| 引用本文: | 毛 荀,董王朝,王京景,等.计及恢复进程影响的电网分区鲁棒优化模型及算法[J].电力系统保护与控制,2025,53(17):79-90.[点击复制] |
| MAO Xun,DONG Wangchao,WANG Jingjing,et al.Robust optimization model and algorithm for power grid partitioning considering the impact of system recovery process[J].Power System Protection and Control,2025,53(17):79-90[点击复制] |
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| 摘要: |
| 合理、科学的电网分区可显著加快系统恢复进程及提升恢复的成功率。对新型电力系统下计及系统恢复进程影响的电网分区鲁棒优化模型及算法进行研究。首先，构建了以系统恢复过程的停电损失、分区联络线功率及分区恢复时间差的加权和最小为目标函数，并考虑相关完备约束条件的电网分区鲁棒优化模型。该模型考虑了频率及电压调节容量约束和分区问题的新能源出力不确定性，使得分区优化模型更加接近于工程实际，同时基于此所得到的分区方案也更加合理。由于该模型目标函数中包括分区恢复过程的节点负荷恢复时间变量而难以直接求解，提出了一种有效的分解求解策略。即将模型转换为两层优化模型，上层模型为既定节点负荷恢复时间的分区优化鲁棒模型，采用约束生成法(constraint generation, CG)进行求解；下层模型为既定分区方案的分区近似恢复模型，采用列与约束生成法(column and constraint generation, C&CG)进行求解。基于此，通过上下层模型间交互迭代而实现问题的最终求解。该求解算法实现了分区优化与系统恢复两个相互耦合子问题的有效求解。算例及实际系统验证了模型及算法的有效性和先进性。 |
| 关键词: 新型电力系统 电网分区优化 系统恢复进程 鲁棒优化 分解求解方法 |
| DOI：10.19783/j.cnki.pspc.240904 |
| 投稿时间：2024-07-12修订日期：2025-02-19 |
| 基金项目:国家自然科学基金项目资助(51177107)“基于图论的智能电网最优电力孤岛形成模型和算法”；国网安徽省电力有限公司科技项目资助(521205230013)“基于储能和新能源的区域电网黑启动及恢复关键技术研究” |
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| Robust optimization model and algorithm for power grid partitioning considering the impact of system recovery process |
| MAO Xun1,DONG Wangchao1,WANG Jingjing1,LÜ Kai1,TANG Wei1,YU Dengyan1,HUANG Kai2,LI Tangming3,LIN Jikeng3 |
| (1.State Grid Anhui Electric Power Corporation Research Institute, Hefei 230601, China; 2. Anhui Jiyuan Software Limited
Company, Hefei 230093, China; 3. College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China) |
| Abstract: |
| Rational and systematic grid partitioning can significantly accelerate system recovery and improve restoration success rates. This paper investigates a robust optimization model and algorithm for power grid partitioning that accounts for the impact of the recovery process in new power systems. First, a robust optimization model for grid partitioning is constructed, with the objective of minimizing a weighted sum of outage losses during recovery, tie-line power flows between partitions, and differences in partition restoration times, while incorporating comprehensive operational constraints. The model also considers frequency and voltage regulation constraints, and uncertainties in renewable energy output within grid partitions, making it more consistent with engineering practice and yielding more practical partitioning schemes. Since the objective function includes node load restoration time variables in the recovery process, which makes direct solution difficult, an effective decomposition-based solution strategy is proposed. Specifically, the model is reformulated as a two-level optimization framework: the upper-level model is a robust partitioning optimization problem with given node load restoration times, solved using the constraint generation method (CG); the lower-level model is an approximate recovery optimization model for a given partition scheme, solved using the column and constraint generation method (C&CG). This solution algorithm effectively addresses the two interdependent subproblems of partitioning optimization and system recovery. Case studies and practical system tests verify the effectiveness and superiority of the proposed model and algorithm. |
| Key words: new power system grid partitioning optimization system recovery process robust optimization decomposition-based solution method |
