Optimization of combined cooling, heating, and power systems for rural scenario based on a two-layer optimization model

In the process of rural urbanization, the burden of energy and carbon emissions has increased. To alleviate these problems, this study proposes a combined cooling, heating, and power with a photovoltaic/thermally assisted ground source heat pump system to meet the load demand of rural users. A two-layer optimization model is constructed with the goal of optimal economy. The upper layer is a capacity allocation model simulated with MATLAB software and solved with an accelerated particle swarm optimization algorithm to obtain the optimal equipment parameters. The optimization variables are constrained by the operation strategy of the lower layer. The lower layer is the operation optimization model. The operation model is established on the TRNSYS platform, and the operation strategy is formulated. The charging and discharging time of its energy storage device is constrained by the local time-of-use electricity price system. Taking a rural house nearby Huaihe river in Huainan, China as an example, a combined cooling, heating, and power system is designed. And an annual simulation of the system is conducted and optimized based on the local parameters. The optimization results show that the economy and performance of the optimized system are improved. During the heating period, the average COP of the optimized system increased from 4.94 to 6.59, and its energy saving rate reached 7.18%. The operating income increased by 862.93 CNY and 2866.87 CNY, respectively, compared with the non-optimized system and the separate production system. And the optimized PV/T waste heat utilization system can provide 3254.70 kWh of heat. The utilization rate of waste heat reaches 72.26%, which realizes the full utilization of renewable energy. • A photovoltaic/thermally auxiliary ground source heat pump energy supply system is proposed. • A two-layer optimization model is constructed. • The precision of the upper-level optimization model through the accelerated particle swarm optimization algorithm is improved. • The performance, economics, and carbon emissions of the optimized system are discussed.