A novel approach for optimal power scheduling of distributed energy resources in microgrids

In microgrids, to have minimal operation cost, optimal power scheduling among distributed energy resources is an essential task. Owing to nonlinearity and complexity of power scheduling problem, it is vital to use a high-performance optimization technique to efficiently solve this problem. To achieve this goal, in this paper, crow search algorithm (CSA) is proposed and improved to optimally solve the power scheduling problem in a microgrid including photovoltaic modules, wind turbines and combined heat and power system. In the improved CSA (ICSA), there are three modifications: (1) oriented differential operator, (2) adaptive flight length and (3) repulsion factor. These modifications help the algorithm to provide a better balance between diversification and intensification. In order to validate the effectiveness of the proposed algorithm, the results obtained by ICSA are compared with the results found by the other algorithms. Over IEEE 37-node feeder, simulation results indicate that the proposed algorithm finds more accurate results than the other methodologies. In comparison with artificial fish swarm, additive increase multiplicative decrease, memory-based genetic algorithm, genetic algorithm, particle swarm optimization with inertia weight (PSOw) and particle swarm optimization with constriction factor (PSOcf), when ICSA is applied, operation cost decreases around 19, 19.7, 0.7, 36, 6.1 and 5%, respectively.