| 大停电后含分布式电源的电网分区及负荷恢复方案 |
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| 引用本文： | 刘仲,牟龙华,杨智豪.大停电后含分布式电源的电网分区及负荷恢复方案[J].继电器,2015,43(22):55-61. |
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| 作者姓名： | 刘仲 牟龙华 杨智豪 |
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| 作者单位： | 同济大学电气工程系，上海 201804;同济大学电气工程系，上海 201804;同济大学电气工程系，上海 201804 |
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| 基金项目： | 国家自然科学基金资助项目(51407128)；浙江省科技计划项目(2013C01063) |
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| 摘 要： | 含有分布式电源(Distributed Generation,DG)的电网在发生大停电事故后,为提高系统恢复效率,一般将电网进行合理分区,并采用分区并行恢复的方案。首先运用灰色决策理论选取了各分区的黑启动电源,结合最短路径快速算法(Shortest Path Faster Algorithm,SPFA)搜索各黑启动电源到待恢复节点的最优恢复路径;然后据此建立了兼顾恢复时间性和安全性的最优分区多目标优化模型,并采用多目标粒子群优化算法(Multi-objective Particle Swarm Optimization,MOPSO)对模型进行了求解。基于所得的最优分区方案,以构建初步网架为目标,结合遗传算法对各分区内的负荷恢复问题进行了优化。IEEE 30节点算例验证了所提方案的有效性。
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| 关 键 词： | 黑启动 分布式电源 系统分区 多目标粒子群算法 负荷恢复 |
| 收稿时间： | 2015/4/19 0:00:00 |
Scheme for partitioning and load restoration of power grid containing
distributed generation after blackout |
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| LIU Zhong,MU Longhua and YANG Zhihao.Scheme for partitioning and load restoration of power grid containing
distributed generation after blackout[J].Relay,2015,43(22):55-61. |
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| Authors: | LIU Zhong MU Longhua and YANG Zhihao |
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| Affiliation: | Department of Electrical Engineering, Tongji University, Shanghai 201804, China;Department of Electrical Engineering, Tongji University, Shanghai 201804, China;Department of Electrical Engineering, Tongji University, Shanghai 201804, China |
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| Abstract: | After blackout, in order to improve the efficiency of system restoration for power grid containing distributed generation (DG), this paper partitions the grid into several subsystems and restores them in parallel. Firstly, black-start sources for each subsystem are selected by employing grey decision theory, and optimal restoration paths from black-start sources to candidate nodes are obtained by utilizing shortest path faster algorithm (SPFA). Secondly, multi-objective optimization model for optimal partitioning, which considers rapidity and security of restoration, is established. Then, the model is solved by multi-objective particle swarm optimization (MOPSO). Afterwards, for the purpose of reconfiguring primary skeleton network, load restoration inside each subsystem is optimized by adopting genetic algorithm (GA) based on the optimal partitioning scheme acquired previously. The effectiveness of the proposed scheme is validated by the simulation results of IEEE 30-bus system. |
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| Keywords: | black start distributed generation system partitioning multi-objective particle swarm optimization load restoration |
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