基于监督学习约束线性化的资源协同优化配置在高比例新能源场站中应用研究

doi:10.19595/j.cnki.1000-6753.tces.251700

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摘要

为解决高比例新能源接入导致的系统安全稳定性下降问题,本文综合考虑小信号稳定性与暂态稳定性,同时保障系统经济最优,提出一种基于监督学习约束线性化的资源协同优化配置策略。首先,深入分析将新能源建模为电压控制型电流源后,对稳定性指标的影响机制;其次,构建含调相机与储能的协同配置优化模型;再次,引入基于监督学习的非线性约束线性化方法,将原问题重述为MILP（Mixed-Integer Linear Programming）模型,同时对拟合模型进行理论分析;最后,利用数学仿真模型进行验证,在全时段内系统稳定性指标均满足安全约束要求,gSCR（generalized Short Circuit Ratio）与SCC（Short Circuit Current）平均值分别增至3.48和5.95,且较阈值标准保留一定安全裕度;在此优化策略下,系统总经济成本显著降低,也有效平抑系统经济对参数变化的敏感性;本文所提模型具有线性收敛特性,可在32.67min内求取最优解,相较于其他模型,计算时间最高提升约7倍,拟合精度最高提升约5.6倍,各场景均无稳定性指标越限,且在大规模系统中仍有良好适用性。仿真结果表明,该资源协同优化配置方案,不仅提升了新型电力系统的稳定性与经济性,相较于其他模型还存有显著优势。

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关键词 ：高比例新能源, 小信号稳定性, 暂态稳定性, 监督学习, 调相机, 协同优化

Abstract：

With the continuous increase of high-penetration renewable energy, power system stability issues concerning small-signal and transient security have become increasingly prominent. Current studies critically lack a coordinated optimization framework that jointly enforces multiple stability constraints and integrates multiple types of devices. As a result, unmodeled stability metrics are often not validated for security compliance, posing potential operational risks, while device deployment remains largely confined to single-type configurations, hindering economic optimality. Moreover, existing transient analyses still model renewable generators as conventional impedance-voltage-source equivalents, which fail to accurately capture their true fault response characteristics and may lead to assessment results that deviate significantly from real behavior. Compounding these issues, the optimization models and solution algorithms employed often struggle to achieve an effective balance between computational efficiency and solution accuracy. To address these challenges, this paper proposes a coordinated resource allocation strategy based on supervised learning enabled constraint linearization.
Initially, the impact mechanism of modeling renewable energy as voltage-controlled current sources on stability indices is thoroughly analyzed. Subsequently, an optimization model for the coordinated configuration of synchronous condensers and energy storage is constructed. Furthermore, a supervised learning-based nonlinear constraint linearization method is introduced to reformulate the original problem as a MILP (Mixed-Integer Linear Programming) model, accompanied by theoretical analysis of the fitted model in terms of convergence of the linearization coefficients, model complexity, and approximation accuracy. Finally, mathematical simulations are conducted to validate the proposed optimization strategy.
Simulation results demonstrate that, throughout the entire time horizon, the system stability metrics satisfy all security constraints, with the average values of gSCR and SCC increased to 3.48 and 5.95, providing safety margins of 6.33% and 2.41% relative to their respective thresholds. Under this optimization strategy, the total system economic cost is significantly reduced, and the system's economic sensitivity to parameter variations is effectively mitigated. Moreover, the proposed model exhibits linear convergence and obtains the optimal solution within 32.67min. Compared with existing models, it achieves up to a 7-fold reduction in computational time and improves fitting accuracy by a factor of 5.6, with no violations of stability constraints observed across all test scenarios, demonstrating strong applicability and scalability in large-scale systems.
The following conclusions can be drawn from the analysis: 1) In terms of security and stability, the proposed strategy ensures that all system buses satisfy both small-signal and transient stability constraints throughout the entire scheduling horizon, with no violations observed. This effectively mitigates the safety risks associated with single-constraint optimization, where neglecting one stability dimension may compromise overall system security. 2) In terms of an economic perspective, the coordinated configuration significantly reduces the total system cost and eliminates resource allocation redundancy arising from decoupled planning. Furthermore, the resulting economic cost demonstrates strong robustness to variations in key parameters, such as renewable penetration levels and stability thresholds, rendering the approach well suited for electricity market operation scenarios which require comprehensive economic considerations. 3) Compared with existing methods, the proposed model demonstrates superior performance in convergence, solution accuracy, computational efficiency, and applicability; furthermore, the supervised learning-based constraint reconstruction step accounts for only 20% of the total computation time, imposing negligible overhead on solution efficiency.

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

收稿日期: 2025-10-09

PACS:

TM712

基金资助:

国家自然科学基金资助项目（62303183）

通讯作者: 铉子逸男,2002年生,硕士研究生,研究方向为新能源电力系统无功优化,新能源电力系统分析与控制。E-mail：1263759549@qq.com

作者简介: 李永刚男,1967年生,教授,博士生导师,研究方向为新能源电力系统分析与控制、大型同步电机故障诊断及分析等。E-mail：lygzxm0@163.com

引用本文:

铉子逸, 李永刚, 郭潇镁, 张峰睿, 周一辰. 基于监督学习约束线性化的资源协同优化配置在高比例新能源场站中应用研究[J]. 电工技术学报, 0, (): 20251700-. Xuan Ziyi, Li Yonggang, Guo Xiaomei, Zhang Fengrui, Zhou Yichen. Research on Resource Coordinated Optimal Allocation in High-Penetration Renewable Energy Power Stations Based on Supervised Learning-Constrained Linearization. Transactions of China Electrotechnical Society, 0, (): 20251700-.

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