Optimal energy managing and power scheduling of microgrid in grid-connected mode using modified multi-objective manta ray technique

Abstract

In a hybrid renewable energy source microgrid (MG), there are several components with different performances and control that should be able to work together in a compatible manner. Therefore, there is a persistent need for developing optimal strategies of energy management to ensure a consistent operation of the MG in conjunction with the main grid via mitigating the losses and optimizing the usage of the storage system. This paper proposes a methodology based on a modified multi-objective manta-ray foraging optimizer (MOMRFO) as an energy management strategy for a hybrid solar-wind microgrid with energy storage elements. The load shifting approach has been taken into account during scheduling the power among the MG elements and the utility grid. The considered MG comprises wind turbine (WT) and solar photovoltaic (PV) system as generating units in addition to battery bank and fuel cells as storage systems. Three fitness functions are considered in this work for minimization of the system operational cost, mitigation of the environmental emission, and minimization of the customer inconvenience factor. The manta-ray foraging optimizer has been selected due to its flexibility and free of external adjustable parameters. The optimization process has been established over 24 h. The proposed MOMRFO has been assessed in comparison with the other conventional energy management strategies as well as the other state of art techniques, including multi-objective particle swarm optimizer (MOPSO), multi-objective hunger game search optimizer (MOHGS), multi-objective jellyfish optimizer (MOJF). The analysis and comparisons have confirmed the reliability and efficiency of the proposed MOMRFO in shifting the flexible parts of the load to adequate horizons where the grid tariff is low and the generated energy is high. The suggested approach achieved the best saving in operating cost of 81.859% outperforming MOPSO and MOJF that achieved 80.961% and 54.259%, respectively. Additionally, the energy-sharing process based on the proposed algorithm has achieved extra savings in expenses and more mitigation of environmental emissions.