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Optimal discharge scheduling of energy storage systems in MicroGrids based on hyper-heuristics

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  • Mallol-Poyato, R.
  • Salcedo-Sanz, S.
  • Jiménez-Fernández, S.
  • Díaz-Villar, P.

Abstract

In this paper we tackle the optimal Discharge Scheduling of Energy Storage systems Problem (DSESP) in MicroGrids, considering renewable generation, and applying hyper-heuristic (HH) algorithms. The problem consists of, given the generation and load profiles in the MicroGrid, obtaining the optimal discharge scheduling of the Energy Storage System (ESS) that minimizes the consumption from the utility grid. HHs are a novel methodology in optimization problems that constructs a solution to a given problem by means of the application of basic heuristics, evolved using a global search algorithm. This methodology can be easily adapted to solve the DSESP, in this case by using an evolutionary algorithm as global approach. In this paper we detail the adaptations performed to a HH to tackle the DSESP, mainly in the encoding of solutions, and new evolutionary operators that improve the evolution of good solutions to the problem. The performance of the proposed approach has been evaluated in a real Microgrid, with different scenarios of generation and load profiles, obtaining around 5% reduction of the energy consumption from the utility grid.

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  • Mallol-Poyato, R. & Salcedo-Sanz, S. & Jiménez-Fernández, S. & Díaz-Villar, P., 2015. "Optimal discharge scheduling of energy storage systems in MicroGrids based on hyper-heuristics," Renewable Energy, Elsevier, vol. 83(C), pages 13-24.
    • Handle: RePEc:eee:renene:v:83:y:2015:i:c:p:13-24
    DOI: 10.1016/j.renene.2015.04.009
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    5. Moradi, Mohammad H. & Foroutan, Vahid Bahrami & Abedini, Mohammad, 2017. "Power flow analysis in islanded Micro-Grids via modeling different operational modes of DGs: A review and a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 248-262.
    6. Kumar, K. Prakash & Saravanan, B., 2017. "Recent techniques to model uncertainties in power generation from renewable energy sources and loads in microgrids – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 348-358.
    7. Mallol-Poyato, R. & Jiménez-Fernández, S. & Díaz-Villar, P. & Salcedo-Sanz, S., 2016. "Joint optimization of a Microgrid's structure design and its operation using a two-steps evolutionary algorithm," Energy, Elsevier, vol. 94(C), pages 775-785.
    8. Raji Atia & Noboru Yamada, 2016. "Distributed Renewable Generation and Storage System Sizing Based on Smart Dispatch of Microgrids," Energies, MDPI, vol. 9(3), pages 1-16, March.
    9. Yeon-Ju Choi & Byeong-Chan Oh & Moses Amoasi Acquah & Dong-Min Kim & Sung-Yul Kim, 2021. "Optimal Operation of a Hybrid Power System as an Island Microgrid in South-Korea," Sustainability, MDPI, vol. 13(9), pages 1-18, April.
    10. Purkayastha, Sagar N. & Chen, Yujun & Gates, Ian D. & Trifkovic, Milana, 2020. "A kelly criterion based optimal scheduling of a microgrid on a steam-assisted gravity drainage (SAGD) facility," Energy, Elsevier, vol. 204(C).
    11. Alexander Vaninsky, 2018. "Optimal environment-friendly economic restructuring: the United States–China cooperation case study," Economic Change and Restructuring, Springer, vol. 51(3), pages 189-220, August.
    12. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    13. Reynolds, Jonathan & Ahmad, Muhammad Waseem & Rezgui, Yacine & Hippolyte, Jean-Laurent, 2019. "Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 699-713.
    14. Hemmati, Reza & Saboori, Hedayat & Saboori, Saeid, 2016. "Assessing wind uncertainty impact on short term operation scheduling of coordinated energy storage systems and thermal units," Renewable Energy, Elsevier, vol. 95(C), pages 74-84.
    15. Chen, Y. & Trifkovic, M., 2018. "Optimal scheduling of a microgrid in a volatile electricity market environment: Portfolio optimization approach," Applied Energy, Elsevier, vol. 226(C), pages 703-712.
    16. Tabar, Vahid Sohrabi & Jirdehi, Mehdi Ahmadi & Hemmati, Reza, 2017. "Energy management in microgrid based on the multi objective stochastic programming incorporating portable renewable energy resource as demand response option," Energy, Elsevier, vol. 118(C), pages 827-839.
    17. Zhang, Jingrui & Wu, Yihong & Guo, Yiran & Wang, Bo & Wang, Hengyue & Liu, Houde, 2016. "A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints," Applied Energy, Elsevier, vol. 183(C), pages 791-804.

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