Research On Dispatching Model Of Home Energy Management System Under Smart Grid

In the context of the rapid development of smart grids,the Home Energy Manage System(HEMS)is an indispensable tool for residential users to respond to power demand side.Its research and development is of great significance for the future development of China's power demand side response resources.The research of this thesis focuses on the core issues of HEMS: scheduling model and algorithm.The paper combines HEMS static scheduling with dynamic scheduling,and proposes a HEMS environment adaptive scheduling scheme based on dynamic decision real-time correction.Through simulation experiments,the scheduling scheme can save household electricity consumption and reduce electricity consumption costs on the premise of meeting user needs.Improve the user load curve and meet the grid demand response,overcome the prediction error problem of the traditional HEMS static scheduling,and to some extent make up for the shortcomings of the HEMS dynamic scheduling economy.Firstly,the paper analyzes the status quo of HEMS research at home and abroad,and summarizes the achievements of existing research.The HEMS related technologies including demand response technology are expounded,and the various components of HEMS are introduced.The emphasis is on the analysis of system scheduling models and algorithms.The analysis pointed out that the traditional HEMS static scheduling may make unreasonable energy scheduling schemes when the PV power prediction is not accurate and the actual temperature does not match the forecast temperature,resulting in system economic degradation or even violation of user settings;However,the traditional HEMS dynamic scheduling only considers the running status of the current scheduling time.Although it can meet the constraint operating conditions of the scheduling time,there is a lack of coordination between the dynamic scheduling of each scheduling moment,and the overall scheduling economy is not strong.Then,based on the system composition,the paper establishes the basic structure of the intelligent home appliance management system,and builds a static optimization scheduling model and load model.Then the improved particle swarm optimization algorithm is used to solve the problem.The improved particle swarm optimization algorithm increases the adaptability of the particle by introducing the attenuation factor,and uses the variable acceleration coefficient to improve the rationality of the particle search.The simulation example shows that the improved dispatching plan obtained by the improved algorithm reasonably arranges the household electricity,meets the user's needs and settings,has strong economic performance,and verifies the rationality of the static optimization scheduling model and the load model.The adaptability of the improved particle swarm optimization algorithm used to the model was also confirmed.Further,based on the smart home appliance management system,a distributed power generation system(photovoltaic power generation system)model and an energy storage system(battery)model are added to form a basic structure of the HEMS.A reasonable and economical static scheduling plan is obtained through the improved particle swarm optimization algorithm.Construct a real-time prediction model of environmental data such as PV output and temperature,and use the current time period data to estimate the next time period data in real time,and provide data support for the judgment of the later dynamic correction.Then,the environment discriminating and decision-making mechanism is established.The mechanism discriminates according to the real-time forecasting data and the plan formulated by the static scheduling in the past,dynamically corrects the original plan,and makes the best scheduling decision.Through the simulation test of the example,the scheduling scheme can adjust the actual decision in real time under the condition that the prediction of the PV output power and temperature is inaccurate,which can not only maintain the economicality of the static scheduling in the overall planning,but also can be used in each time period.Ensure that the system does not violate user settings.