基于引力搜索算法的水火电短期优化调度方法

发布时间：2018-03-31 13:34

本文选题：水火电系统　切入点：节能环保　出处：《华中科技大学》2014年硕士论文

【摘要】：水火电系统短期优化调度在电力系统运行中具有可观的经济效益，它一直是国内外学者的研究热点。对于电力系统调度问题，传统调度模型仅追求系统经济效益最大化，使水电厂尽可能多发电，以火电厂耗煤最小为目标。随着现代化社会的建设，能源短缺和环境恶化问题日渐严重，节能环保是实施可持续发展战略的必然选择，将减少火电厂废气排放作为调度问题的优化目标具有重要意义。许多学者成功应用智能优化算法求解水火电短期优化调度问题，然而大多数智能算法都有早熟收敛的缺点，并且对于问题复杂约束条件的处理方法也较少。本文对引力搜索算法进行改进，提高其搜索性能，并提出多目标的引力搜索算法，用于解决综合考虑经济效益和环境保护的水火电系统短期优化调度问题。本文的主要研究工作如下： 1)针对引力搜索算法容易陷入局部最优的缺陷，本文提出一种改进的引力搜索算法(IGSA)。首先在速度更新过程引入个体记忆和群体信息，，提出改进的速度和位置更新公式。然后在算法进化过程中引入混沌变异操作，使算法从局部最优解附近转到全局范围内搜索最优解，增强其全局寻优性能。并采用基于选择操作的种群进化规则，保证种群总是向最优解方向进化。为了处理多目标优化问题，本文将非支配排序和聚集距离引人引力搜索算法中，提出带混沌变异的非支配排序引力搜索算法(NSGSA-CM)。通过基准问题测试表明，NSGSA-CM算法有较好的搜索性能，适用于解决高维多目标优化问题。 2)本文在节能环保的重要背景下，建立了综合考虑经济效益和环境保护的水火电系统短期优化调度模型。该模型追求电力系统运行时燃料消耗最小和污染气体排放最少，是一个双目标优化问题。并通过引入基于时段的可变权重，将该问题转化为单目标优化问题，使其可以用改进的引力搜索优化算法求解。 3)在处理问题众多约束条件时，本文采用一系列启发式策略使个体满足约束。对于动态水量平衡约束，首先将违反约束水量平均分成若干份，然后将其调整到随机选出的时段对应的下泄流量中，直到所有水量调整完从而满足约束。在处理水库库容约束时，基于超过库容限制的水量，在可行域内同对当前时段和后一时段的下泄流量做出等量调整，在保持水库动态水量平衡的情况下使库容约束得到满足。采用基于火电厂优先级的规则调整各火电厂每时段的出力，以满足系统负荷平衡约束。 4)为了验证IGSA和NSGSA-CM算法求解水火电短期优化调度问题的有效性，本文选取了两个实例进行仿真计算。实例结果表明，与文献中的结果相比，IGSA和NSGSA-CM算法有更好的全局优化性能，在满足所有约束的情况下获得了较小的燃煤成本与污染气体排放。可知，文中的算法与约束处理策略解决水火电系统短期调度问题是可行和有效的。
[Abstract]:Short-term optimal dispatching of hydro-thermal power system has considerable economic benefits in the operation of power system. It has always been a hot research topic of scholars at home and abroad. For the power system scheduling problem, the traditional dispatching model only pursues the maximization of system economic benefits. With the construction of modern society, the problems of energy shortage and environmental deterioration are becoming more and more serious. Energy saving and environmental protection is the inevitable choice to implement the strategy of sustainable development. It is of great significance to reduce exhaust gas emissions from thermal power plants as the optimization objective of scheduling problems. Many scholars have successfully applied intelligent optimization algorithms to solve short-term optimal scheduling problems for hydro-thermal power plants. However, most intelligent algorithms have the disadvantage of premature convergence. In this paper, the gravity search algorithm is improved to improve its search performance, and a multi-objective gravity search algorithm is proposed. The main work of this paper is as follows: (1) to solve the short-term optimal scheduling problem of hydro-thermal power system considering economic benefits and environmental protection comprehensively. 1) aiming at the defect that gravity search algorithm is easy to fall into local optimum, an improved gravity search algorithm is proposed in this paper. Firstly, individual memory and group information are introduced into the speed update process. An improved speed and position updating formula is proposed, and then chaotic mutation operation is introduced in the evolution of the algorithm, which makes the algorithm move from the local optimal solution to the global optimal solution, and search for the optimal solution in the global range. The global optimization performance is enhanced, and the population evolution rule based on the selection operation is adopted to ensure that the population always evolves towards the optimal solution. In order to deal with the multi-objective optimization problem, the undominated ordering and aggregation distance are introduced into the gravitational search algorithm in this paper. An undominated sorting gravitational search algorithm with chaotic mutation is proposed. The benchmark test shows that the NSGSA-CM algorithm has good search performance and is suitable for solving high dimensional multiobjective optimization problems. 2) under the important background of energy saving and environmental protection, this paper establishes a short-term optimal dispatching model of hydro-thermal power system considering economic benefits and environmental protection. The model pursues the minimum fuel consumption and the least emission of polluting gas when the power system is running. By introducing variable weight based on time interval, the problem is transformed into a single objective optimization problem, which can be solved by an improved gravitational search optimization algorithm. 3) in dealing with many constraints, a series of heuristic strategies are used to make individuals satisfy the constraints. For dynamic water balance constraints, first of all, the amount of water in violation of constraints is divided into several parts. It is then adjusted to the lower discharge corresponding to the randomly selected time period until all the water is adjusted to meet the constraints. In dealing with the reservoir capacity constraints, based on the amount of water exceeding the reservoir capacity limit, In the feasible region, the same amount of downward discharge is adjusted for the current period and the later period, Under the condition of keeping the dynamic water balance of the reservoir, the reservoir capacity constraints are satisfied, and the output force of each period of time is adjusted based on the priority rule of the thermal power plant to satisfy the system load balance constraint. 4) in order to verify the effectiveness of IGSA and NSGSA-CM algorithms in solving hydro-thermal power short-term optimal scheduling problem, two examples are selected for simulation. The results show that the algorithm has better global optimization performance than the results in literature. The low cost of coal combustion and the emission of polluting gas are obtained under all constraints. It can be seen that the algorithm and constraint treatment strategy in this paper is feasible and effective to solve the short-term scheduling problem of hydro-thermal power system.
【学位授予单位】：华中科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM73

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