原文信息
Load profiling and Monte Carlo simulation for load variety and variability in voltage optimization
原文链接：
https://www.sciencedirect.com/science/article/pii/S030626192402213X
Highlights
1) 有功无功负荷分析应对负荷多样
2) 蒙特卡罗模拟应对负荷多变
3) 有监督与无监督学习融合聚类，而非有监督归类独立于无监督分类
4) 探寻含负荷分析多场景评估的稳态应用
Abstract
Voltage optimization has increasingly turned to the demand side for greater flexibility, a pursuit complicated by variety and variability of the loads. To address these challenges and maximize demand-side potential, a load profiling method embedded in a Monte Carlo framework is proposed in this study. The variety of loads is captured by load profiling that delineates the power system’s operational boundaries by identifying typical consumption patterns, which is achieved via a novel clustering technique that uniquely combines supervised and unsupervised learning. Unlike existing combinations of the two learning algorithms that use unsupervised learning to set the classes and supervised learning to fill in them in two separate steps, the newly developed clustering integrates both unsupervised and supervised learning exclusively for clustering. The variability of loads is represented by the active – reactive load curves, sampled by the Monte Carlo simulation to create multiple scenarios for the coordinated dispatch of active and reactive powers. This multi-scenario voltage optimization, enabled by the new load profiling technique, aims to enhance a wide range of power system operation and planning applications, particularly voltage evaluation and reactive power planning, which are utilized here to demonstrate the effectiveness of the proposed method.
Keywords
Load profiling
Monte Carlo simulation
Variability
Variety
Voltage optimization
Graphics
图 1. 有无监督融合聚类与先无监督分类再有监督归类的对比：所提算法以无监督学习预聚类，再以有监督学习校正类边界，图中双色球即表示有监督校正类边界后另划他类的样本。这一开放框架可适配不同的有、无监督学习算法，本文所用的分别是支持向量机与k-均值。
图 2. 样本近似度的评判标准：兼顾负荷样本之间大小的接近与形状的相似，二者分别以欧氏距离与余弦相似度衡量。
作者简介
作者简介：
林腾，博士生，就读于上海交通大学，2018、2021年从同校获得学士、硕士学位，从事含新能源电力系统运行优化、电力系统可靠性等领域研究。
尚策，副教授，2017年始从教于上海交通大学电气工程系，2011年于浙江大学获学士学位，2016年于新加坡国立大学获博士学位，关注能源电力系统、储能与海上风电的运行规划与可靠性评估。
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