Advanced Operation Methodology For Combined Cooling,Heating And Power Integrated Energy System

In the context of wordwide commitment to carbon neutrality,renewable and sustainable energy technologies have been highly valued,and diversed new energy utilization systems have been proposed.Among them,the combined cooling heating and power integrated energy system(CCHP-IES)can realize convenient conversion between different energy forms and comsume more clean energy through flexible flow topology,thereby comprehensively improving the system performance in terms of high overall efficiency and freindly accomadation for renewables.Therefore,it has received increasingly attention in recent years.In multiple research fields of CCHP-IES,optimal operation can control the direction of energy flow and coordinate the output of equipment,which plays a key role in the actual system operation.However,there are still many challegnes for the optimal operation of the integrated energy system such as strong uncertainty,complicated coupling,multiple timescales and dynamics.For this reason,the dissertation attempts to deal with these challenges by some advanced optimal operation methodoldoies,and the main works are summaried as follows:(1)A configuration structure of a typical CCHP-IES is proposed,and the scheduling models for the cooling,heating,power and energy storage equipments in the system are established.Based on the scheduling model taking heat-electricity as the optimized variables,a mixed integer linear programming optimization of the CCHP-IES is established.In order to evaluate the influence of the uncertainty of the energy parameters,technical parameters and economic parameters on operation results,a variance-based Sobol method is proposed to analyze the global sensitivity of a large number of parameters in the CCHP-IES,in order to quantitatively compare the degree of influence of the individual,interactional and total effects of the parameters on the output.The case studies found that among the 17 typical parameters,fluctuations of the economic parameter have the greatest impact on the optimal operation results of CCHP-IES,and the changes in supplied and demand parameters have the widest range of influence on the dispatch results.Although with a large number of technical parameters in the CCHP-IES,fluctuations of only a few technical parameters will have a critical impact on the optimal operation results.(2)In addition,the dissertation proposes to exploit temperature-flowrate as the optimization variables to model the thermal processes in the CCHP-IES so as to better consider the strong coupling relationship among the cooling,heating and power in the system,thus solving the problem that the traditional method of taking the heat transfer of thermal processes as the optimization variables oversimplifies the complicated relationship of the process state variables in the system.Moreover,in order to cope the various uncertainties in the system,stochastic model predictive control operation strategy(SMPC)is applied to enhance the capacity of the optimal operation of systems to fully suppress the influence of the parameters uncertainty on the scheduling results.Through a comparative study of the four operating modes with the SMPC operation strategy and the proposed temperature-flowrate scheduling model,it is found that the methodology could easily handle the complex thermal characteristics and process constraints in the system.The results also demonstrated that among these constraints,the changes in the network temperature have the greatest impact on the scheduling economy,and the environmental temperature changes also have a significant impact on the scheduling results,proving the necessity of adopting temperature and flowrate as optimization variables in the optimal operation of CCHP-IES.(3)In order to coordinate the multi-timescale characteristics of the supplied and demanded parameters,this dissertation proposes a single-layer multi-timescale scheduling approach,which can achieve a tradeoff between uncertainty alleviation performance with respect to shorttimescale and economic performance with regard to long-timescale.At the same time,the least squares support vector machine(LS-SVM)method is applied to forecast the supply and demand parameters in multiple timescales,to ensure the consistency of timescales of the prediction model and the multi-timescale optimization window.The optimization window further servers as the optimization horizon of the SMPC operation strategy,which thereby promotes the performance of the traditional SMPC operation strategy.Case studies show that the proposed single-layer multi-time scale SMPC operation methodogloy can greatly reduce the cost time,while still maintain good economy and robustness compared with the traditional method.It thus has the best comprehensive performance.(4)In addition to the multi-timescale characteristics of the supply and demand parameters,the dynamic characteristics of the equipments in the CCHP-IES also have an important impact on operation results.To this end,a mixed logic dynamic scheduling model of the CCHP-IES is established,and the constraint priority method is adopted to differentiate and relax the constraints of levels in the system to ensure the feasibility of operation optimization.Finally,combined with the multi-time-scale appraoch proposed in this paper,a multi-time-scale operation methodology for the CCHP-IES based on the mixed logic dynamic model is proposed.This method can account for not only the multi-time scale characteristics but also the influence of dynamic characteristics.Case study found that the scheduling results based on the dynamic model are more in line with the characteristics of the underlying equipment than the scheduling based on the steady-state model,which can significantly improve the robustness of the scheduling results.With the improved robustness,the proposed multi-time-scale methodology based on the mixed logic dynamic model can still maintain the computational efficiency and economic performance.