Retrofitting wind power plants into hybrid PV–wind power plants: Impact of resource related characteristics on techno-economic feasibility

The concept of hybrid power plants (HPPs), wherein co-located solar photovoltaic (PV) and wind assets share a common point of interconnection (POI) with the grid, is gaining traction. The wind and solar resources can be anti-correlated, and this opens for oversizing the capacity of these HPPs relative to their nominal POI capacity while ensuring low curtailment losses. Thus, retrofitting existing wind power plants into PV-wind HPPs can be a promising strategy in regions where access to the grid is a barrier to capacity expansion. However, it is not clear how the resource availability and anti-correlation of the solar and wind resources at a given location impact the techno-economic feasibility for retrofitting an existing wind power plant with PV capacity. In this study, 128 existing wind power plants in Norway and Sweden were assessed for retrofitting with PV using a techno-economic model, measured wind power and modeled PV power generation data. Multiple linear regression (MLR) analysis was applied to the resulting cost-optimal HPPs in order to determine which of a site’s resource characteristics that are influencing the feasibility of such retrofitting, and to what extent. The results suggests that the top three key characteristics sorted in order of decreasing importance are: (i) high mean PV capacity factors, (ii) low mean wind capacity factors and (iii) strong anti-correlation between the hourly PV and wind power generation. The results thus demonstrate that developers aiming to retrofit wind power plants with PV capacity should target those located in areas with high solar irradiance and performing badly, i.e., with low wind capacity factors, rather than prioritizing wind power plants at sites with strong anti-correlation between the PV and wind generation. Finally, it is demonstrated how the analysis framework can be used as a screening tool, i.e., as a means of predicting the techno-economic potential for PV retrofitting, also for wind power plants where power generation time series are unavailable. • A retrofitting study on 128 existing wind power plants in the Nordics is performed. • Cost-optimal PV systems and curtailment levels are determined per wind power plant. • Multiple linear regression (MLR) analysis identifies the traits of the best cases. • Presence of high anti-correlation is insufficient to decide retrofitting feasibility. • The derived MLR model can be used as screening tool for new retrofitting projects.

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Publication Details

Journal
Applied Energy
Published
2024-11-21
DOI
https://doi.org/10.1016/j.apenergy.2024.124895
Citations
7
Primary Topic
Integrated Energy Systems Optimization
Type
article
Field-Weighted Citation Impact
1.46

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article

Retrofitting wind power plants into hybrid PV–wind power plants: Impact of resource related characteristics on techno-economic feasibility

David Lingfors, Erik Stensrud Marstein, Oskar Lindberg, Magnus Moe Nygård et al.
7 citations
Applied Energy
Integrated Energy Systems Optimization
1.46
article

Retrofitting wind power plants into hybrid PV–wind power plants: Impact of resource related characteristics on techno-economic feasibility

David Lingfors, Erik Stensrud Marstein, Oskar Lindberg, Magnus Moe Nygård, Øyvind Sommer Klyve, Ville Olkkonen
article en
7 citations

Abstract

The concept of hybrid power plants (HPPs), wherein co-located solar photovoltaic (PV) and wind assets share a common point of interconnection (POI) with the grid, is gaining traction. The wind and solar resources can be anti-correlated, and this opens for oversizing the capacity of these HPPs relative to their nominal POI capacity while ensuring low curtailment losses. Thus, retrofitting existing wind power plants into PV-wind HPPs can be a promising strategy in regions where access to the grid is a barrier to capacity expansion. However, it is not clear how the resource availability and anti-correlation of the solar and wind resources at a given location impact the techno-economic feasibility for retrofitting an existing wind power plant with PV capacity. In this study, 128 existing wind power plants in Norway and Sweden were assessed for retrofitting with PV using a techno-economic model, measured wind power and modeled PV power generation data. Multiple linear regression (MLR) analysis was applied to the resulting cost-optimal HPPs in order to determine which of a site’s resource characteristics that are influencing the feasibility of such retrofitting, and to what extent. The results suggests that the top three key characteristics sorted in order of decreasing importance are: (i) high mean PV capacity factors, (ii) low mean wind capacity factors and (iii) strong anti-correlation between the hourly PV and wind power generation. The results thus demonstrate that developers aiming to retrofit wind power plants with PV capacity should target those located in areas with high solar irradiance and performing badly, i.e., with low wind capacity factors, rather than prioritizing wind power plants at sites with strong anti-correlation between the PV and wind generation. Finally, it is demonstrated how the analysis framework can be used as a screening tool, i.e., as a means of predicting the techno-economic potential for PV retrofitting, also for wind power plants where power generation time series are unavailable. • A retrofitting study on 128 existing wind power plants in the Nordics is performed. • Cost-optimal PV systems and curtailment levels are determined per wind power plant. • Multiple linear regression (MLR) analysis identifies the traits of the best cases. • Presence of high anti-correlation is insufficient to decide retrofitting feasibility. • The derived MLR model can be used as screening tool for new retrofitting projects.

Applied EnergyVol. 379
University Graduate Center (NO), Uppsala University (SE), Institute for Energy Technology (NO), Kjeller Innovasjon (Norway) (NO)
Energimyndigheten, Norges Forskningsråd
Openalex Percentile: Top 17%
Integrated Energy Systems Optimization
1.46
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