Ghiassi-Farrokhfal, Yashar ORCID: 0000-0001-6365-1001, Ketter, Wolfgang ORCID: 0000-0001-9008-142X and Collins, John (2021). Making green power purchase agreements more predictable and reliable for companies. Decis. Support Syst., 144. AMSTERDAM: ELSEVIER. ISSN 1873-5797

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To comply with sustainability goals, many companies buy green energy to serve their energy demand. This is typically done by engaging in bilateral power purchase agreements (PPA) with renewable energy producers (REP). A PPA can be flexibly structured, but the core principle is that a buyer (company) agrees to buy future energy production of a seller (REP) at an agreed-upon fixed price. PPAs are financially attractive for sellers, providing price certainty, unlike trading in electricity markets. However, PPAs can bring quantity uncertainty for buyers due to the uncertainty of future green energy delivery. This uncertainty in the long-term endangers sustainability targets, and in the short-term complicates reliable and cost-efficient demand matching. Thus, multiple strategies have been used in PPAs to encourage sellers to provide accurate and good-faith predictions of their short-term and longer-term future production. Yet, it has been shown that REPs can have incentives to misreport predicted values. This has discouraged some companies from engaging in PPAs. In this paper, we first investigate how PPA structure and pricing can incentivize REPs to provide more reliable predictions. This shifts the risk of production uncertainty to REPs, increasing the chance that REPs adopt batteries. We further study how having batteries for REPs affects their own revenue as well as the reliability of their energy predictions for buyers. We use analytical and simulation approaches to propose a decision tree for a win-win PPA structure, which improves reliability for buyers while maintaining profitability for REPs.

Item Type: Journal Article
CreatorsEmailORCIDORCID Put Code
URN: urn:nbn:de:hbz:38-581908
DOI: 10.1016/j.dss.2021.113514
Journal or Publication Title: Decis. Support Syst.
Volume: 144
Date: 2021
Publisher: ELSEVIER
Place of Publication: AMSTERDAM
ISSN: 1873-5797
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
Computer Science, Artificial Intelligence; Computer Science, Information Systems; Operations Research & Management ScienceMultiple languages


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