Integration of Community Energy Storage for Congestion Management and Energy Cost Optimization: A TLBO Solution Approach

The idea of this paper is to provide a framework for simultaneous energy cost optimization and congestion management by using shared energy storage. As a case study, this paper addresses this aim by proposing the integration of a community energy storage (CES) within a distribution system, connected to four microgrids (MGs). The shared storage system enables the MGs to reduce their energy costs by optimizing the operation of the battery using a Heuristic optimization algorithm, specifically the Teaching-Learning-Based Optimization (TLBO) algorithm. Simultaneously, the distribution system operator (DSO) leverages the shared storage to alleviate congestion by purchasing charged power from the storage manager. Interestingly, the DSO is willing to pay a premium price for the charged power from the shared storage, surpassing the prevailing electricity price during congested hours. Moreover, to account for uncertainties arising from load variations and intermittent renewable energy resources (RES), Monte Carlo simulation is employed in this study. Through comprehensive simulations and analyses, the proposed approach demonstrates the potential of CES as an effective tool for congestion relief and operational cost optimization in distribution systems, and providing economic benefits to both the MGs and the DSO.