Preprints
https://doi.org/10.5194/wes-2024-43
https://doi.org/10.5194/wes-2024-43
15 Apr 2024
 | 15 Apr 2024
Status: this preprint is currently under review for the journal WES.

Designing wind turbines for profitability in the day-ahead markets

Mihir Kishore Mehta, Michiel Zaaijer, and Dominic von Terzi

Abstract. 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 kWh 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 (Mehta et al., 2023), which explicitly focused on the drivers for turbine sizing w.r.t. 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 Multi-disciplinary 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 w.r.t. profitability-based economic metrics like MIRR/PI, indicating a limited need to redesign turbines for a specific day-ahead market scenario.

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Mihir Kishore Mehta, Michiel Zaaijer, and Dominic von Terzi

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2024-43', Anonymous Referee #1, 06 May 2024
  • RC2: 'Comment on wes-2024-43', Anonymous Referee #2, 07 May 2024
Mihir Kishore Mehta, Michiel Zaaijer, and Dominic von Terzi
Mihir Kishore Mehta, Michiel Zaaijer, and Dominic von Terzi

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Short summary
In a subsidy-free era, there is a need to optimize turbines to maximize the revenue of the farm instead of minimizing the LCoE. A wind farm-level modeling framework with a simplified market model to optimize the size of wind turbines to maximize revenue-based metrics like IRR/NPV. The results show that the optimum turbine size is driven mainly by the choice of the economic metric and the market price scenario, with an LCoE-optimized design already performing well w.r.t. metrics like IRR.
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