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Joint optimization of a Microgrid's structure design and its operation using a two-steps evolutionary algorithm

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

Abstract

In this paper a novel problem in MGs (Migrogrids) is analyzed. The problem consists in the joint optimization of the MG structure and operation, by obtaining on the one hand an optimal sizing of its elements and structural parameters, and on the other hand the scheduling for the ESS (energy storage system) in the MG. For this joint problem, a novel two-steps EA (evolutionary algorithm) is proposed. The EA operates in such a way that a first EA obtains the best MG structure, mainly the optimal values for the sizing of generators and ESS, and a second EA determines the operational part of the MG (ESS scheduling). A real scenario of variable electricity prices is considered. In this scenario, power demanded from the main grid has different prices depending on the day and the hour of the day when it is demanded. The MG considered in this paper is formed by wind and photovoltaic generators, different residential and industrial loads, as well as ESS. Moreover, four different settings with different natural resource availabilities have been analyzed, and the results obtained show a significant cost improvement in the MG's performance.

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  • 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.
    • Handle: RePEc:eee:energy:v:94:y:2016:i:c:p:775-785
    DOI: 10.1016/j.energy.2015.11.030
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    Cited by:

    1. Marcelino, C.G. & Leite, G.M.C. & Wanner, E.F. & Jiménez-Fernández, S. & Salcedo-Sanz, S., 2023. "Evaluating the use of a Net-Metering mechanism in microgrids to reduce power generation costs with a swarm-intelligent algorithm," Energy, Elsevier, vol. 266(C).
    2. Forough, Atefeh Behzadi & Roshandel, Ramin, 2018. "Lifetime optimization framework for a hybrid renewable energy system based on receding horizon optimization," Energy, Elsevier, vol. 150(C), pages 617-630.
    3. Guo, Li & Wang, Nan & Lu, Hai & Li, Xialin & Wang, Chengshan, 2016. "Multi-objective optimal planning of the stand-alone microgrid system based on different benefit subjects," Energy, Elsevier, vol. 116(P1), pages 353-363.
    4. Shang, Ce & Srinivasan, Dipti & Reindl, Thomas, 2016. "Generation-scheduling-coupled battery sizing of stand-alone hybrid power systems," Energy, Elsevier, vol. 114(C), pages 671-682.

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