Multi-Scenario Optimization of PID Controllers for Hydro-Wind-Solar Complementary Systems Based on the DEAGNG Algorithm

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 (KP, KI) 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.