| 引用本文: | 王光瑞,李正烁,刘聪聪.柔性负荷聚合灵活性的多时间颗粒度鲁棒评估方法[J].电力自动化设备,2023,43(7): |
| WANG Guangrui,LI Zhengshuo,LIU Congcong.Multi-time granularity robust evaluation method for flexible load aggregation flexibility[J].Electric Power Automation Equipment,2023,43(7): |
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| 摘要: |
| 面向柔性负荷虚拟电厂(FLVPP)，提出了包含日内滚动时间窗-调度周期-分钟级的多时间颗粒度的动态聚合灵活性的鲁棒评估方法。分析了多时间颗粒度评估的必要性，构造了最大化多个时段聚合灵活性的鲁棒评估模型，考虑了多时段耦合、电力网络拥塞、负荷运行状态在调度周期内发生变化以及时延响应的影响。该模型属于决策依赖的不确定性优化模型，可通过数学变换转化为常规两阶段鲁棒优化问题。针对第二阶段问题存在的二进制变量，设计了基于嵌套列和约束生成算法的迭代求解策略。通过算例分析验证了相比于现有常见方法，所提方法可以更加准确地评估FLVPP的聚合灵活性，同时验证了在评估聚合灵活性时考虑电力网络约束的必要性，并展示了算法性能。 |
| 关键词: 柔性负荷 虚拟电厂 鲁棒优化 多时间颗粒度 决策依赖不确定性 嵌套列和约束生成算法 |
| DOI：10.16081/j.epae.202211019 |
| 分类号:TM73 |
| 基金项目:国家自然科学基金资助项目(52007105)；国家重点研发计划项目(2019YFE0118400-1)；国网山东省电力有限公司科技项目(SGSDJY00NYJS2100050)；新型电力系统运行与控制全国重点实验室开放基金资助项目(SKLD22KZ08) |
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| Multi-time granularity robust evaluation method for flexible load aggregation flexibility |
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WANG Guangrui1,2, LI Zhengshuo1,2, LIU Congcong1,2
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1.School of Electrical Engineering, Shandong University, Jinan 250061, China;2.Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan 250061, China
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| Abstract: |
| For flexible load virtual power plant(FLVPP),the robust evaluation method of dynamic aggregation flexibility containing intra-day rolling time window, dispatching cycle, and minute-level multi-time granularity is proposed. The necessity of multi-time granularity evaluation is analyzed. The robust evaluation model for maximizing multi-time period aggregation flexibility is constructed, which takes into account the effects of multi-time period coupling, power network congestion, variation of load operating state during the dispatching period, and time-delay response. This model is a decision-dependent uncertainty optimization model, which can be transformed into a conventional two-stage robust optimization problem using mathematical transformation, and an iterative solution strategy based on nested column-and-constraint generation algorithm is designed for the problem of binary variables existing in the second-stage. Case study analysis verifies that the proposed method can evaluate the aggregation flexibility of FLVPP more accurately than the existing methods. Meanwhile, the necessity of considering the power network constraint in evaluating the aggregation flexibility is verified, and the computational performance of the algorithm is demonstrated. |
| Key words: flexible load virtual power plants robust optimization multi-time granularity decision-dependent uncertainty nested column-and-constraint generation algorithm |
