蚁群算法在生物质发电配网规划中的应用研究
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摘要
配电网规划是电力系统规划的重要组成部分,对其进行科学合理的优化,寻找最佳配网决策将带来可观的经济和社会效益。生物质发电配电网规划是低压的配电网规划,根据变电站的容量及用户的负荷容量,设计最佳的网络结构,为用户提供长期稳定、并能满足用户需要的电力。生物质低压配电网涉及每个自发电站的位置、容量大小等非线性数据问题,同时服从每个自发电站容量、辐射状网络结构以及可靠性要求等约束,是一个非线性、多目标、多约束的组合优化问题。
蚁群算法(Ant Colony Algorithm)是一种基于种群的模拟进化,用于解决复杂线性及非线性优化问题的启发式算法。该算法最初应用在旅行商问题(TSP)、二次分配问题(QAP)和车间调度问题(JSP)的求解中并且取得了较好的效果。现在蚁群算法以其良好的寻优性能已经成功的在电机优化设计问题、函数优化问题和集成电路布线问题等领域得到应用。
本文对基本蚁群算法和蚁群系统进行了深入研究,同时分析国内外配电网规划研究现状的基础上结合生物质生长位置、存储运输条件、小发电网的应用用户层等特点给出了生物质配电网规划的数学模型、约束条件和适应度目标函数。针对配电网络的辐射性和连通性等特点,提出将向上节点法应用于配电网规划中,旨在保证生物质发电配电网络规划的辐射状网络解；针对基本的蚁群算法在信息素的选取种类单一、信息素的更新机制容易使得算法陷入停滞的缺点,对算法进行了信息素的选取、信息素的更新规则两方面改进：首先,根据选择的生物质发电配电网网的约束条件适当的增加信息素的种类,并利用不同的权值给出多种信息素之间的制约关系；在信息素更新机制方面利用平均线路长度比较的方法,每条新规划线路与平均线路相比较之后再判别该线路的规划是否有效。在以上两点改进的方法上提出了适合于生物质发电低压配电网络规划的改进蚁群算法(IACOA)。
利用51个城市的TSP典型问题与遗传算法、蚁群系统算法进行比较,实验结果表明,本文提出的IACOA算法相对于遗传算法和蚁群系统算法而言,IACOA算法具有更好的全局寻优能力,算法运算速度快,抑制了“早熟收敛”,避免陷入局部最优值,表现出较强的寻优性能,更加适用于非线性规划问题的求解。最后将IACOA算法应用到72个负荷点、6个自发电站的村镇生物质低压配电网络规划问题中,弥补了传统蚁群系统算法在配电网规划中计算速度慢、易于陷入局部最优解的不足,并改善了解的收敛性得到比较好的规划结果。
Distribution network planning is an important component of power system planning. The great economic and social benefits will be achieved by the aid of scientific planning and optimal power network investment decision. In order to supply abundant and high-qualified power to customers, low-voltage (LV) biomass distribution network planning needs to provide a powerful and flexible scheme based on the substation capacity and the load capacity. To sum up, distribution network planning is a nonlinear, multi-objective, multi-constraint optimal problem, and is associated with the radiate power line routing, network-loss and power supply reliability etc.
The traditional Ant Colony Algorithm is an algorithm that simulates evolution, used to solve complex linear and nonlinear optimization heuristic algorithm. It was originally obtained the solution in good results applying to the traveling salesman problem, quadratic assignment problem and shop scheduling problem. Now ant colony optimization algorithm has been successful in the motor optimal design, function optimization problem and the areas of integrated circuit routing problem because of its good performance.
In this paper, first we investigate the research status of the basic ant colony algorithm and study the principle and mathematical model of ant colony algorithm in depth. The mathematical model of distribution network planning and Constraints and objective function was obtained based analyzing the status of distribution network planning in and abroad. After analyzing the radiate and connective characters of distribution network and the state transfer principles and the pheromone initialization and pheromone update, this paper reformulates the existing Improved Ant Colony Optimization Algorithm (IACOA), on which a new approach for the LV distribution network planning applied in real streets is created. The reformulated method can obtain a satisfied solution in extending searching scope and better computing speed for distribution network Planning.
Comparing IACOA with typical problems genetic algorithms and ant colony system algorithm used TSP of 51 cities, the experimental results show that the IACOA algorithm is better than this genetic algorithm and ant colony system algorithm in global search ability and fast operation to avoid falling local optimal value, The IACOA algorithm is more suitable for solving un-linear programming problems showing a strong optimization properties. IACOA algorithm is applied to the biomass power plant low-voltage distribution network planning problem of 72 load points and six power station, the real tests prove the practicability and validity of this paper method.
蚁群算法(Ant Colony Algorithm)是一种基于种群的模拟进化,用于解决复杂线性及非线性优化问题的启发式算法。该算法最初应用在旅行商问题(TSP)、二次分配问题(QAP)和车间调度问题(JSP)的求解中并且取得了较好的效果。现在蚁群算法以其良好的寻优性能已经成功的在电机优化设计问题、函数优化问题和集成电路布线问题等领域得到应用。
本文对基本蚁群算法和蚁群系统进行了深入研究,同时分析国内外配电网规划研究现状的基础上结合生物质生长位置、存储运输条件、小发电网的应用用户层等特点给出了生物质配电网规划的数学模型、约束条件和适应度目标函数。针对配电网络的辐射性和连通性等特点,提出将向上节点法应用于配电网规划中,旨在保证生物质发电配电网络规划的辐射状网络解；针对基本的蚁群算法在信息素的选取种类单一、信息素的更新机制容易使得算法陷入停滞的缺点,对算法进行了信息素的选取、信息素的更新规则两方面改进：首先,根据选择的生物质发电配电网网的约束条件适当的增加信息素的种类,并利用不同的权值给出多种信息素之间的制约关系；在信息素更新机制方面利用平均线路长度比较的方法,每条新规划线路与平均线路相比较之后再判别该线路的规划是否有效。在以上两点改进的方法上提出了适合于生物质发电低压配电网络规划的改进蚁群算法(IACOA)。
利用51个城市的TSP典型问题与遗传算法、蚁群系统算法进行比较,实验结果表明,本文提出的IACOA算法相对于遗传算法和蚁群系统算法而言,IACOA算法具有更好的全局寻优能力,算法运算速度快,抑制了“早熟收敛”,避免陷入局部最优值,表现出较强的寻优性能,更加适用于非线性规划问题的求解。最后将IACOA算法应用到72个负荷点、6个自发电站的村镇生物质低压配电网络规划问题中,弥补了传统蚁群系统算法在配电网规划中计算速度慢、易于陷入局部最优解的不足,并改善了解的收敛性得到比较好的规划结果。
Distribution network planning is an important component of power system planning. The great economic and social benefits will be achieved by the aid of scientific planning and optimal power network investment decision. In order to supply abundant and high-qualified power to customers, low-voltage (LV) biomass distribution network planning needs to provide a powerful and flexible scheme based on the substation capacity and the load capacity. To sum up, distribution network planning is a nonlinear, multi-objective, multi-constraint optimal problem, and is associated with the radiate power line routing, network-loss and power supply reliability etc.
The traditional Ant Colony Algorithm is an algorithm that simulates evolution, used to solve complex linear and nonlinear optimization heuristic algorithm. It was originally obtained the solution in good results applying to the traveling salesman problem, quadratic assignment problem and shop scheduling problem. Now ant colony optimization algorithm has been successful in the motor optimal design, function optimization problem and the areas of integrated circuit routing problem because of its good performance.
In this paper, first we investigate the research status of the basic ant colony algorithm and study the principle and mathematical model of ant colony algorithm in depth. The mathematical model of distribution network planning and Constraints and objective function was obtained based analyzing the status of distribution network planning in and abroad. After analyzing the radiate and connective characters of distribution network and the state transfer principles and the pheromone initialization and pheromone update, this paper reformulates the existing Improved Ant Colony Optimization Algorithm (IACOA), on which a new approach for the LV distribution network planning applied in real streets is created. The reformulated method can obtain a satisfied solution in extending searching scope and better computing speed for distribution network Planning.
Comparing IACOA with typical problems genetic algorithms and ant colony system algorithm used TSP of 51 cities, the experimental results show that the IACOA algorithm is better than this genetic algorithm and ant colony system algorithm in global search ability and fast operation to avoid falling local optimal value, The IACOA algorithm is more suitable for solving un-linear programming problems showing a strong optimization properties. IACOA algorithm is applied to the biomass power plant low-voltage distribution network planning problem of 72 load points and six power station, the real tests prove the practicability and validity of this paper method.
引文
[1]2010-2015年中国生物质能发电产业投资分析及前景预测报告(上下卷),2010：25-27
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[2]王锡凡电力系统规划基础．北京水利电力出版社,1996.
[3]Singh, G, Das, S. Gosavi, S., Pujar, S.:Ant Colony Algorithms for Steiner Trees:An Application to Routing in Sensor Networks, Recent Developments in Biologically Inspired Computing, Eds. L. N. de Castro, F. J. von Zuben, Idea Group Publishing, 2004,181-206.
[4]M. Dorigo, V.Maniezzo,& A. Colorni. Positive feedback as a search strategy. Technical report 91-016, Dipartimento di Elettronica, Politecnico di Milano, Milan, 1991.
[5]Issmail Ellabib, Paul Calamai, Otman Basir Exchange strategies for multiple Ant Colony SystemInformation Sciences, Volume 177, Issue 5,1 March 2007, Pages 1248-1264.
[6]Angel Fernando Morales and Carlos Villegas Quezada, A Universal Eclectic Genetic Algorithm for Constrained Optimization. In Proceeding 6th European Congress on Intelligent Techniques, Germany, sept.1998:518-522..
[7]Tao ZHANG, Wen-xin TIAN, Yue-jie ZHANG, Shi-xin LIU. Improved Ant Colony System for VRPSPD with Maximum Distance Constraint Systems Engineering-Theory & Practice, Volume 28, Issue 1, January 2008, Pages 132-140.
[8]F.van den Bergh, A.P. Engelbrecht.A study of particle swarm optimization particle trajectories Information Sciences, Volume 176, Issue 8,22 April 2006, Pages 937-971.
[9]Zhang, Y., Kuhn, L., Fromherz, M:Improvement on Ant Routing for Sensor Networks, In:Workshop on Ant Colony Optimization and Swarm Intelligence, Sept.2004.
[10]倪秋龙,黄民翔,基于支路交换的模拟退火算法在配电网规划中的应用,电力系统及其自动化学报,2000,12(4)：31～35.
[11]Liu Y, Zhang H, et al. Particle Swarm Optimization for Fault State Power Supply Reliability Enhancement.In:Proc of the IEEE International Conference on Intelligent Systems Application to Power Systems. Budapest, Hungary,2001:172-176.
[12]屈稳太,丁伟,一种改进的蚁群算法及其在TSP中的应用,系统工程理论与实践,2006,(5)：93～98.
[13]Nabil Nahas, Mustapha Nourelfath, Daoud Ait-Kadi Coupling ant colony and the degraded ceiling algorithm for the redundancy allocation problem of series-parallel systems Reliability Engineering & System Safety,2007,92(2):211-222.
[14]王会颖,贾瑞玉等．一种求解0-1背包问题的快速蚁群算法[J].计算机技术与发展,2007,17(1)：104-107.
[15]Marco Dorigo, L M Gambardella. Ant Colony System:A Cooperative Learning Approach to Traveling Salesman Problem. IEEE Trans. On Evolutionary Computation, 1997,1(1):53-66.
[16]金慧敏,马良.遗传退火进化算法在背包问题中的应用[J].上海理工大学学报,2004,26(6)：561-564.
[17]Liu S, Dou Z T, Li F, et al. A new ant colony clustering algorithm based on DBSCAN. Proceedings of the 2004 International Conference on Machine Learning and Cybernetics,2004, (3):1491-1496.
[18]陈珩.电力系统稳态分析.北京：中国电力出版社,1995：150-153.
[19]王锡凡电力系统规划基础.北京水利电力出版社,1990
[20]贺国庆.基于改进蚁群算法的输电网络扩展优化规划.[硕士学位论文].华北电力大学大学系统工程,2006.
[21]侯云鹤,熊信良,吴耀武等.基于广义蚁群算法的电力系统经济负荷分配[J].中国电工程学报,2003,23(3)：59-64.
[22]Peak selection from MALDI-TOF mass spectra using ant colony optimization H. W. Ressom, R. S. Varghese, S. K. Drake, G. L. Hortin, et al. Bioinformatics. Oxford:Mar 1,2007,23(1):619
[23]C. S. Chang, L. Tian, F. S. Wen.A new approach to fault section estimation in power systems using Ant system Electric Power Systems Research,2008,49(1):63-70.
[24]胡泽春,王锡凡.配电网无功优化的分时段控制策略.电力系统自动化,2002,26(6)：45-49.
[25]Song Y H, Irving M R. An overview of heuristic optimization techniques for power system expansion planning and design[J]. Power Engineering Society General Meeting, 2004,1(6):933.
[26]钱峰，10kV配电网规划研究：[硕士学位论文],河海大学,2003.
[27]孙洪波.电力网络规划.重庆：重庆大学出版社,2000.
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