Regulation-aware combinatorial optimization of energy community formation under geographic proximity constraints

作者：Yaçine Merrad, Seifeddine Ben Elghali · 发表于：Applied Energy · 年份：2026 · DOI：10.1016/j.apenergy.2026.128776 · 研究领域：Smart Grid Energy Management、Integrated Energy Systems Optimization、Building Energy and Comfort Optimization

Renewable Energy Communities (RECs) enable collective self-consumption by allowing geographically proximate households to share locally generated renewable energy. While most existing studies optimize the operation of a pre-defined community, the prior problem of how to partition a set of households into multiple regulation-compliant communities remains underexplored. This paper formalizes REC formation as a pre-operational combinatorial design problem: households are partitioned under a strict diameter-based proximity constraint derived from European national regulatory rules. Within this feasible space, we minimize the quadratic imbalance of yearly community energy balances, a structural proxy for self-consumption potential equivalent to variance minimization over community balances, and prove it tightens the theoretical upper bound of annual aggregate self-consumption. The problem is shown to be NP-hard via reduction from 3- Partition . A hybrid particle swarm optimization (PSO) framework with strict feasibility repair and local memetic refinement is proposed. Experiments on residential data from two urban zones of Murcia, Spain ( 𝑁 = 2 0 0 , 𝑁 = 1 5 1 households; 130 profiles from the FlexCHESS European project) under the Spanish regulatory distance cap of 2 km (Real Decreto-ley 29/2021) demonstrate 86–93% reduction in quadratic imbalance over random and geographic baselines, and 7–9% over greedy local search (Wilcoxon 𝑝 < 0 . 0 0 4 , 10 independent runs). Hourly validati...