电力系统高维目标协同优化决策及区间最优调度模型

With the grid integration of large-scale intermittent sustainable energy resources as well as more and more operational objectives involved in grid dispatch, these transitions and features have brought concerns and challenges for power system real-time dispatch problems. It is the aim of this research to develop an interval uncertainty based high-dimensional multiobjective synergistic optimization methodology for optimal equilibrium dispatch decision-making scheme with multi-uncertainty energy resources. This project first investigates an interval modeling approach for various stochastic and volatile renewable generations and loads with reasonable accuracy based on their prediction deviations, and then formulates a novel linearized model of interval load flow using uncertainty interval metrics. Furthermore, the theoretical studies on power system many-objective interval optimal power dispatch are implemented and proposed based on the above linearized load flow model, and the influence of increased number of optimization objectives on the real-time power dispatch solution would be further investigated. Moreover, this research will analyze the inherent coupling components and relationship among various operational objectives in power system dynamic dispatch optimization process, and then specifically formulate the dominant sorting metrics of elite frontier solutions for many-objective optimal power flow. Finally, with the introduction of equilibrium game and synergistic parallel search strategies, the systematized theoretical achievements on uncertainty optimal coordinated dispatch with the solution algorithms for many-objective optimization space would be obtained. The core technologies developed in this research not only could improve the system accommodation capability for randomness and volatility of large-scale wind, solar energy and electric vehicles, but also contribute towards the theoretical foundation of smart grid real-time coordinated dispatch and optimal decision-making scheme.

针对大规模间歇性可再生能源接入以及大量目标联合优化对电网实时调度决策带来的影响与挑战，本课题提出利用基于区间不确定性的高维目标协同优化来研究含多随机源的电力系统最优均衡调度决策问题。首先基于多种随机波动性能源及负荷的功率预测偏差建立具有合理精度的区间近似模型，并推导出基于区间不确定性度量的区间潮流线性化数学模型；在此基础上研究优化目标个数增加对实时调度决策的影响，并分析电网动态优化调度过程中各个运行目标的内在耦合关系与成分，提出一套针对高维多目标最优潮流前沿精英解集的支配性排序指标体系，从而推进和完善电力系统区间高维多目标调度优化的理论研究工作；最后借鉴均衡对策论与协同并行优化方法，获得面向高维目标空间的不确定性最优协调调度及其最优化求解算法的系统化理论成果。本项目研究工作将有助于提升系统对风光发电和电动汽车随机性的消纳能力，为智能电网实时协调调度优化与决策提供理论依据。

针对高比例间歇性可再生能源接入背景下智能电网分散式、多主体、多目标协调运行对系统调控与优化决策带来的影响与挑战，从“源-网-荷”互动协调的角度，开展高维目标能量协同调度与最优决策的架构、模型与算法研究。首先，构建了考虑高渗透率可再生能源、分布式储能与主动负荷响应的智能电网多目标能量管理及其滚动优化调控框架，深入分析了“源-网-荷”各个运行主体优化目标的协同调控机理与模型，提出了电网多目标经济/节能/减排发电调度的均衡决策模型及其帕累托求解算法，建立了多能源微网系统的分布式多目标互补协调调度模型，进一步地，从多样性负荷调控的角度，提出了考虑多异构家庭电量电价弹性的高维目标群体协同需求响应模型框架；其次，分析了优化目标个数逐步增加对动态调度决策的影响，并研究了不同能源形式供需主体的各个运行目标之间内在耦合关系与成分，在此基础上基于超立方几何学提出了一套针对高维目标前沿精英解集的支配性排序指标，并结合超平面空间变换及其投影模型，提出了一种面向高维目标空间的群体协同进化算法，来求解各种运行场景下电力系统多主体协同的滚动调度问题；最后，根据多种随机波动性能源及负荷的功率预测偏差建立了具有合理精度的区间近似模型，并基于不确定性度量与区间数序关系，将随机性优化目标函数与非线性区间约束条件转化为一种确定性多目标和线性潮流约束的智能配电网最优调度数学模型，进一步形成了电力系统多主体互动式随机调度方法及其高维目标协同优化决策的系统化理论成果。本项目研究工作将有助于提升系统对风光可再生能源发电和电动汽车充电负荷的消纳能力，为智能电网实时协调调度优化与决策提供理论依据。