00932323_optimal Distributed Generation Allocation in Mv Distribution Networks_2001

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OPTIMAL DISTRIBUTED GENERATION ALLOCATION

IN MV DISTRIBUTION NETWORKS

G. Celli Member,

IEEE, F.

Pilo, Member,

IEEE

Department of Electric and Electronic Engineering

Universityof Cagliari

Piazza d

Armi 09 123

Cagliari,

Italy

Abstract: The necessity for flexible electric systems,

changing regulatory and economic scenarios, energy savings

and environmental impact are providing impetus to the

development of Distributed Generation (DG), which is

predicted to play an increasing role in the electric power

system of the future. With

so

much new distributed generation

being installed, it is critical that the power system impacts be

assessed accurately so that DG can be applied in a manner that

avoids causing degradation of power quality, reliability and

control of the utility system. For these reasons, the paper

proposes a new software procedure, based on a Genetic

Algorithm, capable to establish the optimal distributed

generation allocation on an existing MV distribution network,

considering all the technical constraints, like feeder capacity

limits, feeder voltage profile and three-phase short circuit

current in the network nodes.

Keywords: MV Distribution Networks, Distributed

Generation, Genetic Algorithms, Network Planning.

I. INTRODUCTION

Distributed Generation (DG) includes the application of

small generators, scattered throughout a power system, to

provide the electric power needed by electrical customers. DG

often offers a valuable alternative to traditional sources of

electric power for industrial, commercial and residential

applications. DG makes a large use of the latest modern

technology and can be efficient, reliable, and simple to own

and operate that it can compete with electrical power systems.

In some cases DG can offer significantly lower cost and higher

reliability than a customer can obtain from the electrical grid.

In others, it can augment the grid so that the combination of

grid and DG can provide higher performance than either could

alone. But regardless, it offers an alternative that utility

planners should explore in their search for the best solution to

electric supply problems [1-31.

Power system planning involves identification of the best

equipment, along with its locations, manner of interconnection

to the system, and schedule of deployment. Since cost is an

important attribute in power planning, almost invariably one of

the planners chief goals is to minimize overall cost [3,

41.

Whatever planning philosophy or paradigm is adopted for

planning a power system, DG can influence other resources

(i.e. transmission and distribution) and its correct impact is of

the greatest importance to take the right decisions. In this

paper, the important task of planning the optimal number and

position of DG generators has been faced. This optimization

permits the best location of generators to be found

so

that

power losses in an existing distribution network are minimized,

and investments for electric grid upgrade, due to the growth of

the energy demand of loads, can be deferred or reduced.

Furthermore, the voltage profile and the three phases short

circuit currents are checked and only those solutions able

to

remain within the range imposed by the planner are accepted.

In order to perform this optimization a new software

procedure, based on a Genetic Algorithm (GA), has been

developed and tested on real size MV distribution networks.

The structure of the paper is the following: in section I1 the

most important features of DG are briefly described, in section

some generalities on Genetic Algorithms are provided, in

section IV the proposed GA for the optimal allocation of DG

units is presented and, finally, in section V some results are

shown and discussed.

11.

DISTRIBUTED GENERATION

The need for more flexible electric systems, changing

regulatory and economic scenarios, energy savings,

environmental impact and the need to protect sensitive loads

against network disturbances are providing impetus to the

development of dispersed generation and storage systems

based on a variety of technologies.

In particular, the term DG implies the use of any modular

technology that is sited throughout a utilitys service area

(interconnected to the distribution or sub-transmission system)

to lower the cost of service. DG can comprise diesel and

intemal combustion engines, small gas turbines, fuel cells and

photovoltaics. The purpose of these plants is to cope with the

growing demand for electricity in certain areas and render

certain activities self-sufficient in terms of power production

thus achieving energy savings

[

1-31.

The main reasons for the increasingly widespread use of

dispersed generation can be summed up as follows:

DG units are closer to customers so that Transmission and

Distribution (T&D) costs are avoided or reduced;

the latest technology has made available plants ranging in

capacity from 1OkW to 15MW.

some technologies have been perfected and are widely

practiced (gas turbines, intemal combustion engines), others

are finding wider application in recent years (wind, solar

energy) and some particularly promising technologies are

currently being experimented or launched (fuel cells, solar

panels integrated into buildings);

it is easier to find sites for small generators;

0-7803-6681-6/01/ 10.00 001

IEEE 81


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CHP (Combined Heat and Power) groups do not require

large and expensive heat networks;

natural gas, often used as fuel in DG stations is distributed

almost everywhere and stable prices are to be expected;

usually DG plants require shorter installation times and the

investment risk is not

so

high;

DG plants yield fairly good efficiencies especially in

cogeneration and in combined cycles (larger plants);

the liberalization of the electricity market contributes to

creating opportunities for new utilities in the power generation

sector;

T&D costs have risen while DG costs have dropped, as a

result the avoided costs produced by DG are increasing;

DG offers great values as it provides a flexible way to

choose a wide range of combinations of cost and reliability.

For these reasons, the first signs of a possible technological

change are beginning to arise on the international scene, that

could involve in the next future the presence of a consistently

generation produced with small and medium size plants

directly connected to the distribution network (MV and LV)

and characterized by good efficiencies and low emissions.

In this context, the need to provide access to the distribution

network to any company intending to install DG groups

clashes with the need for utilities to manage these networks,

maintaining adequate levels of security and quality.

Consequently, the utilities are now faced not only with the

technical problems involved in managing the networks that

have been passing from passive to active (voltage regulation,

protection policy, disturbances and interfacing problems), but

also with new tasks. Undeniably, system planning and

operation have become a much more uncertain task than in the

past. For example, electric utilities may decide to resort to DG

in the place of enforcing transmission and distribution plants.

This will create new problems and probably the need of new

tools for developing and managing these systems.

III. GENETIC ALGORITHMS

Genetic Algorithms are a family of computational models that

rely on the concepts of evolutionary processes

[5, 61.

It is a

well known fact that according to the laws

of

natural selection,

in the course of several generations, only those individuals

better adapted to the environment will manage to survive and

to pass on their genes to succeeding generations.

Correspondingly, the GAS operate on a set (population) of

possible solutions (individuals) of a generic problem, applying

selection and reproduction criteria whereby new solutions

(offspring) are generated containing the information enclosed

in the solutions fiom which they originated (parents). Clearly,

the better the solution, the more possibilities there are of

reproducing and passing on genes to the offspring.

The first step to be taken in implementing these algorithms is

to encode a potential solution in a simple data structure of the

chromosomal type (generally a vector) in which each element

is represented by means of a specific alphabet (usually binary).

Once the initial population has been randomly generated, every

solution is evaluated by means of the objective function.

The strategy followed by GAS is very simple. To ensure an

amelioration in the population, in each generation a selection

operator sees to it that the solutions with higher fitness have

greater possibilities

of

reproducing. At this point some

individuals are coupled and cross-bred by means of a crossover

operator, which recombines the salient information brought by

the parent structures in a significantly non-destructive way.

The crossover operator produces offspring, that will then

replace some of the old individuals of the population. Lastly,

the strings can undergo mutation, which involves selecting,

with little probability, a string element and changing the

symbol contained therein with another symbol of the alphabet

being used (Fig.

1).

Once the procedures performed by the three operators have

been completed, the offspring produced are evaluated and

compared with their parents. If the GA is generational, then the

offspring will replace all their parents, creating a new

population. If on the other hand the GA is steady state then the

offspring will replace their parents only if they are better.

Several parameters normally influence the search for the

optimum solution by GAS: population size, the probability of

mutation, the maximum number of generations to be explored,

etc.. These parameters should be accurately calibrated,

adapting them to the size of the problem in question.

IV. GENETIC ALGORITHM

FOR

THE OPTIMAL

ALLOCATION

OF

DG UNITS

The distribution network planning generally considers a

temporal horizon of 5-20 years. During these years it is

normally hypothesized that loads draw more energy fiom the

grid, that new MVILV nodes appear and, eventually, that one

or more substations can be built. The dynamic nature of the

problem, jointly with its dimension (normally, thousands

of

MVILV nodes should be considered simultaneously), makes

really difficult to examine all possible network configurations

to find the optimal network arrangement that minimizes the

cost of construction, maintenance and energy losses. If the

network architecture is assumed to be invariable during the

planning period, changes in load energy demand or the

appearance of new loads could require investments for network

SELECTION

Population

CROSSOVER

MUTATION

Parents Offspring

Fig.

1.

GAoperators

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upgrade. DG, in addition to the advantage of reducing power

losses, can be a valuable option for the planning engineer to

defer or reduce investments for grid upgrade. Moreover, the

greater attention should be paid in the siting and sizing of DG

units because their installation in non optimal locations can

result in an increasing of power losses having opposite effects

than those described. For these reasons, optimization tools,

able to find the correct siting and sizing of DG units in a given

network, can be a valid aid for the planner who has to face with

the worldwide growth of DG penetration. In the paper, a GA

optimization technique has been developed for the optimal DG

allocation in MV distribution networks that is deeply described

in the following sections.

A . Coding of the solution

The first important aspect of a correct implementation of the

GA is the coding of the potential solution. Considering that the

network structure is fixed, all the branches between nodes are

known, and the evaluation of the objective function depends

only on size and location of the DG units. For this reason each

solution can be coded by using a vector, whose size is equal to

the number of nodes, in which each element contains the

information on the presence or not of a DG unit. In order to

perform not only the siting but also the sizing of DG, a

prefixed number

NDG)

of generator sizes have been assumed

and classified (e.g. size number 1 corresponds to a 100 kVA

DG unit, size number 2 corresponds to a

200

kVA DG unit,

etc.). Therefore, each element of the vector solution is

represented by means

of

the following alphabet:

no DG located on the node;

size index of the DG installed in the node.

0

1,

.

NDG

Of course, the vector elements corresponding to the HV/MV

primary substations are fixed to 0.

The type of code used is suitable for every kind of network

structure (radial, meshed, etc.), that influences only the

assessment of the usual technical constraints (voltage profile

and thermal feeder capacity) considered during the evaluation

of the objective function, but does not affect the optimal

allocation procedure. In the paper the algorithm has been

applied to open loop distribution networks, that are

commonly used in Italy, described in subheading D.

B.

GA Implementation

is randomly generated by means of the following procedure:

In the first phase, an initial population of possible solutions

for each solution a value of DG penetration is chosen

between

0

and a maximum limit of DG penetration, fixed

by the planner on the ground of economical and network

security justifications;

a number of DG units of different sizes is randomly

chosen until the total amount of power installed reaches

the DG penetration level assigned;

the DG units are randomly located among the nodes of

the network;

the objective fimction (OF) for each solution is evaluated

verifying all the technical constraints; if one of them

is

violated, the individual is discarded.

Regarding the population size, the best results have been

found assuming it equal to the dimension of the problem, i.e.

the number of nodes in the network.

In the second phase, the genetic operators are applied in

order to produce the new solutions. In the paper the following

implementation details for the operators have been considered:

Selection: the remainder stochastic sampling without

replacement scheme has been adopted, whereby the

number of selections of each individual is calculated in

the following way: expected individual count values are

calculated as a fraction between the OF value of the

individual and the average of

OF

value of the whole

population. Then integer parts of the expected numbers

are assigned, and fiactional parts are treated as

probabilities. For example, a solution with an expected

number of copies of

1.4

would receive one sure single

copy and another with probability 0.4. This process

continues until the population is hll .

Crossover: the uniform crossover is adopted, by which

each allele is swapped with probability

0.5.

Mutation: all the vector elements are mutated, according

to a small mutation probability, choosing a different value

in the defined alphabet.

Each offspring is accepted if all technical constraints are

verified and the total amount of DG does not exceed the

maximum level of DG penetration.

After several tests, a generational GA model has been

implemented, because it seems to guarantee better solutions

than the steady state model, even if with a greater number of

iterations. Therefore, the offspring replaces all their parents,

creating the new population. The procedure terminates when a

maximum number of generations has been explored.

C. Evaluation ofthe objective unction

The objective function to be optimized within the technical

constraints refers

to

the total cost of the network which

considers [7,

81:

site of the substations and loads

geographical, geological and urbanistic features of the area

concerned

power demands of the loads and their growth versus time

duration of the planning period

some cost parameters such as inflation and interest rates

unit cost of kWh lost due to Joule effect (cost of losses)

construction and maintenance costs of feeders of different

cross-sections and for different types of lines (overhead,

underground).

Due to the current Italian standard, that does not admit

islanded portion of MV network directly supplied by DG, the

most common reliability indices for long interruptions are not

modified by DG units and thus no service quality

improvements can be achieved by normal customers. For this

reason, the costs of disruptions are not considered in the

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objective function. Of course, each customer with DG units can

use them to supply, totally or partially, its loads during gnd

faults or scheduled interruptions, increasing the availability of

energy and reducing the number and duration of interruptions.

The objective function to be minimized in the problem at

hand is thus represented by the total cost

CO, of

the generic

network, with present value taken at the beginning of the whole

planning period of N years. This cost can be expressed by

using the sum:

N

COG= C c o j (1)

j = l

where N T ~ ~s the number of network nodes, Ncp is the

number of substations, NTorNcphe number of branches in the

network and C the present cost of the branch.

The cost of every branchj is the sum of the construction,

residual, management costs, and cost of losses in the

subperiods, transferred o the cash value at the beginning of the

planning period by using economical expressions based on the

inflation rate, the interest rate and the load growth rate (all

of

them constant) [7]

The cost of every branch can be expressed by using:

k = l

where C is the total cost of the branchj Cwphe portion of

cost independent of power flow, CoMk he cost term

proportional to the power flow through the branch in the Ph

subperiod (cost of losses) and

m

is the number of subperiods

into which the planning period ofN years has been divided.

COG

s the constructioncosts,

R , is the residual value,

CO@s the management costs,

ej is a binary factor that is equal to

1

for a resized branch

and

0

for an existing one.

the cost

C

independent of power, can be written by using

Denoting with:

(3):

Coil

= e .

cocj Roj )

+

C0g-j

(3)

The cost of resizing the branch Cod akes into account the

year of reconstruction to transfer the cash value to the

beginning of the planning period, while the residual value R ,

considers the fact that the planning period does not coincide

with the life durationof the component.

The cost of Joule losses in the h subperiod opjkan be

calculated transferring, to the cash value at the beginning of the

planning period, the annual cost of such losses

CHk

evaluated

by using:

c p j k =CkWh. 38760.coeffrj.Lj.ccpj z : k ) 4)

where:

Ckwhs the cost of kWh,

coeff is the utilization factor

of

energy losses under full

load, different for overhead and underground,

8760

are the number of hours per year,

yj is the resistance per mof line [ 2/km],

Lj is the branch length

[km],

4 k is the phase current in thefh branch [A] at the beginning

of the

Ph

subperiod,

ccpj is a corrective coefficient of the losses due to the

simultaneityof loads.

D. Distribution Network Structure

Distribution networks always have a radial structure and are

often subdivided into two different levels: trunk feeders and

lateral branches.The degree of reliability obtainable with this

network arrangement is limited by the fact that a fault in one

part of the network results in outage in a large number of load

points. To improve service reliability, emergency ties provide

alternative routes for power supply in case of outages or

scheduled interruptions. Emergency ties end with an open

switch so that radial structure is maintained during normal

conditions; firthermore trun s are subdivided in some

segments by means of normally closed switches, generally

positioned in

MVILV

nodes. During emergencies segments

can be reswitched to isolate damaged sections and route power

around outaged equipment to customers who would otherwise

have to remain out of service until repairs were made [9].

n

important class of such networks are the open loop networks

which are usually employed in urban power distribution

systems.

If

there are no laterals (pure open loop networks)

then service restoration is ensured through the emergency tie

that connects the ends

of

the feeder. n intermediate

alternative is to install laterals (spurious open loop networks)

in which top priority customers are supplied through the main

feeder and can be completely re-energized in the event of a

fault. The main characteristic of both open loop networks is

that only two branches can converge in a trunk node

(topological constraint)

[lo].

Automatic switching devices

along trun feeders and emergency ties may reduce both the

duration of service interruption and the number of customers

affected thereby (Fig.

2) [9,

101.

Emergency connection

A Pure open loop network with no lateral MV nodes

mergency connection

B) Spurious open loop network with lateral MV nodes

Fig. 2. Open loop

networks

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E.

Technical

Constraints

Cost of investments

Cost of losses

Each individual produced by GA operators has to comply

with all technical constraints usually adopted by planning

engineers, i.e. the voltage profile along the network trun s and

the three-phase short circuit currents in the network nodes

[

11,

121. Indeed, the presence of generation nodes in the

distribution system can cause a voltage drop or an overvoltage

in some points of the network. This situation depends

particularly on the transformer control system used. Generally

speaking, the connection of a generator to a network can result

in an increase in the voltage that depends on the power

supplied by the generator. For this reason, in the proposed

methodology the voltage profile is checked and only those DG

allocations able to maintain the voltage within prefixed ranges

both in normal and emergency situations are evaluated.

Calculations are performed by determining the impedance

matrix 2 of each feeder examined and by calculating the

voltage in each node of the network. The calculation of

Z

can

be noticeable simplified considering the particular network

architecture (open loop network).

The presence of DG can change the magnitude, duration,

and direction of the fault current. The fault current is modified

since the connection of rotating generators modifies the

characteristics (impedance) of distribution networks. In this

context, one needs to verify that the alteration in magmtude,

duration and direction of the fault current due to dispersed

generation groups does not affect the selectivity of protection

devices. In fact, the selectivity must be checked for each

connection of a new generator to the distribution network. In

the paper, fault currents are calculated for each DG

configuration examined by using the diagonal elements of the

short circuit matrix and the voltages in each node. Again all

those situations which do not comply with this technical

constraint cannot be accepted.

With DGithout DG

1,000,000US 5,000 US

626,000 US 486,000

US

V. RESULTS AND DISCUSSION

In order to show the capability of the proposed

methodologies, an area of the real M Y Italian network has been

considered. As shown in Fig. 3, it is constituted by 48 nodes

divided into 3 HVMV substations and 145 MVLV trun

nodes. The whole chosen area covers a surface of about 600

km. The period taken into consideration for the planning study

is 20 years long, with all nodes existing at the beginning of the

period. For each

MVLV

node, a constant power demand

growth rate of 3% per year has been assumed; the size of the

installed transformer ranges from 100 kVA to 630 kVA. The

majority of the branches is of the overhead type, but some

buried cables exist. The thermal capacity constraint is verified

for all the branches at the beginning of the planning period, but

some of them will have to be resized according to the growing

energy demand.

In order to test the proposed methodology, DG units have

been considered, ranging between 100-500 kVA. It is

straightforward noticing that there are no limits on the size of

DG units that can be treat by the optimization procedure

MV/LV node with DG unit

Fig.

3.

Test network 154 nodes

proposed. The maximum level of DG penetration rate admitted

for the study is

20%

of the total amount of power demand.

The cost of Joule losses has been taken as

0.05

US /kWh,

the cost for section unity has been assumed 0.3 US /* for

buried cables and

0,5 US /mm

for overhead lines (no

adjunctive costs for digging or poles, considering that no new

paths are built).

In Table I,

the costs of investments for grid upgrade and of

power losses are reported for the network in Fig. 3, without DG

and with the optimal arrangement of DG units obtained with

the proposed GA.

It is worth noticing that DG units allow reducing both costs

considerably. In particular, the greater saving is represented by

the reduction of the investments for upgrading the existing

branches. This is really an important result, considering that

T&D costs represent almost 30-50 % of the kWh cost and the

deferment of investments will produce benefits to both utilities

and final customers regardless the type of distribution energy

market adopted.

Table I.

Comparison

between costs for the

MV distributionnetwork inFig. 3

Total cost 1,626,000US 491,000 US

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VI.

CONCLUSIONS

DG is predicted to play an increasing role in the electric

power system of the near future. In fact, studies have predicted

that distributed generation may account for up to

20%

of all

new generation going online by the year 2010. With so much

new

distributed generation being installed, it is critical that the

power system impacts be assessed accurately so that these DG

units can be applied in a manner that avoids causing

degradation of power quality, reliability, and control of the

utility system. On the other hand, DG has much potential to

improve distribution system performance and it should be

encouraged. For this reason, it is really important that

distribution planners can have useful and efficient tools to take

into account the opportunities of DG, avoiding costly and time

consuming impact studies. On the basis of these

considerations, the paper deals with the important task of

finding the optimal siting and sizing of DG units for a given

network

so

that the cost of power losses during a prefixed

period of study can be minimized and investments for grid

upgrades can be deferred. As shown in the discussion, the GA

developed by the authors can be successfully applied in real

size scenarios with several hundreds of nodes. The examples of

application show that considerable savings can be achieved

simply by adding some generation units in the right position.

Further studies will deal with the development of new models

for DG units that can consider not only synchronous

generators, but also generation units with electronic power

conditioners for network interfacing.

VII. REFERENCES

[l] CIGRE WG 37-23: Impact of increasing contribution of

dispersed generation on the

power

system

-

Final Report. Electra,

September 1998.

[2] CIRED WGO4: Dispersed generation - Preliminary Report,

CIRED'99, Nizza (Fr), 2-5 Giugno1999.

[3] H. L. Willis, W. G. Scott, Distributed Power Generation, Marcel

Dekker, New York, 2000.

[4] Muscas, F. Pilo, W. Palenzona: Expansion of large MV

networks: a methodology for the research of optimal network

configuration, Proc. of CIRED96 Conference, Buenos Aires,

Argentina, pp. 69-74.

[5] D. E. Goldberg, Genetic Algorithms in Search, Optimization&

Machine Learning, Addison Wesley, 1989.

[6] T. Back, D. Fogel,

Z.

Michalewicz, Handbook of Evolutionary

Computation, Oxford University Press, New York, 1997.

[7] Invemizzi, F. Mocci, M. Tosi: Planning and Design Optimization

of MV Distribution, Proc. of T&D World '95 Conference, New

Orleans, USA, 1995, pp. 549-557.

[SI F. Mocci, C. Muscas,

F.

Pilo: Network planning and service

reliability optimization in MV distribution systems, Proc. of

ESMO95 Conference, Columbus (U.S.A), 95CH35755, pp. 36-

46.

[9]

G.

Celli, F. Pilo: Optimal Sectionalizing Switches Allocation in

Distribution Networks, IEEE Trans. Power Delivery, vol. 14, no.

[IO] B. Cannas,

G.

Celli, F. Pilo: Optimal MV distribution networks

planning with heuristic techniques, Proc. of AFRICON'99

Conference, Cape

Town

(South Afiica), 28 Sept.-1 Oct. 1999,

[ I] N. Hadisaid, J.

F.

Canard, F. Dumas: Dispersed generation

impact on distribution networks, IEEE Computer Applications in

Power, Vol. 12, No. 2, April 1999, pp. 22-28.

[12]

P. P. Barker, R. W. de Mello: Determining the Impact of

Distributed Generation on Power Systems: Part

I -

Radial

Distribution Systems Proc. of IEEE PES Summer Meeting,

Seattle (USA), 16-20 July 2000, vol. 3, pp. 1645-1656. ISBN 0

3, July 1999, pp.1167-1172.

pp. 995-1000.

7803-6420-1.

VIII.

BIOGRAPHIES

Gianni Celli

(M 1999) was bom in Cagliari,

Italy, in 1969. He graduated in Electrical

Engineering at the University of Cagliari

in

1994.

He became Assistant Professor of Power System

in 1997 at the Dept. of Electrical and Electronic

Engineering of the University of Cagliari. Current

research interests are in the field of MV

distribution network planning optimization,

power quality and use of neural Networks in the field of Power

System. He is IEEE member.

Fabrizio Pilo

(M 1998) was bom in Sassari,

Italy, 1966. He received the Dr. Eng. degree in

Electrical Engineering at the University of

Cagliari in 1992 and the Ph.D. at the University

of Pisa in 1998. Since 1996 he has been Assistant

Professor of Electrical Engineering at the

Department of Electrical and Electronics

Engineering of the University of Cagliari. His

current research interests include electrical power systems, network

planning and optimization and neural networks. He is IEEE and EI

member.

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00932323_optimal Distributed Generation Allocation in Mv Distribution Networks_2001