An efficient substation placement and sizing strategy based on GIS using semi-supervised learning
An efficient substation placement and sizing strategy based on GIS using semi-supervised learning
复制标题
使用半监督学习的基于 GIS 的高效变电站布局和规模调整策略
DOI：
10.17775/cseejpes.2017.00800
复制
发表时间：
2018
影响因子：
7.1
通讯作者：
C. Jing
中科院分区：
文献类型：
--
作者：
Li Yu;Di Shi;Xiaobin Guo;J. Zhen;Guangyue Xu;Ganyang Jian;J. Lei;C. Jing
随着负荷和可再生能源渗透率的持续增长，变电站的优化布局和规模在配电系统规划中变得越来越重要。本文提出了一种改进的方法来解决变电站选址和规模的问题，基于地理信息和监督学习。该方法能以最小的投资和年运行费用优化变电站的位置、容量和供电范围。土地的资本成本增加了变电站布局问题的复杂性和难度，特别是对于高度发达的城市地区。本文提出了一个理论框架，以确定考虑土地成本的变电站的最佳位置。采用最先进的并行计算技术，使多个电压等级的变电站的协同优化，可以直接进行计算效率的方式。案例研究表明所提出的方法的有效性。
As load and renewable penetration continues to grow, optimal placement and sizing of substations is becoming increasingly important in distribution system planning. This paper presents an improved methodology to solve the substation siting and sizing problem based on geographic information and supervised learning. The proposed approach can optimize the locations, capacities, and power supply ranges of substations with minimum investment and annual operation costs. Capital cost of land adds complexity and difficulty to the substation placement problem, especially for highly developed urban areas. This paper presents a theoretical framework to determine the optimal location of substations considering the cost of land. The state-of-the-art parallel computing techniques are employed so that co-optimization for substations of multiple voltage levels can be directly conducted in a computational efficient way. Case studies are presented to demonstrate the effectiveness of the proposed approach.