Optimizing virtual power plant allocation for enhanced resilience in smart microgrids under severe fault conditions using the hunting prey optimization algorithm

The rapid expansion of renewable energy sources (RESs), such as photovoltaic (PV), wind turbine (WT), micro turbine (MT), and fuel cell (FC), has presented both opportunities and challenges to power systems, particularly in terms of environmental sustainability and economic feasibility. While RESs offer benefits like reduced environmental pollution, decreased power losses, and enhanced power quality, their intermittent nature and uncertainties pose challenges, resulting in variable generation and instability in distribution systems. To address these challenges, the concept of aggregating distributed energy resources (DERs), battery energy storage system (BESS), electric vehicles (EVs), and controllable loads into a virtual power plant (VPP) managed by an energy management system (EMS) has emerged. This study aims to determine the optimal location and size of VPPs within radial distribution system (RDS) while considering network resilience to severe weather events. The problem is formulated as an optimization task with dual objectives: minimizing the operating cost of VPPs and reducing energy not supplied (ENS) during natural disasters such as floods and earthquakes. To address this optimization problem, a novel meta-heuristic optimization algorithm called the hunting prey optimization algorithm (HPOA) is applied. HPOA serves various functions within the RDS, specifically targeting the optimization of VPP location, VPP resource management, and the objective functions. A case study is conducted, incorporating RESs, BESS, and EVs, and compared against existing algorithms such as BESA and SMA using a standard IEEE 85-bus RDS. Simulation results conducted in MATLAB demonstrate that the proposed HPOA algorithm effectively determines the optimal size and location of VPPs, leading to improved economic, operational, and resilience indices in the network.