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The increasing volatility of electricity prices due to increased uncertain renewable energy generation gives rise to interesting short-term arbitrage opportunities for Energy Storage Systems (ESS) operators. Whereas prior research has shown the possibility to exploit inter-temporal arbitrage opportunities in the real-time balancing market, this paper formulates a two-stage optimization methodology that allows ESS operators to also engage in inter-market arbitrage by participating in both the day-ahead and real-time balancing markets. However, the effectiveness of such a strategy is heavily influenced by expected inter-market price differentials, which is known to be difficult to predict when it involves the imbalance price. To address this issue, we propose a risk-averse approach by incorporating the conditional value at risk in the ESS objective function. The proposed methodology is applied to a case study of the Belgian electricity market, where we demonstrate the effectiveness of (i) the combined market participation compared to an ESS participating in either market, and (ii) the risk-aware methodology by showcasing improved ex-post out-of-sample profit performance of a risk-averse compared to a risk-neutral ESS. • Implicit balancing has been shown an effective yet risky strategy for energy storage systems. • This strategy is complemented with day-ahead market participation. • A two-stage bi-level optimization methodology is employed. • Conditional value at risk is used to model risk-averse behaviour in the day-ahead stage. • A case study of Belgium shows the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• Not considering alternative sources of flexibility can lead to significant errors. • Unit commitment constraints have limited impact with alternative flexibility sources. • Unit commitment constraints mainly impact investments in storage technologies. • Sensitivity to assumed flexibility of power plants and reserve requirements is high. • Errors due to approximate formulations are small compared to the sensitivities. In the context of an increasing penetration of intermittent renewable energy sources, the impact of neglecting unit commitment constraints in generation expansion planning models has been widely assessed and demonstrated in the literature. However, the focus is often on thermal generators being the only source of flexibility, and the sensitivity to certain assumptions (e.g., system requirements and technical characteristics) has not been investigated. This paper contributes to the existing literature by revisiting the relevance of considering unit commitment constraints in generation expansion planning models for varying assumptions regarding both the available flexibility and the need for operating reserve requirements. The results indicate that if storage technologies are considered, integrating technical constraints has only a minor impact on both overall cost projections and most investments, with the exception of investments in storage technologies themselves. Furthermore, the investments in storage technologies are shown to be highly sensitive to the assumptions made regarding the assumed flexibility of thermal generators and the future need for operating reserves. These sensitivities are shown to be of an order of magnitude higher or of an equal order of magnitude compared to the impact of integrating technical constraints with continuous rather than integer commitment variables. [ABSTRACT FROM AUTHOR]

Flow-based market coupling is a critical element of the electricity market in Europe. Transmission System Operators determine the commercial transmission capacity that can be implicitly traded in a zonal day-ahead market. However, this entails a trade-off: higher commercial transmission capacities increase market efficiency, affecting the electricity market prices, but also increase redispatch costs, affecting the network tariff. The decision on the commercial transmission capacity should optimally balance day-ahead welfare and redispatch costs, but depends on the rules and regulated incentives enforced on the TSOs. A MinRAM criterion, i.e., imposing minima for the commercial transmission capacity, is a one-size-fits-all policy without variation in time and space that unlikely leads to optimal transmission capacity allocation and is hard to tune because regulators have incomplete information. Incentive regulation is an alternative policy instrument promoting welfare-maximizing commercial transmission capacities, robust against information asymmetry. We provide a set of mathematical conditions to properly design an incentive scheme that rewards price convergence and penalizes excessive redispatch costs. Therefore, this paper serves as a stepping stone towards tapping the full potential of cross-border trade in zonal markets for policymakers, regulators, TSOs and market participants. • Flow-based market coupling is prone to welfare losses because its performance depends on parameters, set by TSOs. • Regulatory oversight is important to stimulate welfare-optimal commercial transmission capacities. • MinRAM criteria could be useful, but are static by design and could lead to welfare losses. • Incentive regulation, if well designed, allows to consider the multiple objectives of TSOs. • Moreover, it can overcome the issue of information asymmetry between TSO and regulator. [ABSTRACT FROM AUTHOR]

In liberalized electricity markets, empirical findings suggest that power plant investment decision-making under uncertainty exhibits bounded rationality. This study leverages the Nobel-prize-winning theory – prospect theory – to evaluate the impact of various boundedly rational elements on electric power system transition. Results show that boundedly rational investment behavior renders higher uncertainties and higher system costs. Further analysis highlighted that reference dependence, a factor overlooked in existing studies, can dominate the system's evolution. In light of the presented results, it could be relevant for energy policy evaluation to consider, among others, the perspective and status of investors. • The theoretical foundation of boundedly rational investment behavior is established. • Prospect theory is incorporated into a long-term agent-based simulation model. • The impact of boundedly rational investment behavior on system evolution is assessed. • Reference dependence element can largely shape the electric power system evolution. • The status of the investors should be considered in energy policy evaluation. [ABSTRACT FROM AUTHOR]

Flow-Based Market Coupling (FBMC), inaugurated in May 2015 as a means of cross-border capacity allocation in the Central Western Europe’s day-ahead markets, is an important cornerstone of the European target model. If successful, this methodology is likely to be extended to other market zones and markets. Despite its importance, the FBMC methodology is complex and poorly understood by market participants. A thorough description of its concepts and definitions can serve as a starting point for further research into the methodology and its market impact. [ABSTRACT FROM AUTHOR]

Exposing residential consumers to real-time pricing (RTP) can yield significant efficiency gains, but may also pose challenges due to the inherent complexity. Using an extensive spectrum of temporal granularities for retail electricity pricing, we analyze to what extent these capture the benefits of RTP while concurrently considering the influence of three distribution tariff types in distorting the pricing signal. Via a mixed complementarity model, we consider both operational and investment decisions at retail as well as wholesale levels, accounting for their interdependencies. Our analysis reveals three key findings. Firstly, we find that decreasing temporal granularity of retail electricity prices increases total system cost due to an increasingly inefficient generation mix, however, three-hourly or six-hourly pricing can approximate RTP benefits. Secondly, decreasing temporal granularity of retail electricity prices raises average offtake and lowers average injection prices for consumers due to changes in wholesale market prices as well as consumption patterns of consumers. Thirdly, capacity-based tariffs reduce system offtake peaks and associated price increases by incentivizing battery discharge during peak consumption while volumetric tariffs, over-incentivizing solar PV investments, result in highest offtake prices and injection peaks. • A mixed complementarity problem allowing for the interactions between wholesale and retail levels is presented. • The model is applied to examine 24 cases with a wide range of temporal granularities for retail prices and three distribution tariffs. • Decreasing temporal granularity leads to an asymmetric increase in offtake prices and decrease in injection prices. • Volumetric tariff yields the highest offtake price while capacity-based tariff yields the lowest. • A three-hourly or even six-hourly granularity for retail electricity pricing can approximate the economic benefits provided by real-time pricing (RTP). [ABSTRACT FROM AUTHOR]

The EU endeavors to stimulate the use of renewable energy cooperation mechanisms. These cooperation mechanisms can significantly reduce the policy cost for meeting renewable targets. Several authors, however, have raised concerns that such cooperation mechanisms can be subject to efficiency losses due to different national regulatory conditions, and due to an ill-advised selection of cross-border support instruments. A quantitative evaluation of these effects remains missing. To address this gap, we first introduce a unifying analytical framework to show how optimal cross-border renewable energy trade should be organized and how these mechanisms could be distorted. We then develop a partial equilibrium model, formulated as a large-scale mathematical program with equilibrium constraints, to assess the impact of (i) different national grid cost allocation regimes and (ii) different cross-border feed-in premium implementations. Our results indicate that EU-wide auctions for renewable electricity should (i) not be based on sliding feed-in premiums and should (ii) ideally be discriminatory if national regulatory conditions differ across Member States. We also consider country-level distributional effects and confirm that Member States can lose when engaging in renewable cooperation. [ABSTRACT FROM AUTHOR]

To reduce the computation time of Energy System Optimization Models and Generation Expansion Planning Models operational detail is typically limited to several hours, days, or weeks in a year selected using Time Series Aggregation methods. We compare time series aggregation methods and generation expansion planning models which aim to capture the value of long-term storage for the first time in the literature. Time Series Aggregations methods were compared by varying the number of representative periods and then running a full year generation expansion planning model on novel synthetic time series. Generation Expansion Planning Models were run on selections and ordering of representative periods in order to compare them. Our results suggest that approximating the full-year time series does not necessarily translate to approximating the full-year generation expansion planning solution and that selecting hours or days is a greater determinant of performance than the time series aggregation method itself. Two of the generation expansion planning models considered, Enhanced Representative Days and Chronological Time Period Clustering, could capture the value of long-term storage, though over or underinvestment in long-term storage by more than a factor of 2 was also possible and the latter formulation exhibited a clear bias towards long-term storage. Based on these results we formulate recommendations for modelers seeking to include long-term storage in generation expansion planning models. • Comparison of methods which allow for long term storage arbitrage. • Weak link between approximating original time series and full year solution. • Minimal differences between time series aggregation methods. • Two methods capture value of long term storage, but risk of over or underinvestment. • Care should be taken with temporal scope reduction and long term storage. [ABSTRACT FROM AUTHOR]

• We develop a rolling planning unit commitment model with detailed start-up times. • The model adjusts scheduling in energy-operating reserve and balancing markets. • Wind power forecast updates are generated via a novel scenario generation technique. • More frequent market clearing with scheduling adjustments leads to cost-savings. • Enhanced start-up capabilities of combined-cycle gas turbines increase cost savings. To cope with the variability and uncertainty introduced by, i.a., intermittent renewable energy sources, the flexible planning and operation of generation units is crucial. Their reaction time is constrained by the lead times on the start-up decisions, whereas the demand for flexibility and operating reserves depends on the market clearing frequency. The start-up lead times are limited by the operator's tolerance for increased maintenance on the asset, which should be reflected in short-term scheduling models. To study this interaction between the market clearing frequency and the start-up capabilities of combined-cycle gas turbines, we develop a unit commitment model. The model considers multiple start-up trajectories and the scheduling decisions in joint energy-operating reserve and balancing markets. The uncertainty on wind power forecasts is presented via wind power forecast updates generated by a dedicated data-driven tool. Leveraging this model, we investigate the interaction between (i) the frequency of wind power forecast updates, linked to the market clearing frequency, (ii) cost-optimal operating reserve volumes and (iii) combined-cycle gas turbine start-up decisions. Results show that, in general, higher market clearing frequencies lead to lower operating costs, driven by decreasing volumes of operating reserves and facilitated by the fast start-up capabilities of combined-cycle gas turbines. [ABSTRACT FROM AUTHOR]

• Bid structures delimit the level playing field in electricity markets. • We study their impact on the market participation of strategic energy storage owners. • Assuming perfect foresight, bid structures do not limit market participation. • Assuming limited foresight, simple price-quantity bids reduce profits. • Linked charging/discharging bids (complex bids) allow higher profits. Energy storage Systems (ESS) may play a pivotal role in the cost-efficient integration of renewable energy sources. Integrating large volumes of grid-scale energy storage into electricity markets, however, raises questions related to their profitability and impact on electricity prices. This paper explores the implications of different bid structures on the strategic behavior of a storage owner that participates in the wholesale market and is able to influence prices to maximize its profits. We conduct a comparative analysis on four bid structures present in existing markets and the scientific literature: (i) simple quantity-based bids, (ii) simple price-based bids, (iii) price-quantity pairs bids and (iv) complex bidding in which the ESS owner provides full information of its technical characteristics through the bids. The decision problem of the ESS owner is formulated as a bilevel programming model, where the upper-level problem represents the profit maximization of the ESS and the lower-level problem simulates possible market clearing outcomes. These bilevel models are reformulated as equivalent mixed-integer linear programming problems by means of the Karush-Kuhn-Tucker optimality conditions, the strong duality theorem and the Big-M method. In a case study, we show that when the ESS owner is uncertain about the market clearing outcome, the bid structure affects the ESS's profitability and, to a lesser extent, the market's efficiency and the system's generation costs. [ABSTRACT FROM AUTHOR]

It is anticipated that the decarbonisation of the entire energy system will require the introduction of large shares of variable renewable electricity generation into the power system. Long term integrated energy systems models are useful in improving our understanding of decarbonisation but they struggle to take account of short term variations in the power system associated with increased variable renewable energy penetration. This can oversimplify the ability of power systems to accommodate variable renewables and result in mistaken signals regarding the levels of flexibility required in power systems. Capturing power system impacts of variability within integrated energy system models is challenging due to temporal and technical simplifying assumptions needed to make such models computationally manageable. This paper addresses a gap in the literature by reviewing prominent methodologies that have been applied to address this challenge and the advantages & limitations of each. The methods include soft linking between integrated energy systems models and power systems models and improving the temporal and technical representation of power systems within integrated energy systems models. Each methodology covered approaches the integration of short term variations and assesses the flexibility of the system differently. The strengths, limitations, and applicability of these different methodologies are analysed. This review allows users of integrated energy systems models to select a methodology (or combination of methodologies) to suit their needs. In addition, the analysis identifies remaining gaps and shortcomings. [ABSTRACT FROM AUTHOR]

Different storage technologies enable an increasing share of variable renewable generation in the electricity system by reducing the temporal mismatch between generation and demand. Two storage ratings are essential to time-shift delivery of electricity to loads: electric power, or instantaneous electricity flow [W], and electric energy, or power integrated over time [Wh]. An optimal storage portfolio is likely composed of multiple technologies, each having specific power and energy ratings. This paper derives and explains the link between the shape of the time-varying demand and generation profiles and the amount of desirably installed storage capacity, both energy and power. An analysis is performed for individual storage technologies first, showing a link between the necessary power and energy capacity and the demand and generation profile. Then combinations of storage technologies are analyzed to reveal their mutual interaction in a storage portfolio. Results show an increase in desirability for storage technologies with low cost power ratings when the mismatch between generation and demand occurs in daily to weekly cycles. Storage technologies with low cost energy ratings are preferred when this mismatch occurs in monthly to seasonal cycles. The findings of this work can help energy system planners and policy makers to explain results from generation expansion planning studies and to isolate the storage benefits accountable to temporal arbitrage in broader electricity storage studies. [ABSTRACT FROM AUTHOR]

The economic efficiency of distribution network tariffs may be enhanced by applying marginal cost pricing principles. This paper investigates the Long-Run Incremental Cost (LRIC) methodology, a marginal cost-based approach that has been applied in Great Britain since 2012. The long-term social welfare achieved under a coincident peak tariff set according to three different methods, i.e., LRIC, traditional historical cost-based allocation, and a theoretical benchmark, is analytically derived. The resulting social welfare is compared for a range of key system characteristics and the underlying mechanisms are discussed. The results provide evidence that LRIC is more efficient than historical cost-based allocation. • We assess the economic efficiency of distribution network cost allocation methods. • Varying levels of demand growth, elasticity, and upgrade costs are considered. • Long-Run Incremental Cost (LRIC) raises welfare compared to a historical cost method. • Social welfare gains of LRIC are not always driven by network cost savings. [ABSTRACT FROM AUTHOR]

Active demand response (ADR) is a powerful instrument among electric demand side management strategies to influence the customers’ load shape. Assessing the real potential of ADR programmes in improving the performance of the electric power system is a complex task, due to the strict interaction between supply and demand for electricity, which requires integrated modelling tools. In this paper an analysis is performed aimed at evaluating the benefits of ADR programmes in terms of electricity consumption and operational costs, both from the final user’s and the overall system’s perspective. The demand side technologies considered are electric heating systems (i.e. heat pumps and electric resistance heaters) coupled with thermal energy storage (i.e. the thermal mass of the building envelope and the domestic hot water tank). In particular, the effect of the penetration rate of ADR programmes among consumers with electric heating systems is studied. Results clearly show that increasing the number of participating consumers increases the flexibility of the system and, therefore, reduces the overall operational costs. On the other hand, the benefit per individual participant decreases in the presence of more ADR-adherent consumers since a reduced effort from each consumer is needed. Total cost saving ranges at most between about 400 € and 200 € per participant per year for a 5% and 100% ADR penetration rate respectively. [ABSTRACT FROM AUTHOR]

Heat pumps are widely recognized as a key technology to reduce CO 2 emissions in the residential building sector, especially when the electricity-generation system is to decarbonize by means of large-scale introduction of renewable electric power generation sources. If heat pumps would be installed in large numbers in the future, the question arises whether all building types show equal benefits and thus should be given the same priority for deployment. This paper aims at answering this question by determining the CO 2 -abatement cost of installing a heat pump instead of a condensing gas boiler for residential space heating and domestic hot-water production. The electricity system, as well as the building types, are based on a possible future Belgian setting in 2030 with high RES penetration at the electricity-generation side. The added value of this work compared to the current scientific literature lies in the integrated approach, taking both the electricity-generation system and a bottom up building stock model into account. Furthermore, this paper analyzes the possible benefits of active demand response in this framework. The results show that the main drivers for determining the CO 2 -abatement cost are the renovation level of the building and the type of heat pump installed. For thoroughly insulated buildings, an air-coupled heat pump combined with floor heating is the most economic heating system in terms of CO 2 -abatement cost. Finally, performing active demand response shows clear benefits in reducing costs. Substantial peak shaving can be achieved, making peak capacity at the electricity generation side superfluous, hence lowering the overall CO 2 -abatement cost. [ABSTRACT FROM AUTHOR]

Active Demand Response (ADR) can contribute to a more cost-efficient operation of, and investment in, the electric power system as it may provide the needed flexibility to cope with the intermittent character of some forms of renewables, such as wind. One possibly promising group of demand side technologies in terms of ADR are electric heating systems. These systems could allow to modify their electrical load pattern without affecting the final, thermal energy service they deliver, thanks to the thermal inertia in the system. One of the major remaining obstacles for a large scale roll-out of ADR schemes is the lack of a thorough understanding of interactions between the demand and supply side of the electric power system and the related possible benefits for consumers and producers. Therefore, in this paper, an integrated system model of the electric power system, including electric heating systems (heat pumps and auxiliary resistance heaters) subjected to an ADR scheme, is developed, taking into account the dynamics and constraints on both the supply and demand side of the electric power system. This paper shows that only these integrated system models are able to simultaneously consider all technical and comfort constraints present in the overall system. This allows to accurately assess the benefits for, and interactions of, demand and supply under ADR schemes. Furthermore, we illustrate the effects not captured by traditional, simplified approaches used to represent the demand side (e.g., price elasticity models and virtual generator models) and the supply side (e.g., electricity price profiles and merit order models). Based on these results, we formulate some conclusions which may help modelers in selecting the approach most suited for the problem they would like to study, weighing the complexity and detail of the model. [ABSTRACT FROM AUTHOR]

• A framework to investigated strategic and risk-averse investment behavior. • Numerical case study to disentangle risk aversion and strategic investment behavior. • Distinguishability of risk aversion and strategic investment behavior is technology-dependent. • Distinguishability of risk aversion and strategic investment behavior depends on capacity target. Both risk-averse behavior and strategic bidding may distort the outcome of energy and capacity markets. Although these two behavioral aspects may trigger similar bidding behavior and investments into generation capacity, they are fundamentally different. While in the literature risk-aversion and strategic behavior have been investigated separately, the presented research offers a comparative analysis to identify situations in which both types of behavior result in the same outcome. First, a framework is introduced in which both strategic and risk-averse behavior can be investigated for a generation expansion planning problem. The problem including the risk-averse investor is modeled as a Nash-equilibrium problem and solved via Alternating Direction Method of Multipliers, whereas the Stackelberg game between the strategic agent and the markets is formulated as a bi-level optimization problem and solved using disjunctive constraints. Using a set of 1920 problem instances we reveal that the distinguishability of the two types of behavior is highly technology-dependent and influenced by the way the capacity target is set. Implications of this work may support regulators in disentangling risk-aversion and strategic behavior based on observed market outcomes. [ABSTRACT FROM AUTHOR]

In the face of growing balancing requirements, there is increased interest in cross-border coordination of balancing capacity markets. Despite improved coordination of balancing energy, only limited progress has been made on coordinating balancing capacity procurement and sizing. That is in part because the coordination of procurement and sizing is more complex as it requires the reservation of cross-zonal transmission capacity. In this paper, a model to simulate a joint day-ahead energy and balancing capacity market clearing is presented, with coordinated procurement and sizing of balancing capacity, and co-optimized allocation of cross-zonal transmission capacity. We apply this model to a highly renewable Central Western European-based case study to illustrate how regional coordination of balancing capacity markets generates benefits. Results show that balancing capacity coordination enables a more cost-effective day-ahead scheduling of electricity production. Notably, the scheduling of peak-load technologies is impacted. Potentially, the greatest benefit of balancing capacity coordination lies in its ability to substantially reduce the need for back-up capacity. Sharing infrequently used generation capacity across borders rather than having individual self-sufficient control areas, especially when paired with alternative sources of balancing capacity (e.g., demand response), could greatly reduce generation capacity needs. • Model to study the coordination of the procurement and sizing of balancing capacity. • Explicit allocation of cross-zonal capacity for balancing capacity transfers. • We apply this model to a highly renewable Central Western European-based case study. • Balancing capacity coordination enables more cost-effective day-ahead scheduling. • Back-up capacity needs can be substantially reduced by sharing balancing capacity. [ABSTRACT FROM AUTHOR]

• Study of the temporal granularity of joint energy-operating reserve markets. • Analysis based on state-of-the-art unit commitment model accounting for uncertainty. • Benefits in terms of cost-efficiency and scarcity on operating reserve markets. • Case study reveals potential total operating cost reductions of 1.5–1.8%. The cost-efficient integration of variable renewable power (vRES) requires adequate operating reserve and energy markets. This paper focusses on the benefits of increasing the temporal granularity of joint energy-operating reserve markets in a case study on two high-vRES systems. Specifically, the impact of the frequency (i.e., how often reserves are sized and procured) and the resolution or contract duration (i.e., the length of the sizing and procurement blocks) is isolated by comparing year-long unit commitment and economic dispatch simulation results. We optimize the power system's operation in consecutive stages: scheduling of generation units with procurement of reserves, intra-day adjustments of generation schedules and real-time activation of flexibility to compensate for imbalances. The results suggest that dynamic and more frequent sizing, limiting the reserve procurement contract duration and a higher procurement frequency allow reducing the total operating cost by 1.5–1.8%, while eliminating scarcity on reserve markets. These design changes facilitate the integration of intermittent renewables in reserve markets. Finally, some challenges following the proposed market design changes are highlighted. [ABSTRACT FROM AUTHOR]

Long-term electric power system planning models are frequently used to provide policy support in the context of the ongoing transition towards a low-carbon electric power system. In a liberalized market, this transition relies on generation company investment decisions. These decisions are shaped by both economic and behavioral factors. Agent-based modeling allows the incorporation of both these factors in the description of the investment decision making process. Nevertheless, there are several challenges associated with the design of agent-based models such as the definition of the model structure and its lack of transparency. In this study, we aim to increase the transparency of investment decision making algorithms by shedding light on how implicit assumptions of the price projection methods used in these algorithms impact model results. To achieve this goal, we developed a core long-term agent-based model to assess different investment decision making algorithms from the literature and we introduced a novel price projection method based on optimization modeling. Our results show that investment decisions vary enormously depending on the assumptions and parameters used in investment decision making algorithms. We also found that our proposed price projection method is robust to parametric deviations. Thus, the proposed investment decision making algorithm enables agent-based modelers to mitigate the potential impacts of hidden implicit assumptions. • Investment decision making algorithms in long-term agent-based models are reviewed. • An investment decision making algorithm based on optimization modelling is proposed. • Investment decision making algorithms are evaluated on a level playing field. • Existing investment decision making algorithms are sensitive to parametric deviations. • The novel investment decision making algorithm is robust, transparent, and flexible. [ABSTRACT FROM AUTHOR]

• Accommodating electricity from renewable sources leads to volatile natural gas off-take. • Power-to-gas technologies strengthen the link between the power and natural gas system. • We model joint scheduling decisions in the electric power system and natural gas system. • We present a case study of a future Belgian power and natural gas system. The possibly increasing volatile gas off-take from gas-fired power plants to accommodate volatile renewable generation in combination with the integration of power-to-gas (P2G) warrants further study into the operation of a coupled electrical power and natural gas system. Therefore, this paper presents and validates a novel operational model comprising both the electrical power and gas systems. Model improvements include (i) the use of zonal gas loads in addition to nodal loads, (ii) ramp rates for conventional gas production facilities and, (iii) an improved detailed technological model of P2G units, which all increase the realism of the obtained results. Results of several small-scale case studies illustrate the relevance of these model additions. In addition, a case study inspired by the Belgian electrical power and gas systems shows that the Belgian gas network has abundant capacity to integrate a possibly volatile injection of synthetic methane from P2G. This model may be used by electricity and gas transmission system operators to study the interaction between their systems and inform policy makers and regulators. [ABSTRACT FROM AUTHOR]

• Ignoring trade potentials affect technology choices and domestic welfare. • Congestion rents constitute an important welfare component in planning exercises. • Including foreign dispatch decisions is the recommended modeling approach. • Approaches based on annual average trade potentials are not recommended. Long-term energy system optimization models can be designed to model systems with a broad geographical scope that comprises multiple countries. However, due to computational limitations, often the geographical scope is restricted to a single country. This raises the problem of correctly accounting for cross-border trade of electricity in models with a limited geographical scope. Therefore, this paper assesses the impact of not correctly representing cross-border trade flows in geographically restricted long-term planning models. To this end, we use a planning model for the interconnected Central-Western European power system to compare technology choices and welfare estimates for Belgium when (i) cross-border trade of electricity is ignored and (ii) cross-border trade flows are an endogenous part of the planning model. Furthermore, this paper presents two sets of methodologies to account for transmission flows in planning models. A first methodology is to extend the model's geographical scope and fix the capacity variables in the neighboring countries in line with pre-designed scenarios for those countries. A second methodology further reduces the computational cost by using specially tailored import and export curves to represent each country's trade opportunities. The results indicate that for highly interconnected systems, neglecting cross-border trade or having a highly stylized representation of cross-border flows can lead to inaccurate welfare estimates and technology biases. In addition, a key insight presented in this paper is that congestion rents can constitute a major share of the welfare gains attained by trading electricity. Finally, endogenizing the dispatch decisions in neighboring countries is the most accurate method to deal with cross-border trade, while by correctly designing cross-border trade curves computational time can be reduced, but planning model outcomes become less accurate. [ABSTRACT FROM AUTHOR]