| In recent years,the household heat metering model has been widely promoted in the residential district heating systems.The main reasons are as follows: First,compared with the previous charging policy based on heating area,household measurement and separate charging can better improve the user’s energy-saving enthusiasm;Second,household heat metering enables users to transform from passive acceptance to autonomous regulation of heat supply according to its own needs,so as to improve indoor thermal comfort.However,due to the uncertainty of the occupancy of the building and the differences of people’s behavior in the building,the heat load demand of the end-users is strong random,which is manifested as the heating demand of the end-users in part-time and part-space.After each heat consumer performs decentralized and autonomous adjustment according to their actual needs,the heating system cannot respond well to changes in end-user flow rate and heat supply through centralized adjustment at the heat source,resulting in poor matching between supply and demand,serious energy waste and low operating performance of the heating system.Therefore,it is a key problem to improve the supply and demand matching degree and reduce the heating energy consumption to better realize the coordination of centralized and decentralized regulation to adapt to the change of thermal load demand of end-users after the heat consumers disperse and adjust independently as required in the household heat metering mode.Firstly,this article divides the staff into three typical types of office workers,students and the elderly.Using the occupancy behavior model to describe the above three different types of people’s presence rules,and combined with De ST software to simulate the parttime and part-space heat load demand of the end-user.Secondly,the graph theory is applied to establish the hydraulic model of the heating system in residential district.The definition of supply and demand matching degree is given and the optimized mathematical model for achieving the optimal supply and demand matching degree of heating system is established.And particle swarm optimization algorithm is applied to solve this model in MATLAB environment.The influence of pump operation control strategy and different valve grading adjustment methods on heating system supply and demand matching and operation performance is discussed.Finally,the supply and demand matching degree and the operating performance of the heating system are explored under the two different thermal load characteristics of full-time and full-space and part-time and part-space mode.The results show that the matching degree of supply and demand proposed in this paper can objectively reflect the matching degree between the centralized regulation ability of heating system and the decentralized random load demand.When the system adopts different valve grading adjustment method under two different heating modes,the supply and demand matching degree of each end-user branch is obviously different.The heat load demand of the end-users is greatly different and fluctuates significantly under the part-time and part-space mode,and the supply and demand matching degree of the system optimized under four different valve grading adjustment methods is greatly different.In the full-time and full-space mode,the heat load demand of the end-users has little difference and very small fluctuation.When four different valve grading adjustment methods are adopted,the system can achieve good match between supply and demand.From the perspective of balancing the supply and demand matching of the heating system and the energy saving of the system operation,the valve grading adjustment methods suitable for the two different thermal load characteristics of full-time and full-space mode and part-time and part-space mode are given respectively. |
