【摘要】：穩(wěn)定約束最優(yōu)潮流是電力系統(tǒng)運(yùn)行與控制決策中的重要研究課題,它能夠在最小化系統(tǒng)運(yùn)行成本的同時(shí),通過調(diào)整穩(wěn)態(tài)運(yùn)行點(diǎn)提升系統(tǒng)受擾后的動態(tài)性能,包括系統(tǒng)的暫態(tài)穩(wěn)定性和短期電壓穩(wěn)定性。穩(wěn)定約束最優(yōu)潮流在數(shù)學(xué)上屬于動態(tài)優(yōu)化問題,即含有微分代數(shù)方程組約束條件的非線性規(guī)劃問題,在涉及含復(fù)雜模型的大規(guī)模電力系統(tǒng)、長仿真時(shí)間窗口和多預(yù)想故障時(shí),其求解過程計(jì)算時(shí)間長、耗用內(nèi)存多,計(jì)算復(fù)雜性是該問題研究的主要理論和技術(shù)障礙。本文著重研究了基于數(shù)值優(yōu)化理論和高性能計(jì)算技術(shù)高效求解穩(wěn)定約束最優(yōu)潮流問題的優(yōu)化算法及其并行化實(shí)現(xiàn),主要研究內(nèi)容及其學(xué)術(shù)成果包括： 1)提出了統(tǒng)一考慮電力系統(tǒng)暫態(tài)穩(wěn)定和短期電壓穩(wěn)定約束的穩(wěn)定約束最優(yōu)潮流模型,給出了其基于動態(tài)優(yōu)化問題的數(shù)學(xué)模型。同時(shí)針對復(fù)雜電力設(shè)備元件的動態(tài)模型集成問題,基于面向?qū)ο笤O(shè)計(jì)和自動微分技術(shù),提出了應(yīng)用于穩(wěn)態(tài)和暫態(tài)分析的系統(tǒng)化復(fù)雜模型集成方法,進(jìn)而設(shè)計(jì)并實(shí)現(xiàn)了應(yīng)用于穩(wěn)定約束最優(yōu)潮流的模塊化框架,提升了其算法實(shí)現(xiàn)的靈活性,拓展了該優(yōu)化模型的應(yīng)用前景。 2)針對動態(tài)優(yōu)化問題的兩個(gè)算法階段,即微分代數(shù)方程組的轉(zhuǎn)化階段和非線性規(guī)劃問題的求解階段,提出了基于直接多重打靶法和簡約空間內(nèi)點(diǎn)法的兩階段數(shù)值優(yōu)化算法。與已有研究成果相比,該優(yōu)化算法能夠充分利用穩(wěn)定約束最優(yōu)潮流的問題特點(diǎn)和結(jié)構(gòu)性質(zhì),從而顯著提高優(yōu)化算法的收斂性能和計(jì)算效率。通過一系列大規(guī)模電力系統(tǒng)算例的數(shù)值實(shí)驗(yàn),驗(yàn)證了所提出兩階段優(yōu)化算法的有效性。 3)對于穩(wěn)定約束最優(yōu)潮流問題的優(yōu)化求解過程,在不同的算法層面提出了可組合使用的四種并行分解策略,即預(yù)想故障分解策略、矩陣分塊分解策略、打靶區(qū)間分解策略和軌跡靈敏度參數(shù)分解策略。能夠充分利用基于多核CPU的計(jì)算集群、對稱多處理平臺和圖形處理器(GPU)等多種高性能計(jì)算平臺的計(jì)算資源,實(shí)現(xiàn)了問題求解的多層并行化,有效提高算法執(zhí)行的計(jì)算效率,拓展能夠求解的計(jì)算規(guī)模。
[Abstract]:Stable constrained optimal power flow is an important research topic in power system operation and control decision. It can minimize the operating cost of the system and improve the dynamic performance of the system after disturbance by adjusting the steady operation point. It includes transient stability and short-term voltage stability. Stable constrained optimal power flow is a dynamic optimization problem in mathematics, that is, nonlinear programming problem with constraints of differential algebraic equations. When large scale power systems with complex models, long simulation time windows and many preconceived failures are involved. The computational complexity is the main theoretical and technical obstacle in the research of the problem. This paper focuses on the optimization algorithm based on numerical optimization theory and high performance computing technology for solving stable constrained optimal power flow problem and its parallel implementation. The main research contents and academic achievements are as follows: 1) an optimal power flow model with stability constraints considering power system transient stability and short-term voltage stability constraints is proposed and its mathematical model based on dynamic optimization problem is presented. At the same time, aiming at the dynamic model integration problem of complex power equipment components, based on object-oriented design and automatic differential technology, a systematic complex model integration method applied to steady state and transient analysis is proposed. Furthermore, the modularization framework applied to stable constrained optimal power flow is designed and implemented, which improves the flexibility of the algorithm and expands the application prospect of the optimization model. 2) for the two stages of dynamic optimization, namely, the transformation of differential algebraic equations and the solving of nonlinear programming problems, a two-stage numerical optimization algorithm based on direct multiple target shooting method and reduced space interior point method is proposed. Compared with the existing research results, the proposed optimization algorithm can make full use of the characteristics and structural properties of the stable constrained optimal power flow, thus significantly improving the convergence performance and computational efficiency of the optimization algorithm. The effectiveness of the proposed two-stage optimization algorithm is verified by a series of numerical experiments of large-scale power system examples. 3) for the optimization of stable constrained optimal power flow problem, four combinable parallel decomposition strategies are proposed at different algorithm levels, that is, preconceived fault decomposition strategy and matrix partitioning decomposition strategy. Shooting interval decomposition strategy and trajectory sensitivity parameter decomposition strategy. It can make full use of the computing resources of multi-core CPU computing cluster, symmetric multi-processing platform and graphics processor (GPU), and realize multi-layer parallelization of problem solving, which can effectively improve the efficiency of algorithm execution. Expand the computational scale that can be solved.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TM744

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