Energy management of community energy storage in grid-connected microgrid under uncertain real-time prices. (March 2021)

• Record Type: Journal Article
• Authors: Hossain, Md Alamgir; Chakrabortty, Ripon K.; Ryan, Michael J.; Pota, Hemanshu Roy
• Is Part Of: Sustainable cities and society. Volume 66(2021)
• Publication Date: 2021-03
• Subjects: Energy management schemes -- Particle swarm optimisation -- Community energy storage -- Scheduling battery energy -- Real-time energy management -- Renewable energy; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2020.102658
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: MPSO algorithms are proposed to solve scheduling problems Extensive analysis for energy management schemes is conducted Parameter tuning using the Taguchi method and sensitivity analysis is performed Scheduling programs demonstrate better performance even under uncertain environments Abstract: Although several energy management schemes are developed in the literature, they are not thoroughly examined in order to reduce the impact of uncertain power generation, demand and electricity prices to minimise the operating cost of a small-scale power system. This paper investigates energy management schemes under uncertain environments and proposes a scheduling scheme to minimise the operating cost of a grid-connected microgrid. Optimisation problems are formulated as real-time scheduling approaches, and are solved by developing modified particle swarm optimisation (MPSO) algorithms. The modification in the MPSO algorithm is performed through structural change in incorporating a mechanism that regulates the selection procedure for decision variables. The effectiveness of the proposed algorithm is justified by comparing the results with other recent algorithms. All the algorithms are separately tuned using the Taguchi technique to demonstrate a fair comparison in solving the scheduling problem. The scheduling program demonstrates superior performance in all cases, including when there is uncertainty in prediction, as compared to other energy management approaches, although solutions have significant deviations due to prediction errors. It is also shown that the proposed MPSO algorithm for the scheduling program can save 16.80 per cent operational cost as compared to the PSO algorithm.