The Optimization Of Ground Source Heat Pump System Based On The Particle Swarm Optimization Algorithm

As a method of renewable energy utilization, ground source heat pump system (GSHPS) is capable of mitigating building energy consumption and air pollution problems. The operating condition, an important determinant of energy efficiency, does not only correlates with the ground heat exchanger, but also with the heat pump and thermal load of buildings. The reasonable coupling among the ground heat exchanger, heat pump and building load is extremely important for energy and economic efficiency. Therefore, a thorough investigation is conducted targeting on the overall optimization design for GSHPS in this paper.The systematic model of GSHPS, which includes the soil heat storage model, the heat pump model and building model, was built and verified through comparison with the experiment data, obtained using the GSHPS experimental rig of DUT. Introducing the orthogonal experimental design method, this paper made simulations for a small-sized office building in Beijing. Based on the results, the comparative weight of various factors of ground heat exchangers affecting GSHPS operational effects is analyzed. The results show that the length of ground heat exchanger and the pipe distance are two dominating factors.Based on the features of GSHPS design parameters, the particle swarm optimization (PSO) algorithm was introduced and the discrete variables PSO algorithm mathematical model was established. The paper discussed the variables of ground heat exchanger and heat pump system, and determined the selection principles of optimization variables . Meanwhile, with the lowest annual cost as the objective function, the platform of the GSHPS optimization is established and optimization methods proposed. The results of a real project, the GHSP optimization of a sanatorium in Dalian, show that the initial investment of the optimization plan is higher than the primary value—125,400 RMB, while the operating costs are reduced up to 21,700 RMB annually, due to its high operational efficiency. As a result, the energy consumption can be saved up to 27.1MWh. Thus the energy saving potential is fully realized through the optimization design, which greatly enhances the economic efficiency.Incorporating the orthogonal experimental design method and PSO algorithm, this paper proposes a new optimization method with the lowest annual cost as the objective function and establishes the widely applicable optimization platform of the GSHPS, thus providing some guidance in the GSHPS design.