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.

Due to the volatility of renewable energy resources (RES) and the lag of power grid construction, grid integration of large-scale RES will lead to the curtailment of wind and photovoltaic power. Pumped storage hydro (PSH) and electrochemical energy storage (EES), as common energy storage, have unique advantages in accommodating renewable energy. This paper studies the optimal configuration of EES considering the optimal operation strategy of PSH, reducing the curtailment of wind and photovoltaic power in the power grid through the cooperative work of PSH and EES. First, based on the curtailment of RES, with the goal of improving the accommodation of RES, a combined operation optimization model of PSH and EES is proposed. Then, an optimal configuration method of EES capacity is proposed to meet the power curtailment rate in the power grid. Finally, the simulation is carried out in the actual power grid and the CPLEX solver is used to solve the optimization, and the rationality and economy of the optimization are analyzed and discussed. The simulation results show that, based on the combined operation of PSH and EES, by rationally configuring the capacity of EES, the desired power curtailment rate of the power grid can be achieved, and the necessity of configuring variable speed units is verified.

With the advancement of China’s electricity markets and the continuous development of renewable energy sources (RESs), it is of great importance to investigate the strategic behavior of RESs in electricity markets. In this paper, a two-stage stochastic optimization model is proposed for a hybrid energy system composed of cascade hydropower plants, wind farms, and photovoltaic stations. Firstly, typical scenarios are generated based on Latin hypercube sampling (LHS) and the K-means clustering algorithm to represent uncertainties of wind–photovoltaic power outputs. Then, with an analysis of China’s electricity market structure, a two-stage coordinated scheduling model of hydropower–wind–photovoltaic hybrid systems in electricity markets is established with the objective of maximizing total revenues considering bilateral contract decomposition, the day-ahead energy market, and the real-time balance market. In addition, the proposed model is transformed into a mixed-integer linear programming (MILP) problem for computational convenience. As shown in an analysis of case studies, cascade hydropower plants can compensate for the fluctuation in wind and photovoltaic power outputs to reduce financial risks caused by uncertainties of wind and photovoltaic power generation. Simulation results show that compared with uncoordinated operation, the coordinated operation of hydropower–wind–photovoltaic hybrid systems increases total revenue by 1.08% and reduces the imbalance penalty by 29.85%.

The coupling between multiple energy sources such as electricity, gas, and heat is strengthened in an integrated energy system (IES), and this, in turn, improves the operational flexibility of the IES. As an upper-level energy supply system, an IES can play a role as virtual energy storage, which can provide regulating power to smooth out the volatility from large-scale renewable energy generation. The establishment of an aggregating virtual energy storage model for IESs has become an important issue. Under this background, a multi-objective optimization-based adjustable capacity evaluation method is proposed in this paper. Firstly, the mathematical model of an IES considering the coupling of multiple kinds of energy forms is proposed. Then, an aggregating model considering demand response and economic constraints is established to demonstrate the adjustable capacity of the IES. In addition, multi-objective optimization is used to identify parameters in the proposed model, and the normal boundary intersection (NBI) method is used to solve the problem. Finally, a simulation example is provided to verify the effectiveness and feasibility of the proposed method. The external energy demand boundary of the IES can be modeled as virtual energy storage, and the coupling relations of electricity and gas can be presented. Case studies demonstrate that economic constraints narrow the adjustable capacity of the IES while the demand response extends it.

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.

Traditional pumped storage capacity configuration uses static, year-targeted approaches, leading under-capacity in the early planning stages—wasting renewable energy—and over-capacity in later stages, thus wasting resources. In order to solve the above problems, this article innovatively proposes a dynamic, time-sequenced construction timeline and annual capacity configuration strategy, synchronized with new energy and load development, enhancing sustainability through optimized investment allocation and efficient resource utilization. It presents a two-layer model that considers multiple scenario operational dispatch. The upper layer aims to minimize the curtailment of wind and solar energy, providing a planning scheme to the lower layer, which focuses on multi-scenario economic dispatch, taking into account the peak-valley difference indicators. The models co-iterate: lower-layer operational outcomes feed back to refine the upper-layer’s capacity plan. This process continues until the predicted curtailment calculated by the upper layer aligns closely with that observed in the lower-layer operational simulations, or until capacity changes stabilize, ultimately determining the optimal time-phased capacity configuration. Simulations on a provincial power grid during three typical scenarios in winter, transitional seasons, and summer, as well as extreme weather scenarios, confirm that timely, dynamic configuration strategy significantly enhances renewable absorption, proving the model’s effectiveness.