This study analyzes the production of green hydrogen using dedicated offshore wind power in the Dutch North Sea region. The analysis is based on a detailed techno-economic model that simulates physical flows and estimates the levelized cost of hydrogen (LCOH). However, the model’s outputs depend on user-provided inputs and evaluating all possible inputs is computationally infeasible. To this end, “optimization with constraint learning” is employed, where surrogate machine learning models are trained on simulation data and embedded in mixed-integer optimization problems. The surrogate models are trained on 4096 simulation runs and achieve a mean absolute percentage error of ≤[jls-end-space/]3% for physical flow-related outputs, and an error of ≈[jls-end-space/]10% for the LCOH-related outputs. Once trained, these surrogates enable one to solve stakeholder–specific problem instances in sub-second solve times, supporting rapid scenario analysis and trade-off exploration.

Vehicle-to-Grid (V2G) technology represents a key advancement in integrating electric vehicles with the power grid, enabling bidirectional energy flow to improve grid stability and optimise energy use and storage. Despite its technical feasibility, adoption remains hindered by social acceptance that evolves over time. The reviewed period from 2017 to 2025 has been marked by significant turbulence in sectors directly affecting V2G, including energy markets, mobility trends, and policy frameworks. Given that V2G has long been technically feasible yet struggled to achieve widespread acceptance, these disruptions present a unique window of opportunity. In light of this, rather than offering the usual snapshot perspective, this review focuses on the connections between its perception and trends influencing it. Aiming to identify the key drivers that could propel it toward widespread implementation. This review unconventionally combines academic literature with diverse grey literature, providing a broader perspective by incorporating industry developments and identifying trends. Factors are classified into three categories - emerging, persistent, and diminishing - capturing their changing significance over time. Our findings suggest that while financial incentives and lack of standardisation remain crucial, motivations for V2G acceptance are shifting, with sustainability and energy autonomy gaining importance. Some previously critical concerns, such as range anxiety and battery degradation, are diminishing as technology advances. Beyond identifying trends, we propose strategies to overcome key barriers, providing a deeper understanding of evolving consumer priorities and technological advancements.

Recent energy justice scholarship has argued for the need to reflect more explicitly on the normative assumptions that underpin claims to justice in energy systems. While such reflections increasingly inform energy policy, less attention has been paid to how these assumptions shape the design of algorithmic systems central to energy system planning and operations. This paper explores how normative assumptions in the design of algorithmic systems used to request flexibility from electricity consumers and producers to manage grid congestion may influence distributive justice outcomes. By systematically reviewing the scientific literature presenting such systems, we define two categories of assumptions: (1) scope assumptions , which set the boundaries of the justice analysis by determining which burdens and benefits, scale, subjects, and timeframe are considered relevant; and (2) design assumptions , which specify how these considerations are translated into the structure of algorithmic systems, such as allocation principles, technical problem framing, data availability and evaluation metrics. We find that the particular assumptions adopted within each category determine the distributive outcomes of these algorithmic systems. Recognizing their normative character, we propose that scope assumptions should be informed by context-specific risks of injustice identified by policymakers, while engineers should reflect on and validate their design assumptions in relation to these risks.

Vehicle-to-grid (V2G) could help balance and regulate the electricity grid. While research papers have focused primarily on the technological potential of V2G services and consumer adaptation, the institutional barriers obstructing the industry from implementing V2G are hardly researched. This study, therefore, explored these institutional barriers using grounded theory and stakeholder interviews. The results showed an array of barriers related to communication standard ambiguity, non-harmonised and undefined network codes, charging standard ambiguity resulting in uncertainties and financial risks, and conflicting stakeholder needs about who should control V2G operations. We conclude that large-scale adoption of V2G in Europe is hindered because it is unclear to the actors involved how to become ”V2G-ready”. This lack of clarity results in an innovation that is in a wait-and-see phase. We give practical recommendations to potentially become V2G-ready and for further research.

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.

As renewable energy and electrification expand rapidly, many electrical distribution grids experience grid congestion. This situation leads to long waiting lists for parties seeking a new grid connection or aiming to expand their existing grid connection. In addition to traditional grid enforcements, distribution system operators are developing ways to manage congestion by steering electricity supply and demand. As grid congestion limits the previously abundant resource of grid capacity, the challenge of how to fairly distribute this now-scarce resource raises new questions about nondiscrimination and broader notions of justice. This study, grounded in energy justice, explores the distributive and procedural injustices people experience with increasing grid congestion. Our research focuses on The Netherlands, where more than 10,000 parties await new grid connections. Through 16 semi-structured interviews with people either affected by or involved in mitigating grid congestion, our thematic analysis reveals three key categories: (1) injustices arising from legacy policies, legislation, and social norms; (2) injustices due to unclear regulations, inconsistent policies, and policy gaps; and (3) injustices related to changing relationships between DSOs and affected parties. These findings highlight that grid congestion is fundamentally sociotechnical; while congestion is both constrained and addressed by technical factors, institutional and social factors such as legacy policies, social norms and communication, significantly influence perceptions of injustice. Our findings call for a comprehensive integration of justice principles within the institutional (e.g. regulation, policy, markets, social norms), technical (e.g. grid infrastructure, IT systems), and social (e.g. community engagement, communication) components of grid infrastructure.

Fairness has recently gained significant attention in the scientific literature on algorithmic control systems for congestion management. However, many diverse conceptualizations of fairness have been presented. This paper aims to categorize these varying conceptualizations by reviewing existing literature on congestion management. It examines how researchers approach decisions concerning the scoping of fairness problems, the selection of fairness principles, and the choice of evaluation metrics. Findings highlight a need for more justification of fairness conceptualizations in literature as well as a need for standardized evaluation metrics and more empirical grounding and validation. The insights provided can help researchers and practitioners consider fairness comprehensively in the design of algorithmic control systems for congestion management.

Charging infrastructure in neighborhoods is essential for inhabitants who use electric vehicles. The development of public charging infrastructure can be complex because of its dependency on local grid conditions, the responsibility to prepare for anticipated fleet growth policies, and the implicit biases that may occur with the allocation of charging resources. How can accessible EV charging be ensured in the future, regardless of energy infrastructure and socio-economic status of the neighborhood? This study aims to represent the decision-making in the allocation of public charging infrastructure and ensure that various key issues are accounted for in the short-term and long-term decision making. The paper first identifies these issues, then describes the decision-making process, and all of these are summarized in a visual overview describing the short-term and long-term decision loop considering various key indicators. A case study area is identified by comparing locally available data sources in the City of Amsterdam for future simulation.

Hydrogen is becoming increasingly popular as a clean, secure, and affordable energy source for the future. This study develops an approach for designing a PV–battery–electrolyzer–fuel cell energy system that utilizes hydrogen as a long-term storage medium and battery as a short-term storage medium. The system is designed to supply load demand primarily through direct electricity generation in the summer, and indirect electricity generation through hydrogen in the winter. The sizing of system components is based on the direct electricity and indirect hydrogen demand, with a key input parameter being the load sizing factor, which determines the extent to which hydrogen is used to meet seasonal imbalance. Technical and financial indicators are used to assess the performance of the designed system. Simulation results indicate that the energy system can effectively balance the seasonal variation of renewable generation and load demand with the use of hydrogen. Additionally, guidelines for achieving self-sufficiency and system sustainability for providing enough power in the following years are provided to determine the appropriate component size. The sensitivity analysis indicates that the energy system can achieve self-sufficiency and system sustainability with a proper load sizing factor from a technical perspective. From an economic perspective, the levelized cost of energy is relatively high because of the high costs of hydrogen-related components at this moment. However, it has great economic potential for future self-sufficient energy systems with the maturity of hydrogen technologies.

The Netherlands aims at reducing natural gas consumption for heating in the housing sector. Although homeowners are responsible for replacing their heating systems and improving dwelling insulation, they are not always able to make individual decisions. Some projects require group decisions within and between buildings. We use an agent-based modelling and simulation approach to explore how these individual and group decisions would influence natural gas consumption and heating costs in an illustrative neighbourhood, under a set of assumptions. We model individual household preferences over combinations of insulation and heating systems as a lifetime cost calculation with implicit discount rates, and we use quorum constraints to represent group decisions. We model three fiscal policies and a policy to disconnect all dwellings from the natural gas network. Results show that the disconnection policy was the only necessary and sufficient condition to incentivize households to replace their heating systems and that group decisions influenced the alternatives that were chosen. Since results were influenced by group decisions within buildings and by the market discount rate, we recommend further research regarding policies around these topics. Future work can apply our approach to case studies, incorporate new empirical knowledge, and explore group decisions in other contexts.

Flexibility coming from consumers in residential and service sectors has received significant attention to deal with uncertainty and variability of renewable energy sources. Since these consumers are too small individually to participate in the electricity markets, their assets can be pooled by an aggregator. The aggregator can implement business models by trading flexibility obtained from these consumers’ assets in different electricity markets. However, the aggregator and the consumers are only motivated to implement a business model, if it is economically feasible. The economic feasibility of a business model depends on (1) financial aspects: how much profit the aggregator makes, and how much money the consumers save, and (2) operational aspects: how the consumers’ assets are operated to increase the financial aspects. This paper aims to provide insights in these operational and financial aspects of the aggregator's business models in residential and service sectors. For this purpose, a literature review is conducted, and a framework is presented to analyze the selected papers on these operational and financial aspects. Based on this analysis, different strategies for the aggregator to implement business models are determined. Moreover, knowledge gaps are identified and several recommendations for future research are provided.