基于大系统理论的电网负荷动态调度优化

发布时间：2018-03-27 05:01

本文选题：机组组合　切入点：分解协调　出处：《华北电力大学(北京)》2017年硕士论文

【摘要】：机组组合问题的建模和优化是电力系统调度管理的重要部分,对资源的有效利用和降低系统运行成本起着关键作用。由于用连续量表示的机组负荷和用离散量表示的机组启停状态存在耦合关系,该问题成为强NP难的数学问题。随着机组数量的增加,所对应的机组组合数目呈指数式增加,在常规解法中不可避免的存在迭代时间长的问题,难以在规定时间内求出该问题的最优解。针对上述存在的问题,本文采用时间序列理论对历史负荷数据建立了ARIMA模型并预测了一天内的负荷范围。在Java程序中通过Rserve调用R语言对预测方法进行了仿真,分析了负荷的自相关函数和偏自相关函数,结合模型选择准则得到负荷最大最小值的预测模型,预测出的未来五组数据经过了残差的白噪声检验。根据预测出来的负荷范围,计算出最多和最少需要启动的机组数目,对所有不符合要求的机组组合进行了高效的删减,大大缩小了机组组合的选择范围。经仿真分析该算法能够在保证精确的前提下,经过删减后剩余的机组组合数目缩小了几个数量级,取得了较好的效果。在此基础上采用大系统分解协调法将负荷分配问题分解为三层结构。中间层和底层之间通过拉格朗日乘子协调各自的输入输出,上层对符合条件的机组组合进行迭代,最终得到各时段最优机组组合与各机组输出功率。经过对算例的仿真分析和比较,算法计算速度极快,计算结果具有一致性。本文深入分析了机组组合问题中存在“维数灾”和“对偶间隙”的原因,将时间序列理论与大系统相关算法相结合,提出一种新的求解方法使机组组合问题得到极大简化且易于求解,并经过仿真分析对算法的可行性进行了验证。
[Abstract]:The modeling and optimization of unit commitment problem is an important part of power system dispatching management. It plays a key role in the efficient utilization of resources and reducing the operating cost of the system. Because of the coupling relationship between the unit load expressed by the continuous quantity and the unit starting and stopping state expressed by the discrete quantity, This problem has become a strong NP-hard mathematical problem. With the increase of the number of units, the corresponding number of units increases exponentially, and the problem of long iteration time is inevitable in the conventional solution. It is difficult to find the optimal solution of the problem within the specified time. In this paper, the ARIMA model of historical load data is established by using time series theory and the load range in one day is predicted. The prediction method is simulated by Rserve calling R language in Java program. The autocorrelation function and partial autocorrelation function of load are analyzed. Combined with the model selection criterion, the forecasting model of maximum and minimum load value is obtained. The predicted five groups of data are tested by residual white noise. According to the predicted load range, The maximum and least number of units needed to be started are calculated, and all units that do not meet the requirements are deleted efficiently, which greatly reduces the selection range of unit combinations. The simulation results show that the algorithm can ensure accuracy. The number of units remaining after the reduction has been reduced by several orders of magnitude. On the basis of this, the load distribution problem is decomposed into three layers by using the large scale system decomposition and coordination method, and the input and output between the middle layer and the bottom layer are coordinated by Lagrange multiplier. The upper layer iterates the qualified unit combination, and finally obtains the optimal unit combination and the output power of each unit in each time period. Through the simulation analysis and comparison of the example, the calculation speed of the algorithm is very fast. The calculation results are consistent. In this paper, the causes of "dimensionality disaster" and "dual gap" in the unit commitment problem are deeply analyzed, and the time series theory is combined with the large-scale system correlation algorithm. A new solution method is proposed to simplify the problem of unit commitment and to solve the problem easily. The feasibility of the algorithm is verified by simulation analysis.
【学位授予单位】：华北电力大学(北京)
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：N941.4;TM73

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