Published forecasts underestimate renewable energy capacity growth and potential cost reductions, creating uncertainty around investment decisions and slowing progress. Scenario-based projections diverge widely, driven by variations in modelling techniques and underlying assumptions, with policy-based models typically being overly conservative. With historical generation capacity and cost data readily available, this research demonstrates that data-driven approaches can be leveraged to improve long-term capacity and cost forecasts of solar, wind, and battery storage technologies. Unlike exponential growth models prevailing over shorter time scales, logistic curves requiring asymptotic limits, or machine learning algorithms dependent on extensive datasets, this analysis demonstrates that temporal quadratic regressions are a better starting point to represent capacity growth trends over two to three decades. When coupled with published learning rates, trend-based capacity forecasts provided tighter and lower capital and levelized cost of energy outlooks than most reviewed scenarios, with photovoltaics global average levelized cost of energy reducing from 0.057/kWh to below USD 0.03/kWh by 2030 and below USD 0.02/kWh by 2040. Greater transparency on manufacturing ecosystems is proposed so that more advanced analytical techniques can be utilized. This analysis indicates that without direct interventions to accelerate the growth in wind power generation, global renewable energy technology deployment will fall short of the generation capacities required to meet climate change objectives.

The development of an efficient and reliable method of evaluating the performance of an air-cooled steam condenser (ACC) under windy conditions using computational fluid dynamics (CFD) is presented. A two-step modeling approach is employed as a result of computational limitations. The numerical ACC model developed in this study makes use of the pressure jump fan model, among other approximations, in an attempt to minimize the computational expense of the performance evaluation. The accuracy of the numerical model is verified through a comparison of the numerical results to test data collected during full-scale tests carried out on an operational ACC. Good correlation is achieved between the numerical results and test data. The effect of wind on ACC performance at El Dorado Power Plant (Nevada, USA) is investigated. It is found that reduced fan performance due to distorted flow at the inlet of the upstream fans is the primary contributor to the reduction in ACC performance associated with increased wind speed in this case. The model developed in this study has the potential to allow for the evaluation of large ACC installations and provides a reliable platform from which further investigations into improving ACC performance under windy conditions can be carried out.