| 摘要: |
| 在配电网分布式可再生能源(Renewable Distributed Generation, RDG)优化规划问题上，考虑可控分布式电源和可中断负荷的调度运行情况，建立RDG双层优化模型。上层模型以配电网系统电压偏移最小、网络损耗最小为目标，求解RDG接入配电网的最优位置和容量。下层模型以配电网发电综合运行成本最小为目标，求解各时段可控分布式电源和可中断负荷最优出力安排。利用场景分析方法处理多种分布式可再生能源优化中的不确定因素。为减少模型计算量，采用K-means聚类算法获取典型场景。最后，采用改进的自适应遗传算法求解，以IEEE-33节点网络系统为例对所提模型与方法的有效性进行验证。仿真结果表明，该方法能够有效提高RDG优化方案在系统实际运行中的适用性。 |
| 关键词: 可再生能源 可控分布式电源 场景分析法 改进自适应遗传算法 双层优化模型 |
| DOI：10.19783/j.cnki.pspc.191240 |
| 投稿时间：2019-10-11修订日期：2019-11-23 |
| 基金项目: |
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| A bi-level optimization planning method for a distribution network considering different types of distributed generation |
| SONG Qianyun |
| (State Grid Fujian Electric Power Economic Research Institute, Fuzhou 350001, China) |
| Abstract: |
| To optimize renewable distributed generation in a distribution network, a bi-level optimization model is established considering the scheduling of controllable distributed generation and interruptible load. The upper level model aims to solve the location and capacity allocation of renewable distributed generation connected to the network with the goal of minimum voltage offset and minimum network loss in the distribution network system. The lower model solves the optimal output arrangement of controllable distributed generation and interruptible load in each period with the goal of minimum comprehensive operational cost of the distribution network. Scenario analysis is used to deal with the uncertainty in the optimization of renewable distributed generation. In order to reduce the amount of calculation, the K-means clustering algorithm is used to obtain typical scenes. Finally, an improved adaptive genetic algorithm is used to solve the model. The IEEE-33 node network is used as an example to analyze and calculate to verify the effectiveness of the proposed model and method. Results show that the method can effectively improve the applicability of the RDG optimization scheme in actual operation of the distribution network. |
| Key words: renewable distributed generation controllable distributed generation scenario analysis improved adaptive genetic algorithm bi-level optimization model |
