含分布式電源的直流配電網協(xié)調控制研究
發(fā)布時間:2018-03-24 11:35
本文選題:直流配電網 切入點:分布式電源 出處:《華北電力大學(北京)》2017年博士論文
【摘要】:隨著分布式電源在配電網中滲透率的增加,其穩(wěn)定性和可靠性問題變得尤為復雜。直流配電網兼具靈活性與高效性的優(yōu)點,且不存在無功環(huán)流及功角穩(wěn)定等問題,是分布式電源更為理想的組網方式。由于直流配電網中沒有無功和頻率問題,其功率-電壓控制是穩(wěn)定及優(yōu)化運行的核心技術。對于端數(shù)較多的直流配電網可采用一次調壓與二次調壓相結合進行電網功率調節(jié),把復雜電網的穩(wěn)定運行與優(yōu)化運行在不同的時間尺度上進行解耦控制。此外,在直流配電網中,各分布式電源通過電力電子變流器并網,對直流系統(tǒng)的慣性幾乎沒有貢獻,這將成為分布式能源大規(guī)模接入配電網之后面臨的新問題。隨著能源結構多樣化及用戶需求的精益化,對直流配電系統(tǒng)供電質量及運行特性的要求也在不斷提高。本文主要研究含分布式電源的直流配電網協(xié)調控制方法,以提高其暫態(tài)運行穩(wěn)定性,改善系統(tǒng)運行效率及供電質量。主要工作及成果如下:1.建立了包含風力發(fā)電的直流配電網各單元數(shù)學模型,包括永磁風電機組、蓄電池儲能單元、聯(lián)網變流器以及交直流負荷的數(shù)學模型。研究了直流配電網中各單元的傳統(tǒng)控制策略及系統(tǒng)運行特性,為各單元慣性控制策略的提出,及預測控制模型的建立奠定了基礎。2.提出了直流配電網一次與二次電壓控制相結合的調壓體系。在電壓分段一次調壓方法的基礎上,深入研究系統(tǒng)各單元控制響應特性,提出了直流配電網自適應慣性調節(jié)方法,以解決直流配電網低慣性問題。對于風力發(fā)電單元通過控制旋轉動能的吸收或釋放來改善功率輸出特性,對于儲能及并網單元也可以通過改變相應的控制參數(shù),虛擬出與直流電壓變化相適應的慣性響應,增強了直流配電網的抗擾能力及暫態(tài)運行穩(wěn)定性。3.提出了一種基于變流器模型的本地預測控制方法,以配合系統(tǒng)慣性調節(jié)時對變流器響應快速性的要求。該方法中,在對各單元變流器數(shù)學模型進行分析推導的基礎上,分別建立了機側變流器,網側變流器及儲能變流器預測控制模型。為消除數(shù)字執(zhí)行過程的單步延時,引入了兩步預測誤差函數(shù)從而形成了多目標優(yōu)化。采用分類排序法選擇目標向量,解決了傳統(tǒng)基于PI的雙閉環(huán)控制參數(shù)整定繁瑣的問題。將基于變流器模型的預測控制方法與慣性調節(jié)相結合,提高了系統(tǒng)控制響應速度,增強了直流配電網暫態(tài)運行穩(wěn)定性。4.設計了基于模型預測控制器的風電直流配電網集散控制系統(tǒng)。為滿足系統(tǒng)經濟運行及高質量電能的要求,采用模型預測控制器作為其集散控制體系架構中的主控制單元,以產生本地控制器優(yōu)化參考信號。本地控制器根據(jù)系統(tǒng)運行狀態(tài)特性,在優(yōu)化參考值的基礎上對系統(tǒng)運行狀態(tài)進行細調。同時采用冗余參考信號,通信故障時采用基于直流電壓的系統(tǒng)功率分配方案,以規(guī)避該分層控制結構中通信故障對系統(tǒng)穩(wěn)定性的影響,如此,既能夠保證正常運行時,系統(tǒng)高效運行及對電能質量的要求,又能在故障時保證系統(tǒng)不失穩(wěn)。
[Abstract]:With the increase of distributed generation in distribution network penetration, its stability and reliability problems become more complex. The DC power distribution network has advantages of both flexibility and efficiency, and does not exist without the problems of power circulation and power angle stability, power is distributed more ideal network. Because of the lack of reactive power and frequency of the DC power distribution in the net, the power voltage control is the core technology of stable and optimal operation. Using a voltage and the two voltage combined power adjustable DC power distribution network for a large number of end, the complex grid stable operation and optimal operation of decoupling control in different time scales in addition. And in the DC distribution network, the distributed power grid through power electronic converters of DC, the inertia of the system is almost no contribution, it will be faced after the large-scale distributed energy distribution network access The new problem. With the energy structure diversification and user needs to lean, DC distribution system and operation characteristics of the power quality requirements are constantly improved. Coordinated control method of DC power distribution network is mainly studied in this paper with distributed power, in order to improve the transient stability, improve system efficiency and power quality. The main work and achievements are as follows: 1. the DC distribution network of each unit mathematical model including wind power, including the permanent magnet wind generator, battery energy storage unit, the mathematical model of AC / DC converter and network load. On the operating characteristics of the traditional control strategy and system of each unit of DC distribution network, for each unit inertia control strategy the prediction model, and laid the foundation for.2. proposed a DC distribution network and the two voltage control combined pressure regulating system. In section one voltage Based on the voltage regulating method, in-depth study of the system of each unit control response characteristics, put forward the adaptive inertia DC distribution network adjustment method to solve the DC distribution network low inertia problem. For wind power generation unit by controlling the rotational kinetic energy absorption or release to improve the power output characteristics for energy storage and grid unit can shift the corresponding control parameters, virtual and DC voltage changes to adapt to the inertial response, enhance the anti disturbance capability and transient stability of the.3. DC power distribution network presents a predictive control method based on local model of converter, to match the system inertia quick response requirements for converter during adjustment. In this method, the for each unit converter mathematical model is derived on the basis of machine side converter are established respectively, and the grid side converter converter model for predictive control. Eliminate one step delay of digital implementation process, the introduction of the two step prediction error function to form a multi-objective optimization. Select the target vector by sorting method, to solve the traditional double closed loop PI control parameter tuning based on complicated problems. The prediction model of converter control system and inertial modulation method based on improved system the control response speed, enhance the transient stability of the.4. DC power distribution network design model predictive controller of wind power DC power distribution network based on the distributed control system. In order to satisfy the system requirements of high quality and economic operation of electric energy, a model predictive controller as the main control unit of distributed control system architecture, to optimize the local controller reference signal the local controller. According to the characteristics of the running state of the system, for fine-tuning the operation state of the system on the foundation of the optimum reference value. At the same time using redundancy More than the reference signal, the system power allocation scheme based on DC voltage communication fault, to avoid the influence of hierarchical control structure in communication fault on the stability of the system, which can ensure the normal operation, efficient operation of the system and the requirements of power quality, but also in the failure to prevent system instability.
【學位授予單位】:華北電力大學(北京)
【學位級別】:博士
【學位授予年份】:2017
【分類號】:TM721.1
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