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 (Mehta et al., 2024), 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.@en

Large-scale exploitation of offshore wind energy is deemed essential to provide its expected share to electricity needs of the future. To achieve the same, turbine and farm-level optimizations play a significant role. Over the past few years, the growth in the size of turbines has massively contributed to the reduction in costs. However, growing turbine sizes come with challenges in rotor design, turbine installation, supply chain, etc. It is, therefore, important to understand how to size wind turbines when minimizing the levelized cost of electricity (LCoE) of an offshore wind farm. Hence, this study looks at how the rated power and rotor diameter of a turbine affect various turbine and farm-level metrics and uses this information in order to identify the key design drivers and how their impact changes with setup. A multi-disciplinary design optimization and analysis (MDAO) framework is used to perform the analysis. The framework uses low-fidelity models that capture the core dependencies of the outputs on the design variables while also including the trade-offs between various disciplines of the offshore wind farm. The framework is used, not to estimate the LCoE or the optimum turbine size accurately, but to provide insights into various design drivers and trends. A baseline case, for a typical setup in the North Sea, is defined where LCoE is minimized for a given farm power and area constraint with the International Energy Agency 15 MW reference turbine as a starting point. It is found that the global optimum design, for this baseline case, is a turbine with a rated power of 16 MW and a rotor diameter of 236 m. This is already close to the state-of-the-art designs observed in the industry and close enough to the starting design to justify the applied scaling. A sensitivity study is also performed that identifies the design drivers and quantifies the impact of model uncertainties, technology/cost developments, varying farm design conditions, and different farm constraints on the optimum turbine design. To give an example, certain scenarios, like a change in the wind regime or the removal of farm power constraint, result in a significant shift in the scale of the optimum design and/or the specific power of the optimum design. Redesigning the turbine for these scenarios is found to result in an LCoE benefit of the order of 1 %–2 % over the already optimized baseline. The work presented shows how a simplified approach can be applied to a complex turbine sizing problem, which can also be extended to metrics beyond LCoE. It also gives insights into designers, project developers, and policy makers as to how their decision may impact the optimum turbine scale.@en

To limit the consequences of climate change, generation from renewables coupled with large scale electrification is necessary. However, the deployment of renewables has its own challenges and not all sectors can be electrified. Hydrogen production from wind energy emerges as a promising solution that can alleviate these challenges. The current costs of green hydrogen production are high due to the high costs of electricity used for electrolysis. This study looks into the benefits of optimizing a turbine specifically for hydrogen production and the reduction in the Levelized Cost of Hydrogen (LCoH) compared to the use of conventional Levelized Cost of Energy (LCoE) optimized turbine. The case presented shows that turbines designed specifically for hydrogen production tend to have a higher specific power but these provide only a marginal advantage over using LCoE-optimized turbines for hydrogen production. Oversizing the electrolyzer compared to the turbine was shown to be a good design strategy. In the future, designing turbines specifically for hydrogen production could have certain benefits, depending on how the electrolyzer efficiencies, hydrogen production costs and the hydrogen market evolve.@en

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 could be completely exposed to the volatility of market prices, where the price paid per kWh of energy would not be a constant anymore. The developer might then have to maximize the revenue earned by participating in di_erent energy, capacity, or ancillary services markets. In such a scenario, a turbine designed for maximizing its market value could be more pro_table 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 _eld of turbine and farm design. The goal is to optimize the de- sign for a new set of objective functions and constraints, and analyze the impact of these new designs on the system as a whole. The power density of the turbine is optimized to maximize the Internal Rate of Return (IRR) and is compared to the turbine design optimized for LCoE. A multivariate model is developed to derive the spot price from the existing nationwide wind power and demand forecast. For the future years, the forecasts are scaled up w.r.t the increase in installed wind turbine capacity and demand derived from trends/government targets. Various scenarios are simulated wherein the installed wind turbine capacity and demand are varied. A gradient-free optimization is performed by using the rotor diameter as a design variable while keeping the machine rating constant. Using IRR as an objective function results in larger rotor sizes enabling the turbine to produce a higher power at lower wind speeds, corresponding to times with higher spot prices. The result of a scenario (Target) where the installed wind turbine capacity follows government targets and demand is extrapolated linearly, is shown in Figure 1a. Here, the power density of a 5 MW baseline turbine is optimized for IRR, where the revenue from the Dutch day-ahead market is considered along with the turbine costs. Results for a single (onshore) turbine will be compared with a similar IRR optimization of power density of a turbine in a sample o_shore wind farm. At a wind farm level, the e_ects of power density variations on the farm layout, wake losses, ca- bling costs, etc. are also included. Moreover, insights into the consequences of optimizing the turbines on 'system-friendliness' are provided. Figure 1b illustrates a comparison between the farm capacity factor and farm power ramps. It is observable that while the capacity factor of the farm with a revenue-driven turbine is higher, the power ramps are steeper as well. A system-level trade-o_ is apparent as higher capacity factors ensure a better supply of demand at lower wind speeds while higher ramps need further compensation. This shows how moving beyond LCoE, by only considering energy markets, might not necessarily produce the most system-friendly turbines. To avoid negative implications, this study emphasizes the need to examine the consequences of selecting a revenue-based objective function on the system as a whole. @en

The potential technical benefits of wind-storage hybrids, mainly arbitrage, imbalance reduction, and frequency support, are convincing enough to launch demonstration projects. However, a quantitative analysis of these benefits, including economic considerations, is lacking. The aim of this study is to establish at what costs such technical benefits can be achieved, and whether developers reap sufficient economic advantage to make the development of such hybrid plants attractive. A wind-storage power plant is simulated for arbitrage, imbalance revenue maximization, and secondary frequency support using the Internal Rate of Return as a parameter to measure the economic performance. It is found that, for a wind-farm developer, deploying batteries just for arbitrage and/or imbalance revenue maximization does not improve profitability at current levels of battery costs. However, there is a strong economic incentive for a wind farm developer to deploy batteries to participate in the secondary frequency market. @en