Steffen, and 3 co-authors, reported in 2020 that operations and maintenance (O&M) cost for renewables become increasingly important, accounting for 20%–25% of lifecycle costs for wind and solar plants in 2017. Understanding the dynamics of these costs will allow us to increase the precision of modeling results in energy system and integrated assessment models and improve planning by various stakeholders. The authors estimated O&M experience curves for renewable energy technologies, evaluated mechanisms regarding how O&M experience led to lower costs, and demonstrated a projection of O&M costs until 2030.
Highlights:
- New data show O&M cost for onshore wind and solar PV strongly improved over time,
- Underlying learning mechanisms support applying the experience curve concept,
- Experience rates between 9.2% and 18.2% estimated for different cost components,
- Endogenizing O&M cost dynamics can improve precision of energy system models.
Given the decline in investment costs for renewable energy technologies, other cost components have become increasingly important. In 2017, operations and maintenance (O&M) accounted for 20%–25% of lifecycle costs for wind and solar plants in Europe, but the understanding of O&M dynamics is limited.
Presenting new data from Germany, here, the authors considered the operating experience to estimate O&M experience curves for onshore wind and solar photovoltaic (PV). Results indicate O&M cost reductions with each doubling of cumulative experience of 9.2%–12.8% for onshore wind and of 15.7%–18.2% for solar. Drawing on expert interviews and archival information, the authors described mechanisms regarding how O&M experience led to lower costs, including economies of scale, upsizing, learning by doing, and learning by using and learning by interacting, comparing both technologies.
Moreover, regulatory changes fostered competitive pressure toward cost reductions.
The authors concluded that dynamic cost reduction potentials for O&M should be considered in energy system planning, modeling, and optimization. Furthermore, projections of O&M costs follow established methods for experience curve-based extrapolations. In order to apply the experience curves derived in this study, the authors drew on projected values for the explanatory variable (cumulative produced 14 Joule 4, 1–17, February 19, 2020
Please cite this article in press as: Steffen et al., Experience Curves for Operations and Maintenance Costs of Renewable Energy Technologies,
Joule (2019), https://doi.org/10.1016/j.joule.2019.11.012 electricity). To this end, the authors needed to estimate annual capacity additions and plant
retirement as well as capacity factors. For the capacity projections, the authors referred to the grid development plan (Netzentwicklungsplan) for Germany.
This is the official planning document for grid operators, signed-off by the German power grid regulation agency (Federal Network Agency for Electricity, Gas, Telecommunications, Post, and Railway). For both wind and solar PV electricity generation, the authors chose scenario C (scenario with high solar PV and onshore wind deployment), which projects 104.5 GW cumulative installed solar PV capacity and 85.5 GW cumulative installed wind capacity by 2030.
Because of the absence of higher resolution data, the authors assumed a linear increase in cumulative capacity from 2017 to 2030, which is in line with recent market developments. For solar PV, the authors estimated the projected capacity larger than 100 kWp by assuming its share of total solar PV deployment is the same as the average of the last ten years (52%). Capacity factors from 2017 are assumed to remain constant until 2030. When applying the derived experience curves, the authors used the point estimates from model specification I as well as the 95% confidence interval
to produce three curves.
To put the authors' results into perspective, they illustrated values for projected O&M cost development through 2030 from other studies. To identify relevant sources, the authors referred to a summary of cost projections for power generation technologies by the DIW (German Institute for Economic Research), which is widely used as a data source by German energy system models (e.g., models LIMES, DIETER, EMMA, and dynELMOD).
[Note: with decreasing capital cost of wind and solar project means lower quality of equipment, which means after construction, higher O&M costs will likely appear?]
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