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风电变流技术 储能系统 ★ 5.0

考虑多状态不确定性的鲁棒机组组合:一种新模型与可扩展求解方法

Robust Unit Commitment With Multi-State Uncertainty: A Novel Formulation and Scalable Solution Method

作者 Jikeng Lin · Zihang Zeng · Lingjie Liu · Guangyuan Zhu · Fushuan Wen
期刊 IEEE Transactions on Sustainable Energy
出版日期 2025年6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 两阶段鲁棒机组组合模型 多状态不确定性集 风电功率输出 改进的不精确列与约束生成法 保守性缓解
语言:

中文摘要

为降低两阶段鲁棒机组组合模型调度方案的保守性,本文提出一种考虑风电出力不确定性的新型多状态不确定性集(MSUS)。该集合通过在不确定区间内引入多个离散状态值,提升不确定性建模分辨率,并结合嵌入多组转移约束的转移指标限制极端波动。状态值与转移指标通过分位数回归与马尔可夫链分析确定,有效排除低概率场景,显著降低保守性。为加速求解,本文还提出改进的不精确列与约束生成(II-C&CG)方法,采用自适应容差与精细回溯策略,在保证有限收敛的同时大幅缩短计算时间。算例验证了所提方法的有效性与优势。

English Abstract

To mitigate the conservatism of scheduling schemes derived from the two-stage robust unit commitment model (TS-RUCM), this paper proposes a novel multi-state uncertainty set (MSUS) considering the uncertain wind power output (WPO). The MSUS formulates WPO uncertainties with higher resolution by introducing multiple discrete state values within the uncertain interval. Additionally, the MSUS limits extreme fluctuations of WPO uncertainties among state values through transition indicators embedded in multiple transition constraints. The state values and transition indicators are calculated using the conditional quantile regression technique and Markov chain analysis. This systematic and scientific approach allows the MSUS to effectively exclude low-probability WPO scenarios, thereby significantly mitigating the conservatism of the scheduling schemes. Moreover, to accelerate the computation of the TS-RUCM based on the MSUS, this paper proposes an improved inexact column and constraint generation (II-C&CG) method. The II-C&CG method employs an adaptive tolerance strategy and refined backtracking to solve master and subproblems inexactly while ensuring finite convergence, significantly reducing computational time. Case studies demonstrate the effectiveness and advantages of both the MSUS and the II-C&CG method.
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SunView 深度解读

该研究提出的多状态不确定性建模方法对阳光电源的储能调度系统具有重要参考价值。可直接应用于PowerTitan大型储能系统的智能调度优化,通过引入多状态不确定性集和转移约束,提升系统对新能源波动的适应性。具体可优化ST系列储能变流器的调度策略,降低储能调度保守性,提高经济效益。同时该方法也可集成到iSolarCloud平台,为光储联合调度提供更精准的决策支持。研究中提出的改进求解算法可提升储能调度的实时性能,对构建更高效的储能管理系统具有启发意义。