【作者】 朱佳梅；

【导师】 任洪娥； 董本志；

【作者基本信息】 东北林业大学 ， 计算机应用技术， 2010， 硕士

【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。更多还原

【Abstract】 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.更多还原