分布式模型预测控制在新能源电力系统负荷频率控制中的应用研究

发布时间：2018-05-31 09:04

本文选题：负荷频率控制 + 规模化风电场　；参考：《华北电力大学(北京)》2017年博士论文

【摘要】：负荷频率控制(Load Frequency Control,LFC)旨在发电量实时自动跟踪电力系统负荷的变化,维持发电功率和负载功率的平衡,使电力系统频率保持为规定值,是保证电能质量的重要手段。随着世界能源战略格局的变化,规模化的风电场逐步投入运行,由于风电具有较强的随机性和间歇性,规模化的风电场给互联电力系统有功平衡带来较大的影响,需准确掌握规模化风电并入互联电力系统LFC后电力系统的频率特征,进而研究负荷频率控制方案与措施,以适应传统电力系统LFC向规模化风电场介入后的现代互联电力系统LFC的转变。本文在深入分析规模化风电场介入后的多区域互联电力系统动态特征的基础上,采用分布式模型预测控制理论分析与数值仿真结合的方法,对含规模化风电场的多区域互联电力系统负荷频率控制问题开展深入研究。本文的创新性研究内容包括:(1)单台风机容量和风电场总装机容量较小情况下,提出规模化风电场与常规机组在同一区域的互联电力系统LFC结构,风电机组根据当前风速通过调节桨距角实现最大风能输出,风电场的发电量作为常规机组的补充,跟踪负荷变化由常规机组来实现。综合桨距角控制系统模型、风力机模型和变速恒频发电机模型,建立由风电场、常规机组构成的互联电力系统LFC分布式数学模型。(2)单台风机容量和风电场总装机容量较大情况下,提出风电场处于单独区域互联电力系统LFC结构,与常规机组共同跟踪负荷变化,保持系统频率稳定。构建了含规模化风电场互联电力系统LFC分布式数学模型。(3)研究不同风电介入互联电力系统LFC结构下不同的控制策略,提出控制器设计方法。针对风电场与常规机组处于同一区域的互联电力系统,提出分布式模型预测控制(Distributed Model Predictive Control,DMPC),并考虑发电机变化速率约束(Generation Rate Constraints,GRCs)和阀门位置约束。以四区域互联电力系统为例,系统外界负荷发生变化时,DMPC控制器与集中模型预测控制算法相比,DMPC在频率恢复速度和稳定性显示出良好的优越性能的同时,计算负担小,运算速度快也是一大优势。(4)针对规模化风电场单独区域的互联电力系统,提出协同DMPC控制算法。研究不同风速条件下各个区域的控制目标,设计不同目标函数。同时,要求常规机组满足GRC和阀门位置约束,风电机组还需满足风速约束。以四区域互联电力系统为例,协同DMPC控制策略能有效地协调风电机组与常规机组之间出力,控制性能良好,能有效控制系统频率波动在允许范围之内。(5)研究由于系统运行工况变化带来系统模型参数发生变化和系统结构变化引起的频率波动问题,分别以传统互联电力系统和含规模化风电场的互联电力系统为平台,构建鲁棒分布式模型预测控制(Robust Distributed Model Predictive Control,RDMPC)算法。RDMPC将优化问题转化成‘min-max’问题,等效成求解上限问题,利用线性矩阵不等式进行迭代求解得到最优控制率。所有控制区域控制量最终能够实现纳什平衡且接近Pareto最优解。与DMPC对比,RDMPC在存在不确定性情况下具有更好地鲁棒性,体现出应用的可行性和有效性。
[Abstract]:Load Frequency Control (LFC) is designed to automatically track the changes of power system load in real time, maintain the balance of power and load power, keep the power system frequency as the prescribed value, and be an important means to ensure the quality of power. In operation, due to the strong randomness and intermittency of wind power, the large-scale wind farm has a great influence on the active balance of the interconnected power system. It is necessary to accurately grasp the frequency characteristics of the power system after the large-scale wind power is incorporated into the interconnected power system LFC, and then study the load frequency control schemes and measures to adapt to the traditional power system LFC. The transformation of the modern interconnected power system LFC after the intervention of the large-scale wind farm. Based on the in-depth analysis of the dynamic characteristics of the multi region interconnected power system after the intervention of the large-scale wind farm, this paper uses the method of combining the distributed model predictive control theory analysis and the numerical simulation to the multi region interconnected power containing the large-scale wind farm. The innovative research contents of this paper are as follows: (1) the LFC structure of the interconnected power system of the scale wind farm and the conventional unit in the same area is proposed under the case of the single typhoon capacity and the small wind farm assembly capacity, and the wind turbine group can achieve the maximum by adjusting the pitch angle according to the current wind speed. Wind power output, wind power generation capacity as a supplement to conventional units, tracking load changes are realized by conventional units. Integrated pitch angle control system model, wind turbine model and variable speed constant frequency generator model, LFC distributed mathematical model of interconnected power system composed of wind farms and conventional units. (2) single typhoon capacity and wind power In the case of large field assembly capacity, it is proposed that the wind farm is in the LFC structure of the interconnected power system in a separate area, tracking the load change with the conventional unit and keeping the system frequency stable. A distributed LFC mathematical model of the large-scale wind farm interconnected power system is constructed. (3) the study is different from the LFC structure of the wind power interconnected power system. Control strategy, a controller design method is proposed. A distributed model predictive control (Distributed Model Predictive Control, DMPC) is proposed for an interconnected power system in the same area as a wind farm and a conventional unit, considering the variation rate constraint of the generator (Generation Rate Constraints, GRCs) and the valve position constraints. The four area interconnection is interconnected. As an example of the power system, when the system external load changes, the DMPC controller is compared with the centralized model predictive control algorithm. DMPC shows a good performance in the frequency recovery speed and stability, while the calculation burden is small and the operation speed is fast. (4) the interconnected power system in the separate area of the large-scale wind farm is proposed. The cooperative DMPC control algorithm is used to study the control targets of each area under different wind speed conditions and design the different target functions. At the same time, the conventional units are required to meet the GRC and valve position constraints. The wind turbines need to meet the wind speed constraints. The four area interconnected power system is taken as an example, and the coordinated DMPC control strategy can effectively coordinate the wind turbine and the routine. The output of the unit is well controlled and the frequency fluctuation of the system can be effectively controlled within the allowable range. (5) research on the frequency fluctuation caused by the change of system model parameters and the change of system structure caused by the change of the operating condition of the system, and the interconnected power system with the traditional interconnected power system and the scale wind farm, respectively. On the platform, the Robust Distributed Model Predictive Control (RDMPC) algorithm.RDMPC is constructed to transform the optimization problem into the 'min-max' problem, which is equivalent to solving the upper limit problem. The optimal control rate is obtained by using linear matrix inequalities to get the optimal control rate. All control area control quantities can eventually realize Nash. It is balanced and close to the Pareto optimal solution. Compared with DMPC, RDMPC has better robustness in the presence of uncertainty, and shows the feasibility and effectiveness of the application.
【学位授予单位】：华北电力大学(北京)
【学位级别】：博士
【学位授予年份】：2017
【分类号】：TM712

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