Two-Level Decentralized-Centralized Control of Distributed Energy Resources in Grid-Interactive Efficient Buildings
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The operation of long-distance quantum networks requires photons to be synchronized and must account for length variations of quantum channels. We demonstrate a 200 MHz clock-rate fiber optic-based telecommunication C-band quantum network using off-the-shelf components combined with custom-made electronics. The network is backed by a scalable and fully automated synchronization system with ps-scale timing resolution. Synchronization of the photons is achieved by distributing O-band-wavelength laser pulses between network nodes. Specifically, we distribute photon pairs between three nodes, and measure a reduction of coincidence-to-accidental ratio from 77 to only 42 when the synchronization system is enabled, which permits high-fidelity qubit transmission. Our demonstration sheds light on the role of noise in quantum communication and represents a key step in realizing deployed co-existing classical-quantum networks.
As the U.S. electrifies the transportation sector, cyberattacks targeting vehicle charging could impact several critical infrastructure sectors including power systems, manufacturing, medical services, and agriculture. This is a growing area of concern as charging stations increase power delivery capabilities and must communicate to authorize charging, sequence the charging process, and manage load (grid operators, vehicles, OEM vendors, charging network operators, etc.). The research challenges are numerous and complicated because there are many end users, stakeholders, and software and equipment vendors interests involved. Poorly implemented electric vehicle supply equipment (EVSE), electric vehicle (EV), or grid operator communication systems could be a significant risk to EV adoption because the political, social, and financial impact of cyberattacks — or public perception of such — would ripple across the industry and produce lasting effects. Unfortunately, there is currently no comprehensive EVSE cybersecurity approach and limited best practices have been adopted by the EV/EVSE industry. There is an incomplete industry understanding of the attack surface, interconnected assets, and unsecured inter faces. Comprehensive cybersecurity recommendations founded on sound research are necessary to secure EV charging infrastructure. This project provided the power, security, and automotive industry with a strong technical basis for securing this infrastructure by developing threat models, determining technology gaps, and identifying or developing effective countermeasures. Specifically, the team created a cybersecurity threat model and performed a technical risk assessment of EVSE assets across multiple manufacturers and vendors, so that automotive, charging, and utility stakeholders could better protect customers, vehicles, and power systems in the face of new cyber threats.
Abstract As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are impeded by the costs of communication, thermal management, power delivery and clocking. To improve scalability, we propose a digital optical neural network (DONN) with intralayer optical interconnects and reconfigurable input values. The path-length-independence of optical energy consumption enables information locality between a transmitter and a large number of arbitrarily arranged receivers, which allows greater flexibility in architecture design to circumvent scaling limitations. In a proof-of-concept experiment, we demonstrate optical multicast in the classification of 500 MNIST images with a 3-layer, fully-connected network. We also analyze the energy consumption of the DONN and find that digital optical data transfer is beneficial over electronics when the spacing of computational units is on the order of $$>10\,\upmu $$ > 10 μ m.
RMEM - a prototyping platform to explore the usage of RMA-based strategies in GPU-to-GPU communication. The implementation relies on libfabric (BSD-2 or GNUv2) to explore different synchronization approach and their associated performance. It is compatible with different network technologies such as InfiniBand, SlingShot-11, and TCP. It provides to the user host-to-host and device-to-device communication. The latter can be initiated either from the host, or from the device.
This report introduces the concept of wireless controls in heliostat-based concentrating solar thermal power (CSP) systems. Specifically, the need for wireless communications to be implemented within the heliostat-based CSP systems is identified versus the existing approaches that follow wireline connections which suffer from high installation, operation, and maintenance cost. Furthermore, this report analyzes the most recent advances in the field of developing wireless controls in heliostat-based CSP systems, which are highlighted to be in their infancy given the primitive wireless networking solutions that they currently utilize and test. The main contribution of this report is the introduction of a novel HELIOCOMM system that supports the wireless controls in heliostat-based CSP systems by exploiting the next generation networking technology of integrated access and backhaul. Furthermore, the proposed HELIOCOMM system performs an artificial intelligent clustering approach of the heliostats and clusterhead selection and develops an entropy-based routing model to enable the heliostats to communicate with the central station by considering their energy availability and network traffic. Also, towards efficiently exploiting the limited resources in the developed wireless communication system, a resource allocation module is introduced that performs the maximization of the energy efficiency of each heliostat by minimizing its corresponding experienced end-to-end latency to communicate with the central station, while performing an intelligent bandwidth splitting in the access and backhaul wireless links. The overall architecture is presented, and its individual building components are discussed in detail.
A multiple input multiple output (MIMO) power line communication (PLC) model for industrial facilities was developed that uses the physics of a bottom-up model but can be calibrated like top-down models. The PLC model considers 4-conductor cables (three-phase conductors and a ground conductor) and has several load types, including motor loads. The model is calibrated to data using mean field variational inference with a sensitivity analysis to reduce the parameter space. The results show that the inference method can accurately identify many of the model parameters, and the model is accurate even when the network is modified.
In order to deal with the increasing number of mobile devices and with their demand for Internet services, particularly social media platforms, streaming video, and online gaming, Radio-Frequency (RF) wireless networks have been pushed to their capacity limits. In addition to this, 80% of the total data traffic is carried out by users inside buildings. Therefore, new technologies have started to be considered for indoor wireless communications. Visible Light Communications (VLC) can provide both illumination and communications, appearing as an alternative or complement to RF wireless networks. VLC offers high bandwidth and immunity to interference from electromagnetic sources. This manuscript reviews recent high-capacity VLC demonstrations. The main focus of this work is to present digital-signal-processing techniques used in VLC systems. Different modulation formats are analyzed, which can be divided into two large groups, namely single-carrier and multi-carrier modulation schemes. Finally, some recently proposed capacity-achieving strategies are presented. We discuss how to implement these techniques and how they will be useful for the continued development of VLC systems.
An important challenge for smart grid security is designing a secure and robust smart grid communications architecture to protect against cyber-threats, such as Denial-of-Service (DoS) attacks, that can adversely impact the operation of the power grid. Researchers have proposed using Software Defined Network frameworks to enhance cybersecurity of the smart grid, but there is a lack of benchmarking and comparative analyses among the many techniques. In this work, a distributed three-controller software-defined networking (D3-SDN) architecture, benchmarking, and comparative analysis with other techniques is presented. The selected distributed flat SDN architecture divides the network horizontally into multiple areas or clusters, where each cluster is handled by a single Open Network Operating System (ONOS) controller. A case study using the IEEE 118-bus system is provided to compare the performance of the presented ONOS-managed D3-SDN, against the POX controller. In addition, the proposed architecture outperforms a single SDN controller framework by a tenfold increase in throughput; a reduction in latency of > 20%; and an increase in throughput of approximately 11% during the DoS attack scenarios.
Gigapixel images are prevalent in scientific domains ranging from remote sensing, and satellite imagery to microscopy, etc. However, training a deep learning model at the natural resolution of those images has been a challenge in terms of both, overcoming the resource limit (e.g. HBM memory constraints), as well as scaling up to a large number of GPUs. In this paper, we trained Residual neural Networks (ResNet) on 22,528 x 22,528-pixel size images using a distributed spatial decomposition method on 2,304 GPUs on the Summit Supercomputer. We applied our method on a Whole Slide Imaging (WSI) dataset from The Cancer Genome Atlas (TCGA) database. WSI images can be in the size of 100,000 x 100,000 pixels or even larger, and in this work we studied the effect of image resolution on a classification task, while achieving state-of-the-art AUC scores. Moreover, our approach doesn't need pixel-level labels, since we're avoiding patching from the WSI images completely, while adding the capability of training arbitrary large-size images. This is achieved through a distributed spatial decomposition method, by leveraging the non-block fat-tree interconnect network of the Summit architecture, which enabled GPU-to-GPU direct communication. Finally, detailed performance analysis results are shown, as well as a comparison with a data-parallel approach when possible.
Module-OT is a bump- in- the- wire solution acting as a secure conduit for data between devices or systems across a network. Using the latest in open-source cryptographic libraries, all defined communications undergo authentication, authorization, and encryption. The core system can be easily installed through industry standard processes, and the use of popular open-source packages allows for it to be customizable customized by the developer community or deployed in unique embedded environments.
Devices, methods, and systems for distributed gather and scatter operations in a network of memory nodes. A responding memory node includes a memory; a communications interface having circuitry configured to communicate with at least one other memory node; and a controller. The controller includes circuitry configured to receive a request message from a requesting node via the communications interface. The request message indicates a gather or scatter operation, and instructs the responding node to retrieve data elements from a source memory data structure and store the data elements to a destination memory data structure. The controller further includes circuitry configured to transmit a response message to the requesting node via the communications interface. The response message indicates that the data elements have been stored into the destination memory data structure.
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Abstract A coordinated set point automatic adjustment with correction enabled (C‐SPAACE) framework that uses 5G communication for real‐time control coordination between inverter‐based resources (IBR) in microgrids is proposed. Utilising slicing capability, 5G offers low‐latency communication to C‐SPAACE under normal conditions. However, given the multitude of power grid use cases, a certain 5G slice for C‐SPAACE may have access only to limited radio spectrum resources, which if not managed well, greatly undermines the communication needs of C‐SPAACE framework. Thus, optimally scheduling the available spectrum resources among IBRs in a sliced 5G network‐based C‐SPAACE framework becomes a critical problem. To address this issue, the authors utilise a novel age of information (AoI) metric and designs an AoI‐based 5G scheduler to provide low‐latency communication to C‐SPAACE. Following this, a co‐simulation environment is designed using PSCAD/EMTDC and Python to simulate a microgrid supported by 5G communication. Time‐domain simulation case studies are performed using the proposed co‐simulation environment to evaluate the performance of C‐SPAACE using 5G with both AoI‐based and other baseline (non‐AoI) schedulers.
The integration of quantum computers and sensors into a quantum network enables new capabilities in quantum information science. Most networks with atom-like qubits operate at visible or near-ultraviolet wavelengths and require conversion to the telecom band for long-distance communication, which reduces efficiency and potentially introduces noise. In this article we report high-fidelity entanglement between ytterbium-171 atoms and optical photons generated directly in the telecommunication band, where fibre loss is low. The nuclear spin of the atom is entangled with a single photon in the time-bin basis, yielding a high atom-measurement-corrected atom–photon Bell state fidelity. This can be further improved by addressing photon measurement errors. By imaging the atom array onto an optical fibre array, we also implement a parallelized networking protocol that can increase the remote entanglement rate proportionately with the number of channels. We also preserve coherence on a memory qubit during operations on communication qubits. These results support the integration of atomic systems into scalable quantum networks.
The ability to distribute entanglement between quantum nodes may unlock new capabilities in the future that include teleporting information across multinode networks, higher resolution detection via entangled sensor arrays, and measurements beyond the quantum limit enabled by networked atomic clocks. These new quantum networks also hold promise for the Aerospace community in areas such as deep space exploration, improved satellite communication, and synchronizing drone swarms. Although exciting, these applications are a long way off from providing a “real-world” benefit, as they have only been theoretically explored or demonstrated in small-scale experiments. An outstanding challenge is to identify near-term use cases for quantum networks; this may be an intriguing new area of interest for the aerospace community, as the quantum networking field would benefit from more multidisciplinary collaborations. This paper introduces quantum networking, discusses the difficulties in distributing entanglement within these networks, highlights recent progress toward this endeavor, and features two current case studies on mobile quantum nodes and an entangled clock network, both of which are relevant to the aerospace community.
While the Experimental Physics and Industrial Control System (EPICS) is widely used at large laboratories for slow controls and instrumentation, the deployment of a full EPICS installation can be difficult, with a steep learning curve to new users. Taking advantage of the pythonSoftIOC module, we developed an EPICS slow controls implementation for Jefferson Lab's Hall B cryotarget written entirely in Python and based on software IOCs that communicate with instruments over Ethernet. Here, this system ran successfully, interfacing with Jefferson Lab's full EPICS network, and we offer it as an example of the capabilities of pythonSoftIOC to build lightweight, yet robust and flexible instrumentation platforms that would be easily adapted for use at a small-scale laboratory. University groups can use these examples to build complete slow controls systems, from device communication to data archiving and display, using open-source, mature EPICS tools and student-friendly Python as an alternative to expensive and proprietary systems such as LabVIEW.
With the increasing adoption of distributed energy resources (DERs) in distribution networks, DERMS is becoming an attractive option to coordinate the control of DERs, especially primal-dual-based DERMS which is a well-developed class of them. In order to help lower the uncertainty in commercializing them, we evaluate and find the upper limits of severity for some common issues that can occur within the communication systems on which a primal-dual-based DERMS relies on; these include packet losses, link failures, and delays. This information can help a DERMS operator decide on the specifications for the communication infrastructure it will invest in for the DERMS. Our evaluation is based on numerical simulations of a real-world feeder with around 2,000 nodes and a DERMS that controls 163 PV generators and 140 energy storage batteries. In general, we found that the DERMS becomes less resistant to communication issues as the information flows closer to the DERs implementing the controllable power injections.