【作者】 郝晋；

【导师】 周家启；

【作者基本信息】 重庆大学 ， 电力系统及其自动化， 2002， 硕士

【摘要】 电力系统优化运行是典型的非线性优化问题，并且涉及到许多非线性、离散、随机性及不确定性等因素。常规的数学方法在处理此类问题时有一定的局限性。而模拟进化优化方法最适于解决那些传统优化方法难以求解的高度非线性、离散性问题及组合优化问题。本文引入最近几年才提出来的一种新型模拟进化优化算法-蚁群优化算法，并以机组最优启停问题为研究对象，对算法本身及其在最优启停问题中的应用进行探索性工作。主要成果有：从随机优化技术出发，提出了一种新颖的随机扰动蚁群优化算法,该算法包含了两个重要方面：一是提出了采用倒指数曲线来描述的放大因子，可以有效地防止早熟；二是设计出了相应的随机选择策略和扰动策略，避免算法陷入停滞。对改进算法的参数选取进行了分析，在大量数值仿真的基础上提出了具有普遍意义的参数选取方法，并基本确定了各参数的最佳取值范围。通过算例并与基本蚁群算法进行比较，验证了该算法的逃脱局部极值的全局优化能力。利用提出的随机扰动蚁群优化算法求解机组最优启停问题。首先引入了状态、决策等概念，同时提出了路径的概念，最终把机组最优启停问题设计成类似于TSP的模式，从而可以方便地利用蚁群优化算法来求解。提出了不同的方法来处理各种约束。用表限制不满足旋转备用约束和机组最小启、停时间约束的状态；通过附加惩罚项处理线路N安全性约束。数值仿真证明利用蚁群优化算法求解机组最优启停问题是可行的、有效的。分别用基本蚁群优化算法及随机扰动蚁群优化算法对机组最优启停进行计算比较，仿真结果表明：随机扰动蚁群优化算法较基本蚁群优化算法具有更强的全局优化能力。考虑到负荷预测的不确定性和发电设备的可能停运，还必须安排一定的发电备用。论文拟用概率方法评估系统的旋转备用，即在机组最优启停模型中引入可靠性约束。解算时，通过计算安全函数排除不满足可靠性要求的组合，并进行了期望安全水平对优化解影响的敏感度分析。更多还原

【Abstract】 Optimal operation problems in electric power systems are typical non-linear programming problems, in addition, many non-linear, discrete, stochastic and undeterministic factors are involved. Conventional mathematical programming techniques are imperfect for the optimal operation of power systems due to inherent complexity. However, the optimization methods by simulated evolutions are appropriate for solving the non-linear, discrete problems and combination optimization problems which are difficult to be solved via conventional methods. A novel optimization method by simulated evolutions - Ant Colony Optimization algorithm and its application in the Unit Commitment problem (UC) are exploringly investigated in this thesis. The main achievements are as follows:According to the stochastic mechanism, a novel Ant Colony Optimization algorithm with Random Perturbation Behavior(RPACO) is presented in this paper. The new algorithm includes two important aspects: a amplify factor formulated by inverse exponent function is developed, which is used to avoid premature, on the other hand, corresponding transition strategy with random selection and perturbation behavior is designed, which is designed to prevent the algorithm from stagnating. Furthermore, selection of parameters in the new algorithm is analyzed, a selection method of parameters which possesses a generality is proposed based on many numerical simulations, and finally the best numerical range of each parameter is determined. Numerical simulations demonstrate that the new algorithm possesses more strong global optimization capability.The optimal unit commitment is solved via the Ant Colony Optimization algorithm in this paper. Concepts of a state and a decision are introduced, and the definition of a path is proposed. Thereby, the unit commitment problem is designed with a simulation to TSP. Because only the problem without constrains can be solved through ACO, two effective methods are developed to deal with all kinds of constrains of UC. First, the list is used to restrict states which don’t satisfy the constrains, such as spinning reserve,<WP=7>minimum start up/shut down time. Second, the transmission line capacity limit is handled by an additive penalty item. Numerical results demonstrate the feasibility and availability of the algorithm.Considering the uncertainty of load forecast and likelihood of component failure, a certain generation reserve should be established. In this paper, the probabilistic technique is used to evaluate the spinning reserve requirements of the system, that is, the reliability constraint is considered in the model of unit commitment. The operating risk of the combination states in the system is evaluated via the security function method, and combinations, in which the security constraint doesn’t be satisfied, will be eliminated. Moreover, the sensitivity of the desired security level to the optima is investigated.更多还原