How stochastic optimization enhances modelling of energy communities’ outcomes

Energy communities are expanding as a contributing means to decarbonisation goals. These often operate at the edge of the grid with small portfolios, high renewable shares, and sometimes peer-to-peer trading, all of which are conditions that make outcomes sensitive to uncertainty in demand, solar output, and wholesale prices. Yet most studies evaluate community operations deterministically or address uncertainty in isolation, raising questions about whether reported benefits reflect what communities would actually experience. This work evaluates how uncertainty treatment decisions by the modeller affect energy communities' performance metrics and outcomes. A peer-to-peer energy community model coordinating consumers, prosumers, and shared storage is developed, and five optimization frameworks are compared - deterministic (single and multi-scenario), stochastic, least-regret, and robust - each representing a different approach to incorporating uncertainty into operational decisions. Using real data across seasons, the findings demonstrate that deterministic methods systematically overestimate the welfare gains of energy communities by up to 17% in the case of a single scenario or 21% in the case of multiple independent scenarios. When these deterministic decisions are deployed under actual uncertainty in winter, they result in a 63.7 and 35.8 percentage point cost increase compared to the stochastic optimisation, respectively. These results provide evidence-based guidance for energy communities, emphasising that considering uncertainty is not a modelling detail, but rather a decision essential for estimating the true deployment value, which will ultimately determine resilience and economic efficiency in energy community operations.