利用机器学习将频率最低点约束纳入小型电力系统机组组合问题
Inclusion of Frequency Nadir constraint in the Unit Commitment Problem of Small Power Systems Using Machine Learning
AI总结：
针对清洁能源占比提升导致的电力系统停运后频率不稳定问题，提出用机器学习将非线性频率最低点约束纳入机组组合问题，经拉帕尔马岛电力系统仿真验证，该方法比传统解析线性化方法求解速度更快且频率响应质量达标。
摘要：
随着减少火力发电量、提升清洁能源占比的目标推进，电力系统因惯性水平下降，在发生停运事故后愈发容易出现频率不稳定问题。为解决这一问题，调度流程中正在纳入频率约束，以确保任何突发事故下的频率偏差都处于可容忍范围内。本文提出一种方法，借助机器学习将非线性频率最低点约束整合到机组组合问题中。首先生成合成训练数据集，随后采用两种经典机器学习方法，即logistic regression（逻辑回归）和support vector machine（支持向量机）来预测频率最低点。为将机器学习方法与传统的频率约束机组组合方法进行对比，研究针对拉帕尔马岛的电力系统开展了仿真，仿真对象既包括所提出的两种机器学习方法，也包括频率最低点的解析线性化公式。结果表明，采用基于机器学习的频率最低点约束的机组组合问题，求解速度远快于采用解析公式的情况，同时仍能在停运事故后达到可接受的频率响应质量。
英文摘要：
As the intention is to reduce the amount of thermal generation and to increase the share of clean energy, power systems are increasingly becoming susceptible to frequency instability after outages due to reduced levels of inertia. To address this issue frequency constraints are being included in the scheduling process, which ensure a tolerable frequency deviation in case of any contingencies. In this paper, a method is proposed to integrate the non-linear frequency nadir constraint into the unit commitment problem, using machine learning. First a synthetic training dataset is generated. Then two of the available classic machine learning methods, namely logistic regression and support vector machine, are proposed to predict the frequency nadir. To be able to compare the machine learning methods to traditional frequency constrained unit commitment approaches, simulations on the power system of La Palma island are carried out for both proposed methods as well as an analytical linearized formulation of the frequency nadir. Our results show that the unit commitment problem with a machine learning based frequency nadir constraint is solved considerably faster than with the analytical formulation, while still achieving an acceptable frequency response quality after outages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
