Xu Ling

Hybrid pumped-storage hydropower plants can provide substantial flexibility for renewable energy integration, but their available power range is strongly coupled with reservoir dynamics and future grid requests. This paper proposes a non-anticipative flexibility assessment method for constructing a multi-period active power request region that can be reliably tracked under causal operation. A sufficient storage-envelope certificate is developed for a continuous aggregate plant model, ensuring that every request trajectory within the resulting hyperrectangular region can be followed using only information available up to the current period, while satisfying hydraulic constraints and terminal storage requirements. Under the stated hydraulic and spill assumptions, monotonicity of the one-period storage transition set reduces the semi-infinite forward-invariance conditions to endpoint dispatch checks. The resulting single-stage nonlinear formulation is implemented as a mixed-integer linear program using piecewise-linear approximations. The certified active power intervals are further used to construct compatible active–reactive power capability regions. A case study based on the Baishan–Hongshi hybrid pumped-storage hydropower plant in China shows the effectiveness of the proposed method. The resulting capability region can help operators screen requested power profiles and set defensible day-ahead or intraday flexibility limits, while the storage envelope provides a state-dependent rule for causal scheduling-level redispatch that preserves deliverability later in the horizon.

Pumped storage hydropower (PSH), with its advantages of fast response and large-scale energy storage, has become a key means of enhancing power system flexibility. However, the time-varying nature of transmission line capacity may constrain the effective utilization of its regulating capability. Most existing studies treat transmission capacity as a fixed boundary, failing to adequately account for the impact of its dynamic variations on scheduling strategies. To address this gap, this paper constructs three typical transmission capacity scenarios: fixed high (no congestion), fixed low (persistent severe congestion), and time varying (capacity reduced during daytime and restored at night). A power system dispatch optimization model incorporating wind power, solar power, and pumped storage is established, with the objective of minimizing total system operating cost. Under different capacity scenarios, the pumping/generating behavior of PSH, unit output structure, controlled line operation status, and economic indicators are compared and analyzed. Furthermore, sensitivity analyses are conducted from three dimensions—congestion severity, congestion time window, and PSH installed power capacity—to comprehensively evaluate the marginal impacts of key factors on system performance. Results based on a modified IEEE 14-bus system indicate that under the fixed high scenario, PSH can achieve free arbitrage with the best economic performance, but does not account for capacity fluctuation risks. Under the fixed low scenario, persistent congestion leads to substantial wind and solar curtailment, significantly increased operating costs, and prolonged full loading of lines, posing the highest security risk. Under the time-varying scenario, PSH is forced to pump at full power during nighttime and generate continuously during daytime, effectively alleviating line flow pressure. Although the operating cost is slightly higher than that of the fixed high scenario, line overload is avoided, renewable energy accommodation is significantly improved, and substantial security gains are achieved at a moderate economic cost. This paper reveals the forcing mechanism of time-varying transmission capacity on PSH scheduling strategies, verifies the feasibility of the time-varying capacity strategy as an effective trade-off between economics and security, and provides a theoretical basis for optimal PSH operation and transmission capacity management in power systems with high shares of renewable energy.

The high penetration of renewable energy introduces strong uncertainty into power system operating conditions, making it difficult for conventional planning methods to effectively satisfy N-1 security verification requirements. To address this issue, this paper proposes an N-1 security-constrained 8760-hour power flow generation method (N-1-SC-8760-PFGM), formulated as a mixed-integer nonlinear programming (MINLP) problem. To overcome the computational intractability of direct solution, an energy-power-reactive power co-optimization method is developed based on objective consistency and solution path coordination. By employing a consistency-oriented objective function, a pre-embedded N-1 constraint mechanism, and a “verification–augmentation–re-optimization” solution strategy, the proposed method overcomes the limitations of traditional sequential optimization approaches, such as stage-wise independent objectives, insufficient coordination of N-1 constraints, and the lack of iterative re-optimization mechanisms during operating state adjustments. Case studies on modified IEEE 39-bus and IEEE 300-bus systems demonstrate that the proposed method achieves superior convergence and computational efficiency compared with conventional decomposition-based methods, and can efficiently generate full-year power flow profiles that satisfy N-1 security constraints.

Under the global imperative for climate action and sustainable development, accelerating the transition towards high-penetration renewable energy systems remains a universal priority, central to achieving the United Nations Sustainable Development Goals. However, the inherent uncertainty and volatility of renewables such as wind and solar PV pose fundamental challenges to power system stability and flexibility worldwide. These challenges, if unaddressed, could significantly hinder the reliable and sustainable integration of clean energy on a global scale. While pumped storage hydropower (PSH) represents a mature, large-scale solution for enhancing system regulation capabilities, existing planning methodologies frequently suffer from critical limitations. These included oversimplified scenario representations—particularly the inadequate consideration of escalating extreme weather events under climate change—and computational inefficiencies in solving large-scale stochastic optimization models. These shortcomings ultimately constrained the practical value of such approaches for advancing sustainable energy planning and building climate-resilient power infrastructures globally. To address these issues, this paper proposed a bi-level stochastic planning method integrating scenario optimization and improved Benders decomposition. Specifically, an integrated framework combining affinity propagation clustering and isolation forest algorithms was developed to generate a comprehensive scenario set that covered both typical and anomalous operating days, thereby capturing a wider range of system uncertainties. A two-layer stochastic optimization model was established, aiming to minimize total investment and operational costs while ensuring system reliability and renewable integration. The upper layer determined PSH capacity, while the lower layer simulated multi-scenario system operations. To efficiently solve the model, the Benders decomposition algorithm was enhanced through the introduction of a heuristic feasible cut generation mechanism, which strengthened subproblem feasibility and accelerated convergence. Simulation results demonstrated that the proposed method achieved a 96.7% annual renewable energy integration rate and completely avoided load shedding events with minimal investment cost, verifying its effectiveness, economic efficiency, and enhanced adaptability to diverse operational scenarios.