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.

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.