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Economic analysis of the early market of centralized photovoltaic parks in Sweden

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  • Lindahl, Johan
  • Lingfors, David
  • Elmqvist, Åsa
  • Mignon, Ingrid

Abstract

Sweden are one of the countries that experience growing installation volumes of Solar photovoltaic. Traditionally, in Sweden, most of the solar photovoltaic investments and policy incentives have focused on distributed photovoltaic systems. Yet, despite limited policy incentives and pessimistic forecasts, an increasing number of centralized photovoltaic parks have been commissioned and plans for substantial new capacities are communicated. Hence, the current paper investigates why. Detailed information about the underlying costs of six PV parks commissioned in 2019 and 2020 in Sweden were obtained by in-depth interviews with stakeholders and were analysed through levelized cost of electricity calculations. We conclude that the unsubsidised levelized cost of electricity ranged from 27.37 to 49.39 €/MWh, with an average of 40.79 €/MWh. This is lower than what are assessed for photovoltaic parks in some recent Swedish electricity system scenario studies. The main reason for the discrepancy is identified to be the assumed interest rates in the system scenario studies and the actual cost of capital experienced in the market. Comparing the levelized cost of electricity values with the market value of solar photovoltaic electricity on the spot market show that four of the six studied parks would be profitable under a merchant business model with the last years spot prices. If the downward price trend continues, Sweden may face an unexpected expansion of photovoltaic parks.

Suggested Citation

  • Lindahl, Johan & Lingfors, David & Elmqvist, Åsa & Mignon, Ingrid, 2022. "Economic analysis of the early market of centralized photovoltaic parks in Sweden," Renewable Energy, Elsevier, vol. 185(C), pages 1192-1208.
    • Handle: RePEc:eee:renene:v:185:y:2022:i:c:p:1192-1208
    DOI: 10.1016/j.renene.2021.12.081
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    4. Kellil, N. & Aissat, A. & Mellit, A., 2023. "Fault diagnosis of photovoltaic modules using deep neural networks and infrared images under Algerian climatic conditions," Energy, Elsevier, vol. 263(PC).

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