Optimal Operation of Self-Healing Networked Microgrids Using Pufferfish Optimization Algorithm

This paper presents an approach for optimal operation of self-healing networked microgrids (NMGs) under both normal operation and emergency conditions using the pufferfish optimization algorithm (POA). The proposed methodology is based on an energy management system (EMS) with two levels and independent functions. The lower-level is designed for normal operation, where the local controller of each microgrid (MG) performs the optimal dispatch of power from the dispatchable sources. During an emergency case in any MG, the higher-level EMS is activated, and the global controller is brought into operation. Physically, the NMGs are connected by tie-lines, while cyber links are established to exchange information and control signals for coordinated operation. Each MG operates to supply its local demand during normal operation conditions, resulting in no electrical power exchange between MGs. İn case of generation deficiency or a fault leading to generation outage, electrical power can be exchanged through the existing interconnections, enabling the affected microgrid to receive support from neighboring MGs. The main objective of POA is to minimize the total operating cost, in which the economic impact of network power losses is incorporated into the single objective function. Simulation studies were conducted using MATLAB and DIgSILENT software over one day. The performance of POA was compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) under the same computational settings. Statistical and convergence analyses show that POA achieves the lowest mean operating cost across all studied cases, with low run-to-run variability and favorable convergence behavior. The results demonstrate the effectiveness of the proposed approach in improving the economic operation of NMGs under both normal and emergency conditions.