Tradewind Grid Modelling and Power System Data En

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parts, except with prior written consent of the TradeWind consortium.

FFuurrtthheerr DDeevveellooppiinngg EEuurrooppeess PPoowweerr MMaarrkkeett

ffoorr LLaarrggee SSccaallee IInntteeggrraattiioonn ooff WWiinndd PPoowweerr

D3.2 Grid modelling and power system data

M. Korps, L. Warland, J. O. G. Tande, K. Uhlen Sintef Energy Research

K. Purchala, S. Wagemans

Suez - Tractebel SA

December 2007

Agreement n.: EIE/06/022/SI2.442659

Duration November 2006 October 2008

Co-ordinator: European Wind Energy Association

Supported by:

Document Name: Grid modelling and power system data Date: 05/02/2009 Document Number: TR F6604 Page: 2/92

Document information

Diffusion list All TradeWind Consortium Partners

Documents history Revision Date Summary Author

01 30/11/07 Original release M Korpas et.al.

02 03/12/07 Updated map of UCTE K Purchala

03 18/06/08 Added appendix on Model Updates L Warland,M Korps

Document Name: Grid modelling and power system data

Document Number: TR F6604

Author: M Korps, L Warland, J O G Tande, K Uhlen, K Purchala, S Wagemans

Date: 01.12.2007

WP: WP3

Task: 1-3

Revision:

Approved:


SUMMARY This report describes the main activity of Work Package 3 in the Tradewind project. The work package includes collection of required load data, generation data and grid data and furthermore preparation of a European grid model allowing simulation of cross border flows relevant for wind power integration studies. The European grid model is simulated by using the Power System Simulation Tool (PSST), developed by SINTEF Energy Research. The simulation tool is based on an existing market model with simplified grid representation, assuming aggregated capacities and marginal costs of each generator type within specified grid zones. The simulation tool is programmed in Matlab using the Matpower functionality and runs an optimal power flow problem for a given power system model for each hour of a year. The optimal power flow minimises the total generation cost, using a simplified grid representation and with the assumption of a perfect market. The European grid model that is used in the simulations consists of separate power flow data files for the UCTE system, the Nordel system and Great Britain + the island of Ireland. The power flow data for the three systems are merged together, making it possible to run an optimal power flow for the whole system. The continental transmission network (UCTE) is represented by aggregated zonal PTDFs (Power Transfer Distribution Factors). DC representations of individual lines are used for Nordel and Great Britain + the island of Ireland, and these are converted to PTDFs when running simulations of the full European model. PDTFs allow for fast calculations compared to solving the optimal power flow using the underlying DC representation. Data for generators and loads are mainly collected from UCTE, EURELECTRIC and IEA. Aggregated generator units are divided into different types based on the primary fuel used. Marginal costs of the same generator type in different countries are for simplicity set equal. However, the input data structure offers the possibility to use different marginal costs for different countries. Marginal costs of hydro power are treated as a special case due to the possibility of storing water in reservoirs for later use. Pumped hydro operation is included in the model. Scenarios for installed wind power capacity were constructed in Tradewind WP 2, and the installed capacity for each country is divided into different wind regions. To use this data in the simulation program, it was necessary to relate these wind regions to the grid model zones within each country. Wind speed data from the Reanalysis global weather model, combined with regional wind power curves and wind speed adjustment factors from WP 2, is used for constructing synthetic wind power time series for the different grid model zones.


Table of Content

1 INTRODUCTION...................................................................................................................6

2 SIMULATION PROCEDURE................................................................................................7 2.1 THE OPTIMAL POWER FLOW DESCRIPTION .....................................................8

2.1.1 Cost function.....................................................................................................9 2.1.2 Optimal DC power flow description...............................................................12 2.1.3 Power Transfer Distribution Factors (PTDF) .................................................14

2.2 UPDATING THE CONSTRAINTS FOR A GIVEN HOUR ....................................17 2.2.1 Wind generation..............................................................................................17 2.2.2 Generation cost of hydro units........................................................................17

3 COMPUTER MODEL STRUCTURE..................................................................................19

4 EUROPEAN GRID MODEL................................................................................................20 4.1 POWER FLOW DESCRIPTION...............................................................................20 4.2 SYSTEM MODEL .....................................................................................................20

4.2.1 UCTE..............................................................................................................21 4.2.2 Nordel .............................................................................................................29 4.2.3 Great Britain and Ireland ................................................................................31

4.3 GENERATION ..........................................................................................................33 4.3.1 Capacity and cost scenarios ............................................................................33 4.3.2 Thermal power................................................................................................36 4.3.3 Hydro power ...................................................................................................37 4.3.4 Wind power.....................................................................................................41

4.4 LOAD .........................................................................................................................42 4.4.1 Load profiles ...................................................................................................42 4.4.2 Load forecast...................................................................................................42

5 RESULTS FROM TEST CASES .........................................................................................42 5.1 NORDEL ....................................................................................................................43 5.2 EUROPE (UCTE + NORDEL + GB/IRELAND)......................................................51

6 SUMMARY AND CONCLUSIONS....................................................................................58

REFERENCES ..............................................................................................................................60

APPENDIX A: HOURLY LOAD PROFILES .............................................................................62

APPENDIX B: CONNECTING WIND REGIONS TO GRID ZONES ......................................63

APPENDIX C: DATA FILES.......................................................................................................65

APPENDIX D: COMPUTER MODEL STRUCTURE ................................................................67


D.1 INSTALLATION ...........................................................................................................67 D.2 FUNCTIONS..................................................................................................................67

D2.1 Running the simulation......................................................................................68 D2.2 Presenting the results .........................................................................................69 D.2.3 Output of simulation and storing results...........................................................72

D.3 CASE DESCRIPTION (FORMATS) ............................................................................72 D.3.1 Power flow case description .............................................................................73 D.3.2 Generation capacity and marginal cost of production ......................................75 D.4.3 Time series of load............................................................................................76 D.3.4 Wind series .......................................................................................................77 D.3.5 Revision plans...................................................................................................77

APPENDIX E: MODEL UPDATES.............................................................................................78 E.1 REDUCED POWER FLOW DESCRIPTION ...............................................................78 E.2 EUROPEAN GRID MODEL - NEW DC POWER FLOW

DESCRIPTION ..........................................................................................................79 E.3 DISTRIBUTION OF GENERATION TYPES AND DEMAND ..................................79 E.3.1 NORDEL .....................................................................................................................80 E.3.2 UK AND IRELAND....................................................................................................80 E.3.3 UCTE ...........................................................................................................................80 E.4 ETSO NET TRANSFER CAPACITIES ........................................................................80 E.5 GRID DEVELOPMENT ................................................................................................82 E.6 MARGINAL COST SCENARIOS.................................................................................82 E.7 DEMAND SCENARIOS................................................................................................85 E.8 GENERATION CAPACITY SCENARIOS...................................................................86 E.9 REFERENCES TO APPENDIX E .................................................................................91


1 Introduction This report describes the main activity of Work Package 3 in the Tradewind project. The work package includes collection of required load data, generation data and grid data and furthermore preparation of a European grid model allowing simulation of cross border flows relevant for wind power integration studies. The European grid model is simulated by using the Power System Simulation Tool (PSST), developed by SINTEF Energy Research. The simulation tool is based on an existing market model with simplified grid representation, assuming aggregated capacities and marginal costs of each generator type within specified grid zones. Furthermore, this document explains the methodology assumed behind data acquisition and production of equivalent grid. Chapter 2 described the overall structure of the model used for simulating the European grid over a year with hourly time steps. The chapter includes:

overview of the algorithm used for yearly simulations, formulation of the optimal power flow problem solved for each time step, formulation of grid equivalents, description of how time-varying parameters (wind, load, hydro inflow) are

updated each time step

Chapter 3 describes briefly the simulation tool (PSST), which is implemented in Matlab. The detailed description given in Appendix D may serve as a simple user guide for the Matlab model. Chapter 4 gives detailed information on how the European power system is represented with grid equivalents, generator types and loads. Chapter 5 presents preliminary results from two test cases. The first test case is the Nordel system and the second test case is the integrated European grid, comprising the four synchronous zones UCTE, Great Britain, the island of Ireland and Nordel with HVDC connections in between. A summary of the work and concluding remarks are given in Chapter 6. Appendix A and B contains additional information on load profiles and wind data, respectively. Appendix C provides a list of the data files that are distributed together with this document and Appendx D gives an overview of the computer program structure. Appendix E describes important updates on modelling and input data that have been carried out after the original release of this report. The most important change is that the PTDF approach has been replace by full DC power flow representation.


2 Simulation procedure The structure of the computer program which is used for simulating the European power systems is shown in Figure 1. The inputs to the program are the grid model, time series for load, time series for wind, generation capacity forecast for all generator types and generation costs for all generator types. Both the load and wind are given as relative hourly profiles for a given reference year. The load and wind in any given hour can then be found using the total load in GWh and installed wind capacity in MW for all grid zones. The generation capacity forecast is given as total installed capacity for a given year and country.

Aggregate and present

- Load series- Inflow (hydro)- Watervalues

Time dependent

hour +1

- branch/hvdc flow- sensitiveties of constraints- power exchange (countries)

- Total load and production

True

False

hours==8760

DC/PTDF/AC

Solve Optimal power flow

Year (hour=1)- Power flow case description- Generator capacities- Generator cost curves (marginal cost)- Reservoire levels (hydro)

Input data for given year

Parameter updating- Wind and load by hour

- Cost of hydro production

- Clp

- bpmpd

External LP/QP solvers for DC and PTDF

results

- Wind series

Figure 1. The main simulation structure


For each hour the program will update the load, wind production and marginal cost of hydro units and run an optimal power flow, which determines the power output of all generators and the power flow on all lines. In general, the power flow description can be either DC, PTDF or AC formulation, though only the two first have been considered in this project due to the availability of data and also because of the calculation time needed to solve the power flow for all the hours within a year. The free (controllable) variables in the optimal power flow problem are the power output of all generators and the flow on HVDC interconnections. The power output of the generators is dependent on the maximum and minimum capacity, the marginal cost relative to other generators and limitations of power flow on lines.

2.1 The optimal power flow description The quadratic optimization problem, which is solved for each iteration in the simulation loop, is given as:

min ( ) 0.5 ' subject to:Tx

eqeq

lower upper

F x x Hx c x

A x bA x b

x x x

(1)

where x is the state variable vector F(x) is the cost function to be minimized (total generating costs)

H and c determine the cost of all the second and first order elements respectively in the cost function

A and b describe the transmission constraints between grid zones Aeq and beq are given by the power flow equations xlower and xupper defines the lower and upper bounds on the state variables

The state variables x includes generator production, HVDC flow and voltage angles. The voltage angles are only used in DC optimal power flow. The HVDC connections are modelled as loads with opposite sign on each side of the connections. Both elements of the cost function, H and c, are given by the generator cost curves. The elements of the cost function for voltage angles and HVDC part of the state variable x are zero. When the cost curves are given as piecewise linear or linear costs the quadratic part, H, is set to zero. The equality and inequality constraints typically represent the power flow description and the branch flow limitations respectively. Through the lower and upper bound on the state variable x it is possible to limit the flow on the HVDC connections as well as including maximum and minimum generation levels.


2.1.1 Cost function For each generator the total cost can given as either linear, piecewise linear or quadratic as as shown in Figure 2 through Figure 4. For non-generator state variables, such as HVDC power flow and voltage angles, the cost coefficient is zero.

Marginal cost (P) [Euro/MW]

Installed capacity

P [MW]

Installed capacity

Cost(P) [Euro]

P [MW]

Figure 2. Linear cost function

For linear cost functions the elements of the cost vector ci are equal to the marginal cost for any generator unit xi in the state variable x.


P [MW]


MC3

MC2

MC1

Installed capacityP1 P2

P [MW]

Cost(P) [Euro]

Installed capacityP1 P2 Figure 3. Piecewise linear cost function

Piecewise linear cost is handled by introducing an extra state variable xci for each generator with more than one segment, such as MC1, MC2 and MC3 in Figure 3, representing the cost for given generator with cost coefficient cci equal to one. The cost coefficient ci for the corresponding generator production, also given as a state variable, is set to zero. Each linear segment in the cost function for a given generator is then represented by an inequality constraint as show in the equations below, where the state variable xci must be higher or equal to the linear curve for all segments.

ci ji i ix b MCj x (2)

ci i i jix MCj x b (3) The inequality constraint representing segment j and generator i is shown in the equations above, where:


bji - The cost value for linear segment j in the cost function for generator i crossing the y-axis.

MCji - Marginal cost for segment j. xci - Cost for generator i xi - Production for generator i

P [MW]

MC2

MC1


Installed capacity

Installed capacity

Cost(P) [Euro]

P [MW]

Figure 4. Quadratic cost function

The quadratic cost Fi(xi) for generator i is shown in the equation below:

2

max

2 11( ) 12

i ii i i i i

i

MC MCF x MC x xP

(4)

When a generator is modelled with a quadratic cost function the current implementation of the program requires that all generators must be represented by quadratic descriptions. Thus, only generators on the form shown in Figure 2 and Figure 4 can be used.


The optimization problem does not include costs for startup and shutdown units. All the generator units in the power flow description represent an aggregation of all units in a given area or zone.

2.1.2 Optimal DC power flow description The DC power flow is a linearization of the power flow description under the following assumptions:

1. The voltage angles differences ( ) are small, small sin( ) 2. Line resistance is negligible ri 0 3. Flat voltage profile, i.e. all voltage magnitudes are close to 1.0 pu

1

3

2 Figure 5. Example 3-node network

Given the example network in Figure 5, the DC power flow equations, being nodal active power balances (5) and line flows (6), are found as:

1 2 1 3 1 2 1 3 1 1

1 2 1 2 2 3 2 3 2 2

1 3 2 3 2 3 1 3 3 3

inj

inj

inj

B B B B PB B B B PB B B B P

(5)

1 3 1 3 1 1 3

1 2 1 2 2 1 2

2 3 2 3 3 2 3

B B FlowB B Flow

B B Flow

(6)

where:

i - Voltage angle on node i


Bi-j - Branch suseptance between two nodes i and j Pinj i - Power injected into node i Flowi-j - Branch flow between node i an j

The DC power flow equations shown in (5)-(6) are linearly dependent, thus one of the nodes, the reference bus, needs to be removed. For the optimal power flow accounting for the reference bus can be done by removing column j in the B matrix and row j in the angle matrix, that is j=0. The DC power flow description can be generalized to:

inj G L hvdcG hvdcB P I P P I P (7) and

FlowfB P (8) where:

Pinj - Vector containing the power injected into buses. Sum of production, load and HVDC power injected.

PL - Load vector B - The nodal admittance matrix Bf - The flow admittance matrix IG - Connection matrix for generators containing ones where state variable

for given generator is connected into the system. Ihvdc - Connection matrix for HVDC links, containing plus/minus one

depending on the direction of flow on the connection. The voltage angle vector includes all but the reference angle. If there are several synchronous areas separated by HVDC connections these will each have their own reference bus. The equality constraints can be found by rearranging the power flow balance shown in equation (9).

G LG hvdchvdc eqeq

B I I P PbPA

x

(9)

The inequality constraints of the optimization problem in equation (1) the branch flow limitations shown below.


,max

,max

0 00 0

f FlowG

f Flowhvdc

B PP

B PP

bAx

(10)

The flow on the connections are directional, thus the duplication of the rows using negative sign to limit the flow in both directions. The method for handling HVDC links is based on refs. [1]-[2]. 2.1.3 Power Transfer Distribution Factors (PTDF) Generally speaking, power flows are determined by impedances of individual lines and branches, as well as system state variables such as nodal voltages or power injections. However, knowing all parameters of the system does not directly indicate which is the influence of a given transaction on a given line flow. The nodal PTDF matrix does offer such a possibility as it translates nodal injections into individual line flows by explicitly stating the contributions of each nodal injection to a given line flow. It can be calculated based on network topology and line parameters. In its simplest form, assuming a DC representation of a transmission network, PTDFs can be calculated directly from line parameters.

2.1.3.1 Formulation of nodal PTDFs Any nodal PTDFn,i-j shows how much of a given transaction Pn between nodes n and the reference node flows through a line i-j (Figure 6). By assuming a reference node and referring all transactions to it, the nodal PTDF matrix can be limited to only nodal injections. This matrix shows the incremental influence of a transaction, or nodal injection referred to a reference node for that matter, on a given line. In order to get the actual flow, all transactions in the system have to be considered.

jinjinjin

n

n

n

ji PTDFPTDFPTDF

PTDFPTDFPTDFPTDF

nodePTDF

nodePTDF

node

line

lineline

PTDF

,,,

31,31,231,1

21,21,2

2

21,1

1

31

21

................

Figure 6. Example PTDF matrix

One of the ways to derive the PTDF matrix is to assume a DC representation of the power system and derive the PTDF matrix directly from the line parameters of the network. In DC power flow terms, nodal PTDF matrix depends only on the line


parameters and not on the dispatch of production and demand. In other words, such a flow factors matrix does not depend on the operating point. The flow factors matrix can be derived as follows. The DC power flow equations shown in (5)-(6) are linearly dependent, one of the nodes needs to be removed. Therefore an arbitrarily chosen node, in this case node 3, is designated as a reference node and removed, indicated by the black lines, from both above sets of equations.

1 2 1 3 1 2 1 11 3

21 2 1 2 2 3 2 23

1 3 2 3 2 3 1 3 3 3

i

in

j

nj

j

n

i

BB

B B B PB B B PB B B B P

(11)

1 3

2 3 3

1 3 1 1 3

1 2 1 2 2 1 2

2 3 2 3

B FlowB B Flow

B Flo

B

B w

(12)

Substituting from equation (11) to equation (12) gives

11 3 1 3

11 2 1 3 1 21 2 1 2 1 2

21 2 1 2 2 32 3 2 3

inj

inj

B FlowPB B B

B B FlowPB B B

B Flow

(13)

1,1 3 2,1 3 1 3

11,1 2 2,1 2 1 2

21,2 3 2,2 3 2 3

inj

inj

PTDF PTDF FlowP

PTDF PTDF FlowP

PTDF PTDF Flow

(14)

FlowinjPTDF P P

(15)

Note, that due to erasing one of the nodes from equations (11) and (12), PTDFs in equations (13) and (14) are coupled to a reference node, or one reference node for each synchronous area. This means that PTDFk,n-m is a flow on line n-m spanning nodes n and m caused by a unit of injection in node k and withdrawal at the reference node. This allows the PTDF matrix to be limited to one number per node per line, instead of having to store all the possible combination of nodal transactions. Such a nodal-based PTDF matrix is a factor (NrNodes-1) smaller than a full transaction-based PTDF matrix. However, it is very easy to derive a transaction-based PTDF matrix from a nodal-based one. If one wants to know the influence of a transaction between nodes j and k on a line n-m between nodes n and m, it can be easily calculated by subtracting the corresponding factors from each other.

, , , j k n m j n m k n mPTDF PTDF PTDF (16)


As for the DC power flow, the flow equations (13)-(14) represent the equalities in the optimization problem. In addition there is a need for one extra equality constraint for each of the reference nodes.

2.1.3.2 Formulation of zonal PTDFs Due to the limited availability of the data needed to conduct a full-fledged power flow study (i.e. lack of detailed nodal demands, course network model available, etc), and in order to simplify the model and limit the problem size, it has been decided to use a zonal PTDF model. The zonal PTDFs follow directly from the nodal ones, and result of aggregation of the nodal factors into zonal ones. In other words, the zonal PTDFs depend on the reparation of the generation and the load in each zone. Zonal PTDF is defined as a weighted sum of each nodal PTDF of the zone for each monitored line. The sum is weighted by the generation of the load of the node. Two zonal PTDFs are defined for each line in each zone. There is one PTDF representing the contribution of flow passing through the line due to the load of the zone and another one representing the contribution of the flow passing on the line due the generation in the zone.

Azonej

L

AzonejL

klineTieL

klineTieloadAzone

j

jj

P

PPTDFPTDF ,

Azonei

G

AzoneiG

klineTieG

klineTiegenerationAzone

i

ii

P

PPTDFPTDF ,

where: Linear relations between zonal load and generation and each tie-line are now determined. This simplification does not imply any additional approximation in the computation of the power flowing on each tie-line. Indeed, a DC load flow is a linear system. The result of this computation is a matrix of the following type.


The limit of this zonal model is that the zonal PTDF is dependant of the generation and the load pattern. 2.2 Updating the constraints for a given hour For each hour the constraints which are time dependent are updated before running the optimal power flow. The constraints which are updated each time step are the load and available wind power that are given as hourly profiles for each area/node, as well as marginal cost of hydro units which is a function of the reservoir level. The available wind power is defined here as the wind power output that can be fed into the grid in a non-congested case. 2.2.1 Wind generation Aggregated wind farms are modelled as generators with maximum power equal to the available wind power for the specific hour. The minimum production is set to zero so that it is possible to reduce the wind power output in constrained areas. The marginal cost is set low, so that wind power plants always will produce if not limited by grid constraints. 2.2.2 Generation cost of hydro units If the cost function of any hydro unit with reservoir were given by a fixed marginal cost value, typically lower than any other generation types expect wind power, the unit would produce on its maximum level unit the reservoir was empty. This would have resulted in an unrealistic production profile over the year. Therefore, the marginal cost of hydro units is chosen to be a function of the reservoir level. Thus, the marginal cost reflects the value of saving the water for later use, referred to as the water value method


[3]. Typically, the marginal cost is low when the reservoir level is near its maximum and vice versa. The same water value function is used for pumping operation. As an example, consider a system with only gas power and hydro power. If the water value is lower than the marginal cost of the gas power plant, the hydro unit will generate power and thus cause a reduction of the reservoir level. If the water value is higher than the marginal cost of the gas power plant, the hydro unit consume power by pumping water from a lower reservoir to a higher reservoir. The reservoir level is updated each hour, by the following equation:

( 1) ( ) ( ) ( )Reservoirlevel t Reservoirlevel t Inflow t dt Production t dt The inflow is the flow of water into the reservoir, represented as an energy flow (MWh pr time step). The term dt is included so that the equation is also valid for other time steps than 1 hour. The production is negative for pumped hydro operation. It is also ensured that the maximum production capacity of the hydro unit is limited by the available energy:

( ) min , ( ) ( ) /max installedP t P Reservoirlevel t Inflow t dt Run of river units are not implemented as a separate generator type in the present version of the model. Instead, by specifying a hydro unit with very low reservoir capacity, the production will follow the hydro inflow as would be the case for a run-of-river station. The marginal cost will then always be low, due to the rapid filling of the reservoir.


3 Computer model structure Appendix D presents the computer program PowerSystemSimulationTool (PSST) that is used for simulations of the European grid. The simulation program is based on an existing market model developed by SINTEF Energy Research and adapted and further developed for the purposes of the Tradewind project. The simulation program is the property of SINTEF Energy Research. The PSST toolbox contains functionality for reading case-description, such as network data, time series of load, wind data, water capacities/values and available production capacities. There are also functions for running the power flow and presenting the results from the simulation.


4 European Grid model Due to various reasons, the decision was taken to base the grid models on public data. The chosen approach is to build models of the different European synchronous zones and based on these to create dedicated equivalents. This task will consist of treating different synchronous zones in a different way. The reason behind that is that the availability of network data is different in various regions. The continental transmission network (UCTE) is represented by aggregated zonal PTDFs. DC representations of individual lines are used for Nordel and Great Britain + the island of Ireland, and these are converted to PTDFs when running simulations of the full European model. The purpose of having a grid model is to analyze the influence of wind power fluctuations on continental power flows in Europe. This will be analyzed using hourly time series of wind power injections (based on historical wind power data or wind speed data) and demand, complemented by the behaviour of conventional generation obtained using an optimal power flow model. 4.1 Power flow description Due to difficulties in obtaining detailed data (generation, demand and network data), and due to the problem size (detailed European network of thousands of nodes needs to be solved 8760 times to get one year of data), it has been chosen to reduce the problem size. Each country has been aggregated into a number of geographical zones. The basis for this zone definition is the number of generators and available network infrastructure. As far as the network is concerned, all cross-border lines are kept as physical lines in the model. In each synchronous zone, the flow on each monitored AC line is defined thanks zonal PTDF (for more theoretical explanation see the chapter 2.1.3). The zonal PTDF matrix enables computing the flow passing through each monitored lines based on the load and the generation of all the zones of the studied synchronous area. The flows on the DC lines are defined by the optimization process based on generation costs and networks constraints. There is one zonal PTDF matrix by synchronous zone. 4.2 System model Load and generation data for the countries and country codes given in the table below, has been established from the available data from UCTE [7], Nordpool [8], National grid [9] and Eirgrid [10] .

Table 1. Areas/countries described in publications available from [7].

Code Country Code Country DE 1 Germany GR 16 Greece NL 2 The Netherlands HU 17 Hungary


BE 3 Belgium GB 18 Great Britain LU 4 Luxemburg PT 19 Portugal FR 5 France HR 21 Croatia CH 6 Switzerland CS 22 Serbia & Montenegro IT 7 Italy RO 23 Romania AT 8 Austria BG 24 Bulgaria ES 9 Spain BA 26 Bosnia-Herzegovina DK_W 10 Denmark West SK 27 Slovak Republic DK_E 11 Denmark East PL 29 Poland NO 12 Norway MC 40 Macedonia SE 13 Sweden UA 31 Ukraine West CZ 14 Czech Republic IR 72 Ireland SI 15 Slovenia SF 35 Suomi Finland

For all the countries in Table 1 internal bottle necks, installed capacity, load profiles and transmission line capability have been established in a model containing 132 nodes, 384 generators, 67 loads 213 transmission lines and 6 HVDC links. The transmission line capacities are based on thermal limits 4.2.1 UCTE

4.2.1.1 Description of the model Due to the failure of the attempts to acquire the high voltage grid data from the European TSOs, the TradeWind consortium was bound to base its investigations on on public data. As a starting point, the approximated UCTE network created by the team of prof. Janusz Bialek of University of Edinburgh has been chosen. This network covers the former first UCTE synchronous zone (i.e. excludes the Balkan states, Greece, etc). It is a patchwork of publicly available data such as national generation, peak load, power flow exchanges (UCTE), generation/substation data from websites of individual TSOs, geographic information of population and industry. Electrical parameters of transmission lines estimated from their lengths and voltage levels as standard /km values have been assumed. The voltage levels covered include 220kV and above. The network size is some 1200 nodes, some 380 generators. There are three load levels represented: summer, winter peak, winter off-peak. The zonal PTDF matrix has been created based on the winter peak case. However, this file represents the status of 2002. In order to get other time horizons, grid reinforcements since had to be added, again based on public data. Moreover, the same case is with the former second UCTE synchronous zone. This task has been done based on an approach that is similar to the one envisaged by prof. Bialek. Among other, documents like UCTE grid adequacy reports, SYSTINT Report on European, CIS and Mediterranean Interconnection has been used. The network has been updated till end of 2006.


As far as the accuracy of Edinburgh model is concerned, the article describing the grid model proves that the correlation of the PTDFs calculated using the model and the ones available from public sources (EC reports) is in the range of 95-97% [11]. Such range of accuracy seems acceptable for the TradeWind study considering that the complete data are not publicly available.

Figure 7. Map of the UCTE zone modelled. Please refer to Table 2 for overview of

zones.


Table 2. UCTE zone names and codes in the European grid model.

code zone name code zone nameA1 Austria F7 FranceA2 Austria FX France external (DC to England)B Belgium GR GreeceBH Bosnia and Hercegovina GX Greece external (DC to Italy)BU Bulgaria HR CroatiaCZ Czech Republic HU HungaryD1 Germany I1 ItalyD2 Germany I2 ItalyD3 Germany I3 ItalyD4 Germany IX Italy external (DC to Greece)D5 Germany L LuxemburgD6 Germany MC MacedoniaDK Denmark N NetherlandsDX Germany external (for DC Denmark ) P PortugalDY Germany external (for DC Sweeden ) P1 PolandE1 Spain P2 PolandE2 Spain PX Poland external (DC to Sweeden)E3 Spain RO RomaniaE4 Spain S1 SwitzerlandF1 France S2 SwitzerlandF2 France SC Serbia and MontenegruF3 France SK SlovakiaF4 France SV SloveniaF5 France UA UkraineF6 France

4.2.1.2 Validation of PTDF accuracy In order to validate the accuracy of the aggregated zonal PTDFs that will be used in the frame of the Tradewind project, for different scenarios (production and consumptions patterns), a comparison is done between the flows on the lines computed by the PTDFs and by a DC load flow. The study methodology is as follows:

1) introduce changes to the full nodal model and compute the power flow using the detailed network model (extended Edinburgh model)

2) translate the nodal variations into zonal variations (i.e. summing up the nodal generation into zonal generation, nodal load into zonal load) and compute the zonal power flow using aggregated zonal PTDFs

3) compare the power flows on cross-border lines


In order to have a representative number to illustrate the power flow estimation mismatches between the two models, each scenario is represented with a weighted average mismatch. This gives also an objective way to compare the different scenarios.

linesnr

iizonal

linesnr

iizonalinodal FlowFlowFlowerror

_

1,

_

1,, )(/)(

where: error weighted average mismatch between the nodal and zonal network models nr_lines number of lines Flownodal,i power flow on line i given by the nodal model (Edinburgh model) Flowzonal,i power flow on line i given by the zonal model (aggregated PTDFs) The total of 20 scenarios have been analyzed:

Scenario 1 : Reference network (no variation), Scenario 2: Variation of the load inside a country (the load increase by 2141

MW in the region D6 and decrease by the same amount of load in the region D2),

Scenario 3: Variation of the production inside Germany (the production in region D2 increase by 10% (= 582 MW) and decrease by the same value in the region D6),

Scenario 4: Variation of the production inside France (the production in region F2 increase by 10 % (= 636 MW) and decrease by the same value in the region F6),

Scenario 5: Variation of the loop flows between two regions from different countries (the production in a region of Germany increase by 10 % and the production in a region of Spain decrease by the same value),

Scenario 6: Variation of the loop flows between two countries (the production in France increase by 15 % and the production in Germany decrease by the same value),

Scenario 7: Randomization of the production (each production unit is changed by a random factor between -10 % and +10 % and the total production stay unchanged),

Scenario 8: Variation of the production inside Spain (the production in region E3 increase by 50 % and decrease by 20 % in the region E1).

Scenario 9: Increase of the generation in region D2 (+5000 MW) and decrease of the same value in the region D6.

Scenario 10: Same as scenario 9, but for 10 000 MW. Scenario 11: Increase of the generation in region D2 and DK (+5000 MW) and

decrease of the same value in D4 and D6.


Scenario 12: Same as scenario 11, but for 10 000 MW. Scenario 13: Uniform increase of load in EU (+10%). Increase of production:

+30 % in DK, D1, D2, N, E1, F6; +10% in D3, D4, B, F, E, I1, I2, A, HU; and +4.8% for others.

Scenario 14: Uniform increase of load and production: +10% in EU. Scenario 15: Random increase of load and production: between 0 and +10% in

EU.

Scenario 16: Random increase of load and production: between 0 and +20% in EU.

Scenario 17: Uniform increase of the load (+20%) in south (regions P, E, I, F4, F5, F6, HR, GR) and uniform increase of the production in EU (increase proportional to the actual production).

Scenario 18: Uniform increase of load and generation: +30%. Scenario 19: Uniform decrease of load and generation: -30%. Scenario 20: Random increase/decrease of load and generation: +/- 30%.


Table 3. The percentages of accuracy of the PTDFs for each scenario.

# brief scenario description error

1 Reference network (no variation), 0.01% 2 load increase by 2141 MW in D6 and the same decrease in D2 0.01% 3 load increase in D2 by 10% (= 582 MW) and the same decrease in D6 0.01% 4 load increase in F2 by 10% (= 636 MW) and the same decrease in F6 0.01%

5 production in all German regions increases by 10 % and decreases in Spain by the same value 0.01%

6 production in all French regions increase by 15 % and the production in Germany decrease by the same value 0.01%

7 each production unit is changed by a random factor between -10 % and +10 % and the total production stay unchanged 3.55%

8 production in region E3 in Spain increase by 50 % and decrease by 20 % in the region E1. 0.01%

9 Increase of generation in region D2 (+5000 MW) and decrease of the same value in the region D6. 0.01%

10 Increase of generation in region D2 (+10000 MW) and decrease of the same value in the region D6. 0.01%

11 Increase of the generation in region D2 and DK (+5000 MW) and decrease of the same value in D4 and D6. 0.01%

12 Increase of the generation in region D2 and DK (10000 MW) and decrease of the same value in D4 and D6. 0.01%

13 Uniform increase of load in Europe (+10%). Increase of production: +30 % in DK, D1, D2, N, E1, F6; +10% in D3, D4, B, F, E, I1, I2, A, HU; and +4.8% for others.

0.12%

14 Uniform increase of load and production: +10% in EU. 0.17% 15 Random increase of load and production: between 0 and +10% in EU. 1.30% 16 Random increase of load and production: between 0 and +20% in EU. 2.50%

17 Uniform increase of the load (+20%) in south (regions P, E, I, F4, F5, F6, HR, GR) and uniform increase of the production in EU (increase proportional to the actual production).

0.11%

18 Uniform increase of load and generation: +30%. 0.27% 19 Uniform decrease of load and generation: -30%. 0.28% 20 Random increase/decrease of load and generation: +/- 30%. 6.68%

As can be seen on the above table, the error percentages stay lower than 1 % for most of the scenarios. In case of scenarios assuming a uniform variation of zonal dispatch (i.e. respecting the initial contributions of individual nodes to the zonally aggregated sum), the precision is very high even in case of large variations (scenarios 9-12). The scenarios for which the error rises upon 1% are scenario with a random increase of the generation and/or load on individual nodes. As soon as the production pattern in the zone changes, the precision of zonal aggregation is decreasing. As the aggregated PTDF model neglects the information of the dispatch within the zone (i.e. assumes that this dispatch is relatively unchanging), it cannot take these variations into account. However, even with random variations of these nodal injections by a factor of up to 30%, the estimation mismatch precision seems by far acceptable. For a random variation of the nodal production and the load of 30%, the error stays still under 7%.


The figure below shows the difference between the flux on the lines computed by the load flow and via the PTDFs. For the four scenarios having the worst accuracy, (7, 15, 16 and 20), the average mismatch between the power flows on each of the monitored lines each of the monitored lines estimated using the nodal and zonal model is calculated and plotted. For majority of the lines this mismatch is lower than 20 MW. Given the fact that the lines in question are cross-border lines, usually of the capacities in the range of thousands of MWs, this mismatch seems quite low.

Mean absolute error on the lines

0

10

20

30

40

50

60

70

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141Line number

Abs

olut

e er

ror (

MW

)

Figure 8. Mean absoloute error on the lines.

Figure 9 confirms that the absolute mismatch in the range of tens of MW is actually quite low. It presents the same mismatch but this time related to the nominal capacity of the line. The error of the flux computed by the PTDFs is less than 5 % for more than 95 % of the lines.


Mean estimation error in % of nominal power

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141Line number

Abs

olut

e er

ror (

MW

)

Figure 9. Mean estimation error in % of nominal power.

4.2.1.3 Network reinforcements There are two options to include the impact of network reinforcements:

Recalculate the new set of PTDFs. That implies to have a different set for each possible grid.

Introduce a new set of incremental PTDFs. This means that the current set of zonal PTDF will still be used, but in order to take the network reinforcements into account, one would need to calculate the effect of this reinforcement, and apply it to correct the power flows. This incremental PTDFs set would be used in the same way the generic factors are used (multiplication of zonal injections and the PTDFs give the line power flows), but the result would be only the impact of this particular network reinforcement on power flows.

As can be seen, both options actually imply recalculation of the PTDFs. The choice therefore depends on the goal of the analysis. If one wants to quantify the impact of a given reinforcement option on the European power flow, option 2 would seem the adequate one. If one wished to study the continental power flow at some other time horizon, option 1, implying a new adopted set of PTDFs, is the one to opt for.

4.2.1.4 Phase-shifting transformers Phase-shifting transformers are currently not modeled in the UCTE grid model described above. These could in principle be introduced in the model by extending the PTDF matrix. The flow on each tie-line will be then a function of the generation, the


load of each region and the tap of the phase shifters. However, as currently there is no European optimization of phase shifter taps positions to maximize the transmissible flow in the UCTE, each country choose in function of its own needs the tap of the phase-shifter. Therefore it has been chosen to abandon the use of tap changers as a optimization variable. 4.2.2 Nordel The basis for all calculations performed on the Nordel power system is the 23 generator model of the Northern European system. The 23 generator model determines the topology of the grid, and the distribution of loads and generation units except wind. The 23 generator model of the Nordic system has been developed at SINTEF Energy Research, and it is implemented in Matlab using Matpower format as a DC optimal power flow model. The model has been developed through several steps and updated with recent grid and generation data for the use in the TradeWind project. The original development of this model is described in [12], and further model developments in [13]. The original Nordel model (as used in the example in Chapter 5) includes a bus representing Denmark West and a bus representing Germany. These buses are removed from the Nordel grid used here, since they are parts of the UCTE grid model. The HVDC connections to Denmark West and Germany are kept, since they link the Nordic grid model with the UCTE grid model. The grid model is visualised in Figure 10. In the context of the TradeWind project, the 23 generator model is suitable as it have a significant correspondence in power flow if comparing with a full scale model of the Nordel system. For analysis connected to active power flow the reduced size and still significant accuracy makes the 23 generator model favourable. In the 23 generator model of Northern Europe the lines and generators are located and adjusted in such a way that they to a significant degree reflect the real production and the most interesting bottlenecks in the Nordel system. The impedances are adjusted in such a way that the power flow to a significant degree will correspond to a full-scale model. HVDC-links that are not modelled (for instance Finland-Russia) can be treated as loads in the model, although not included in the data set used here. In Figure 10 the locations of the different generators equivalents in the 23 generator model are shown. The node number of the different generators is also shown. In Figure 11 the one-line circuit diagram of the 23 generator model is shown. The installed aggregated generation capacity and generator type (wind is not included since it is treated separately, see Chapter 4.3.4) at each bus are listed in Table 4 . The total installed capacity of each generator type in each country sums up to the values reported in the EURPROG statistics [14].


G

G

G

G

G G

G G G

G G

G

G

G G

G

G

G

G

G

G

G

G

G

G G

G G G

G

G

G

G

GG

G

G

G

G

G

G

67007100

7000

311532493245

3000

6500

3100

32003359 3300

8500

5100 5300

5500 5603 5600

6000 6100

5400

Figure 10. The Nordic grid equivalent. The numbers corresponds to generator

buses. Load buses are not shown.

Figure 11. Nodes, generators, and loads in the original 23 generator model of the Northern European system. For this project, The DK_WEST and Germany areas

removed from the model, since they are parts of the UCTE system.

8001 8002

5603

5602 5600

5601

6000

6100 5300

5301

5501 5401

5402

6001 5102

5103

5100

5101

6500

6700 6701

3701

3244

7100 3115

3249 3000

7000

3245

3100 3200

3300

3360

8003

8004

8005

8500

9000

9001

5500

5400 3359

NO_S

DK_WEST

Germany

NO_M

SF SE

DK_EAST


Table 4. Generator specification for the Nordel system.

Country Bus number

Generator types Capacity 2005 (GW)

Sweden 3000 Nuclear 5.3 Sweden 3100 Hydro 3.0 Sweden 3115 Hydro 4.8 Sweden 3200 Nuclear 0.35 Sweden 3245 Hydro 1.8 Sweden 3249 Hydro 6.6 Sweden 3300 Fossil (oil) + other renewable 5 + 3.1 Sweden 3359 Nuclear 3.5 Norway 5100 Hydro 2.4 Norway 5300 Hydro 3.7 Norway 5400 Hydro 2.5 Norway 5500 Hydro 1.3 Norway 5600 Hydro 3.8 Norway 5603 Hydro 0.13 Norway 6000 Hydro 1.9 Norway 6100 Hydro 3.9 Norway 6500 Hydro 2.5 Norway 6700 Hydro 4.5 Finland 7000 Fossil + other renewable +

Nuclear 8.4 + 2.2 + 2.7

Finland 7100 Hydro 3 Denmark (east)

8500 Fossil (coal & gas) + other renewable

2.8 + 0.8

4.2.3 Great Britain and Ireland Figure 14 shows the simplified system model for the synchronous zones of Great Britain and the island of Ireland (the numbers on the figure are only for illustration). It has been developed at the University of Manchester. All the transmission lines are modelled with inductive impedances. This is a simplification, but the flow of power should reflect the real flow of power in the system with these impedances [13]. As the system is radial the actual impedance values does not make a difference for the power flow, when using a DC or PTDF description. The island of Ireland is attached to bus number 2 in South of Scotland through an HVDC connection.


North of Scotland

3

6

1 2 5

7

4

Ireland

South of Scotland

England and Wales

Figure 12. Great Britain and Ireland system (Ireland+North-Ireland) grid equivalent

Installed production and load is given Table 5, for specified nodes, as percentage of total installed production and load respectively. The generation type capacities are distributed on the nodes based on the percentage given in the table.

Table 5. Installed production and load percentage of total installed capacity and load respectively.

Node Generation Load 1 15.38 % 11.44 % 3 70.41 % 40.67 % 5 7.82 % 4.67 % 6 6.37 % 40.67 % 7 100 % 100 %

The maximum power flow on the HVDC connection between Ireland and South of Scotland is set to 500 MW in both directions [15], while there are not limits for the flow on all other branches. For the optimal power flow this is the same as putting Scotland, England and Wales on the same node.


4.3 Generation 4.3.1 Capacity and cost scenarios The generation type and capacities is specified for two scenarios of generating capacity evolution, where [16]: Conservative Scenario A: only new generation projects known as firm are integrated. This scenario is used to identify the expected need for new investments in generation. Best estimate Scenario B: it takes into account future power plants whose commissioning can be considered as reasonably probable according to the information available for the TSOs. This scenario is used to give the best view of possible evolution of adequacy provided expected investments in generation are made.


Figure 13. Installed capacity and marginal cost.

For each year 2005, 2006, 2007, 2008, 2010, 2015, 2020 and 2030 the generation capacity available on the third Wednesday in both January and July is specified. The type of generation is given as either hydro, nuclear, fossil, renewable and not clearly identifiable energy source, where fossil can be specified as lignite, hard coal, gas, oil or a mix of oil and gas. The installed capacity is the aggregated electricity generating capacity of the given type at the given area and year, as described in Table 1. The provided spreadsheet (see file list in Appendix C), shown in Figure 13, also includes columns for specification of generation cost data by generation type as given below:


The non-fuel O&M cost (cnf) is the non-fuel operation and maintenance cost in EUR/MWh, e.g. about 6 EUR/MWh for nuclear power.

The fuel efficiency (nf) is the ratio of electric energy output and fuel energy input,

e.g. maybe 60 % for modern natural gas turbines. The fuel cost (cf) is the fuel cost per fuel energy unit specified in EUR/MWh. The tax (ct) is the (equivalent) taxation of the electricity generation in EUR/MWh. It

is stated as an equivalent taxation, as the tax may not be directly on the generation, but indirectly through tax on greenhouse gas emissions or others.

The marginal cost (mc) is the marginal operating cost in EUR/MWh that is

calculated from the above given parameters.

tf

fnf cn

ccmc

100/

The marginal cost of generation will to a large degree govern the power system operation. Hence it is important that we use the best available generation cost data. It is however out of the scope of this WP3 (and the project as a whole) to prepare detailed generation cost estimates. The suggestion is thus that we use generation cost data from well established references. In IEA reports we can find estimates for most of the cost parameters given above, but the uncertainties are pronounced both on future fuel costs and taxation. Indeed, taxation can be considered an instrument for modifying the operation of the power system, and as such it may seem relevant to consider using in the analysis phase of the project not only one forecast for taxation, but rather applying a span of taxation levels. Using several scenarios for taxation will of course increase the total number of possible simulation scenarios significantly, since there are three different wind development scenarios and two scenarios for other generating types. The aggregated cost estimates used are shown in Table 6. For simplicity, and because of difficulties of obtaining exact operating costs for all types in all countries, there are no differences in fuel costs between the countries. Several sources have been used for specifying the cost data for each generator type [17]-[22]. Especially uncertain is the marginal cost of renewable energy sources other than wind and hydro, as this type comprises several different sources (e.g. biomass, biogas, waste) and a number of different converting technologies. The costs of bio fuel will also vary much, depending on the availability of local resources. Another issue is that the electricity generation from bio fuelled plants may be linked directly to heat production in which electricity is a secondary product. In these cases, the electricity production could be defined as an


input time series, in the same way as wind and demand is treated in the simulation program. However, it is chosen here to use the same cost characteristics for all generators of the same type. For renewable energy sources (other than wind and hydro), it is assumed that the power plant technology is similar to natural gas plants regarding efficiency, but with a higher non-fuel cost. The chosen fuel cost results in a marginal cost higher than gas but lower than oil and mixed oil/gas.

Table 6. Cost estimates for the different generation types. See 4.3.3 for details on hydro power marginal costs.

Non-fuel

O&M cost Fuel

efficiency Fuel cost Tax Marginal

cost [/MWh] [%] [/MWh] [/MWh] [/MWh] hydro power stations 3.0 100 0.0 0.00 3 nuclear power stations 6.0 100 5.0 0.00 11 fossil fuel power stations 1.5 49 12.6 0.00 27 of which, lignite 3.3 37 5.4 0.00 18 of which, hard coal 3.3 37 5.7 0.00 19 of which, gas 1.5 49 12.6 0.00 27 of which, oil 5.0 30 15.0 0.00 55 of which, mixed oil / gas 5.0 30 14.0 0.00 52 of which, non attributable 5.0 30 16.0 0.00 58 renewable energy sources (other than hydro) 4.0 49 14.0 0.00 33 of which, wind 2.0 100 0.0 0.00 2 not clearly identifiable energy sources 5.0 30 17.0 0.00 62

4.3.2 Thermal power The present and forecasted generation capacity of thermal power given in the UCTE System Adequacy forecast has been used for all countries in the UCTE synchronous zone. The aggregated thermal units are represented with constant marginal costs and full flexibility to operate between zero and maximum power. As no information was available on where the units are located, it is chosen here to distribute the known national thermal capacity evenly inside each country (for countries divided into several zones). For example Spain has 7.6 GW installed nuclear power, and is in the European grid model divided into four areas; E1-E4. Hence, each of these areas has installed 7.6 GW / 4 = 1.9 GW nuclear. For the Nordic countries, the locations and capacities of the thermal units are specified in the Matpower power flow input file, see Appendix C.


Renewable energy sources other than hydro and wind are treated as thermal power plants, i.e. dispatchable generators. 4.3.3 Hydro power For the Nordic countries, the locations of the hydro power units and installed capacity are specified in the Matpower power flow input file, see Table 4 and Appendix C. For the other countries, on the other hand, the hydro power data is only known at national level. For the countries that are divided into several zones, such as France, the total hydro capacity is simply distributed evenly among these zones. For the simulation studies in the later work packages, the location of hydro units should be looked at in more detail. The hydro generation capacity from the UCTE System Adequacy Forecast and EURPROG Statistics is used for all countries. The UCTE data does not diversify between run of river, pumped hydro and conventional hydro. The production capacity used in the model is the sum of all types of hydro, and all hydro units are connected to a reservoir. This is a rough approximation; especially for countries where the main type is run-of-river plants. Regarding countries with mostly run-of-river, this approximation is to some extent accounted for as the reservoir capacities in these countries are very low. Pumped hydro operation is included in the model by setting the negative minimum generating capacity equal to the installed pumping capacity. The UCTE data does include any information on reservoir capacity and pumping capacity, therefore this required data has been collected from various other sources [26], [23]. For countries with missing data, the annual generation is found by assuming 2000 utilization hours, which e.g. corresponds to Italy, France and Slovakia. Furthermore, the reservoir capacity is set to 0.24 times the annual production, which corresponds (in per unit of annual production) to the reservoir capacity in Switzerland. The annual inflow to the hydro reservoir is set equal to the annual production. The assumed hydro power data is shown in Table 7.


Table 7. Assumed hydro power data, year 2005. The numbers in italic are estimated based on general assumptions on utilization hours as explained in the text.

Capacity Reservoir Inflow Pumping Start Inflow

Country Code (GW) (TWh) (TWh/yr) capacity (GW)reservoir

(%) pattern Germany DE 8,7 0,30 16,8 3,8 70 1 Belgium BE 1,4 0,03 0 1,3 70 1 Luxemburg LU 1,1 0,03 0 1,1 70 1 France FR 25,5 9,80 55 4,3 70 3 Switzerland CH 13,3 8,60 30,4 1,6 70 3 Italy IT 21 7,90 35,5 4,2 70 3 Austria AT 12 3,20 31,5 2,9 70 3 Spain ES 18 18,40 24,8 3,3 70 2 Norway NO 28 82,00 136 0 70 4 Sweden SE 16 28,00 72,6 0 70 4 Czech CZ 2,1 0,54 2,5 1,1 70 3 Slovenia SI 0,9 0,00 3,1 0 70 3 Greece GR 3 2,40 6 0,7 70 2 Great Britain GB 4,3 1,20 5 2,8 70 1 Portugal PT 5 2,60 10,6 0,8 70 2 Croatia HR 2 1,44 6 0 70 3 Serbia CS 3,5 2,00 11,8 0 70 3 Romania RO 6 4,30 17,9 0 70 3 Bulgaria BG 2,8 0,98 4,1 0,6 70 2 Bosnia BA 2 1,44 6 0 70 1 Slovakia SK 2,4 0,63 4,2 0,9 70 2 Poland PL 2,23 0,41 1,7 1,7 70 2 Finland SF 3 5,00 13,6 0 70 4 Ireland IR 0,5 0,24 1 0,3 70 1 Long-term statistics for weekly hydro inflow is well known for the Nordic countries. Approximate weekly inflow patterns for other countries has been constructed based on information from the hydrological study FRIEND (Flow Regimes from International Experimental and Network Data) [24], and from [25]. It has been chosen here to specify four general, representative inflow patterns, and assigning them to the different countries, see Figure 14 and Table 7. This is assumed to be a sufficient detail level for the purposes of the Tradewind project. However, it is possible for the user of the program to specify new inflow patterns, if a further diversification between the countries is considered necessary.


0 10 20 30 40 50 600

1

2

3

4

5

6

Week

% o

f ann

ual i

nflo

w

Inflow pattern 1Inflow pattern 2Inflow pattern 3Inflow pattern 4

Figure 14. Hydro inflow patterns used in the data set.

The water values for the Norwegian hydro units have been constructed by using the EMPS-model (Multi-Area Power Market Simulator), a commercial model developed at SINTEF Energy Research in Norway for hydro scheduling and market price forecasting [1]. This is a complex stochastic optimisation model that simulates the optimal operation of the hydro power resources in a region with a stochastic representation of inflow to the hydro power stations and a number of physical constraints taken into account. Water values for the first two weeks of January are shown in Figure 15, and the seasonal variations are shown in Figure 16. The marginal cost of hydro units is set equal to the water value, and it is seen that this is a function of reservoir level and the time of the year. Due to the similarities between the Nordic countries, the water values for the reservoirs in Finland and Sweden is calculated from the same function as for Norway. For countries outside the Nordic region, the water values from Norway for the first week of January has for simplicity been used for all weeks of the year, since no information has been available on how the water values (or other measures for the marginal cost of hydro) is calculated. The main reason for choosing the same water value function for all weeks of the year is that countries outside the Nordic regions are not dominated by hydro power with large reservoirs, meaning that the special case with very low water values during summer may be invalid. For the simulation studies in the later work packages, a sensitivity analysis may be carried out on how the water value functions influence the power flow between the grid zones, especially regarding countries such as France and Switzerland with relatively large amounts of hydro power.


0 50 100 150 200 250 300 350 400 4500

20

40

60

80

100

Water values (/MWh)

Res

ervo

ir le

vel (

%)

Week 1 (january)Week 2 (january)

Figure 15. Water values for the fist two weeks of January, calculated from the

EMPS model.

0 10 20 30 40 50 600

50

100

150

200

250

300

350

400

450

Week

Wat

er v

alue

(/M

Wh)

0 %25 %50 %75 %100 %119 /MWh

Figure 16. Sesonal variations in water values for Norway. The different graphs represents different hydro reservoir levels. For example, if the reservoir is 75 % filled in a specific hour in week 30, the water value for that hour is 119 /MWh.


4.3.4 Wind power Wind power scenarios for all relevant countries have been constructed in WP 2 of the Tradewind project [27]-[28]. In WP 2, the installed capacity for each country is divided into different wind regions. To use this data in the simulation program, it was necessary to relate these wind regions to the grid model zones within each country. The capacity scenarios comprise estimates for the installed capacity in years 2008, 2010, 2015, 2020 and 2030, divided into low, medium and high wind power development. Also included is an estimate for the installed capacity for 2005 in each zone, based on the actual installed capacity within each country. An extract of the data file containing the scenarios of installed capacity is shown in Table 8. For UCTE, the bus numbers are created automatically in the simulation model. Therefore, the bus numbers are not specified in the wind data file. The wind power units are automatically located at the generator buses inside the zones specified in the data file. For Nordel, Great Britain and the Ireland system on the other hand, the generator buses are specified manually in Matpower .m files (see Figure 10-Figure 12) and must therefore also be specified in the wind data file. The complete overview on how the wind regions from WP2 are allocated to the grid model zones is found in Appendix B. There are 128 wind regions from WP2 that lies within the geographical area of the European grid model. These regions are put into a total of 56 different grid zones. The total wind power production in a zone is consequently the sum of production from all wind regions inside that zone.

Table 8. Extract from Excel-sheet winddata.xls, which specifies the scenarios for installed wind power capacity in the different zones.

2005 2008 2008

Area Zone Bus

Region Identifier (Node)

RDP mapping Actual L M

SF SF 7000 24 222 0 0 0 SF SF 7100 25 242 0 15 30 FR F7 26 100 91 317 408 FR F1 27 119 91 317 408 FR F2 28 120 91 317 408 FR F3 29 102 123 436 560 FR F5 30 63 132 218 280 FR F4 31 83 132 218 280

Wind speed data from the Reanalysis global weather model, combined with regional wind power curves and wind speed adjustment factors from WP 2, is used for constructing synthetic wind power time series for the different grid model zones. The preparation of the wind speed data is based on the RDP-mapping (Renanalysis Data Points) from WP 2. The Reanalysis model, RDP-mapping and different regional power


curves are described in more detail in [27], [29] and [30]. Reanalysis wind speed data from year 2000 to 2006 is provided. It is possible for the user of the simulation model to choose which Reanalysis year to base the power calculations on. Chapter 5.2 shows some example duration curves for wind power based on the Reanalysis wind speed data. 4.4 Load For the Nordic countries the hourly load profiles have been provided by Nordpool [21] and the forecast from Nordel, National grid [9] for Great Britain and Eirgrid [10] for Ireland, while UCTE [7] have provided the load data for all the other countries. 4.4.1 Load profiles Hourly load profiles for all areas have been collected for a given year, 2006, which in the simulation tool is stored in a Matlab binary mat-file called UCTELoad.mat. This contains two variables allhours and year, where both are cells and the index in cell corresponds to the area number for given country (see Table 1 on page 20). The variable allhours contains the hour by hour load in each area, while the variable year contains relative load for all countries and given year using 2006 as a reference. 4.4.2 Load forecast The load forecast for the years 2007, 2008, 2010, 2015, 2020 and 2030 is given in the Excel spreadsheet WP_loadscenarios.xls, see Appendix C. As for the generation, the load forecast is given for specified hours on the third Wednesday in both January and July, with one sheet for each area, where the sheet is named by the country code for given area. Assuming these hours are representative for the whole year, the relative increase/decrease in load demand can be found using the same hours in 2006 as reference. The forecast for any of the specified years can be calculated using the relative increase/decrease and the hour by hour load profile for year 2006. 5 Results from test cases In the development of the simulation tool used in this project, and models for the synchronous zones UCTE, Nordel, Great Britain and Ireland as well as in the gathering of system data some preliminary results have been prepared. These are test cases, which need and will be further tuned, though they do provide promising result as both load and production in the different areas are reasonable.


5.1 Nordel A test case with the Nordic system has been provided as an example of the use of the PSST toolbox and what results can be expected when running a simulation. The data used in this simulation is not complete, so assumption and simplifications have been made in order to get the simulation running. For simplicity, wind data measurement from one location in Norway has been used for zones NO1 and NO2 in Norway. No wind power is specified for Finland and Sweden in the test case. The geographical location of power lines and the generator buses are shown in Figure 17, which is similar to the Nordel grid description in 4.2.2, except that Denmark-West and Germany is included here (these countries are part of the UCTE system in the full European grid model, which is not used in this test case).

Figure 17. Bus numbering in the Nordel test case.

All wind power series are first scaled to a maximum power output of 1, see Figure 18. Then they are upscaled to the desired installed capacity in each area:

DK-W: 2393 MW DK-E: 748 MW NO1: 9 MW NO2: 381 MW


This approach for wind power representation differs from what is used in the full European model, where Reanalysis wind speed data is used as a basis for wind power series in all zones.

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

0.5

1

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

0.5

1

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

0.5

1

Pow

er o

utpu

t rel

ativ

e to

inst

alle

d ca

paci

ty

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

0.5

1

Hour of the year

Figure 18. Relative wind power output in (from top to bottom): DK-West, DK-East, NO1 and NO2. Note that the same time series is used in NO1 and NO2.

In the test case, revision and maintenance of nuclear plants is simplified by reducing the available power output of all generators by the same factor. A monthly aggregated reduction factor has been used, as shown in Figure 19.


1 2 3 4 5 6 7 8 9 10 11 120

0.2

0.4

0.6

0.8

1

Month

Rel

ativ

e av

aila

ble

capa

city

Figure 19. Monthly available nuclear capacity.

The simulation program has been set up to solve 8760 optimal power flow problems, one for each hour of the year. The marginal cost of hydro, which is a function of the reservoir level, links consecutive hours and therefore the optimal power flow problems must be solved chronologically. After the complete simulation is run, the program returns the load, generation, power line flows for each hour and plots aggregated results as shown in Figure 20-Figure 24. Furthermore, Figure 27-Figure 29 shows examples of hourly load, hourly hydro production and hydro reservoir development over the year. Figure 20 shows the annual load in each of the Nordic countries. The annual load shall in normal cases always be equal to the annual load specified in the input file. Simulation results with unfulfilled load demand should be avoided, but if this is the case, it is important to find out what causes reduced load (e.g. too low generating capacity or power line capacity) and run the simulation again with modified input data. The reduced load is stored as flexible load in the program plotted in the same graph as the annual load.


DK NO SE SF0

50

100

150

Country

Load

[TW

h]

Annual load in 2006

LoadFlexible load

Figure 20. Annual load per country.

In Figure 21, the annual production of each generator type in each country is plotted. In the simplified Nordic case all generation other than hydro, nuclear and wind is denoted as fossil generation (which is a simplification since e.g. Finland has many bio fuelled generators).

DK NO SE SF0

20

40

60

80

100

120

140

Country

prod

uctio

n [T

Wh]

Annual production in 2006

HydroNuclearFossileWind

Figure 21. Annual production of each generator type per country.


The branch (or power line) flows are plotted as duration curves, see Figure 22. One should first investigate the duration curves to check whether the power line capacities used in the simulation are reasonable. With a well-tuned data set, the duration curves are useful for investigating which power lines that may cause constrained power flow situations when increasing the wind generation in the system. As an illustration, the duration curves in Figure 23 shows the situation when increasing the wind power capacity in the Northern Norway (bus 6700) from 381 MW to 1000 MW. It is seen when comparing with Figure 22 that the exchange with Northern Sweden is changed from net import to net export. Furthermore, the increased wind power in Northern Norway reduces the import from Sweden to Mid-Norway.

0 1000 2000 3000 4000 5000 6000 7000 8000 9000-1500

-1000

-500

0

500

1000

1500

Duration [hours]

Flow

MW

3115 -67013244 -65003249 -71005101 -51005300 -6100

Figure 22. Duration curves for branch flows between buses.


0 1000 2000 3000 4000 5000 6000 7000 8000 9000-1500

-1000

-500

0

500

1000

1500

Duration [hours]

Flow

MW

3115 -67013244 -65003249 -71005101 -51005300 -6100

Figure 23. Duration curves for branch flows when increasing wind capacity at bus

6700, as compared with Figure 22. A parameter that is useful in combination with the duration curves is the sensitivity of power line capacity. The sensitivity, which is an output of the optimization routine, is the change in the objective function (total operating costs of the system) by increasing the power line capacity by one unit. The sensitivity is zero for lines that are not operated at its limits. By summing up the sensitivities for every hour of the year, one gets an indicative measure for the value of increasing the transmission capacity between two buses. Figure 24 shows the sum of hourly sensitivities for a selection of power lines and Figure 26 shows the corresponding results when increasing the wind power capacity in Northern Norway (bus 6700). By comparison of the figures it is seen that the added wind at bus 6700 reduces the sensitivity of the line between buses 3115-6701 (Northern Sweden to Northern Norway) and the line between buses 3244-6500 (Northern Sweden to Northern Norway). This is because added wind in Northern Norway reduces the need for importing power from Sweden, resulting in less hours with maximum import to buses 6500 and 6700. With a further significant increase in the installed wind power capacity in Northern Norway, it might be necessary to reduce the power output in some hours due to limited capacity of the power lines to Sweden. The sensitivity of the power line capacities will then increase, meaning that there are operational benefits of


increasing the power export limits, since wind power then can displace generators with higher operating costs. An alternative visualisation of the sensitivity is the duration curve, shown in Figure 26, which gives an indication of the hourly operational costs induced by the constraint.

3115 -6701 3115 -7100 3244 -6500 3249 -7100 3300 -8500 5101 -5100 5300 -6100 5500 -5501 6000 -60010

0.5

1

1.5

2

2.5

3

3.5

4

4.5x 104

From bus - To bus

Sen

sitiv

ity [

/ (M

W *

yea

r)]

Sensitivity of bottlenecks

Figure 24. Sensitivity of bottlenecks, i.e. what is the monetary value of increasing

the tie-line capacities.

3115 -6701 3115 -7100 3244 -6500 3249 -7100 3300 -8500 5101 -5100 5300 -6100 5500 -5501 6000 -60010

0.5

1

1.5

2

2.5

3

3.5

4

4.5x 10

4

From bus - To bus

Sen

sitiv

ity [

/ (M

W *

yea

r)]

Sensitivity of bottlenecks

Figure 25. Sensitivity of bottlenecks when increasing wind capacity at bus 6700, as

compared with Figure 24.


0 1000 2000 3000 4000 5000 6000 7000 8000 90000

10

20

30

40

50

60

Duration [hours]

Sen

sitiv

ity [E

uro/

MW

]

3115 -67013115 -71003244 -65003249 -71003300 -85005101 -51005300 -61005500 -55016000 -6001

Figure 26. Duration curves for sensitivity of bottlenecks, i.e. the instantaneous

(hourly) value of increasing the line capacity. When the sensitivity is zero, the full capacity of the line is not used.

0 1000 2000 3000 4000 5000 6000 7000 8000 90001000

1500

2000

2500

3000

3500

4000

4500

hour

Load

at b

us 6

100

(MW

)

Figure 27. Hourly consumption at bus 6100.


0 1000 2000 3000 4000 5000 6000 7000 8000 90000

1000

2000

3000

4000

hour

Pro

duct

ion

at b

us 6

100

(MW

)

Figure 28. Production at bus 6100.

0 1000 2000 3000 4000 5000 6000 7000 8000 90002

3

4

5

6

7

8

9x 106

hour

Res

ervo

ir le

vel a

t bus

610

0 (M

Wh)

Figure 29. Reservoir level of the hydro plant at bus 6100.

5.2 Europe (UCTE + Nordel + GB/Ireland) This chapter gives some example results from running the simulation program with the data for generation, load and grid described in Chapter 4. The numeric data is provided in the files listed in Appendix C: Data files. Simulation studies and analysis of simulation results are parts of other work packages, therefore example results are only briefly presented here.