Simulating the impact of investment preference on low-carbon transition in power sector

Authored by Can Wang, Huadong Chen, Wenjia Cai, Jianhui Wang

Date Published: 2018

DOI: 10.1016/j.apenergy.2018.02.152

Sponsors: Chinese National Natural Science Foundation

Platforms: No platforms listed

Model Documentation: Other Narrative Flow charts Mathematical description

Model Code URLs: Model code not found

Abstract

With the deepening marketization of the electric power industry in China, its low-carbon transition relies increasingly on enterprise investment decisions. These decisions can be influenced by the risk preferences and technical preferences of the enterprises, thus deviating traditional estimation with respect to both economic optimization and uncertainty. To evaluate the impacts of investment preferences on the development path of the power sector, we developed an agent-based model combined with Monte Carlo simulation to quantitatively capture the risk preferences and adaptive technical preferences of power enterprises in their decision-making process. Two scenarios were established with and without risk preferences and adaptive technical preferences, respectively. The results indicate that both the risk aversion and the adaptive technical preference of power generation enterprises play significant roles in promoting the low-carbon transition of the power sector and that they exhibit a synergistic effect. In addition, the risk aversion of power generation enterprises increases the stability of transition in the power sector. However, these two preferences lead to income loss and additional subsidy burden in the power sector. The preferences of power generation enterprises should be recognized and considered in the design and evaluation of low-carbon policies in China's power sector.

Tags

Agent-based model China Policy Risk Electricity Generation Risk preference Adaptive technical preference Long-term low-carbon transition China's power sector Multiregion optimization model Real-options Resource assessment Solar power Uncertainties