王小明,徐斌,倪静怡,宋浩杰,陈智华,吴红斌
WANG Xiaoming,XU Bin,NI Jingyi,SONG Haojie,CHEN Zhihua,WU Hongbin

• 1:国网安徽省电力有限公司电力科学研究院
• 2:国网安徽省电力有限公司黄山供电公司
• 3:合肥工业大学电气与自动化工程学院

摘要(Abstract)：

针对柔性互联配电网中大规模分布式电源及负荷并网后带来的不确定性的问题，综合考虑了多类型资源的出力特性，提出了融合多场景分析的多时间尺度优化调度模型。首先，运用多场景分析技术，对风光发电及负荷的不确定性进行了模拟分析。其次，在日前调度阶段，考虑储能充放电特性、微型燃气轮机的启停特性及电容器组的投切状态，建立了基于DRO（分布鲁棒优化）的两阶段优化调度模型，在日内阶段建立MORO（多目标滚动优化）模型。然后，利用1-范数和∞-范数寻找最恶劣场景下的概率分布，并采用列与约束生成算法与多目标粒子群算法分别对日前与日内调度模型求解。最后，在改进的IEEE 28节点算例系统中进行仿真验证，结果表明，所提调度模型能够降低柔性配电网在最恶劣条件下的运行成本，同时提高供电的可靠性。
To address the uncertainties introduced by the integration of large-scale distributed generation(DG) and loads in flexible interconnected distribution networks, this paper proposes a multi-timescale optimal scheduling model incorporating multi-scenario analysis while considering the output characteristics of diverse resource types. First, multi-scenario analysis is employed to simulate and analyze the uncertainties associated with wind and PV power generation and load. For the day-ahead scheduling stage, a two-stage distributionally robust optimization(DRO) model is established, accounting for the charging/discharging characteristics of energy storage, the startstop behavior of micro gas turbines, and the switching states of capacitor banks. For the intraday scheduling stage, a multi-objective rolling optimization(MORO) model is formulated. Subsequently, the 1-norm and ∞-norm are employed to identify the worst-case probability distribution, while the column-and-constraint generation(C&CG) algorithm and multi-objective particle swarm optimization(MOPSO) are applied to solve the day-ahead and intraday scheduling models, respectively. Finally, simulation tests on a modified IEEE 28-node test system demonstrate that the proposed scheduling model effectively reduces operational costs under extreme conditions while enhancing power supply reliability in flexible distribution networks.

关键词(KeyWords)： 多时间尺度;分布鲁棒优化;不确定性;智能软开关
multi-timescale;DRO;uncertainty;intelligent soft switching

Abstract:

Keywords:

基金项目(Foundation): 安徽省高校协同创新项目(GXXT-2022-023);; 国网安徽省电力有限公司科技项目(B3120524001L)

作者(Author): 王小明,徐斌,倪静怡,宋浩杰,陈智华,吴红斌
WANG Xiaoming,XU Bin,NI Jingyi,SONG Haojie,CHEN Zhihua,WU Hongbin

DOI: 10.19585/j.zjdl.202509010

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