Research On Supply And Demand Matching Promotion Mechanism Of Distributed Energy System Based On Energy And Information Coupling

For the distributed energy system（DES）that incorporates a high proportion of renewable energy,the uncertainties caused by the volatility of renewable energy and randomness of demand not only limit the improvement of the supply and demand matching,but also cause the disorderly flow of energy,which leads to a greater increase in entropy.Based on the classical thermodynamic entropy analysis method,this research tries to find the law of promoting the negative entropy transformation of the DES,by introducing the negative entropy of information and making full use of the efficient coupling mechanism of energy and information.In this way,the supply and demand matching of DES can be improved to provide support for the large-scale application of renewable energy.Based on the consistency of thermodynamic entropy and information entropy to the uncertainty of system microstates,the mechanism of supply and demand matching improvement of DES based on energy and information coupling is studied thoroughly.Firstly,the generalized information work was defined in the macroscopic system by drawing from the relationship between energy and information in the microscopic process of information thermodynamics.In the research of DES,the uncertainty analysis was adopted to describe the unknown states of the system,and the function of information was to reduce the uncertainty.The energy property of information was proposed through the measurement of uncertain events.The generalized information work,which is represented as the energy property of information,does not mean that information do work directly,but the part that can’t do any work due to uncertainty becomes the useful work under the control of information,and this part of work is considered to be generalized information work.Secondly,a DES modeling method considering multiple uncertainties was studied,and the dependence between uncertainty parameters and system performance was quantified based on information entropy.Taking a solar collector and air source heat pump combined heating system as an example,the fluctuation of system performance caused by the uncertainty of environmental parameters will decrease with the improvement of building insulation performance.In order to reduce the impact of multiple uncertainties on the actual operation of the system,the design solar radiation value of typical cities were obtained,which can guarantee 95%economic reliability.The design solar radiation for Xi’an is 243 W/m²,Tianjin is 286 W/m²,Yinchuan is 485 W/m²,and Lhasa is 658 W/m².Then,the influence of information as negative entropy on the improvement of system supply and demand matching was quantitatively analyzed.The optimal capacity configuration of a typical DES was obtained according to the multiobjective optimization,which was carried out based on Pareto optimal solution.The total entropy change(ΔS_tot)and On-site Energy Fraction（OEF）were taking as the evaluation parameters.On this basis,the influence of the accuracy of load forecasting,a typical process of information utilization in DES,on system uncertainty was quantitatively analyzed,and compared with the effect of energy storage.Afterwards,the application of entropy theory in DES optimization was described.For a practical solar energy and heat source tower heat pump integrated multi-energy complementary heating system,the solar fraction of the system was test according to the short-term testing methods firstly,was 47.77%±1.58%.Then exergic analysis method was adopted to optimize the operation mode of the system:by adding oil storage device and optimizing the operating temperature of thermal oil at 105 ^℃,the solar fraction of the heating season was increased to 53.04%.Finally,the system optimization method based on energy and information coupling proposed in this paper was adopted on this practical system.The feasibility of the method proposed in Chapter 4 to improve the matching between supply and demand of distributed energy system by increasing the negative entropy of the system was verified.In addition,the conclusion that"the system performance fluctuation caused by the uncertainty of environmental parameters will decrease with the improvement of the thermal insulation performance of buildings"in Chapter 3 was also verified.Finally,an evaluation method of the amount of information tha model could use was proposed.Taking the photovoltaic system as example,a theoretical model（TH-model）of thermo-electric coupling was established based on the physical process of photovoltaic power generation,and the Back Propagation Neural network models with seven parameters（BP-7）,five parameters（BP-5）and three parameters（BP-3）were constructed based on the actual monitoring data.From the perspective of model accuracy,the accuracy of BP-5 model is higher than that of the TH-model.In terms of information utilization of photovoltaic power prediction model,the information utilization of the the theoretical model is close to that of the data driven model.