低保守性自适应鲁棒优化及其在含大规模风电电网调度中的应用

项目名称： 低保守性自适应鲁棒优化及其在含大规模风电电网调度中的应用

项目编号： No.61503211

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 自动化技术、计算机技术

项目作者： 张玉利

作者单位： 清华大学

项目金额： 21万元

中文摘要： 具有强不确定性和波动性的风电大规模并网，对电网安全经济运行带来了新挑战。消纳大规模风电的一个重要途径在于提升电网应对不确定性的能力。现有随机调度方法计算复杂，并依赖于风电功率的精确概率分布函数；而可计算性强、不依赖于精确分布函数的鲁棒方法往往过于保守。为此，项目提出一种低保守性自适应鲁棒优化方法，并应用于电网机组组合和经济调度问题。低保守性来自于不确定性集的精炼和新型鲁棒模型的构建；自适应性在于调度决策对不确定性风电功率的动态响应。针对不确定性的表示问题，提出数据驱动的不确定性集构造方法，给出其概率保证，并应用于不确定性风电功率的表示。利用具有概率保证的不确定性集，提出融合概率信息的低保守性自适应鲁棒模型，并应用于机组组合—经济调度问题，进而研究基于策略的分层算法。最后在东北电网调度决策中进行应用验证。项目研究成果将为电网调度提供安全可行、实用有效的决策支持，提升电网消纳大规模风电的能力。

中文关键词： 不确定优化；混合整数规划；区间优化

英文摘要： Due to the significant uncertainty and variability of wind energy, its rapid growth brings great challenges for the secure and economic operation of power systems. It is critical to enhance the ability of system scheduling under uncertainty for handling the wind power integration problem. The existing stochastic optimization approach is computationally intensive and depends on the accurate probability distribution of the uncertainty. Although the computable robust optimization approach is independent of the probability distribution, it always faces the challenge on its over conservatism. This project aims to propose a less conservative adaptive robust optimization method and applies it to the unit commitment and economic dispatch (UC-ED) problem. Its less conservativeness depends on well-designed uncertainty sets and robust models, and its adaptability refers to the dynamic response to uncertain wind energy. To construct proper uncertainty sets with probability guarantee, we present a data-driven method and apply it to characterizing the correlation of wind uncertainty. Based on the constructed uncertainty sets, a probability based less conservative robust model is proposed and used to solve the UC-ED problem. The project will further design policy based multi-level solution method. The proposed models and methods will be validated by data from the northeast power grid. The research results will provide a set of secure and efficient decision support for power systems scheduling, and enhance the ability of power systems to integrate wind energy.

英文关键词： uncertainty optimization;mixed integer programming;interval optimization