Decision support for choice optimal power generation projects: Fuzzy comprehensive evaluation model based on the electricity market

ZHANG, Hongwei, LIAN, Zhihong, YAN, Kun, SUN, Yaowei, YUAN, Jiahai and ZHANG, Zhizheng (2006). Decision support for choice optimal power generation projects: Fuzzy comprehensive evaluation model based on the electricity market. Energy Policy, 34 (17), 3359-3364.

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In 2002, China began to inspire restructuring of the electric power sector to improve its performance. Especially, with the rapid increase of electricity demand in China, there is a need for non-utility generation investment that cannot be met by government finance alone. However, a first prerequisite is that regulators and decision-makers (DMs) should carefully consider how to balance the need to attract private investment against the policy objectives of minimizing monopoly power and fostering competitive markets. So in the interim term of electricity market, a decentralized decision-making process should eventually replace the centralized generation capacity expansion planning. In this paper, firstly, on the basis of the current situation, a model for evaluating generation projects by comprehensive utilization of fuzzy appraisal and analytic hierarchy process (AHP) is developed. Secondly, a case study of generation project evaluation in China is presented to illustrate the effectiveness of the model in selecting optimal generation projects and attracting private investors. In the case study, with considerations of attracting adequate private investment and promoting energy conservation in China, five most promising policy instruments selected as evaluation factors include project duration, project costs, predicted on-grid price level, environmental protection, enterprise credit grading and performance. Finally, a comprehensive framework that enables the DM to have better concentration and to make more sound decisions by combining the model proposed with modern computer science is designed.

Item Type: Article
Research Institute, Centre or Group - Does NOT include content added after October 2018: Materials and Engineering Research Institute > Modelling Research Centre > Microsystems and Machine Vision Laboratory
Identification Number:
Page Range: 3359-3364
Depositing User: Margaret Boot
Date Deposited: 01 Aug 2017 13:37
Last Modified: 18 Mar 2021 17:16

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