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DOE OSTI2023

Joint Resource Modeling and Assessment for Hybrid Distributed Solar and Wind Systems

The inherent variability and uncertainty in distributed energy resources can presents myriad challenges to the planning and operations of power systems. These risks are poised to become larger as the penetration of renewable energy sources rises in the power generation mix. Hybrid solar-wind energy systems are able to mitigate some of these risks by their complementary resource availability. Surface solar and wind fields are coupled and correlated in both space and time. Appropriately estimating the hybrid solar wind energy system requires simulating the spatio-temporal structure of these fields that can be produced for each time horizon. We introduce a novel joint spatio-temporal stochastic differential equation (SPDE) approach that captures the spatio-temporal dynamics of solar and wind fields and their joint dependency over a domain for each time step. In the case study on Colorado, we consider nonstationary three-level hierarchical spatio temporal models for both hourly solar irradiance data and wind speed data in Colorado. Dependence between the solar irradiance data and wind speed data is captured by a shared spatio-temporal random effect. Our approach performs well in terms of the prediction score criterion.

joint modeling↗

DOE OSTI2022

Advanced Distributed Wind Turbine Controls Series: Part 2-Wind Energy in Isolated Grids – Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL)

In an isolated grid, wind turbines are typically deployed to provide energy to maximize energy production, reduce diesel-fuel consumption, reduce carbon emissions, and reduce costs for energy and fuel transportation. However, in addition to solely providing energy to the power system, wind turbines contain rotating masses and inverter-based controls that can enable various reliability and resilience services through advance controls. As part of the Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL) this paper demonstrates, through desktop simulations, advanced wind turbine controls that can be employed to support higher contributions of wind in isolated grids, and to demonstrate ways that wind can play a role in supporting stability of an isolated grid. This isolated grid used in these desktop simulations is comprised of a wind turbine (600 kW), solar PV (430 kW), battery energy storage system (1 MW/1MWh), a simulated diesel generator (2 MW) and various types of loads (critical, dynamic). We developed a model of the subsystems in MATLAB/Simulink and validated them with available data from real-world components on NREL's Flatirons Campus. These validated models are then configured for various case studies. We compare the output of the desktop simulation with a baseline case with the diesel generator. Active and reactive power control of the wind turbine can help improve frequency and voltage responses in the isolated grid, respectively. By utilizing a small integrated battery energy storage system in the DC-link of the wind turbine, we also demonstrate that wind turbines can help blackstart a critical load comparable to its rated power and support other renewables (e.g. solar PV) come online and pick up an additional load. This report illustrates some of these reliability and resilience services a wind turbine can provide in an isolated grid.

17 WIND ENERGY↗

DOE OSTI2024

Developing Data-Driven Synthetic Infrastructure Models for Resilience Analysis

Research on infrastructure resilience has produced promising methods to simulate and optimize complex networks to improve performance. However, restrictions on sharing infrastructure models and the steep cost of developing and maintaining infrastructure models presents a roadblock to adoption. To overcome this limitation, this research focuses on methods to create data-driven infrastructure models that will help improve infrastructure resilience and security. The analysis couples incomplete utility data, geospatial data, machine learning, and synthetic network generation methods to rapidly develop and update infrastructure models. The methods are validated using realistic utility models and site-specific data, with a focus on Puerto Rico due to its unique infrastructure challenges and available data. This research highlights promising opportunities for the use of synthetic network generation and machine learning to create infrastructure models when very little data is available. Results demonstrate that hybrid methods, which combine sparse utility data with synthetic models, can enhance model accuracy, and machine learning can predict model attributes using training data from other models. However, the complexity of infrastructure systems means that even minor changes in network connectivity can significantly impact simulation results. Resilience analysis using synthetic infrastructure models shows that while some system behaviors are preserved, the magnitude of disruptions may not be accurately represented, indicating the need for more research and validation before using synthetic models for critical infrastructure investment decisions. The framework outlined in this report represents a significant advance to infrastructure model development and could be applied to additional domains and sites. Future research will continue to streamline and validate methods to help reduce roadblocks to resilience analysis.

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DOE OSTI2023

Power Flow Models of Grid-Forming Inverters in Unbalanced Distribution Grids

Load unbalance in electric distribution systems is unavoidable. Unbalanced load currents will lead to negative sequence (NS) voltages that may damage electric equipment. Unbalanced power flow analysis is a common tool to detect and mitigate NS voltage issues and requires accurate models of grid components. While traditional source models are available, grid-forming (GFM) inverter models are not well developed. GFM inverters implement various control strategies, which affect their power flow models. Here, in this paper, a novel GFM inverter model considering control effects is proposed. We show that for some control methods, unbalanced system loading will lead to unbalanced terminal voltages of the GFM inverters, which are modeled through an equivalent negative sequence impedance. The proposed models are initially validated using a simple test circuit. Then, they are applied in the power flow analysis on the IEEE 13-bus and 34-bus systems to demonstrate the accuracy improvement over the state-of-the-art. Using time-domain simulations as benchmarks, we show that the proposed models reduce the calculation error of negative sequence voltages by at least 25% in unbalanced distribution systems.

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DOE OSTI2020

CRADA Final Report: Open Microgrid Platform

Current microgrid control technology is mostly proprietary, expensive, and inflexible. An open-source, vendor-neutral, publish-subscribe controller will significantly lower the barrier to entry for new technologies and market entrants: inverter, battery, and load control manufacturers; cloud and services providers; and energy aggregators. An OpenFMB controller can significantly reduce the acquisition, integration, and ongoing operating and maintenance costs and increase revenue opportunities for energy producers and consumers. It allows distributed energy resource (DER) owners to replace individual DER components as richer-function, lower-cost devices become available or to incorporate new forecast and optimization algorithms as they are developed. A standard, secure, full-function DER field controller will also provide a low-cost means for grid operators to effectively manage the variability and uncertainty of solar photovoltaics (PV). Under the terms of this CRADA, ORNL has worked with Open Energy Solutions (OES) to advance the transition of ORNL’s existing open-source microgrid controller into a platform geared toward mainstream implementation. Using a more prevalent and memory-safe programming language, Rust, OES has converted ORNL’s current generation on-grid optimization into an application more suitable for industry use. The results of the ported optimization were analyzed by inputting the same arguments in both the current-generation and next-generation optimizers and verifying that the results were equivalent.

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DOE OSTI2025

Bridging the Gap on Data and Analysis for Distribution System Planning: Information That Utilities Can Provide Regulators, State Energy Offices and Other Stakeholders

Electric utilities conduct planning annually to ensure their distribution system meets technical standards, policies, and regulations; addresses forecasted grid conditions; satisfies customer needs; and advances utility priorities. The plan identifies grid deficiencies, analyzes potential solutions, and prioritizes capital investments and other expenditures. About 20 U.S. states and jurisdictions require regulated utilities to file some type of distribution system plan with the public utility commission for review. Requirements for sharing distribution system data and analyses vary widely, from few specific requirements to a detailed list of information that must be provided. While utilities conduct extensive analysis to develop distribution system plans, in most jurisdictions regulators and stakeholders do not know what data are available and how the utility uses the data in planning and investing. This report aims to bridge the gap by increasing understanding of the types of data and analyses utilities employ to develop distribution system plans and how the information affects their decision-making. The report describes information that states and stakeholders can ask for related to 11 data categories: -Forecasting loads and distributed energy resources (DERs) -Scenario analysis -Worst-performing circuits -Asset management strategy -Hosting capacity analysis -Value of DERs -Grid needs assessment -Cost-effectiveness framework for investments -Distribution system investment strategy and implementation -Geotargeted programs -Non-wires alternatives procurements.

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DOE OSTI2021

A Nonstationary and Non-Gaussian Moving Average Model for Solar Irradiance

Historically, power has flowed from large power plants to customers. Increasing penetration of distributed energy resources such as solar power from rooftop photovoltaic has made the distribution network a two-way-street with power being generated at the customer level. The incorporation of renewables introduces additional uncertainty and variability into the power grid. Distribution network operation studies are being adapted to include renewables; however, such studies require high quality solar irradiance data that adequately reflect realistic meteorological variability. Data from satellite-based products are spatially complete, but temporally coarse, whereas solar irradiances exhibit high frequency variation at very fine timescales. We propose a new stochastic method for temporally downscaling global horizontal irradiance (GHI) to 1 min resolution, but we do not consider the spatial aspect due to limited availability of the in situ irradiance measurements. Solar irradiance's first and second-order structures vary diurnally and seasonally, and our model adapts to such nonstationarity. Empirical irradiance data exhibits highly non-Gaussian behavior; we develop a nonstationary and non-Gaussian moving average model that is shown to capture realistic solar variability at multiple timescales. We also propose a new estimation scheme based on Cholesky factors of empirical autocovariance matrices, bypassing difficult and inaccessible likelihood-based approaches. The model is demonstrated for a case study of three locations that are located in diverse climates through the United States. The model is compared against competitors from the literature and is shown to provide better uncertainty and variability quantification on testing data.

Cholesky factor↗

DOE OSTI2019

Compounding climate change impacts during high stress periods for a high wind and solar power system in Texas

Power system planning aims at ensuring that sufficient supply- and demand-side assets exist to meet electricity demand at all times. For a Texas electric power system with high wind and solar penetrations, we quantify how climate change will affect supply and demand during three types of high stress periods for the power grid: high demand hours, high net demand hours, and high system ramp hours. We specifically quantify effects on demand, reductions in available thermal capacity (i.e. thermal deratings), wind and solar generation, and net demand. We estimate each using meteorological variables from five climate change projections (2041-2050) assuming Representative Concentration Pathway 8.5 and from a reference period (1996-2005). All five projections indicate that climate change will increase demand by up to 2 GWh during high demand hours (4% of demand in the reference period) and increase net demand by up to 3 GWh during high net demand periods (6% of net demand in the reference period). All five projections also indicate thermal deratings will increase during high demand and net demand periods by up to 2 GWh and high net demand ramps will increase by up to 2 GW. Overall, our results indicate compounding effects of climate change in Texas will necessitate greater investment in peak and flexible capacity.

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DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, volt/VAR optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure↗

DOE OSTI2023

Small Reactors in Microgrids: Technology Modeling and Selection (Net-Zero Microgrid Program Project Report)

This report demonstrates the capabilities of the net-zero microgrid (NZM) Xendee platform for modeling an SR module with electricity, heat extraction and thermal storage in microgrids configurations. The model effectively captures the most important technical and economic considerations for SR technology specific analysis: cost and operational characteristics of SR technology and financial costs and incentives. The model can analyze multiple scenarios to establish metrics for cost-competitive and zero-carbon microgrids connected to the grid or completely isolated. The model is fully integrated within the Xendee platform for modeling and analysis of clean energy microgrids with storage and generation from renewable energy sources. The model captures the capabilities, constraints, and nuances of SR by incorporating parameters related to plant economics, design efficiency and performance, plant operation and component and fuel lifespan. The cost and operational parameters modeled in the SR module are specific to the technology selected for integration in the microgrid. Cost parameters recognize advanced nuclear technology for modular production and installation based on economies of scale from factory manufacture and related commissioning, and cost reduction through technology maturation—first-of-a-kind (FOAK) and nth-of-a-Kind (NOAK). The cost parameters include installation, operations and maintenance (O&M), fuel refueling cycle, and reactor life. Installation cost reflects economies of scale due to unit sizing at scale and colocation. O&M economies of scale for both fixed- and variable-cost fuel life-cycle costs are incurred at every refueling interval, with separate front- and back-end fuel costs, as well as waste-handling and disposition costs. This report investigates key characteristics of different SR technologies suitable for microgrid applications, including design principles, sizing, coolant properties, temperature ratings, fuel structures, and life-cycle considerations. This also includes fuel technologies applicable to these SR systems, alongside strategies for nuclear-waste and spent-fuel management and approaches to address safety, security, and proliferation challenges. Four primary groups of SR technologies are examined: water-cooled, liquid-metal-cooled, high-temperature gas-cooled, and molten-salt-cooled systems. In this report, an initial guideline for technology selection is established, aligning the characteristics of the technologies with the requirements of microgrids. The selection of technology in a microgrid is influenced by various factors, including financial capacity, location and accessibility, demand type and characteristics, reliability and resilience requirements, area constraints, and the lifespan of the microgrid. The types of electrical and non-electrical applications within the microgrid also play a significant role in technology selection. The characteristics of SRs, such as their smaller size, modularity, transportability, long refueling interval, improved safety features, ability to operate in autonomous or semi-autonomous mode, and provision of high-grade heat, are particularly appealing for microgrids. Furthermore, a list of considerations for implementing SRs in microgrids is outlined. The SR model is created to be continuously improved with the acquisition of actual data on investment and operational costs, experience with supply chains, production at scale, and field deployments. In the near term, performance data on applications in microgrids will become available from lessons learned from laboratory tests, such as those planned for the Microreactor Applications Research Validation and Evaluation Project (MARVEL), led by Idaho National Laboratory (INL). The SR model incorporates scenario data and known SR design specifications, enabling technoeconomic analysis for SR deployment in microgrids. It specifically considers the distinctive attributes of SRs as generators in technoeconomic studies. SRs can be modeled and analyzed with generation from renewable-energy sources, energy storage, and flexible loads over a range of functionality and applications. This offers a comprehensive tool for feasibility studies, scenario development, and sensitivity analysis for “what-if” consideration of any range of assumptions about SRs in microgrids and other aggregations of distributed-energy resources, including virtual power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Five Year Wildfire Risk Reduction Action Plan for the Electric Power Industry

The U.S. DOE and the Electric Power Research Institute convened a wildfire advisory group to bring together knowledge of ignition risks, electric utility needs, the applicability of available technology, and existing technology gaps. The outcome is a five-year action plan, containing recommendations on RD&D projects, that if completed by the year 2030, may accelerate the power industry’s ability to substantially reduce wildfire ignition risks. When reviewing this document please consider it is a snapshot in time for the years 2023 and 2024, could be obsolete by the end of the decade. The following list comprises the titles and topics for the proposed follow-on demonstrations: 1. Hybrid Undergrounding RD&D, 2. Live Downed Conductor Detection RD&D, 3. Fault Energy Reduction RD&D, 4. Advanced Inspection and Response Drone, 5. Fault and PQ Event Signature Repository, 6. Advanced and Intelligent Sensor Nodes, 7. Fire Friendly Asset Coatings and Coverings, and 8. Environmental Monitoring Action Plan.

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DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data: Preprint

advanced metering infrastructure↗

DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, Volt/VAr optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder-head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure (AMI)↗

DOE OSTI2024

Can Wholesale Electricity Markets Achieve Resource Adequacy and High Clean Energy Generation Targets in the Presence of Self-Interested Actors?

Wholesale electricity markets are intended to incentivize system generation investments and operations outcomes that meet evolving system needs. In this work, we evaluate the effectiveness of wholesale market structures, rules and policies in achieving system resource adequacy (RA) and clean energy targets in the presence of self-interested generation investors using the Electricity Markets and Investment Suite Agent-based Simulation (EMIS-AS) model. Results highlight that both capacity markets and operating reserve demand curves (ORDCs) can help achieve a reliable system but with different RA compliance timelines and distribution of generation technologies. Structures with capacity markets tend to favor more capital-intensive peaking technologies while reducing wind and solar build-outs due to suppressed energy and clean energy market prices, particularly in the absence of strong clean energy targets. Conversely, ORDCs improve the commitment of available generation units, but this comes at the expense of higher system costs and renewable generation curtailment. We also find that well-calibrated static capacity demand curves can yield similar reliability and total cost compared to capacity market demand curves informed dynamically by resource adequacy while also yielding stable annual capacity prices. Different approaches to formulating ORDC curves can also yield key trade-offs, namely that a more efficient treatment of storage chronology results in lower ORDC curves and prices, yielding less investment and cost but at the expense of reliability. Finally, the effectiveness of wholesale electricity markets in practically achieving very high clean energy generation targets highly depends on the cost-competitiveness of clean energy technologies that can support critical balancing needs across multiple timescales.

capacity expansion↗

DOE OSTI2021

Thermal energy grid storage: Liquid containment and pumping above 2000 °C

As the cost of renewable energy falls below fossil fuels, the key barrier to widespread sustainable electricity has become availability on demand. Energy storage can enable dispatchable renewables, but only with drastic cost reductions compared to current battery technologies. One electricity storage concept that could enable these cost reductions stores electricity as sensible heat in an extremely hot liquid (>2000°C) and uses multi-junction photovoltaics (MPV) as a heat engine to convert it back to electricity on demand hours, or days, later. Furthermore, this paper reports the first containment of silicon in a multipart graphite tank above 2000°C, using material grades that are affordable for energy storage at GWh scales. Low cost molded graphite with particle sizes as large as 10 μm successfully contained metallurgical grade silicon, even with as much as twothirds iron by mass for up to 10 hours and temperatures as high as 2300°C, in tanks as large as two gallons.

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DOE OSTI2021

Visualization of Multi-Fidelity Approximations of Stochastic Economic Dispatch

As renewable energy generation deployment increases, the operation of electrical grids becomes more complex. Economic dispatch is part of a grid operator's regular decision process where the amount of energy to generate is determined based on the number of available generators and the actual level of energy demand. Renewable generators are inherently stochastic due to the chaotic nature of weather patterns, and thus, real-time decisions of economic dispatch become increasingly complex. Modeling efforts to assist in these decisions in the highest fidelity typically take hours to days to solve on leadership-class computers; too long for the 5-minute operational time-frame demanded of operators. Alternatively, multi-fidelity approximations can be used to predict generation levels quickly and with sufficient accuracy to be used for real-time operations. We have developed a visualization tool to demonstrate the utility of multi-fidelity approximations by displaying contextual results of economic dispatch approximations, comparisons across fidelity levels of generation levels and possible failures to meet demand, and meta-data on the modeling setup.

interactive visualization↗

DOE OSTI2024

Modifications to Solar Titan-130 Combustion Systems for Efficient, High Turndown Operation

The project team of Southwest Research Institute® (SwRI®), Solar Turbines Incorporated (Solar), the Electric Power Research Institute (EPRI), the University of California, Irvine (UCI), and the Georgia Institute of Technology (Georgia Tech) investigated methods to allow higher efficiency part-load operation of a Solar Titan 130 gas turbine. The objective was to develop a low-emission combustion system capable of sustaining combustion and avoiding lean blowout during high turndown operation, which would allow the gas turbine to operate as efficiently as possible at part load. Currently, electric utility markets are beginning to experience substantial increases in renewable energy generation. Some of these renewable energy sources have highly variable output in an uncontrolled manner. In order to maintain grid stability, there is a need for power plants to ramp up power to the grid rapidly to make up for drops in renewable generation. This is often termed spinning reserve, but the size of this reserve may need to increase as renewable penetration into the electric utility market increases. Small combined heat and power (CHP) power plants provide a promising option for meeting this spinning reserve requirement. In order to operate in spinning reserve while still meeting the heat requirements for the CHP, the gas turbine needs to operate efficiently at very low loads. Efficient, high turndown operations in this engine are limited by the lean flammability limit of the premixed combustion system. This project sought enhance the lean operability range of the Titan 130 combustor. First, the project team participated in a brainstorming activity and ultimately selected two concepts to explore: fuel augmentation with hydrogen (H2) to improve the stability at lean operating conditions and modifications to the fuel nozzle to improve the emissions performance at lean operating conditions. Analytical and laboratory investigations were accomplished by UCI to investigate the efficacy of H2 addition at improving lean blow out (LBO) limits and the resulting emissions. These investigations used a variety of chemical reactor network (CRN) and CFD models, validated against laboratory data, to model the impact of H 2 and inform the experimental efforts accomplished by SwRI and Solar. Ultimately, both the CRN and CFD models yielded generally good agreement with the experimental data below a particular temperature threshold. Atmospheric tests of a full-scale T130 annular combustor were performed at SwRI facilities in San Antonio, Texas, to investigate the use of H 2 addition. For these tests, the T130 combustion system remained largely unchanged; minor modifications were performed to the fuel ducting to allow for the safe use of H 2 . The test ultimately demonstrated that the addition of H 2 to the fuel mixture significantly increased the AFR ratio at which the combustor could operate. This improvement to the LBO limit should allow for less use of compressor bleed and less throttling needed by the inlet guide vanes (IGV). This in turn could result in more efficient operation of the gas turbine at lower load points. The second modification explored in this work was a direct modification to the T130 injector. The project team hypothesized that modifications to the pilot of the T130 injector could provide lower emissions at high turn-down operations. These modifications were manufactured and explored by the team at Solar. High pressure rig tests, originally slated to occur at SwRI, were ultimately accomplished by Solar to maintain overall project budget and mitigate cost growth attributable to supply chain issues and inflation. The pressurized rig tests ultimately showed that the SwRI Project No. 18.24153 - DE-EE0008415 Page 2 Final Technical Report January 24, 2024 modifications did not significantly alter the performance of the combustion system at the high turn-down conditions; both the modified injectors and the baseline configuration exhibited elevated emissions comparted to the full-load operating condition. A final set of studies performed by EPRI investigated the benefit-cost of flexible CHP as well as a grid interconnection study for the California Independent System Operator (CAISO) grid. These studies considered: traditional CHP with no spinning reserve available for on-demand grid support, 50% flexible CHP where 50% of the machine’s capacity is consumed by on-site baseload operations while providing an additional 50% capacity for on-demand grid support, and 70% flexible CHP where 70% of capacity is consumed on-site by baseload operations and 30% is available for on-demand grid support. In all cases, the analyses showed a benefit-to-cost ratio greater than unity implying a positive net present value for all configurations. However, the traditional CHP showed the most economic benefit. These results are sensitive to several factors, many of which are not fully known and may vary over time. Thus site owners must be convinced that taking up the increased costs and risks from flexible CHP would be worth implementing. As the grid in California and across the country transition to incorporate larger renewable energy generation, flexible CHP can provide much needed operating reserves and dispatchability. Alternative fuel options, such as hydrogen blending and biofuels, may also lower carbon intensities of CHP. Flexible CHP should be examined in the evolving market to understand innovative business models, changes market rules and services, and new technologies.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2025

Best Practices for Grid Communications

As the grid evolves, the communications architecture will need to evolve with it. That architecture affords a structured means by which the evolving complexities of the modern electric grid can be managed. This document provides best practices that can be implemented in the grid of today and evolve towards the grid and grid architecture of the future. The evolving grid and its control communications increasingly rely on commercial communications providers and a variety of technologies, from wireless (e.g., 5G, microwave, Wi-Fi) to wireline (fiber, copper) to radio communications (P25, other repeater-based systems), and all these communications systems rely on electric power. A reliable and resilient grid must account for this complex set of interdependencies in its planning activities, especially those involving restoration and recovery. The participation of all relevant parties in both planning and exercising of plans can prevent unexpected conditions that impede the reliable operation and recovery of the grid. Best practices for grid communications include using a Network Management System to document the operational state, define and monitor baselines, detect changes, and accelerate response to abnormalities. If transitioning from SONET to IP/packet-based systems, translating grid requirements into communications requirements for latency, bandwidth and throughput, IP packet delay variation, packet loss, and availability should inform and drive technology planning and selection as well as that communication system’s Quality of Service (QoS) policies and Service Level Agreements (SLAs). Secure and reliable timing is another key component of a reliable and resilient grid that can operate through adverse events. A trusted internal NTP configuration, an integrated and diverse timing delivery system, optimizing the timing architecture based on the transport technologies of the communications system, and using established standards can deliver the level of timing accuracy required by a range of time-sensitive power system applications.

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