Assessing the capability of a distribution grid to accommodate new solar PV installations, namely its hosting capacity (HC), has been a prevalent research topic. Although providing a technical limit to how much additional solar PV can be integrated into a distribution grid without trespassing operational limits, commonly used HC analysis (HCA) does not consider consumer preferences or the economic feasibility of installations. Using a market-based optimal power flow (MBOPF) and HCA, we compare the economic and technical limits of solar PV capacity integration in low voltage distribution systems (LVDS). Findings illustrate that (1) the PV HC computed using grid limits only does not give a complete picture of solar PV capacity integration potential, (2) linear, deterministic power flow is not a foolproof method for assessing the network-secure amount of PV, and (3) the number of technically feasible installation sites supersedes the economically feasible ones. @en

Following the formulation of certain strategic policy goals, such as reducing greenhouse gases (GHGs) and including more renewable sources (RES) as part of the energy mix in several parts of the world, the practical translation and actual implementation of these goals have led to the introduction of substantial volumes of intermittent renewable electric sources. Because affordable bulk storage for electricity is still lacking, demand and supply need to be (instantaneously) balanced. The resulting challenge that intermittent renewable power sources pose to the controllability of the electric power system requires greater flexibility from other parts of the system, as well as flexibility through interaction with other energy sectors such as the heating sector, the natural gas sector, and the transportation sector.@en

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.@en

In competitive electricity markets, the optimal bid or offer problem of a strategic agent is commonly formulated as a bi-level program and solved as a mathematical program with equilibrium constraints (MPEC). If the lower-level (LL) part of the problem can be well approximated as a convex problem, this approach leads to a global optimum. However, electricity markets are governed by non-convex (partially known) constraints and reward functions of the participating agents. In this paper, an alternative data-driven paradigm, labeled as a mathematical program with neural network constraint (MPNNC), is developed. The method uses a neural network to represent the mapping between the upper-level (agent) decisions and the lower-level (market) outcomes, i.e., it replaces the lower-level problem with a surrogate model. In the presented case studies, the proposed model is used to find the optimal load shedding strategy of a strategic load-serving entity. First, the MPNNC performance is compared to the MPEC approach, both in convex and non-convex environments, showing that the proposed MPNNC achieves similar performance to an ideal MPEC that has perfect knowledge of the simulated market environment. Then, aggregated supply curves from the Belgian spot exchange are used to assess the potential gains of using the developed model in real-life applications.@en

This paper formulates an energy community's centralized optimal bidding and scheduling problem as a time-series scenario-driven stochastic optimization model, building on real-life measurement data. In the presented model, a surrogate battery storage system with uncertain state-of-charge (SoC) bounds approximates the portfolio's aggregated flexibility. First, it is emphasized in a stylized analysis that risk-based energy constraints are highly beneficial (compared to chance-constraints) in coordinating distributed assets with unknown costs of constraint violation, as they limit both violation magnitude and probability. The presented research extends state-of-the-art models by implementing a worst-case conditional value at risk (WCVaR) based constraint for the storage SoC bounds. Then, an extensive numerical comparison is conducted to analyze the trade-off between out-of-sample violations and expected objective values, revealing that the proposed WCVaR based constraint shields significantly better against extreme out-of-sample outcomes than the conditional value at risk based equivalent. To bypass the non-trivial task of capturing the underlying time and asset-dependent uncertain processes, real-life measurement data is directly leveraged for both imbalance market uncertainty and load forecast errors. For this purpose, a shape-based clustering method is implemented to capture the input scenarios' temporal characteristics. @en

The potential contribution of short term storage technologies such as batteries to resource adequacy is becoming increasingly important in power systems with high penetrations of Variable Renewable Energy Sources (VRES). However, unlike generators, there are multiple ways in which storage may be operated to contribute to resource adequacy. We investigate storage operational strategies which result in the same amount of Expected Energy Not Served (EENS) but differing Loss of Load Expectation (LOLE) to investigate the range of LOLE possible and what factors affect this range. A case study of a Belgium-like power system using an economic dispatch model, typical of state-of-the-art adequacy assessments, results in a LOLE ranging between 2 and 6 h/yr, with the difference decreasing for greater storage duration and increasing for higher installed capacities of storage. Capacity Credits (CCs), which give the relative contribution of a resource to system adequacy, may also be affected by storage operation and the CC of storage is shown to differ by up to 30% depending on the operation and how the CC is calculated. Given these findings, it is recommended that modellers be explicit and transparent about the storage operation they assume in adequacy assessments and capacity credit calculations.@en

Since 2015 available cross-border transmission capacity is determined using flow-based market coupling (FBMC) in the day-ahead electricity markets of Central Western Europe. This paper empirically estimates the effect of introducing FBMC on day-ahead electricity price convergence and cross-border exchange volumes. In the month following the introduction of FBMC, hourly cross-border exchange volumes increased by 1,700 MWh/h, while prices between countries converged by 10.4 €/MWh. Since then, observed cross-border exchange volumes decreased to 400 MWh/h below their levels before the introduction of FBMC by the end of 2017. However, when controlling for changing market conditions in the years following the introduction of FBMC, we find that FBMC still has a persistent positive effect of around 1,150 MWh/h on hourly cross-border exchange volumes and of 2 €/MWh on price convergence. Finally, we provide suggestive evidence that decreased commercial transmission capacity on critical branches might have contributed to the decline of the benefits over time. This paper is useful for policymakers, regulators, TSOs, and other stakeholders in light of the extension of FBMC to other regions as it is the target methodology for coupling market zones in the European single electricity market.@en

The European Union (EU)'s resource adequacy framework consists of the reliability standard calculation, adequacy assessments and capacity remuneration mechanisms, three pillars which should conspire to deliver a realised adequacy in liberalised markets which is close to that which would be delivered by a central planner minimising total system costs. However, this framework is vulnerable to inconsistencies in the parameters used in each of the three pillars, making the framework internally incoherent and potentially increases system costs. We illustrate and discuss how an inconsistent choice of storage operation can lead to a sub-optimal level of adequacy. However, a consistent storage operation challenges the stated purpose of adequacy assessments in the EU which is to predict adequacy resulting from market operations. @en

The market integration of hybrid off-shore projects, consisting of wind farms and transmission assets connecting different market zones, requires re-examining bidding zone definitions. Policy makers consider separate off-shore bidding zones to optimally integrate off-shore wind farms in power systems. In this paper, we apply Advanced Hybrid Coupling to include off-shore DC transmission lines in flow-based market coupling, and compare different bidding zone configurations. We find that off-shore bidding zones lead to a transfer of welfare: the producers' surplus of off-shore wind farm owners decreases as a result of a lower average price and the congestion rent for TSOs increases. Despite that an off-shore bidding zone signals transmission scarcity better, it impacts the need for support instruments for off-shore wind farms.@en

Michiel Kenis is a PhD researcher at the Energy Systems Integration & Modeling Group at the University of Leuven with a doctoral mandate from the Flemish Institute for Technological Research (VITO). He was a visiting researcher at the Massachusetts Institute of Technology. His research focuses on cross-border electricity markets. He holds a MSc in energy engineering and a MSc in policy economics, both from the University of Leuven. Luca Lanzilao completed his MSc degree in mathematical engineering from Politecnico di Torino in 2018. Currently, he is pursuing a PhD at KU Leuven. His research focuses on studying the response of the atmospheric boundary layer to wind farm forcing, with particular emphasis on meso-scale phenomena, such as gravity waves. Kenneth Bruninx received a MSc degree in energy engineering in 2011, a MSc in management, and a PhD degree in mechanical engineering in 2016, all from the University of Leuven (KU Leuven), Belgium. Currently, he is an assistant professor at the Faculty of Technology, Policy, and Management of TU Delft, Netherlands and a research fellow at the Department of Mechanical Engineering, KU Leuven, Belgium. His research interests include market design, policies, and regulation for integrated energy systems. Johan Meyers is a professor of mechanical engineering at KU Leuven since 2009. His research focuses on the simulation of turbulent flows and the atmospheric boundary layer with applications in wind energy. In 2012, he obtained an ERC grant on wind farm control and has been involved in various European projects on wind energy since. He served as the vice president of the European Academy of Wind Energy from 2017 to 2019 and as its president from 2019 to end of 2021. He has been active as an associate editor for Computers & Fluids and is currently an associate editor for Wind Energy Science. Erik Delarue received MSc and PhD degrees in mechanical engineering from the University of Leuven, Belgium, in 2005 and 2009, respectively. He is currently an associate professor with the University of Leuven, TME Branch (energy conversion) and active with EnergyVille. His research focus and expertise are on quantitative tools, supporting an efficient operation of, and transition toward, a low-carbon energy system (mathematical modeling of energy systems). Applications relate to flexibility through energy systems integration, market design, and energy policies.@en

This paper presents new risk-based constraints for the participation of an energy community in day-ahead and real-time energy markets. Forming communities offers indeed an effective way to manage the risk of the overall portfolio by pooling individual resources and associated uncertainties. However, the diversity of flexible resources and the related userspecific comfort constraints make it difficult to properly represent flexibility requirements and to monetize constraint violations. To address these issues, we propose a new risk-aware probabilistic enforcement of flexibility constraints using the conditional-valueat- risk (CVaR). Next, an extended version of the model is introduced to mitigate the distributional ambiguity faced by the community manager when new sites with limited information are embedded in the portfolio. This is achieved by defining the worstcase CVaR based-constraint (WCVaR-BC) that differentiates the CVaR value among different sub-clusters of clients. Both reformulations are linear, thus allowing to tackle large-scale stochastic problems. The proposed risk-based constraints are then trained and evaluated on real data collected from several industrial sites. Our findings indicate that using the WCVaRBC leads to systematically higher out-of-sample reliability, while decreasing the exposure to extreme outcomes. @en