需求侧管理峰谷分时电价多目标优化方法研究

发布时间：2018-04-15 17:36

本文选题：需求侧管理 + 峰谷分时电价　；参考：《天津大学》2014年硕士论文

【摘要】：峰谷分时电价(Time of use pricing,简称TOU)是电力需求侧管理(Demand Side Management,简称DSM)中一项非常重要的措施。依据原始负荷大小情况,对不同用电时段制定不同的电价,以利用电价在电力市场中的经济引导作用,调节电力用户的用电行为,改变负荷的分布情况,实现削峰填谷、节能减排的作用,更符合国家可持续发展的大政方针。从而解决高峰时段的电力缺口,有效缓解了供电方和用电方之间的矛盾,同时为双方带来效益,实现双赢的局面。本文对国内外关于需求侧管理峰谷分时电价的研究进行分析,发现分时电价的研究中仍存在着问题。以往在制定分时电价的过程中,往往过于侧重削峰填谷效果而忽略了不同电价对于用户的影响,忽略了用户的接受程度及其对分时电价实施效果的影响,使得优化结果准确度欠佳。对此,本文基于智能算法建立了一套从时段划分到各时段电价制定都综合考虑的多目标分时电价递进优化方案,综合考虑各方面因素对分时电价进行制定和优化。本文利用聚类分析的方法进行时段划分,以使各时段之间有更大的区分度,使划分结果更具科学性。同时,采用带有精英策略的快速非支配遗传算法,对峰谷分时电价的多个目标同时优化,以得到平衡多个目标的Pareto最优解集,进而利用多属性决策原理选择出具有更高综合满意度的最优折衷解。在此基础上,本文综合考虑实施分时电价后用户对于电价方案的满意程度和接受程度对于实施效果的影响,提出了对峰谷分时电价的递进优化策略,对用户响应曲线和负荷曲线进行递进优化。对响应曲线进行递进修正,以更准确地预测实施分时电价后的用户负荷曲线;同时,用优化后的负荷曲线代替原始的负荷曲线进行递进优化,以进一步削峰填谷,探索最优的削峰填谷效果。对于分时电价实施过程中用户接受程度的考虑以及对分时电价进一步递进优化是在需求侧管理领域的创新应用,对于峰谷分时电价的普及及优化具有重要意义。通过仿真分析,验证该优化方法在削峰填谷上的显著效果的同时,也证实了递进策略通过效地实现了负荷的进一步优化。
[Abstract]:Time of use pricing (TOU) is a very important measure in DSM (demand and Side Management).According to the size of the original load, different electricity prices are made for different periods of time, in order to make use of the economic leading role of electricity price in the electricity market, to adjust the power consumption behavior of the power users, to change the distribution of the load, and to realize cutting the peak and filling the valley.The role of energy conservation and emission reduction, more in line with the national policy for sustainable development.In order to solve the power gap during the peak period, effectively alleviate the contradiction between the power supply side and the power side, at the same time bring benefits to both sides, and achieve a win-win situation.In this paper, the domestic and foreign research on demand side management peak-valley time-sharing price is analyzed, and it is found that there are still some problems in the study of time-sharing price.In the past, in the process of making time-sharing electricity price, the effect of peak cutting and valley filling was often emphasized too much, and the influence of different electricity price on the user, the acceptance degree of the user and the effect on the implementation effect of time-sharing electricity price were ignored.The accuracy of the optimization results is poor.In this paper, based on the intelligent algorithm, a set of multi-objective time-sharing price progressive optimization scheme is established, from the time division to the pricing formulation of each time period, which considers all factors to formulate and optimize the time-sharing electricity price.In this paper, the method of clustering analysis is used to divide the time interval, so that there is a greater degree of distinction between the different periods and the result of the division is more scientific.At the same time, a fast non-dominated genetic algorithm with elitist strategy is used to optimize multiple targets of peak-valley time-sharing electricity price at the same time, so as to obtain the Pareto optimal solution set that balances multiple targets.Then the optimal compromise solution with higher comprehensive satisfaction degree is selected by using the principle of multiple attribute decision making.On this basis, this paper considers the effect of customer satisfaction and acceptance on the effect of electricity price scheme after the implementation of time-sharing price, and puts forward a progressive optimization strategy for peak and valley time-sharing electricity price.The user response curve and load curve are progressively optimized.The response curve is modified step by step in order to predict the user load curve more accurately, and the optimized load curve is used instead of the original load curve to further cut the peak and fill the valley.To explore the optimal peak cutting and filling effect.It is an innovative application in the field of demand-side management to consider the user acceptance degree in the implementation process of time-sharing electricity price and to further optimize the time-sharing electricity price, which is of great significance to the popularization and optimization of peak and valley time-sharing price.The simulation results show that the proposed optimization method has significant effect on peak cutting and valley filling, and it also proves that the progressive strategy can achieve further optimization of load effectively.
【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F426.61

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