Designing wind turbines for profitability in the day-ahead market
<p>Traditionally, wind turbine and wind farm designs have been optimized to minimize the cost of energy. Such a design would make sense when bidding in price-based auctions. However, in a future with a high share of renewables and zero subsidies, the wind farm developer is exposed to the volat...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2024-12-01
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| Series: | Wind Energy Science |
| Online Access: | https://wes.copernicus.org/articles/9/2283/2024/wes-9-2283-2024.pdf |
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| Summary: | <p>Traditionally, wind turbine and wind farm designs have been optimized to minimize the cost of energy. Such a design would make sense when bidding in price-based auctions. However, in a future with a high share of renewables and zero subsidies, the wind farm developer is exposed to the volatility of market prices, where the price paid per kilowatt-hour of energy would not be constant anymore. The developer might then have to maximize the revenue earned by participating in different energy, capacity, or ancillary services markets. In such a scenario, a turbine designed for maximizing its market value could be more profitable for the developer compared to a turbine designed for minimizing the levelized cost of electricity (LCoE). This study is in line with this paradigm shift in the field of turbine and farm design. It is a continuation of a previous study conducted by the same authors <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx31">Mehta et al.</a>, <a href="#bib1.bibx31">2024</a>)</span>, which explicitly focused on the drivers of turbine sizing with respect to LCoE. The goal of this study is to optimize the design for a new set of objective functions and analyze how various day-ahead market conditions and objectives drive turbine design. A simplified market model that can generate hourly day-ahead market prices is developed and coupled with a wind-farm-level multidisciplinary design analysis and optimization (MDAO) framework to evaluate key economic indicators of the wind farm. The results show how the optimum turbine design is driven by both the choice of the economic metric and the market scenario. However, an LCoE-optimized design is found to perform well with respect to profitability-based economic metrics like modified internal rate of return (MIRR) or profitability index (PI), indicating a limited need to redesign turbines for a specific day-ahead market scenario.</p> |
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| ISSN: | 2366-7443 2366-7451 |