Liao Siyang,Shen Nuoqing,Xu Jian,et al.Optimization method for inter-provincial peak shaving and supply guarantee based on synergistic quantification of support capability and flexibility[J].Electric Power Automation Equipment,2026,46(05):183-190.
Liao Siyang,Shen Nuoqing,Xu Jian,et al.Optimization method for inter-provincial peak shaving and supply guarantee based on synergistic quantification of support capability and flexibility[J].Electric Power Automation Equipment,2026,46(05):183-190. DOI: 10.16081/j.epae.202512001.
Optimization method for inter-provincial peak shaving and supply guarantee based on synergistic quantification of support capability and flexibility
The current research about inter-provincial peak shaving generally has the problems of single decision-making dimension and insufficient synergistic quantification of support capability and flexibility. Therefore,an optimization method for inter-provincial peak shaving and supply guarantee is proposed based on the synergistic quantification of support capability and flexibility. The flexibility supply,demand,and their balance relationship are defined,and a quantitative indicator system is constructed to realize system flexibi-lity assessment. The probability distribution of power flow under the coupling of renewable energy and load uncertainties is characterized under the generalized polynomial chaos method,and a quantitative model for inter-provincial support capability-flexibility boundary is built to generate the synergistic boundary curves of each province. A decision-making model of peak shaving and supply guarantee for regional power grid is built with the synergistic boundaries as the constraints,and an optimal inter-provincial dispatch scheme is formed. The simulation verification of an actual regional power grid show that the proposed method can effectively improve the system flexible regulation capability,ensure secure and stable operation of power grid,facilitate optimal allocation of energy resource,which verifies the feasibility and effectiveness of the model.
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