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A robust real-time energy scheduling strategy of integrated energy system based on multi-step interval prediction of uncertainties

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  • Dong, Fuxiang
  • Wang, Jiangjiang
  • Xu, Hangwei
  • Zhang, Xutao

Abstract

The uncertainties of renewable energies and loads in integrated energy system (IES) result in unstable operation and performance degradation. To consider the impact of the uncertainties, the scheduling strategies of IES with the prediction of uncertainties are required. This paper designs a hybrid solar and gas turbine IES, in which the thermal and electrical storage units are integrated to address the uncertainties. A robust real-time energy scheduling strategy is proposed according to the real-time load and the predicted loads in the future time, in which the errors of multi-step interval prediction of renewable sources and loads are combined. The multi-step prediction method based on gated recurrent unit and time classification is constructed for the interval prediction of uncertain sources and loads by analyzing the probability statistics of prediction errors. In the robust optimization, the penalty factors of time sequences are proposed and considered to decrease the influences of future predicted information on current real-time scheduling, in which the future uncertainties in larger interval with current time has less influence. The optimal results in the hybrid IES demonstrate that the daily operation cost is 13.67 % lower than the method that does not consider the prediction parameters. Compared to the traditional scheduling strategies, the proposed strategy declines the operation cost by 2.93 %. The analysis of the confidence level of predictions ranging from 60 % to 98 % illustrates that the operation cost under the penalty factor of 0.94 is the lowest when the confidence level is from 94 % to 96 %.

Suggested Citation

  • Dong, Fuxiang & Wang, Jiangjiang & Xu, Hangwei & Zhang, Xutao, 2024. "A robust real-time energy scheduling strategy of integrated energy system based on multi-step interval prediction of uncertainties," Energy, Elsevier, vol. 300(C).
    • Handle: RePEc:eee:energy:v:300:y:2024:i:c:s0360544224014129
    DOI: 10.1016/j.energy.2024.131639
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