Yuye Li

The increasing penetration of wind and photovoltaic power intensifies power fluctuations and raises the requirement for reserve capacity allocation in hydro-wind-solar (HWS) systems. To address this issue, this study proposes a two-stage optimization framework for coordinated reserve configuration. In the first stage, the entropy weight method is used to evaluate heterogeneous reserve resources according to unit capacity cost, response time, and carbon emission intensity, thereby determining their response priority and obtaining an initial reserve allocation. In the second stage, alternative preference coefficient ratios for economy, rapidity, and low-carbon performance are assessed, and the resulting allocation proportions are fed back to the first stage to form a closed-loop optimization process. To solve the model, an improved Osprey Optimization Algorithm incorporating a Lens Imaging Opposition-Based Learning mechanism is adopted. A case study based on the Wudongde regional grid shows that the 2:1:2 preference-ratio scenario provides the best overall trade-off among the tested cases, with a reserve cost of 18,640.38 CNY (Chinese Yuan), carbon emissions of 8718.30 kg CO₂, and a response time of 4336.7 s. Compared with representative benchmark models, the proposed method achieves lower carbon emissions and faster response while maintaining competitive economic performance. The results demonstrate that the proposed framework can improve reserve allocation quality and operational adaptability in HWS systems with high renewable penetration.

This paper focuses on the hydro–solar–wind complementary system, targeting two typical scenarios (both include PV output fluctuations driven by solar radiation intensity: wind power not participating in frequency regulation and wind power participating in frequency regulation) to conduct research on system frequency characteristic analysis and Proportional-Integral-Derivative (PID) controller parameter (K_P, K_I) optimization. By constructing a frequency response model that accounts for wind power penetration and output fluctuations, the dynamic regulation characteristics of the system under different scenarios are quantitatively analyzed. Given the limitations of single-objective optimization algorithms in balancing multiple performance indicators, the Decomposition-based Evolutionary Algorithm Guided by Growing Neural Gas (DEAGNG) multi-objective algorithm is introduced, with the Integral Time Absolute Error (ITAE) and the Integral Time Squared Error (ITSE) as the objective functions for parameter collaborative optimization. The results show that the optimization method based on DEAGNG can effectively improve the frequency stability of the system, reduce the mean value and maximum deviation of frequency fluctuations, and exhibit good adaptability in both scenarios. This study provides a multi-scenario-adapted PID parameter optimization scheme for hydro–solar–wind complementary systems, offering theoretical and technical support for achieving high-precision frequency control and enhancing the operational reliability of the system.

In the integrated hydro–wind–solar–storage system, the strong output fluctuations of wind and solar power, along with prominent system nonlinearity and time-varying characteristics, make it difficult for traditional PID controllers to achieve high-precision and robust dynamic control. This paper proposes a fuzzy fractional-order PID control strategy based on a multi-objective optimization algorithm, aiming to enhance the system’s frequency regulation, power balance, and disturbance rejection capabilities. The strategy combines the adaptive decision-making ability of fuzzy control with the high-degree-of-freedom tuning features of fractional-order PID. The multi-objective optimization algorithm AGE-MOEA-II is employed to jointly optimize five core parameters of the fuzzy fractional-order PID controller (K_p, K_i, K_d, λ, and μ), balancing multiple objectives such as system dynamic response speed, steady-state accuracy, suppression of wind–solar fluctuations, and hydropower regulation cost. Simulation results show that compared to traditional PID, single fractional-order PID, or fuzzy PID controllers, the proposed method significantly reduces system frequency deviation by 35.6%, decreases power overshoot by 42.1%, and improves renewable energy utilization by 17.3%. This provides an effective and adaptive solution for the stable operation of hydro–wind–solar–storage systems under uncertain and variable conditions.