Optimal energy management for stand‐alone microgrids based on multi‐period imperialist competition algorithm considering uncertainties: experimental validation. (2nd October 2015)

• Record Type: Journal Article
• Authors: Marzband, Mousa; Parhizi, Narges; Adabi, Jafar
• Is Part Of: International transactions on electrical energy systems. Volume 26:Number 6(2016:Sep.)
• Page Start: 1358
• Page End: 1372
• Publication Date: 2015-10-02
• Subjects: demand response -- imperialist competition algorithm -- microgrid -- optimal energy management -- optimal scheduling -- uncertainty; Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/etep.2154
• Languages: English
• ISSNs: 2050-7038

Abstract

Summary: Microgrid (MG) constitutes non‐dispatchable resources and responsive loads, which can serve as a basic tool to reach desired objectives while distributing electricity more effectively, economically, and securely. However, high penetration of distributed generations into the grid leads to fundamental and critical challenges to ensure a reliable power system operation. This paper presents a general formulation of optimum operation strategy with the objective of cost optimization plan and demand response regulation. MG energy management problem can be formulated as an optimization problem in order to minimize the cost‐related to generation resources and responsive loads. An expert heuristic approach based on multi‐period imperialist competition algorithm is applied to implement an energy management system for optimization purposes. A comparison is carried out between the proposed algorithm and classical techniques, including particle swarm optimization and a modified conventional energy management system algorithms. An artificial neural network combined with Markov‐chain approach is used to predict non‐dispatchable power generation and load demand under uncertainty conditions. The proposed algorithm is evaluated experimentally on an MG testbed, and the obtained results demonstrate the efficiency of the proposed algorithm to minimize the total generation cost with a fast calculation time, which makes it useful for real‐time applications. Copyright © 2015 John Wiley & Sons, Ltd.