Whale Optimization Algorithm-based Optimum Energy-management And Battery-sizing Method For Grid-connected Microgrids

In recent years,the penetration of intermittent renewable energy sources(RESs),such as wind and solar,into existing distribution networks has increased significantly to meet the growing electricity demand and fulfil the target of reducing greenhouse gas emissions.However,these RESs,depending on weather conditions and time of day,are stochastic in nature,resulting in variable power generation.As a result,battery energystorage systems have become an integral feature of microgrids.On the other hand,determining the optimal size of battery energy storage systems can reduce the operational costs of MGs.Intelligent energy management and battery sizing are essential requirements in the microgrids to ensure the optimal use of the renewable sources and reduce the operational cost in such complex systems.Therefore,the mathematical model of common distributed generators in microgrid is analyzed and the operation mode of energy storage is discussed firstly.On this basis,the objective of microgrid economic optimization under on-grid mode is established.Aiming at the constraints of microgrid optimal operation and objective function,the intelligent optimization solution method based on whale optimization algorithm is analyzed.Considering the characteristics of the microgrid optimization model,this paper put forward a strategy of switching mechanism based on improved whale optimizer that controls the storage devices' operation.Morever,we solved the comprehensive economic benefits of mode based on the improved algorithm on the on-grid mode.The whale optimization algorithm is implemented for different scenarios,and the numerical simulation results are compared with other optimization methods including the genetic algorithm(GA),particle swarm optimization(PSO),the Bat algorithm(BA),and the improved bat algorithm(IBA).The proposed method(WOA)shows outstanding results and superior performance compared with other algorithms in terms of solution quality and computational efficiency.The numerical results show that the WOA with a smart utilization of battery energy storage(BES)helped to minimize the operational costs of microgrid by 34.072% in comparison with GA,PSO,BA and IBA.In a word,the main contribution of this study can be presented as follow:(1)proposing a novel approach to an intelligent energy-management method that increases the penetration of the renewable-energy sources and reduces the dependence on fossil fuel in the microgrid;and(2)takes into account the effect of the optimum size of battery-storage system on the operation management as well as the overall cost of the microgrid and optimize the size.