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本文研究的重点为机组组合问题，其目的是针对在指定周期内，满足系统负荷、备用容量、机组最小运行时间和停机时间等限制，考虑机组启停费用和发电费用特性，确定机组的开停机计划，使周期内发电总费用最小。 本文通过对机组组合问题进行深入的研究，对电力系统实际运行调度方式作了充分了解，以此对含风电的日前机组组合问题进行建模研究。针对风功率预测不确定性问题，本文作了详细讨论，引入风功率预测误差随机变量及其概率密度函数，通过选择合适的置信区间并结合初始风功率预测结果获得了在制定日前计划时风功率的预测值。在此基础之上，通过充分研究一些经典的机组组合问题的解决方案，本文决定采用结合等耗量微增率-迭代法整合离散二进制粒子群优化算法（BPSO）用于解决机组组合问题。BPSO 算法用于解决机组启停问题，等耗量微增率-迭代法用于解决经济调度问题。 本文参考相关文献建立出合理的机组组合模型，在此基础上对所建机组组合模型进行测试实验，最后得出结果表明：解决方案接近最优，也完全满足 UC 问题的约束条件，本文建立的机组组合模型能够根据具体预测信息类型进行机组组合决策。7780
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关键词 机组组合问题 风功率预测不确定性 等耗量微增率-迭代法
粒子群算法 Title The dynamic economic dispatch problem of Large-scale wind Power access to network
Abstract
The focus of this paper is the unit commitment problem, which is meeting the
system’s load and the reserve capacity and the unit’s minimum run time and down time
constraints during a specified period. Considering the cost of unit commitment and
power generation cost characteristics, we will determine the off and on plan of the unit
at the minimum total cost.
In this paper, we made a full understanding of the actual operation of power
system operation mode and made up a unit commitment model. For wind speed and
wind power is not be sure, we discuss about the problem in detail. When we talk about
the problem of wind speed prediction error we study the random variables and their
probability density function, and combine the problem with the wind speed forecast,
then we get the results of wind speed probability distribution, and then get the
distribution function of wind electric power. On this basis, we study the solutions of
some classical unit commitment problem, decide to use a combination of
Lambda-iteration method and discrete binary particle swarm optimization algorithm
(BPSO) to solve the unit commitment problem. The BPSO algorithm is used to solve
the crew scheduling problem, the Lambda-iteration method is used to solve the problem
of economic dispatch.
In this paper, we establish a reasonable unit commitment model, on this basis, we
make a test about the unit commitment model, then we get the conclusion that: the
solution we get is close to optimal, fully meet the constraints of the UC problem.
Keywords Unit commitment problem Uncertainty Lambda-iteration method Particle
swarm optimization 目 录
1 引言 . 1
1.1 课题的研究背景 1
1.1.1可再生能源及风电发展状况[1]
1
1.1.2风电场接入电力系统给电网调度管理带来的问题 2
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