Abstract
Optimal inclusion of a photovoltaic system and wind energy resources in electrical grids is a strenuous task due to the continuous variation of their output powers and stochastic nature. Thus, it is mandatory to consider the variations of the Renewable energy resources (RERs) for efficient energy management in the electric system. The aim of the paper is to solve the energy management of a micro-grid (MG) connected to the main power system considering the variations of load demand, photovoltaic (PV), and wind turbine (WT) under deterministic and probabilistic conditions. The energy management problem is solved using an efficient algorithm, namely equilibrium optimizer (EO), for a multi-objective function which includes cost minimization, voltage profile improvement, and voltage stability improvement. The simulation results reveal that the optimal installation of a grid-connected PV unit and WT can considerably reduce the total cost and enhance system performance. In addition to that, EO is superior to both whale optimization algorithm (WOA) and sine cosine algorithm (SCA) in terms of the reported objective function.
Highlights
Load demand is increasing rapidly and the growth of energy will increase by 40% from 2006 to 2030 [1]
This review found that the reliability of the micro-grids increases when the wind turbine (WT) and PV are combined with the storage devices
The captured results are compared with whale optimization algorithm (WOA) and sine cosine algorithm (SCA) techniques for verifying the effectiveness of the proposed method
Summary
Load demand is increasing rapidly and the growth of energy will increase by 40% from 2006 to 2030 [1]. The fuzzy self-adaptive particle swarm optimization (FSAPSO) algorithm was proposed and implemented to solve MG’s energy management to reduce the operation cost and emission with optimal scheduling multi resources including WT, PV, battery, and fuel cell unit system [12]. The efficient salp swarm algorithm (ESSA) was proposed in [14] and applied for reducing the operation cost of a micro-grid connected with renewable energy sources and storage systems. Ghadimi solved the energy management problem for a MG to minimize the operation cost and emission using multi-objective particle swarm algorithm [9]. Most of the presented efforts solved the energy management problem at the deterministic conditions where the uncertainties of the renewable energy resources and the load demands were not considered.
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