基于PSO算法的DSM分时电价定价设计

发布时间：2018-12-05 19:53

【摘要】：智能用电则是智能电网的重要组成部分之一,它能大大的提高电能的利用率,目前最普遍实行的有效手段为需求侧管理(Demand Side Management,DSM),它通过比较理想的激励使用电用户合理地改善用电方式,使电力负荷运行更加平稳,需求响应(Demand Response,DR)则是DSM的最重要实现方式之一,是指用电的用户因为价格或其他激励的市场信息而主动改变日常的用电习惯以此达到节能减耗的效果。其最主要实现方式为分时电价(Time of Use,TOU),通过改变峰谷时段价格的方式引导用户改变用电负荷,优化用电方式。那么对于供电公司和用电用户来说,在实施分时电价的过程时应该如何设定更好价格信号以达到控制用户用电负荷的目的,从而实现负荷的削峰填谷,节能减排的效果,这是未来电力市场能智能用电能否成功的关键问题之一。首先,本文在智能用电发展的背景下,对分时电价定价策略的制定和实施以及居民用户的用电行为进行研究、分析。介绍需求响应的概念,总结国内和国外现阶段开展的分时电价定价研究的现状。其次,针对国内用电用户的响应行为建立有效地分时电价定价项目的模型。根据供电公司和居民的实际需求设计优化策略及目标函数。根据模型所设定目标函数提出优化算法,并对选取的遗传算法(Genetic Algorithm,GA)和粒子群算法(Particle Swarm Optimization,PSO)的可行性进行分析。最后,在MATLAB平台下,当不考虑用户舒适度的情况下,利用GA算法和PSO算法对模型进行单目标仿真,得出两种算法均可实现削峰填谷的目标,其中粒子群算法在本模型中的优化效果更佳。当考虑到用户舒适度的情况下,设定目标函数后利用多目标粒子群算法(Multi-objective Particle Swarm Optimization,MOPSO)对模型进行多目标优化,得到的优化结果可以同时达到提高用户舒适度和削峰填谷的目标。
[Abstract]:Intelligent power consumption is one of the important components of smart grid, it can greatly improve the utilization of electricity, the most common effective means is demand side management (Demand Side Management,DSM). It can reasonably improve the power consumption mode and make the power load run more smoothly through the more ideal incentives. The demand response (Demand Response,DR) is one of the most important ways to realize DSM. Electricity users change their daily habits actively because of price or other incentive market information in order to save energy and reduce consumption. The main way to realize it is time-sharing price (Time of Use,TOU). By changing the price of peak and valley period, it can guide the user to change the load of electricity and optimize the mode of electricity consumption. Well, for power supply companies and power users, how to set a better price signal in the process of implementing time-sharing price to achieve the purpose of controlling the user's power load, thus realizing the effect of cutting the peak and filling the valley of the load and saving energy and reducing emissions. This is one of the key questions about the success of intelligent power consumption in the future electricity market. First of all, in the context of the development of intelligent electricity, this paper studies the formulation and implementation of pricing strategy of time-sharing pricing and the consumer's behavior of electricity consumption. This paper introduces the concept of demand response and summarizes the present situation of time-sharing pricing research both at home and abroad. Secondly, an effective pricing model of time-sharing price is established according to the response behavior of domestic consumers. Design optimization strategy and objective function according to the actual needs of power supply companies and residents. The optimization algorithm is proposed according to the objective function set by the model, and the feasibility of the selected genetic algorithm (Genetic Algorithm,GA) and particle swarm optimization algorithm (Particle Swarm Optimization,PSO) is analyzed. Finally, on the MATLAB platform, when the user comfort is not considered, the GA algorithm and the PSO algorithm are used to simulate the model, and the results show that the two algorithms can realize the target of peak cutting and valley filling. Particle swarm optimization algorithm is more effective in this model. When the user comfort is considered, the multi-objective particle swarm optimization (Multi-objective Particle Swarm Optimization,MOPSO) is used to optimize the model by setting the objective function. The optimized results can achieve the goal of increasing the user's comfort and cutting the peak and filling the valley simultaneously.
【学位授予单位】：哈尔滨理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;F426.61

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