Modified PSO algorithm for real-time energy management in grid-connected microgrids. (June 2019)

• Record Type: Journal Article
• Authors: Hossain, Md Alamgir; Pota, Hemanshu Roy; Squartini, Stefano; Abdou, Ahmed Fathi
• Is Part Of: Renewable energy. Volume 136(2019)
• Page Start: 746
• Page End: 757
• Publication Date: 2019-06
• Subjects: Converter-based microgrids -- Renewable energy sources -- Optimum battery control -- Real-time energy management -- And particle swarm optimisation; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2019.01.005
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: In real-time energy management of a converter-based microgrid, it is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. The complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating a cost function, it is suitably analysed and then a dynamic penalty function is proposed in order to obtain the best cost function. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 h. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management. Highlights: A regularised PSO algorithm is presented for the application of real-time energy management. Cost functions for determining charging/discharging energy amount of a battery are analysed. A dynamic penalty function to efficiently manage battery energy is proposed to the cost function. Optimisation model is developed to optimise battery energy.