Research On Neighborhood Adaptive Particle Swarm Optimization Dispatch Method For Ground Source Heat Pump District Energy System

With the continuous development of economy and society,the demand for energy is increasing,and the excessive use of fossil energy has led to increasingly prominent environmental problems.In the "Paris Agreement",countries around the world proposed to achieve "carbon neutrality" by the middle of this century.Therefore,ensuring the rational use of energy has become an inevitable trend to promote social progress and the only way to achieve sustainable economic development.In response to the above problems,many scholars and researchers are committed to promoting clean and efficient use of energy.Ground source heat pump system converts the low-temperature heat source into a high-temperature heat source by using the shallow low-grade ground temperature or water temperature as the cold/heat source and assisting part of the electric energy,so as to achieve the purpose of energy saving and emission reduction.Combined with the actual ground source heat pump regional energy system,this paper focuses on the optimal dispatch model and swarm intelligence optimization method of the ground source heat pump regional energy system.The main contents are summarized as follows:First,in order to solve the optimal scheduling model of the ground source heat pump regional energy system more efficiently and accurately,the neighborhood adaptive particle swarm optimization(NAPSO)algorithm was proposed.First,the algorithm topology is constructed based on the individual spatial position and Euclidean distance,and is adjusted accordingly according to the change of the number of iterations.Then,two update strategies in the differential evolution algorithm are used to balance the exploration and mining capabilities of the algorithm.Finally,the performance of the NAPSO algorithm is tested in 23 standard test functions.The experimental results show that the NAPSO algorithm has good convergence accuracy and convergence speed,and exhibits good global exploration and local exploitation capabilities.Second,in order to formulate the dynamic optimal allocation strategy of the user-side time-varying load among multiple ground source heat pump units,the hourly cooling/heating capacity of the heat pump unit is taken as the optimization variable,and the system operating cost is taken as the optimization objective,the optimal scheduling model of the ground source heat pump regional energy system is established,and the NAPSO algorithm is used to solve the model.Compared with the experience operation strategy,the system operation cost is reduced and the system operation efficiency is improved.Third,in response to the government’s call to encourage enterprises to use time-of-day electricity price to stabilize the peak-to-valley difference in the power grid,a energy storage device was introduced into the ground source heat pump regional energy system to cut peaks and fill valleys,and an optimal dispatch model for the composite ground source heat pump regional energy system considering pipeline losses was established,then the operation strategy of the heat pump unit within a dispatch period was given.At the same time,the operation plan of the energy storage device is formulated,so that it can store energy in the period of low electricity price and release energy in the period of high electricity price,thereby reducing the operating cost of the system.Aiming at the intractable problem of integer variables in the model,the original mixed integer programming problem is transformed into a nonlinear continuous optimization problem by using constraint scaling and auxiliary variables,thereby reduces the difficulty of solving the model.Fourth,aiming at the uncertainty problem in the ground source heat pump district energy system,a robust optimal dispatch model considering double uncertainty is established.First,the robust variables in the model are described based on the polyhedral uncertainty model.Then,the uncertainty of building cooling/heating load and the uncertainty of performance of unit are discussed respectively.For the former,the duality principle is used to simplify the optimization model,and the latter is analyzed by the scenario method.Finally,a multi-objective optimization constraint processing method is used to deal with the constraints in the robust optimal scheduling model.