Research On Experiment And Control Of Solar Organic Rankine Cycle In Cold Regions

With the rapid population growth and economic development,China’s demand for energy has grown rapidly.Compared with developed countries,China’s energy consumption structure is more unbalanced.Over-reliance on fossil energy will cause many problems such as the greenhouse effect and environmental pollution.Therefore,the transformation of energy consumption structure and the vigorous development of new renewable energy and energy utilization technologies are urgent tasks.Organic Rankine Cycle(ORC)is a technology that can effectively convert medium and low temperature heat sources into electrical energy.It has the advantages of high safety,low maintenance requirements,good thermal performance and environmental friendliness.Combining ORC with solar energy and other clean and renewable energy has very broad application prospects.In the past two decades,the number of researches on ORC technology at home and abroad has grown rapidly,but at present there are fewer ORC experimental units using natural cold sources in cold regions,and the dynamic performance research and online control technology of micro-scale ORC system are still immature.This paper constructs a solar-powered Organic Rankine Cycle system that combines the climate conditions of Harbin,Heilongjiang Province.First,the influence of operating parameters such as heat source temperature,heat source mass flow rate on the performance of the system is analyzed using off-design simulation for five working fluids(R245fa,R123,R141 b,R11,R236ea).The optimal operating parameters of the system under different working conditions and thermodynamic goals or economic goals are solved through global optimization,and the thermoecomomic optimization problem of the system is studied.Based on a micro-scale 2 k W ORC experimental platform using R245 fa as working fluid,experiments were performed using simulated solar heat sources and low-temperature natural cold sources in Harbin in winter.Through steady-state experiments,the thermodynamic performance(thermal efficiency,heat recovery efficiency,etc.)of the system and the power generation performance(power generation,power generation efficiency,etc.)with heat source conditions and operating parameters are studied,and system operating parameters such as evaporation temperature and superheat are explored.It was found that the thermal efficiency of the system and the power generation efficiency have the opposite local extreme value characteristics.When the temperature of heat source is 120℃,the maximum thermal efficiency of the system reaches 15.4%,and the maximum power generation reaches 1569.75 W.The interaction between system operating parameters such as evaporation temperature and superheat degree was explored,and it was found that the increase of superheat degree had a negative effect on the thermodynamic performance of the unit,but it had a positive effect on power generation efficiency.Through dynamic experiments,the dynamic response law and sensitivity of the system under external and internal disturbances are studied.A prediction-control method for the ORC system is designed using machine learning algorithms.The prediction loss value(mean absolute error)of the prediction model at most measuring points drops to less than 0.007,and the control model achieves a good control effect on the goal of controlling the maximum net output power of the system.After the controller acts on an average of 15.7 s,the net output power of the system can reach a maximum value.