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 Swim - Accessories 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 Limited-memory 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 Board Game model but also exhibits scalability and preserves participant privacy.