Advanced Generation of Uncertainty Scenarios to Enhance a Stochastic Day-Ahead Scheduling in a Local Energy Community

Abstract

Local energy communities (LECs) represent a collaborative approach to managing energy resources, where community members share and optimise the use of distributed energy resources (DERs). Hence, they require multiple objective functions to optimise a set of objectives, including economic, environmental, social and technical considerations while addressing the diverse interests of community members and stakeholders. Due to the complexity of LECs and the unpredictability of DERs, real-time operations in LECs often deviate significantly from day-ahead scheduling. To tackle these challenges, this paper presents a stochastic multi-objective optimization framework designed to improve day-ahead scheduling by accounting for forecasting errors in DERs. The proposed method employs advanced scenario generation techniques, including multivariate copulas and quantile forecasting, to capture uncertainties in load demand and renewable production without relying on prior distribution assumptions. The results demonstrate significant improvements in energy bill savings, grid management and user comfort, highlighting the effectiveness of the proposed optimization framework using a real-world dataset from a living lab in the Netherlands.