Energy Management in Smart Inter-Connected Micro-Grids Using a Hybrid Sperm Swarm Optimization and Gravitational Search Algorithm

Abstract

In order to achieve global optimal solutions, this paper proposes a new hybrid sperm swarm optimization and gravitational search algorithm (HSSOGSA), which is based on the idea of balancing the exploration and exploitation capabilities by combining sperm swarm optimization (SSO), which has a fast convergence rate, and a gravitational search algorithm (GSA), which can efficiently explore a search domain. Here, the suggested algorithm is utilized to assess how interconnected micro-grids (IMGs) should operate economically, including optimization energy management for charging the system of electric vehicles (EVs), where the optimal charging system for the EV is applied in one micro-grid of interconnected micro-grids. Each micro-grid (MG) consists of various distributed generation (DG) units, including solar photovoltaic (PV), wind turbine (WT), and micro-turbine (MT). The primary objective function of each micro-grid seeks to minimize the cost of total power generation while taking into account the power exchange between IMGs and utility with a focus particular emphasis on technical constraints. The suggested HSSOGSA algorithm is compared with another optimization technique based on sperm swarm optimization (SSO) and war strategy optimization (WSO) algorithms to demonstrate its effectiveness. Results obtained from the HSSOGSA algorithm demonstrate how to manage energy transfer between each MG and the utility. It has small electricity consumption for a variety of daily loads Including the loads for charging EVs as well as small the price of overall electricity output, small utility costs, raising micro turbine (MT) efficiency and smart regulation of EVs charging throughout the day.

Optimal placement of distribution network‐connected microgrids on multi‐objective energy management with uncertainty using the modified Harris Hawk optimization algorithm

“…The findings demonstrate that, in comparison to optimal centralized programming techniques, employing a decentralized model with stochastic approaches can lower operational costs and computing complexity. Reference [28] suggests the hybrid sperm swarm optimization and gravitational search algorithm (HSSOGSA), a hybrid optimization algorithm, for smart interconnected microgrids (IMGs) energy management. The program combines the effective exploration capabilities of the gravitational search program (GSA) with the quick convergence rate of sperm swarm optimization (SSO).…”