Development Of Inexact Urban Energy Systems Planning Models And Their Applications

With the rapid development of China's urbanization, the ratio of urban energy consumption to total energy consumption in China is continuously growing. This necessitates the effective management of urban energy systems for facilitating managers to secure urban energy supply, reducing energy costs, mitigating environmental impacts, improving the living standard of urban inhabitants, and implementing low-carbon policy, regulations and strategy. Energy systems planning model is one of the important tools that can support the management of urban energy systems.Urban energy systems are complicated with dynamic, multi-objective, nonlinear and uncertain characteristics. Particularly, uncertainties existed in urban energy management systems could be expressed in various formats. Previsously, there were lacks of studies on the development of urban energy systems planning models for supporting urban energy systems management under uncertainty. Thereore, this study aimed at the development of inexact urban energy systems planning models for addressing uncertainty in interval format and probabilistic distribution through using techniques of interval-parameter and chance-constaints programming.The developed models were then applied to Beijing energy systems. The results indicated that Beijing would tend to adjust its energy structure, with the proportion of high-quality energy supply continuously growing. In terms of energy conversion, coal-fired power cogeneration and gas-steam combined-cycle power generation would make a major contribution. Heat pumps, geothermal heating and other new technologies would play an important role. Renewable and other new energy would also be developed. Energy saving retrofit of existing buildings would be recommended for reducing end-use energy demands. NOx emission from power and thermal industries would increase. This would necessitate the actions for reducing NOx emission by the power and thermal industries.In short, the developed models could effectively handle a variety of uncertainties in urban energy management systems. The developed models and the applied techniques could be referred to other research of energy and environmental systems. However, in the practical energy managment systems, uncertainties may not be limited to interval value and probabilistic distribution. In addition, urban energy systems may consist of many subsystems. These subsystems may interact among themselves and further be affected by environmental, social and economic factors. These complexities will be addressed in the future studies.