Grid-Edge Optimization for Maximizing the Utilization of Solar-Battery and Wind-Biomass Systems in Large-Scale Networked Microgrids to Minimize Annual Overall Cost
Raja Dhanavath, Satish Kumar Injeti and Rambabu Busi
The integration of dispersed generation into the utility grid presents significant challenges in maintaining grid safety, stability, and efficiency. Microgrids, which connect distributed energy resources to the low-voltage grid, have emerged as viable grid-edge solutions that enhance energy quality, reliability, and environmental sustainability. As the energy landscape evolves, greater reliance on grid-edge technologies and distributed resources such as wind turbines, photovoltaic systems, micro-turbines, fuel cells, and energy storage systems is anticipated. This research explores the optimal planning and scheduling of Solar-Battery Energy Storage Systems and Wind-Biomass units at the grid edge within largescale networked microgrids. By leveraging advanced grid-edge optimization strategies, this study computes the Annual Overall Cost on an hourly basis over one year, integrating time-varying loads, solar and wind generation data, and real-time grid price fluctuations. Distributed generators are represented as PV nodes rather than traditional PQ nodes, requiring sophisticated methodologies to ensure voltage stability, reactive power control, and seamless interaction with other grid-edge components. The investigation utilizes two advanced optimization techniques Butterfly Optimization Algorithm and Chaotic Velocity-based Butterfly Optimization Algorithm with Adaptive Control Strategy to minimize the objective functions while enhancing grid-edge operations. A benchmark interconnected microgrid, comprising four independent microgrids, serves as a testbed for validating the proposed approach. Load flow analysis conducted using the MATPOWER tool assesses operational constraints and technical metrics, including total active power loss and slack bus power, during the optimal planning of PV-BESS and WT-Biomass units. The study focuses on maximizing the efficiency of grid-edge resources to minimize overall annual costs while ensuring reliability in both dispatchable and non-dispatchable distributed generators. Results demonstrate that the CVBOA-ACS algorithm significantly optimizes energy management at the grid edge, reducing annual costs and showcasing its effectiveness in enhancing the performance of networked microgrids.
Keywords: Benchmark, load flow data, Networked Microgrids (NMG), Battery energy storage system (BESS), Energy Management System (EMS), Distributed Energy Resources (DER), Butterfly Optimization algorithm (BOA), Distributed Energy Resources (DERs), Chaotic Velocity Bidirectional Butterfly Optimization Algorithm with Adaptive Control (CVBBOA-ACS)