Rolling Horizon-Based Stochastic Optimization with Multivariate Copulas for Energy and Flexibility Dispatch: LEC BAM Campus Case Study

This paper delves into a rolling horizon-based (RH) stochastic optimization (SO) augmented with multivariate copulas (MC) within the context of the local energy community (LEC) BAM campus case study. The objective is to make decisions regarding energy and flexibility dispatch while considering all profile scenarios that emerge due to forecasting errors. Taking into account the interrelation between different time steps, we explore MC based on historical data to generate profile scenarios along with their corresponding probabilities via the scenarios reduction method. The RH SO ensures the continuous update of forecast profiles with the latest data, thus aligning the flexibility provision commitments of the LEC with the requirements of the system operator. Moreover, as the LEC participates in flexibility services, the transformer significantly reduces its loading, thanks to the support of optimized battery energy storage system operations.