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This report describes the capability requirements for flexibility services offered in U.S. electricity markets. It is intended to serve as a directory of such services—a Flexibility Services Directory—used to design and assess grid resources, typically generation or storage resources, able to offer such services.
Flexible Financial Credit Agreements is a broad term used to describe a suite of solar products with innovative features not currently offered in traditional solar financing programs. The NREL Low- and Moderate-Income Solar Flexible Financing Credit Agreement Rubric brief document is designed to help evaluate innovative strategies for increasing the accessibility of solar and increasing adoption among LMI households. The rubric is modeled after a version developed to support plug-in electric vehicle policy adoption (NASEO and Cadmus).
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Investigations of coupled multiphysics processes in rivers and hyporheic zones have extensively used numerical models. Most existing models use a sequential, one-way coupling between the surface and subsurface domains. Such one-way coupling potentially introduces error. To overcome this, a fully coupled model, hyporheicFoam, was developed using the open-source computational platform OpenFOAM. It captures the coupled flow and multicomponent reactive transport processes within both surface and subsurface domains and across their interface. The coupling between two domains is implemented by mapping conservative flux boundary conditions at the interface through an iterative algorithm. Reactive transport is enabled by specifying a reaction network. To start, we have implemented reaction kinetics following the double Monod-type model with inhibition. The model capability is illustrated through modeling of both conservative and reactive hyporheic flow and transport through dune bedforms. With the novel coupled model, it is now possible to quantify reactions wherein the reactants and products are constantly exchanging between domains and have feedbacks. hyporheicFoam can simulate large, three-dimensional cases owing to the computational flexibility and power offered by the code structure and parallel design of OpenFOAM.
Demand flexibility is increasingly important for power grids. Careful coordination of thermostatically controlled loads (TCLs) can modulate energy demand, decrease operating costs, and increase grid resiliency. We propose a novel distributed control framework for the Coordination Of HeterOgeneous Residential Thermostatically controlled loads (COHORT). COHORT is a practical, scalable, and versatile solution that coordinates a population of TCLs to jointly optimize a grid-level objective, while satisfying each TCL’s end-use requirements and operational constraints. To achieve that, we decompose the grid-scale problem into subproblems and coordi- nate their solutions to find the global optimum using the alternating direction method of multipliers (ADMM). The TCLs’ local problems are distributed to and computed in parallel at each TCL, making COHORT highly scalable and privacy-preserving. While each TCL poses combinatorial and non-convex constraints, we characterize these constraints as a convex set through relaxation, thereby making COHORT computationally viable over long planning horizons. After coordination, each TCL is responsible for its own control and tracks the agreed-upon power trajectory with its preferred strategy. In this work, we translate continuous power back to discrete on/off actuation, using pulse width modulation. COHORT is generalizable to a wide range of grid objectives, which we demonstrate through three distinct use cases: generation following, minimizing ramping, and peak load curtailment. In a notable experiment, we validated our approach through a hardware-in-the-loop simulation, including a real-world air conditioner (AC) controlled via a smart thermostat, and simulated instances of ACs modeled after real-world data traces. During the 15-day experimental period, COHORT reduced daily peak loads by an average of 12.5% and maintained comfortable temperatures.
Block copolymer (BCP) self-assembly is a fundamental process in which incompatible blocks spontaneously form organized microstructures with broad practical applications. Classical understanding is that the domain spacing is limited by the contour length of the polymer backbone. Here, using a combination of molecular design, chemical synthesis, small-/wide-angle X-ray scattering, transmission electron microscopy, and electron tomography, we discover that this molecular picture does not hold for architecturally semiflexible BCPs. For strongly segregated linear–semiflexible bottlebrush–linear triblock copolymers, the size of the bottlebrush domain can be twice the bottlebrush backbone contour length. The mechanism of such anomalous self-assembly is likely that the interfacial repulsion between the incompatible blocks is large enough to pull a part of the linear end blocks into the bottlebrush domain. This effectively increases the bottlebrush domain size. Moreover, the semiflexible bottlebrush widens the regime for the cylinder morphology that is associated with the volume fraction of the end blocks fC SFB ∈ (0.10, >0.41). This window is much wider than that for flexible linear BCPs, fC F ∈ (0.14, 0.35), and that predicted by the recent self-consistent field theory for linear-bottlebrush BCPs of the same chemistry and molecular architecture. Finally, our experimental findings reveal previously unrecognized mechanisms for the self-assembly of architecturally complex BCPs.
This work reports a method of producing flexible cobalt nanowires (NWs) directly from the chemical conversion of bulk precursors at room temperature.
This paper proposes a machine-learning (ML)-aided cognitive approach for effective bandwidth reconfiguration in optically interconnected datacenter/high-performance computing (HPC) systems. The proposed approach relies on a Hyper-X-like architecture augmented with flexible-bandwidth photonic interconnections at large scales using a hierarchical intra/inter-POD photonic switching layout. We first formulate the problem of the connectivity graph and routing scheme optimization as a mixed-integer linear programming model. A two-phase heuristic algorithm and a joint optimization approach are devised to solve the problem with low time complexity. Then, we propose an ML-based end-to-end performance estimator design to assist the network control plane with intelligent decision making for bandwidth reconfiguration. Numerical simulations using traffic distribution profiles extracted from HPC applications traces as well as random traffic matrices verify the accuracy performance of the ML design estimator ( < <#comment/> 9 % <#comment/> error) and demonstrate up to 5 × <#comment/> throughput gain from the proposed approach compared with the baseline Hyper-X network using fixed all-to-all intra/inter-portable data center interconnects.
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