Research On Optimal Design Of District-scale CCHP Systems And Exploration On Optimal Design Nnder Uncertainty

Combined Cooling Heating and Power (CCHP) systems have great significance in rational use of energy as well as energy-saving and emission-reducing for China. A number of industrial parks, business parks, and science and technology parks are being planned and constructed, which provides a rare historical opportunity for the development of district-scale CCHP systems in China. Design planning is of great significant for the success of a district-scale CCHP system project, which is usually a kind of multi-input and multi-output complex energy system. Furthermore, the optimal design of CCHP systems under various uncertainties, which are distinguished by source, type, size, and nature, poses significant challenges in terms of decision-making process. Aiming at this problem, research on models and methods for the optimal design of district-scale CCHP systems, and exploration on methods for the optimal design of CCHP systems under uncertainties were carried out in this paper. The main works are as follows:Firstly, two kinds of models for the optimal design of district-scale CCHP systems were developed based on superstructure modeling method combined with the methodology of regional energy planning. One focuses on decentralized generation systems, in which the conversion technologies are adopted in virtually every building, permitting energy distribution between buildings through the connected pipelines or wires. The other focuses on centralized generation systems, in which all forms of energy (including medium-voltage electricity and steam which are produced in the thermal power plants far away from the buildings, and low-voltage electricity, cold water, and hot water which are produced in the energy distribution stations near the buildings and used to meet the energy demands of the buildings) are produced outside the buildings and distributed among the thermal power plants, the energy distribution stations, and the buildings through the corresponding energy distribution networks. Results of case studies show that both models can achieve simultaneous optimization of location and structure of system, discreteness of number and capacity of equipment, energy distribution network as well as operation strategy. The introduction of storages can increase the capacity of absorption chillers and decrease that of the compression chillers and gas boilers, improving the system’s economic efficiency because of the removal some of the strong coupling relationship between cooling/heating demands and prime movers and the full use of the exhaust heat generated by the prime movers.Secondly, two kinds of methods for the optimal design of CCHP systems under uncertainties were explored. First of all, five uncertain programming models for the optimal design of CCHP systems with consideration of large-scale uncertainties in energy demands in a long period of time were developed based on five decision-making criteria in decision-making theory including optimistic criterion, pessimistic criterion, Hurwicz criterion, Laplace criterion, and minimax regret criterion, respectively. Results of case studies show that the five methods have high sensitivity to the choice of scenarios. When the five methods are based on the situation of cost minimization, as for the optimal capacity of the main and auxiliary equipment, optimistic method recommends the least value, pessimistic method presents the largest value, and both Laplace method and minimax regret method identify a moderate value. The minimax regret method is to find a balance point between the electricity purchased cost and the equipment investment cost, so that the energy system can achieve the optimal robustness in economic characteristic under the large-scale uncertainties in energy demands. Next, based on superstructure modeling method combined with stochastic programming theory, two mathematical models were developed to optimally design CCHP systems under the stochastic uncertainties in energy demands in a short period of time, energy prices, and renewable energy intensity. One is two-stage stochastic programming model, and the other is chance-constrained stochastic programming model. Both models are transformed into their deterministic equivalents and solved. Results of case studies show that compared with the deterministic optimization results, the optimal capacity of hot-water gas boliers and energy storages tends to increase when the uncertainties in energy demands are considered. The optimal capacity of other equipment including gas engines also tends to increase with further increase in the size of the uncertainty in energy demands because the system must ensure the feasibility of the energy supply for higher energy demands. The uncertainties in energy prices and renewable energy intensity mainly affect the capacity of storages. The system’s economic efficiency is overestimated if the system is designed without considering the uncertainties.The effective methods were provided in this paper, which can achieve simultaneous optimization of energy stations and energy distribution networks in district-scale CCHP systems, as well as which can mitigate the risk of decision-making process for CCHP systems planning under uncertainties.