电力系统多目标混合优化调度问题研究
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摘要
随着电力系统中风、光等清洁可再生电源渗透率的不断增加,不确定间歇性功率波动给电网计划安排等工作带来的困难越发突出。同时,由于面临着巨大的能源与环境压力,我国已全面实施节能减排政策,电力工业需要完成国家下达的分阶段节能减排考核目标。如何在包含风电系统的日调度问题中,在考虑强制性节能减排指标的基础上,合理制定经济发电计划与旋转备用计划,已经成为亟待解决的关键问题。
针对需要考虑电力系统节能减排的优化调度问题,论文首先基于单时段的多目标期望值优化模型,拓展构建了以发电煤耗总量、污染物排放总量和总购电费用为期望值目标的多时段期望值负荷优化分配模型,并讨论了它的解算方法。对照期望值优化模型,拓展建立了以发电煤耗总量和污染物排放总量为期望值目标、总购电费用为最小化目标的混合负荷优化分配模型,并讨论了相应的解算方法。通过有关算例分析,验证了多时段负荷优化分配问题的期望值优化模型、期望值与最小值混合优化模型及其求解方法的正确性与实用性。
为考虑风电波动对调度计划制定的影响,论文定义了以风电功率间歇波动引起系统备用紧张程度为指标的规范化的上调和下调风电备用风险。将该指标列入优化目标中,构建了电网公司的购电总费用目标采取最优值形式,系统煤耗总量、污染物排放总量、上调和下调备用风险采取期望值形式的多目标混合优化调度模型,可以一体化制定优化的经济发电计划与旋转备用计划。
在求解考虑风电备用风险的多目标混合优化调度模型时,采用了分解交替迭代求解策略:将原问题分解为经济发电计划混合优化子问题和旋转备用计划混合优化子问题,并在2个优化子问题间通过发电出力与旋转备用的特定关系进行交替迭代求解。算例分析结果表明,所建立的考虑风电备用风险的发电计划与备用计划一体化多目标混合优化模型,可统筹并有区别地考虑安全、质量、经济、环境等多个目标,相应的求解策略与方法有助于降低求解难度、提高计算效率。
考虑到电力系统的一次调频可以调节部分风电波动引起的有功不平衡量,在原来的不计系统调频特性的多目标混合优化调度模型的基础上,拓展添加了系统静态调频能力关系,而后通过算例对比分析了系统调频特性对调度计划制定的影响。
在衡量风电备用风险时,需要通过适当抽样方式对风电功率波动场景进行概率抽样,抽样的宽度会对分析结果存在一定影响。论文最后定义用于风险目标中的风电有功波动场景抽样方式的性价比指标,来分析风速分布曲线上的不同抽样宽度对恒上调、下调备用风险指标下的备用计划制定的影响,并讨论了性价比较优的抽样方法。
本文工作得到国家自然科学基金项目《含间歇式电源的大型电力系统绿色能效协联优化理论研究》(50877014)和高等学校博士学科点专项科研基金课题《电力网络复合源流分析与调控理论研究》(20102302110021)资助。
With the permeability of the renewable power, such as the wind power and the solar power increasing in the Grid, the volatility of such uncertain and interrupted power has brought more difficulties for optimal dispatch. At the same time, facing high tension from the environmental and energy problems, the task of saving energy and reducing emissions for China has been implemented widely. Whole of the electric power industry has to accomplish the palnning index of the environmental and energy problems, which is drawn up by the government. Based on the consideration of the energy-saving and emission-reduction indexes, how to formulate the generation schedule and spinning reserve schedule in daily optimal disptch has, become a key problem to be solved.
To solve the problems of optimal dispath considering the saving energy and reducing emissions, in the first place, on the base of the research on the model of multi-objective desired value optimation for single time-period power load distribution, this paper sets up the model of multi-objective desired value optimation for multi-time-periods power load distribution. The targets of the model above include the energy consumption, the emission of pollutants, and the Grid Corporation’s purchasing cost. The solving method for this model will also be discussed. Comparing with the multi-objective desired value optimation model, this paper will discuss the model of multi-objective hybrid optimation for load distribution. The purchasing cost target will be turned to a desired value controled target, and its solving method will be given as well. Through some examples, the accuracy and practicality of these models and solving methods are verified.
In order to consider the wind power’s effect on optimal dispath, this paper defines a normalized reserve risk in order to evaluate the tension of the spinning reserve caused by the volatility of wind power, thus establishes the model of multi-objective hybrid optimal dispatch, which can both consider the reserve risk and formulate the generation schedule and spinning reserve schedule at the same time. In this model, the target of Grid Corporation’s purchasing cost reaches for the minimum value, and the targets of the energy consumption, emission of pollutants and the reserve risk reach for the desired value.
When solving the model of multi-objective hybrid optimal dispatch, a solving method is adopted, which disintegrates the original problem into two sub-problems, the economic load optimal dispatching problem and the spinning reserve optimization problem.Then, it uses the alternating solution according to the specific relationship between the two sub-problems. Examples show that the model can make an overall plan of safty, quality, economy and environment discriminatively, and the sovling method could help reducing the difficulty and increasing the efficiency for caculating.
Considering that the PFCA (Primary Frequency Control Ability) of power system can adjust a part of unbalanced active power caused by the fluctuating wind power, based on the previous model of multi-objective hybrid optimal dispatch without the ability of PFCA, the PFCA is added to the model. Then, examples show the different effect on the dispatching work between the two models.
When calculating the reserve risks, a proper sampling method will be adopted to forming different scenes of wind power fluctuations, because the width of sampling window will affect the analysis results to some extent. In the last part, to analyze effect of different sampling width through sampling on different wind-speed distribution curves on the reserve plan, the paper defines the index of cost performance and discusses the sampling method with better cost performance.
This work was conducted as a part of the Project 50877014 supported by Natural Science Foundation of China and the Project 20102302110021 supported by Specialized Research Fund for the Doctoral Program of Higher Education.
针对需要考虑电力系统节能减排的优化调度问题,论文首先基于单时段的多目标期望值优化模型,拓展构建了以发电煤耗总量、污染物排放总量和总购电费用为期望值目标的多时段期望值负荷优化分配模型,并讨论了它的解算方法。对照期望值优化模型,拓展建立了以发电煤耗总量和污染物排放总量为期望值目标、总购电费用为最小化目标的混合负荷优化分配模型,并讨论了相应的解算方法。通过有关算例分析,验证了多时段负荷优化分配问题的期望值优化模型、期望值与最小值混合优化模型及其求解方法的正确性与实用性。
为考虑风电波动对调度计划制定的影响,论文定义了以风电功率间歇波动引起系统备用紧张程度为指标的规范化的上调和下调风电备用风险。将该指标列入优化目标中,构建了电网公司的购电总费用目标采取最优值形式,系统煤耗总量、污染物排放总量、上调和下调备用风险采取期望值形式的多目标混合优化调度模型,可以一体化制定优化的经济发电计划与旋转备用计划。
在求解考虑风电备用风险的多目标混合优化调度模型时,采用了分解交替迭代求解策略:将原问题分解为经济发电计划混合优化子问题和旋转备用计划混合优化子问题,并在2个优化子问题间通过发电出力与旋转备用的特定关系进行交替迭代求解。算例分析结果表明,所建立的考虑风电备用风险的发电计划与备用计划一体化多目标混合优化模型,可统筹并有区别地考虑安全、质量、经济、环境等多个目标,相应的求解策略与方法有助于降低求解难度、提高计算效率。
考虑到电力系统的一次调频可以调节部分风电波动引起的有功不平衡量,在原来的不计系统调频特性的多目标混合优化调度模型的基础上,拓展添加了系统静态调频能力关系,而后通过算例对比分析了系统调频特性对调度计划制定的影响。
在衡量风电备用风险时,需要通过适当抽样方式对风电功率波动场景进行概率抽样,抽样的宽度会对分析结果存在一定影响。论文最后定义用于风险目标中的风电有功波动场景抽样方式的性价比指标,来分析风速分布曲线上的不同抽样宽度对恒上调、下调备用风险指标下的备用计划制定的影响,并讨论了性价比较优的抽样方法。
本文工作得到国家自然科学基金项目《含间歇式电源的大型电力系统绿色能效协联优化理论研究》(50877014)和高等学校博士学科点专项科研基金课题《电力网络复合源流分析与调控理论研究》(20102302110021)资助。
With the permeability of the renewable power, such as the wind power and the solar power increasing in the Grid, the volatility of such uncertain and interrupted power has brought more difficulties for optimal dispatch. At the same time, facing high tension from the environmental and energy problems, the task of saving energy and reducing emissions for China has been implemented widely. Whole of the electric power industry has to accomplish the palnning index of the environmental and energy problems, which is drawn up by the government. Based on the consideration of the energy-saving and emission-reduction indexes, how to formulate the generation schedule and spinning reserve schedule in daily optimal disptch has, become a key problem to be solved.
To solve the problems of optimal dispath considering the saving energy and reducing emissions, in the first place, on the base of the research on the model of multi-objective desired value optimation for single time-period power load distribution, this paper sets up the model of multi-objective desired value optimation for multi-time-periods power load distribution. The targets of the model above include the energy consumption, the emission of pollutants, and the Grid Corporation’s purchasing cost. The solving method for this model will also be discussed. Comparing with the multi-objective desired value optimation model, this paper will discuss the model of multi-objective hybrid optimation for load distribution. The purchasing cost target will be turned to a desired value controled target, and its solving method will be given as well. Through some examples, the accuracy and practicality of these models and solving methods are verified.
In order to consider the wind power’s effect on optimal dispath, this paper defines a normalized reserve risk in order to evaluate the tension of the spinning reserve caused by the volatility of wind power, thus establishes the model of multi-objective hybrid optimal dispatch, which can both consider the reserve risk and formulate the generation schedule and spinning reserve schedule at the same time. In this model, the target of Grid Corporation’s purchasing cost reaches for the minimum value, and the targets of the energy consumption, emission of pollutants and the reserve risk reach for the desired value.
When solving the model of multi-objective hybrid optimal dispatch, a solving method is adopted, which disintegrates the original problem into two sub-problems, the economic load optimal dispatching problem and the spinning reserve optimization problem.Then, it uses the alternating solution according to the specific relationship between the two sub-problems. Examples show that the model can make an overall plan of safty, quality, economy and environment discriminatively, and the sovling method could help reducing the difficulty and increasing the efficiency for caculating.
Considering that the PFCA (Primary Frequency Control Ability) of power system can adjust a part of unbalanced active power caused by the fluctuating wind power, based on the previous model of multi-objective hybrid optimal dispatch without the ability of PFCA, the PFCA is added to the model. Then, examples show the different effect on the dispatching work between the two models.
When calculating the reserve risks, a proper sampling method will be adopted to forming different scenes of wind power fluctuations, because the width of sampling window will affect the analysis results to some extent. In the last part, to analyze effect of different sampling width through sampling on different wind-speed distribution curves on the reserve plan, the paper defines the index of cost performance and discusses the sampling method with better cost performance.
This work was conducted as a part of the Project 50877014 supported by Natural Science Foundation of China and the Project 20102302110021 supported by Specialized Research Fund for the Doctoral Program of Higher Education.
引文
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[2]国办发[2007]53号文件:国务院办公厅关于转发发展改革委等部门节能发电调度办法(试行)的通知[EB/OL]. [2008-4-1] http://law.baidu.com/pages/ chi-nalawinfo/9/60/25b6ec5f1470eb30c3102692f6c98257_0.html.
[3]孙静,于继来.节能发电调度问题的多目标期望控制模型及解法[J].电力系统自动化, 2010, 34(11): 23-27.
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