Optimising feed-in tariff design through efficient risk allocation

Mel T. Devine, Niall Farrell, William T. Lee

Research output: Contribution to journalArticle

6 Citations (Scopus)

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.

LanguageEnglish
Pages59-74
Number of pages16
JournalSustainable Energy, Grids and Networks
Volume9
Early online date21 Dec 2016
DOIs
Publication statusPublished - 1 Mar 2017
Externally publishedYes

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Game theory
Electricity
Optimal design

Cite this

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Optimising feed-in tariff design through efficient risk allocation. / Devine, Mel T.; Farrell, Niall; Lee, William T.

In: Sustainable Energy, Grids and Networks, Vol. 9, 01.03.2017, p. 59-74.

Research output: Contribution to journalArticle

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