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 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 model but also exhibits scalability and preserves participant privacy.