The coordination of pumped storage and renewable energy is regarded as a promising avenue for renewable energy accommodation. Considering wind power output uncertainties, a collaborative capacity optimization method for wind–pumped hydro storage hybrid systems is proposed in this work. Firstly, considering the fluctuation of wind power generation caused by the natural seasonal weather and inherent uncertainties of wind power outputs, a combined method based on the generative adversarial network and K-means clustering algorithm is presented to construct wind power output scenarios. Then, a multi-objective wind–pumped storage system capacity optimization model is established with three objectives consisting of minimizing the levelized cost of energy, minimizing the net load peak–valley difference of regional power grids, and minimizing the power output deviation of hybrid systems. An inner and outer nested algorithm is proposed to obtain the Pareto frontiers based on the strength of the Pareto evolutionary algorithm II. Finally, the complementarity of wind power and pumped storage is illustrated through an analysis of numerical examples, and the advantages of variable-speed pumped storage in complementary operation with wind power over fixed-speed units are verified.

As flexible resources, cascaded hydropower stations can regulate the fluctuations caused by wind and photovoltaic power. Constructing pumped-storage units between two upstream and downstream reservoirs is an effective method to further expand the capacity of flexible resources. This method transforms cascaded hydropower stations into a cascaded pumped-hydro-energy storage system. In this paper, a flexibility reformation planning model of cascaded hydropower stations retrofitted with pumped-storage units under a hybrid system composed of thermal, wind, and photovoltaic power is established with the aim of investigating the optimal capacity of pumped-storage units. First, a generative adversarial network and a density peak clustering algorithm are utilized to generate typical scenarios to deal with the seasonal fluctuation of renewable energy generation, natural water inflow, and loads. Then, a full-scenario optimization method is proposed to optimize the operation costs of multiple scenarios considering the variable-speed operation characteristics of pumped storage and to obtain a scheme with better comprehensive economy. Meanwhile, the proposed model is retransformed into a mixed-integer linear programming problem to simplify the solution. Case studies in Sichuan province are used to demonstrate the effectiveness of the proposed model.

The coordinated scheduling of cascade hydropower with photovoltaic (PV) power stations can significantly improve the utilization rate of delivery transmission lines. However, the inherent uncertainty associated with photovoltaic (PV) forecasts challenges the reliable and economic operation of the complementary energy system. Against this background, in this paper, a day-ahead, chance-constrained scheduling for cascaded hydro–photovoltaic complementary generation systems (CHPSs) considering the transmission capacity is proposed. Firstly, the uncertainty of PV forecast errors is simulated by a probability density function fitted using kernel density estimation with historical sampling data. Then, a chance-constrained optimization model considering peak-shaving demands of the receiving-end power grid is developed to determine the day-ahead optimal schedules of CHPSs. Also, complex hydraulic coupling and unit operation constraints of cascade hydropower are considered in the proposed model. To deal with the nonlinear and stochastic constraints, an efficient linearization method is adopted to transform the proposed model into a mixed-integer linear programming (MILP) problem. Finally, the effectiveness and feasibility are verified by case studies. The results show that the day-ahead schedule optimized by the proposed method can fully balance peak-shaving and photovoltaic accommodation while considering photovoltaic output uncertainty.