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Optimising feed-in tariff design through efficient risk allocation
Author(s)
Date Issued
2017-03
Date Available
2019-08-12T08:02:37Z
Abstract
Many Feed-in Tariff designs exist. This paper provides a framework to determine the optimal design choice through an efficient allocation of market price risk. Feed-in Tariffs (FiTs) incentivise the deployment of renewable energy technologies by subsidising remuneration and transferring market price risk from investors, through policymakers, to a counterparty. This counterparty is often the electricity consumer. Using Stackelberg game theory, we contextualise the application of different FiT policy designs that efficiently divide market price risk between investors and consumers, conditional on risk preferences and market conditions. Explicit consideration of policymaker/consumer risk burden has not been incorporated in FiT analyses to date. We present a simulation-based modelling framework to carry this out. Through an Irish case study, we find that commonly employed flat-rate FiTs are only optimal when policymaker risk aversion is extremely low whilst constant premium policies are only optimal when investor risk aversion is extremely low. When both policymakers and investors are risk averse, an intermediate division of risk is optimal. We provide evidence to suggest that the contextual application of many FiT structures is suboptimal, assuming both investors and policymakers are at least moderately risk averse. Efficient risk allocation in FiT design choice will be of increasing policy importance as renewables deployment grows.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Programme for Research in Third-Level Institutions (PRTLI) Cycle 5
European Regional Development Fund (ERDF)
Type of Material
Journal Article
Publisher
Elsevier BV
Journal
Sustainable Energy, Grids and Networks
Volume
9
Start Page
59
End Page
74
Copyright (Published Version)
2016 Elsevier
Language
English
Status of Item
Peer reviewed
ISSN
2352-4677
This item is made available under a Creative Commons License
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