Stochastic Algorithms for Controller Optimization of Grid Tied Hybrid AC/DC Microgrid with Multiple Renewable Sources

Abstract

The hybrid AC/DC microgrid (MG) configuration is efficient as it reduces the need for multiple power conversions and hence losses. Therefore, this paper focuses on the study of grid assisted hybrid AC/DC MG comprising of solar PV and fuel cell (FC) systems on DC subgrid with supercapacitor (SC) as the short term storage device and wind energy conversion system (WECS) on the AC subgrid. A comprehensive study of the operation of MG is performed under varying system conditions in MATLAB/Simulink software. The real and reactive power (PQ) control scheme is used to regulate the DC bus voltage and power flow between the subgrids. Genetic algorithm (GA), artificial bee colony (ABC) optimization, particle swarm optimization (PSO) and the PSO with new update mechanism (PSOd) are used to compute the optimum gain values of proportional-integral (PI) controller in the PQ control scheme. The SC bank effectively reduces the power stress on the subgrids of the proposed hybrid MG system during intermittent conditions of load and generation. In addition, a comparative study of the heuristic optimization techniques is presented in detail. The ABC algorithm is found to arrive at the best results in determining the optimal gains of PI controller.

Keywords

• fuel cells
• heuristic algorithms
• microgrid
• renewable energy sources
• supercapacitors