Alexander Hoogsteyn obtained his MSc from KU Leuven in 2021 with a major in electrical engineering and minor in power system engineering. He then completed a traineeship at the EU Agency for Cooperation of Energy Regulators, after which he returned to KU Leuven to pursue a PhD in mechanical engineering at the Energy Systems Integration and Modeling research group. He is also affiliated with Energyville in Genk. Here, he investigates the role of hydrogen in industrial decarbonization and the impact hydrogen policies have on energy markets. Kenneth Bruninx received MSc and PhD degrees in energy engineering in 2011 and 2016, respectively, and an MSc degree in management in 2015 from KU Leuven, Belgium. Currently, he is an associate professor at the Delft University of Technology (TU Delft) and a research fellow at KU Leuven. His main research interests include market design, policy, and regulation for integrated net-zero energy systems. He serves as the chair of the IEEE EPPC WG on Energy and is an associate editor of the IEEE Transactions on Energy Markets, Policy & Regulation. Erik Delarue received MSc and PhD degrees in mechanical engineering from KU Leuven, Belgium, in 2005 and 2009, respectively. He is a professor at KU Leuven, TME Branch (energy conversion), leading the Energy Systems Integration and Modeling research group, and he is active within 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, energy market design, and energy policies. Erik has published widely in the academic literature.

Ambitious offshore wind energy targets in the North Sea necessitate innovative solutions for efficiently delivering energy to onshore demand locations. Wind-to-hydrogen systems offer a promising pathway, with three archetypes of system configurations: centralized onshore electrolysis (C-ON), centralized offshore electrolysis (C-OFF), and decentralized offshore electrolysis at each wind turbine (D-OFF). This study introduces a high-resolution, time-dependent simulation framework capable of analyzing offshore wind-to-hydrogen systems with a focus on operational dynamics and comprehensive cost estimation. The framework enables detailed analysis of D-OFF, capturing its unique dynamics driven by direct connections to individual wind turbines, including the impacts of dynamic operation. A comprehensive system analysis, spanning from the wind farm to the hydrogen offtaker, reveals a wide cost range, with Levelized Cost of Hydrogen (LCOHs) ranging from 3.0 to 10.5€/kgH₂ post 2030. Among the different scenarios analyzed, C-OFF with proton exchange membrane electrolysis achieves the lowest LCOHs due to a reduced need for offshore electrical infrastructure, economies of scale, and efficient dynamic operating characteristics. D-OFF with alkaline electrolysis incurs the highest costs and faces operational challenges, such as electrolyzers shutting down when they occasionally fail to reach the minimum load thresholds, lowering hydrogen production. We illustrate the trade-offs between system configurations’ cost, production rate, and electrolyzer stack lifetime across configurations. Insights from this study can be utilized as a starting point for informed decision-making for large-scale wind-to-hydrogen deployment in the Dutch North Sea region.

We assess the effect of different incentive schemes with future financial benefits on photovoltaic (PV) adoption patterns in the residential sector for the two biggest regions of Belgium - Flanders and Wallonia. Due to variation in incentive schemes across the regions and within regions across months, we can identify the effect of changes in future benefits on adoption. We combine and compare three distinct and widely used incentive schemes for PV, namely output-based, capacity-based and net metering, which is electricity cost saving-based. We find that PV adoption in the residential sector is highly sensitive to overall future financial benefits. A one percent increase in overall benefits per kW of installed capacity increases the number of installations by around 6%. However, the different incentive schemes have varying degrees of effectiveness. In particular, capacity- and output-based incentive schemes, which directly compensate households with a fixed price per produced amount of electric energy or per installed capacity annually are around 6 percentage points more effective in fostering PV uptake than the cost saving-based net metering, which depends on future electricity prices and production. Furthermore, the average size of new installations depends on whether incentive schemes remunerate for additional capacity.

Motivated by generation system adequacy concerns, many European countries have introduced capacity remuneration mechanisms (CRMs) to ensure sufficient investments in power generation. However, it is uncertain whether the existing CRMs will promote sufficient adequacy and flexibility in a decarbonized power system, where supply and demand will become more weather-dependent. We assess the effectiveness of a centralized capacity market, a strategic reserve, and a decentralized capacity market via capacity subscriptions in a climate-neutral, weather-driven power system. We develop a co-simulation of two agent-based models simulating myopia in both operational and investment decisions. We simulate weather uncertainty by running the model with 40 different weather years. Our results from a case study based on the Netherlands indicate that a strategic reserve may increase electricity price volatility in the long-term. A centralized capacity market is more cost-effective than a strategic reserve, but administratively setting its parameters is prone to over- or underprocurement. Capacity subscription allows consumers to select their desired level of reliability. Results indicate that these decentralized capacity markets may yield a clearer signal for the needed dispatchable capacity and promote demand-side response, but it may be challenging to provide long-term certainty for investors.

Energy Storage Systems (ESS) play a crucial role in managing renewable energy variability. Forecast-informed optimization is typically used to maximize ESS profit in electricity markets. Whereas traditional forecaster training methods use accuracy-based loss functions, Decision-Focused Learning uses a task-aware loss function with the aim of improving ESS profits. This can be achieved by integrating the downstream optimization in the forecaster training procedure. A common task-aware loss function is the Smart Predict-then-Optimize (SPO+) loss. However, its current implementation is prone to overfitting and is limited to linear forecasting models. Here, we extend the SPO+ framework to neural network forecasters with non-linear activation functions while introducing an interior-point training method to mitigate overfitting risks. When applied to an ESS participating in the day-ahead market, our approach outperforms both traditional and other decision-focused benchmarks in terms of obtained ESS profits.

Introducing electricity purchase conditions in renewable fuel regulations and carbon accounting is a controversial issue in the US and Europe. We argue that their impact must be assessed considering demand flexibility, local grid conditions, and overlapping policy instruments such as emissions trading schemes and renewable portfolio standards. The introduction of more stringent requirements has a significant impact on companies' reported progress in reducing indirect emissions. In addition, we currently lack reliable carbon intensity data with sufficient temporal and spatial granularity outside the US and Europe. Assessing hydrogen imports should consider strategic behavior. In summary, a broader systems perspective is needed to analyze the impact of electricity purchase conditions on markets, prices, and emissions, integrating voluntary actions to decarbonize hard-to-abate sectors under increasing renewable energy shares. This will have implications not only for the (re)design and integration of certificate markets for renewable electricity but also for renewable fuels and carbon.

The main aim of this paper is to illustrate the added value at system level of explicitly accounting for the closed-loop feedback aspect in the open-loop optimal control problem of model predictive control strategies for thermostatically controlled loads participating in demand response programs, when subject to uncertainties. To this end, an integrated system-level optimization problem is set up, merging an economic dispatch problem to represent the supply side and an open-loop stochastic optimal control problem incorporating affine disturbance feedback to represent the demand side. The incorporation of affine disturbance feedback enables the simultaneous optimal scheduling of the demand for electrical energy, reserve capacity and real-time flexibility required to guarantee thermal comfort at the demand side, thereby disclosing very valuable information for an aggregator or system operator, since the load uncertainty can be revealed and controlled ahead of real time. To solve the mathematically complex integrated problem, a distributed solution strategy based on the alternating direction method of multipliers is developed. With the help of an illustrative case study, it is demonstrated that the day-ahead coordination of the demand for reserve capacity in addition to the energy demand is able to reduce the system operating cost while guaranteeing thermal comfort, and hence, enables a more cost-efficient electrification of the residential heating sector. Cost reductions up to 10.7 % are shown to be achievable for a demand side consisting of 900 000 flexible heat pumps combined with low-temperature radiators.

Increasing shares of renewable generation are leading to more volatile electricity prices, presenting an opportunity for Energy Storage Systems (ESS) participating in short-term electricity markets. Model Predictive Control (MPC) has been shown to be a powerful tool to leverage the latest information at the time of optimization, yet its efficacy depends on the quality of the employed price forecasts. So far, these forecasts have been developed with traditional forecasting methods instead of value-oriented approaches, which consider the downstream decision problem during the forecaster training phase. Existing value-oriented methods, however, often rely on a specific downstream problem structure. This paper addresses these shortcomings by introducing a universally applicable, value-oriented forecasting methodology that employs a generalized loss function designed to account for inter-temporal price variability, using the downstream value (i.e., profit from ESS market participation) as the selection criterion in the hyperparameter tuning step. The proposed methodology is tested on a case study considering different types of ESS participating in the Belgian balancing market through MPC. The method is benchmarked against other forecasting techniques including a neural network trained in traditional, accuracy-oriented fashion. Using real-life data over a test set of two months, we show that the methodology outperforms those traditional techniques in terms of ex-post out-of-sample profit.

This paper explores various policies to support climate-neutral hydrogen production, focusing on their interaction with energy markets and cap-and-trade systems such as the EU emission trading scheme. We develop and deploy a state-of-the-art equilibrium model to examine the effect of hydrogen support policies on the interactions between hydrogen, electricity and emission markets. Our analysis shows that mechanisms remunerating hydrogen production can distort spot prices of electricity and hydrogen more strongly than mechanisms that remunerate hydrogen production capacity. Hydrogen support mechanisms furthermore promote renewable electricity production and deter investment in conventional generation assets. The associated decrease in emissions in the power sector leads to an increase of emissions in the industrial and hydrogen sector due to the waterbed effect in the EU emission trading scheme. Our case study on an emission-capped area inspired by the EU shows that the operational distortions that production-based mechanisms exhibit, typically increase costs more than the investment distortions that capacity-based mechanisms entail.

Due to computational limits, temporal details within Energy System Optimisation Models are often reduced, for example by reducing the time horizon or by resampling via Time Series Aggregation (TSA) techniques. In high RES energy systems, this may lead to undersizing of Long-Duration Energy Storage (LDES) capacities, necessary for system flexibility, due to the omission of long-term interannual weather effects. Via comparative analysis between the capacity expansion results for different subsets of weather years, this paper shows the extent to which single year models underpredict LDES. but also that a small cluster (n=2, 3) of weather years can adequately capture key system-defining weather patterns. Identifying these weather years ex-ante is non-trivial, as there is no obvious correlation with how well they describe the full set of weather years. As this assumed correlation underpins current time series aggregation techniques, new techniques are required.

This study delves into the interplay of residential electricity customers in low voltage distribution systems (LVDS) with market designs that manage local grid constraints. Within this context, residential electricity customers are self-interested agents, exposed to real-time pricing, that can invest in distributed energy resources (DER) and participate in wholesale energy as well as ancillary service market. The study specifically evaluates the trade-off between market design complexity and economic efficiency by examining market designs that employ Static Limits (SLs), Dynamic Operating Envelopes (DOEs), and Distribution Locational Marginal Pricing (DLMP) to ensure network integrity. Using a long-run equilibrium problem the study comprises both operational and investment perspective, considering feedbacks between distribution and higher voltage levels. The analysis reveals three key insights. Firstly, simpler market designs, namely SL-based and DOE-based designs, can approximate the economic efficiency of DLMP-based design, contingent on the network's characteristics. Effective in networks comprised of shorter feeders and larger consumers, the efficacy of simpler designs in approximating DLMP-based design diminishes in networks comprising longer feeders and numerous small consumers. Secondly, consumer preferences play a crucial role in DOE-based design, with consumers having a high willingness-to-pay (WTP) for grid capacity influencing economic efficiency. Thirdly, despite differences in distribution, energy, and DER investment costs, for the majority of consumers, total costs remain comparable across the three designs.

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.

The electrification of end-energy use and the increasing integration of distributed energy resources (DERs) are significantly reshaping the landscape of low voltage (LV) distribution grids. However, many LV networks were originally designed without considering these transformative factors, potentially leading to congestion and overloads. Assessing the hosting capacity of these networks has become crucial, as it quantifies the ability of the distribution network to accommodate additional DERs while maintaining stable and reliable operations. In this context, we introduce the concept of remaining hosting capacity as a metric to evaluate LV distribution networks' capacity to absorb additional DERs, considering the existing DER deployment. We present two simulation methodologies: Gaussian mixture model-based stochastic power flow simulations that deliver a detailed network analysis, including specific current and voltage data but require substantial computational resources, and a data resampling simulation methodology that employs detailed load and DER profiles to rapidly approximate load demands at the transformer level. Furthermore, we conduct a sensitivity analysis for different levels of DER penetration to calculate the networks' capability to accommodate more DERs. The results obtained illustrate the effectiveness of GM models and the data resampling simulation methodology proposed in this work. The remaining hosting capacity concept provides essential insights into the networks' capabilities to accommodate additional DERs in the future, facilitating informed decisions for both Distribution System Operators (DSOs) and DER developers regarding grid operation, necessary upgrades, and sustainable DER expansion.

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.

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.

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.

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.

Demand Response (DR) programs offer flexibility that is considered to hold significant potential for enhancing power system reliability and promoting the integration of renewable energy sources. Nevertheless, the distributed nature of DR resources presents challenges in developing scalable optimization tools. This paper explores a novel data-driven approach in which DR resources are modeled through their aggregate forecasts using Inverse Optimization. The proposed method utilizes historical price-consumption data to deduce DR price-response behavior via a flexibility curve. The model is assessed within the Belgian single imbalance market context, where a Balance Responsible Party (BRP) employs the inferred flexibility curve to optimize its strategic imbalance positions by managing DR resources through suitable real-time price signals. The accuracy of the estimated flexibility provided by the proposed algorithm is evaluated by comparing it with the XGboost method. The results demonstrate that the model can effectively capture DR behavior and generate profit from providing balancing energy.

This paper proposes a multi-level segmented tariff to encourage consumers to provide demand response using a battery. The aim of the tariff is to (i) properly reflect consumers’ contribution to the distribution grid cost while ensuring cost recovery for the distribution network operator and (ii) to provide consumers with a financial incentive to flatten their load profile and avoid peak demand. An optimization problem is formulated to describe how consumers can provide demand response by managing their batteries. To evaluate the effectiveness of the proposed multi-level segmented tariff, four case studies were conducted. The results indicate that the multi-level segmented tariff is the most effective in reducing coincident peak demand, with a reduction of 22%. Policymakers and regulators are recommended to consider multi-level segmented distribution tariffs, as it provides an incentive to consumers to manage their assets to provide demand response.