Rule-based energy management strategies for a hybrid microgrid using grey wolf optimizer

Abstract

This study utilizes grid-connected microgrids using photovoltaics (PVs) and wind turbines (WTs) in a residential system. For improved reliability, the system uses battery storage and diesel generators (Dgen). The proposed system uses supervisory controllers (as a rule-based energy management system) for energy management strategy implementations. The essence of using the grey wolf optimizer (GWO) is to strategize the rule-based energy management system in the proposed microgrid operations. The primary objectives are to achieve a low levelized cost of energy (LCOE) and determine the optimal number of microgrid components. The performance of the GWO is compared with three other optimization algorithms, namely, antlion optimizer (ALO), particle swarm optimizer (PSO), and cuckoo search algorithm (CSA), for benchmarking purposes. The findings indicate that the proposed GWO supersedes ALO, PSO, and CSO in energy cost reduction by 30.3% (0.0448 $/kWh), 65.6% (0.0971 $/kWh), and 120% (0.1774 $/kWh), respectively. The suggested algorithm selects the optimum number of the system’s components, which is 46 PV modules, 30 wind turbines, and 10 units of batteries. An improved GWO-based algorithm based on hybridization with gradient descent algorithms is envisaged to implement a customer-centered energy management that can ensure customer satisfaction and further reduce energy cost.