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This presentation introduces NREL's advanced distribution management systems test bed and related projects that use the test bed to study DER management solutions.
Devices based on gallium nitride (GaN) have great potential for high power switching applications due to the high breakdown field and high electron mobility. In this work, we present a vertical GaN-on-GaN PN power diode using high dielectric constant material, BaTiO3, for electrical field management and high breakdown voltages, in together with an optimized guard-ring and field plate design. Numerical simulation shows that with high-k dielectrics implemented, the peak electrical field at the PN interface is mitigated from 3.5 to 3.1 MV/cm under a reverse bias of −9.05 kV. The device design with BaTiO3 shows a breakdown voltage of 9.65 kV or about 600 V improvement. The fabricated diodes with a 57 μm thick drift layer demonstrate a breakdown voltage of 7.86 kV on a bulk GaN substrate. The device has an on-resistance of 2.8 mΩ cm2 and a Baliga figure of merit of 22 GW/cm2.
This is an updated risk management plan and risk register for the design, build and test of a novel, remote, low-power wave energy converter (WEC) for non-grid applications. This Columbia Power Technologies project seeks to develop a prototype low-power WEC that lowers the total cost of ownership and provides robust, new capabilities for customers in the maritime environment. The testing location for this prototype is the U.S. Navy Wave Energy Test Site (WETS) in Kaneohe Bay, O'ahu, Hawai'i. Detailed in the Risk Management Plan document is a Failure Modes, Effects, and Criticality Analysis (FMEC) that systematically identifies all potential failure modes and their effects on the system. Risk registers for major subsystems were completed according to the methodology described in the Risk Management Plan and are also included here.
Measurements have been an essential part of managing the electric power system from the beginning. Surprisingly, today’s measurements are not always particularly trustworthy. Countless factors can influence measurement in real-world conditions. In this report, we look at the measurement system as a comprehensive information acquisition system with complicated interdependencies setting the limiting factors and affecting system performance. The first chapter provides an overview of the whole system and introduces the key elements of the limitations checklist. The second part of the document is a component checklist for measurement system limitations that concisely describes effects, common remedies, and underlying factors.
The convergence of transportation electrification, other load growth, and the integration of many power sector technologies presents a complex planning problem requiring realistic, region-specific modeling and analysis to ensure the power system can support new loads quickly and affordably.
The ability to control tens of thousands of residential electricity customers in a coordinated manner has the potential to enact system-wide electric load changes, such as reduce congestion and peak demand, among other benefits. To quantify the potential benefits of demand-side management and other power system simulation studies (e.g. home energy management, large-scale residential demand response), synthetic load datasets that accurately characterize the system load are required. This study designs a combined top-down and bottom-up approach for modelling individual residential customers and their individual electric assets, each possessing their own characteristics, using time-varying queueing models. The aggregation of all customer loads created by the queueing models represents a known city-sized load curve to be used in simulation studies. The three presented residential queueing load models use only publicly available data. An open-source Python tool to allow researchers to generate residential load data for their studies is also provided. The simulation results presented consider the ComEd region (utility company from Chicago, IL) and demonstrate the characteristics of the three proposed residential queueing load models, the impact of the choice of model parameters, and scalability performance of the Python tool.
Commercial nuclear power plants typically use nuclear fuel that is enriched to less than five weight percent in the isotope 235 U. However, recently several vendors have proposed new nuclear power plant designs that would use fuel with 235 U enrichments between five weight percent and 19.75 weight percent. Nuclear fuel with this level of 235 U enrichment is known as “high assay low-enriched uranium.” Once it has been irradiated in a nuclear reactor and becomes used (or spent) nuclear fuel, it will be stored, transported, and disposed of. However, irradiated high assay low-enriched uranium differs from typical irradiated nuclear fuel in several ways, and these differences may have economic effects on its storage, transport, and disposal, compared to typical irradiated nuclear fuel. This report describes those differences and qualitatively discusses their potential economic effects on storage, transport, and disposal.
To effectively engage demand-side and distributed energy resources (DERs) for dynamically maintaining the electric power balance, the challenges of controlling and coordinating building equipment and DERs on a large scale must be overcome. Although several control techniques have been proposed in the literature, a significant obstacle to applying these techniques in practice is having access to an effective testing platform. Performing tests at scale using real equipment is impractical, so simulation offers the only viable route to developmental testing at scales of practical interest. Existing power-grid testbeds are unable to model individual residential end-use devices for developing detailed control formulations for responsive loads and DERs. Furthermore, they cannot simulate the control and communications at subminute timescales. To address these issues, this paper presents a novel power-grid simulation testbed for transactive energy management systems. Detailed models of primary home appliances (e.g., heating and cooling systems, water heaters, photovoltaic panels, energy storage systems) are provided to simulate realistic load behaviors in response to environmental parameters and control commands. The proposed testbed incorporates software as it will be deployed, and enables deployable software to interact with various building equipment models for end-to-end performance evaluation at scale.
Intermittent renewable systems and increasing electric vehicle (EV) penetrations increase load intermittency—and will require expensive distribution system infrastructure upgrades. Power-to-gas (P2G) systems are highly controllable loads that can be modulated to regulate the net load behind the meter. In this work, we present an active load management approach using P2G systems to regulate the net load for an EV charging station with high penetrations of renewable generation. The system also validates the feasibility of the proposed approach using a 750-kW electrolyzer operated with the distribution system in a real-time power-hardware-in-the-loop test. Finally, we evaluate the impact of the size of the P2G system on its ability to regulate the net load.
To meet the grand challenge of a sustainable energy future, there has been a surge of interest in renewable energy. Today, the uncertainty associated with renewable resources is handled by using operating reserves. The high penetration of renewable resources, however, introduces difficult-to-control dynamics and challenges for power system operation. Decision support tools are necessary at the bulk system operational level to recognize and efficiently utilize renewable resources and distributed demand response products in concert with traditional grid resources. It is envisaged that responsive load can potentially have very significant cost advantages over either spinning or non-spinning ramping reserve. Critical decisions are made during hour(s)-ahead and real-time power system operation regarding the commitment and dispatch of generators to ensure power delivery is both reliable and economic. These decisions are typically made by a security constrained optimal flow, which determines future generator commitments, dispatches, and ensures adequate reserves are available in the event of a contingency (unexpected outage) or if future system conditions deviate from forecasts. However, security has been always based on a pre-specified subset of contingency constraints whose enforcement does not guarantee security under all possible future possibilities while also giving little or no weight to the likelihood of each contingent event or the severity of its consequences. Existing tools, which are based exclusively on deterministic optimization models, do not yield optimal operational decisions to address these new challenges, in terms of both reliability and cost-effectiveness. This project has focused on developing a stochastic optimal power flow (SOPF) framework, which integrates renewable resource uncertainty, load uncertainty, distributed storage (DS), demand response (DR) products, in a holistic manner to address the uncertainty associated with ever-increasing renewable resources, along with the inclusion of distributed demand response products in future power systems. A proof-of-concept problem was created using the Pennsylvania-Jersey-Maryland (PJM) power system network. Synthetic wind generation was added to the system to simulate 50% wind penetration. A 1-hour test of SOPF operation indicated more than 6% operational cost savings. The project continued by adding the Midwestern Independent System Operator (MISO) as a partner, with focus shifting from SOPF to Stochastic Look-Ahead Unit Commitment (SLAC). Unlike PJM, MISO is faced with significant renewable energy resources within its footprint and is challenged with substantial uncertainty in its operations. The SLAC distinguishes itself from existing tools that operators use. At best, today’s tools solve two to three cases independently, where one or two system parameters, such as forecasted load level (e.g., a low, base, and high forecast), are varied and the resulting scenarios are analyzed independently. The stochastic-based optimization of SLAC leverages statistical information from an ensemble of potential operational scenarios and their respective likelihood. The SLAC output can be translated into valuable information to the operator such as suggested commitments, optimal scheduling and dispatch of resources, reserve requirements at both locational and zonal resolutions, ramping availability and requirements, availability of demand response including operational guidance concerning the near-term and real-time coordination between distributed energy resources, and utilization of distributed storage resources. The developed SOPF/SLAC tool, a stand-alone tool compatible with existing EMSs, will provide system operators with unprecedented visibility, flexibility and predictability to these resources and operational guidance concerning the real-time coordination between DERs and DR/DS products. The game changing and practical impact of this disruptive technology will be dramatic and will usher in a new era in the electric power industry, wherein green energy concepts are fully embraced, and electric power costs are lowered throughout the nation.
Electric Power Group, LLC (EPG) was awarded DOE OE0000850 to design, develop, and demonstrate a real-time software application for substation secondary equipment health monitoring and management at host utility American Electric Power (AEP), a cost share partner. This project addresses the need for monitoring substation equipment health and providing operators with tools to identify and take pre-emptive action to avoid catastrophic equipment failure. By monitoring synchrophasor data in real time, data anomalies that indicate potential asset failure can be detected and alerts can be sent to operators in time to take corrective actions. EPG developed two data driven algorithms to identify abnormal equipment signature patterns as well as a method using substation linear state estimation (SLSE). The software has been deployed on hardened PC’s and tested and validated for cost share partner AEP’s substations - 138kV and 765 kV. The SSHM software has been accepted by AEP and final demonstration of DOE - OE0000850 for AEP and DOE was successfully completed on March 17th, 2020. EPG developed the Substation Secondary Asset Health Monitoring (SSHM) Platform to analyze equipment failure signatures in PMU data and alert substation personnel when pre-emptive inspection, repairs and other actions are warranted to prevent potential catastrophic failures. This is the first system that automatically monitors the health of substation secondary assets and alerts users to emerging failures using synchrophasor measurements. SSHM uses high resolution PMU data from PTs, CTs, and CCVTs to analyze equipment signatures and identify anomalies that are precursor indicators of potential equipment failure. When an anomaly is detected, there is a likelihood of potential failure of monitored equipment, the system alerts the user with visual alarms including alarm trend charts and indicator lights on a oneline diagram of the substation.
The project's primary objective is to develop a digital-twin based Photovoltaic (PV) Analysis and Response Support (PARS) platform, which aims to provide real-time situational awareness and optimal response plans. This platform is designed to enhance the performance of hybrid PV systems, making them competitive with or even superior to conventional generation resources. The PARS platform enabled the project team to develop and evaluate an extensive suite of grid support functionalities for the hybrid PV systems to enhance grid performance, across key areas including visibility, dispatchability, security, resilience, and reliability. Given the global push toward achieving 100% clean energy by 2035, there is a significant increase in the integration of inverter-based resources (IBRs) throughout the energy grid. Effectively managing the inherent variability and uncertainty associated with IBRs is crucial for ensuring cost-effectiveness, reliability, and security in both the main grid and islanded microgrids. Constrained to a limited array of IEEE test systems or standard feeder models, traditional IBR modeling struggles to assimilate new field data, accurately reflect system dynamics, and adapt to the evolving energy landscape. In our project, we embraced a Digital Twin (DT) strategy for crafting the PARS platform. A digital twin acts as a precise virtual counterpart of a physical system, built on historical data and continuously honed with real-time insights. This enables the high-fidelity DT to accurately mirror current system operations and forecast future scenarios. Consequently, the PARS platform becomes an ideal environment for testing and refining monitoring, control, power, and energy management algorithms designed to boost hybrid PV system performance. The defining feature of the PARS platform, distinguishing it from other advanced simulation tools, is its exceptional adaptability. This is achieved by employing actual network topologies and utilizing real-time field data for fine-tuning and calibration, ensuring a close emulation of real-world conditions. The project deliverables include: 1) High-fidelity IBR models and tools for real-time parameterization, utilizing real-time field measurements to refine IBR models for enhanced accuracy and performance; 2) Grid-forming and Grid-following capabilities to deliver resilience services, including blackstart, voltage and frequency support, cold-load pick-up, power reserves, and three-phase load balancing across grid-connected and microgrid settings; 3) Machine learning-based forecasting tools and methods for generating synthetic data and topologies, creating diverse and realistic simulation environments for evaluating varied operational scenarios; 4) Advanced microgrid power and energy management algorithms for optimizing the integration and operation of PV, storage, and demand response resources within both feeder and community scales. The power grid data sets are provided by four utility companies in North Carolina and the New York Power Administration. Acting as industry advisors, our industry partners communicated stakeholder needs and regulatory standards to the research teams, aiding technology transfer by incorporating the developed methodologies into their daily operations. This collaboration ensures that the PARS platform, functioning as a power system digital twin, enhances our understanding of IBR dynamic behaviors and enables the development and evaluation of IBR control functions that match or exceed the capabilities of conventional synchronous generators.
In Nuclear Power Plants VVER 440, Russian type of reactor there was designed auxiliary building where according to basic design all operational liquid radioactive waste (RAW) should be stored in large capacity tanks during the whole operational period. That's why there are in the auxiliary building several types of large storage tanks with different internal volume from 110 m{sup 3}, 460 m{sup 3} to 550 m{sup 3}. Storage tanks are usually situated in rooms with limited access and minimal handling space. Operational waste was here transported by the system of pipes from the reactor building. There is possibility to handle with the waste and set the pipeline in to the chosen storage tanks. Nobody takes care about the quality of waste in the storage tanks and waste waits for the period of decommissioning when it should be treated. The storage capacity was designed for the whole lifetime of nuclear power plant. Currently the life time of Nuclear Power Plant's (NPP) is extended and there is necessity to retrieve and treat the RAW in advance. Another reason why the stakeholder, who operates the NPPs, decides to treat the operational RAW during the operational period of NPP, is that there are still new requirements from safety point of view which need to have free available space for example because of potential accident. So treating the operational RAW during the life time of NPP is present issue. Because in basic design nobody cares how the RAW will be retrieved and treated in the future, there is usually no technology for that in existing NPP. Some of the NPPs build an additional building with suitable technologies for treatment or hire the company which offer mobile technology for retrieving the waste from the storage tanks. Wood Nuclear Slovakia PLC provides all activities within liquid RAW management projects by its own staff including management, design, construction and deployment of equipment, engineering support, as well as all RAW retrieval and solidification activities. We offer RAW management as a service without any significant changes in existing NPP's buildings and technology. It is always a technical challenge to conduct successful retrieval, pumping and treatment of RAW. The complex RAW retrieval system can be designed as tailored solution for any RAW storage according to known boundary conditions and specific needs. These systems are based on years of experience with RAW manipulation, processing and treatment and the whole RAW management knowledge by Wood Nuclear Slovakia LTD. (authors)
Final Technical ReportThis project aimed to research, develop, and test electric traction drive system technology for use in vehicle applications that are capable of meeting the targets set by the Department of Energy Vehicle Technologies Office. The project is categorized into three major thrusts: Bonding interfaces for packaging, thermal management of electric vehicle (EV) power inverters, and electric motor thermal management. Device- and System-Level Thermal Packaging for Electric-Drive Technologies project aimed to develop, analyze, and validate transformative approaches in thermal management and packaging for power electronics and electric motor systems, with the ultimate goal of enhancing power density, efficiency, and reliability in electrified transportation platforms.
Fast simultaneous feasibility testing (SFT) for management of an electrical power grid is achieved through various innovations. The computation problem relates to evaluation of candidate solutions for external power flows into a power grid, with respect to predetermined constraints and contingencies. Storage and computations are reduced by formulating the problem in terms of transactional nodes (e.g. third party connections for generators and loads) instead of the larger number of bus nodes. Further advantages are achieved by precomputing matrices that can be reused across multiple SFT invocations, organizing matrices and operations to reduce storage and computation, and eliminating branches that have no contingency violations. The fast SFT further enables new applications for integrating SFT with a Security-Constrained Unit Commitment (SCUC) optimizer for efficient single pass solutions; operating SFT as a service to multiple SCUC solvers; or integration with Security-Constrained Economic Dispatch (SCED) optimization.
This manuscript focused on analyzing electric vehicles’ (EV) charging behavior patterns with a functional data analysis (FDA) approach, with the goal of providing theoretical support to the EV infrastructure planning and regulation, as well as the power grid load management. 5-year real-world charging log data from a total of 455 charging stations in Kansas City, Missouri, was used. The focuses were placed on analyzing the daily usage occupancy variability, daily energy consumption variability, and station-level usage variability. Compared with the traditional discrete-based analysis models, the proposed FDA modeling approach had unique advantages in preserving the smooth function behavior of the data, bringing more flexibility in the modeling process with little required assumptions or background knowledge on independent variables, as well as the capability of handling time series data with different lengths or sizes. In addition to the patterns revealed in the EV charging station’s occupancy and energy consumption, the differences between EV driver’s charging time and parking time were analyzed and called for the needs for parking regulation and enforcement. The different usage patterns observed at charging stations located on different land-use types were also analyzed.
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The Lunar Surface Electromagnetic Experiment at Night (LuSEE-Night) is a low-frequency, 0.5 to 50 MHz, radio experiment on the radio-quiet far side of the Moon. The instrument will be launched by NASA Commercial Lunar Payload Services in 2026. The LuSEE-Night instrument core is composed of a radio frequency spectrometer (SPT) processing signals from four antennas, the Data Controller Board (DCB), low electromagnetic interference (EMI) Picket Fence Power Supply (PFPS), and Power Distribution Unit (PDU). The battery powers the instrument during the lunar night and stores energy harvested by the solar panel array during the lunar day. The battery charging is controlled by the Power Conditioning and Distribution Unit (PCDU). The unregulated power is supplied either by the SpaceCraft (S/C) or the battery and gets distributed to the PFPS, communication radio, heaters, and deployables through the PDU. The PFPS generates all regulated low-voltage rails using switching regulation synchronized to the LuSEE-Night clock, which ensures self-generated EMI will be confined to well-defined frequency bins. Here, we discussed the unregulated power distribution system architecture and functionality. The PDU engineering and flight modules are developed and characterized to confirm compliance with LuSEE-Night requirements. At the time of writing, all power subsystem components have been integrated into the payload.