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Multi-objective mean-semi-entropy model for optimal standalone micro-grid planning with uncertain renewable energy resources

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  • Jiao, P.H.
  • Chen, J.J.
  • Peng, K.
  • Zhao, Y.L.
  • Xin, K.F.

Abstract

Standalone renewable energy system holds the most promising solution to the electrification of remote areas without utility grid access as well as to reduce fossil fuel consumption and environmental pollution. However, the random volatility and unpredictability of renewable energy are key factors to restrict its large-scale accommodation. In the present study, a multi-objective mean-semi-entropy model is proposed for a standalone micro-grid with photovoltaic-wind-battery-diesel generator hybrid system, with the aim of providing a trade-off solution between maximum profits and minimum risk in consideration of photovoltaic and wind uncertainties. Then, the preference-inspired co-evolutionary algorithm, along with Pareto optimality concept, is used for the system techno-economic optimization, i.e., to maximize the profits defined as the mean value of the return and to minimize the risk defined as the semi-entropy simultaneously. Subsequently, the preference ranking organization method is used for decision making to determine the optimal trade-off dispatch solution. Simulation results show that the multi-objective mean-semi-entropy model is well applicable to deal with standalone micro-grid operation, considering the integration of uncertain renewable energy resources.

Suggested Citation

  • Jiao, P.H. & Chen, J.J. & Peng, K. & Zhao, Y.L. & Xin, K.F., 2020. "Multi-objective mean-semi-entropy model for optimal standalone micro-grid planning with uncertain renewable energy resources," Energy, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:energy:v:191:y:2020:i:c:s0360544219321929
    DOI: 10.1016/j.energy.2019.116497
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    7. Shahbazbegian, Vahid & Shafie-khah, Miadreza & Laaksonen, Hannu & Strbac, Goran & Ameli, Hossein, 2023. "Resilience-oriented operation of microgrids in the presence of power-to-hydrogen systems," Applied Energy, Elsevier, vol. 348(C).
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