On the influence of jurisdiction on the profitability of residential photovoltaic-storage systems: A multi-national case study. (October 2017)

• Record Type: Journal Article
• Authors: Kazhamiaka, Fiodar; Jochem, Patrick; Keshav, Srinivasan; Rosenberg, Catherine
• Is Part Of: Energy policy. Volume 109(2017)
• Page Start: 428
• Page End: 440
• Publication Date: 2017-10
• Subjects: Residential PV system -- Residential storage system -- Economic analysis; Energy policy -- Periodicals; Politique énergétique -- Périodiques; Electronic journals; 333.79
• DOI: 10.1016/j.enpol.2017.07.019
• Languages: English
• ISSNs: 0301-4215

Abstract

Abstract: Policy makers in many jurisdictions have implemented incentive schemes such as 'feed-in tariffs' (FIT) and upfront purchase subsidies to encourage consumers to self-generate parts of their power requirements by solar energy. We quantitatively study the impact of jurisdiction-specific solar radiation profile, the typical residential loads, the cost of system components, the price of grid electricity, and incentive programs on photovoltaic (PV) and storage system profitability in Germany, Ontario, and Austin, Texas. In each jurisdiction, for a range of PV and storage system sizes, we compute the optimal use of the system, and hence the best possible profitability of that system in that jurisdiction over a 20 year life span. This methodology allows us to quantitatively estimate the influence of a jurisdiction on the (best possible) profitability of PV-storage systems. We find that the choice of jurisdiction has significant impact on the profitability of PV-storage systems. We also find that policy makers can use the price of grid electricity as well as upfront subsidies to influence profitability, and therefore adoption. Abstract : Highlights: Characterize jurisdiction from the context of household PV-storage system adoption. Methodology to estimate PV-storage system profitability in a given jurisdiction. Data-driven study to give policy recommendations tailored for three jurisdictions. Open-sourced optimization model implementation in AMPL (link in paper).