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1.电力传输与功率变换控制教育部重点实验室(上海交通大学),上海市 200240
2.上海非碳基能源转换与利用研究院,上海市 200240
张锞(1998—),女,博士研究生,主要研究方向:电力系统优化运行、电力市场。E-mail:kelsey_zhang@sjtu.edu.cn
王旭(1989—),男,通信作者,博士,副教授,博士生导师,主要研究方向:电力系统优化运行、智能电网、新能源、弹性配电网等。E-mail:wangxu1989@sjtu.edu.cn
杨宏坤(1999—),男,硕士研究生,主要研究方向:电力系统优化运行。E-mail:yanghongkun@sjtu.edu.cn
收稿:2023-07-20,
修回:2023-10-20,
网络出版:2024-01-15,
纸质出版:2024-04-10
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张锞,王旭,杨宏坤等.数据中心集群灵活边界下电力系统分布鲁棒优化调度方法[J].电力系统自动化,2024,48(7):235-247.
ZHANG Ke,WANG Xu,YANG Hongkun,et al.Distributionally Robust Optimal Scheduling Method for Power System Under Flexibility Boundaries of Data Center Clusters[J].Automation of Electric Power Systems,2024,48(7):235-247.
张锞,王旭,杨宏坤等.数据中心集群灵活边界下电力系统分布鲁棒优化调度方法[J].电力系统自动化,2024,48(7):235-247. DOI: 10.7500/AEPS20230720018.
ZHANG Ke,WANG Xu,YANG Hongkun,et al.Distributionally Robust Optimal Scheduling Method for Power System Under Flexibility Boundaries of Data Center Clusters[J].Automation of Electric Power Systems,2024,48(7):235-247. DOI: 10.7500/AEPS20230720018.
分布式资源的广泛接入与风、光等不可调资源的波动性使电力系统运行调控的难度显著增加。文中研究可再生能源不确定性影响下的分布式集群资源优化调度策略,挖掘多种可调资源灵活性,提高不可调资源利用率。为实现分布式可调资源的灵活调控,首先,提出了数据中心与光储集群聚合体的可调潜力时变边界计算方法,保证其聚合最优性与分解可行性。然后,基于历史数据构建契合风电随机特性的∞-Wasserstein模糊集,该方法具有良好的样本外信息描述能力。最后,提出自适应多面逼近方法解决分布鲁棒优化问题固有的无限维求解难题,将两阶段分布鲁棒调度模型转化为有限维问题以实现快速求解,并基于修改的IEEE-RTS 24节点系统仿真验证了所提方法的有效性。
The widespread integration of distributed resources and the volatility of uncontrollable resources such as wind and solar power pose significant challenges to the operation and control of power systems. This paper investigates optimal scheduling strategies for distributed cluster resources under the uncertainty of renewable energy
exploits the flexibility of various controllable resources
and enhances the utilization rate of centralized uncontrollable resources. To achieve the flexible control of distributed controllable resources
this paper first proposes a time-variant adjustable potential boundary computation method for aggregating data centers and photovoltaic-energy storage clusters
ensuring their aggregation optimality and decomposition feasibility. Furthermore
the ∞-Wasserstein fuzzy set is constructed based on historical data to capture the stochastic characteristics of wind power
providing reliable out-of-sample guarantee. Finally
an adaptive polyhedral approximation method is proposed to address the inherent infinite-dimensional solving challenge in the distributionally robust optimization problem. The two-stage distributionally robust scheduling model is transformed into a finite-dimensional problem to achieve the rapid solution. The effectiveness of the proposed method is verified based on the simulation on the modified IEEE-RTS 24-bus system.
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