基于全过程频率响应与电压稳定性的异构资源电力系统协同优化策略

doi:10.19595/j.cnki.1000-6753.tces.260650

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Abstract

With the continuous increase in high penetration of renewable energy, system frequency and voltage stability issues have become increasingly prominent. Regarding frequency stability, existing studies often lack modeling of the whole-process frequency response and overlook secondary frequency regulation and frequency regulation capacity reserves, while failing to distinguish the differential response characteristics of heterogeneous resources. In terms of voltage stability, approximating the stability of new power systems using the gSCR index poses risks related to voltage dip safety thresholds. Furthermore, there is a lack of collaborative optimization mechanisms for heterogeneous resources under multi-constraint conditions. Current research has neither established detailed dynamic models for the support characteristics of various renewable sources nor thoroughly analyzed the impact of heterogeneous resource dynamics on frequency and voltage stability constraints, along with their coupling effects. Meanwhile, existing linear fitting methods struggle to balance computational efficiency with fitting accuracy, thereby constraining the performance of overall optimization models. To address these gaps, this paper proposes a collaborative optimization strategy for heterogeneous resources based on full-process frequency response and voltage stability.
Initially, dynamic modeling is conducted to analyze the frequency support characteristics of various renewable energy sources. Subsequently, based on the heterogeneous resource system, full-process frequency response indices and stability constraints are derived, alongside voltage security indices and constraints grounded in Site-Dependent Short-Circuit Ratio(SDSCR). Furthermore, a two-stage distributionally robust optimization model is constructed to balance system robustness and economic efficiency, while incorporating a supervised learning-based linear fitting method to linearize the original high-dimensional nonlinear constraints. Finally, mathematical simulations are conducted to validate the proposed optimization strategy.
Simulation results demonstrate that under the worst-case probability distribution, the strategy significantly enhances system frequency and voltage security levels, ensuring sufficient margins for all indices relative to their thresholds. Concurrently, the total system operating cost is substantially reduced, and the renewable energy accommodation rate reaches up to 88.65%. Coupling analysis indicates that when metric ?f_nadir is below 0.75Hz, the voltage stability constraint can be omitted, as the frequency stability constraint alone suffices to maintain adequate voltage strength in high renewable penetration system. Furthermore, the proposed supervised learning-based fitting method determines the optimal linear coefficients within 8.96min with a fitting error constrained to less than 0.5%, thereby ensuring the overall performance of the optimization strategy.
The following conclusions can be drawn from the analysis: (1) Regarding frequency stability enhancement, the full-process frequency response model of heterogeneous resources ensures sufficient frequency regulation capacity, ultimately achieving zero steady-state error. Notably, the dynamic support characteristics of renewable energy sources contribute significantly more to the improvement of frequency stability performance. (2) In terms of voltage stability enhancement, a voltage stability index constructed based on the SDSCR effectively mitigates the weakening effect of renewable energy integration on node voltages, ensuring excellent safety levels for bus voltages across all time periods. Among various measures, synchronous condensers demonstrate a particularly significant role in voltage support. (3) Concerning economic enhancement, the collaborative optimization of heterogeneous resources yields substantial economic benefits while avoiding redundant configuration and energy waste. (4) With respect to indicator coupling, frequency constraints are the dominant factor determining system stability and exert a more pronounced impact on economic costs. Consequently, practical planning necessitates an optimal trade-off between security/stability and economic efficiency. (5) Regarding model advantages, the proposed supervised learning-based linear fitting model achieves a balance between computational efficiency and fitting accuracy, thereby endowing the overall optimization strategy with applicability to large-scale systems and scalability under multi-constraint conditions.

Key words： High penetration renewable energy small-signal stability transient stability supervised learning synchronous condenser coordinated optimal allocation

Received: 29 April 2026

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TM712

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	Xuan Ziyi
	Li Yonggang
	Li Runqian
	Guo Xiaomei
	Zhou Yichen

Cite this article:

Xuan Ziyi,Li Yonggang,Li Runqian等. Coordinated Optimization Strategy for Heterogeneous Resources in Power Systems Based on Full-Process Frequency Response and Voltage Stability[J]. Transactions of China Electrotechnical Society, 0, (): 650-.

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