基于时序-卷积概率模型的电力系统灵活性优化调度OA

The high penetration of uncertain renewable energy sources(RESs)poses significant challenges to the opera-tional flexibility of power systems.However,considering the omission of time-series correlations among RESs and the high dispatch costs of flexibility,we propose an optimal flexibility scheduling method based on a time-series convolution probabilistic model.First,based on the convolution theory,three uncertain inputs,including the wind power,photovoltaic,and load,are transformed into a net load probabilistic model with enhanced computational efficiency.Meanwhile,Gauss-ian Copula is newly employed to characterize the time-series correlations of net load,thereby,the errors in flexibility demand quantification resulted from ignoring time correlations are reduced.Second,to lower the dispatch costs of flexi-bility supply resources,a dynamic selection model for controllable loads is proposed.The operators can dynamically choose the optimal regulation strategies of controllable loads among three standard modes,achieving a trade-off between flexibility supply capability and regulation costs.Furthermore,a day-ahead optimal scheduling model considering the probabilistic power balance of system is established.Meanwhile,the flexibility regulation costs and risks are co-optimized after considering the chance-constrained programming and conditional value-at-risk theory,which can ob-tain an optimal and economical allocation of flexibility supply resources.Case studies demonstrate that the proposed method can be adopted to effectively enhance both system flexibility and economic performance.