Downscaling ERA5 wind speed data: a machine learning approach considering topographic influences

Energy system modeling and analysis can provide comprehensive guidelines to integrate renewable energy sources into the energy system. Modeling renewable energy potential, such as wind energy, typically involves the use of wind speed time series in the modeling process. One of the most widely utilized datasets in this regard is ERA5, which provides global meteorological information. Despite its broad coverage, the coarse spatial resolution of ERA5 data presents challenges in examining local-scale effects on energy systems, such as battery storage for small-scale wind farms or community energy systems. In this study, we introduce a robust statistical downscaling approach that utilizes a machine learning approach to improve the resolution of ERA5 wind speed data from around 31 km × 31 km to 1 km × 1 km. To ensure optimal results, a comprehensive preprocessing step is performed to classify regions into three classes based on the quality of ERA5 wind speed estimates. Subsequently, a regression method is applied to each class to downscale the ERA5 wind speed time series by considering the relationship between ERA5 data, observations from weather stations, and topographic metrics. Our results indicate that this approach significantly improves the performance of ERA5 wind speed data in complex terrain. To ensure the effectiveness and robustness of our approach, we also perform thorough evaluations by comparing our results with the reference dataset COSMO-REA6 and validating with independent datasets

Cropland and rooftops: the global undertapped potential for solar photovoltaics

The utilization of cropland and rooftops for solar photovoltaics (PVs) installation holds significant potential for enhancing global renewable energy capacity with the advantage of dual land-use. This study focuses on estimating the global area suitable for agrivoltaics (PV over crops) and rooftop PVs by employing open-access data, existing literature and simple numerical methods in a high spatial resolution of 10 km × 10 km. For agrivoltaics, the suitability is assessed with a systematic literature review on crop-dependent feasibility and profitability, especially for 18 major crops of the world. For rooftop PV, a non-linear curve-fitting method is developed, using the urban land cover to calculate the PV-suitable built-up areas. This method is then verified by comparing the results with open-access building footprints. The spatially resolved suitability assessment unveils 4.64 million km ^2 of global PV-usable cropland corresponding to a geographic potential of about 217 Terawatts (TW) in an optimistic scenario and 0.21 million km ^2 of rooftop-PV suitable area accounting for about 30.5 TW maximum installable power capacity. The estimated suitable area offers a vast playground for energy system analysts to undertake techno-economic assessments, and for technology modellers and policy makers to promote PV implementation globally with the vision of net-zero emissions in the future

Impact of COVID-19 on Electricity Demand: Deriving Minimum States of System Health for Studies on Resilience

To assess the resilience of energy systems, i.e., the ability to recover after an unexpected shock, the system's minimum state of service is a key input. Quantitative descriptions of such states are inherently elusive. The measures adopted by governments to contain COVID-19 have provided empirical data, which may serve as a proxy for such states of minimum service. Here, we systematize the impact of the adopted COVID-19 measures on the electricity demand. We classify the measures into three phases of increasing stringency, ranging from working from home to soft and full lockdowns, for four major electricity consuming countries of Europe. We use readily accessible data from the European Network of Transmission System Operators for Electricity as a basis. For each country and phase, we derive representative daily load profiles with hourly resolution obtained by k-medoids clustering. The analysis could unravel the influence of the different measures to the energy consumption and the differences among the four countries. It is observed that the daily peak load is considerably flattened and the total electricity consumption decreases by up to 30 under the circumstances brought about by the COVID-19 restrictions. These demand profiles are useful for the energy planning community, especially when designing future electricity systems with a focus on system resilience and a more digitalised society in terms of working from home

Monitoring resilience of future energy systems

Energy systems of the future are expected to be more resilient and digitalised. Resilience of an energy system refers to its capacity to withstand and recover from perturbations of extreme events. This relates to a reduced probability of failure, reduced ramifications, and reduced time to recovery. Extreme events are qualitatively characterised as having a low probability of occurrence but a high impact, and can be of a climatic, economic, technological or social origin, to cite a few examples. The idea of resilience can also transcend to the system's ability to adapt after an extreme event: to rebuild and renew the system afterwards, making stronger than before. In the wake of climate change, the occurrence of said extreme events is expected to become more critical. This requires energy systems to be more prepared and not only reliable under known and predictable threats. Furthermore, digital technologies will enable energy networks of the future to be more decentralised than ever with the adoption of renewable energy and other distributed technologies such as electro-mobility. This new environment entails a complex grid for which resilience is critical. On the digitalisation front, the greater adoption of digital technologies significantly impacts energy demand: they can potentially improve energy efficiency and reduce energy usage, while also leading to higher energy consumption. This thesis will study the resilience of the national power sector, with consideration of a more digitalised society. The thesis primarily aims to develop a framework to monitor the resilience of such a system and subsequently achieve an improvement in its resilience, while also assessing the impact of a shift to a society which is predominantly engaged in remote working, on the national power demand. The work is structured into four broad work packages. The first package provides a critical review of resilience and presents a method to measure resilience, i.e. a resilience metric which integrates all phases of a system’s performance after attack from the extreme event. The second entails assessing the damage incurred to components of power systems from the extreme event considered. This package comprises the development of fragility and recovery curves to account for hazard data and infrastructural data, which is crucial for generating a time series of the functionality of each component of the power system. The third package involves the development of a framework to measure the resilience of power systems using an energy system model. This includes obtaining relevant quantities from model runs so as to measure resilience using the aforementioned resilience metric. It also aims to provide recommendations for effective monitoring and enhancing resilience of power systems, upon measuring system resilience. Finally, the fourth work package addresses the impact of digitalisation on the electricity demand, as well as obtaining a data basis to describe the 7minimum operational state of power systems, which is pertinent for the recovery phase of resilience studies . This package uses real-time data from the COVID-19 pandemic to generate representative power demand profiles under various degrees of stringency of COVID-19 measures adopted. The results from the thesis help in providing insights into effective monitoring of and strategies to enhance power system resilience. The datasets presented in the thesis show a strong work-from-home behaviour as a first step towards a digitalised society, and also serve as a proxy for the minimum state of health of systems to be achieved in the recovery phase of power systems affected by such extreme event