Grid Modelling and power system data

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This document contains information, which is proprietary to the “TradeWind” Consortium. Neither this document nor the information contained herein shall be used, duplicated or communicated by any means to any third party, in whole or in

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

FFuurrtthheerr DDeevveellooppiinngg EEuurrooppee’’ss PPoowweerr MMaarrkkeett

ffoorr LLaarrggee SSccaallee IInntteeggrraattiioonn ooff WWiinndd PPoowweerr

D3.2 Grid modelling and power system data

M. Korpås, 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 Korpås

Document Name: Grid modelling and power system data

Document Number: TR F6604

Author: M Korpås, 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:T

x

eqeq

lower upper

F x x Hx c x

A x b

A 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( ) 1

2i i

i i i i ii

MC MCF x MC x x

P

(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 P

B B B B P

B 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 Flow

B B Flow

B B Flow

(6)

where:

δi - 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 δj=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 δ 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 hvdc

hvdc eqeq

B I I P P

bPA

x

(9)

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


,max

,max

0 0

0 0f Flow

Gf Flow

hvdc

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

node

PTDF

node

PTDF

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

B

B

B B B P

B B B P

B 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 Flow

B B Flow

B Flo

B

B w

(12)

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

11 3 1 311 2 1 3 1 2

1 2 1 2 1 221 2 1 2 2 3

2 3 2 3

inj

inj

B FlowPB B B

B B FlowPB B B

B Flow

(13)

1,1 3 2,1 3 1 31

1,1 2 2,1 2 1 22

1,2 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.

AzonejL

AzonejL

klineTieL

klineTieloadAzone

j

jj

P

PPTDF

PTDF ,

AzoneiG

AzoneiG

klineTieG

klineTiegenerationAzone

i

ii

P

PPTDF

PTDF ,

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 PTDF’s 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

http://www.ucte.org/�


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 name

A1 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

Ab

so

lute

err

or

(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

Ab

so

lute

err

or

(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 tap’s 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

31153249

3245

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 c

n

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 an

nual

inflo

w

Inflow pattern 1

Inflow pattern 2Inflow pattern 3

Inflow 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

(€/

MW

h)

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 re

lativ

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

[T

Wh]

Annual load in 2006

Load

Flexible 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

Hydro

NuclearFossile

Wind

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]

Flo

w M

W

3115 -6701

3244 -6500

3249 -71005101 -5100

5300 -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]

Flo

w M

W

3115 -6701

3244 -6500

3249 -71005101 -5100

5300 -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 10

4

From bus - To bus

Sen

sitiv

ity [

€ /

(MW

* y

ear)

]

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 [

€ /

(MW

* y

ear)

]

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 [

Eur

o/M

W]

3115 -6701

3115 -71003244 -6500

3249 -7100

3300 -8500

5101 -5100

5300 -61005500 -5501

6000 -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

bus

6100

(M

W)

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 10

6

hour

Res

ervo

ir le

vel a

t bu

s 61

00 (

MW

h)

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.


Two different years have been simulated; 2005 and 2015. The wind power scenario ‘hi’ is chosen for 2015, with a total installed wind power capacity of 185 GW, which is 4.4 times higher than the 2005 situation with 42 GW. Figure 30 and Figure 31 show the duration curves for power output in all grid zones for the two years, while Figure 32 compares the duration curves of the total wind power output. It is evident from the figures that there is a significant smoothing of wind power output when considering the modelled areas of Europe as a whole.

0 1000 2000 3000 4000 5000 6000 7000 80000

1000

2000

3000

4000

5000

6000

7000

Duration (hours)

Win

d po

wer

out

put

(MW

)

2005

Figure 30. Duration curves of simulated wind power output for each of the 56 grid zones with installed wind power, year 2005.

0 1000 2000 3000 4000 5000 6000 7000 80000

2000

4000

6000

8000

10000

12000

14000

16000

Duration (hours)

Win

d po

wer

out

put

(MW

)

2015 'hi'

Figure 31. Duration curves of simulated wind power output for each of the 56 grid zones with installed wind power, ‘hi’ scenario year 2015.


0 1000 2000 3000 4000 5000 6000 7000 80000

20

40

60

80

100

120

140

Duration (hours)

Tot

al w

ind

pow

er o

utpu

t (G

W)

2005

2015 'hi'

Figure 32. Duration curves of simulated total European wind power output for 2005 and ‘hi’ scenario for 2015.

Example results from running a simulation for the years 2005 and 2015 on the European system are shown in Figure 33 to Figure 39. The results for the production in a few selected countries, as shown Figure 33, generally correspond well with actual annual production for the same countries [14]. There are some discrepancies in the use of different types of fossils compared to statistics, but the total fossil production seems to correspond well. Typically, the simulations give less production from natural gas than reported in the statistics [14], which can be explained by the marginal cost estimates, see Table 6. The total annual load, shown in Figure 35 and Figure 36, is given as input to the simulation. However, if there are any transmission line constraints which put a limit to the load in some areas, this will result in a flexible load which should be avoided, as also discussed in Chapter 5.1.


CH DE ES FR IT0

50

100

150

200

250

300

350

400

450

500

Country

prod

uctio

n [T

Wh]


HydroNuclear

hard_coal

gas

Renew_other_than_windlignite_coal

oil

oil_gasWind

Figure 33. Annual production of each generator type per (selected) country, year 2005.

CH DE ES FR IT0

50

100

150

200

250

300

350

400

450

500

Country

prod

uctio

n [T

Wh]


HydroNuclear

hard_coal

gas

Renew_other_than_windlignite_coal

oil

oil_gasWind

Figure 34. Annual production of each generator type per (selected) country, year 2015 (hi wind scenario).


CH DE ES FR IT0

100

200

300

400

500

600

Country

Load

[T

Wh]

Annual load in 2005

Load

Flexible load

Figure 35. Annual load per (selected) country, year 2005.

CH DE ES FR IT0

100

200

300

400

500

600

Country

Load

[T

Wh]

Annual load in 2015

Load

Flexible load

Figure 36. Annual load per (selected) country, year 2015.


Figure 37 shows the line flow duration curves for the lines that connect Spain to France. It is seen that the high wind scenario in 2015 reduces the net export from France to Spain, compared to the case for 2005. This is due to the large increase in installed wind capacity in Spain. It should be noted that the flow limits in this case are set by the physical limits of the lines, not including the upper limit on the cross-border exchange as given by the Net Transfer Capacity (NTC) [7].

1000 2000 3000 4000 5000 6000 7000 8000-3000

-2000

-1000

0

1000

2000

3000

4000

Duration (hours)

Pow

er f

low

on

lines

(M

W)

E2 - F6 2005E2 - F6 2005

E2 - F6 2005

E2 - F6 2005

E2 - F6 2005E2 - F6 2015

E2 - F6 2015

E2 - F6 2015

E2 - F6 2015E2 - F6 2015

Figure 37. Duration curves for line flow on individual lines between France and Spain.

Figure 38 shows the duration curve for the use of the HVDC link between Norway and Denmark. Since the HVDC link is modelled as a free variable in the optimization problem, the full capacity is often used in both directions. For the 2015 scenario, the installed wind power capacity in Norway has increased to 4000 MW (from 274 MW in 2005), leading to increased export to Denmark, even though Denmark also has a noticeable increase in wind power capacity. As seen in Figure 39, this leads to higher filling of Norwegian water reservoirs. The figure shows that the reservoir reaches its maximum capacity in parts of the year, indicating a need for transmission expansions in order avoid hydro spilling.


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

-500

0

500

1000

Duration (hours)

Pow

er f

low

on

HV

DC

cab

le (

MW

)

NO to DK 2005

NO to DK 2015

Figure 38. Duration curve for the HVDC link between Norway and Denmark.

0 1000 2000 3000 4000 5000 6000 7000 8000 90001

2

3

4

5

6

7

8x 10

6

Hour of the year

Res

ervo

ir co

nten

t (M

Wh)

year 2015

year 2005

Figure 39. Reservoir filling of hydro power in Mid-Norway (bus 6500 in the Nordic grid equivalent, see Figure 10).


6 Summary and conclusions This report has presented the developed model of the European power system and the collected data on load, generation and grids, which will be used for carrying out simulations and analysis in WP5 and WP6 of the Tradewind project. The simulations are carried out using the Power System Simulation Tool (PSST), developed by SINTEF Energy Research. 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. Although Great Britain and the island of Ireland are two separate synchronous zones, they are aggregated in the power flow data file, linked by HVDC. 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. 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. Grid models that include future interconnections will be established as part of WP5 and WP6. An important aspect of using PTDFs is that when introducing a new interconnection, a new PTDF matrix must be constructed from the underlying DC grid representation since the new line influences the line flows in other parts of the grid. However, 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. Constant marginal costs are assumed, but this is not a strict limitation regarding the bpmpd and clp (Coin) optimal power flow solvers used by the simulation tool, since they handle piece-wise linear cost curves. Bpmpd also handles and quadratic cost functions. The marginal cost of hydro can be specified as either constant or linear. Further modelling work could include an extension of the user interface so that it would be possible to choose between constant and linear marginal costs for other generator types than hydro.


Marginal costs of hydro power are treated as a special case due to the possibility of storing water in reservoirs for later use. Pre-calculated water value tables, which are functions of the reservoir level and week of the year, are used for finding the marginal costs of hydro units, which links consecutive hours. Therefore the optimisation problems for the different time steps must be solved chronologically. Pumped hydro operation is included in the model. Wind power scenarios for all relevant countries have been constructed in WP 2 of the Tradewind project. 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. 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. Two example simulations are presented in the report. The first simulation is of the Nordel grid only, since the core of the Nordel grid model was available at project start. The Nordel system is represented by a DC 23 generator model, allowing for fast simulations of a whole year. It is shown how it is possible to investigate the influence of wind power on power flows between different countries and to point out critical bottlenecks in the grid. The second simulation example is of the complete European grid model. It is time-consuming to simulate the European system for a whole year of operation due to the complexity of the optimization problem that must be solved for each of the 8760 time steps. By using warm-start (i.e. defining a start-vector for the optimization variables) it is possible to speed up the computation of the optimization problem. Warm start is implemented in the Matlab interface to Coin and shows a very significant decrease in simulation time. However, this can only be used when the optimization problem is given as an LP description, as the current Matlab interface to Coin for QP descriptions is unstable. For QP, bpmpd should be used but this has the disadvantage of long computation time.


References [1] Uhlen K,Grande O, Warland L, Solem G, Norheim I. Alternative Model for Area

Price Determination in a Deregulated Power System, Proc. 2004 IEEE PES Power System Conference & Exposition, PSCE (Panel paper), New York, NY, October 10-13, 2004.

[2] Uhlen K, Warland L, Grande O. Model for Area Price Determination and Congestion Management in Joint Power Markets. 2nd CIGRE/IEEE PES Symposium on Congestion Management in a Market Environment. San Antonio, Texas, USA, 5-7 October 2005

[3] Fosso OB, Gjelsvik A, Haugstad A, Mo B, Wangensteen I. Generation scheduling in a deregulated system. The Norwegian case. IEEE Trans. Power Systems; 14(1):75-81, 1999.

[4] http://www.pserc.cornell.edu/matpower/ [5] http://www.pserc.cornell.edu/bpmpd/ [6] http://www.coin-or.org/ [7] Union for the Co-ordination of Transmission of Electricity UCTE),

http://www.ucte.org/ [8] Nordpool ftp server, ftp://ftp.nordpool.no [9] http://www.nationalgrid.com [10] http://www.eirgrid.ie/ [11] Zhou Q, Bialek JW. Approximate Model of European Interconnected System as

a Benchmark System to Study Effects of Cross-Border Trades, IEEE Trans. Power Systems; 20(2), May 2005.

[12] Bakken, BH. Technical and economic aspects of operation of thermal and hydro power system, Doctoral Dissertation, Norwegian University of Science and Technology, 1997.

[13] GreenNet EU/27. Guiding a least cost grid integration of RES-electricity in an extended Europe. Deliverable D8: Case Studies on System Stability with Increased RES-E Grid Integration.

[14] Union of the Electricity Industry. Statistics and prospects for the European electricity sector (EURPROG), 2005 and 2007.

[15] Northern Ireland Energy Holdings. The Moyle Interconnector. http://www.nienergyholdings.com/The_Moyle_Interconnector/Index.php

[16] UCTE, “System Adequacy Forecast 2007-2020”, January 16th, 2007. [17] IEA, NEA. Projected Costs of Generating Electricity, www.iea.org [18] Reinaud, J. Emissions Trading and its Possible Impacts on Investment Decisions

in the Power Sector, IEA Information Paper, OECD/IEA, Paris, France, 2003. [19] Energy Information Administration, U.S. Government, http://www.eia.doe.gov [20] Holltinen H, Tuhkanen S. The effect of wind power on CO2 abatement in the

Nordic Countries, Energy Policy, Vol. 32/14, pp. 1639-1652, 2004.

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http://www.pserc.cornell.edu/bpmpd/�

http://www.coin-or.org/�


ftp://ftp.nordpool.no/�

http://www.nationalgrid.com/�

http://www.eirgrid.ie/�

http://www.nienergyholdings.com/The_Moyle_Interconnector/Index.php�

http://www.iea.org/�

http://www.eia.doe.gov/�


[21] Norwegian Water Resources and Energy Directorate. www.nve.no [22] BiofuelB2B web page. http://www.biofuelsb2b.com/biomass_facts.php [23] Lehner B, Czisch G, Vassoloa S. The impact of global change on the

hydropower potential of Europe: a model-based analysis. Energy Policy 33: 839-855, 2005.

[24] FRIEND, Flow Regimes from International Experimental and Network Data. Vol. I Hydrological studies, Institute of Hydrology, UK, 1993.

[25] Arnell, N.W. A simple water balance model for the simulation of streamflow over a large geographical domain. J. Hydrology 217: 314-335, 1999.

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http://www.nordel.org/�


Appendix A: Hourly load profiles In the spreadsheet “UCTELoad.xls” the historical load profiles are stored, where the load for each area is given in separate sheets named by the country code. The total load in GWh/year is specified for the years 2004 through 2006, as given in Table 9, and similar for the monthly values given in MWh in Table 10.

Table 9. Specification of annual load in GWh/year.

2004 2005 2006Annual load GW/year

Table 10. Specification of load profile data (Month) in MWh.

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2004 2005 2006 The load profile describing the load variation over a 24 hour period is given for the third Wednesday in each month for the year 2005, while every hour for 2006. The hourly load data for 2006 for the Nordic countries are supplied in separate ascii files, in a format given by Nordpool, and has not been included in the excel file. There are however made Matlab routines for reading and merging both data sets when running the optimal power flow scenarios.

Table 11. Specification of load profile data (hour) in MWh/h.

Date 01:00 02:00 … 24:00 19.01.2005 16.02.2005 16.03.2005 20.04.2005 18.05.2005 15.06.2005 20.07.2005 17.08.2005 21.09.2005 19.10.2005 16.11.2005 21.12.2005


Appendix B: Connecting wind regions to grid zones

Table 12. Wind regions from WP2 and their corresponding grid model zones (UCTE) and grid model bus numbers (Nordel, GB and the Ireland system).

Area Zone Bus

Region Identifier (Node) Area Zone Bus

Region Identifier (Node)

AT A1 1 NO NO2 6500 74 BE BE 2 NO NO2 6700 75 BE BE 3 NO NO2 6700 76 BG BG 4 PL P1 77 BG BG 5 PL P2 78 BG BG 6 PT PT 79 HR HR 7 PT PT 80 HR HR 8 PT PT 81 HR HR 9 PT PT 82 CZ CZ 11 PT PT 83 CZ CZ 12 IR IR 7 84 CZ CZ 13 IR IR 7 85 DK DK_E 14 IR IR 7 86 DK DK 15 IR IR 7 87 DK DK 16 IR IR 7 88 DK DK 17 IR IR 7 89 DK DK_E 18 RO RO 90 SF SF 7000 22 RO RO 91 SF SF 7100 23 RO RO 92 SF SF 7000 24 RO RO 93 SF SF 7100 25 RO RO 94 FR F7 26 RO RO 95 FR F1 27 RO RO 96 FR F2 28 CS CS 97 FR F3 29 SK SK 98 FR F5 30 SK SK 99 FR F4 31 SK SK 100 FR F6 32 SI SI 101 DE D1 33 SI SI 102 DE D2 34 ES E4 103 DE D3 35 ES E4 104 DE D4 36 ES E4 105 DE D5 37 ES E2 106 DE D5 38 ES E2 107 DE D6 39 ES E2 108 DE D1 40 ES E1 109 DE D2 41 ES E1 111 DE D2 42 ES E3 112 GB GB 3 43 ES E1 113 GB GB 6 44 ES E1 114 GB GB 3 45 ES E1 115 GB GB 5 46 ES E2 116


GB GB 1 47 ES E2 117 GB GB 6 48 ES E3 118 GB GB 3 49 ES E3 119 GR GR 50 ES E4 120 GR GR 51 ES E1 121 GR GR 52 ES E1 122 HU HU 53 ES E3 123 IT I1 54 ES E3 124 IT I3 55 ES E2 125 IT I3 56 ES E1 126 IT I3 57 ES E1 127 IT I3 58 ES E4 128 LU LU 63 SE SE 3359 129 NL NL 65 SE SE 3000 130 NL NL 66 SE SE 3249 131 NO NO1 6000 67 SE SE 3115 132 NO NO2 6500 68 SE SE 3300 133 NO NO2 6500 69 SE SE 3300 134 NO NO2 6700 70 SE SE 3200 135 NO NO2 6700 71 SE SE 3245 136 NO NO1 5600 72 SE SE 3115 137 NO NO2 6500 73 CH S1 138


Appendix C: Data files Wind data Spreadsheet document “WP3_winddata.xls” contains the wind capacity scenarios, Regional power curves, wind speed adjustment factors and Reanalysis Data Point mapping from WP2 in a format readable to the WP3 simulation program. The WP2 wind regions are mapped to the WP2 grid zones/buses as shown in Table 12. Load data Spreadsheet document “WP3_loadscenarios.xls” contains the scenarios for peak demand for all countries in the European grid model, in a format readable to the WP3 simulation program. Spreadsheet document “WP3_loadprofiles.xls” contains the hourly load profiles that are used together with the peak demand scenarios to create hourly load time series for the target years. Generation data Spreadsheet document “WP3_genscenariosA.xls” contains the scenarios for installed capacity, divided into generator type and country. For the UCTE countries for the target years up to 2020, the capacities are based on “UCTE - SAF2007 - scenarioA.xls” which can be downloaded from www.ucte.org. For the other countries and for 2030, the capacities are based on the EURPROG Statistics [14]. The spreadsheet also contains the assumed marginal costs for each generator type, as reported in Table 6. Spreadsheet document “WP3_genscenariosB.xls” contains the scenarios for installed capacity, divided into generator type and country. For the UCTE countries for the target years up to 2020, the capacities are based on “UCTE - SAF2007 - scenarioB.xls” which can be downloaded from www.ucte.org. For the other countries and for 2030, the capacities are based on the EURPROG Statistics [14]. The spreadsheet also contains the assumed marginal costs for each generator type, as reported in Table 6. Hydro data Spreadsheet document “WP3_hydrodata.xls” contains data on reservoir, inflow and pumping capacity, as showed in




Table 7. Grid data Spreadsheet document “WP3_PTDF_UCTEgrid.xls”, provided by Tractebel, contains the PTDFs for all zones in the UCTE grid model. It also contains the line capacities and information on how the load and generating capacity is subdivided in each grid zone. Matlab file “WP3_nordelgrid.m” contains the grid data for Nordel in Matpower format, readable to the WP3 simulation program. The .m file can be opened in any text editor. Matlab file “WP3_GB_Irelandgrid.m” contains the grid data for the GB and Ireland systems in Matpower format, readable to the WP3 simulation program. The .m file can be opened in any text editor. Spreadsheet document “WP3_Interconnection_development.xls” provides information on interconnection developments for the coming years. Updated grid models including future interconnections will be established as part of WP5 and WP6.


Appendix D: Computer model structure This chapter presents the computer program 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 PowerSystemSimulationTool (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. All the necessary toolboxes required to run the simulation are provided in the file PowerSystemSimulationTool_1.0.0.zip. D.1 Installation The PSST toolbox must located in MATLABPATH together with Matpower[4], bpmpdmex [5], optionally an improved mex interface, mexclp, to the Coin Clp [6] solver and some miscellaneous functions provided along with the above mentioned toolboxes. The Matpower toolbox is a slightly modified version of release “3.2”. When bpmpdmex is located in Matlab path this will be the preferred solver, otherwise Clp will be used. The Clp mex interface has a considerable improvement in calculation speed due to the reuse of previous problem formulation, as only the cost coefficients and upper and lower constraints are changed when running hour by hour optimal power flow calculations. A test run on an Intel Pentium 3.20 Ghz CPU took six hours to complete a one year simulation using bpmpdmex, while the improved Clp mex interface completed the simulation in 30 minutes. D.2 Functions Available functions with description can be shown using the Matlab help command. >> help psst PSST Power System Simulation Tool 1.0.0. Run timeseries of dc optimal power flow scenario - Run DC optimal powerflow for given year outrev - Reduce installed capacity due revision of generators psstformat - case description formats (also see caseformat in Matpower) RESULTS

http://www.pserc.cornell.edu/matpower/�

http://www.pserc.cornell.edu/bpmpd/�

http://www.coin-or.org/�


areaprod - Bar plot of annual production areaload - Bar plot of annual load in area or zone branchbar - Bar plot of sensitivity index (lambda) for branch capacity branchplot - Duration curves for bottlenecks in the grid (sensitivety or flow) Exchange - Plot the total exchange for given country INPUT/OUTPUT readload - Read load data from Excel file readpg - Read generator data from Excel file updatexcel - Update load and/or generation data based on Excel spreadsheet MISC LoadInHour - Retrieve load for given hour areas - Return structure with the areas in UCTE branchsens - "Branch sensitivties". Get info on bottlenecks in the grid genindx - Return index for generation (from Excel sheet) loaddist - Find the distribution of load within an area mgcost - Make generator cost data plotcap - Plot installed production capacity relprofiles - Generation relative profiles for month,day,hour remcountries - remove parts of system for which we don't have data showAreaprod - Show production in given area PSST Power System Simulation Tool

All the functions listed here are described in further detail, showing both functionality and input and output parameters, by using the Matlab help <function> for any function listed above. D2.1 Running the simulation The main function for running a series of power flows is scenario: >> help scenario SCENARIO Run DC optimal powerflow for given year or time [Pl,Pg,Plflex,SensBr,BRflow,Sensgen,Price,Wres,F,failed,mpc,wind,HVDCan,HVDCse]=... scenario(year,mpc,wind,revision,progress,useCoin,hydroData,GenCase,UseQuadSolv

e) year - Year to be "simulated", or vector with specific hours in datenum format. NB! There is a dependency in the use of water between hours. mpc - struct containing casedescription (see psstformat and caseformats from Matpower). wind - struct containg Wind data (see psstformat) revision - matrix giving the reduction in installed capacity due to revision of generators (see psstformat and outrev). progress – workbar showing how much of the simulation is completed. useCoin – Use Clp solver hydroData – Hydro data input (see private/hydrocapacities)


GenCase – ‘A’ or ‘B’ (Default ‘A’) UseQuadSolve - Use a quadratic solver Pl - Annual load hour by hour for all load buses where rows are given by mpc.Lbus and columns by hours simulated (8760 in a normal year). Pg - Annual production where rows are given by mpc.gen and columns by hour. Plflex - Flexible load SensBr - Sensitivities of branch restrictions, where rows are given by mpc.branch and columns by hour. BRflow - Branch flow in each hour, where direction is given by From and To bus in mpc.branch. Rows and colums as for SensBr. Sensgen - Sensitivety in restriction (available capacity) of production. Price - water values for each hour, where rows are given by mpc.gentype.Hydro. Wres - Water resevoire for each hour. Rows and columns as for Price. F - Total cost in each hour. failed - hours where the solver could not find a solution. HVDCan - Annual HVDC flow HVDCse - Sensitivities of constraints on HVDC connections.

The input variable wind, that includes both time profile and capacities, makes it possible to run several different wind scenarios for any given year and power flow description. Similar can the revision variable be used to alter the available generation capacity in a specific hour for a given “generator”, emulating scheduled outages. The function outrev is provided in order to easily generate the revision matrix. OUTREV Reduce installed capacity due revision of generators revision = outrev(gen_idx,hour,perct,type) revision - input to scenario gen_idx - index in mpc.gen hour - houre for which perct(age) available capacity perct - Perctenage of avaiable capacity for given hour type - 'mnt','day','hour' for which this is the case

D2.2 Presenting the results


Five functions for presentation are provided in the toolbox, both bar plots of annual production, load and sensitivities of line restrictions as well as duration curve of the sensitivities and branch flows and the power exchange. The total production within all countries, given by generation type, can be displayed by using the command areaprod: >> help areaprod AREAPROD Bar plot of annual production h = areaprod(mpc,wind,P,zone,year,count,fname) mpc - Matpower case description (with PSST updates) wind - time series. P - Annual production as given by the output of scenario. zone - 1: Use zone (with internal regions) 0: Use country totals year - Display year simulated in title count – Countries shown, ex. {'DE','FR','NO'}, Default: empty [] for all fname - Write result to fname h - figure handle

Total load in each area, which is, except for the flexible load, input to the simulation, can be shown by the function areaload: >> help areaload AREALOAD Bar plot of annual load in area or zone h = areaload(mpc,Pload,Pflex,zone,year,count,fname) mpc - Matpower case description (with PSST updates) Pload - Annual load as given by the output of scenario. Pflex - Flexible load zone - 1: Use zone (with internal regions) 0: Use country totals year - simulated count - Countries shown fname - Save to file instead of plotting h - figure handle

Both functions areaprod and areaload have the option of plotting results based on internal regions or country totals given by the input parameter zone. They also have the ability to write the result to an ASCII file. An important result from the simulation is the sensitivities, or the lambda values, for the transmission line constraints. The total value for a given constraint for a one year simulation can be plotted using the command branchbar: >> help branchbar LOADBAR Bar plot of sensitivity index (lambda) for branch capacity h = branchbar(SensBR,year,Xlabel_info,synczone,nlim,fname) SensBR .br : branch number .sens : Sensitivity [€ / (MW * year)]


Xlabel_info 0 : Bus number 1 : Area name (default) synczone - Synchronous area {2:UCTE, 1:Nordel, 3:GB,4:Irland} (Optional) nlim - Just show the nlim greatest sensitiveties (default: []-all) fname - Write to file (Optional)

To see the duration curves, either as power flow on constrained lines, or the duration of the sensitivities can be seen using the command branchplot: >> help branchplot BRANCHPLOT Duration curves for bottlenecks in the grid h = branchplot(mpc,SensBRresult,BRflow,year,legend_info,sense,synczone,nlim) SensBRresult .br : branch number .sens : Sensitivity [€ / (MW * year)] BRflow (or SenceBr from scenario): Power flow or sensitiveties of contrained branches (from bus -> to bus) Year : E.g. 2006 legend_info 0 : Bus number 1 : Area name (default) sence - 0: Assume input is flow 1: Assume input is sensetivieties synczone - Shown synchronous zone ( 1: Nordel, 2: UCTE, 3: Greate Britain, 4: Irland) nlim - Shown the nlim greatest (based on total sensitiveties)

The input variables SensBrR and BRflow are generated by the function branchsens: >> help branchsens BRANCHSENS "Branch sensitivties". Get info on bottlenecks in the grid SensBRresult = branchsens(SensBR,mpc); SensBRresult .br : branch number .sens : Sensitivity [€ / (MW * year)] ConstrBRflow (Constrained branch flows)

where sensBR is one of the output variables from scenario. The total power exchange for the simulated year can be shown using the command exchange: >> help exchange EXCHANGE Plot the total exchange for given areas h = exchange(mpc,BRflow,HVDCflow,year,countries) mpc - Matpower case BRflow - annual flow on branches (given by scenario) HVDCflow - annual flow on HVDC connections (given by scenario)


year - year simulation (for the title) countries - Countries to show countries = {'DE','FR','IT','GB'}

D.2.3 Output of simulation and storing results Even though some of the functions for presenting the result, shown in previous section, have the ability to save data to ASCII formatted files, due to the size of the output from the simulation, it is recommended to use Matlab for the presentation of the result and the binary “MAT-file” to store results from a simulation. Typical size for one year simulation of the entire European system, including Nordel, UCTE and, is about 45 Mb in a compressed “MAT-file”. D.3 Case description (formats) The power flow case format used by scenario is described in psstformat and caseformat, provided in PSST and Matpower toolboxes respectively for documentation purposes only. >> help psstformat PSST case description formats (see also: caseformat in Matpower) WIND-DATA (structure with the following variables assuming n buses with wind production) LOCATION - bus number at which any given wind production is connected (see bus in caseformat). wind.location(n,1) = [ bus_1; bus_2; . bus_n; ] ; HOUR - hour for production (given in datenum format) for a whole year wind.hour(8760,1) = [ hour_1; hour_2; .... ;hour_8760]; POWER - Production (normalized) for given hour (row) and location (column) converted from wind measurements. wind.P(8760,n) = [P_{1,1} P_{1,2} ... P_{1,n} ; P_{2,1} P_{2,2} ... P_{2,n} ; ... ; P_{8760,1} P_{8760,2} ... P_{8760,n} ]; CAPACITY - Total maximum wind production for given location. Makes it possible to alter total installed capacities using the same wind scenarios.

wind.cap(n,1) = [ P_max(1); P_max(2); . P_max(n);]; REVISION Matrix for reduction of installed capacity due revision of generators 1 Generator indx (mpc.gen)


2 Hour (datenum format) for which the generator is running with reduced capacity 3 pu generation available. AREAS Description the BUS_AREA/ZONE used in PSST. See areas function. Ex.: ar = areas; bus(:,BUS_AREA) = 10 -> ar(10,:) => 'DK_W' 'Denmark West' EXTENTIONS to Matpower caseformat (version '2') HVDC data format (mpc.hvdc) 1 f, from bus number 2 t, to bus number 3 P, Active power flow (directional from f -> t bus). 4 Plim f-bus, flow limit injected to HVDC at from bus 5 Plim t-bus, flow limit injected to HVDC at to bus 6 Mu (of Plim f-bus) 7 Mu (of Plim f-bus) GENTYPE structure where the fields define what kind of generator where the fieldname defines type and value gives indecies to gen. The Ex.: mpc.gentype.Hydro = [1 2 10]'; mpc.gentype.Nuclear = [3 5 6]'; mpc.gentype.Fossile = [7 8 9]'; Here number 1,2 and 10 in the gen description is a hydro generator, 3,5 and 6 is Nuclear and 7,8 and 9 is Fossile.

The PSST toolbox uses the same power flow description as Matpower, with some extra input such as HVDC and description of generation type. In addition to the powerflow case the toolbox needs input of time series of wind and load, marginal cost of production and revision schedule for generation plants. D.3.1 Power flow case description The PSST toolbox uses Matpower caseformat version ‘2’ for description of the power flow: >> help caseformat MATPOWER Case Version Information: A version 1 case file defined the data matrices directly. The last two, areas and gencost, were optional since they were not needed for running a simple power flow. In version 2, each of the data matrices are stored as fields in a struct. It is this struct, rather than the individual matrices that is returned by a version 2 M-casefile. Likewise a version 2 MAT-casefile stores a struct named 'mpc' (for MATPOWER case). The struct also contains a 'version' field so MATPOWER knows how to interpret the data. Any case file which does not return a struct, or any struct which does not have a 'version' field is considered to be in version 1 format.


See also IDX_BUS, IDX_BRCH, IDX_GEN, IDX_AREA and IDX_COST regarding constants which can be used as named column indices for the data matrices. Also described in the first three are additional columns that are added to the bus, branch and gen matrices by the power flow and OPF solvers. Bus Data Format 1 bus number (1 to 29997) 2 bus type PQ bus = 1 PV bus = 2 reference bus = 3 isolated bus = 4 3 Pd, real power demand (MW) 4 Qd, reactive power demand (MVAr) 5 Gs, shunt conductance (MW (demanded) at V = 1.0 p.u.) 6 Bs, shunt susceptance (MVAr (injected) at V = 1.0 p.u.) 7 area number, 1-100 8 Vm, voltage magnitude (p.u.) 9 Va, voltage angle (degrees) (-) (bus name) 10 baseKV, base voltage (kV) 11 zone, loss zone (1-999) (+) 12 maxVm, maximum voltage magnitude (p.u.) (+) 13 minVm, minimum voltage magnitude (p.u.) Generator Data Format 1 bus number (-) (machine identifier, 0-9, A-Z) 2 Pg, real power output (MW) 3 Qg, reactive power output (MVAr) 4 Qmax, maximum reactive power output (MVAr) 5 Qmin, minimum reactive power output (MVAr) 6 Vg, voltage magnitude setpoint (p.u.) (-) (remote controlled bus index) 7 mBase, total MVA base of this machine, defaults to baseMVA (-) (machine impedance, p.u. on mBase) (-) (step up transformer impedance, p.u. on mBase) (-) (step up transformer off nominal turns ratio) 8 status, > 0 - machine in service <= 0 - machine out of service (-) (% of total VAr's to come from this gen in order to hold V at remote bus controlled by several generators) 9 Pmax, maximum real power output (MW) 10 Pmin, minimum real power output (MW) (2) 11 Pc1, lower real power output of PQ capability curve (MW) (2) 12 Pc2, upper real power output of PQ capability curve (MW) (2) 13 Qc1min, minimum reactive power output at Pc1 (MVAr) (2) 14 Qc1max, maximum reactive power output at Pc1 (MVAr) (2) 15 Qc2min, minimum reactive power output at Pc2 (MVAr) (2) 16 Qc2max, maximum reactive power output at Pc2 (MVAr) (2) 17 ramp rate for load following/AGC (MW/min) (2) 18 ramp rate for 10 minute reserves (MW) (2) 19 ramp rate for 30 minute reserves (MW) (2) 20 ramp rate for reactive power (2 sec timescale) (MVAr/min) (2) 21 APF, area participation factor Branch Data Format 1 f, from bus number 2 t, to bus number (-) (circuit identifier) 3 r, resistance (p.u.) 4 x, reactance (p.u.) 5 b, total line charging susceptance (p.u.) 6 rateA, MVA rating A (long term rating) 7 rateB, MVA rating B (short term rating)


8 rateC, MVA rating C (emergency rating) 9 ratio, transformer off nominal turns ratio ( = 0 for lines ) (taps at 'from' bus, impedance at 'to' bus, i.e. ratio = Vf / Vt) 10 angle, transformer phase shift angle (degrees) (-) (Gf, shunt conductance at from bus p.u.) (-) (Bf, shunt susceptance at from bus p.u.) (-) (Gt, shunt conductance at to bus p.u.) (-) (Bt, shunt susceptance at to bus p.u.) 11 initial branch status, 1 - in service, 0 - out of service (2) 12 minimum angle difference, angle(Vf) - angle(Vt) (degrees) (2) 13 maximum angle difference, angle(Vf) - angle(Vt) (degrees) (+) Area Data Format 1 i, area number 2 price_ref_bus, reference bus for that area (+) Generator Cost Data Format NOTE: If gen has n rows, then the first n rows of gencost contain the cost for active power produced by the corresponding generators. If gencost has 2*n rows then rows n+1 to 2*n contain the reactive power costs in the same format. 1 model, 1 - piecewise linear, 2 - polynomial 2 startup, startup cost in US dollars 3 shutdown, shutdown cost in US dollars 4 n, number of cost coefficients to follow for polynomial cost function, or number of data points for piecewise linear 5 and following, cost data defining total cost function For polynomial cost: c2, c1, c0 where the polynomial is c0 + c1*P + c2*P^2 For piecewise linear cost: x0, y0, x1, y1, x2, y2, ... where x0 < x1 < x2 < ... and the points (x0,y0), (x1,y1), (x2,y2), ... are the end- and break-points of the cost function.

In addition any HVDC connections must be specified in field hvdc. HVDC data format (mpc.hvdc) 1 f, from bus number 2 t, to bus number 3 P, Active power flow (directional from f -> t bus). 4 Plim f-bus, flow limit injected to HVDC at from bus 5 Plim t-bus, flow limit injected to HVDC at to bus 6 Mu (of Plim f-bus) 7 Mu (of Plim f-bus)

D.3.2 Generation capacity and marginal cost of production The generation capacity and marginal cost for the different countries is provided through the Matlab file “UCTEGenerationA.mat” or “UCTEGenerationB.mat”. The file name is in the current version of the program hardcoded in scenario, where it is only possible to choose between the two through the input parameter GenCase. The file must contain a cell array a, where the index correspond to the areas in mpc.bus. Each cell element contains a matrix with 90 rows and 26 columns, which contains the installed capacity and generation marginal cost based on generation type. The row of the matrix indicate what type, while the column indicates the year.


Table 13. Column location used Year 2005 2006 2007 2008 2010 2015 2020 2030

Column 3 6 9 12 15 18 21 24 Table 14. Row locations used Generation capacity

[MW] Marginal cost [Euro/MW]

Hydro power stations 8 79 Nuclear power stations 9 80 Fossil fuel power stations 10 81

of which, lignite 11 82 of which, hard coal 12 83

of which, gas 13 84 of which, oil 14 85

of which, mixed oil /gas 15 86 of which , non attributable 16 87

Renewable energy sources (Not Wind)

17 88

An example of generation imput data given in an Excel sheet is shown in Figure 13 on page 34. D.4.3 Time series of load The hourly load time series is provided in a Matlab file UCTELoad.mat together with load forecast for all countries/areas. The filename is hardcoded in scenario and is loaded each time scenario is called. The file must contain to variables, allhours and year, where both are cell arrays where the index correspond to area in mpc.bus. Each element in allhours represents a country and contains a matrix with 365 rows and 25 columns. The first column gives the dates as a serial date number, as given by datenum in Matlab, for all the 365 days in the year. The next columns contain the total load in MWh for given country and hour, from hour 1 through 24. The load forecast for each country is given in the second variable year, where each element in the cell array is a matrix with 2 rows and 6 columns. The first row lists the year while the second row lists the scaling factor, where the reference year is given by allhours.


Table 15. Element of cell array year Year 2007 2008 2010 2015 2020 2030scale D.3.4 Wind series The hourly wind series is provide as an input argument to scenario, as a structure with fields ‘location’, ’hour’, ‘P’ and ‘cap’. The field location is a column vector with the bus numbers where the wind power is injected, while the total installed capacity at the site is given by the field cap. The normalized wind power for a specific hour can then be found in the field P, where the rows represent the hour and the columns the location. D.3.5 Revision plans The input variable revision to the scenario function can be used to emulate any scheduled outages. The first column gives the index of the gen variable, the second column is the hour for the reduction in installed capacity, while the third is the reduction factor. The function outrev is provided to generate such a matrix. » help outrev OUTREV Reduce installed capacity due revision of generators revision = outrev(gen_idx,hour,perct,type) revision - input to scenario gen_idx - index in mpc.gen hour - houre for which perct(age) available capacity perct - Perctenage of avaiable capacity for given hour type - 'mnt','day','hour' for which this is the case


Appendix E: Model updates This appendix supplements the original TradeWind WP3 Report, “D3.2 Grid modelling and power system data”, with updates on modelling and input data. The main updates are:

Switch from PTDF to full DC power flow representation Allocation of generator types to generator buses Distribution of demand to load buses Inclusion of ETSO Net Transfer Capacities between countries New scenarios for fuel costs, efficiencies etc. New water value functions for hydro power. Demand forecasts are now based on EURPROG 2006 Grid development scenarios (added lines and HVDC cables for years 2010 and

onwards) E.1 Reduced power flow description In the original WP3 report it was decided to use zonal PTDFs due to the limitation in availability of data needed, such as detailed nodal demands and generation cost, to conduct a full-fledged power flow. Using a reduced model, such as the zonal PTDFs or a reduced DC power flow description, will always suffer from being only valid around a certain operation point which will not be the case when simulating hour by hour for a whole year. The improvements in calculation speed, using a zonal PTDFs compared to a full DC power flow description, is also negligible as:

1. When running 8760 consecutive optimal power flow calculations, the solver can benefit from using the previous solution as starting point for finding the next. Thus it is only the initial calculation (first hour) where there are significant improvements in calculation speed.

2. In general a DC optimal power flow is much faster to solve than a PTDF description due to the sparsity in the description of the constraints for the DC model.

When adding NTC to the model, based on values given by ETSO, a zonal PTDF description, which is based on a snapshot, will have problem satisfying the demand for many of the 8760 hours in many areas due to NTC constrained loop flows.


E.2 European grid model - New DC power flow description For the areas Nordel, UK and Ireland the PTDF was originally just converted to a full PTDF description, and not reduced to a zonal PTDF, as:

For Nordel there are mainly hydro power plants, and it is a relatively simple task to locate thermal power plants in the DC model.

For UK and Ireland there are no internal constraints, and as the model is radial in structure, so that a reduced PTDF and a “full” DC models will be equal.

Demand is scaled based on total demand and the original distribution of demand in a given country, which again give the same results for both models.

The new DC power flow description uses the original DC model for UCTE, that is described in Chapter 4.2.1 in the original TradeWind WP3 Report, “D3.2 Grid modelling and power system data” [31]. This is the model that was the basis for generating the zonal PTDFs for UCTE. In the DC optimal power flow the HVDC connections, which are mainly located between the different synchronous zones, are modelled as demand on each side of the connection just as for running at optimal PTDF power flow solution.

Table 16. Size of DC power flow model.

Type Bus Generators Loads Branches hvdc Number 1381 557 995 2213 12

E.3 Distribution of generation types and demand In the original DC power flow description the generators are only given by size and not type, thus the cost of using any specific generator is not given. Thus, the generator types for all generators in the DC power flow description must be established in order to determine the generator cost. For all regions the installed generation capacities, after distribution of generation types, are scaled to match the total available generation capacity for any given country and generation type. The initial demand distribution in the model is scaled, country by country, hour by hour, to match the estimate of total demand for each country over the year.


E.3.1 Nordel For the Nordel DC power flow description, the generator types are determined based on knowledge about the geographical location of buses in the model and actual generator types in the same locations. In Norway there are mainly hydro units, while the locations of nuclear for both Sweden and Finland are known, thus they can easily be placed in the simplified DC power flow module used for Nordel. E.3.2 UK and Ireland In UK and Ireland, the location of the generators and their type does not affect the result of the optimization due the simplicity of the model for this region, being radial in structure and with know internal transmission constraints, thus they are distributed randomly. E.3.3 UCTE UCTE is the synchronous zone with the highest level of detail in the power flow description. This makes it more difficult to determine the type of any given generator in this part. Hydro units and nuclear units are allocated to generator buses based on geographical information from [32] and [33], respectively. The remaining generation capacities for all countries are distributed using according to the following algorithm:

1. The largest generator in the power flow model is assigned the type with largest available capacity in the country.

2. The second largest generator is assigned the type with second largest available capacity.

3. Keep assigning generators by size until all generator types in the country are used and start on the top of the list of generator types for next generator.

E.4 ETSO Net Transfer Capacities The DC power flow model is a linear mode which does not capture stability constraints such as voltage, transient and angular stability. Based on more detailed studies net transfer capacities can be established which acount for these stability issues. Such detailed studies are not a part of this project, so the net transfer capacities between the countries, available from www.etso.org which also includes political constraints, have been included in the model. For Nordel the NTCs have been taken from [13]. The NTC can and will change through the year, though in the current model only one set of NTC’s are used, as shown in Table 17 and Table 18. The model does not include HVDC connections in the NTC, so the total capacity between two countries is equal to the sum of the NTC and all HVDC capacities.

http://www.etso.org/�


Table 17. NTCs for Nordel (directional).

Country/region Capacity [MW] Country/region Capacity [MW]

NO3 NO2 600 600 SE3 SF2 1600 1200

NO3 SE3 600 700 SE3 SE2 7000 7000

NO2 SE3 600 500 SE1 SE2 4000 4000

NO2 NO1 300 300 SE1 DK_E 1350 1750

NO1 SE2 2050 1850

Table 18. NTCs for UCTE (directional).

Country Capacity [MW] Country Capacity [MW]

AT SV 350 650 ES P 1300 1200

AT IT 220 85 FR IT 2650 995

AT DE 1800 2000 GR BU 500 500

AT CH 1200 1200 GR MC 705 101

AT HU 500 100 IT SV 160 430

B FR 2379 3460 N DE 3000 3850

B N 2400 2400 RO HU 700 600

BH HR 600 600 RO BU 750 750

BH SC 695 540 RO SC 650 750

CH IT 3890 1460 SC MC 0 0

CH DE 4000 2100 SC HR 540 500

CH FR 2300 3200 SC BU 500 650

HR SV 900 900 SC HU 1000 800

CZ DE 2300 700 SK HU 1300 800

CZ PL 800 1660 SK PL 550 550

CZ SK 1300 900 UA SK 450 450

CZ AT 430 1032 UA HU 800 0

DE FR 2750 2850 UA RO 500 200

DE PL 1200 1100 DK DE 1500 950

ES FR 500 1400

If extra branches are added between to countries a new NTC will be calculated according the following equation:

newnew old

old

ATCNTC =NTC

ATC

where:

ATC – Available Transfer Capacity NTC – Net Transfer Capacity.


New HVDC connections do not make any changes to the NTCs. E.5 Grid development Scenarios for new lines and HVDC cables are based on grid development information from UCTE, National Grid and Nordel, and are shown below.

Table 19. New HVDC connections.

From To Name Capacity [MW] Year Netherlands Norway NorNed 700 2009 Netherlands Great Britain BritNed 1000 2011

Sweden Finland Fenno scan 2 800 2015 Denmark West Denmark East Great Belt 600 2010

Norway Denmark Skagerrak 4 600 2020 Great Britain Ireland East-West interc. 500 2010

Table 20. New lines.

From To Capacity

[MW] Year Comments

NO2 SE3 800 2011 Nea – Järpstrømmen B F2 400 2008 Chooz – Jamiolle - Monceau

GR MC 1420 2008 Bitola – Florina CZ A1 1386 2008 2d line Slavetice - Durnrhor E2 F6 3100 2010 France - Spain: eastern reinforcement I2 SV 3100 2015 Udine – Okroglo P E1 1500 2015 Valdigem - Douro Internacional - Aldeadavilla P E4 3100 2015 Algarve - Andaluzia P E1 3100 2015 Galiza – Minho

RO SC 1420 2015 Timisoara - Varsac I2 A2 3100 2020 Thaur – Bressanone (Brenner Basis Tunnel) A1 HU 1514 2020 Wien/Südost - Gÿor A2 I2 530 2020 Nauders – Curon / Glorenza A2 I2 3100 2020 Lienz - Cordignano DK D2 1660 2010 Upgrading of Jutland - Germany

E.6 Marginal cost scenarios Updated marginal cost scenarios have been made in TradeWind WP7. These can be downloaded in Excel form from the 3Eproject web site under: Root Collection / WP07 Analysis of Market Rules / Market Modelling Input. The cost estimates for power generation are adapted as much as possible taking into account the recent EC Energy Baseline scenario [35]. Where this was not possible, other transparent reference publications where found [36],[37]. In the power flow model, there are some sub-categories of fossil generation that are not included in the WP7 scenarios. The cost data for these generator types have been estimated in a simplified manner, by making small alternations of the WP7 cost data for similar fossil types, mainly based on [38]. The cost scenario data are shown in Table 21-Table 25.


Table 21. Operation and Maintenance costs and CO2 content of fuel (for all years). Power plant type O&M CO2 content

(Euro/MWh) (tonnes/MWh)nuclear power stations 6.00 0fossil fuel power stations of which, lignite 2.00 0.45of which, hard coal 2.00 0.346of which, gas 1.70 0.205of which, oil 5.00 0.281of which, mixed oil / gas 5.00 0.281of which, non attributable 5.00 0.281renewables (other than wind and hydro) 3.00 0not clearly identifiable energy sources 5.00 0.281

Table 22. Fuel cost scenarios. *Nuclear fuel prices are referred to electricity, not thermal energy content of uranium. Fuel cost [Euro/MWh] 2005 2008 2010 2015 2020 2030nuclear power stations* 5.00 5.00 5.00 5.00 5.00 5.00fossil fuel power stations 16.29 18.24 19.54 20.44 21.66 22.41of which, lignite 6.27 5.99 5.81 6.06 6.23 6.31of which, hard coal 6.97 6.66 6.45 6.73 6.92 7.02of which, gas 16.29 18.24 19.54 20.44 21.66 22.41of which, oil 25.66 25.66 25.66 27.26 28.77 29.57of which, mixed oil / gas 25.66 25.66 25.66 27.26 28.77 29.57of which, non attributable 25.66 25.66 25.66 27.26 28.77 29.57renewables (other than wind and hydro) 16.20 16.20 16.20 16.20 16.20 16.20not clearly identifiable energy sources 25.66 25.66 25.66 27.26 28.77 29.57

Table 23. Fuel efficiency scenarios. *Nuclear fuel prices are referred to electricity, not thermal energy content of uranium. Fuel efficiency % 2005 2008 2010 2015 2020 2030nuclear power stations* 100 100 100 100 100 100fossil fuel power stations 39 43 46 48 49 49of which, lignite 31 33 34 36 37 37of which, hard coal 31 33 34 36 37 37of which, gas 39 43 46 48 49 49of which, oil 29 34 37 38 38 38of which, mixed oil / gas 28 32 35 36 36 36of which, non attributable 26 30 33 34 34 34renewables (other than wind and hydro) 23 27 29 31 33 33not clearly identifiable energy sources 25 29 31 32 32 32

Table 24. Scenarios for CO2 price and calculated CO2 taxes based on fuel efficiency and CO2 content.

2005 2008 2010 2015 2020 2030CO2 price [€/tonne] 5 20 20 21 22 23CO2 Tax [Euro/MWh]nuclear power stations 0.00 0.00 0.00 0.00 0.00 0.00fossil fuel power stations 2.63 9.53 8.91 8.97 9.20 9.84of which, lignite 7.25 27.26 26.46 26.24 26.74 26.33of which, hard coal 5.58 20.97 20.35 20.18 20.57 20.25of which, gas 2.63 9.53 8.91 8.97 9.20 9.84of which, oil 4.84 16.53 15.19 15.53 16.27 16.06of which, mixed oil / gas 5.02 17.56 16.06 16.39 17.17 16.86of which, non attributable 5.40 18.73 17.03 17.36 18.18 17.75renewables (other than wind and hydro) 0.00 0.00 0.00 0.00 0.00 0.00not clearly identifiable energy sources 5.62 19.38 18.13 18.44 19.32 18.73


Table 25. Calculated marginal costs. Marginal cost [Euro/MWh] 2005 2008 2010 2015 2020 2030nuclear power stations* 11.00 11.00 11 11.00 11.00 11.00fossil fuel power stations 46.10 53.66 53.09327 53.24 55.11 56.37of which, lignite 29.49 47.42 45.53461 45.07 45.58 43.73of which, hard coal 30.06 43.15 41.32604 40.89 41.28 39.37of which, gas 46.10 53.66 53.09327 53.24 55.11 56.37of which, oil 98.34 97.01 89.54636 92.27 96.98 91.46of which, mixed oil / gas 101.67 102.76 94.37758 97.12 102.09 95.79of which, non attributable 109.10 109.27 99.79441 102.54 107.80 100.56renewables (other than wind and hydro) 73.43 63.00 58.86207 55.26 52.09 50.65not clearly identifiable energy sources 113.27 112.87 105.91 108.64 114.22 105.87 With the new marginal cost scenarios, it was also necessary to update the marginal cost functions (water values) of hydro power. Due to limited time available, it was not realistic to find optimized water value functions (i.e. marginal cost as a function of reservoir level) for each country for each simulated year. This would have resulted in 8736 different water value functions to be determined (52 weeks * 6 simulation years * 28 countries with hydro installations). To simplify this process, the data described in the original WP3 report (see pages 37-40) has been used as is, with the following exceptions:

The original water values in Norway, Sweden and Finland have been multiplied by a factor of 1.5 so that the water values better reflect the new marginal cost of competing generator types. See page 40 in [31].

During simulation studies, it was observed that the utilization of hydro power in Austria, Germany, Poland and Portugal were unrealistic with the original water values. The new and improved water values functions for these countries are shown in Figure 40.

0 10 20 30 40 50 60 70 80 90 1000

50

100

150

200

250

300

350

400

450

Reservoir level (%)

Mar

gina

l cos

t (€

/MW

h)

DE

AT and PLP

All other countries except NO, SE and SF

Figure 40. Marginal costs of hydro power as a function of reservoir filling (water value function).


E.7 Demand scenarios Demand scenarios are based on EURPROG 2006 . Deviations from Eurprog 2006 in the forecast for 2030 are based our own jugdement, and is due to lack of data for both demand and generation.

Table 26. Demand scenarios pr. country.

2005 2008 2010 2015 2020 2030 DE 556 566 572 573 575 572 NL 115 122 129 143 157 191 BE 88 93 97 103 109 109 LU 6 7 6 7 7 7 FR 482 493 508 530 552 618 CH 63 64 65 72 80 98 IT 330 352 366 408 450 550 AT 63 65 63 66 70 83 ES 253 288 317 353 390 463 NO 122 128 133 138 143 153 SE 145 148 150 152 154 156 CZ 63 66 68 73 77 83 SI 13 15 16 17 18 20 GR 53 60 67 75 84 101 HU 39 43 45 49 53 58 GB 377 417 458 485 512 523 PT 50 55 59 67 76 97 HR 17 18 19 21 23 28 RS 42 45 48 53 58 58 RO 52 56 59 69 78 105 BG 36 36 36 44 51 62 BA 11 12 12 14 15 18 SK 26 29 31 33 35 39 PL 131 136 136 148 160 181 SF 85 93 96 101 107 117 DK 36 37 38 40 41 45 MK 8 8 8 8 8 8 IE 26 30 34 38 43 43


E.8 Generation capacity scenarios

0 20 40 60 80 100 120 140 160 180

DENLBELUFRCHIT

ATESNOSECZSI

GRHUGBPTHRRSROBGBASKPLUASFDKMKIE

RU

2005

Capacity [GW]

HydroNuclearFossilelignite coalhard coalgasoiloil/gasnon attributableRenew other than windNot identifiable

Figure 41. Generation capacity by country and type in 2005


0 20 40 60 80 100 120 140 160 180

DENLBELUFRCHIT

ATESNOSECZSI


RU

2008

Capacity [GW]




0 20 40 60 80 100 120 140 160 180

DENLBELUFRCHIT

ATESNOSECZSI


RU

2010

Capacity [GW]




0 50 100 150 200

DENLBELUFRCHIT

ATESNOSECZSI


RU

2015

Capacity [GW]




0 50 100 150 200

DENLBELUFRCHIT

ATESNOSECZSI


RU

2020

Capacity [GW]




0 50 100 150 200 250

DENLBELUFRCHIT

ATESNOSECZSI


RU

2030

Capacity [GW]



E.9 References to Appendix E [31] Korpås M, Warland L, Tande JOG, Uhlen K, Purchala K, Wagemans S. Grid

modelling and power system data. D3.2 TradeWind Report, 2007. [32] UCTE Map. Available at www.ucte.org [33] International Nuclear Safety Center. Maps of Nuclear Power Reactors. Available

at http://www.insc.anl.gov/pwrmaps/ [34] GreenNet EU/27. Guiding a least cost grid integration of RES-electricity in an

extended Europe. Deliverable D8: Case Studies on System Stability with Increased RES-E Grid Integration.


http://www.insc.anl.gov/pwrmaps/�


[35] Capros P, Mantzos L, Papandreou V, Tasios N. European Energy and Transport, Trends to 2030 -- Update 2007. ICCS-NTUA for European Commission, DG TREN, April 8, 2008.

[36] Norwegian Energy and Water Directorate, Quarterly report 4 -2005 (in Norwegian), p. 67, ISBN: 82-410-0576-8, January 2006

[37] Interactive Recalculator, Downloads. IEA RETD, http://www.recabs.org/, accessed 18/04/2008.

[38] Vattenfall Annual Report 2006

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