面向家庭能源管理系统的用电调度策略研究

发布时间：2018-01-04 13:20

本文关键词：面向家庭能源管理系统的用电调度策略研究　出处：《上海电力学院》2017年硕士论文　论文类型：学位论文

【摘要】：能源危机与环境恶化问题是当今国际社会的热点问题,而居民侧的电力节能降耗对整个地区和国家的节能减排具有重要意义。需求响应是从居民侧解决能源危机的一项措施。近年来,智能电网整合的高级的信息、控制及通信技术为需求响应的推行提供了强大的技术支持。家庭能源管理系统在高级计量架构的基础上融入数据分析和信息管理功能,能够监测居民家庭中的能源使用情况,并根据需求响应信号和电价信号优化家庭中各类用电设备的运行,最终实现在满足家庭用户用能需求的前提下降低能源成本、提高能源使用效率。目前针对家庭能源管理系统的研究在用电信息采集、智能控制和网络通信等硬件平台的搭建方面较为全面,而在具体设备的运行调度策略方面还存在不足。因此,本文以家庭能源管理系统为对象,进一步研究完善家庭用电调度策略。本文的主要研究内容如下:首先分析了需求响应的研究现状,介绍了家庭能源管理的技术体系,并重点分析其核心技术用电调度策略的研究现状;然后设计了家庭能源管理系统的基本架构,系统中包含居民侧的基本设备、分布式电源和储能设备这三类常见设备,总体架构由通信、检测、预测、优化调度等功能模块构成,优化调度模块是整个系统的核心功能模块,也是本文的重点研究内容。考虑到家庭能源管理与电价变化密切相关,电价的精确预测是家庭能源管理系统实现高效优化的必要条件,因此本文提出了一种基于BP神经网络和马尔可夫链的短期电价预测模型,先采用BP神经网络初步预测短期电价的变化趋势,再基于马尔可夫链对初步预测的误差进行修正,并通过算例分析验证了该预测方法的准确性。最后,提出了面向家庭能源管理系统的用电调度策略。在对家庭能源管理系统中的可调度设备和储能设备建立数学模型的基础上,针对用户的不同需求,分别以用电成本最小化、用户体验与用电成本兼顾、用电隐私与用电成本兼顾为目标建立三种用电调度模型,并基于混合编码的遗传算法对模型进行求解,在算例分析中通过不同场景的对比仿真验证了本文提出的用电调度策略的实用性和有效性。
[Abstract]:Energy crisis and environmental deterioration are hot issues in the international community today. In recent years, the smart grid integrates the advanced information of smart grid, and the demand response is a measure to solve the energy crisis from the resident side. Control and communication technologies provide strong technical support for the implementation of demand response. The household energy management system integrates data analysis and information management functions on the basis of advanced metrological architecture. The utility model can monitor the energy use in the household and optimize the operation of various kinds of electric equipment in the household according to the demand response signal and the electricity price signal. Finally, it can reduce the energy cost and improve the energy efficiency under the premise of meeting the energy needs of the household users. Currently, the research on the household energy management system is in the power information collection. Intelligent control and network communication hardware platform construction is more comprehensive, but in the specific equipment scheduling strategy is still inadequate. Therefore, this paper takes the home energy management system as the object. The main contents of this paper are as follows: firstly, the research status of demand response is analyzed, and the technical system of household energy management is introduced. The research status of the power dispatching strategy of its core technology is analyzed. Then the basic architecture of the household energy management system is designed. The system includes the basic equipment on the residential side, distributed power generation and energy storage equipment. The overall architecture is communication, detection, prediction. The optimal scheduling module is the core function module of the whole system, which is also the key research content of this paper. Considering that the household energy management is closely related to the change of electricity price. The accurate forecasting of electricity price is a necessary condition for the household energy management system to realize the efficient optimization, so this paper proposes a short-term electricity price forecasting model based on BP neural network and Markov chain. First, BP neural network is used to predict the change trend of short-term electricity price, then the error of preliminary prediction is corrected based on Markov chain, and the accuracy of the prediction method is verified by an example. Based on the mathematical model of the schedulable equipment and the energy storage equipment in the household energy management system, the paper puts forward the power dispatching strategy for the household energy management system, aiming at the different needs of the users. To minimize the cost of electricity, user experience and power cost, power privacy and power cost as the goal to establish three power dispatching models, and based on hybrid coding genetic algorithm to solve the model. The practicability and effectiveness of the proposed power dispatching strategy are verified by comparison and simulation of different scenarios in a numerical example.
【学位授予单位】：上海电力学院
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM73

【参考文献】