Optimal Control Method And Application Study For Combined Cooling Heating And Power System

Combined cooling heating and power(CCHP)system is an advanced and high-ly efficient energy technology based on the concept of temperature counterparts and cascade utilization.CCHP can not only achieve energy cascade utilization and im-prove energy efficiency,but also show great advantages in reducing CO2 and PM2.5 emissions,which has better economic,environmental and social benefits.CCHP has been an important development trend of future energy technology,and it has been a research focus for scholars of many countries.However,CCHP system is character-ized by complex structure,variable operating conditions,various modes and mixed disturbance,There are many severe challenges in the optimization control for CCH-P system.Therefore,the key scientific problems of CCHP system are studied in this paper:load forecasting,system design and operation control.Then,this paper designs and builds the 863 project CCHP demonstration application system.The specific research contents and results are as follows:Firstly,It is difficult to predict and improve the prediction accuracy because of the uncertainty and coupling of the combined cooling,heating and power system load.Aiming at this problem,a novel cooling,heating and electrical load forecasting method based on multivariate phase space reconstruction and kalman filter algorithm was proposed.The theoretical method takes into account the coupling relationship between the load and the factors such as temperature,humidity,wind and so on.The multivariate time series were constructed by choosing the cooling load,heating load,electrical load and weather factors time series based on the correlation coefficient be-tween the variables.The phase space of multivariate time series was reconstructed based on the chaos theory and C-C method.It can enrich the system information,and to be more close to the evolution law of the original cooling,heating and electrical load.Simulation results show that novel cooling,heating and electrical load fore-casting method than univariate time series phase space reconstruction and kalman filter prediction method has higher prediction accuracy,It is of great value for the optimal design and energy management of CCHP system,which provides an im-portant theoretical method to ensure the economical,safe and reliable operation for CCHP system.Moreover,It is difficult to realize the whole optimization because of the com-plicated structure,the variety of equipment selection and the changeable operation mode of CCHP system.Aiming at this problem,A novel three-stage collaborative global optimization design method for CCHP system was presented.On the first stage,discrete particle swarm optimization was applied to solve the optimal equip-ment type problem with maximum annual primary energy utilization rate.On the second stage,particle swarm optimization was utilized to solve the optimal equip-ment capacity with minimum annual carbon dioxide emissions.On the third stage,particle swarm optimization was utilized to solve the optimal operation strategy with minimum annual operation costs.It provides a new design method for the optimal design and operation of CCHP system.Simulation results show that this CCHP sys-tem designed by this optimal method is more energy saving,more environmental and more profitable than two CCHP systems separately designed by following the electric load(FEL)and following the thermal load(FTL)operation strategy.Furthermore,It is difficult to realize the high efficiency operation control for C-CHP system which is not matched with the demand of cooling,heating and electrical load.An initiative optimization operation strategy and multi-objective energy man-agement method for combined cooling heating and power with storage systems was presented.Initially,the initiative optimization operation strategy of CCHP system in the cooling season,the heating season and the transition season was formulat-ed.The energy management of CCHP system was optimized by the multi-objective optimization model with maximum daily energy efficiency,minimum daily carbon emissions and minimum daily operation cost based on the proposed initiative opti-mization operation strategy.Furthermore,the pareto optimal solution set was solved by using the niche particle swarm multi-objective optimization algorithm.Ultimate-ly,the most satisfactory energy management scheme was obtained by using the tech-nique for order preference by similarity to ideal solution(TOPSIS)method.A new method for optimal operation and energy management of CCHP system is designed.Simulation results show that CCHP system with storage systems has an energy uti-lization,environmental protection and economic benefits based on optimal operation strategy and the multi-objective energy management method,at the same time,C-CHP system with storage systems can stabilize the load fluctuation,achieve peak cooling load.Eventually,The biogas CCHP demonstrate application system was designed and builded.Firstly,A stable and efficient biogas system was designed and builded.then,A new and efficient biogas combustion engines and control system was optimal designed and developed,and a new type of lithium bromide absorption type absorp-tion refrigerating was developed.Finally,the paper designed and builded CCHP demonstrate application system based on the principle of temperature counterpart-s cascade utilization and multi energy complementary.The intelligent control and management system were designed for the CCHP system.The intelligent control and management system can achieve the overall performance test of biogas com-bustion power generation performance testing,LiBr absorption chiller and CCHP system.It also can verify and apply the new theoretical method.At the same time,it ensure the safe and stable operation of the CCHP system.