Probabilistic PV Power Forecasting-Driven Reserve Quantification under Capacity-Sensitive Prices

In order to address the impacts of uncertainty in photovoltaic (PV) generation to the secure and economic operation of power systems, this paper proposes a reserve quantification method based on probabilistic PV power forecasting under capacity-sensitive prices. First, the study utilizes similar historical data to conduct probabilistic PV power forecasting and risk expectation estimation, which effectively captures the variability and uncertainty of PV generation. Second, a mapping between quantiles and corresponding quantile levels is established through tail-end quantile regression to support reserve decisions. Finally, by considering the capacity-sensitive reserve prices, the method minimizes the overall reserve cost consisting of reserve provision and risk losses to obtain optimal quantile combinations, which successfully optimizes reserve benefits. The effectiveness of the proposed method is validated through case studies on reserve quantification of PV generation in an East China province, which not only demonstrates superior performance in economic efficiency and reliability but also enables flexible adaptation to PV generation uncertainty and future reserve market demands.