Another Ørsted partner announces billion-dollar impairment charges — EnergyWatch
After Denmark’s Ørsted previously announced billion-dollar write-downs on several of its offshore wind projects in the US, it’s no surprise that Ørsted’s US partner on several projects, Eversource Energy, is now doing the same.
In an announcement on Monday, Eversource announced plans for total impairment charges of up to USD 1.6bn across its three offshore wind farms, South Fork Wind, Revolution Wind and Sunrise Wind, which are owned 50/50 with Ørsted.
”During the fourth quarter of 2023, Eversource identified certain impacts that will require further adjustment to the carrying value of its offshore wind investments for the three projects,” the announcements reads.
The US company cites ”revised construction cost estimates, primarily due to supply chain issues related to the project’s installation vessels and foundation fabrication” as one of the reasons.
It also cites uncertainties related to the Sunrise Wind rebid process in New York’s current RFP issued in November after the project was shelved because the two partners failed to negotiate a higher settlement price for future power.
The problems with supply chains, higher prices and delays will result in a write-down of USD 800-900m across all three projects, and at the same time, another USD 600-700m will be shaved off the value of Sunrise Wind. In total, this amounts to USD 1.4-1.6bn.
“Offshore wind projects continue to experience major supply chain disruption and inflationary challenges in the early stage of this growing industry in the US, and this impairment is an unfortunate reflection of the current market conditions we are facing,” says Eversource CEO Joe Nolan.
Eversource continues to pursue a divestment of its ownership interests in the three projects.
In the fourth quarter, Ørsted presented figures for its expected impairment charges, which also include challenges at several other US projects, including onshore, amounting to a staggering DKK 28.4bn (USD 4.16bn).
(Translated using DeepL with additional editng by Katrine Gøthler)
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