Federated learning has recently emerged as a privacy-preserving distributed machine learning approach. Federated learning enables collaborative training of multiple clients and entire fleets without sharing the involved training datasets. By preserving data privacy, federated learning has the potential to overcome the lack of data sharing in the renewable energy sector which is inhibiting innovation, research and development. Our paper provides an overview of federated learning in renewable energy applications. We discuss federated learning algorithms and survey their applications and case studies in renewable energy generation and consumption. We also evaluate the potential and the challenges associated with federated learning applied in power and energy contexts. Finally, we outline promising future research directions in federated learning for applications in renewable energy.

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Surface solar irradiance (SSI) plays a crucial role in tackling climate change — as an abundant, non-fossil energy source, exploited primarily via photovoltaic (PV) energy production. With the growing contribution of SSI to total energy production, the stability of the latter is challenged by the intermittent character of the former, arising primarily from cloud effects. Mitigating this stability challenge requires accurate, uncertainty-aware, near real-time, regional-scale SSI forecasts with lead times of minutes to a few hours, enabling robust real-time energy grid management. State-of-the-art nowcasting methods typically meet only some of these requirements. Here we present SHADECast, a deep generative diffusion model for the probabilistic spatiotemporal nowcasting of SSI, conditioned on deterministic aspects of cloud evolution to guide the probabilistic ensemble forecast, and based on near real-time satellite data. We demonstrate that SHADECast provides improved forecast quality, reliability, and accuracy in all weather scenarios. Our model produces realistic and spatiotemporally consistent predictions extending the state-of-the-art forecast horizon by 26 min over different regions with lead times of 15-120 min. Our physics-informed generative approach leads to up to 60% performance improvement in extreme value prediction over the state-of-the-art deterministic models, showcasing the advantage of probabilistic modeling of cloudiness over the classical deterministic approach. It also surpasses the probabilistic benchmarks in predicting extreme values. Finally, SHADECast empowers grid operators and energy traders to make informed decisions, ensuring stability and facilitating the seamless integration of PV energy across multiple locations simultaneously.

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Wind energy plays a critical role in the transition towards renewable energy sources. However, the uncertainty and variability of wind can impede its full potential and the necessary growth of wind power capacity. To mitigate these challenges, wind power forecasting methods are employed for applications in power management, electricity trading, or maintenance scheduling. In this work, we present, evaluate, and compare four machine learning-based wind power forecasting models. Our models correct and improve 48-hour forecasts extracted from a numerical weather prediction (NWP) model. The models are evaluated on datasets from a wind park comprising 65 wind turbines. The best improvement in forecasting error and mean bias was achieved by a convolutional neural network, reducing the average NRMSE down to 22%, coupled with a significant reduction in mean bias, compared to a NRMSE of 35% from the strongly biased baseline model using uncorrected NWP forecasts. Our findings further indicate that changes to neural network architectures play a minor role in affecting the forecasting performance, and that future research should rather investigate changes in the model pipeline. Moreover, we introduce a continuous learning strategy, which is shown to achieve the highest forecasting performance improvements when new data is made available.

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Floating Modular Energy Islands (FMEIs) are modularized, interconnected floating structures that function together to produce, store, possibly convert and transport renewable energy. Recent technological advancements in the offshore energy sector indicate that the concept of floating offshore energy islands has the potential to become more cost-effective and more widespread than previously anticipated. This review is specifically meant as a basis for the development of new approaches to the sustainable exploitation of multi-energy sources in the offshore environment leveraging the know-how of existing technologies and, at the same time, exploring new solutions for the specific challenges of FMEIs. The paper critically analyzes the current state of data-driven approaches and structural health monitoring techniques in the offshore energy sector. It also covers topics such as met-ocean data, load estimation, platform dynamics, coupling actions, nonlinear dynamics of mooring lines, modelling considerations, and control of electrical subsystems. It is believed that this systematic and multidisciplinary review will facilitate synergies and further enhance research and development of offshore renewable energies.@en