Research On Operational Optimization Of Energy-Flexibility Building Base On Model Predictive Control

Energy is an indispensable resource for human social development,and with economic progress and development,energy issues have become increasingly tense.The construction sector accounts for a significant proportion of global energy consumption and is responsible for a large amount of carbon emissions.According to data from the International Energy Agency,approximately 40% of the total energy demand in the global construction industry comes from heating,ventilation,and air conditioning systems.In response to this situation,China has proposed the goal of carbon peaking and carbon neutrality to address the challenges of energy and climate change.Against the backdrop of increasing energy consumption and worsening energy issues in the construction industry,the low-carbon and efficient use of building energy and the promotion of renewable energy utilization have become important components for achieving the carbon neutrality target by 2060.In order to improve the flexibility and efficient use of building energy,this paper explores the effects of energy storage batteries,air source heat pump systems,and passive energy storage on optimizing building energy flexibility.Taking a residential building as an example,this study explores the impact of different flexibility strategies on improving building energy flexibility when operated separately and in combination.By constructing models of different components and introducing advanced model predictive control methods,this study analyzes energy consumption and economic costs under different operating modes through simulation,and proposes an operation optimization model for energy-flexible buildings based on model predictive control.The main research contents and conclusions of this paper are as follows:1)Firstly,based on the research object and building composition,mathematical models were established for energy storage batteries,air source heat pumps,and thermal storage devices composing the air source heat pump system,the grey-box RC model of the residential building,and the mathematical model of the heating system.By using actual monitoring data of the building,the accuracy and reliability of the established mathematical models were verified to ensure the effectiveness of subsequent research.2)Then,active energy storage optimization models were designed,including energy storage battery electricity storage optimization model and air source heat pump thermal storage optimization model,and the established models were verified and analyzed through simulation experiments.Simulation results show that the electricity storage optimization model successfully accommodated 37.3% of photovoltaic power generation,reduced electricity consumption costs by 27,598 yen,and obtained photovoltaic power selling revenue of 29,181 yen;the thermal storage optimization model achieved on-site accommodation of 26.7% of photovoltaic power generation,saved electricity consumption costs by 5,543 yen,and obtained photovoltaic power selling revenue of 22,094 yen.3)In addition,this study explores the flexibility potential of residential buildings through experiments and simulations,and designs an optimized passive energy storage model for buildings.Simulation experiments show that this model can significantly improve indoor thermal comfort,effectively reduce temperature fluctuations,and maintain room temperature within the comfortable range.The model also increases the on-site consumption rate of photovoltaic power,reduces the demand for grid electricity,and further improves energy efficiency.4)Finally,this study proposes a flexible energy system optimization model based on model predictive control(MPC),which integrates multiple flexibility measures including energy storage batteries,air-source heat pump systems,and building heating systems to achieve efficient and flexible energy utilization in buildings.Simulation results show that during the heating season,the model optimizes indoor temperature and maintains an average indoor temperature of 22.1℃ while reducing system energy consumption.During the non-heating season,the model successfully incorporates59.3% of photovoltaic power generation through flexible allocation of electricity,saving79,358 Japanese yen in grid electricity consumption and earning 28,486 Japanese yen by selling excess photovoltaic power generation to the grid,demonstrating the application of energy flexibility and improving economic efficiency.