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Wasserstein metric-based two-stage distributionally robust optimization model for optimal daily peak shaving dispatch of cascade hydroplants under renewable energy uncertainties

Author

Listed:
  • Jin, Xiaoyu
  • Liu, Benxi
  • Liao, Shengli
  • Cheng, Chuntian
  • Zhang, Yi
  • Zhao, Zhipeng
  • Lu, Jia

Abstract

The integration of large-scale weather-dependent renewable energy (RE) changes how power grids operate, particularly for peak shaving dispatch. This paper uses flexible hydropower to buffer the volatility and the randomness of RE sources and aid peak shaving in response to the transition towards sustainability. Thus, a Wasserstein metric-based two-stage distributionally robust optimization model for the regional power grid's daily peak shaving dispatch is constructed. Peak shaving dispatch, minimizing the maximum residual load, focuses on the first stage, whilst integrating various operational constraints. In the second stage, for hedging against random RE sources' interference, hydropower is adjusted based on the principle of minimum water consumption adjustment. A reformation approach based on strong dual theory and linearization technology is employed to transform the proposed model into a mixed-integer linear programming (MILP) framework. Case studies for a provincial power grid in Southwest China are being conducted. Results show that the proposed model can conduct peak shaving effectively and compensate for RE randomness and volatility by taking full advantage of hydropower's flexible regulation ability. Furthermore, simulation results demonstrate that the proposed model outperforms the benchmark models in most cases and provides flexibility for decision-makers to weigh the trade-off between operational reliability and economy.

Suggested Citation

  • Jin, Xiaoyu & Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Zhang, Yi & Zhao, Zhipeng & Lu, Jia, 2022. "Wasserstein metric-based two-stage distributionally robust optimization model for optimal daily peak shaving dispatch of cascade hydroplants under renewable energy uncertainties," Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:energy:v:260:y:2022:i:c:s0360544222020023
    DOI: 10.1016/j.energy.2022.125107
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    References listed on IDEAS

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