Electric Power Engineering Technology
2096-3203
2019
38
4
48
55
10.12158/j.2096-3203.2019.04.007
article
考虑电能替代负荷接入的配电网鲁棒重构优化
Robust network reconfiguration optimization in distributionnetwork with power energy alternatives integration
为治理大气污染、保护环境，涵盖电采暖和电动汽车的电能替代负荷将越来越多地接入配电网中，配电网的供电能力受到显著影响，传统配电网运行优化方法须要改进。基于电采暖、电动汽车负荷的出力模型，采用拉丁超立方抽样、Cholesky分解和同步回代削减相结合的方法实现概率多场景的快速生成。基于生成的概率多场景，将鲁棒优化和随机规划方法相结合，采用场景分析方法，在满足鲁棒约束条件下，以网络损耗和负载均衡度为优化目标，建立考虑电能替代接入的配电网鲁棒重构优化模型，采用蚁群算法对模型进行求解。最后通过算例验证了文中重构方法能够有效提升含大规模电能替代负荷配电网的供电能力。
In order to control air pollution and protect the environment, more and more power energy alternatives covering electric heating and electric vehicles, emerge in distribution networks. Power supply capability of distribution network receives a significant effect and the traditional distribution network operation optimization method needs to be improved. According to the randomness characteristics of electric heating and electric vehicles, the methods of Latin super cube sampling, Cholesky decomposition and synchronous back generation reduction are adopted to realize the rapid generation of probability multiple scenes. Based on the probability multiple scenes, combining robust optimization and stochastic programming method, the scenario analysis method is accepted. With satisfying robust constraints, taking the optimal expectation values of running cost and load balancing degree as objectives, robust network reconfiguration model in distribution network with power energy alternatives integration is built. Ant colony algorithm is used to solve the model. Finally, a case study is given to verify that the proposed method can effectively improve the power supply capacity of the distribution network with large scale power energy alternatives integration.
电能替代;拉丁超立方抽样;概率多场景;鲁棒重构优化;蚁群算法
power energy alternatives;Latin hypercube sampling;probabilistic multi-scene;robust network reconfiguration optimization;ant colony algorithm
杨烁,杨卫红,刘艳茹,王云飞,侯佳,姜世公
YANG Shuo, YANG Weihong, LIU Yanru, WANG Yunfei, HOU Jia, JIANG Shigong
dlgcjs/article/abstract/181115593