Energy Infrastructure, Renewables Integration, and Smart Grids

Vertical-axis wind turbines offer untapped opportunities for energy generation but suffer from dynamic stall in strong winds. Here, authors implement individual blade pitch control to benefit from stall vortices instead of suppressing them, tripling the power coefficient and reducing load transients by 70%.

Accurate nowcasting of cloud cover or fraction and its movement remains a significant challenge for stable solar photovoltaic electricity generation. Here, the authors combine continuous radiance images with high spatio-temporal resolutions to develop a nowcasting algorithm for predicting cloud cover at a leading time of 0–4 h.

By building transcontinental power pools, Yang and colleagues find global electricity demand can be 100% met by renewables, at an affordable cost.

A resilient battery electric bus transit system design and configuration is proposed. The model is robust against simultaneous charging disruptions without interrupting daily operation. Indeed, additional marginal cost is required, yet it prevents significant service reductions.

A rapid and large-scale reduction in car use, within a well-designed policy mix, is necessary to achieve short-term emission targets and reduce energy demand. Here, the authors introduce the Urban Transport Policy Model and demonstrate, using London as a case study, that current policies will not meet climate targets.

Renewable energy and electric vehicles will be required for the energy transition, but the global electric vehicle battery capacity available for grid storage is not constrained. Here the authors find that electric vehicle batteries alone could satisfy short-term grid storage demand by as early as 2030.

Increasing the capacity of existing lines or adding new lines in power grids may, counterintuitively, reduce the system performance and promote blackouts. The authors propose an approach for prediction of edges that lower system performance and defining potential constrains for grid extensions.

In exploring the energy required to provide decent living for all, the authors find the costs of inequality to be far greater than that of population growth. Nonetheless, population growth remains important for other reasons.

Li-ion batteries are used to store energy harvested from photovoltaics. However, battery use is sporadic and standard diagnostic methods cannot be applied. Here, the authors propose a methodology for diagnosing photovoltaics-connected Li-ion batteries that use trained machine learning algorithms.

A new study assesses the feasibility of a fully renewable based power system by 2050 across India, finding this option to be cost competitive with the status quo and with zero GHG emissions.

Modern power grids undergo a transition due to the integration of renewable energy generation technologies that bring heterogeneity in the grid. The authors study the synchronization and stability of power grids with heterogeneous inertia and damping factors, and demonstrate power feasibility of operating a system consisting of only renewable generation technologies with enhanced stability.

Though a global assessment of rooftop solar photovoltaic (RTSPV) technology’s potential and the cost is needed to estimate its impact, existing methods demand extensive data processing. Here, the authors report a machine learning method to realize a high-resolution global assessment of RTSPV potential.

Worman and colleagues analyse the coordination of wind, solar and hydropower over continental Europe to balance the continental electric load demand. Modelling results show that spatiotemporal management of mixed electricity production over large scale can induce a potential energy storage gain to match the electricity consumption.

The increase of intermittent energy sources and renewable energy penetration generally results in reduced overall inertia, making power systems susceptible to disturbances. Here, authors develop an AI-based method to estimate inertia in real-time and test its performance on a heterogeneous power network.

Data-Driven Quantum Approximate Optimization Algorithm for Power Systems Quantum Approximate Optimization Algorithms can enhance the monitoring, operation, and control of Distributed Energy Resources. Li and coworkers reduce the computational effort required for training these algorithms by efficiently obtaining algorithm parameters.

Here the authors introduce dual communities, characterized by strong connections at their boundaries, and show that they are formed as a trade-off between efficiency and resilience in supply networks.

In network systems governed by oscillatory activity, such as brain networks or power grids, configurations of synchrony may define network functions. The authors introduce a control approach for the formation of desired synchrony patterns through optimal interventions on the network parameters.

In modern power grids, knowing the required electric power demand and its variations is necessary to balance demand and supply. The authors propose a data-driven approach to create high-resolution load profiles and characterize their fluctuations, based on recorded data of electricity consumption.

A study of how the Greater London electric vehicle charging network is affected by flooding reveals disproportionate impacts on already-stressed parts of the network, peaking as far as over 10 km away from the flooded regions.