Transmission System Congestion Analysis Based on a European Electricity Market and Network Simulation Framework

Transcription

1 Transmission System Congestion Analysis Based on a European Electricity Market and Network Simulation Framework Christopher Spieker, Johannes Schwippe, Dennis Klein, Christian Rehtanz Institute of Energy Systems, Energy Efficiency and Energy Economics (ie 3 ) TU Dortmund University, Dortmund, Germany Abstract The coupling of electricity markets and the increasing feed-in of renewable energy sources (RES) cause a rising number of congestions in the European transmission network. In order to ensure a secure network operation suitable measures for resolving these congestions need to be found. In this context, a methodology for analyzing congestions in transmission systems is presented in this paper. The methodology is based on a European electricity market and network simulation framework including internal congestion management by redispatch and control of high-voltage direct current (HVDC) links. In particular, the methodology enables to determine the frequency of occurrence, the causes as well as a ranking of existing congestions regarding their constraining effect on social welfare. The simulation results of a Europe 2030 case study underline that the introduced methodology offers a detailed insight into the system and can help to identify suitable network extension measures for avoiding overloads. Index Terms Congestion Management, Network Planning, Power Generation Dispatch, Power Transmission System I. INTRODUCTION The increasing integration of volatile generation by RES and the transition towards an integrated electricity market cause a rising number of congestions in the European transmission network. For instance, in [1] the European Network of Transmission System Operators for Electricity (ENTSO-E) identified more than 100 bottlenecks in the system which either already exist, or will develop in the future. In order to resolve or to prevent congestions and hence, to ensure operational security different congestion management methods are applied which aim at utilizing the limited transmission capacities in a most efficient manner. Furthermore, as a long term measure, network reinforcements are carried out to enhance the network s transmission capacity, thereby reducing the limiting effect of congestions on social welfare. In order to identify the most suitable network extension options, the information derived from congestion management can be useful. For example, the frequency of occurrence, the causes as well as the associated costs of congestions are of particular interest. For a precise congestion management analysis models are necessary that are capable of simulating realistic loading conditions of the network. With the Model of International Energy Systems (MILES), a European electricity market and network simulation framework is designed and implemented, which enables to determine generation-load-configurations as well as the resulting utilization of the transmission network for current and future scenarios [2], [3]. In this paper, MILES is extended by an internal congestion management model, which aims at finding the most cost-effective countermeasures for resolving congestions, including redispatch, RES curtailment as well as the adjustment of power flow controlling equipment. Moreover, a methodology for analyzing congestions is introduced which basis on the solution information of the congestion management optimization model. By evaluating the Lagrange multipliers of each branch specific marginal costs can be allocated to each bottleneck in the network, so that a ranking of the bottlenecks regarding their constraining effect on social welfare can be derived. In addition, the methodology enables to determine the frequency of occurrence, the causes as well as the interdependencies of overloads. Due to these insights obtained the methodology is capable of supporting the network planning process. The paper is structured as follows: After describing the state of the art in internal congestion management simulation in section II, the developed European electricity market and network simulation framework will be presented with the focus on the congestion management modeling approach. In this context, it will be pointed out how the optimization model s solution information can be interpreted and used for congestion analysis. In section IV, the introduced simulation framework will be applied in a European case study for the year 2030 with a focus on analyzing congestions in the German transmission network particularly with regard to their constraining effect on social welfare, their simultaneity of appearance and their causes. Finally, a conclusion is drawn and an outlook on future research is given in section V. II. STATE OF THE ART Due to their importance for operational security, internal congestion management models have been developed by various researchers. In the following, approaches from the literature are briefly discussed that model the decisionmaking of Transmission System Operators (TSOs) in operational planning or real-time operation in a European environment. Most approaches for simulating internal congestion management apply fundamental optimization problems. The objective of such models is to ensure a secure system operation by resolving internal congestions at minimum costs.

2 Thereby both, market-related measures (such as redispatch and curtailment of RES) and network-related measures (such as control of phase shifting transformers (PSTs), Flexible Alternating Current Transmission System (FACTS) devices and HVDC links as well as the switching of lines) are modeled as potential congestion management procedures of TSOs. In order to simulate the impact of these measures on line loadings linearized load flow models are used in most cases. Furthermore, some approaches consider the (N-1) criterion by incorporating security constraints in the optimization problem formulation. Generally, recent research approaches differ in the set of modeled countermeasures by TSOs. For example, [4], [5] and [6] consider the redispatch of generating units as the only measure for resolving internal congestions. In addition to redispatch, [7] also models the switching of lines as an operational flexibility, whereas [8] simulates the control of FACTS devices. A further significant difference between the introduced approaches is the consideration of the (N-1) security criterion. Whereas [7] does not consider the (N-1) security explicitly, [4] and [5] simplify the (N-1) criterion by analyzing the (N-0) topology and applying a reliability margin of 20 %. In contrast, [6] considers the outages of branches by using Line Outage Distribution Factors (LODFs). III. MARKET AND NETWORK SIMULATION FRAMEWORK The methodology for identifying and analyzing congestions in transmission systems is based on the European electricity market and network simulation framework MILES, which consists of several modules visualized in Fig. 1. Market simulation Regionalized installed capacities Load and RES feed-in time series Cost-minimal power plant schedules Congestion management Objective function: Minimization of rescheduling costs Constraints: Load and control reserve coverage Technical parameters of gen. units Maximum transmission capacities of internal branches Network simulation Tap positions of PST Operating points of HVDC lines Branch loadings and sensitivities Simulation results Adapted schedules of gen. units Adjusted operating points of internal HVDC transmission lines Solution information Lagrange multipliers (shadow prices) Slack variables Internal congestion management simulation Figure 1: Flow chart of the market and network simulation framework First, a market simulation is executed using a model of the European electricity market which has been presented in detail in [2]. As main steps the modeling approach includes the regionalization of the predicted electrical load and installed capacities of RES, the generation of the corresponding time series as well as the determination of the power plants dispatch for meeting the load and the system s reserve requirements. The latter is done by solving a Security-Constraint Unit Commitment model in a Mixed-Integer Linear Program formulation considering for example, Available Transfer Capacity (ATC)- limits on inter-zonal exchanges and the generating units technical parameters such as minimum up- and downtimes, ramping limits and storage capacities. The market coupling simulation s outcome includes high-resolution feed-in time series of RES and Combined Heat and Power (CHP) plants, hourly generation schedules of conventional power plants and storages as well as the energy exchanges (commercial schedules) between different market zones. Using these results a network simulation is carried out basing on a model of the European transmission system [3]. Besides executing AC load flow calculations for determining the loading of every branch, suitable operating points of power flow controlling equipment are identified. In particular, the tap positions of PSTs and the set-points of HVDC transmission lines are determined by solving two separate optimization problems with different objectives. Whereas PSTs are assumed to be used for minimizing the difference between cross border physical flows and commercial schedules, internal (national) HVDC links are assumed to be utilized for relieving the respective AC transmission network 1. On the basis of the market and network simulations results an internal congestion management simulation is performed for single hourly time steps. Since the generating units dispatch determined in the market simulation might cause overloads in the network, the model aims at identifying the most cost-effective countermeasures for resolving congestions and ensuring a secure system operation. As possible congestion management procedures the model includes redispatch of conventional power plants, the curtailment of RES as well as the control of internal HVDC links. The model s outcome includes the total rescheduling costs, the adapted schedules of generating units and the adjusted operating points of HVDC links due to internal congestion management. In contrast to the market simulation only limiting inter-zonal exchanges by ATC-values, the model considers the physical power flows on all internal branches within a certain network region by using a linearized power flow model. The usage of linear sensitivities enables to obtain optimization solution information, in particular the Lagrange multipliers of the network constraints, which can then be assessed to analyze internal congestions with regard to their constraining effect on social welfare, their simultaneity of appearance and their causes. Notably, since MILES is adapted to the European context, congestion management is modeled by a two stage approach 2. In the following sections first the optimization model for resolving internal congestions within a single network region is introduced. Afterwards, section III-B describes how the optimization model s solution information can be interpreted and used for congestion analysis. 1 For a detailed explanation of the problem formulations the reader is referred to [3]. 2 Market platforms in the ENTSO-E region limit cross-zonal exchanges in order to prevent congestions between market zones. If the resulting generation dispatch causes overloads within market zones, the responsible TSOs have to adjust the dispatch for ensuring a secure system operation.

3 A. Congestion Management Simulation The congestion management model s objective is to determine the cost-minimal measures for resolving congestions (within a certain region of the network) that result from the generating units dispatch determined previously in the market simulation. For this purpose, the model is formulated as a linear optimization problem minimizing the total rescheduling costs by the following objective function (cp. [6]): min ΔPg G,up +c down g ΔPg G,down ). (1) (c up g g S g Here, the decision variables ΔPg G,up R 0 and ΔPg G,down R 0 represent the increase respectively the reduction of production of generating unit g (belonging to the set of all units S g ). The associated costs for production increase c up g and reduction c down g are derived from the marginal generation costs of each unit being calculated under consideration of, for example fuel prices and prices for CO 2 emission rights. For reasons of clarity the decision variables ΔPg G,up and ΔPg G,down are summarized to the redispatched power ΔPg G R of each generating unit g: ΔP G g =ΔP G,up g +ΔP G,down g g S g. (2) When executing redispatch several restrictions have to be taken into account. Firstly, the active power balance needs to be ensured further on, yielding the following constraint: ΔPg G =0. (3) g S g Due to their different technical characteristics and the resulting differences in operational flexibility not all types of generating units are suitable for participating in the redispatch process. For example, coal-fired and nuclear power plants can only participate in case they are already in operation, whereas gas-fired power plants can contribute to redispatch even if they are not scheduled to be online due to their fast start-up times and high ramping rates. Besides conventional power plants, the set of all generating units S g also includes RES units that can be curtailed in case they cause congestions in the network. For example, this refers to wind turbines whose feed-in can be reduced by turning them away from the wind. In order to account for the aforesaid technical conditions the following constraint is formulated: Pg min Pg G +ΔPg G Pg max g S g. (4) Thereby, the redispatch volume ΔPg G of each unit is modeled as deviation from its base dispatch Pg G determined in the market simulation. Pg max represents the maximum reachable production of each generating unit g in the regarded time step considering its nominal capacity and its ramping limits for startup or ramp up, respectively. Analogously, Pg min is the minimum possible production of each generating unit g considering its technically required minimum production capacity and its ramping limits for shutdown or ramp down, respectively. In case generating unit g is not in operation, Pg min equals zero. Notably, for RES units the maximum production Pg max equals Pg G, so that their production can only be decreased. Besides the adjustment of the generating units dispatch the model includes the control of internal (national) HVDC links as a further countermeasure for resolving congestions in the network. As stated before, the operating points of national HVDC transmission lines are already determined during network simulation by solving an optimization problem aiming at relieving the respective AC network. In the internal congestion management simulation these operating points are now adapted to model a corrective HVDC control. The active power transmission by HVDC links is modeled as coupled injections (feed-in and feed-out) at the connection nodes. By assuming a lossless transmission the injections at either nodes must have the same value but with reversed sign to account for the different direction of injection: ΔP hvdc h 1 = ΔP hvdc h 2 (h 1,h 2 ) S hl. (5) Thereby, the decision variables ΔPh hvdc 1 and ΔPh hvdc 2 represent the adjusted active power injections h 1 and h 2 at the connection nodes belonging to the set S hl. The latter contains the assignment which pair of injections models one HVDC link. Analogously to AC lines, the active power transmission through HVDC links is limited by their maximum active power transmission capacity. Therefore, each injection needs to be within the maximum flow limit (Ph max ) of the corresponding HVDC link taking into account either flow directions: P max h P hvdc h +ΔP hvdc h P max h h S h : μ h. (6) Here, Ph hvdc denotes the base dispatch of the injection h (belonging to the set of all injections S h ) corresponding to the base set-point of active power transmission through the respective HVDC link determined during network simulation. Moreover, the decision variable ΔPh hvdc represents the corresponding deviation from the original operating point. Notably, as ΔPh hvdc is not part of the objective function the problem formulation does not apply costs for the adjustment of operational HVDC control. Thus, the total rescheduling costs equal the total redispatch costs. As stated before, the introduced measures (redispatch including curtailment of RES and HVDC control) are taken to resolve existing congestions and hence, to ensure a secure network operation. In order to model the impact of these measures on line loadings a linear load flow model is applied. Besides normal operation conditions ((N-0) case) the approach considers the outage of branches as relevant contingency cases. Eq. (7) specifies the power flow constraint for the (N-0) topology and ensures that the active power flow through each branch b (belonging to the set of all branches S b ) after

4 congestion management does not exceed its maximum active power transmission capacity P max b : P max b Pb 0 + β b,n ΔP n Pb max b S b. (7) n S n Using this formulation the power flows resulting from congestion management are calculated by the superposition of the base power flows before congestion management Pb 0 and the impact of the adaption of nodal powers on the branch power flows. The latter is derived from the sensitivity β b,n which is a Power Transfer Distribution Factor (PTDF) modeling the impact of an additional injection of 1 MW at network node n on the power flow through branch b and ΔP n representing the adjusted feed-in respectively feed-out at node n due to the congestion management which is given by: ΔP n = ΔPg G + ΔPh hvdc n S n. (8) g S g,n h S h,n Thereby, S g,n and S h,n comprise the generating units and HVDC injections at the respective node n. In order to be able to consider outages of branches in the model Eq. (7) needs to be modified. Eq. (9) ensures, that the base dispatch is adjusted for meeting the (N-1) criterion: P max b 1 P 0 b 1 + β b1,n ΔP n n S n + γ b1,b 2 (Pb β b2,n ΔP n ) Pb max 1 n S n b 1 S b, b 2 S b : μ b1,b 2. (9) Here, sensitivity γ b1,b 2 is a LODF (cp. [9]) providing the fraction of pre-contingency flow on branch b 2 that appears on branch b 1 in case of an outage of one circuit of b 2. Consequently, the third summand in Eq. (9) represents the change in flow on branch b 1 due to the outage of one circuit of b 2, also taking into account the impact of the adaption of nodal powers resulting from congestion management. Since every branch in the network could fail, the number of power flow constraints for each branch is given by the total number of branches N b, resulting in a high computational effort when simulating large systems. A significant reduction of the number of constraints can be achieved by disregarding the outages of low loaded branches and of branches with a negligible influence (small LODF) on the respective branch (cp. [6]). As a last remark, the variables μ h and μ b1,b 2 in Eq. (6) and Eq. (9) are the Lagrange multipliers of the respective constraints. Their meaning and interpretation in the context of network analysis will be explained in the next section. B. Interpretation of the Solution Information In addition to the optimal decision variables the solution of a linear optimization problem offers information, such as Lagrange multipliers. The Lagrange multiplier or shadow price of a constraint is the change in the optimal value of the objective function due to the relaxation of the respective constraint by one (marginal) unit. A Lagrange multiplier equal to zero means that the constraint is non-binding. Thus, in the context of the proposed internal congestion management model, the Lagrange multiplier of a branch (or more precise: of a branch s power flow constraint) indicates the amount by which the total rescheduling (redispatch) costs would have been lower if the branch s capacity had been higher by one unit (MW). Notably, as redispatch measures can resolve several congestions simultaneously, only the Lagrange multipliers of the most constraining branches are unequal to zero. Therefore a Lagrange multiplier equal to zero either means that the branch is not overloaded or does not represent the most constraining congestion. As mentioned before, the congestion management simulation is performed for single (hourly) time steps. By using the introduced solution information, i.e. the Lagrange multipliers, specific marginal rescheduling (redispatch) costs can be allocated to each branch in every time step. These are determined as follows: μ b1,t =max(μ b1,b b 2,t). (10) 2 S b Here, μ b1,b 2,t represents the Lagrange multiplier of branch b 1 in time step t due to an outage of one circuit of branch b 2 (cp. Eq. (9)). μ b1,b 2,t equals zero if the outage of one circuit of branch b 2 does not cause an overload on branch b 1 in t or if the resulting overload is not the most constraining one. Otherwise, (marginal) costs arise for resolving this congestion, which can be allocated to b 1. Potentially every branch in the network can fail and cause an overload on b 1. Therefore, in every time step the number of Lagrange multipliers of b 1 is given by the number of existing branches N b. For the correct interpretation and application only the maximum value of all N b Lagrange multipliers of the respective branch is relevant being defined in Eq. (10) by μ b1,t. Furthermore, the specific marginal rescheduling (redispatch) costs of HVDC links in every time step can be calculated. Due to the fact, that a DC line is modeled as a pair of coupled generators with both having their own Lagrange multiplier, the marginal costs of each HVDC link h l are given by: μ hl,t = μ h,t. (11) h S hl Since congestion management simulation typically covers more than one time step, an overall economic indicator is derived from the Lagrange multipliers of each branch and each DC line in every time step. The so-called Average Marginal Effect (AME) of a branch or DC line is defined as the sum of their respective marginal costs in each time step divided by the number of time steps T : t S t μ b1,t AME b1 =, AME hl =. (12) T T The AME can be interpreted as the average willingness to pay for enhancing the capacity of the respective equipment by t S t μ hl,t

5 one MW. By calculating the AME for each branch and DC line in the network a ranking of the bottlenecks regarding their constraining effect on social welfare can be derived. As a general remark, it should be noted that the evaluation of optimization solution information is also applied in several Nodal Pricing modeling approaches (cp. [10], [11] and [12]). IV. CASE STUDY In this section, the introduced market and network simulation framework is applied in a European case study for the year 2030 with a focus on analyzing congestions in the German transmission network. For this, section IV-A details the scenario setup, and section IV-B presents the results of the congestion analysis. A. Scenario Setup The congestion analysis is based on the results of a market simulation of the whole ENTSO-E region for the year The underlying scenario considers the predicted installed capacities published in [13]. Assuming no change in the consumption the hourly load values in every country are taken from [14]. The regional distribution of the electrical load and RES is carried out according to [15]. The RES feed-in time series are simulated based on regional meteorological data for all European countries obtained from [16]. The information about the European power plant portfolio is based on [17] and the ATC-values for limiting inter-zonal exchanges in the market coupling simulation are taken from [18]. 106 flow controlling equipment (PSTs and HVDC links). The network model used for the simulation is an aggregated model of the European transmission system described in [19]. The model has been extended by network expansion measures for the year 2030 described in [20]. As the focus of the congestion analysis is set on congestions in the German transmission network only the German part (visualized in Fig. 2) is considered for the subsequently executed congestion management simulation. The German part consists of 31 nodes, 100 AC lines and 12 HVDC links (not illustrated in the figure). B. Simulation Results In Fig. 3 the AMEs of the branches and DC lines in Germany obtained from the one-year simulation are depicted in descending order. It can be seen, that the AC line between nodes 4 and 5 has the highest AME, followed by the lines 5-13 and 3-4. This means, from a macroeconomic point of view there is the highest willingness to pay for enhancing their capacity. AME [e /MW] Figure 2: Aggregated model of the German transmission network On the basis of the market simulation s outcome a network simulation of the synchronous area of Continental Europe is performed for determining the branch loadings before congestion management and the initial operating points of the power AC AC DC 4-31 AC AC AC AC AC AC DC DC AC Lines Figure 3: Average marginal effects of lines in Germany The determined ranking of bottlenecks is verified by the analysis of the bottlenecks capacity utilization in the course of the year, which is derived from AC load flow calculations for the (N-0) case and illustrated in the left and the middle part of Fig. 4. The analysis is performed for the six lines marked in Fig. 3 having the most constraining effect on social welfare. In order to identify the causes of high branch loadings a correlation analysis has been carried out determining the relationship between a high loading of the respective branch and the level of the electrical load or the amount of feed-in (of RES or conventional power plants (CPP)) in Germany. The results of the correlation analysis are depicted in the right part of Fig. 4. The shape and shade of the respective ellipse indicate the level of the correlation, whereas the direction reflects the sign. The following results can be derived: The utilizations of the lines between nodes 4-5 and 5-13 in the north of Germany strongly correlate with the

6 level of wind feed-in. High loadings of these lines occur simultaneously. Line 3-4 is highly utilized during the entire year. The utilization is most likely influenced by the wind. In some hours the line is utilized up to 150 %. Line is highly utilized during the summer. Its loading correlates with the level of PV feed-in. The cross-border line between Germany and France is highly utilized in the winter. This is due to a high load in Germany being covered by imports from France. All considered lines are already overloaded within a few hours of the year for the (N-0) topology (see boxplot). The congestion analysis is now extended by investigating the occurring overloads in the course of the year with the help of the solution information obtained from the congestion management optimization model. Notably, as the model considers the (N-1) security criterion, it can be analyzed which branch outages ((N-1) cases) contribute to the bottlenecks reducing effect on social welfare. In the left part of Fig. 5 the overloads appearance in a specific time step is depicted by a black vertical line. In the optimization model the overloads appearance is stated by a Lagrange multiplier unequal to zero. The middle part of the figure shows a boxplot of the bottlenecks Lagrange multipliers for the considered year, indicating the distribution of the rescheduling costs for resolving the respective congestion. The right part of Fig. 5 illustrates the number of hours in which relevant (N-1) cases (depicted in the lower right) cause an overload on the considered bottlenecks. This information is derived from the model by counting the number of time steps in which the Lagrange multiplier μ b1,b 2 of the considered bottleneck b 1 and the related outage b 2 (see Eq. (9)) is unequal to zero. The following results can be gathered from Fig. 5: The lines 4-5, 5-13 and 3-4 are frequently overloaded throughout the year. The overloads on lines 4-5, 5-13 and 3-4 tend to occur simultaneously resulting in trans-regional congestions. Line AC is presented as being rarely overloaded within the year, although it is already congested in a few hours for the (N-0) case (see Fig. 4). This is because this line does not represent the most constraining bottleneck. The rescheduling costs for resolving congestions on the lines 4-5, 5-13 and 3-4 are very low compared to the other bottlenecks. This is because overloads on these lines are mainly caused by wind feed-in (see Fig. 4) and costs for RES curtailment are not applied in the simulation. Although line is more frequently overloaded than line AC the willingness to pay for enhancing its capacity is lower (see Fig. 3). This is due to the fact, that resolving its congestion is less expensive in most cases. Although line 4-5 is approximately as frequently overloaded as line 3-4 the willingness to pay for enhancing its capacity is significantly higher (see Fig. 3). This is because the rescheduling costs for resolving its congestion are higher in most cases. As expected an overload on a line is most frequently caused by an outage of one circuit of the respective line. An outage of one circuit of line 5-13 causes overloads on all considered bottlenecks except line For instance, line is overloaded in approximately 200 hours due to an outage of one circuit of line From the congestion analysis suitable network extension measures can be derived. For example it seems sensible to enhance lines, which on the one hand produce high rescheduling costs and on the other hand have a great impact on the other bottlenecks in case of an outage. In this study this is the case for the lines and. V. CONCLUSION AND OUTLOOK In the preceding sections first the market and network simulation framework MILES is presented with a focus on modeling internal congestion management by redispatch and HVDC control. Subsequently, a methodology for analyzing bottlenecks in transmission systems is introduced, which is Lines AC AC Time [h] Utilization [%] PV Wind Load CPP Causes Figure 4: Congestion analysis before internal congestion management simulation

7 Lines AC AC # hours Time [h] μ t [e /MW] AC Lines Figure 5: Congestion analysis based on internal congestion management simulation AC based on the solution information of the congestion management optimization model. The results of a case study modeling internal congestion management in the German transmission network underline that the introduced methodology offers a detailed insight into the system and thus, is capable of supporting the network planning process. For instance, a ranking of the bottlenecks in the network can be derived representing the willingness to pay for enhancing the capacity of the respective branches in order to avoid rescheduling costs. Additionally, due to the consideration of the (N-1) security criterion in the model, the resulting effect of branch outages on the bottlenecks can be determined. Taken together, both information enable to identify suitable candidates for network enhancement as they on the one hand produce high rescheduling costs and on the other hand have a great impact on the other bottlenecks in case of an outage. Based on the introduced methodology further research will focus on developing algorithms for an automated identification of suitable network extension measures. Moreover, future work will concentrate on evaluating given sets of network extension options. With the help of the methodology the benefit of these measures can be assessed and compared with regard to their avoided rescheduling (redispatch) costs. REFERENCES [1] European Network of Transmission System Operators for Electricity, Ten-Year Network Development Plan 2014, July [Online]. Available: ten-year-network-development-plan/tyndp-2014/pages/default.aspx [2] C. Spieker, J. Teuwsen, V. Liebenau, S. C. Müller, and C. Rehtanz, European Electricity Market Simulation for Future Scenarios with High Renewable Energy Production, in 2015 IEEE Eindhoven PowerTech, Eindhoven, Netherlands, June 2015, pp [3] C. Spieker, D. Klein, V. Liebenau, J. Teuwsen, and C. Rehtanz, European Electricity Market and Network Simulation for Energy System Analysis, in IEEE Energycon 2016, Leuven, Belgium, April 2016, pp [4] B. Burstedde, The NEULING Model, EWI Working Paper, No 12/10, Nov [5] B. Burstedde, From Nodal to Zonal Pricing: A Bottom-Up Approach to the Second-Best, in th International Conference on the European Energy Market (EEM 2012), 2012, pp [6] C. Linnemann, D. Echternacht, C. Breuer, and A. Moser, Modeling Optimal Redispatch for the European Transmission Grid, in IEEE Trondheim PowerTech, Trondheim, Norway, June 2011, pp [7] F. Kunz, Improving Congestion Management - How to Facilitate the Integration of Renewable Generation in Germany, Electricity Markets Working Papers No. WP-EM-46, July [8] C. Gutschi, A. Jagl, G. Nischler, C. Huber, U. Bachhiesl, and H. Stigler, Scenarios for the Development of the Electricity Economy in Continental Europe, [Online]. Available: at/portal/page/portal/files/i4340/daten/wec_atlantis_2010.pdf [9] G. Jiachun, F. Yong, L. Zuyi, and M. Shahidehpour, Direct Calculation of Line Outage Distribution Factors, IEEE Transactions on Power Systems, vol. 24, no. 3, pp , [10] H. Stigler and C. Todem, Optimization of the Austrian Electricity Sector (Control Zone of VERBUND APG) by Nodal Pricing, Central European Journal of Operations Research, vol. 13, pp , [11] R. Green, Nodal pricing of electricity: how much does it cost to get it wrong? Journal of Regulatory Economics, vol. 31, no. 2, pp , [12] L. Fangxing and B. Rui, DCOPF-Based LMP Simulation: Algorithm, Comparison With ACOPF, and Sensitivity, IEEE Transactions on Power Systems, vol. 22, no. 4, pp , [13] European Network of Transmission System Operators for Electricity, Scenario Outlook and System Adequacy Forecast , [Online]. Available: adequacy-forecasts/soaf /pages/default.aspx [14] European Network of Transmission System Operators for Electricity, Consumption data, [Online]. Available: data-portal/consumption/ [15] J. Teuwsen, V. Liebenau, and C. Rehtanz, Comparison of regionalization methods for network development planning, in 4th IEEE PES Innovative Smart Grid Technologies Europe, Copenhagen, Denmark, Oct. 2013, pp [16] German Meteorological Service, Regional Model COSMO-EU, [Online]. Available: [17] Platts, World Electric Power Plants Database, Dec [18] Consentec/IAEW, Regionalisierung eines nationalen energiewirtschaftlichen Szenariorahmens zur Entwicklung eines Netzmodells NEMO, [19] J. Schwippe, A. Seack, and C. Rehtanz, Pan-European market and network simulation model, in 2013 IEEE Grenoble PowerTech, Grenoble, France, June 2013, pp [20] European Network of Transmission System Operators for Electricity, Ten-Year Network Development Plan 2012, July [Online]. Available: ten-year-network-development-plan/tyndp-2012/pages/default.aspx