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Grid

Illinois Institute of Technology

Wide Bandgap Solid State Circuit Breakers for AC and DC Microgrids

Illinois Institute of Technology (IIT) will develop autonomously operated, programmable, and intelligent bidirectional solid-state circuit breakers (SSCB) using transistors based on gallium nitride (GaN). Renewable power sources and other distributed energy resources feed electricity to the utility grid through interfacing power electronic converters, but the power converters cannot withstand a fault condition (abnormal electric current) for more than a few microseconds. Circuit faults cause either catastrophic destruction or protective shutdown of the converters, resulting in loss of power reliability. Traditional mechanical circuit breakers are too slow to address this challenge. The team's proposed SSCB technology offers a programmable response time to as short as one microsecond, well within the overload-withstanding capability of power converters, and enables a distribution system-level ability to isolate a fault from the rest of the power system before renewable power generation is interrupted. Their design produces a 1000x decrease in response time and 5x reduction in cost in comparison to commercial mechanical circuit breakers. If successful, such devices could be used to help protect microgrids and enable higher penetration of renewable energy sources.

KEMA Inc.

Enabling the Internet of Energy through Network Optimized Distributed Energy Resources

DNV GL together with its partners, Geli and Group NIRE, will develop an Internet of Energy (IoEn) platform for the automated scheduling, aggregation, dispatch, and performance validation of network optimized DERs and controllable loads. The IoEn platform will simultaneously manage both system-level regulation and distribution-level support functions to facilitate large-scale integration of distributed generation onto the grid. The IoEn will demonstrate a novel and scalable approach for the fast registration and automated dispatch of DERs by combining DNV GL's power system simulation tools and independent third-party validation with Geli's networking, control, and market balancing software. The platform will demonstrate the ability of customer-sited DERs to provide grid frequency regulation and distribution reliability functions with minimal impact to their local behind-the-meter demand management applications. The IoEn will be demonstrated and tested at Group NIRE's utility-connected microgrid test facility in Lubbock, Texas, where it will be integrated with local utility monitoring, control and data acquisition systems. By increasing the number of local devices able to connect and contribute to the IoEn, this project aims to increase renewables penetration above 50% while maintaining required levels of grid performance.

Marquette University

Ultra-Fast Resonant DC Breaker

Marquette University will leverage the technology gap presented by the lack of DC breaker technology. The project objective is to create an industry standard DC breaker that is compact, efficient, ultra-fast, lightweight, resilient, and scalable. The proposed solution will use a novel current source to force a zero current in the main current conduction path, providing a soft transition when turning on the DC breaker. A state-of-the-art actuator that can produce significantly more force than current solutions will also be used. The approach represents a transformational DC breaker scalable across voltage and current in medium voltage DC applications, such as power distribution, solar, wind, and electric vehicles.

Michigan State University

Transformer-Less Unified Power Flow Controller for Wind and Solar Power Transmission

Michigan State University (MSU) is developing a power flow controller to improve the routing of electricity from renewable sources through existing power lines. The fast, innovative, and lightweight circuitry that MSU is incorporating into its controller will eliminate the need for a separate heavy and expensive transformer, as well as the construction of new transmission lines. MSU's controller is better suited to control power flows from distributed and intermittent wind and solar power systems than traditional transformer-based controllers are, so it will help to integrate more renewable energy into the grid. MSU's power flow controller can be installed anywhere in the existing grid to optimize energy transmission and help reduce transmission congestion.

National Renewable Energy Laboratory

Real-time Optimization and Control of Next-Generation Distribution Infrastructure

The National Renewable Energy Laboratory (NREL) lead team will develop a comprehensive distribution network management framework that unifies real-time voltage and frequency control at the home/DER controllers' level with network-wide energy management at the utility/aggregator level. The distributed control architecture will continuously steer operating points of DERs toward optimal solutions of pertinent optimization problems, while dynamically procuring and dispatching synthetic reserves based on current system state and forecasts of ambient and load conditions. The control algorithms invoke simple mathematical operations that can be embedded on low-cost microcontrollers, and enable distributed decision making on time scales that match the dynamics of distribution systems with high renewable integration.

National Renewable Energy Laboratory

SMARtDaTa: Standardized multi-scale Models of Anonymized Realistic Distribution and Transmission data

The National Renewable Energy Laboratory (NREL), with partner MIT-Comillas-IIT, will develop combined distribution-transmission power grid models. The team will create distribution models using a version of Comillas' Reference Network Model (RNM) that will be adapted to U.S. utilities and based on real data from a broad range of utility partners. The models will be complemented by the development of customizable scenarios that can be used for accurate algorithm comparisons. These scenarios will take into account unknown factors that affect the grid, such as future power generation technologies, increasing distributed energy resources, varying electrical load, disruptions due to weather events, and repeatable contingency sequences. These enhanced datasets and associated data building tools are intended to provide large-scale test cases that realistically describe potential future grid systems and enable the nation's research community to more accurately test advanced algorithms and control architectures. MIT-Comillas-IIT will assist NREL with the distribution model creation. Alstom Grid will assist in validating the distribution models.

National Rural Electric Cooperative Association

GridBallast - Autonomous Load Control For Grid Resilience

The National Rural Electric Cooperative Association (NRECA) will develop GridBallast, a low-cost demand-side management technology, to address resiliency and stability concerns accompanying the exponential growth in DERs deployment in the U.S. electric grid. Specifically, devices based on GridBallast technology will monitor grid voltage and frequency and control the target load in order to address excursions from grid operating targets. The devices will operate autonomously to provide rapid local response, removing the need for costly infrastructure to communicate with a central controller. If the devices are installed with an optional radio, they will be able to support traditional demand response through peer-to-peer collaborative operation from a central operator. The team includes experts from Carnegie Mellon University, Eaton Corporation, and SparkMeter, and will focus development on two specific devices: a water heater controller, and a smart circuit controller. The GridBallast project aims to improve resiliency and reduce the cost of demand side management for voltage and frequency control by at least 50% using a streamlined design and removing the need for extensive communications infrastructure.

New York University

Grid Dynamics and Energy Consumption Patterns Through Remote Observations of City Light

New York University (NYU) will develop an observational platform to remotely reveal energy usage patterns of New York City using synoptic imaging of the urban skyline. The electrical grid of the future will be a complex collection of traditional centralized power generation, distributed energy resources, and emerging renewable energy technologies. Advanced energy consumption data is required to design and optimize our future grid. At present, the costly and time-consuming installation of smart meters is the only way to obtain this level of building energy information. NYU will harness astronomical lessons from the study of light emitted by stars to propose a method to understand city-level energy consumption using a single platform. This platform will develop proxy measures of energy consumption, monitor the health of the electric grid, and characterize end use. The project will use three different imaging methodologies to measure interior lights at night: persistent broadband visible, hypertemporal, and hyperspectral. Broadband visible imaging of an urban skyline will measure changes in the city lightscape. This variability serves as a proxy for occupancy and behavior patterns that, when combined with "ground truth" meter data, will be used to train models to quantify energy use. Hypertemporal visible imaging can detect and classify tiny changes over time in the oscillations of electrical lights. For urban lightscapes, phase changes in individual units can signal changes in load (e.g. appliances turning on/off), while neighborhood-level changes can indicate the health of distribution transformers. The information from these methods can serve as a low-cost supplement (and potential alternative) to smart meters. Hyperspectral observations, including bands of infrared light not visible by the human eye, allow the team to distinguish lighting technologies at night. By combining this data with their broadband visible observations, the team can uniquely quantify energy use phenomena such as technology penetration and "rebound," where the energy benefits of energy efficient lighting are partially offset by greater use. With these results, utility companies can design targeted outreach efforts to incentivize energy conservation at the consumer level. Utility providers can use these insights to improve grid resilience, preemptively detect outages, and more effectively manage assets in real time. If successful, the system is well suited to deployment in developing countries where the use of modern energy-monitoring technologies is prohibitively expensive.

Newton Energy Group, LLC

Coordinated Operation of Electric And Natural Gas Supply Networks: Optimization Processes And Market Design

The team led by Newton Energy Group will lead the Gas-Electric Co-Optimization (GECO) project to improve coordination of wholesale natural gas and power operators both at the physical and market levels. The team's approach uses mathematical methods and computational techniques that have revolutionized the field of optimal control. These methods will be applied to natural gas pipeline networks, and the final deliverable will consist of three major components. First, they will model and optimize intra-day pipeline operations represented by realistic models of gas network flow. Next, the team will develop economic theory and computation algorithms for the pricing of natural gas delivered to end users, in particular to gas-fired power plants. Finally, they will combine these two analytical components to design practical market mechanisms for efficient coordination of gas and electric systems. The goal of efficient market design is to develop a mechanism under which access to pipeline capacity will be provided on the basis of its economic value as determined by gas buyers and sellers, and not on the current allocation of physical capacity rights. The tool guarantees natural gas will be available when power plants need it, and that the power produced can be sold to consumers at a price sufficient to cover the cost of the natural gas.

Northwestern University

A Novel Hierarchical Frequency-Based Load Control Architecture 

Northwestern University and its partners will develop a frequency-based load control architecture to provide additional frequency response capability and allow increased renewable generation on the grid. The work will focus on developing and demonstrating algorithms that adapt to rapid changes of loads, generation, and system configuration while taking into account various constraints arising from the transmission and distribution networks. The multi-layer control architecture makes it possible to simultaneously ensure system stability at the transmission network level, control frequency at the local distribution network level, and maintain the quality-of-service for individual customers at the building level, all under a single framework. At the transmission level, coordination among different areas will be achieved through a centralized scheme to ensure stable frequency synchronization, while the control decisions within a single area will be made based on local information. The efficiency of the centralized scheme will be ensured by decomposing the network into smaller components on which the control problem is solved individually. At the local distribution network level, the control scheme will be decentralized, in which control decisions are made based on the state of the neighboring nodes. At the building level, dynamic models for flexible appliances and DERs will be developed and used to design algorithms to optimally follow a given aggregated load profile.

Oak Ridge National Laboratory

Magnetic Amplifier for Power Flow Control

Oak Ridge National Laboratory (ORNL) is developing an electromagnet-based, amplifier-like device that will allow for complete control over the flow of power within the electric grid. To date, complete control of power flow within the grid has been prohibitively expensive. ORNL's controller could provide a reliable, cost-effective solution to this problem. The team is combining two types of pre-existing technologies to assist in flow control, culminating in a prototype iron-based magnetic amplifier. Ordinarily, such a device would require expensive superconductive wire, but the magnetic iron core of ORNL's device could serve as a low-cost alternative that is equally adept at regulating power flow.

Ohio State University

T-Type Modular DC Circuit Breaker (T-Breaker) for Future DC Networks

The Ohio State University (OSU) team will develop a MVDC circuit breaker prototype based on its novel "T-breaker" topology. OSU will leverage its unique high voltage and real-time simulation facilities, circuit prototyping experience with MV silicon carbide devices, and capability in developing protection strategies for faults in DC networks. The result will be a circuit breaker with reduced cost and weight, simplified manufacturing, and increased reliability, functionality, efficiency, and power density. The self-sustaining modular structure will allow for inherent scalability while integrating ancillary circuit functions, enabling superior electrical grid stability. This attribute will open markets for the T-Breaker in higher voltage grid applications and address the shortcomings of using a circuit breaker in the growing MVDC application space.

Opcondys, Inc.

A Bidirectional, Transformerless Converter Topology for Grid-Tied Energy Storage Systems 

Opcondys will develop a high-voltage power converter design for energy storage systems connected directly to the power grid. Opcondys' converter design will use a modified switched multiplier topology that will allow connection to utility transmission lines without intervening step-up transformers. It uses a photonic, wide bandgap power switching device called the Optical Transconductance Varistor. This is a fast, high-voltage, bidirectional device which reduces the number of circuit elements required for charging and discharging the storage element. By operating at 100 kHz it is possible to increase efficiency to 99% compared to 95-98% efficiency of traditional converters. The system also reduces the size of the passive elements by 50% and, because of the optical control, mitigates electromagnetic interference issues. The elimination of step-up transformers further reduces system size, and can enable a lower cost than existing systems. If successful, project developments could open the door to increased integration of grid-level energy storage.

Pacific Northwest National Laboratory

Data Repository for Power system Open models With Evolving Resources (DR POWER)

The Pacific Northwest National Laboratory (PNNL) has partnered with the National Rural Electric Cooperative Association (NRECA) to build a power system model repository, which will maintain and develop open-access power grid models and data sets. The DR POWER approach will review, annotate, and verify submitted datasets while establishing a repository and a web portal to distribute open-access models and scenarios. Through the portal, users can explore the curated data, create suitable datasets (which may include time variation), review and critique models, and download datasets in a specified format. Key features include the ability to collaboratively build, refine, and review a range of large-scale realistic power system models. For researchers, this represents a significant improvement over the current open availability of only small-scale, static models that do not properly represent the challenging environments encountered by present and future power grids. The repository and the web portal will be hosted in PNNL's Electricity Infrastructure Operations Center with access to petabytes of computing storage and load-balancing across multiple computing resources.

Pacific Northwest National Laboratory

High-Performance Adaptive Deep-Reinforcement-Learning-Based Real-Time Emergency Control (HADREC) To Enhance Power Grid Resilience In Stochastic Environments

Pacific Northwest National Laboratory (PNNL) will construct an intelligent, real-time emergency control system to help safeguard the U.S. electric grid by providing effective and fast control actions to system operators in response to large contingencies or extreme events. PNNL's scalable platform will utilize advanced machine learning techniques (deep-meta-reinforcement learning) as well as high-performance computing to automatically provide effective emergency control strategies seconds after disturbances or attacks. Platform development will focus on the determination, timing, coordination, and automation of control actions, including adaptation under uncertainty. The technology will diminish the need for costly preventive security measures as well as reduce action time sixtyfold and system recovery time by at least 10%, enabling more efficient and resilient grid operation.

Pacific Northwest National Laboratory

Multi-scale Incentive-Based Control of Distributed Assets

Pacific Northwest National Laboratory (PNNL) will develop and test a hierarchical control framework for coordinating the flexibility of a full range of DERs, including flexible building loads, to supply reserves to the electric power grid. The hierarchical control framework consists of incentive-based control strategies across multiple time-scales. The system will use a slower incentive-based approach to acquire flexible assets that provide services, combined with faster device-level controls that use minimal communication to provide desired responses to the grid. Each DER that chooses to participate will communicate its ability to provide flexibility and the time scale over which it can provide the service. A distribution reliability coordinator will act as an interface between the DERs and the bulk system, coordinating the resources in an economic and reliable manner. The team will characterize various DER types to quantify the maximum flexibility that can be extracted from a collection of DERs in aggregate in order to provide service-level guarantees to the bulk energy market operator. The performance of the resulting hierarchical control system will be tested at scale in a co-simulation environment spanning transmission, distribution, ancillary markets, and communication systems.

Pacific Northwest National Laboratory

High Performance Power-Grid Optimization 

The team led by Pacific Northwest National Laboratory (PNNL) will develop a High-Performance Power-Grid Optimization (HIPPO) technology to reduce grid resource scheduling times to within a fraction of current speeds, which can lead to more flexible and reliable real-time operation. The team will leverage advances in optimization algorithms and deploy high-performance computing technologies to significantly improve the performance of grid scheduling. HIPPO will provide inter-algorithm parallelization and allow algorithms to share information during their solution process, with the objective of reducing computing time by efficiently using computational power. New algorithms will leverage knowledge of the underlying system, operational experience, and past solutions to improve performance and avoid previously encountered mistakes.

Pacific Northwest National Laboratory

Non-Wire Methods for Transmission Congestion Management through Predictive Simulation and Optimization

Pacific Northwest National Laboratory (PNNL) is developing innovative high-performance-computing techniques that can assess unused power transmission capacity in real-time in order to better manage congestion in the power grid. This type of assessment is traditionally performed off-line every season or every year using only conservative, worst-case scenarios. Finding computing techniques that rate transmission capacity in real-time could improve the utilization of the existing transmission infrastructure by up to 30% and facilitate increased integration of renewable generation into the grid--all without having to build costly new transmission lines.

Pacific Northwest National Laboratory

Sustainable Data Evolution Technology for Power Grid Optimization 

The Pacific Northwest National Laboratory (PNNL), along with the National Rural Electric Cooperative Association, PJM, Avista, and CAISO, will develop a sustainable data evolution technology (SDET) to create open-access transmission and distribution power grid datasets as well as data creation tools that the grid community can use to create new datasets based on user requirements and changing grid complexity. The SDET approach will derive features and metrics from many private datasets provided by PNNL's industry partners. For transmission systems, PNNL will develop advanced, graph-theory based techniques and statistical approaches to reproduce the derived features and metrics in synthetic power systems models. For distribution systems, the team will use anonymization and obfuscation techniques and apply them to datasets from utility partners.

PingThings Inc.

A National Infrastructure for Artificial Intelligence on the Grid

PingThings will develop a national infrastructure for analytics and artificial intelligence (AI) on the power grid using a three-pronged approach. First, a scalable, cloud-based platform will store, process, analyze, and visualize grid sensor data. Second, massive open and accessible datasets will be created through (a) deploying grid sensors to capture wide-scale and localized grid behavior, (b) simulating and executing grid models to generate virtual sensor data, and (c) establishing a secure data exchange mechanism. Third, a diverse research community will be developed through focused educational content, online code sharing, and data and AI competitions. The project's goal is to accelerate the development of data-driven use cases to improve grid operation and analysis.

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