| With the continuous increase of social power demand,the peak-valley difference of power load is gradually becoming larger.The imbalance between supply and demand in power systems poses serious challenges to transmission and distribution lines.The application of energy storage system to shift the peak-valley difference of power load is one of the feasible means to solve the imbalance between supply and demand in power system.User-side energy storage is a distributed energy storage technology that can achieve local load balance and has many potential advantages.A new building intelligent platform based on a flat,non-centered network structure abstracts the electromechanical equipment or building space in the building into an intelligent unit,each unit corresponds to a computing process node(CPN),and each CPN is based on physical spatial relationship connect into a CPN network.In the CPN network,each CPN only exchanges data with neighboring CPNs,and through mutual cooperation,completes global calculation and control tasks in a self-organized manner.The new building intelligent platform does not require a central monitoring station for centralized information processing.At the same time,the CPN network has good scalability and is an emerging direction of research on intelligent building control technology.This paper is oriented to a new type of building intelligent platform,combined with the peak load and valley filling control of building electricity load,the following main research work has been carried out:First of all,grasping the user's electricity consumption rule is a prerequisite for charging and discharging scheduling of the user-side energy storage device.Based on the historical data of building electricity load,this paper analyzes the influencing factors of electricity load,extracts the characteristics of electricity load,and uses genetic algorithm to optimize the support vector machine and long-term and short-term memory neural network to construct the electricity load forecasting model.The weather data of the forecast day and the type of working day are used as the input of the forecast model to obtain the electricity load data of the forecast day.By comparing and analyzing the electricity load forecast results obtained by different forecasting models,according to the relevant evaluation indicators of electricity load forecast,choose electricity load forecast data suitable for the study of charging and discharging scheduling strategies of energy storage devices,it laid the foundation for the follow-up study of the charging and discharging scheduling strategy of energy storage devices.Secondly,based on the electricity load forecast data and the parameters of the energy storage device,this paper constructs a charge-discharge scheduling model of the energy storage device for peak and valley filling,a charge and discharge scheduling model of the energy storage device for saving electricity consumption,and a charge and discharge scheduling model of the energy storage device for multi-objective,uses particle swarm optimization to optimize the charging and discharging power of the energy storage device in the model.Use particle swarm optimization to optimize the charge and discharge power of the energy storage device in the model,according to different energy storage device charging and discharging scheduling models,multiple simulation experiments are conducted and the experimental results are compared and analyzed.An experiment result with the smallest fitness value after particle swarm optimization is selected as the solution result of the charge and discharge scheduling model of the energy storage device.According to the result of the charge and discharge scheduling model of the energy storage device,a reasonable charge and discharge strategy of the energy storage device is formulated.Finally,a user-side energy storage system was built on a new building intelligent platform to simulate the charging and discharging scheduling strategy of the energy storage device,and the feasibility and effectiveness of the charging and discharging scheduling strategy of the energy storage device were verified.At the same time,the new building intelligent platform is based on the neighbor interaction mechanism,which can efficiently and flexibly realize the cooperative operation of construction equipment.Figure[44] Table[8] Reference[52]... |
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