【作者】 李阳；

【导师】 贾嵘；

【作者基本信息】 西安理工大学 ， 水利水电工程， 2006， 硕士

【摘要】 水电站厂内经济运行问题作为电力系统优化运行的一个重要方面，相关文献已经对此做了很深入的研究。然而多数研究是基于统一调度的垄断模式下进行的。新的电力市场机制下，水电站的经营思想要从被动计划发电转为主动竞价上网发电。因而经济运行己日益成为各发电公司自觉的行为，实现企业运营的利润最大化，已成为水电站的最终目标。电力市场下水电站的利润在很大程度上取决于该企业对电力市场信息的把握和电站内部运行方式的决策。 针对完全竞争模式下电力市场的特点，本文构建了水电站机组启停决策模型和机组组合规划模型。模型对水电站竞价策略的制定和运营方式的优化具有现实的指导意义和参考价值：前者有助于水电站制定有利于电站取得最大化利润的竞价决策；后者可用以指导水电站优化机组运营，以降低成本，增加利润。 在综合比较各种优化算法的基础上，结合本文数学模型的特点，选用遗传算法作为数学模型的求解方法，并且结合数学模型目标函数和约束条件的特点，对基本遗传算法从多方面做了改进。算法根据所研究的实际问题，采用二进制编码、二维数组方式表示机组启停计划。遗传操作过程中采用无回放余数随机选择算子、两点交叉算子、单点变异算子操作，减轻了求解过程对算法参数的依赖性。此外，还设计了启发式产生初始解、局部交叉算子、两个自检变异算子等均别于其它遗传算法的改进方法，这些设计方法对求解其它复杂优化问题同样具有参考价值。 本文采用了两个算例。以算例1为例阐述了如何利用改进遗传算法求解这种新的机组启停问题，并给出了用此方法求解的详细过程。以算例2为例将其求解结果与其它方法进行比较，结果显示此方法具有一定实用性和通用性。最后对本文提出的数学模型所能做的改进和将来的研究发展方向作了简要描述。更多还原

【Abstract】 The economic operation in hydropower station is an important part of the operation in power system. Many documents have made an intensive study on it. Most of these studies are based on monopolistic market. In the new market, The management of hydropower station transforms into bidding in competitive environment form planned generation. So economic operation has been becoming the conscious behavior of the electric corporations. The goal of hydropower station is to maximize the profits, which to some extent is determined by the information taken form electricity market and the operation strategy.Aim at the character of electricity market in competitive environment, two UC models were constructed in this paper. The first one is based on forecasted marginal price and takes profit as optimizing goal, this model can give hydropower station a valuable reference when they are making their bidding strategy ;The latter is a cost minimization model intending to help hydropower station lower their operation cost.Based on comprehensively comparing some sorts of algorithm available for solving UC and combining the characters of the models being proposed in this paper, genetic algorithm was chosen as numerical computing method. Some improvements concerning coding ,operator designing, constrain handling etc were made so as to make the algorithm more suitable for the models constructed in the paper. Numerical analysis illustrates that the improved genetic algorithm (IGA) designing is successful in solving high-dimension, multi-constrains, and nonlinear UC problem. The designing method is also helpful for solving other complex system optimization problems.Two illustrative examples are given in this paper. We expound that how to use the modified genetic algorithm to solve this new UC Problem with the first example. Comparing results of the second example with two other methods the algorithm’s practicability is proved. Finally, a summary is given and some problems to be further studied are discussed.更多还原