电力用户侧需求响应优化及行为研究

发布时间：2018-05-28 11:00

本文选题：需求响应 + 楼宇集群能量系统　；参考：《上海电力学院》2017年硕士论文

【摘要】：随着各种分布式电源和新型用电设备的日益推广以及电力用户对需求个性化要求的提升,智能用电的理念及其技术发展体系应运而生。在该背景下,高级量测和通信系统与智能负荷调控技术的日渐发展和完善,驱动了以双向互动智能高效用能为目标的需求响应技术的应用和发展。为获得经济、环境效益的同时提升电能服务质量,研究用电行为并主动整合用户侧资源、优化用能调度己成为一项积极而有效的措施。本文首先构建了可实现集群内部能源的互济、储用、控制及信息交互的楼宇集群能量管理系统。然后基于分时电价机制和系统内可控元件的功耗模型,提出了一种电网友好型楼宇集群能量优化调度策略。以多形式储能、冷热电联供系统(CCHP)、制冷机组等可控元件为控制目标,同时考虑系统各类约束条件建立了楼宇集群能量协同优化调度模型。在分时电价机制下,以一天运行成本最低为目标,利用机会约束规划在处理随机变量方面的优势,建立了计及风光出力不确定性的楼宇集群能量系统(BCES)经济优化调度模型。在LINGO语言环境下编写了相应程序,并对模型求最优解。算例结果表明通过优化求解获得的BCES中各单元的出力,可以较好的利用多形式储能系统,引导BCES对主网的削峰填谷的同时也有效地提高了自身的经济效益。然后,本文提出了一种基于用户行为学的家庭日负荷曲线自下向上精细预测模型。在分析影响居民用电行为因素的基础上,通过非齐次马尔科夫链建立用户状态转移矩阵并求取家庭中居民所处的状态;将居民活动与相应电器状态关联,分别建立电器开启及其使用持续时长的联合概率分布函数;对居民家庭中常用电器进行建模,并基于序贯抽样法可进行不同日类型、气象数据、家庭人数下的居民日负荷曲线预测。算例仿真的结果表明负荷曲线预测精度较高,验证了该模型的有效性。最后,本文对电力用户侧的需求响应行为展开研究。为了揭示用户用电量对电价、气象、日类型等相关变化因素的响应程度,提出了一种需求响应综合模型。该模型实现了用户在分时电价下的需求响应行为规律的模拟,可为电力公司制定需求侧管理策略提供基础性指导。
[Abstract]:With the increasing promotion of various distributed power sources and new power equipment and the improvement of the demand individuation requirements of power users, the concept of intelligent power consumption and its technological development system have emerged as the times require. Under this background, the development and perfection of advanced measurement and communication system and intelligent load control technology drive the application and development of demand response technology which aims at two-way interactive intelligence and high utility. In order to achieve economic and environmental benefits and improve the quality of power service, it has become an active and effective measure to study the power consumption behavior and actively integrate the resources of the user side and optimize the energy use scheduling. In this paper, a building cluster energy management system which can realize the mutual aid, storage, control and information interaction of energy in the cluster is constructed. Then, based on the time-sharing pricing mechanism and the power consumption model of controllable components in the system, a grid friendly building cluster energy optimal scheduling strategy is proposed. Taking the controlled elements such as multi-form energy storage, combined cooling and heat supply system, refrigeration unit and so on, as the control target, and considering all kinds of constraints of the system, a building cluster energy cooperative optimal scheduling model is established. Under the time-sharing pricing mechanism, taking the lowest daily operating cost as the goal, and taking advantage of the advantage of opportunistic constrained programming in dealing with random variables, an economic optimal scheduling model of building cluster energy system (BCES) considering the uncertainty of wind power is established. The corresponding program is written in LINGO language environment, and the optimal solution of the model is obtained. The result of example shows that the output force of each unit in BCES can be obtained by optimization, which can make good use of multi-form energy storage system, and guide BCES to cut the peak and fill the valley of the main network, at the same time, it can effectively improve the economic benefit of itself. Then, this paper presents a fine forecast model of family daily load curve from bottom to bottom based on user behavior. Based on the analysis of the factors influencing the residents' electricity consumption behavior, the user state transfer matrix is established through the non-homogeneous Markov chain, and the state of the residents in the family is obtained, and the residents' activities are correlated with the corresponding electrical appliance states. Establishing the joint probability distribution function of the electric appliance opening and the duration of using, modeling the electrical appliances commonly used in the household, and based on the sequential sampling method can carry out the different types of day, meteorological data, Forecast of daily load curve under household size. The simulation results show that the accuracy of load curve forecasting is high, and the validity of the model is verified. Finally, the demand response behavior of power user side is studied in this paper. In order to reveal the response degree of electricity consumption to electricity price, weather, day type and other related factors, a comprehensive model of demand response is proposed. The model can simulate the demand response behavior of users under time-sharing price and can provide basic guidance for power companies to formulate demand-side management strategy.
【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

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