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A heuristic methodology to economic dispatch problem incorporating renewable power forecasting error and system reliability

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  • Lujano-Rojas, J.M.
  • Osório, G.J.
  • Matias, J.C.O.
  • Catalão, J.P.S.

Abstract

With the constant increment of wind power generation driven by economic and environmental factors, the optimal utilization of generation resources has become a critical problem discussed by many authors. Within this topic, determination of optimal spinning reserve (SR) requirements is a key and complex issue due to the variable and unpredictable nature of renewable generation besides of generation unit reliability. Cost/benefit relationship has been suggested as a way to determine the optimal amount of power generation to be committed by taking into account renewable power forecasting error and system reliability. In this paper, a technique that combines an analytical convolution process with Monte Carlo Simulation (MCS) approach is proposed to efficiently build cost/benefit relationship. The proposed method uses discrete probability theory and identifies those cases at which convolution analysis can be used by recognizing those situations at which SR does not have any effect; while in the other cases MCS is applied. This approach allows improving significantly the computational efficiency. The proposed technique is illustrated by means of two case studies of 10 and 140 units, demonstrating the capabilities and flexibility of the proposed methodology.

Suggested Citation

  • Lujano-Rojas, J.M. & Osório, G.J. & Matias, J.C.O. & Catalão, J.P.S., 2016. "A heuristic methodology to economic dispatch problem incorporating renewable power forecasting error and system reliability," Renewable Energy, Elsevier, vol. 87(P1), pages 731-743.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p1:p:731-743
    DOI: 10.1016/j.renene.2015.11.011
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    References listed on IDEAS

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    2. Wang, Guang Chao & Ratnam, Elizabeth & Haghi, Hamed Valizadeh & Kleissl, Jan, 2019. "Corrective receding horizon EV charge scheduling using short-term solar forecasting," Renewable Energy, Elsevier, vol. 130(C), pages 1146-1158.
    3. Li Han & Rongchang Zhang & Xuesong Wang & Yu Dong, 2018. "Multi-Time Scale Rolling Economic Dispatch for Wind/Storage Power System Based on Forecast Error Feature Extraction," Energies, MDPI, vol. 11(8), pages 1-27, August.
    4. Pei Zhang & Chunping Li & Chunhua Peng & Jiangang Tian, 2020. "Ultra-Short-Term Prediction of Wind Power Based on Error Following Forget Gate-Based Long Short-Term Memory," Energies, MDPI, vol. 13(20), pages 1-13, October.

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