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Optimal sizing for a hybrid power system with wind/energy storage based in stochastic environment

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  • Mohamed Abd el Motaleb, Ahmad
  • Kazim Bekdache, Sarah
  • Barrios, Lázaro Alvarado

Abstract

For isolated power networks supplied by intermittent energy sources, several doubts have emerged regarding the impact of the uncertainties on the networks reliability. This paper performs optimal sizing for a hybrid power system with wind/energy storage sources based on stochastic modeling of historical wind speed and load demand. The autoregressive moving average is used to stochastically model the uncertainty of the load demand/wind speed and, the sequential Monte Carlo simulation is performed to chronologically sample the system states. The contribution of the paper can be summarized as follows: (1) an objective function based on self-adapted evolutionary strategy in combination with the Fischer–Burmeister algorithm is proposed to minimize the one-time investment and annual operational costs of the wind/energy storage sources; and (2) the effect of the cycle efficiency and charging/discharging rate of different energy storage units on the system cost is investigated under different reliability and load shifting levels. The computational performance of the proposed optimization solver is proven in order to obtain the minimum possible investment cost. The presented case studies in this paper provide the decision makers with the flexibility to choose the suitable capacity installation at different values of reliability and load shifting levels.

Suggested Citation

  • Mohamed Abd el Motaleb, Ahmad & Kazim Bekdache, Sarah & Barrios, Lázaro Alvarado, 2016. "Optimal sizing for a hybrid power system with wind/energy storage based in stochastic environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1149-1158.
    • Handle: RePEc:eee:rensus:v:59:y:2016:i:c:p:1149-1158
    DOI: 10.1016/j.rser.2015.12.267
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    References listed on IDEAS

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    1. Paliwal, Priyanka & Patidar, N.P. & Nema, R.K., 2014. "Planning of grid integrated distributed generators: A review of technology, objectives and techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 557-570.
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    Cited by:

    1. Theo, Wai Lip & Lim, Jeng Shiun & Ho, Wai Shin & Hashim, Haslenda & Lee, Chew Tin, 2017. "Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 531-573.
    2. 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.
    3. Alvarado-Barrios, Lázaro & Rodríguez del Nozal, Álvaro & Boza Valerino, Juan & García Vera, Ignacio & Martínez-Ramos, Jose L., 2020. "Stochastic unit commitment in microgrids: Influence of the load forecasting error and the availability of energy storage," Renewable Energy, Elsevier, vol. 146(C), pages 2060-2069.
    4. Das, Choton K. & Bass, Octavian & Kothapalli, Ganesh & Mahmoud, Thair S. & Habibi, Daryoush, 2018. "Overview of energy storage systems in distribution networks: Placement, sizing, operation, and power quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1205-1230.

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