本文關鍵詞：含風電的電力系統(tǒng)備用決策及小擾動隨機穩(wěn)定分析　出處：《華北電力大學》2014年博士論文　論文類型：學位論文

  更多相關文章： 風電 優(yōu)化調度 隨機微分方程 隨機激勵 隨機系數(shù) 小擾動隨機穩(wěn)定

【摘要】：風力發(fā)電是開發(fā)利用可再生清潔能源的主要形式。大力發(fā)展風電對優(yōu)化能源結構、實現(xiàn)能源供應多元化、應對氣候變化、保護生態(tài)環(huán)境具有非常重要的意義。大規(guī)模、集中開發(fā),遠距離、高電壓輸送是我國風電發(fā)展的主要特征。與常規(guī)電源的可調可控相比,風電機組的調節(jié)能力弱且其出力具有隨機波動性,大規(guī)模風電接入給電力系統(tǒng)的安全穩(wěn)定運行帶來很大影響。傳統(tǒng)的基于確定性狀態(tài)方程的穩(wěn)定性分析方法在解決風電隨機波動引發(fā)的穩(wěn)定問題時受到局限,有必要借助隨機微分方程和隨機穩(wěn)定理論對風電接入后的電力系統(tǒng)穩(wěn)定性進行系統(tǒng)研究。而目前這方面的研究還很薄弱。本文針對風電隨機性進入電力系統(tǒng)狀態(tài)方程的不同層面,有針對性地對含風電電力系統(tǒng)的運行優(yōu)化和小擾動隨機穩(wěn)定機理展開探索研究：針對風電不確定性給系統(tǒng)運行平衡點帶來的影響,研究風電不確定性建模和含風電電力系統(tǒng)的備用和調度決策模型；以風電機組隨機動態(tài)建模為突破口,基于隨機微分方程理論研究隨機激勵和隨機系數(shù)下的電力系統(tǒng)小擾動隨機穩(wěn)定性建模及分析方法。研究旨在將電力系統(tǒng)穩(wěn)定性建模和分析方法拓展到隨機空間下。具體的研究內容及成果如下： 1.基于場景法對風電功率的不確定性進行了建模研究。針對傳統(tǒng)場景縮減算法無法有效處理海量初始場景的問題,提出了一種基于粒子群優(yōu)化算法的改進場景縮減方法。該場景縮減算法的尋優(yōu)空間是整個初始場景集,但粒子迭代尋優(yōu)時的速度更新僅與自身之前的最優(yōu)位置及粒子群中和其Kantorovich距離最小的粒子有關,相比傳統(tǒng)算法,尋優(yōu)時不需要對初始場景集進行遍歷,計算耗時大大減少,從而有效解決了海量場景的縮減問題,為進一步的含風電電力系統(tǒng)優(yōu)化調度研究打下了基礎。 2.針對風電出力不確定性對系統(tǒng)運行平衡點的影響,研究了含風電電力系統(tǒng)的備用決策和調度問題�；诠收蠄鼍凹岢隽艘环N綜合反映電源、負荷不確定性的可靠性指標,并推導了風電接入后系統(tǒng)備用需求的量化表達式,進而建立了含風電的電力系統(tǒng)發(fā)電和備用協(xié)調優(yōu)化模型。利用該模型,不僅可以得到系統(tǒng)每個時段所需的運行備用總量,還可得到每個時段機組間的最優(yōu)發(fā)電和備用分配方案。通過算例仿真驗證了模型的有效性。所提方法很好地解決大規(guī)模風電接入后備用容量的量化和分配問題,給出的優(yōu)化調度方案,為計算系統(tǒng)穩(wěn)態(tài)運行平衡點提供了依據。 3.在梳理隨機微分方程及隨機穩(wěn)定理論的基礎上,研究了受隨機激勵影響的電力系統(tǒng)小擾動隨機穩(wěn)定機理。將異步風機機械功率視為隨機過程,基于Ito隨機微分方程建立了隨機激勵下的異步風力發(fā)電機動態(tài)模型,該模型克服了Riemann積分無法處理被積函數(shù)中隨機項的局限,將電力系統(tǒng)的動態(tài)模型由確定性的常微分方程框架拓廣到了隨機微分方程框架下；在此基礎上提出并證明了系統(tǒng)隨機均值穩(wěn)定和均方穩(wěn)定的判據；并進一步推導得到了系統(tǒng)小擾動響應過程期望和方差的計算方法。推導得到的系統(tǒng)狀態(tài)變量統(tǒng)計特征解析表達式準確地描述了隨機激勵下系統(tǒng)的動態(tài)過程。論文還通過數(shù)值方法進行了仿真驗證,證明了所提分析方法的有效性和合理性。 4.研究了考慮隨機系數(shù)的電力系統(tǒng)小擾動隨機穩(wěn)定機理。進一步考慮風機隨機功率波動與系統(tǒng)其它電氣量之間的耦合作用導致的狀態(tài)方程系數(shù)的隨機性,建立了基于Ito隨機微分方程的含隨機系數(shù)的系統(tǒng)動態(tài)模型；應用Ito公式將該系統(tǒng)的隨機均方穩(wěn)定性問題轉化為確定性系統(tǒng)的均值穩(wěn)定性問題,利用Lyapunov函數(shù)證明了這種系統(tǒng)的隨機均方穩(wěn)定判據；之后結合電力系統(tǒng)隨機參數(shù)靈敏度分析方法得到了系統(tǒng)隨機穩(wěn)定概率的計算方法；并通過算例仿真驗證了所提方法的合理性和有效性。該方法可有效且快速的計算有隨機系數(shù)的電力系統(tǒng)穩(wěn)定概率,其本質是解析的,雖然結果保守,但方法嚴謹可靠,且計算量較小。
[Abstract]:Wind power is the main form of exploitation and utilization of renewable and clean energy. The development of the wind power to optimize the energy structure, realize the diversification of energy supply, climate change, has very important significance to protect the ecological environment. The large-scale, centralized development, long-distance, high voltage transmission is the main feature of China's wind power development and conventional power supply. Adjustable and controllable compared regulating capacity of wind turbine and its output is weak with stochastic volatility, large-scale wind power access brings great impact to the safe and stable operation of power system. The traditional stability analysis methods based on deterministic state equation in solving the stability problem of wind power fluctuation caused by random limitations, it is necessary to use random differential equation and stochastic stability theory, a systematic research on the stability of the power system after wind power access. And the current research in this area is still very weak. The needle The different levels of wind power system state equation into random, targeted operation optimization of power system containing wind power and small perturbation stochastic stability mechanism research: in view of the influence of the uncertainty of wind power system operation to balance the backup and scheduling decision model of wind power uncertainty and modeling the power system with wind power; in a random dynamic model of wind turbine power system as a breakthrough, the stochastic differential equation theory of stochastic excitation and random coefficients of the small signal modeling and analysis method based on stochastic stability. The aim of the research on the stability of power system modeling and analysis method is extended to random space. The main research contents and contributions the following:
1. based on the scene of the uncertainty of wind power was researched. According to the traditional scenario reduction algorithms cannot effectively deal with the problem of the initial scene, a method for reducing the improvement of scene based on particle swarm optimization algorithm. The algorithm to reduce the search space of the scene is the initial scene set, but the optimal location and particle particle swarm iterative optimization speed when the update is only with their own before and its Kantorovich distance of particles, compared with the traditional algorithm, the optimization does not need to traverse the initial scenarios, the computing time is greatly reduced, so as to effectively solve the problem of massive cut scenes, including wind power system scheduling research it laid a good foundation.
2. for the wind power uncertainty on the system operation of the balance point of reserve decision and scheduling problem with wind power system. The fault scenario set presents a comprehensive reflection of the power based on the reliability index of the load uncertainty, and the quantitative expression of wind power system reserve demand is derived, and then the establishment of the power system with wind power generation and reserve coordination optimization model. Using this model, the total operating reserve can not only obtain the required period of each system, also can get the optimal power and standby time between each unit allocation scheme. The simulation results verify the validity of the model. The proposed method is very good to solve the large-scale wind power integration reserve quantification and capacity allocation problem, optimal scheduling scheme, provides the basis for the calculation of steady-state operation point of balance.
3. on the basis of stochastic differential equations and stochastic stability theory, the power system under random excitation of small perturbation stochastic stability mechanism. The asynchronous wind turbine mechanical power is considered as a stochastic process and Ito stochastic differential equation is established based on the random excitation asynchronous wind generator dynamic model, the model overcomes the Riemann integral unable to deal with the limitations of random integrand function, the dynamic model of the power system by the framework of ordinary differential equations are extended to the framework of deterministic stochastic differential equations; based on this system and stochastic mean stability and mean square stability criterion is proved; and further deduced the calculation method of response process of expectation and variance the small disturbance system. Analysis of the state variables of the system are derived from statistical characteristics accurately describes the dynamic process under random excitation system. The paper also The validity and rationality of the proposed method are proved by numerical simulation.
4. of the power system with random coefficients small perturbation stochastic stability mechanism considered. Further considering random coefficient equation of state leads to the coupling effect between the fan power fluctuation and other electric quantity of the system, establishes the system dynamic model with random coefficient Ito based on stochastic differential equation; application of Ito formula will mean the stability problem the stochastic mean square stability problem is transformed into a deterministic system, using the Lyapunov function to prove the system stochastic mean square stability criterion; then combined with power system stochastic parameter sensitivity analysis method to obtain the calculation method of stochastic stability probability; and validate the rationality and validity of the simulation calculation. This method can effectively and quickly the power system stability probability random coefficient, its essence is analytical, although the results are conservative, but The method is rigorous and reliable, and the amount of calculation is small.

【學位授予單位】：華北電力大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TM614

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