Battery Storage Systems Control Strategies with Intelligent Algorithms in Microgrids with Dynamic Pricing

Guilherme Henrique Alves (), Geraldo Caixeta Guimarães and Fabricio Augusto Matheus Moura
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Guilherme Henrique Alves: Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, MG, Brazil
Geraldo Caixeta Guimarães: Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, MG, Brazil
Fabricio Augusto Matheus Moura: Unit 2, Institute of Technological and Exact Sciences, Electrical Engineering Department, Federal University of Triangle Mineiro, Uberaba 38025-180, MG, Brazil

Energies, 2023, vol. 16, issue 14, 1-30

Abstract: The current microgrid (MG) needs alternatives to raise the management level and avoid waste. This approach is important for developing the modern electrical system, as it allows for better integration of distributed generation (DG) and battery energy storage systems (BESSs). Using algorithms based on artificial intelligence (AI) for the energy management system (EMS) can help improve the MG operation to achieve the lowest possible cost in buying and selling electricity and, consequently, increase energy conservation levels. With this, the research proposes two strategies for managing energy in the MG to determine the instants of charge and discharge of the BESS. A heuristic method is employed as a reference point for comparison purposes with the fuzzy logic (FL) operation developed. Furthermore, other algorithms based on artificial neural networks (ANNs) are proposed using the non-linear autoregressive technique to predict the MG variables. During the research, the developed algorithms were evaluated through extensive case studies, with simulations that used data from the PV system, load demands, and electricity prices. For all cases, the AI algorithms for predictions and actions managed to reduce the cost and daily consumption of electricity in the main electricity grids compared with the heuristic method or with the MG without using BESSs. This indicates that the developed power management strategies can be applied to reduce the costs of grid-connected MG operations. It is important to highlight that the simulations were executed in an adequate time, allowing the use of the proposed algorithms in dynamic real-time situations to contribute to developing more efficient and sustainable electrical systems.

Keywords: energy management; uncertainty; energy markets; microgrids; dynamic electricity pricing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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