Distributed Energy and Reserve Scheduling in Local Energy Communities Using L-BFGS Optimization

Encouraging citizens to invest in small-scale renewable resources is crucial for transitioning towards a sustainable and clean energy system.Local energy communities(LECs)are expected to play a vital role in this context.However,energy scheduling in LECs presents various challenges,including the preservation of customer privacy,adherence to distribution network constraints,and the management of computational burdens.This paper introduces a novel approach for energy scheduling in renewable-based LECs using a decentralized optimization method.The proposed approach uses the Limitedmemory Broyden–Fletcher–Goldfarb–Shanno(L-BFGS)method,significantly reducing the computational effort required for solving the mixed integer programming(MIP)problem.It incorporates network constraints,evaluates energy losses,and enables community participants to provide ancillary services like a regulation reserve to the grid utility.To assess its robustness and efficiency,the proposed approach is tested on an 84-bus radial distribution network.Results indicate that the proposed distributed approach not only matches the accuracy of the corresponding centralized model but also exhibits scalability and preserves participant privacy.