 1 6 1 7 1 8 7 8 9 1 0 1 1 1 2 1 2 3 4 5 6 4 3 4 4 4 5 4 6 4 7 4 8 3 7 3 8 3 9 4 0 4 1 4 2 3 1 3 2 3 3 3 4 3 5 3 6 2 5 2 6 2 7 2 8 2 9 3 0

1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 A

B 1 2 6 x 8 x

2 x 9 x 3 x 1 0 x 4 x 1 1 x 5 x 7 x 1 x E t h e r n e t

A 1 2 x 6 x 8 x

2 x 9 x 3 x 1 0 x 4 x 1 1 x 5 x 7 x 1 x C

m r S

R L I N T 1 5 0

P O L A T 8 0 R R O L I A N T 1 5 R

Communication

Communication

equipment

access to APN

Third-party systems

Processing and communication servers

Fig. 4. Converge system implementa-tion as of 25 April 2010

Columns on the left– implemented unitsColumns on the right– units to be implementedblue-red: all ENERGA – OPERATOR S A,green-yellow: individual divisions


66

4. SCALE OF THE AMI UNDERTAKING IN ENERGA-OPERATOR SA

The subject-matter of the project is to introduce technical-organisational changes which will automate electricity meter operation processes in the whole ENERGA-OPERATOR SA area of operation. The scope of the project includes the replacement of approximately 3 million energy meters, ensuring two-way data transmis-sion network between the meters and the central IT system, construction of the central IT system which will automate the process of reading out the meters’ readings and facilitate the remote control of meters. The implemented environment will automate numerous processes so far operated manually. In near-real time, in-formation unavailable today will be provided in order to use it for network operation optimisation and energy generation and distribution processes. The basis for numerous future applications will be created, i.e. control of power network elements in locations where it is presently impossible, stimulating the electric energy recipients’ demand and facilitating the readout of other media meters.

5. OUR APPROACH TO THE AMI PROJECT IMPLEMENTATION

A dedicated team has been established in ENERGA-OPERATOR SA which consists of specialists with ex-tensive knowledge in project management and the required technical expertise. The combination of these two competencies will enable us to achieve the assumed project goals. The project team consists of ENERGA-OPERA-TOR SA personnel supported by external specialists employed for the project duration in order to complete given tasks connected with the project. The design works have been carried out in cooperation with AT Kearney.

The first stage of the project implementation process consists in analytical studies compliant with themodel shown in fig. 5.

Fig. 5. Model for analytic studies in the AMI project (advancement stage as of 13 May 2010)

The works carried out will include a review of similar systems implemented and operated in other coun-tries. The review will include the solutions whose size is comparable or larger than the implementation planned by ENERGA-OPERATOR SA We intend to profit from the experience gained by measuring technologies producersand recognised consulting companies specialising in advisory services on the global market. The conclusions drawn from the systems implemented so far will be taken into account for project implementation purposes.

We intend to actively cooperate with other operators; in particular, we intend to intensify our cooperation with PSE Operator SA in order to create a common vision for constructing a smart system for network metering and the planned establishment of the Independent Measuring Operator.

In order to optimise the management of the undertaking, it will be divided into individual parts managed by dedicated managers. We assume that the undertaking will be divided into three areas: measuring systems, data transmission network, central application (fig. 6).

Recom-mendations

regarding the implementa-

tion

Cost analysis

75%Review of

experiences gained so far

in AMI

100%

Analysis of available

solutions and development

directions

100%

Recom-mendations regarding function-

alities and technical solutions

90%

Recom-mendations regarding implemen-tation and operation

model

90%

Benefit analysis

75%

Analysis of the influ-ence of the implementa-

tion on the regulatory policy 50%

Defining risk factors

50%

Imple-mentation

profitabilityanalysis

60%


67

Fig. 6. Basic technological layers in the AMI project

The project implementation will be compatible with the organisational changes planned in ENERGA-OP-ERATOR SA The investment project will be centrally supervised and financed. The majority of works is to be performed using providers’ resources. If required, the providers will be selected on the basis of proceedings su-pervised by the Public Procurement Office. We assume that several providers will be involved in the undertaking. The ENERGA-OPERATOR SA personnel will define requirements, manage the undertaking and accept the results of works performed. Special emphasis will be put on securing long-term ENERGA-OPERATOR SA interests.

Taking into account the fact that this undertaking is very innovative (such large-scale projects have not been implemented in Poland before) we intend to conscientiously document all performed works and created products. This documentation plus our experiences will be available for other entities operating on the Polish energy market in the future.

6. MAIN TECHNICAL AND ORGANISATIONAL CHALLENGES RESULTING FROM AMI IMPLEMENTATION

Taking into account the innovative character and large scale of the undertaking, the persons involved in its implementation will face numerous challenges both in the technical and management fields.

As far as the technical challenges are concerned, the most serious is the selection of technologies in the communication layer and selection / implementation of target equipment and IT systems functionalities. Another considerable challenge is to design a scalable IT hardware infrastructure and software architecture to build a system which will facilitate collecting, processing and providing massive amounts of data.

In order to meet the system demands defined by ENERGA-OPERATOR SA, it is necessary to provide a reliable data transmission network which will facilitate two-way communication of an efficiency required to implement the present and future AMI functions. Implementation of a given technology in a given area will also depend on the power network structure, available communication infrastructure and economic factors. The selection of a solution for a given area is defined during design works performed by teams of experts.

Meters

Network

Application

PLC / MESH

Radio / MESH

GSM

data transmission network

central meter data processing and collection system


68

It is required to replace all electricity meters with cutting-edge devices facilitating not only registration and remote energy consumption readouts but also their remote configuration, alteration of parameters, controland communication with other devices in a household. Such requirements for measuring devices will enable us to further develop the system towards the smart grid and smart house concepts.

Another task to be performed within the framework of this undertaking will be to construct the central IT system responsible for automatic acquisition of measuring data and storing it in a central repository. The data collected in the repository will be operated by the measuring data management system responsible for prepar-ing and providing the data for the purposes of business processes undertaken in ENERGA-OPERATOR SA

The IT systems architecture and technologies used will be required to ensure efficient operation of thegrowing amounts of collected and processed data. Taking into account the critical character of the AMI system in relation to the OSD business processes, the implemented solutions must be scalable, ensure very high avail-ability and reliability and facilitate easy and efficient integration with IT systems that are in operation.

As far as the management challenges are concerned, the most serious are: cooperation with a large num-ber of entities involved in the undertaking, long duration of the project and project risk management. During the project, managing its scope will be a vital issue which also includes the necessity to manage the scope of alterations inevitable for a project of such size.

The undertaking must be planned and implemented in cooperation with numerous internal and external entities which will have to closely cooperate and their activities will require ongoing coordination. It is neces-sary to ensure proper identification and prevention concerning the risk factors in all areas of the undertaking.The key to success is to precisely define the scope, responsibilities and schedules and also to provide all meansnecessary to successfully complete all tasks. For an undertaking lasting for several years, change management is vital, both in the technical and organisational scopes.

The process of installing and replacing approximately 3 million electricity meters over a few years will be a massive organisational challenge. In order to succeed, numerous factors must be identified and taken intoaccount, i.e. installation units’ resources, period of existing meters verification and manufacturing/logistic ca-pabilities of entities providing the new equipment.

In parallel with the AMI system implementation, other works will be performed with a view to adapting the ENERGA-OPERATOR SA operating processes and other ENERGA-OPERATOR SA IT systems to operate with the AMI system.


70Anna Lisowska-Oleksiak; Andrzej P. Nowak; Monika Wilamowska / Gdańsk University of Technology


Andrzej P. Nowak Gdańsk / Poland

Graduate of studies in the field of biotechnology at the

Faculty of Chemistry, Gdańsk University of Technology

(2003). Obtained his doctoral degree in chemical science

at the same university in 2008. Laureate of the First

Prize in the competition of the Gdańsk Department of

Polish Chemical Society for the best doctoral dissertation

defended at the Faculty of Chemistry, Gdańsk University

of Technology. Currently on a scholarship at Technische

Universität Darmstadt. His scientific interest is focused on

conductive polymers, hybrid materials, supercapacitors

and galvanic cells.

Anna Lisowska-OleksiakGdańsk / Poland

Completed her Master’s studies at the Faculty of Mathe-

matics, Physics and Chemistry at the Nicolaus Copernicus

University in Toruń in 1975. Obtained her doctoral degree

in electrochemistry in 1982, and then DSc title in 2003

at Gdańsk University of Technology in electrochemistry.

Between 1991 and 1994 worked at the University of

St Andrews in Scotland where she researched electro-

chemistry of lithium cells. Currently holds the position of

Associate Professor at the Faculty of Chemistry, Gdańsk

University of Technology. Her main research focus areas

are electrode phenomena, materials technology and

materials engineering applicable for chemical sources of

current.

Monika Wilamowska Gdańsk / Poland

Graduate of the Faculty of Chemistry, Gdańsk University

of Technology (2007). Currently PhD student at the same

faculty, specializing in electrochemistry. Her scientific

interest includes hybrid materials for electrochemical

catalysis and conductive polymers in the role of electrode

materials for supercapacitors.

71Supercapacitors as Energy Storage Devices

SUPERCAPACITORS AS ENERGY STORAGE DEVICES

Anna Lisowska-Oleksiak / Gdańsk University of Technology Andrzej P. Nowak / Gdańsk University of Technology

Monika Wilamowska / Gdańsk University of Technology

1. INTRODUCTION

During recent years the fuel crisis combined with the issue of climate change has forced administrations of highly developed countries to introduce legislative solutions aimed at decreasing CO2 emissions and diversi-fication of energy sources. Novel systems using renewable energy sources, i.e. wind turbines or solar batteries are now being rapidly developed, on research, implementation and operation levels. Incorporation of multiple sources of electrical power into one system requires appropriate tools for energy storage and conversion. Countries experienced in wind and solar power applications propose various solutions. These include systems of galvanic cells, so-called redox flow cells (RFCs) and electrochemical capacitors. Technologies based on electro-chemical capacitors have already been practically implemented in the world [1]. Cars with hybrid systems are also equipped with supercapacitors, which act as components of high power density [2].

Electrochemical capacitors (ECs) have been known for many years. In 1957 Becker (General Electric) patented a capacitor design in which carbon material with a well-developed surface area acted as an electrode and sulphuric acid was used as electrolyte [3]. In 1970, which is seen as the beginning of EC commercial ap-plications, the company SOHIO attempted to introduce these devices into the market [4]. The 1990s saw a huge intensification of scientific and technical research on electrochemical capacitors. This is related to application of ECs in vehicles with electric or hybrid propulsion systems. Electrochemical capacitors have been comprehen-sively described in Conway’s monograph [5].

2. PRINCIPLES OF ENERGY STORAGE IN ELECTROCHEMICAL CAPACITORS

Generally electric energy can be stored in electrochemical devices in two main ways:1) by using chemical reactions and/or2) directly by the concentration of electrostatic charge in the interface between electrode and electrolyte.In the first case the energy of chemical reaction is transformed into electricity according to the equation

W = -z × F × E (W – work which can be performed, z – number of transferred electrons, F – Faraday constant 96485 C/mol, E – potential change. This type of conversion process is known as faradaic reaction. Devices which utilize faradaic reactions are galvanic cells (batteries1, accumulators, fuel cells) and redox supercapacitors.

Abstract

Electrochemical capacitors, also known as superca-pacitors or ultracapacitors, store energy in an electric field within an electrochemical double layer. Using electrodes with a developed surface allows obtaining high capacitance values. Small electrochemical capacitors have been avail-able on the market for many years and applied in small electronic devices. Rapid progress in materials engineer-ing evolving towards nanotechnologies has resulted

in increasing reliability of supercapacitors which work both with wind turbines and systems of photovoltaic cells. Fur-ther development of supercapacitor technology is achieved by improving their operational parameters, particularly voltage range and power rating. This paper presents basic principles of supercapacitors, their characteristics and ex-amples of their application.

1. Theoretically the word “battery” should only be used to describe a set of cells connected in parallel or series, but it has become a standard of common language to call such a commercial popular product [6].

72

The other mechanism for charge storage, the so-called nonfaradaic mechanism, is a principle for electro-chemical capacitor operation. At the electrode/electrolyte interface a capacitor with an electrical double layer dl is formed. It is composed of charges on a metal surface and ions of opposite charge in the solution directly adjacent to the electrode surface.

ELECTRICAL DOUBLE LAYER MODELSThe concept of the electrical double layer has a long history. In modern times it can be traced back to

studies on dispersed phases carried out by Helmholtz (1857). Scientific development in this field is presented in fig. 1. First models take into account the charge ordering (Helmholtz 1857) (fig. 1a) and diffuse layer effect caused by thermal movements (Gouy-Chapman model) (fig. 1b). The Stern model (1927) (fig. 1c) combines these two approaches. The resulting theory says that there are actually two capacitors connected in series. One of them is a Helmholtz capacitor with a capacitance CH and the other – a capacitor with diffused layer with a capacitance Cdif. Total capacitance of an electrical double layer is Cdl

-1 = CH-1 + Cdyf

-1. . Therefore charge order-ing at the interface of two conductive phases results in the formation of a capacitor. Capacitance C of a double layer capacitor results from a charge accumulated at a proper range of potentials C = dq/dV and depends on the geometry (area A and plate separation d). An interfacial capacitor has a thickness of d depending on the size of solvent molecules, and in this case d denotes the diameter of those molecules of their clusters. Studies by Graham (1947) and the model created by Parsons (1978) take into account the presence of solvent dipoles in an inner layer capacitor, see fig. 1d.

a) b) c)

d)

Fig. 1. Electrode/electrolyte interface according to Helmholtz model (a), Gouy-Chapman diffuse layer (b), Stern diffuse layer (c) and Graham diffuse layer (d), where φM denotes Galvani potential and ψ is Volta potential, IHP and OHP are inner and outer Helmholtz planes respectively [5]

diffuse layer

solvent particle

anion

cation

Gouy-Chapman diffuse layer

IPH OHP

OHP

Anna Lisowska-Oleksiak; Andrzej P. Nowak; Monika Wilamowska / Gdańsk University of Technology

73

2.1. ELECTROCHEMICAL DOUBLE-LAYER CAPACITORSThe literature refers to the group of electrochemical capacitors using electrical double layer charge as

Electrochemical Double-Layer Capacitors or ECDLs.Capacitance of a capacitor is proportional to the surface of its plates and dielectric constant of the sub-

stance contained between those plates, and inversely proportional to the plate separation.

(1)

where C is capacitance [Farad], A – electrode surface, d – plate separation, ε0 – permittivity of free space, and εr – relative permittivity of the medium.

Capacitance of a capacitor with an electrical double layer metal (e.g. Pt, Au)/electrolyte Cdl varies be-tween 16 and 50 μF/cm². This value is not attractive for practical applications.

According to the equation (1) significant increase of capacitance Cdl can be achieved by using electrode materials with a so-called developed surface area. Those may be conductive carbons with porous structure, oxides of transition metals and electroactive polymers. Activated carbon with a surface of 1000 m²/g and double layer capacitance Cdl 15 μF/cm² allows achieving a specific capacitance of 150 F/g (1000 m²/g × 10,000 cm²/m² × 15 μF/cm² = 150 F/g). Hence the name “supercapacitor” or “ultracapacitor” used for devices which use capaci-tance with a dual layer of electrodes made of materials with highly developed surfaces. In practical applications electrode layers with a thickness of several micrometers are used.

2.2. ELECTROCHEMICAL CAPACITORS USING SO-CALLED REDOX PSEUDOCAPACITANCEElectrochemical capacitors using so-called redox pseudocapacitance are another type of chemical capaci-

tor widely used in practical applications. They are known as redox electrochemical capacitors or redox super-capacitors. These devices, apart from utilizing charge of electrical double layer, are a source of current result-ing from a charge transfer process occurring at substances adsorbed on the surface and undergoing faradaic reaction. The difference between a normal redox reaction and the described process is substrate availability. In capacitors charge transfer is limited to the electrode surface only. The process occurs in multi-electron mode and in a wide range of potentials. This type of device use materials able to undergo surface redox reactions (e.g. ruthenium oxides, electroactive polymers). An example reaction where redox pseudocapacitance is used may be a reaction which occurs on the surface of ruthenium oxides, where surface hydroxyl groups in an acidic environment undergo reaction according to the following equation [7]:

(2)

It is worth noting that the stoichiometry of this reaction is very complex and the surface layer of –OH groups is treated as a whole. Stoichiometric coefficients of the reaction refer to the monolayer of active surfacegroups.

3. DIFFERENCES BETWEEN ELECTROCHEMICAL CAPACITORS AND GALVANIC CELLS

Both capacitors and galvanic cells consist of two electrodes separated with electrolyte (fig. 2). The differ-ence lies in the character of used electrode material and charge accumulation mechanisms. In electrochemical capacitors both electrodes are in most cases made of the same material. In galvanic cells the electrodes have different chemical properties (the anode material is different from that of the cathode).

�

dAC r×× 0ε ε

� δ yδzyz (OH)RuO(OH)RuO eH δ δ

Supercapacitors as Energy Storage Devices

74

Fig. 2. Schematic diagram of an electrochemical capacitor

In the process of capacitor charging or discharging an internal change of electrode potential V is continu-ously observed. It follows the equation:

C = q/V or = C × V (3)

Curves for discharge of an electrochemical capacitor and a battery are presented in fig. 3.

Fig. 3. Discharge curves of an electrochemical capacitor and a battery

In contrast to that characteristic, charging/discharging of galvanic cells is carried out at constant poten-tial, except for values near 0 and 100% (fig. 2).

This difference results in the fact that energy E accumulated by a capacitor is:

E = 1/2 C × U2 or E = 1/2 q × U (4)

while for a battery the energy level is q × U, i.e. it is twice higher than the energy level in a capacitor of the same voltage U = ΔV. Power of a supercapacitor is expressed by the equation:

Separator with electrolyte

Conductive casing wall

Electrode material in contact with electrolyte

Battery discharge curve

Electrochemical capacitor discharge curve

Time

U [V

]


75

where R stands for the device’s resistance.

(5)

Equation (4) indicates that increase of energy accumulated in a capacitor can be achieved by:1) capacitance increase, which may be realized by:

a. increasing electrode active surface,b. decreasing plate separation,c. increasing relative dielectric permittivity of the medium

2) voltage increase.

According to the equation (5) power can be increased by:1) voltage increase2) resistance decrease.When designing a device we can influence the useful power level by appropriate selection of materials,

electrode geometry and electrochemical stability of the electrolyte.Electrochemical capacitors as energy storage and conversion devices can be placed between electrolytic

capacitors2 and batteries. This is illustrated by the Ragone diagram (fig. 4).

� 2

4UPR

Fig. 4. Ragone diagram for various electrochemical devices [8, 9]

Time constants (RC) (dashed lines in the diagram) indicate that the charging/discharging time for revers-ible galvanic cells is considerably longer than corresponding times for electrochemical capacitors.

Batteries, just like low-temperature fuel cells, display low power density when compared to electrolytic capacitors. At the same time batteries have higher energy density than capacitors. Using both batteries and electrochemical capacitors in the same device can improve its operational parameters. The number of charg-ing/discharging cycles for electrochemical capacitors is much higher than for batteries. This results from the fact that in the cells new phases are created during the charge transfer process and difficulties resulting fromside effects occur. An electrochemical capacitor uses mainly electrostatic charge, so from a theoretical point of view there is no limit for the number of charging/discharging cycles.

2 In contrast to electrochemical capacitors, electrolytic capacitors (with dimensions of several centimetres) have very small capacitances, measu-red in micro- or nanofarads. Electrolytic capacitors owe their name to the method of separator creation between the plates. Plates of electrolytic capacitors are made of metals like aluminium, tantalum, titanium, niobium, etc. Plates are separated with a thin (10…100 nm) film of an oxide of therespective metal. This film occurs as a result of anodic polarization of both plates. Electrolytic capacitors should not be confused with electrochemicalcapacitors.

Spe

cific

pow

er[W

/kg]

Electrolyte capacitor

Electrochemical double-layer

capacitor

Cadmium nickel and nickel-metal hydride cells

UltracapacitorLithium-ion batteries

Acid-head cells

Fuel cells

Specific energy [Wh/kg]


76

4. ELECTRODE MATERIALS

4.1. CONDUCTIVE CARBONSCarbon materials are often used for electrodes of electrochemical capacitors [5, 7, 10]. As one knows,

they owe their ability to conduct electric current to the presence of graphene layers where carbon atoms C have a hybridization sp2 (as opposed to non-conductive carbon types sp3). Resistance of graphite or so-called highly oriented pyrolitic graphites (HOPG) is high and depends on their structure and porosity.

Activated carbons are materials of very well developed surface. In a technical scale they are obtained from natural materials i.e. fossil fuels and organic substances (e.g. wood, fruit stones, nutshells). In laboratory conditions also sucrose and synthetic resins are used.

Available carbon electrodes can have specific surfaces area reaching even 2500 m²/g. Carbon materialis used in the form of powder, fabric, felt or fibres. Electricity storage on carbon electrodes is capacitive in anelectrochemical double layer. These are so-called electrochemical double-layer capacitors (ECDLs).

Progress in the field of nanotechnology allows one to expect that in the near future application of carbonnanomaterials in the form of single-walled and multi-walled nanotubes or nanoparticles will enable obtaining a higher specific capacitance of electrode materials.

4.2. METAL OXIDESOxides of transition metals are commonly used in redox electrochemical capacitors. The most popular

types are ruthenium oxides (RuOx) [5,7] for which x value varies from 1.9 to 2.0. Specific capacitance for ruthe-nium oxide capacitors can even reach 720 F/g. This is the highest value of specific capacitance achieved for anyknown electrode material; however, RuOx applications are limited due to the high cost of this material. Promis-ing alternatives include oxides of manganese, iron, indium, tin, vanadium and their combinations. For these, specific capacitance is around 150 F/g. A supercapacitor composed of Fe3O4 as a negative electrode and MnO2 as a positive electrode is characterized by an operating voltage up to 1.8 V in aqueous electrolyte. Specific ca-pacitance of such a device is 21.5 F/g, actual specific power – 405 W/kg and specific energy – 8.1 Wh/kg [11].

Oxide materials, just like carbons, are stable during thousands of charging/discharging cycles.

4.3. CONDUCTIVE POLYMERSConductive polymers, also known as synthetic metals, represent a very attractive group of electrode ma-

terials, which have found application in supercapacitors [5, 12]. These are mixed electron-ion conductors. The most popularly used polymers include polypyrrole and derivatives of tiophene. Advantages of those materials include fast oxidation and reduction processes during charging and discharging, high charge density (~500 C/g) and easy synthesis of electrode material.

Thanks to their energy accumulation properties, conductive polymers have found application as electrode materials in supercapacitors. These can be both p and n type polymers. Rudge et al have divided polymer super-capacitors into three categories.

Type I, where both electrodes are made of identical p-conducting polymer. When the capacitor is fully charged one electrode is oxidized (positively charged) and the other remains neutral (without charge). Potential difference between electrodes is ca 0.8-1.0 V.

Type II, where electrodes are made of different p-conducting polymers which have different oxidation-reduction potentials. Using different polymers allows enhancing the potential range.

Type III – contains both p-conducting polymer (e.g. polythiophene, poly(3-methylthiophene)) and an n-conducting polymer (derivative of bithienyl). Type III supercapacitors offer a wide range of operational poten-tials (up to 3 V for non-aqueous electrolytes) and appropriately higher energy density.

Conductive polymers used as electrodes in electrochemical capacitors may be modified in order to im-prove their operational parameters. Most often modification is made with oxides of transition metals – man-ganese and vanadium. Another option is modification of conductive polymers by attaching a redox group to apolymer chain. Yet another method is the introduction of inorganic substance into a polymer matrix. This addi-tive fulfils the role of a multicentric redox system [13-15]. There are also methods for modifying electroactivepolymers with nanomaterials [16]. In all cases activity of the composite material is created by both electroactive material and the modifying agent.


77

5. ELECTROLYTES

Electrolyte type is another classification criterion for electrochemical capacitors. Both aqueous and non-aqueous electrolytes are used (with aprotic solvents and ionic liquids).

5.1. AQUEOUS ELECTROLYTESAqueous electrolytes restrict operational voltage to 1 V, as above this value during the charging process

molecules decompose on the positively polarized electrode and oxygen is generated, while on the negative electrode water is decomposed and hydrogen is generated. An advantage of aqueous electrolytes is the high conductivity value (e.g. 0.8 S/cm for sulphuric acid), and simple cleaning and drying of electrode material dur-ing the manufacturing process. Moreover, the price of aqueous electrolytes is considerably lower than that of non-aqueous ones.

In order to avoid problems related to decreasing effectiveness of supercapacitor charging, high-concen-tration electrolytes are used. They guarantee sufficiently low resistance values.

5.2. NON-AQUEOUS ELECTROLYTES WITH APROTIC SOLVENTSUsing organic liquids which do not contain chemically active hydrogen atoms in their molecules in the role

of solvent enhances the stability window of the system (decomposition of the solvent’s molecules does not oc-cur). This allows achieving higher operating voltage values. The higher the voltage level, the higher the amount of energy that can be accumulated (see equation (4)). Non-aqueous electrolytes allow achieving voltages up to 3 V. Higher values are prevented by traces of water present in solvents.

An adverse effect of using non-aqueous electrolytes is their high specific resistance value, which affectsthe capacitor’s power. Nonetheless, the loss of power is usually compensated by a possibility to achieve higher voltage.

5.3. IONIC LIQUIDSIonic liquids are salts which are liquid in ambient temperatures. A low melting point results from the

structure of those salts, which consist of a large and asymmetric cation (e.g. 1-alkyl-3-methylimidazolium, 1-alkyl-pyridinium) and a small anion. The range of their electrolytic stability depends only on the type of ions which the ionic liquid is composed of. Appropriate ion selection allows constructing supercapacitors operating in a wide potential spectrum. There are known designs where the operational voltage is 3 V. Usage of ionic liq-uids is limited by low conductivity value, in the range of mS/cm. Because of this feature ionic liquids are used in supercapacitors which are operated at higher temperatures.

6. SUPERCAPACITOR APPLICATION EXAMPLESElectrochemical capacitors are increasingly reliable devices which can work with wind turbines or pho-

tovoltaic cell systems [17]. Very fast charging/discharging rates offered by supercapacitors allow them to promptly adapt to load changes. Supercapacitors have found applications in household appliances, electronic tools, mobile telephones, cameras etc. They are also used in the power supply systems of electrically driven cars. In the automotive industry the main purpose of supercapacitors is to provide support for classic batter-ies – they act as an additional buffer during acceleration and braking. Such an arrangement lowers operational costs of the vehicle, as it extends battery lifetime. Supercapacitors protect the battery from harmful effects of peak loads. Recovery of braking energy by supercapacitors also allows reducing operational costs by decreasing energy consumption.


78

REFERENCES

7. CONCLUSIONSupercapacitors are dynamically entering the power engineering market. Legal regulations concerning

environmental protection and sustainable development foster installation of renewable energy sources, and this in turn generates the demand for reliable energy storage and conversion systems.

Electrochemical capacitors are able to quickly charge and discharge. They also have long lifetimes, though they are not able to store volumes of electricity as high as classic batteries or fuel cells. Comparison of elec-trochemical capacitors and batteries reveals that they are in fact complementary systems. For that reason it is a very good solution to combine supercapacitors with chemical sources of electricity.

1. http://www.dailyreckoning.com.au/supercapacitors/2008/02/28/2. Shukla A.K., Arico A.S., Antonucci V., Renewable Sustainable Energy Rev., vol. 5, 2001, p. 1373. Becker H.E., U.S. Patent 2 800 616 (1957).4. Boos D.I, U.S. Patent 3 536 963 (to Standard Oil, SOHIO) (1970).5. Conway B.E., Electrochemical Supercapacitors, Plenum Publishing, New York 1999.6. Czerwiński A., Akumulatory, baterie, ogniwa, WKt, Warsaw 2005. 7 7. Frąckowiak E. and Bequin F, Carbon, vol. 39, 2001, p. 9378. Kótz R. and Carlen M., Electrochim. Acta, vol. 45, 2000, p. 2483.9. Plitz I., Dupasquier A., Badway F, Gural J., Pereira N., Gmitter A., Amatucci G.G., Appl. Phys. A, vol. 82, 2006, p. 615.10. Lota G., Lota K., Frąckowiak E., Electrochem. Commun., vol. 53, 2008, p. 2210.11. Cottineau T., Toupin M., Delahaye T., Brousse T., Belanger D., Appl. Phys. A, vol. 82, 2006, p. 599.12. Mastragostino M., Arbizzani C., Soavi F, Solid State Ion., vol. 148, 2002, p. 493.13. Gómez-Romero P, Chojak M., Kulesza PJ., Asensio J.A., Electrochem. Commun., vol. 5, 2003, p. 149.14. Gómez-Romero P, Cuentas-Gallegos K., Lira-Cantu M., Mater J. Sci., vol. 40, 2005, p. 1423.15. Lisowska-OleksiakA., Nowak A.P, J. Power Sources, vol. 173, 2007, p. 829.16. Arico A.S., Bruce P, Scrosati B., Tarascon JrM., Schalkwijk van W., Nat. Mater, vol. 4, 2005, p. 366. 17. Lisowska-OleksiakA., Wilamowska M., Szybowska K., Przegląd Komunalny, August 2008.


80


Kamil Kompa Warsaw / Poland

Graduated in BSc and MSc studies at the Warsaw Univer-

sity of Technology with excellent grades and a distinction

for both theses. Currently a fourth year PhD student.

For more than two years has collaborated with research

institutes and universities in Dresden, Germany. Special-

ized in the design of electronic and power-electronic

devices, as well as signal processing and control systems.

Successfully completed numerous industrial and research

projects in those fields.

Antoni DmowskiWarsaw / Poland

Full professor at the Warsaw University of Technology,

working there for more than forty years. Between 1998

and 2009 Head of the Division of Power Plants and

Energy Management of the Institute of Electrical Power

Engineering, Warsaw University of Technology. Between

1988 and 1990 on multiple placements at prominent

European universities and in industrial plants in Germany

and the Netherlands, including TU Aachen, TU Darmstadt,

Ruhr-Universität Bochum, BBC (ABB) plant, Benining Von

Mongold, REO, Huzer. Has collaborated with most of

those institutions ever since. Author of six monographs,

author and co-author of more than 100 domestic and

foreign publications, 34 patents and over 45 industrial

implementations. Supervisor of 17 completed doctoral

theses, with a further four underway. Member of the

Committee on Electrical Engineering, Polish Academy of

Sciences.

Antoni Dmowski; Kamil Kompa; Łukasz Rosłaniec; Bernard Szymański Warsaw University of Technology

Bernard SzymańskiWarsaw / Poland

Obtained Master’s degree in 2005. Currently PhD student

at the Faculty of Electrical Engineering, Warsaw Univer-

sity of Technology. In 2007 and 2008 on a placement

at the Center of Applied Research and Development of

the Hochschule fur Technik und Wirtschaft Dresden in

Germany. In 2009 and 2010 participated in ten monthly

research placements at E.ON Energy Research Center

at RWTH Aachen, Germany. Focuses his research on

renewable energy sources, modern power systems and

electric drives.

Łukasz Rosłaniec Warsaw / Poland

Obtained his Master’s degree in 2008. In the same year

commenced doctoral studies at the Institute of Electrical

Power Engineering, Warsaw University of Technology. In

2009 went on four monthly placements at RWTH Aachen,

Germany. Focuses his research on energy transmission

between renewable sources and the power grid. Particu-

larly interested in issues related to improving energy

quality at an interconnection point.

81

MODERN PHOTOVOLTAIC POWER STATIONS WITH ENERGY STORAGE SYSTEMS CONNECTED TO POWER GRIDS

Antoni Dmowski / Warsaw University of TechnologyKamil Kompa / Warsaw University of Technology

Łukasz Rosłaniec / Warsaw University of TechnologyBernard Szymański / Warsaw University of Technology

This study was co-funded by grant N N510 325537 awarded by the Ministry of Science and Higher Education of the Republic of Poland carried out at the Institute of Electrical Power Engineering,

Warsaw University of Technology.

1. INTRODUCTION

Solar energy is the most basic form of energy. Biochemical processes combined with solar energy enabled the creation of fossil fuel deposits which are the primary source of energy today. Fig. 1 compares the amount of solar energy reaching the Earth during a year to the total deposits of primary energy [1, 2]. The presented values show that the total amount of energy transmitted from the Sun to the surface of the Earth is higher than the total energy cumulated in all primary energy sources. Using solar energy is currently enabled by technolo-gies like thermal solar power plants, photovoltaic (PV) plants and solar concentrators [3].

In a solar thermal power plant the process of converting solar energy starts with focusing solar radiation from a designated area and converting solar radiation to heat, which is then used to drive a thermodynamic turbine [3]. The turbine drives a generator which generates electricity. This thermal cycle is typical for fossil fuel power plants. In Europe a plant of this type has been constructed in Seville, Spain; however the largest project of this type is now planned in the Sahara desert. It is named DESERTEC [4] and was officially launched in July

Modern Photovoltaic Power Stations with Energy Storage Systems Connected to Power Grids

Abstract

The paper presents photovoltaic power systems in-terconnected with a power grid. Power generated by rene-wable energy sources tends to be unstable as it is affected by weather conditions. However, cooperation of solar po-wer stations with energy storage systems (e.g. electroche-mical cells) significantly improves operational parameters and stability of the power system. Moreover, solar power stations often operate at sites accessible for people, so

they require galvanic isolation between the cells and the power grid. As the PV plants are required to maintain high energy conversion efficiency, the energy conversion is car-ried out with resonant power electronic converters with very low energy losses. Another significant component of the system is the line-side converter which transmits elec-tricity from photovoltaic cells to the grid.

Fig. 1. Global primary energy deposits [1]

EJ = 1018 J

Coal 42,831 EJ Gas

17794 EJOil

12,602 EJ

Annual primary

energy con-sumption

Uranium1,507 EJ Solar

energy reaching

lands during a year

820,000 EJ

Methane hydrates

236,588 EJ

480 EJ

Minerals together

74,734 EJ

82

2009 by a consortium created by European utilities. The generated electricity is supposed to be transmitted to European and African countries via DC transmission lines.

In a photovoltaic (PV) power plant the solar radiation energy is converted directly into electricity using phenomena occurring in a semiconductor junction. Apart from photovoltaic cells, a PV plant also uses electronic power converters which adapt parameters of electricity generated by cells for the grid requirements.

We can observe a rapid development of PV power generation. It is mainly used by residential buildings in the role of a local power supply system. There are also some larger plants (> 200 kWp) connected to the power grid, e.g. PV plant Braindis (Germany) with a capacity of 40 MWp or the plant at Puertollano (Spain) of 47 MWp [5].

The growth in capacity installed in European solar power stations has been enabled mainly by the in-troduction of feed-in tariffs for electricity generated in renewable sources [6]. This was the main reason for installing 2500 MW of solar power generation in Spain, 1500 MW in Germany and 50 MW in the Czech Republic in 2008. It can be expected that the introduction of feed-in tariffs in other European countries, for example Poland, could bring similar results.

In 2008 the total installed capacity of large (> 200 kWp) PV plants was approximately 3.8 GWp [5], while in 2006 it was only around 500 MWp. Currently the specific power of an electronic power converter already ex-ceeds 1 MVA. This technology therefore poses an alternative to fossil fuels.

2. PHOTOVOLTAIC SYSTEMS

Photovoltaic systems can be divided into two main groups:• Operating in island mode• Connected to the grid.

In most cases the capacity of photovoltaic power plants operating in island mode reaches several mega-watts. Such systems are not usually connected to public power grids and are only used to supply power to local consumers.

Photovoltaic plants can also work with a power grid. They are connected to the grid with appropriate DC/AC electronic power converters. Most DC/AC converters used at PV plants use fully controllable semiconductor components like power MOSFET transistors or IGBTs and an appropriate PWM control method. This enables precise and quickly responding control of power flow between DC and AC systems. Moreover the current at ACside has no undesirable harmonics and no reactive power flows occur.

The most expensive component of a photovoltaic system is the battery of PV cells. Therefore, designers aim to maximize output of a single cell. Consequently, an electronic power converter installed between solar cells and the power grid achieves the so-called Maximal Power Point Tracking (MPPT) algorithm [7].

3. POWER VARIATIONS

Output of a photovoltaic power plant is not constant over time and depends on weather conditions and time of day. The variability of current generated by a PV plant and a wind turbine are shown in fig. 2 [8]. For thisreason high capacity photovoltaic plants connected to the grid can affect operational parameters and stability of the entire power system. This particularly applies to high capacity plants located far away from the main power supply point. Instability of a photovoltaic plant output can also cause flickers [9, 10, 11].


83

These problems can be eliminated by applying appropriate output control methods for a PV plant. Some-times it might require restricting output (when sufficiently large energy storage is not available). First of all, how-ever, it is necessary to equip the plant with a control system connected to the distribution system operator. If a high capacity photovoltaic plant is connected at a point of the power grid with an appropriately high short-circuit power value and stable voltage-frequency parameters, operation of the plant can be compensated with appropriate power reserves in the system. All that, however, results in high operational costs of renewable energy sources.

4. PHOTOVOLTAIC POWER SUPPLY SYSTEMS WITH ENERGY STORAGE

The problems described above can be significantly alleviated or even eliminated if a PV plant works with an energy accumulator. The most popular energy storage systems are flywheels, superconductors, supercapaci-tors and electrochemical cells [12, 13]. Nowadays the most effective energy storage solutions for photovoltaic systems are electrochemical batteries. The most frequently used types are lead-acid and cadmium-nickel cells. Designing a cost-effective electricity storage system is one of the main issues of modern PV system develop-ment.

A photovoltaic system combined with batteries should operate in the following modes (depending on control input from the grid operator):

• Power supply to the grid from PV cells • Power supply to the grid from batteries and PV cells • Power supply to the grid from batteries• Charging batteries from the power grid when excessive power is available• Power supply for selected consumers from batteries during a grid breakdown.

5. GALVANIC SEPARATION

Photovoltaic panels are usually installed in locations accessible to people. This requires isolating panels from the power grid. Isolation can be achieved by a traditional method, using a transformer operating at 50 Hz on the grid side (fig. 3a). It is also possible to use a modern transformer with much higher operational frequency, characterized by low losses and small dimensions. Such a transformer is an internal component of an electronic power converter (fig. 3b). This solution has the following advantages:

• Small dimensions• High density of transformed power• High efficiency.

Fig. 2. Current generated by a PV plant (blue) and a wind turbine (red) [8]


84

If a photovoltaic power plant working with an energy storage system is appropriately controlled, it can be fully dispatchable and helps the operator to control the power grid [14, 15]. Fig. 4 shows a fully dispatchable PV plant with an electrochemical electricity storage system. Such a plant consists of several functional units, i.e.:

• Photovoltaic panels• DC/DC converter where the maximum cell power point is tracked• DC/AC converter which feeds the power generated at PV cells into the grid• AC/DC and DC/DC converters responsible for supervision and control of the electrochemical energy

storage system.

(a) Galvanic separation with a 50 Hz transformer

(b) Galvanic separation with a high-frequency transformer

Fig. 3. Electronic power converter with galvanic separation

In this case the DC/DC converter discharges the electrochemical battery and assures constant power input for the DC/AC converter. Moreover, the control unit supervises and controls electronic power converters according to the setpoints given by the grid operator. In this solution the galvanic separation is achieved by us-ing high-frequency transformers installed within DC/AC and AC/DC converters.

Fig. 4. Photovoltaic power plant with an energy storage system and galvanic separation

DC/DC converter

GRID

GRID

GRIDMAXIMUM

POWER POINT TRACKING

CONTROLUNIT

GRIDOPRTATOR

BATTERYENERGY STORAGE

SYSTEM


85

6. RESONTANT CONVERTERS IN PHOTOVOLTAIC CELLS

Currently, the density of transmitted power is an important factor for electronic power converters. In or-der to decrease their dimensions and increase energy density a high energy transformation frequency is used. This, however, results in high switching losses of power transistors. Therefore, in order to limit power losses, soft techniques for transistor switching are used. They enable application of small yet efficient separation trans-formers. Moreover, soft switching of a converter’s transistors results in low electromagnetic distortion emission levels.

Fig. 5 shows a method for using soft-switched DC/DC converters in a photovoltaic system [16].

Fig. 5. Multiphase series resonant converter in a photovoltaic power plant [16]

In the discussed solution each photovoltaic panel is integrated with a small electronic power converter which tracks the maximum power point of the cell. A multiphase resonant converter is also used. It is equipped with a high-frequency separating transformer (common for all panels). This solution assures proper galvanic separation between the accessible components and the power grid. In this case the transformer also adjusts the voltage level between the panels and the DC/AC converter which is connected to the load.

Fig. 6 shows a three-phase DC/DC resonant converter. It is used as an element of a photovoltaic system as shown in fig. 4. This DC/DC converter is equipped with a high-frequency separation transformer and uses series electromagnetic resonance for electricity conversion.

Fig. 6. Three-phase series resonant converter

Fig. 7 shows results of measurements of voltage uR (yellow) and current iR (blue) in the resonant circuit of the discussed three-phase converter. The frequency of the parameter shown is:

f ≈ 200 kH z (1)

Fig. 8 shows the output characteristic of the converter shown in fig. 6. The converter is frequency-con-trolled with pulse density modulation [17]. In addition, the converter can operate when loaded with either re-sistance or voltage. It can be used as a component of a DC/AC converter which transmits electricity from a PV plant to the grid or as a component of an AC/DC converter used to charge an electrochemical energy storage system (fig. 4).

LOADMULTIPHASE

SERIES RESONANT CONVERTER


86

Fig. 7. Variability of voltage and current – three-phase series resonant DC/DC converter with a separation transformer

Fig. 8. Output characteristics of a three-phase resonant converter

7. ELECTRONIC POWER CONVERTERS TRANSMITTING POWER FROM PV CELLS TO THE POWER GRID

The most important component of each photovoltaic plant is the frequency converter coupled to the grid, which is responsible for transmitting power from the intermediate DC circuit to the power system. In most cases voltage source inverters are used. This type of device must have appropriate design features and control software enabling it to safely work with the power grid and maintain appropriate quality of output power. Volt-age source inverters allow power flow in both directions, so they can be used for reactive power compensation applications. Reactive power compensation can also positively influence consumer voltage values which are particularly important in low voltage grids.

Hard switching(energy losses

Pulse frequency

modulation

Frequency control

Thermal inductance limitation

Battery charging curve

Frequency control limit

150 kHz & 200 kHz curves at 565 V in the DC circuit

Output voltage

Uwy [V]


87

A typical design of a single-phase voltage source inverter is shown in fig. 9. The system consists of input capacitance, instrumentation for input parameter measurements, IGBT bridge, LCL output filter, overvoltage limiter, switching device and instrumentation for measuring output parameters. LCL filters and EMI filters are essential elements for the inverter parameters. The quality of power fed into the grid mainly depends on those two filters, as well as on the control system of the inverter. The model shown in fig. 9 is currently being tested at the Institute of Electrical Power Engineering, Warsaw University of Technology. The project includes testing three primary control techniques for this type of inverter:

• Hysteresis current controller• PI current controller• PQ controller.The stage completed so far included mainly simulations carried out with the PSIM package. The design of

the simulated device is shown in fig. 10. Fig. 11 shows variability of the output current of the inverter working with the current hysteresis controller.

Using a DLL module (available in PSIM) during the simulation allowed identifying problems which occur while applying digital control to this type of equipment.

Fig. 9. Schematic diagram of a single-phase voltage source inverter

8. CONCLUSIONS

The paper discussed issues related to power generation at photovoltaic plants. PV plants working with electrochemical energy storage systems have been presented. The functions of such a plant within a power system have been described. Additionally, the issue of galvanic separation and the possibility of using resonant electronic power converters at PV plants have been discussed. The article also highlighted the issue of transmit-ting power from a PV plant to a power grid.

Controlsystem

Gatecontrollers


88

Fig. 10. Simulation model of a single-phase voltage source inverter connected to an ideal voltage source

Fig. 11. Variability of inverter output current I(RL3) [A] and bipolar PWM signal PWM V(PWM) [V]


89

REFERENCES

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Acta Energetica Electrical Power Engineering Quarterly no. 01/2010