电能替代下的家居智能用电控制策略研究

发布时间：2018-11-01 15:58

【摘要】：近年来,我国雾霾问题日益严重,而电能替代正是缓解大气污染问题的有效措施。随着电能替代的推进以及智能用电相关技术的发展,对于居民侧电器的优化控制成为了可能。通过对居民侧用户的用电行为进行分析,进而提出相应的用电优化策略,可以达到削峰填谷,消纳新能源的目的。本文首先基于传统分时电价机制以及电力市场化两种电力供给模式下对居民智能用电的架构、交互方式以及实施机理进行了分析。进而对家庭用户的用电行为特征进行分析,并提取出影响家庭用户用电行为的主要影响因素。在此分析的基础上,提出了基于支持向量回归机的用户用电行为的预测方法,并通过仿真实验证明了此方法能够较为准确的对不同用户的电器开始使用时间进行预测。基于对用户行为的分析与预测,本文在传统分时电价的机制下对家居智能用电的优化控制策略进行研究。总结分析现有的智能用电优化策略相关的研究成果,提出了家电使用关联度的概念并建立了家电使用关联度矩阵。进而以用户的电费成本最小为优化目标建立了成本最小化算法,通过仿真验证了算法能够在保证用户正常用电的同时,有效降低用户的电费成本,同时改善用户的负荷曲线。最后,本文提出了电力市场环境下的新型电力供给模式。用户将电器的控制权授权给负荷聚合商并从负荷聚合商处领取补贴,而负荷聚合商则可以对大量获得授权的同种负荷进行统一调控。本文提出了基于遗传算法的负荷群调控策略,并以电动汽车为例,对算法进行了仿真验证。仿真结果表明,算法能够在保证用户用电不受到影响的前提下,达到削峰填谷,消纳新能源的目的。
[Abstract]:In recent years, the problem of haze is becoming more and more serious in China, and the substitution of electric energy is an effective measure to alleviate the problem of air pollution. With the development of electric power substitution and intelligent electricity-related technology, it is possible to optimize the control of residential electrical appliances. By analyzing the power consumption behavior of the residents, and then putting forward the corresponding power consumption optimization strategy, the purpose of cutting the peak and filling the valley and absorbing the new energy can be achieved. Firstly, based on the traditional time-sharing pricing mechanism and the electricity supply mode of electricity marketization, this paper analyzes the structure, interactive mode and implementation mechanism of residential intelligent electricity consumption. Then, the characteristics of household users' electricity consumption behavior are analyzed, and the main influencing factors of household users' electricity consumption behavior are extracted. On the basis of this analysis, a prediction method of user's electricity behavior based on support vector regression machine is proposed, and the simulation results show that this method can accurately predict the time when different users begin to use electrical appliances. Based on the analysis and prediction of user behavior, this paper studies the optimal control strategy of household intelligent power consumption under the traditional time-sharing pricing mechanism. This paper summarizes and analyzes the related research results of the existing intelligent power use optimization strategy, puts forward the concept of household appliance usage correlation degree and establishes the household appliance usage correlation degree matrix. Furthermore, a cost minimization algorithm is established with the aim of minimizing the user's electricity cost. The simulation results show that the algorithm can effectively reduce the user's electricity cost and improve the user's load curve while ensuring the user's normal electricity consumption. Finally, this paper puts forward a new power supply mode in the electricity market environment. The user authorizes the control of the appliance to the load aggregator and receives the subsidy from the load aggregator, and the load aggregator can uniformly regulate the same load of a large number of authorized loads. In this paper, a load group control strategy based on genetic algorithm is proposed, and an example of electric vehicle is given to verify the algorithm. The simulation results show that the algorithm can reduce the peak and fill the valley and absorb the new energy on the premise that the user's electricity is not affected.
【学位授予单位】：华北电力大学(北京)
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM76

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