基于信息共享的智能电网低频减载优化研究

发布时间：2019-04-19 01:25

【摘要】：现有的低频减载方法多基于区域变电站的就地信息进行负荷开断决策,没有充分利用智能电网发展带来的信息化、互动化和网络化优势。本文围绕基于信息共享的智能电网低频减载优化技术,依次从信息基础、技术架构、决策方法三个层面展开研究,提出了一种网络化的低频减载优化控制策略,在架构上可实现用户侧协调和系统侧协调,在功能上可实现减载负荷与减载容量的智能决策。依据低频减载的动作流程梳理归纳其正确动作所应掌握的关键信息,在系统、变电站与负荷不同层面上,深入挖掘智能电网环境下低频减载可利用的各类信息,其中重点研究了负荷的典型特征信息与友好型用户(电动汽车)的互动特性,并对信息的获取途径与传输架构展开分析。分析传统低频减载技术的特点与不足,研究符合智能电网发展趋势的通信网络化低频减载技术。在此基础上结合上述多类可用信息,研究以智能变电站为平台的低频减载优化架构与控制策略,研究以变电站为单元的低频减载广域协调控制策略,从而在技术架构上实现智能电网低频减载的“双重协调”优化。针对用户侧的协调优化,提出了基于灰色关联分析的切负荷排序算法,以量化分析负荷重要性、负荷频率效应系数、负载率等负荷特征信息对低频减载动作的影响。在满足系统切负荷总量需求前提下,给出了基于实时负荷检测的变电站精确切负荷策略,研究了计及电动汽车友好互动的低频减载协调优化策略,并通过仿真算例验证了算法对低频减载动作特性的优化。针对系统侧的协调优化,构建了基于广域实时共享的减载容量智能分配模型。引入单位负荷切除因子来考量系统低频减载控制的综合代价,并给出了基于层次分析法的切除因子计算方法。采用频率稳定直接分析法,以广域测量数据为输入来计算模型的稳态频率约束。进而,利用粒子群算法实现了模型在多约束条件下的最优求解,并通过仿真算例验证了模型有效性。论文研究了智能电网环境下低频减载智能决策减载负荷和分配减载容量的问题,为提高智能电网环境下低频减载的适应性开拓了思路。
[Abstract]:The existing low-frequency load-shedding methods are mostly based on the local information of regional substations to make load cut-off decision, and do not make full use of the advantages of information, interaction and network brought by the development of smart grid. This paper focuses on the intelligent grid low-frequency load-shedding optimization technology based on information sharing, then studies it from three aspects: information foundation, technical architecture and decision-making method, and puts forward a network-based low-frequency load-reducing optimization control strategy. User-side coordination and system-side coordination can be realized in architecture, and intelligent decision-making of load-shedding load and load-reducing capacity can be realized in function. According to the operation flow of low frequency load shedding, the key information that should be grasped in the correct operation is summarized. In the different levels of system, substation and load, the various kinds of information that can be used in low frequency load reduction in smart grid environment are deeply excavated. The typical characteristic information of load and the interactive characteristic of friendly user (electric vehicle) are studied, and the way of obtaining information and the structure of transmission are analyzed. This paper analyzes the characteristics and shortcomings of traditional low-frequency load-shedding technology and studies the communication network-based low-frequency load-reducing technology which accords with the development trend of smart grid. Based on the above-mentioned available information, the optimal architecture and control strategy of low-frequency load-shedding based on intelligent substation are studied, and the wide-area coordinated control strategy of low-frequency load-shedding based on substation is studied. So as to achieve the "dual coordination" optimization of smart grid low-frequency load-shedding on the technical framework. Aiming at the coordination and optimization of user side, a load shedding scheduling algorithm based on grey relational analysis is proposed to quantitatively analyze the influence of load characteristic information such as load importance, load frequency effect coefficient and load ratio on low frequency load shedding action. On the premise of meeting the demand of system load shedding, the accurate load shedding strategy of substation based on real-time load detection is given, and the coordination and optimization strategy of low frequency load shedding taking into account the friendly interaction of electric vehicles is studied. A simulation example is given to verify the optimization of the low-frequency load-shedding performance of the algorithm. Aiming at the coordination optimization of system side, the intelligent allocation model of load-reducing capacity based on wide-area real-time sharing is constructed. The unit load cutting factor is introduced to consider the overall cost of the low frequency load shedding control of the system, and the calculation method of the cutting factor based on the analytic hierarchy process (AHP) is presented. The steady-state frequency constraints of the model are calculated by direct frequency stability analysis with wide-area measurement data as input. Furthermore, the particle swarm optimization algorithm is used to realize the optimal solution of the model under multi-constraint conditions, and the validity of the model is verified by a simulation example. In this paper, the problem of low-frequency load-shedding intelligent decision-making and load-reducing capacity distribution in smart grid environment is studied in order to improve the adaptability of low-frequency load-shedding in smart grid environment.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM76

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