基于遗传算法的电力市场分段竞价交易决策研究

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论文）毕业设计

基于遗传算法的电力市场分段竞

价交易决策研究

专业：电气工程及其自动化 班级：电气065班 作者：

指导教师： 职称：教授 答辩日期：2010-6-18

摘 要

近年来，在电力市场环境下，采用什么样的竞价模型，以及竞价模型的好与坏直接影响着电力市场运营过程的公平、公开、合理性原则，而如何使用更好的竞价算法来对电力市场竞价模型进行求解，以利降低购电费用，同样具有重要的理论价值和应用前景，对电力市场的发展具有重要的意义。

电力市场分段竞价模型是近年来针对分时竞价模型的弊端提出的一种新的竞价模式。针对其排队法只能处理阶梯型报价曲线，而无法应用于其它常用报价曲线的缺点，提出了一种基于遗传算法的电力市场分段竞价交易决策算法，并设计了相应的编码方式、混合交叉变异算子等；针对简单遗传算法容易产生早熟现象的缺点，采用了自适应交叉变异率，小生境排挤技术等手段；竞价结果产生后，根据此竞价结果制定了将全天时刻分为48点的日发电计划。

应用算例，在Matlab软件平台上，对所提出的基于遗传算法的电力市场分段竞价交易算法进行了阶梯型报价曲线、线性报价曲线这两种常用报价曲线情况下的算例验证，计算结果显示算法的寻优效果因报价曲线性质的不同而略有区别，但两种情况均可得到优化的购电费用。实验结果证明了该算法应用于分段竞价模型的有效性和可行性，且不受报价曲线形式的限制。

关键词：电力市场, 分段竞价，竞价模型，遗传算法

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Abstract

In recent years, under the environment of Electric Power Market, what the bidding model we used, and the advantage and disadvantage of bidding model has directly effect the fairness, justice and reasonableness of Electric Power Market’ operation process. It also has very important theoretical value and application prospect that how to using better and more universal bidding algorithm which can effectively reduce the cost of electric energy in order to reduce the price of electric power.

Block bidding model is a new bidding mechanism aiming at the disadvantage of hourly bidding model. The Merit-Order Method which been commonly applied at Block Bidding Power Market can only process step-like bidding curves, but be useless in linear bidding curves, so provides a new way to solve this problem. According to the characteristic of block bidding mechanism, the coding and genetic operators are designed. This algorithm also adopt self-adaptively crossover rate and mutation rate, niche technology to keep population’s diversity, avoid premature and enhance local search ability. Basing on the result of the algorithm, the daily generation scheduling is planned in which a whole day time be divided into 48 points.

On Matlab software platform, the improved genetic algorithm has been applied to the cases using linear and step-like bidding curves. The results prove that optimizing effect of the algorithm is slightly difference according to the difference of bidding curves’ properties, but under the both two situation gains optimized power purchase cost. The results prove the effectiveness and feasibility of this algorithm, and has no limits of bidding curves’ form.

Keywords: electricity market, block bidding, bidding model; genetic algorithm;

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目 录

第1章 前 言 .......................................................................................... 1

1.1电力市场概述 ................................................................................. 1 1.2 研究电力市场的模型和算法的重要意义 ..................................... 2 1.3电力市场竞价问题的发展与研究现状 .......................................... 2

1.3.1 电力市场竞价模型的发展 .................................................. 2 1.3.2 电力市场竞价算法 ..................................................................... 5 1.4本文的主要工作 ............................................................................. 7 第二章 遗传算法概述 ............................................................................... 8

2.1遗传算法的基础理论及其特点 ...................................................... 8 2.2遗传算法的小生境技术 ............................................................... 10 第三章 电力市场分段竞价模型的遗传算法求解 .................................. 12

3.1分段竞价模型概述 ....................................................................... 12 3.2分段竞价的运营方式及规则 ........................................................ 14 3.3基于遗传算法的分段竞价算法的实现 ........................................ 16

3.3.1 采用分段竞价的原则........................................................ 16 3.3.2 分段竞价的电力市场中水电机组的处理 ......................... 16 3.3.3 编码方式 ........................................................................... 18 3.3.4 适应值函数 ....................................................................... 18 3.3.5 初始种群的形成 ............................................................... 19 3.3.6 约束的处理 ....................................................................... 19 3.3.7 遗传算子 ........................................................................... 20 3.3.8 自适应交叉变异率 ........................................................... 22 3.3.9 小生境技术的应用 ........................................................... 22 3.3.10 遗传算法的电力市场分段竞价流程图 ........................... 24

第四章 算例验证及其分析 ....................................................................... 25

4.1算例的原始数据 ........................................................................... 25 4.2 水电机组的处理 .......................................................................... 26 4.3 阶梯型报价曲线的算例分析 ....................................................... 26

4.3.1 性能分析 ........................................................................... 27 4.3.2 优化结果分析 ................................................................... 29 4.4 线性型报价曲线的算例分析 ....................................................... 30

4.3.1 性能分析 ........................................................................... 31 4.3.1 优化结果分析 ................................................................... 33

第五章 总结和展望 .................................................................................. 36

5.1 总结 ............................................................................................. 36 5.2 展望 ............................................................................................. 36

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致 谢 ......................................................................................................... 38 参考文献 .................................................................................................... 39

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