本文選題：機組組合　切入點：分解協(xié)調　出處：《華北電力大學(北京)》2017年碩士論文

【摘要】：機組組合問題的建模和優(yōu)化是電力系統(tǒng)調度管理的重要部分,對資源的有效利用和降低系統(tǒng)運行成本起著關鍵作用。由于用連續(xù)量表示的機組負荷和用離散量表示的機組啟停狀態(tài)存在耦合關系,該問題成為強NP難的數(shù)學問題。隨著機組數(shù)量的增加,所對應的機組組合數(shù)目呈指數(shù)式增加,在常規(guī)解法中不可避免的存在迭代時間長的問題,難以在規(guī)定時間內求出該問題的最優(yōu)解。針對上述存在的問題,本文采用時間序列理論對歷史負荷數(shù)據(jù)建立了ARIMA模型并預測了一天內的負荷范圍。在Java程序中通過Rserve調用R語言對預測方法進行了仿真,分析了負荷的自相關函數(shù)和偏自相關函數(shù),結合模型選擇準則得到負荷最大最小值的預測模型,預測出的未來五組數(shù)據(jù)經(jīng)過了殘差的白噪聲檢驗。根據(jù)預測出來的負荷范圍,計算出最多和最少需要啟動的機組數(shù)目,對所有不符合要求的機組組合進行了高效的刪減,大大縮小了機組組合的選擇范圍。經(jīng)仿真分析該算法能夠在保證精確的前提下,經(jīng)過刪減后剩余的機組組合數(shù)目縮小了幾個數(shù)量級,取得了較好的效果。在此基礎上采用大系統(tǒng)分解協(xié)調法將負荷分配問題分解為三層結構。中間層和底層之間通過拉格朗日乘子協(xié)調各自的輸入輸出,上層對符合條件的機組組合進行迭代,最終得到各時段最優(yōu)機組組合與各機組輸出功率。經(jīng)過對算例的仿真分析和比較,算法計算速度極快,計算結果具有一致性。本文深入分析了機組組合問題中存在“維數(shù)災”和“對偶間隙”的原因,將時間序列理論與大系統(tǒng)相關算法相結合,提出一種新的求解方法使機組組合問題得到極大簡化且易于求解,并經(jīng)過仿真分析對算法的可行性進行了驗證。
[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.
【學位授予單位】：華北電力大學(北京)
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：N941.4;TM73

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本文編號：1670074