Multi-objective Operation Management of a Renewable Micro Grid with Back-up Micro Turbine/Fuel Cell/Battery Hybrid Power Source

As a result of today’s rapid socioeconomic growth and environmental concerns, higher service reliability, better power quality, increased energy efficiency and energy independency, exploring alternative energy resources, especially the renewable ones, has become the fields of interest for many modern societies. In this regard, Micro-Grid (MG) which is comprised of various alternative energy sources can serve as a basic tool to reach the desired objectives while distributing electricity more effectively, economically and securely. In this paper an expert multi-objective Adaptive Modified Particle Swarm Optimization algorithm (AMPSO) is presented for optimal operation of a typical micro-grid with renewable energy sources accompanied by a back-up Micro Turbine/Fuel Cell/Battery hybrid power source to level the power mismatch or to store the surplus of energy when it’s needed. The problem is formulated as a nonlinear constraint multi-objective optimization problem to minimize the total operating cost and the net emission simultaneously. To improve the optimization process, a hybrid PSO algorithm based on a Chaotic Local Search (CLS) mechanism and a Fuzzy Self Adaptive (FSA) structure is utilized. The proposed algorithm is tested on a typical micro-grid and its superior performance is compared to those from other evolutionary algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).