A hybrid constrained Particle Swarm Optimization-Model Predictive Control (CPSO-MPC) algorithm for storage energy management optimization problem in micro-grid. (November 2022)

• Record Type: Journal Article
• Authors: Gbadega, Peter Anuoluwapo; Sun, Yanxia
• Is Part Of: Energy reports. Volume 8(2022)Supplement 8
• Page Start: 692
• Page End: 708
• Publication Date: 2022-11
• Subjects: Model predictive control -- Particle swarm optimization -- Linear programming -- Energy management system -- Energy storage -- MATLAB/Simulink; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.10.035
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: This paper investigates energy management systems in micro-grid using an optimization-based approach, optimizing the operating cost related to the energy purchased from the utility grid, the operation cost of the energy storage system, and revenue from the selling of energy to the utility grid. This research uses a constrained Particle Swarm Optimization-Based Model Predictive Control (CPSO-MPC) and a Linear Program-Based Optimization approach to solve the constrained optimization problem formulated in micro-grid energy management. Due to the absence of constraint management strategies in the traditional PSO algorithm, it is incapable of solving constrained optimization problems. Hence, to overcome this drawback, an intuitive approach known as Deb's rule is applied to handle the constraints. The simulation results show the modified particle swarm optimization's effective performance embedded in the model predictive control algorithm compared to the linear programming algorithm.