家庭智能用电任务调度优化及其对电网负荷影响分析模型

Smart electricity consumption emphasizes and encourages the interaction between users and the grid with the difference of traditional electricity consumption, therefore it’s an important problem of constructing the mechanism of the interaction between the users and the grid in the study of smart electricity consumption. This project is intended to research on the problems of scheduling optimization of smart electricity consumption task and its impact analysis model to the grid load in the household perspective, in order to analyze the interaction mechanism between household users and the grid company. The main contents of this research are listed as follows: constructing a task model and a behavior model of household smart electricity consumption, analyzing the characteristics and acquiring the preferences of household smart electricity consumption behavior; constructing task scheduling optimization models of household smart electricity consumption in a single family and in multi-families, designing the optimal scheduling algorithms based on such genetic algorithms, NSGA-II and so on, and analyzing the impact of the household smart electricity consumption behavior and the electricity price pattern on the scheduling optimization; studying the impact analysis model of household smart electricity consumption task’s scheduling to the grid load, and building an adaptive load forecasting model of smart electricity consumption. The purposes of this research is to develop optimal scheduling schemes of electricity consumption task for families with saving their electricity costs, and also to reduce the grid’s peak load and provide decision support for the grid’s load scheduling and dynamic real-time tariff setting.

智能用电与传统用电方式的区别是强调和鼓励用户和电网之间的双向互动，因此构建一个用电用户和电网之间的互动影响机制是智能用电研究中的一个重要问题。本课题拟从家庭角度研究智能用电任务调度优化及其对电网负荷影响分析模型，分析家庭用户与电网公司之间互动的影响机制，主要研究内容为：构建家庭智能用电任务模型及智能用电行为模型，进行家庭智能用电行为的特征分析和偏好获取；构建单家庭和多家庭下的智能用电任务调度优化模型，设计基于遗传算法、NSGA-II等方法的调度优化算法，并分析家庭智能用电行为及电网电价模式对调度优化的影响；研究家庭智能用电对电网负荷影响分析模型，建立一个智能用电下的自适应负荷预测模型。本课题研究的目的在于为家庭用户制定有效的用电任务调度优化方案，节省用户用电费用，同时降低电网高峰使用负荷，为电网公司负荷调度和动态实时电价制定提供决策支持。

智能用电与传统用电方式的区别是强调和鼓励用户和电网之间的双向互动，因此构建一个用电用户和电网之间的互动影响机制是智能用电研究中的一个重要问题。本课题主要从家庭角度研究智能用电任务调度优化及其对电网负荷影响，目的在于为家庭用户制定有效的用电任务调度优化方案，节省用户用电费用，同时降低电网高峰使用负荷。.首先，对家庭用户的用电行为进行特征分析，并将居民用电行为作为负荷识别的特征之一，设计基于结构熵权法的非侵入式家电识别方法。然后，采用集合论语言对不同用电任务进行描述定义，建立以家庭负荷特性及运行状态为约束的多目标优化问题，并提出了基于混合编码遗传算法的求解算法，可以实现对家庭智能用电任务调度问题的单目标和多目标优化求解。.其次，针对电力市场中电网售电公司与用户间互动关系，基于博弈论构建一种非合作的Stackelberg模型研究家庭用户的需求响应策略。模型通过对用户进行分类以实现对不同偏好用户的综合考虑，同时将电网负荷波动对售电公司效益的影响、用户满意度对用电舒适度的影响进行量化处理以完善供、用电双方效用函数。采用逆向归纳法对模型的纳什均衡解进行求解，并以此实现分时电价下对用户用电任务的优化调整。.最后，提出基于动态电价提出考虑分布式光伏消纳的居民需求响应模型。对用户进行分类以实现对不同偏好电力用户的综合考虑，对用电舒适度及经济度进行量化处理用以描述用户满意度。以用户满意度最大及上网光伏最小为目标，采用NSGA-II算法对模型进行求解。