Analysis of Energy Technology Development Strategy in Shanghai

Future energy technology development strategy plays an important role in guaranteeing the realization of economic development requirements proposed by the 11^th five-year plan in China. This paper builds a MESSAGE energy-system model to formulate the future energy technology choices and strategy for Shanghai, which is a metropolis with great energy consumption and needs. The MESSAGE model structure is briefly described firstly. The overall objective is to fulfil various demands by the utilization of technologies and resources with the minimal total discounted system costs. The model incorporates and explores three cases for the evolution of the energy system from 2005 to 2030, enabling the insight to be gained into different energy development choices. To overcome the limitation of MESSAGE, the future costs of all technologies are assumed uncertain and to be distributed according to the log-normal distribution. Also the technologies are related through the structure of energy system in MESSAGE. Besides, endogenous learning is defined as a function of cumulative experience measured by cumulative installed capacity. Electricity generation is taken as an example to analyze the results in three different cases. The analysis indicates that technological dynamics produce a wide range of different emergent energy systems but can be considered to be approximately equivalent with respect to optimal criteria. The analysis also indicates that future R&D efforts and investments in new technologies should be distributed across related technologies rather than be directed at only one technology from the technology cluster even if it appears to be the best choice at present. Shanghai need policies and programs that encourage the development of advanced clean energy conversation technologies and support aggressive end-use energy efficiency. An important direction of further studies concerns the development of generators for dependent random parameters with a possible- reduction of uncertainty