We present an efficient neural-based approach to estimate the instantaneous flow field around an airfoil from limited surface pressure measurements. The model, denoted SNN-POD, relies on two independent shallow neural networks to predict the instantaneous flow over a wide range of angles of attack $ \left[10{}^{\circ},20{}^{\circ}\right] $. At all angles the global model correctly recovers the average characteristics of the flow from single-time sensor data, thus allowing combination with local, angle-dependent models. The method is applied to 2D URANS simulations of a thick airfoil at a Reynolds number of $ \mathit{\operatorname{Re}}=4.5\times {10}^6 $. The training set consists of snapshots obtained from a coarse sampling $ \left(1-2{}^{\circ}\right) $ of the angle of attack range. A variance-based criterion is used to determine the number and positions of sensors. Tests are carried out for unseen snapshots at angles of attack within the set (sampled angles) as well as outside the set (interpolated angles). The maximum MSE error of attack for sampled and interpolated angles is respectively $ 2.9\% $ and $ 6.6\% $. This makes it possible to develop adaptive strategies to improve the estimation if necessary.

Power loss mechanisms in large wind farms are complex due to the multiscale nature of wind farm aerodynamics. Recent studies based on ‘two-scale momentum theory’ have brought new insights into this field; however, most of them have been limited to idealised wind farm scenarios. To better understand power loss mechanisms in real wind farms, in this study, we extend the framework of the two-scale momentum theory to non-ideal turbine design and layout scenarios, and then introduce simple analytical models to account for the associated power losses. This extension provides a holistic view of how turbine design, layout, operating conditions and atmospheric conditions collectively determine the amounts of different types of power losses in real wind farms, including the losses due to turbine-wake interference (i.e. ‘internal’ power loss) and farm-atmosphere interaction (i.e. ‘external’ power loss). We also present a simple iterative method for calculating the optimal farm induction factor that maximises the overall farm power for a given set of conditions, including the atmospheric boundary layer height. Analogously to blade-element momentum theory playing a key role in wind turbine design optimisation, the present theory may play a key role in wind farm design optimisation.

Despite the proliferation of research, more systematic attempts to trace worker experiences across production networks of renewable energy remain marginal. This limits our potential to offer meaningful insights to guide future political mobilisation and policy measures for organised labour and workers more broadly. As a partial remedy, I provide an initial description of labour processes in wind energy. To do so, I carry out labour regime analysis. The utility of the labour regime framework stems from its ability to help the analyst to understand the labour process better by grounding it into a systematic theoretical framework to capture more effectively how dynamic political economic processes condition workplace outcomes. In my analysis, I highlight how capitals-in-competition within energy production networks facilitate structural conditions that intensify the rate of wind worker exploitation. Critically, the ecological sphere of the labour regime mediates the capital-labour interaction, which helps to explain the significant number of hours and extended timelines expected of wind technicians and those involved on project developments, as project owners push for time intensive schedules to reduce wind turbine downtime. My work also extends labour regime scholarship by arguing that ideational constructions, informed by different spheres of the labour regime, govern the labour process in important ways, which suggests that future studies might more seriously consider how ideational notions of work, such as Gramscian ‘common sense’ expectations, maintain labour regimes. I reason that a combination of these factors, both material and ideational, has made workplace organising in wind difficult.

For efficient wind farm management and optimized power generation under adverse weather conditions, understanding the causal meteorological drivers is essential. In this paper, we investigate the temporal causal influences of wind speed-related meteorological processes within a wind farm using the Heterogeneous Graphical Granger model (HMML). HMML is applied to synthetically generated wind power production data from Eastern Austria. To assess the plausibility of the identified causal processes, we compare the results with those obtained using the state-of-the-art LiNGAM method. Both methods are applied and evaluated across six different scenarios, each defined by distinct hydrological periods. The scenarios are defined by a set of time intervals characterized by either low/high extreme wind speeds or moderate wind speeds. We applied both methods across these scenarios and conducted causal reasoning to identify potential causes of extreme wind speeds within the wind farm. The sets of causal parameters obtained using HMML were found to be more realistic than those derived from LiNGAM. Combining the knowledge of causal variables affecting wind speed at the turbine hub, identified by HMML in each scenario, with weather forecasts can offer practical guidance for wind farm operators. Specifically, this knowledge can support more informed planning regarding when wind turbines should or should not be generating energy. For instance, the strong Granger-causal linkage identified between wind speed and temperature can inform curtailment strategies. In scenarios where rising temperatures are predictive of declining wind speeds, operators may preemptively adjust turbine output or schedule maintenance to optimize efficiency and reduce wear. Moreover, such predictive insights can feed into energy market models, where anticipated curtailment due to meteorological dependencies affects both generation forecasts and pricing strategies. By integrating these causal relationships into operational planning, the proposed tool offers a pathway toward more adaptive and economically efficient wind energy management.

This article explores the responsibility of wind energy developers for the rights of Indigenous Peoples whose lands are affected by wind energy projects. Applying a rights-based approach and drawing on three landmark court rulings involving the struggle of Indigenous communities against the development of wind energy projects, the analysis explores the insights provided by the cases for clarifying the responsibility of business actors involved in developing such projects. It examines how Indigenous Peoples’ rights are frequently marginalized or overlooked in the planning and siting of wind energy projects and the need to respect the rights of Indigenous Peoples throughout a project in order to attain a transition that is just. Based on the analysis, we argue for a rights-based approach as the theoretical framework and analytical tool to advance justice in the green transition and a means to articulate the responsibilities of corporate actors within that context.

Facing increasing nonrenewable and environmental concerns with fossil power generation, renewable energy is being supported by government mechanisms. With the power generation cost of renewables generally higher than fossil fuels, determining the optimal level of these mechanisms requires an understanding of households’ prosocial behavior toward renewables. The issue is determining the magnitude households are willing to pay (WTP) for alternative renewables. Our hypothesis is this behavior varies by the type of renewable energy. As a test of this hypothesis, we apply a discrete choice experiment to measure households’ WTP. Results support our hypothesis with a positive WTP for solar energy, leading to a 62% reduction in solar subsidy, and a negative WTP for biomass and wind sources.

Wind energy is a source of collision fatalities for birds and bats. To evaluate the risk that wind power development projects might pose to the conservation of protected species, it is essential to quantify the impact of collisions on the dynamics of wild populations. To address this challenge, two approaches are primarily employed: potential biological removal (PBR) and population projection analysis (PPA). PBR is a decision rule designed to calculate a sustainable fatality limit for a given population, whereas PPA relies on simulation-based modelling to forecast a population’s future trajectory under various scenarios. In the context of environmental impact assessments (EIAs), we argue that PPA offers a more suitable method than PBR for evaluating population-level impacts resulting from collisions with wind turbines. Unlike PBR, PPA can be focused on a single source of disturbance, aligning with the perspective of the EIA process. By contrast, PBR necessarily adopts a population-centred perspective and is therefore only relevant when considering all sources of mortality that jointly affect a population. Furthermore, robust utilization of the PBR approach requires the definition of quantitative conservation objectives and the implementation of a comprehensive management strategy evaluation, neither of which is ever undertaken within the context of an EIA.

We consider the problem of optimally maintaining an offshore wind farm in which major components progressively degrade over time due to normal usage and exposure to a randomly varying environment. The turbines exhibit both economic and stochastic dependence due to shared maintenance setup costs and their common environment. Our aim is to identify optimal replacement policies that minimize the expected total discounted setup, replacement, and lost power production costs over an infinite horizon. The problem is formulated using a Markov decision process (MDP) model from which we establish monotonicity of the cost function jointly in the degradation level and environment state and characterize the structure of the optimal replacement policy. For the special case of a two-turbine farm, we prove that the replacement threshold of one turbine depends not only on its own state of degradation but also on the state of degradation of the other turbine in the farm. This result yields a complete characterization of the replacement policy of both turbines by a monotone curve. The policies characterized herein can be used to optimally prescribe timely replacements of major components and suggest when it is most beneficial to share costly maintenance resources.

Organizational interactions in fields, including their antecedents and consequences, remain under-researched, in particular with regard to relational distance and transformative skills. Through a comparative study of the German and Japanese wind power sectors, we explore the importance of distance among organizational actors and the development of skills. While in the case of Germany a radical increase in wind energy generation can be witnessed, the situation in the field of Japanese wind power remains largely unchanged. We show how different degrees of distance among organizational actors in these two countries result in the different development of skills that stimulate transformation in the field of energy generation. More precisely, we illustrate the pivotal role of distant challengers with their transformative skills for the successful conversion of already established field structures. Our study contributes to field theory by elaborating on the understanding of the evolution of relational distance, thereby grasping the dynamic interplay between the diversity of actors and their skill formation within a certain strategic action field.

So-called development projects in rural Mexico are heavily contested. Changes in institutional design have had little effect in mediating the exclusivity of the political decision-making processes defining such projects. Why, and how, has the Mexican state been able to maintain a developmentalist agenda, despite growing pressures to incorporate participatory development institutions and consult Indigenous peoples about development projects? This article introduces the geographic concept of scale and the concept of the state’s heterogeneous selectivities into debates on participation to study the politics of development projects. It analyzes the potential and existing obstacles to political participation for Indigenous networks and activists in the corresponding planning processes across institutional scales, examining protest against wind energy development in the state of Oaxaca and the project of “rural cities” in the state of Chiapas. Rather than two separate cases for comparison, both examples represent different planning processes involving the same heterogeneous state and the same promise of progress.

Vertical axis turbine (VAT) arrays can achieve larger power generation per land area than their horizontal axis counterparts, due to the positive synergy from clustering VATs in close proximity. The VATs generate a three-dimensional wake that evolves unevenly over the vertical and transverse directions according to two governing length scales, namely the rotor's diameter and height. Theoretical wake models need to capture such a complex wake dynamics to enable reliable array design that maximises energy output. This paper presents two new theoretical VAT wake models based on super-Gaussian and Gaussian shape functions, which account for the three-dimensional velocity deficit distribution in the wake. The super-Gaussian model represents the initial elliptical shape with the superposition of vertical and lateral shape functions that progressively converge into an axisymmetric circular-shaped wake at a downstream distance that depends on the rotor's height-to-diameter aspect ratio. Our Gaussian model improves the initial wake width prediction taking into account the rectangular rotor's cross-section. Our models were well validated with large-eddy simulations (LES) of single VATs with varying aspect ratios and thrust coefficients operating in an atmospheric boundary layer. The super-Gaussian model attained a good agreement with LES in both near and far wake, whilst the Gaussian model represented well the far-wake region.