Real-time low-carbon scheduling for the wind-thermal-hydro-storage resilient power system using linear stochastic robust optimization

With the large-scale wind power integration, power systems have to address not only the conventional power demand fluctuations but also the wind uncertainty. To improve the economical effectiveness, resilience, and environmental protection of power systems in the source-load uncertainty, a real-time low-carbon scheduling for the wind-thermal-hydro-storage integrated system is proposed. The power imbalance caused by the uncertainty is neutralized by the synergetic linear decision of multiple resources. To address the source-load uncertainty, a stochastic robust optimization is introduced, which establishes the system constraints by robust optimization for the resilience operation, while optimizing the expected operation cost in the empirical uncertainty distribution for economic efficiency. Moreover, a multi-point estimation is applied to formulate the expected operation cost precisely and quickly. By using the dual theory, the proposed real-time power scheduling is derived as a mixed integer bilinear constrained programming. A multi-step sequential convexified solution is developed to solve the complex scheduling problem, which linearizes the bilinear constraints with alternate optimization and relaxes the state variables of energy storages with an estimation-correction strategy. Finally, case studies show the superiority of the proposed scheduling method and convexified solution.

Automated summary

This paper proposes a real-time low-carbon scheduling method for wind-thermal-hydro-storage integrated systems to address the source-load uncertainty. The method neutralizes the power imbalance caused by uncertainty through the synergetic linear decision of multiple resources. To deal with the source-load uncertainty, a stochastic robust optimization is introduced, which establishes system constraints for resilience operation and optimizes the expected operation cost based on empirical uncertainty distribution for economic efficiency. Moreover, a multi-point estimation method is applied for precise and quick formulation of the expected operation cost.Using dual theory, the proposed real-time power scheduling is formulated as a mixed-integer bilinear constrained programming. A multi-step sequential convexified solution is developed to solve the complex scheduling problem, which linearizes the bilinear constraints and relaxes the state variables of energy storages with an estimation-correction strategy. The superiority of the proposed scheduling method and convexified solution is demonstrated through case studies.