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风电变流技术 储能系统 ★ 5.0

用于含不确定风电的电力系统交流网络约束机组组合的两阶段自适应鲁棒模型

Two-Stage Adaptive Robust Model for AC Network-Constrained Unit Commitment in Power Systems With Uncertain Wind Power

作者 Siqi Wang · Xin Zhang · Min Du · Wei Pei
期刊 IEEE Transactions on Sustainable Energy
出版日期 2025年2月
技术分类 风电变流技术
技术标签 储能系统
相关度评分 ★★★★★ 5.0 / 5.0
关键词 风电不确定性 交流网络约束 机组组合模型 Benders分解 系统安全与经济
语言:

中文摘要

随着风电大规模接入电力系统,其固有的不确定性与波动性对系统运行安全构成严峻挑战。传统鲁棒优化方法仅考虑最恶劣场景,导致决策过于保守,且对交流网络约束考虑不足。为此,本文提出一种新型自适应鲁棒交流网络约束机组组合(AC-NCUC)模型,兼顾风电出力不确定性与交流网络安全。通过构建凸多面体不确定性集刻画风电不确定性,并可通过调节其规模控制决策保守性。结合Benders分解法与牛顿-拉夫森法求解该模型,获得最优调度方案。基于改进IEEE 6节点与RTS 79系统的仿真结果验证了所提方法的合理性与有效性,该模型在保障系统安全的同时兼顾经济性。

English Abstract

With wind power being extensively integrated into power systems, its inherent uncertainty and variability pose significant challenges to the power system operational security. Traditional robust optimization methods capture the worst-case scenario, which results in overly conservative decisions, with insufficient considerations on AC network constraints in power systems. To overcome this issue, this paper proposes a novel adaptive robust AC network-constrained unit commitment (AC-NCUC) model that considers both the AC network security and the uncertainty of wind power output in power systems. More specifically, a convex polyhedral uncertainty set is constructed to characterize the uncertain wind power output. Here, the conservativeness of UC dispatch decisions can be adjusted by modifying the size of the convex polyhedral uncertainty set. Then, we combine Benders’ decomposition and Newton-Raphson methods to solve the AC-NCUC model for the optimal dispatch decisions. Simulation results on the modified IEEE 6-bus and IEEE RTS 79 systems validate the rationality and validity of our proposed approach. The proposed AC-NCUC model effectively maintains the system security while ensuring economic effectiveness.
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SunView 深度解读

该研究提出的自适应鲁棒AC-NCUC模型对阳光电源的储能系统和大型电站解决方案具有重要参考价值。其优化算法可应用于ST系列储能变流器的调度控制和PowerTitan系统的容量配置,提升系统经济性。特别是在风电不确定性场景下的交流网络约束处理方法,可用于完善储能系统的GFM控制策略,增强系统稳定性。该模型的自适应特性也可集成到iSolarCloud平台,优化储能调度和经济效益。建议在ST2752XP等大功率储能产品中验证该算法的实际效果,进一步提升阳光储能产品在风电配套领域的竞争力。