Modeling And Optimization Of Combined Cooling Heating And Power (CCHP) System With Energy Storage Under Uncertainties

The combined cooling,heating and power(CCHP)system provides cooling,heating,and electric energy to consumers at the same time by making use of the thermal energy that will be otherwise wasted in the conventional power generation system.However,the CCHP system is challenging to meet time-varying electricity,heating,and cooling demands.The conventional dispatch strategies such as following thermal load and following electric load may cause excess thermal or electric energy being generated.Alternative to developing complex dispatch strategies for the CCHP system,energy storage is expected to play essential roles in the future’s CCHP system by storing the excess energy in the form of heat,cold or electrochemical energy,and release it when needed.Although plenty of previous studies have verified the effectiveness of different kinds of energy storage for improving the techno-economic performance of a variety of CCHP systems,it remains unknown the underlying mechanisms played by different kinds of energy storage for the performance improvement as well as the optimal combinations of energy storage and dispatch strategies of CCHP systems.In addition,the above relations are subject to uncertain parameters for planning the energy-storage-included CCHP systems.Therefore,the implications of the essential parameters such as consumers’ demands and fuel prices should also be addressed.Therefore,the present study paper establishes a NSGA-II-TOPSIS-based,multiobjective optimization model for the CCHP system considering the participation of different kinds of energy storage,with the annualized total cost(capital and operation costs included),equivalent carbon dioxide emissions(ECDE)during the life-time operation of the CCHP,and overall primary energy efficiency(PER)being set as the optimization goals.The resulting techno-economic performance of the CCHP system and optimized energy storage capacities under three different dispatch strategies,i.e.,following thermal load,following electric load,and base-load modes are compared.And underlying drivers for the effectiveness and capacity optimization results of the energy storage are analyzed.The impacts of the uncertainties of the consumers’ demands and fuel prices on the above results and relations are also investigated through the Monte Carlo method.The simulation results for an office building in Shanghai show that the application of the energy storage leads to the optimized system to incorporate the prime mover at a larger rated power compared to the system without the energy storage,reducing the generation from auxiliary parts such as gas boilers and electric refrigerators,thus making the most of the prime mover.Besides,the optimal capacity of specific energy storage is closely related to the amount of the excess energy under the specific operating mode and the cost of the storage.That is,the greater the amount of the excess energy,the lower the energy storage price,the greater the optimal capacity of the energy storage for the system is.With consideration of the uncertainty of consumers’ demand and fuel prices,the overall optimal capacity of the devices increases.Besides,the capacity of the prime mover increases less and the capacity of gas boilers and electric refrigerators increases more when the energy storage is applied.Moreover,the results also indicate that the performances of the system become poor with the presence of uncertainty,with the annualized total cost increasing,ECDE increasing,and PER decreasing.