D6.1. Report describing the power market model, data requirements and results from analysis of initial grid designs

Transcription

1 D6.1 Report describing the power market model, data requirements and results from analysis of initial grid designs Harald G Svendsen, Leif Warland, Magnus Korpås, Daniel Huertas-Hernando SINTEF Energy Research Jakob Völker Deutsche Energie-Agentur GmbH (dena) July 2010 Agreement n.: EIE/08/780/SI Duration May 2009 November 2011 Co-ordinator: European Wind Energy Association Supported by: PROPRIETARY RIGHTS STATEMENT This document contains information, which is proprietary to the OffshoreGrid 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 OffshoreGrid consortium.

2 Document information Document Name: D6.1 Report describing the power market model, data requirements and results from analysis of initial grid designs Document Number: Author: Date: 28 July 2010 Harald G Svendsen, Leif Warland, Magnus Korpås, Daniel Huertas-Hernando, Jakob Völker WP: Task: Revision: Approved: WP6: Offshore Power Market Modelling Task 6.1: Market results and flows for initial grid designs Frans Van Hulle, Achim Woyte Sintef Document Number: Page: 2/33

3 SUMMARY This report describes the power market model used to compute operational costs of the European power system in the OffshoreGrid project. The report aims to give a concise overview of the simulation program itself, the input data and the format of the output data. The actual simulation results have been made available to project partners separately as a number of data files, one for each case plus a summary file. The simulation program, the SINTEF Power System Simulation Tool (PSST) has been developed previously to study effects of integrating large scale wind power into the European power grid. It combines physical power flow with a simplified electricity market, therefore a power market simulator. The simulation program computes the optimal generation dispatch for each hour, where optimum is defined as minimum total cost of generation, and hence represents the socioeconomic optimum. The physical power flow equations are given as constraints to the optimisation problem, together with a number of other constraints such as e.g. cross-border trading limitations and cross-border line capacities. The PSST has previously been used in numerous projects, and recently in the European TradeWind project. Input data required for these simulations are Grid data for Europe, including generation and demand distribution Demand forecasts and hourly demand profiles Forecasts for generator capacities and marginal costs (by type and country) Detailed data for offshore wind farms Region specific data for onshore wind generation Hydro reservoir data Net transfer capacities (country-to-country power trading limitations) The grid data is collected from multiple sources and the model contains in total 1,494 generators, 4,836 buses, and 8,484 branches (connections between buses). A substantial amount of effort was put into the preparation of the grid data to make it consistent and with all the information required by the simulation program. A major task in this respect was to associate tabular data with geographical location for generators (i.e. power grid connection point). Forecasts regarding demand and generation capacities, including onshore and offshore wind were provided by project partners. Other input data was largely taken from the TradeWind project. The main output from the simulations is system cost, defined as the total annual operational costs of power generation. As total cost of generation defines the optimal point in the algorithm, it represents the most direct economic output and has the clearest interpretation. The simulations also give a number of additional output parameters that are used in the subsequent analysis of the results. These include technical and economical parameters such as Costs per country Cost variability (hour-by-hour) Cost difference between countries (hour-by-hour) Utilisation of offshore connections Wind power curtailment The present report focuses on the approach and input data for the market modelling part. The final market modelling results will be published in February The output data is provided in separate files in a tabular format. The explanation of this format, and definitions of the quantities reported are included in this report. System cost results from these simulations are used together with investment cost calculations in work package 5 to form the scientific basis for policy recommendations in the final report (work package 8). Document Number: Page: 3/33

4 Table of contents ts SUMMARY INTRODUCTION MARKET MODEL (POWER SYSTEM SIMULATION TOOL) The optimal power flow description Cost function... 8 Optimal DC power flow description Generation Updating the constraints for a given hour Demand 10 Wind generation Generation cost of hydro units EUROPEAN GRID MODEL Demand and generation scenarios Continental Europe (former UCTE) Production units in the power flow model Countries around the North and Baltic Seas where nodes are identified Countries without geographical information Nordic region (former Nordel) Great Britain and Ireland Baltic region ENTSO-E E Net transfer Capacities Grid reinforcement rcement Wind power Onshore wind power Offshore wind power INPUT DATA HVDC interconnections Document Number: Page: 4/33

5 4.2 Water values and inflow Cost input data RESULTS Simulation cases Generation cost and electricity prices Reporting of simulation results Summary file Individual case result files Document Number: Page: 5/33

6 1 INTRODUCTION This report describes the main activity of Work Package 6 in the OffshoreGrid project. The work package consists of the modelling and analysis of the offshore power market for Northern Europe, with the main objective to provide a scientific basis for further discussions and policy recommendations (Work package 8). This work makes use of the SINTEF Power System Simulation Tool (PSST), which is an existing flow based power market simulator that was used e.g. in the TradeWind project [1]. A detailed model of the offshore grid developed in WP5 is implemented and merged with existing onshore power system data. To the extent possible the project made use of power system and power market data that was collected in the TradeWind project. In addition to ensuring efficiency in the often time consuming data collection task, this also contributes to consistency and confidence in the results obtained. Different study cases have been simulated to analyse different offshore grid designs and sensitivities. These cases, in total 32, have been specified by project partners. The main output from the simulations is system cost, i.e. the total annual operational costs of power generation. The simulations also give a number of additional output parameters that are used in the subsequent analysis of the results. These include technical and economical parameters such as Costs per country Cost variability (hour-by-hour) Cost difference between countries (hour-by-hour) Utilisation of offshore connections Wind power curtailment The present report focuses on the approach and input data for the market modelling part. The final market modelling results will be published in February The simulation cases are based on offshore grid designs specified in Work package 5, and scenario specifications from Work package 4. The grid model is more detailed than the one used in the TradeWind project mentioned above, as it uses the detailed UCTE study grid model for continental Europe. In total, the grid model contains 1,494 generators, 4,836 buses, and 8,484 branches (connections between buses). As the input data was taken from numerous sources, a substantial amount of effort was required for preparation of the grid data to make it consistent and with all the information required by the simulation program. A major task in this respect was to associate tabular data with geographical location for generators (i.e. power grid connection point). As part of this process, the simulation tool itself also had to be upgraded to meet the requirements of the OffshoreGrid project. The main input data required by the simulation program is Grid data for Europe, including generation and demand distribution Demand forecasts and hourly demand profiles Forecasts for generator capacities and marginal costs (by type and country) Detailed data for offshore wind farms Region specific data for onshore wind generation Hydro reservoir data Net transfer capacities (country-to-country power trading limitations) Document Number: Page: 6/33

7 2 MARKET MODEL (POWER SYSTEM SIMULATION TOOL) 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. The load is given as relative hourly profiles for each country for a given reference year. The actual load in any given hour can then be found using the total load in GWh. The generation capacity forecast is given as total installed capacity for a given year and country. Wind power time series have been provided by project partners. Year (hour=1) Parameter updating - Wind and load by hour - Cost of hydro production Input data for given year - Power flow case description - Generator - capacities Generator cost curves (marginal cost) - Reservoir levels (hydro) Time dependent - Wind series - Load series - Inflow (hydro) - Watervalues hour +1 Solve DC optimal power flow External LP solvers from Coin (Clp) False hours==876 0 True Aggregate and present results - Total load and production - Branch/hvdc flow - Sensitivities of constraints - Power exchange (countries) Figure 1. PSST 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. 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 following description of the DC optimal power flow algorithm is only meant as a brief reminder for those already familiar with this type of analysis. A reference for both optimal and DC power flow is ref.[2-3] in addition to [2] for DC power flow. The linear optimisation problem given below is solved for all iterations of the simulation loop in Figure 1. Document Number: Page: 7/33

8 T min F( x) = c x subject to: x A x = b lower eq A x b eq x x x upper (1) where : x F(x) c A and b Aeq and beq xlower and xupper is the state variable vector is the cost function to be minimized (total generation costs) determine the cost of all the second and first order elements respectively in the cost function describe the transmission constraints between grid zones are given by the power flow equations the lower and upper bounds on the state variables The state variables x include generator production, HVDC flow and voltage angles. The HVDC connections are modelled as loads with opposite sign on each side of the connections. Both elements of the cost function, 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. 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. Cost function The cost function F(x) is in general a function of all state variables x. In our case it gives the total generation cost as indicated above. The aim of the optimal power flow algorithm is to find the power flow solution with the optimal (i.e. minimal) generation cost. For each generator the total cost is given as piecewise linear to represent an approximation of quadratic cost, shown in Figure 2. The cost coefficient steps (MC1, MC2 and MC3 in Figure 2) together with the number of equally spaced intervals (3 in Figure 2) are specified explicitly in the program. In the current case, the cost coefficient ranges stepwise from 90% of marginal cost at zero production (P=0) to 100% of marginal cost at full production (P=Installed capacity) for all generators except wind, which has a constant cost coefficient (equal to the marginal cost). The cost is the cost coefficient multiplied by production. For non-generator state variables, such as HVDC power flow and voltage angles, the cost coefficient (marginal cost) is zero and therefore their cost is always zero. Marginal cost (P) [Euro/MW] Cost(P) [Euro] MC3 MC2 MC1 P [MW] P [MW] Figure 2. P1 P2 Installed capacity P1 P2 Installed capacity Example of piecewise p linear cost function with three t steps Document Number: Page: 8/33

9 Optimal DC power flow description The DC power flow [2] is a linearization of the power flow description under the following assumptions: 1. The voltage angles differences ( δ ) are small, 2. Line resistance is negligible, ri 0 3. Flat voltage profile, i.e. all voltage magnitudes are close to 1.0 pu The DC power flow description can be generalized to: Bδ = P = I P P + I P (2) inj G G L hvdc hvdc and B f δ = P (3) Flow 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 as shown in Equation (4). δ A P eq hvdc 123 x [ B IG Ihvdc ] PG = { P L The branch flow limitations encoded in the inequality constraints of the optimization problem in equation (1) are: δ B f 0 0 P Flow,max P G B f 0 0 P (5) Flow,max P hvdc A 123 b x In addition, the sum of rows in A representing a net transfer capacity (NTC) can be limited by an NTC value, putting an upper bound on the flow between countries or areas. b eq (4) 2.2 Generation For all generation types, except Wind and Hydro, the capacity and marginal cost is kept constant in the simulation. These quantities are either known and explicitly given by the model, or specified as a scenario input where the total GW installed capacity as well as marginal cost for all generation types are given for every country in the model. This would be the case when studying future scenarios, where no detailed information regarding location, type and size of generator units, only assumptions on total values and targets are available. Document Number: Page: 9/33

10 When using scenario input for generation capacity, existing generators are scaled so that total capacity by country match that specified in the scenario. If the scenario specifies capacity for a generator type that is not present in the model for a given country, this generation capacity is ignored. Likewise, if the scenario does not specify capacity for a generator type that is present in the model, such generator units are removed before simulation starts. 2.3 Updating the constraints for a given hour For each hour the constraints that are time dependent, i.e. the load, available wind power and marginal cost of hydro units, are updated before running the optimal power flow. The marginal cost of hydro units is a function of the reservoir level. The available wind power, given as hourly profiles for each area (onshore wind) or node (offshore wind), is defined here as the wind power output that can be fed into the grid in a non-congested case Demand For each country in the model an hourly demand profile must be provided. The hourly demand profile together with distribution of demand in any given country is normalized as shown in the equation below: i i P = 1.0, P = 1.0 { all nodes in country} h { all hours in year} where Pi is the normalized demand at node i, and Ph is the normalized hourly value for given country. By multiplying with the total yearly demand for given country the demand on any given node in the country can be found for all hours simulated. The total yearly demand for each country is specified as scenario inputs to the market model. h 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. Generation cost of hydro units Costs associated with hydro units require special care: 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 except wind power, the unit would produce at its maximum level until 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 (see ref. [4]). 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 with reservoir and pumping capacity. 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 and thereby an increase in the water value until it equals the gas marginal cost. If the water value is higher than the marginal cost of the gas power plant, the hydro unit will consume power by pumping water from a lower reservoir to a higher reservoir and thereby decreasing the water value until it equals the gas marginal cost. The energy consumption during pumping is recovered later when the pumped water is used to generate power. Losses in the process are ignored. The reservoir level is updated each hour, according to the following equation: Document Number: Page: 10/33

11 The inflow is the flow of water into the reservoir, represented as an energy flow (MWh per time step dt). 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: ( ) Pmax ( t) = min Pinstalled, 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. Document Number: Page: 11/33

12 3 EUROPEAN GRID MODEL The European grid model used in this project is divided into five synchronous regions: Continental Europe (former UCTE), Great Britain, Ireland, the Nordic region (former Nordel) and the Baltic region. The countries included in the model are shown in Figure 3, where the zones in countries are indicated by red dots and the connection between them as white lines. The countries included in the model are also listed in Table 1. Figure 3. OffshoreGrid zones in the European grid model The power flow descriptions for each of these areas come from different sources, with different levels of details, or simplifications, e.g. in the Irish system there is just two nodes with a single fictitious impedance in between, while for Great Britain a detailed model is available. This simplification of the Irish system is not a problem as the power flow market simulator uses a linear approach where the size of the impedances on radials does not make any difference for the actual flow as seen by the market model. Table 1. Countries included in the OffshoreGrid study Albania Finland Lithuania Russia Austria France Macedonia Serbia Belgium Germany Monte Negro Slovak Republic Bosnia-Herzegovina Great Britain Morocco Slovenia Bulgaria Greece Netherlands Spain Croatia Hungary Norway Sweden Czech Republic Ireland Poland Switzerland Denmark Italy Portugal Ukraine Estonia Latvia Romania Document Number: Page: 12/33

13 The total size of the model for each synchronous area, as they are implemented in the model, is shown in Table 2. Table 2. Size of model used for all synchronous areas # bus # gen # branch UCTE Nordel Great Britain Ireland Baltic Demand and generation scenarios The scenarios for demand, generation and marginal cost data pr. country and fuel type was prepared in work package 2, and distributed in the model as described in Chapter 2.2 and Continental ntal Europe (former( UCTE) The UCTE Study Model Winter (16/01) and Summer (16/7) situation 2008, have been provided for the OffshoreGrid project by ENTSO-E. The two models are snapshots of the flow representative for that period of year. The difference between the two models, summer and winter, is the demand and generation, and as both demand and generation size and type is provided by other sources only one of the models is used (winter). In order to do a power flow analysis it is essential to have a fair overview of the topology and the geographical location of main components of the system, especially for a large system encompassing as many countries as given in the UCTE model. However, the model, as delivered by ENTSO-E, does not provide geographical information nor do the node names used in the model clearly identify the location. For some countries, such as Portugal, it provides only a number, which requires detailed knowledge for that part of the system to make any assumptions regarding generation units. Identifying every single node in the UCTE model with its geographical location was neither necessary nor possible within the scope of this project. It was, however, necessary to identify parts of the main transmission network and locations of production units in order to have correct cost data. The focus of the study is the offshore grid in the North and Baltic Seas both for interconnecting countries and injection of large amount of offshore wind power. Clear knowledge of connection points in the onshore grid is important for identifying the best suited point for connection and for identifying necessary grid upgrades to cope with the large amount of power injected into the onshore system. The main nodes in the countries surrounding the North and Baltic Seas have been identified by the project partners for as many nodes as possible. The countries where nodes have been identified are Poland, Denmark, Germany, The Netherlands, Belgium, France and Luxembourg. Production units in the power flow model The market model uses marginal cost on production units to determine the distribution of power generation for any given hour. The hours are only connected through the water inflow to and the reservoir levels of the Hydro units. The production profile is then selected from a merit order list, while the constraints of the network model are maintained. In this model it is essential to have a good estimate of marginal costs for each unit in order to get a realistic optimal power flow solution. To get the exact value for each unit is for all practical purposes impossible, thus the approach of this project is to base the marginal cost on production types, such as hydro, nuclear, wind and fossil units. While for the Nordic, British and Irish regions it was fairly easy to identify the production units given by type and size, based on publicly available information as well as the knowledge of the project partners, this was not the case for the Continental (UCTE) system. In the model received from ENTSO-E [5] there is no information regarding generator type or size, only an indication Document Number: Page: 13/33

14 whether or not there is production available for any node in the power flow model. The model received is structurally identical to the model received from ENTSO-E at the end of the TradeWind project, thus the work done in the Master thesis of Beharrysingh [6] was used as starting point for further improvements to the allocation of size and type to generator units in the model. Countries around the North and Baltic Seas where nodes are identified For the countries where nodes had been identified and assigned geographical information (longitude and latitude), the Platts database [7] was used for generators. The Platts database provides detailed information on size, type and geographical location. The generators from the Platts database were placed on the node in the model closest to its actual location as described by Platts. Only nodes with existing generation capacity in the original models were considered when placing the generators from Platts. Countries without geographical information For the countries where the nodes have not been identified geographically, the generators of all types, including wind, were allocated to nodes as given in references [6, 8]. Here, data available from IEAE [9] and INSC [10] as well as the ENTSO-E grid map [5] was used to trace each generator within the detailed model to identify the node name referring to the nuclear power plants. All nuclear plants were explicitly assigned. The network map also identifies the hydro power plants within each country. Firstly, the total number of hydro plants in each country was found. Then, nodes referring to substations located in close proximity to these individual hydro plants or clusters of plants were also identified by tracing through the detailed model. Of the 24 UCTE countries in which hydro power was represented, 17 countries were fully described with all hydro plants or clusters of hydro plants explicitly assigned. After the assignment of nuclear and hydro power stations as described in the previous section, a list of unassigned generators was created per country. The generation type and size of these were then assigned according to an algorithm described in detail in ref. [6, 8]. Also, wind generators were distributed based on a wind injection dispersion algorithm, see ref. [6, 8] for further details. 3.3 Nordic region (former ( Nordel) The basis for all calculations performed for the Nordic power system is the 21 generator model of the Northern European system. The 21 generator model determines the topology of the grid, and the distribution of loads and generation units except wind. The original development of this model is described in [11], and further model developments in [12]. 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 Nordic model includes a bus representing Denmark West and a bus representing Germany. These buses have been removed from the Nordic grid used here, since they are parts of the UCTE grid model. The grid model is visualised in Figure 4. In the context of the OffshoreGrid project, the 21 generator model is suitable as it represents the power flow of the system well. This has been demonstrated previously through comparisons with a full scale model of the Nordic system. For analyses related to active power flow the reduced size and good accuracy makes the 21 generator model favourable. In the 21 generator model of Northern Europe the lines and generators are located and adjusted in such a way that they reflect the real production and the most important bottlenecks in the Nordic power system. The impedances are adjusted in such a way that the power flow corresponds to the flow using 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. Document Number: Page: 14/33

15 Figure 4. Nodes and connections in the 21 generator model for the Nordic region 3.4 Great Britain and Ireland Great Britain and Ireland in this context refer to the two geographical islands. Network data describing transmission system, demand and generation by type, for Great Britain, is available from the 2009 Seven Year Statement at the National Grid website [13]. The model has been updated with some missing information, such as missing transmission lines splitting the system into several islands, based on knowledge of the system by project partners Senergy Econnect. For Ireland no data describing the network has been made available for the project. However, as this is a fairly small network, a two bus equivalent, one for the Republic of Ireland and the other for Northern Ireland, was assumed to be adequate for the purpose of the market study within this project. With only two buses in the system the value of the impedance between them does not influence the result of the market model since it is assumed lossless. In other words, this part of the system is modelled as a pure transport model. 3.5 Baltic region For the Baltic countries Estonia, Latvia and Lithuania a reduced equivalent model as shown in Figure 5 is used. The demand is equally distributed between the four nodes in Estonia, while the main generation is in Tallinn with hydro, Tartu with lignite coal and Narva with gas, hard coal and renewable sources other than wind. Document Number: Page: 15/33

16 Tallinn Estonia Narva 2000 MW 1200 MW 750 MW 1200 MW Parnu 1200 MW 700 MW Tartu Latvia 2000 MW Figure 5. Lithuania Baltic reduced network equivalent There is also a 350 MW HVDC connection between Finland (node 7000 in the Nordel model) and Tallinn connecting the Nordic countries and the Baltic area. 3.6 ENTSO-E Net transfer CapacitiesC The DC power flow model is a linear model which does not capture stability constraints such as voltage, transient and angular stability. Based on more detailed studies, maximum transfer capacities can be established which account for these stability issues. Such detailed studies are not a part of this project. Instead, net transfer capacities (NTC) which also include political constraints for allowable power flow between the countries have been included in the model. These NTC values are available from ENTSO-E [5], The values used in this project are based on the Winter 2010 values, except for the Nordic region, where instead more detailed NTC values on zone level from the TradeWind project have been used. In reality, the NTC values vary throughout the year, but in the current model only one set of NTC values are used. These are shown in Table 3 and Table 4. The model does not include HVDC connections in the NTC, so the total transfer capacity between two countries is equal to the sum of the NTC and all HVDC capacities. Document Number: Page: 16/33

17 Table 3. NTC values for Nordic region Country/region Capacity [MW] Country/Region Capacity [MW] NO_3 NO_ SE_3 SF_ NO_3 SE_ SE_3 SE_ NO_2 SE_ SE_1 SE_ NO_2 NO SE_1 DK_E NO_1 NO Table 4. NTC values for other regions Country/region Capacity [MW] Country/Region Capacity [MW] AT SI ES FR AT IT ES PT AT DE FR IT AT CH GR BG AT HU GR MK AT CZ GR AL BE FR IT SI BE NL RO HU BA HR RO BG BA RS RO RS BA ME RS MK CH IT RS BG CH DE RS HU CH FR RS ME HR SI RS AL HR HU ME AL HR RS MK BG CZ DE SK HU CZ PL SK PL CZ SK UA SK DE FR UA HU DE PL UA RO DE NL DK DE Grid reinforcement In an analysis of future scenarios with increased power demand and generation it is generally important to include reinforcements in the grid to avoid an unrealistic amount of grid bottlenecks constraining the power flow in the system. However, accessible information regarding planned reinforcements is only patchy, and it has proven infeasible to include this project by project in the current study. A related problem is lacking information on the geographical location of future increases in demand and generation. Demand and generation scenarios are specified per country, and a uniform scaling is applied to all generators and loads. Various approaches were tested, such as to apply a uniform up-scaling of capacities of bottleneck lines (i.e. lines with very high utilisation) in line with increased generation. In fact, even the original 2010 case, where all or most of the demand was given in the original model, proved to give solutions with unrealistic grid constraints (apparent in the simulation results as high amount of load shedding). The approach to handling grid reinforcements that was adopted was to remove capacity constraints within zones, but retain them on zone-to-zone lines. This way, the most important capacity constraints would be incorporated at the same time as most of the load shedding was avoided. Document Number: Page: 17/33

18 It is worth emphasising that removing capacity constraints does not reduce the model to a simple transport model, as impedances on the lines are still included. (A transport model has effectively zero impedance.) Setting the capacity to infinite is equivalent to the ideal situation where all lines have been upgraded such that they never represent a bottleneck. Then power flow is only dependent on impedances. 3.8 Wind power Wind power time series is provided by work package 3. Onshore wind power For the onshore wind hourly values of the power is given as a total pr. country or zones in country as given in Table 5. The wind units in each country are scaled such that the total production within a country/zone is equal to that given by the scenario for a specified hour. The table and the scenarios Italy consist of two zones, though as it not possible to identify the location of nodes from Italy the scenario wind data for Italy was merged to one value for the entire country. Wind production in Ireland was split between the Republic and Northern Ireland using the same split as in TradeWind. Table 5. Onshore wind w zones Capacity 2020 (MW) Capacity 2030 (MW) Area Zone Country/Region Description Node Coordinates AT AT Austria 47,4 N 14,6 E BE BE Belgium 50,5 N 4,4 E BG BG Bulgaria 42,3 N 25,1 E CH CH Switzerland 47,2 N 7,2 E CZ CZ Czech Republic 49,5 N 17,4 E DK DK_E Denmark East East 55,2 N 11,6 E DK DK_W Denmark West West 56,3 N 9,2 E DE DE_1 Germany 1 East 51,6 N 12,1 E DE DE_2 Germany 2 North-West 53,4 N 9,0 E DE DE_3 Germany 3 Centre 51,4 N 9,3 E DE DE_4 Germany 4 South-East 49,2 N 11,3 E DE DE_5 Germany 5 West 50 N 7,3 E DE DE_6 Germany 6 South West 48,3 N 9,2 E EE EE Estonia 58,9 N 25,7 E ES ES Spain FI SF_1 Finland 1 South 62,3 N 22,0 E FI SF_2 Finland 2 North 66,1 N 26,5 E FR F1 France 1 North West 48,7 N 0,5 E FR F2 France 2 North East 47,6 N 5,2 E FR F3 France 3 South East 45 N 5 E FR F4 France 4 South West 44,4 N 0,6 E England, UK South GB GB-1 Wales GB GB-2 UK Scotland Scotland GR GR Greece HR HR Croatia Coastal 44,6 N 15,2 E HU HU Hungary 47,2 N 18,1 E IE IE Republic of Ireland and Northern Ireland IT IT1 Italy North IT IT2 Italy South LT LT Lithuania 55,5 N 23,8 E LU LU Luxembourg 49,7 N 6,1 E Document Number: Page: 18/33

19 LV LV Latvia 57,1 N 25,5 E NL NL Netherlands 52,4 N 4,7 E NO NO1 Norway 1 South 59,5 N 5,5 E NO NO2 Norway 2 Middle 62,5 N 7,4 E NO NO3 Norway 3 North PL PL Poland PT PT Portugal RO RO Romania RS RS Serbia 42,4 N 21,3 E SE SE_1 Sweden 1 South 56,5 N 14,0 E SE SE_2 Sweden 2 Centre 61,0 N 16,4 E SE SE_3 Sweden 3 North SK SK Slovakia SI SI Slovenia Coastal MT MT Malta Figure 6 and Figure 7 show the wind regions in both Germany and France. Figure 6. Zones in Germany Document Number: Page: 19/33

20 Area 1 Area 2 Area 4 Area 3 Figure 7. Zones in France Offshore wind power For the offshore wind farms, hourly wind power values (potential production) was provided by work package 3 for each individual wind farm. Document Number: Page: 20/33

21 4 INPUT DATA This chapter includes specifications of input data used in the market model simulations that has not already been discussed in the previous chapter. 4.1 HVDC interconnections HVDC connections that are included in the model are listed in Table 6. This list was updated to agree with the list provided by project partner Senergy Econnect. Figure 8 shows a map with the HVDC links plotted in. Table 6. HVDC interconnections Node A Node B Capacity (MW) Name KTJE 1 Nordel: Yes Yes Yes Skagerrak 1 KTJE 1 Nordel: Yes Yes Yes Skagerrak 2 KTJE 1 Nordel: Yes Yes Yes Skagerrak 3 KNVV 1 Nordel: Yes Yes Yes Kontiskan 2 / Kattegat KFGD 1 Nordel: Yes Yes Yes Great Belt / DK E W ZSLK5412 Nordel: Yes Yes Yes SwePol / SE PL D8BW 21 Nordel: Yes Yes Yes Kontek / DE DK E D2HWBC11 Nordel: Yes Yes Yes Baltic cable / DE SE Nordel: 5605 NEEM-E1 700 Yes Yes Yes NorNed FMANDA11 SELL Yes Yes Yes Cross Channel / FR GB NMVL-G1 GRAI Yes Yes Yes BritNed IGLNN111 GARACH Yes Yes Yes Italy Greece AUCH20 IE: Yes Yes Yes Moyle / Scotland NI DEES41 IE: Yes Yes Yes East West / Wales IE Baltic: Tallin Nordel: Yes Yes Yes Estlink Nordel: 3001 Nordel: Yes Yes Yes Fenno Skan Nordel: 3001 Nordel: Yes Yes Fenno Skan 2 KTJE 1 Nordel: Yes Yes Skagerrak 4 Baltic: Tallin Nordel: Yes Yes Estlink 2 IBRIN111 AKOMAN2 500 Yes Yes Italy Albania Nordel: 5605 NEEM-E1 700 Yes Yes NorNed2 KEDR 1 NEEM-E1 700 Yes Yes Cobra (DK NL) HAWP20 PEHE Yes Yes East Coast / GB DEES41 HUNF Yes Yes West Coast / GB CHIC41 FMENUE Yes Yes Enlgand France 2 ICANR111 HKONJS Yes Yes Italy Croatia IVLLR111 0PODG Yes Yes Italy Montenegro IGLNN111 GARACH Yes Yes Italy Greece 2 Nordel: 5605 D2BRUN Yes Yes Nordlink /NO DE SELL40 BSLYKE Yes Yes Nemo / GB BE Nordel: 6000 GRIW Yes Yes BritNor / GB NO Document Number: Page: 21/33

22 Figure 8. HVDC interconnections in 2030 around the North and Baltic Seas 4.2 Water values and inflow Reservoir capacity, pumping capacity and water inflow is specified for each country. Values used in this project are identical to those used in the TradeWind project, and are shown in Table 7. Water values are also based on values used in the TradeWind project, but for non-nordic countries (i.e. all countries except NO, SE, FI) modified with an upper cap such that they never exceed 100 /MWh. This was done to avoid that the electricity price, defined as the cost of the most expensive generator in operation, would be set by the water value. For the generation dispatch this has very little effect due to relatively low reservoir capacities. For the Nordic countries, (NO, SE, FI) however, the hydro reservoirs play the dominant role in the generator dispatch computation, and such a modification is not justifiable. For these the original water value profile is kept (the maximum value, for very low reservoir level, is for these countries 674 /MWh.) Table 7. Hydro input data Country code Reservoir capacity (TWh) Pumping capacity (GW) Reservoir start level (%) Inflow 2010 (TWh) Inflow 2020 (TWh) Inflow 2030 (TWh) AL AT BA BE BG CH CZ DE DK EE ES FI FR GB GR HR HU IE Document Number: Page: 22/33

23 IT LT LU LV MK NL NO PL PT RO RS SE SI SK Cost input data d The total generation cost is computed from marginal costs for all generators which are producing power. The marginal costs are defined per generator type for each country according to: Marginal cost = fuel cost/fuel efficiency + CO2 tax + non-fuel cost. The CO2 tax on electricity, measured in 2007/MWh, is computed for each fuel type according to CO2 tax = CO2 price CO2 content /fuel efficiency. The tables below specify the values of the various marginal cost components used in this project. This data has been provided by Work package 3. Table 8. Specific fuel cost lignite coal ( 2007 ( 2007/MWh) Country AL GR PL Others Table 9. Specific fuel cost oil and hard coal ( 2007 ( 2007/MWh) Fuel type Crude oil Hard coal Table 10. Specific fuel cost gas ( 2007 ( 2007/MWh) Country (LU) (MA) AL AT BA BE BG CH CZ DE DK Document Number: Page: 23/33

24 EE ES FI FR GB GR HR HU IE IT LT LV ME MK NL NO PL PT RO RS SE SI SK UA Table 11. CO2 prices ( prices ( /t /tonne CO2) Scenario Base case Higher price Lower price Table 12. Fuel efficiency and CO2 content (tonne CO2/MWh fuel) Fuel type CO2 content Hydro Wind Other renewable Nuclear Lignite coal Hard coal Gas Oil Oil/gas mix NA Document Number: Page: 24/33

25 Table 13. Non-fuel cost ( 2007 ( 2007/MWh) Fuel type Hydro Wind Other renewable Nuclear Lignite coal Hard coal Gas Oil Oil/gas mix NA Document Number: Page: 25/33

26 5 RESULTS 5.1 Simulation cases Table 14 gives an overview of the cases that have been simulated. Figure 9 shows offshore wind farms and offshore grid in the North Sea region for the direct links case (08) and the hubs case (09). Table 15 contains an overview of key results for each simulation case. Table 14. Simulation cases Case Description 01 Base case Base case Base case T-connection BritNor/Dogger 1 05 T-connection BritNor/Dogger 2 06 T-connection BritNor/Dogger 3 07 T-connection BritNor/Dogger 4 08 Case 01 + Direct links 09 Case 01 + Hubs 10 Meshed UK-NO: Case 01 + direct (line 2) 11 Meshed UK-NO: Case 01 + hubs (line 1) 12 Meshed UK-NO: Case 01 + hubs (line 1) BritNor (UK-NO) 13 Meshed UK-NO: Case 01 + hubs (line 1) + extra UK-NO 14 Meshed UK-DE: Case 01 + direct (line 4) 15 Meshed UK-DE: Case 01 + hubs (line 3) 16 Meshed UK-DE: Case 01 + hubs (line 3) Nemo (UK-BE) 17 T-connection Nordlink/DanTysk 1 ( = Case 01) 18 T-connection Nordlink/DanTysk 2 19 T-connection Nordlink/DanTysk 3 20 T-connection Nordlink/DanTysk 4 21 Sensitivity Case 08 with higher CO2 prices 22 Sensitivity Case 09 with higher CO2 prices 23 Sensitivity Case 05 + extra UK-NO (line 2) 24 Sensitivity Case 06 + extra UK-NO (line 2) 25 Sensitivity Case 07 + extra UK-NO (line 2) 26 Sensitivity Case 04 with 500 MW wind farm 27 Sensitivity Case 05 with 500 MW wind farm 28 Sensitivity Case 06 with 500 MW wind farm 29 Sensitivity Case 07 with 500 MW wind farm 30 Sensitivity Case 01 with higher CO2 prices 31 Sensitivity Case 01 with lower CO2 prices 32 (Sensitivity Nordlink connection point only in preliminary simpulations) 33 Sensitivity Case 01 BritNor (UK-NO) Document Number: Page: 26/33

27 Table 15. Results key numbers Case Total generation cost (M ) Difference from base case (M ) Specific generation cost ( /MWh) ( Price ( /MWh) Document Number: Page: 27/33

28 Figure 9. (a) (b) Offshore grid a) hubs case (case 09), b) direct case (case 08) Energy mix of power generation Annual production (TWh) Wind Hydro Other renewable Nuclear Lignite coal Hard coal Gas Other case 3 (2010) case 2 (2020) case 1 (2030) Figure 10. Energy mix in the 2010, 2020 and 2030 base cases Total generation for the three cases shown in Figure 10 is 2010: 3409 (case 3) 2020: 3886 (case 2) 2030: 4219 (case 1) Document Number: Page: 28/33

29 5.2 Generation cost and electricity prices The simulation program runs an optimal power flow analysis for each hour, where the objective function is the total generation cost. In other words, the program computes the generation cost as part of the optimisation process. Generation cost is computed as the sum of power output times marginal cost for all generators (see chapter 2.1). The main factors that determine the total generation cost is therefore the marginal costs and the energy mix (see Figure 10). To ensure that a solution can be found in all situations, a simple version of load shedding (rationing) is included in the model, i.e. the ability to reduce the demand. This has a cost associated with it that is always higher than the cost of the most expensive generator. In general, load shedding occurs if there is a mismatch between demand/generation and grid capacity. The overall load shedding in the 2030 base case is less than 0.2% of the total demand, when internal constrains within zones are not included. See chapter 3.7. The electricity price ( wholesale price ) is computed as a derived quantity during the postprocessing of the simulation results, and therefore does not affect the power flow. The price is computed for each country for each hour, and is defined as the marginal cost (as defined in chapter 4.3) of the most expensive generator in operation within the given country and hour. For hydro plants outside the Nordic countries (NO, SE, FI), this cost does not include the water values (i.e. equals the base price 3 /MWh). To further filter out water value influence on the electricity price, prices for the Nordic countries (NO, SE, FI) have not been included, i.e. the same prices have been used in all cases (computed as the highest marginal cost in the base case). This definition of price does not take into account the price of imported/exported power. In general it is the most expensive generator type that sets the price. In most cases this is gas generators, which for e.g. Germany 2030 has a marginal cost 74.7 /MWh. Unless water values are higher, and assuming at least one gas generator is in operation, the price for Germany will then be 74.4 /MWh. The annual average should be expected to be of this order. 5.3 Reporting of simulation results The results from the simulations have been collected and presented as Excel files. There is one report file per simulation case, and a summary file. The files have been made available to project partners via the project website. Below are descriptions of the content of these files. Summary file The summary file contains key numbers taken from the individual case result files (see below). The format of the file is explained in Table 16. Table 16. Summary file Column A B C D E F G H I+J Description Case number Total generation cost for entire system Difference in generation cost (column C) from base case Specific generation cost (=total cost /total generation) for entire system Average price (Country average of annual average for each country. Price is computed for each hour for each country and equals the marginal cost of the most expensive generator in operation. Cost of hydro generation is treated in a special way. And variations in price time series for NO,SE,FI are not included prices for these countries are kept equal to the base case ) Difference in price (column E) from base case Average absolute bilateral price difference. The price difference is computed hour by hour and then averaged for each country pair. (cf. case report worksheet called 'abspricediff') The countries AL, MA, ME, RU, UA are not included in the average. Difference in column G from base case As columns G&H, but based on absolute cost differences (worksheet 'abscostdiff') Document Number: Page: 29/33