Energy Grid Management, Optimization and Economic Analysis of Microgrid

This chapter proposes a non-dominated sorting genetic algorithm (NSGAII) for the multi-objective optimal operation management (MOOM) for distributed microgrid. The main objective of the MOOM is to maximize the safe instantaneous system load, and minimizing the pollutant emission produced by the generating sources. Particle swarm optimization (PSO), genetic algorithm (GA) and NSGAII artificial intelligence techniques are studied and optimized for microgrid. The NSGAII control algorithm projected to maintain the grid voltage and angle stability within the IEEE standards while increased penetration. To construct the microgrid structure, the renewable energy sources such as wind energy, solid oxide fuel cells (SOFC) and solar photo-voltaic (SPV) are considered. The robust NSGAII based optimization algorithm continuously monitors the grid conditions and regulates grid parameters for maximizing the instantaneous safe bus loading. Power system stability indices such as fast voltage stability indices (FVSI), line stability indices (LSI) and line stability factor (LQP).