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Grid-enhancing technologies for clean energy systems

Renewable energy source integration into energy systems can contribute to transmission congestion, which requires time-consuming and capital-intensive upgrades to address. Grid-enhancing technologies (GETs) can increase the capacity of grids with minimal investment, preventing congestion and curtailment of renewable energy. In this Review, we discuss the principles and uses of GETs, which use software and/or hardware to interpret real-time conditions to better use the existing capacity of grid assets. GETs include dynamic line ratings, dynamic transformer ratings, power flow controls, topology optimization, advanced conductor technologies, energy storage systems, and demand response. These GETs can enhance system performance individually, but the deployment of multiple GETs together would greatly increase their effect on the grid capacity and stability by removing multiple capacity bottlenecks in parallel. Infrastructure for real-time data acquisition, transmission and analysis is key to successfully deploying GETs but requires further development and commercialization for broader deployment.

Latent circuit inference from heterogeneous neural responses during cognitive tasks

Higher cortical areas carry a wide range of sensory, cognitive and motor signals mixed in heterogeneous responses of single neurons tuned to multiple task variables. Dimensionality reduction methods that rely on correlations between neural activity and task variables leave unknown how heterogeneous responses arise from connectivity to drive behavior. We develop the latent circuit model, a dimensionality reduction approach in which task variables interact via low-dimensional recurrent connectivity to produce behavioral output. We apply the latent circuit inference to recurrent neural networks trained to perform a context-dependent decision-making task and find a suppression mechanism in which contextual representations inhibit irrelevant sensory responses. We validate this mechanism by confirming the behavioral effects of patterned connectivity perturbations predicted by the latent circuit model. We find similar suppression of irrelevant sensory responses in the prefrontal cortex of monkeys performing the same task. We show that incorporating causal interactions among task variables is critical for identifying behaviorally relevant computations from neural response data.

Energy efficiency and carbon savings via a body grid

The climate crisis necessitates decarbonization solutions that transform energy systems across all scales. While attention today focuses on utility-scale power systems, mini-or metro-scale grids, and at end-use device efficiency, the individual user scale remains underexplored. Just as with energy efficiency innovations tailored to micro-environments, body-scale energy savings offer new opportunities alongside technological and behavioral challenges. Here we propose a technique and a suite of potential innovations focused on the “body grid” in which devices, circuits, information network, human body and the environment interact within a universal framework to achieve energy savings, new functionality, and improved comfort. We present and test a prototype body grid supporting inter-device synergy and cooperation with external energy systems indoors and outdoors. This system yields substantial energy and economic savings, enhances personal control and comfort, and enables potential energy market participation. Simulation results demonstrate global energy savings of up to 50% for space cooling and heating.

An Integrative lifecycle design approach based on carbon intensity for renewable-battery-consumer energy systems

Driven by sustainable development goals and carbon neutrality worldwide, demands for both renewable energy and storage systems are constantly increasing. However, the lack of an appropriate approach without considering renewable intermittence and demand stochasticity will lead to capacity oversizing or undersizing. In this study, an optimal design approach is proposed for integrated photovoltaic-battery-consumer energy systems in the form of a m2-kWp-kWh relationship in both centralized and distributed formats. Superiorities of the proposed matching degree approach are compared with the traditional uniformity approach, in photovoltaic capacity, battery capacity, net present value and lifecycle carbon intensity. Results showed that the proposed method is superior to the traditional approach with higher net present value and lower carbon intensity. Furthermore, the proposed method can be scaled and applied to guide the design of photovoltaic-battery-consumer energy systems in different climate zones, promoting sustainable development and carbon neutrality globally.

Advancements in ultrafast photonics: confluence of nonlinear optics and intelligent strategies

Automatic mode-locking techniques, the integration of intelligent technologies with nonlinear optics offers the promise of on-demand intelligent control, potentially overcoming the inherent limitations of traditional ultrafast pulse generation that have predominantly suffered from the instability and suboptimality of open-loop manual tuning. The advancements in intelligent algorithm-driven automatic mode-locking techniques primarily are explored in this review, which also revisits the fundamental principles of nonlinear optical absorption, and examines the evolution and categorization of conventional mode-locking techniques. The convergence of ultrafast pulse nonlinear interactions with intelligent technologies has intricately expanded the scope of ultrafast photonics, unveiling considerable potential for innovation and catalyzing new waves of research breakthroughs in ultrafast photonics and nonlinear optics characters.

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