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基于图卷积神经网络的机组组合问题加速求解方法

来源：电工电气发布时间：2024-04-07 09:07 浏览次数：26

基于图卷积神经网络的机组组合问题加速求解方法

曾贵华，刘明波

(华南理工大学电力学院，广东广州 510640)

摘要：针对传统的精确优化算法求解规模较大的机组组合问题面临时间可行性的挑战, 提出了一种基于图卷积神经网络的机组组合问题加速求解方法。将机组组合问题构建为一个混合整数线性规划模型，根据分支定界法的求解原理，将分支策略定义为从候选变量的特征到候选变量得分的映射关系；提出在离线阶段使用图卷积神经网络来模拟强分支策略的决策行为，并将学习到的映射关系应用到在线分支过程中，从而加速分支定界法求解机组组合问题。通过 IEEE 39 节点 10 机组和 IEEE 118 节点 54 机组系统的算例分析，验证了所提方法的有效性。

关键词： 发电机；机组组合；分支定界法；分支策略；图卷积神经网络

中图分类号：TM744 文献标识码：A 文章编号：1007-3175(2024)03-0044-07

Acceleration Solving Method for Unit Commitment Problem Based on

Graph Convolution Neural Network

ZENG Gui-hua, LIU Ming-bo

（School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China）

Abstract: To solve the challenge of time feasibility faced by traditional accurate optimization algorithms for solving large-scale Unit Commitment (UC) problems, this paper proposes an accelerated solution method for solving the UC problems based on graph convolution neural network. Firstly, the UC problem is constructed as a Mixed Integer Linear Programming (MILP) model. Next, according to the solution principle of the branch-and-bound method, we define the branching strategy as a mapping relationship from the features of candidate variables to the scores of candidate variables. Thus, we propose to mimic the decision-making behavior of strong branching strategy in the offline phase using Graph Convolutional Neural Network (GCNN) and apply the learned mapping relationship to the online branching process to accelerate the process of the branch and bound method to solve the UC problem. Finally, the effectiveness of the proposed method is verified by the analysis of IEEE 39-node 10-unit and IEEE 118-node 54-unit systems.

Key words: generator; unit commitment; branch and bound method; branch strategy; graph convolution neural network

参考文献

[1] XAVIER Á S, QIU F, AHMED S.Learning to solve large-scale security-constrained unit commitment problems[J].INFORMS Journal on Computing，2021，33(2) ：739-756.

[2] SHOULTS R R, CHANG S K, HELMICK S, et al.A practical approach to unit commitment, economic dispatch and savings allocation for multiple-area pool operation with import/export constraints[J].IEEE Transactions on Power Apparatus and Systems,1980，PAS-99(2) ：625-635.

[3] BURNS R M, GIBSON C A.Optimization of priority lists for a unit commitment program[C]//Proceeding IEEE Power Engineering Society Summer Meeting，1975，453-461.

[4] LEE F N . The application of commitment utilization factor (CUF) to thermal unit commitment[J].IEEE Transactions on Power Systems，1991，6(2) ：691-698.

[5] PANG C K, CHEN H C.Optimal short-term thermal unit commitment[J].IEEE Transactions on Power Apparatus and Systems，1976，95(4) ：1336-1346.

[6] PANG C K, SHEBLÉ G B, ALBUYEH F.Evaluation of dynamic programming based methods and multiple area representation for thermal unit commitments[J].IEEE Transactions on Power Apparatus and Systems，1981，PAS-100(3) ：1212-1218.

[7] COHEN A I, YOSHIMURA M.A branch-and-bound algorithm for unit commitment [J] . IEEE Transactions on Power Apparatus and Systems，1983，PAS-102(2) ：444-451.

[8] 谢国辉，张粒子，舒隽，等. 基于分层分枝定界算法的机组组合[J] . 电力自动化设备，2009，29(12) ：29-32.

[9] HABIBOLLAHZADEH H, BUBENKO J A.Application of decomposition techniques to short-term operation planning of hydrothermal power system[J].IEEE Transactions on Power Systems，1986，1(1)：41-47.

[10] SASAK H, WATANAB M, KUBOKAWA J, et al.A solution method of unit commitment by artificial neural networks[J].IEEE Transactions on Power Systems，1992，7(3) ：974-981.

[11] LIANGL R H, KANG F C.Thermal generating unit commitment using an extended mean field annealing neural network[J].IEE Proceedings-Generation, Transmission and Distribution，2000，147(3)：164-170.

[12] JUSTE K A, KITA H, TANAKA E, et al.An evolutionary programming solution to the unit commitment problem[J].IEEE Transactions on Power Systems，1999，14(4) ：1452-1459.

[13] KAZARLIS S A, BAKIRTZIS A G, PETRIDIS V.A genetic algorithm solution to the unit commitment problem[J].IEEE Transactions on Power Systems，1996，11(1) ：83-92.

[14] LIN X, HOU Z J, REN H, et al.Approximate mixedinteger programming solution with machine learning technique and linear programming relaxation[C]//2019 3rd International Conference on Smart Grid and Smart Cities(ICSGSC).IEEE，2019 ：101-107.

[15] 张丽华. 基于内点—分支定界法的最优机组投入研究[D].南宁：广西大学，2006.

[16] LINDEROTH J T, SAVELSBERGH M W P.A computational study of search strategies for mixed integer programming[J].INFORMS Journal on Computing，1999，11(2) ：173-187.

[17] APPLEGATE D, BIXBY R, CHVÁTAL V, et al.Finding cuts in the TSP (A preliminary report)[M].New Jersey: Rutgers University, New Brunswick, USA，1995 ：95-105.

[18] HE H, DAUME Ⅲ H, EISNER J M.Learning to search in branch and bound algorithms[J].Advances in Neural Information Processing Systems，2014，27 ：3293-3301.

[19] GASSE M, CHÉTELAT D, FERRONI N, et al.Exact combinatorial optimization with graph convolutional neural networks[J].Advances in Neural Information Processing Systems，2019，32 ：15554-15566.

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基于图卷积神经网络的机组组合问题加速求解方法