Decision support for choice optimal power generation projects: Fuzzy comprehensive evaluation model based on the electricity market [An article from: Energy Policy] Buy on Amazon

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Decision support for choice optimal power generation projects: Fuzzy comprehensive evaluation model based on the electricity market [An article from: Energy Policy]

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PublisherElsevier
ISBN / ASINB000P6OOUG
ISBN-13978B000P6OOU6
AvailabilityAvailable for download now
MarketplaceUnited States  🇺🇸

Description

This digital document is a journal article from Energy Policy, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
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.
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