Modeling And Optimization Of Active Distribution Network With Integrated Flexible Loads Of Smart Buildings

Increasing attention is being paid to technologies in energy efficiency improvement due to the rapid growth of global energy use and environmental deterioration.According to the Buildings Energy Data Book,buildings' share of the worldwide energy usage is almost 40%,with approximately half of it being used in their Heating,Ventilation and Air-Conditioning(HVAC)systems.With the continuous improvement of the urbanization level around the world and the adjustment of industrial structure,the proportion is growing continually.For this reason,the combined modeling and optimal scheduling for the active distribution network(ADN)with integrated flexible loads of smart buildings are focused on in this paper.Firstly,based on the thermal storage characteristics of buildings,the energy consumption prediction model of buildings considering different heating areas with different orientations is developed by using the Resistor-Capacitor thermal network model.Then,different optimal control methods of HVAC systems in the building are put forward.The energy consumption management of HVAC systems is realized by adjusting the room temperature in a comfortable temperature range.In order to further consider the influence of smart buildings on the economic and security operation of ADN,the modeling and optimal scheduling method of ADN with integrated flexible loads of smart buildings is put forward considering the load factor.Finally,the optimal scheduling results of the aggregation of the smart buildings under different HVAC control methods in the winter heating scene are analyzed.In addition,based on the branch flow model,the optimal power flow model of ADN with on-load tap changer is constructed by piecewise linearization and second-order cone relaxation to reach flexible and optimal operation of ADN.Thus,the impacts of the optimal schedules of the aggregation of smart buildings on the economic and security operation of the ADN are further analyzed and compared.The results show that the proposed method can make full use of the demand response potential of smart buildings and further reduce the operating costs of smart buildings.At the same time,the optimization of the ADN with the load factor can ensure the economic operation of buildings on the basis,and to a certain extent reduce the power loss and increase the voltage quality of ADN utilizing the flexibility of smart buildings.