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Accelerating the deep decarbonization of the world's electric grids requires the coordination of complex energy systems and infrastructures across timescales from seconds to decades. Here, we present a new multiscale simulation framework that integrates process- and grid-centric modeling paradigms to better design, operate, and control integrated energy systems (IESs), which combine multiple technologies, in wholesale energy markets. Traditionally, IESs are analyzed with a process-centric paradigm such as levelized cost of electricity (LCOE) or annualized net revenue, ignoring important interactions with electricity markets. This framework explicitly models the complex interactions between an IES's bidding, scheduling, and control decisions and the energy market's clearing and settlement processes, while incorporating operational uncertainties. Through two case studies, we show the importance of understanding and quantifying complex resource-grid interactions. In case study 1, we demonstrate that optimized bidding from one resource shifts the profit distribution for all energy systems in the market. This result suggests new and more flexible IES technologies can disrupt the economics of all market participants, possibly leading to accelerated retirements of less flexible resources. Interestingly, the optimized bidding has little impact on grid-level aggregate statistics, such as total generation costs and renewable penetration rate. While aggregate modeling strategies may remain valid under some IES adoption scenarios for analysis focused on regional outcomes, direct comparisons of IES technologies at specific locations without considering these interactions may lead to misleading or incorrect conclusions. In case study 2, we consider the design and flexible operation of IESs that hybridize conventional generators with energy storage. Through a sensitivity analysis, we find that as the size of the storage system increases, the total number of start-ups for coal- and natural gas-based IESs reduced by 25% and 33.6%, and the total thermal generator ramping (i.e., mileage) reduced by 86.5% and 62.5%, respectively. This shows the primary benefit of storage may not be reduced operational costs (which do not change significantly) but fewer start-ups and less ramping, which may greatly simplify the design, operation, and control of carbon capture systems. The new modeling and optimization capabilities from this work enable the coupling of rigorous, dynamic process models with grid-level production cost models to quantitatively identify the nuanced interdependencies across these vast timescales that must be addressed to realize clean, safe, and secure energy production. Moreover, the proposed general multiscale simulation framework is applicable to all IES technologies and can be easily extended to consider other energy carriers (e.g., hydrogen, ammonia) and energy infrastructures (e.g., natural gas pipelines).
For individuals, businesses, and communities focused on building resilient electrical grid infrastructure, wind energy can provide an affordable, accessible, and compatible distributed energy resource option that also enhances the capabilities of local grid operations. However, there are technical barriers to realizing the market value and resilience benefits of distributed wind, and there is little to no ability to quantify those benefits so that stakeholders can compare grid investment options. The central aims of this report are: (1) to drive technology transfer of the methods and technologies developed under the Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL) project and (2) increase the number of referenceable case studies available to stakeholders interested in additional value-added capabilities of wind systems beyond bulk energy supply. We achieve this aim by applying three major methods developed under MIRACL to two real-world distributed wind reference systems. The two real-world distributed wind reference systems are the isolated grid of St. Mary’s, Alaska, and the two 10.5-megawatt (MW) front-of-the-meter wind turbine deployments owned and operated by Iowa Lakes Electric Cooperative (ILEC).
Motor systems are an integral part of our industrial and commercial facilities. They provide the motive force behind the fans, pumps, compressors, chillers, and conveyors in these facilities. Given their centrality to any facility’s operations, they are a critical energy end use to understand, particularly when developing technologies and policies to meet sustainability goals, improve productivity, and enhance resilience. In the late 1990s, the U.S. Department of Energy (DOE) conducted two seminal studies to better understand the installed stock and energy savings opportunities of industrial and commercial motor systems. In the industrial sector, The United States Industrial Electric Motor Systems Market Opportunities Assessment used primary data collected through onsite assessments and led to a greater understanding of the installed base of motor systems, their characteristics, and the opportunities for energy savings (U.S. Department of Energy, 2002). Notable findings included the following: Industrial motor systems consumed 679 billion kilowatt-hours (kWh) in 1994, representing 23 percent of U.S. electricity consumption. Cost-effective energy efficiency measures could result in 62-104 billion kWh of energy savings annually. Sixty-two percent of the energy savings potential was from fan, pump, and compressor end use equipment. Nearly half of motor system electricity consumption was attributable to approximately 3,500 facilities, or 1.5 percent of U.S. manufacturing facilities. Opportunities for Energy Savings in the Residential and Commercial Sectors with High Efficiency Electric Motors provided an evaluation of the installed stock of motor driven equipment in U.S. commercial and residential buildings and opportunities for utilization of high efficiency motors and variable speed technologies (Arthur D. Little, 1999). Notable findings included the following: Commercial motor systems consumed 343 billion kWh in 1995, with refrigeration and space conditioning constituting 93 percent of the total. Cost-effective energy efficiency measures could result in 51 billion kWh of energy savings annually. Due in no small part to these seminal studies, motor system technologies and usage characteristics have changed drastically since the late 1990s. Greater awareness of cost-effective strategies for reducing motor system electricity consumption have been developed and deployed. This includes several software tools, literature, and utility and government programs promoting energy efficiency improvements in motor driven systems. Additionally, several rounds of energy efficiency standards have been enacted, resulting in improved installed motor efficiency. The cost of variable speed drives has dropped substantially, and combined with utility rebate programs, has led to their greater adoption. Further, since these results were published, the U.S. manufacturing sector has undergone a massive transformation. Due to global competition, some sectors have relocated operations overseas. Others have brought operations onshore to avail low cost and abundant natural gas. Additionally, automation and robotics have pervaded the entire sector. Consequently, these two reports likely do not represent the current state of motor driven systems in U.S. industrial and commercial facilities. As cited in recent studies, the lack of current information on motor system electricity consumption and use characteristics limits the ability to conduct analysis on energy savings potential, develop technologies to address energy and productivity gaps, and develop programs to promote energy efficiency practices and technologies for motor systems (International Energy Agency, 2007; UNIDO, 2010; McKane and Hasanbeigi, 2011; Waide and Brunner, 2011). Specifically, the lack of information affects a range of stakeholders: Governments must rely on outdated information when setting research agendas, developing policies, and designing energy efficiency programs and offerings. Utilities and energy efficiency programs cannot identify the current market needs or potential impact when designing rebate and energy efficiency programs. Electric grid planners cannot identify motor system usage characteristics when developing plans to support the resilience of the electric grid. Manufacturers of motors, motor driven equipment, and drives are hampered when developing technologies to meet the needs of their market. Motor system end users are limited in their ability to identify energy saving opportunities within their own facilities because they do not have reliable benchmark information. In response to the lack of current information and analysis on industrial and commercial motor systems, the DOE initiated an update to these two studies. Launched in 2016 and led by Lawrence Berkeley National Laboratory (LBNL), the Motor System Market Assessment (MSMA) provides an updated, more comprehensive assessment of the installed stock of motor systems in both the industrial and commercial sectors, a review of the supply chains supporting motor and drives in the U.S., and the performance improvement opportunity available from using best available technologies and maintenance and operation practices. The outcomes of the MSMA are documented in three U.S. Industrial and Commercial Motor System Market Assessment reports, with this report being the first listed: 1. Volume 1: Characteristics of the Installed Base (this report) documents the findings on the installed base of motor systems in the U.S. industrial and commercial sectors. Quantification of energy savings potential is not documented in this report but in Volume 3. 2. Volume 2: Motors and Drives Supply Chain Review reviews the state of supply chains for motors and drives installed in U.S. industrial and commercial facilities, focusing on advanced motor and drive technologies and their constituent materials. 3. Volume 3: Energy Savings Opportunity analyzes the energy performance improvement opportunity for the installed base of U.S. industrial and commercial motor systems. This report has been prepared as a reference for motor system stakeholders. It provides factual information as could be best determined by the assessment results and avoids speculating on any findings.
Annual Merit review of the Smart Electric Vehicle Charging for a Reliable and Resilient Grid (RECHARGE) project. This project will demonstrate the value of smart charge management to reduce the impact of Electric Vehicles at Scale. Assess management of Plug-in Electric Vehicle (PEV) charging at scale to avoid negative grid impacts, identify critical strategies and technologies, and enhance value for PEV / EVSE / grid stakeholders.
This report is a summary of a 3-year LDRD project that developed novel methods to detect faults in the electric power grid dramatically faster than today’s protection systems. Accurately detecting and quickly removing electrical faults is imperative for power system resilience and national security to minimize impacts to defense critical infrastructure. The new protection schemes will improve grid stability during disturbances and allow additional integration of renewable energy technologies with low inertia and low fault currents. Signal-based fast tripping schemes were developed that use the physics of the grid and do not rely on communication to reduce cyber risks for safely removing faults.
The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.
Wind will be a foundational energy source in the electricity grid at the heart of a future integrated energy system, replacing traditional electricity generators powered by fossil fuels and providing grid reliability services in addition to energy. Future capabilities and functions of the wind energy sector will evolve apace with the future expansion and needs of global energy infrastructure; however, wind turbines designed today will not be able to provide the services needed to form and stabilize the grid as a majority supplier. In 2017, organizers for the IEA Wind Technical Experts Meeting (TEM) #89 Grand Vision for Wind Energy workshop assembled a group of experts to consider the question of how to enable a future in which wind energy supplies more than 50% of global electricity consumption. More than 70 experts representing 15 countries attended the workshop and provided diverse perspectives for the Grand Vision for Wind Energy. The IEA Wind TEM #109 was a subsequent gathering that was convened Feb. 28-March 1, 2023, in Boulder, Colorado, USA. The IEA Wind TEM #109 meeting aimed to bring together the leaders of all working groups and the IEA Wind Technology Collaboration Programme (TCP) to identify gaps in scientific knowledge, design, and deployment practice as well as identify recommendations for collaborative pathways, initiatives, and prioritized long-term research needs that can be addressed by IEA Wind. This report captures the outcomes of this meeting of international experts: five Grand Challenge areas (The Atmosphere, The Turbine, The Plant and Grid, Environmental Co-Design, and Social Science). In addition, meeting participants identified eight crosscutting topic areas that are discusses within this report (Environment-Turbine, Turbine-Atmosphere, Atmosphere-Grid/Plant, Grid/Plant-Turbine, Grid/Plant-Environment, Atmosphere-Environment, Turbine-Social, and Social-Grid/Plant).
Maintaining reliability of the bulk power system, which supplies and transmits electricity, is a constant focus of electric grid planners, operators, and regulators. Based on the standards set by power system reliability entities, the U.S. grid has been and continues to be very reliable. Over the past decade the average U.S. customer has only experienced about 15 minutes of outages per year due to supply limitations, with most of those outages occurring due to failures on the distribution system. However, there have been a few notable incidents in recent years tied to extreme weather events. The fact sheet provides and introduction to reliability of the bulk power system in the U.S.
Apparatus and methods are disclosed for control systems for improving stability of electrical grids by temporarily reducing voltage output of electrical generators responsive to transient events on an electrical grid. In one example of the disclosed technology, a controller is coupled is to an automatic voltage regulator, which in turn adjusts excitation current of an electrical generator responsive to changes in frequency detected for the electrical grid. Reducing the output voltage temporarily allows for smaller generators to provide power to the microgrid. Methods for selecting parameters determining how the controller generates a regulation signal used to adjust the excitation current are further disclosed.
The Electric Drive Technologies (EDT) program's mission is to conduct early-stage research and development on transportation electrification technologies that accelerate the development of cost-effective and compact electric traction drive systems that meet or exceed performance and reliability requirements of internal combustion engine (ICE)-based vehicles, thereby enabling electrification across all light-duty vehicle types. The Grid and Charging Infrastructure (G&I) program's mission is to conduct early-stage research and development on transportation electrification technologies that enable reduced petroleum consumption by light, medium, and heavy-duty vehicles. The program identifies and enables the role of vehicles in the future electrical grid.
Sensing and measurement systems are quintessential to the safe and reliable operation of electric power grids. Their strategic placement is of ultimate importance because it is not economically viable to install measurement systems on every node and branch of a power grid, though they need to be monitored. An overwhelming number of strategies have been developed to meet oftentimes multiple conflicting objectives. The prime challenge in formulating the problem lies in developing a heuristic or an optimisation model that, though mathematically tractable and constrained in cost, leads to trustworthy technical solutions. Further, large-scale, long-term deployments pose additional challenges because the boundary conditions change as technologies evolve. For instance, the advent of new technologies in sensing and measurement, as well as in communications and networking, might impact the cost and performance of available solutions and shift initially set conditions. Also, the placement strategies developed for transmission grids might not be suitable for distribution grids, and vice versa, because of unique characteristics; therefore, the strategies need to be flexible, to a certain extent, because no two power grids are alike. Despite the extensive literature on the present topic, the focus of published works tends to be on a specific subject, such as the optimal placement of measurements to ensure observability in transmission grids. There is a dearth of work providing a comprehensive picture for developing optimal placement strategies. Because of the ongoing efforts on the modernisation of electric power grids, there is a need to consolidate the status quo while exposing its limitations to inform policymakers, industry stakeholders, and researchers on the research-and-development needs to push the boundaries for innovation. Accordingly, this paper first reviews the state-of-the-art considering both transmission and distribution grids. Then, it consolidates the key factors to be considered in the problem formulation. Finally, it provides a set of perspectives on the measurement placement problem, and it concludes with future research directions.
Abstract Electricity and water systems in the Western US (WUS) are closely connected, with hydropower comprising 20% of total annual WUS generation, and electricity related to water comprising about 7% of total WUS electricity use. Because of these interdependencies, the threat of climate change to WUS resources will likely have compounding electricity impacts on the Western Interconnect grid. This study describes a WUS‐wide water system model with a particular emphasis on estimating climate impacts on hydropower generation and water‐related electricity use, which can be linked with a grid expansion model to support climate‐resilient electricity planning. The water system model combines climatically‐driven physical hydrology and management of both water supply and demand allocation, and is applied to an ensemble of 15 climate scenarios out to 2050. Model results show decreasing streamflow in key basins of the WUS under most scenarios. Annual water‐related electricity use increases up to 4%, and by up to 6% during the summer months, driven by growing agricultural demands met increasingly through a shift toward energy‐intensive groundwater to replace declining surface water. Total annual hydropower generation changes by +5% to −20% by mid‐century but declines in most scenarios, with decreases in summer generation by up to nearly −30%. Water‐related electricity use increases tend to coincide with hydropower generation declines, annually and seasonally, demonstrating the importance of concurrently evaluating the climate signal on both water‐for‐energy and energy‐for‐water to inform planning for grid reliability and decarbonization goals.
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.
Maintaining reliability of the bulk power system, which supplies and transmits electricity, is a critical priority for electric grid planners, operators, and regulators. As we move toward a cleaner electricity system with more technologies like wind, solar, and battery storage, the way in which we plan for and achieve reliability will change. This fact sheet provides additional technical background to the topics covered in three other reliability explainers on how we measure, enforce, and plan for reliable systems with more clean electricity.
Advanced research tools are essential to illustrate the effects of turbine-based power system operations on the electric grid network. This paper presents the development of an open-source tool to visualize the operation of turbine-based electricity generation. The tool exposes power system models, control parameters, and corresponding values of the turbine model and its electrical system, allowing them to customize the simulation according to their needs. The developed tool enables users to simulate power profiles for different turbinebased energy generation such as wind, tidal, and gas turbines. It allows users to investigate the details of the generated profiles of various types of turbine systems at various scales. It can provide valuable insights into model development and facilitate the analysis of integrated power systems. By enabling access to turbine-based operations visualization, the tool aims to bridge the gap between advanced research tools and users, facilitating broader adoption of renewable energy technologies and aiding in developing sustainable power systems.
To meet the increasing demand for electricity and to have a more reliable and resilient electric grid against conventional and extreme events, grid modernization is more crucial now than ever before. This will require the development and deployment of devices that provide advanced communication capabilities. The overall efficiency, reliability, and resilience of the smart grid will be inextricably linked to the exchange of information between these devices. Unfortunately, the increased information flow will increase the potential attack surface and introduce new vulnerabilities. While a smarter grid will depend critically on information flow, these benefits will be accrued only if that information can be protected. Nowadays, information is secured in smart grids primarily through cryptography. However, with the increasing number of sophisticated attacks as well as the increasing computational power, the security of the “classical” cryptographic algorithms is threatened. Quantum information science offers solutions to this problem, specifically quantum key distribution (QKD), which provides a means for the generation and secure distribution of symmetric cryptographic keys. The security of QKD stems ultimately from the very nature of quantum physics. In this paper, we investigate the applicability of QKD to the various smart grid sectors and specific use cases. We have identified 18 smart grid use cases of interest for QKD suitability together with 7 QKD factors used for the assessment of the various use cases. For each use case, the impact to security of the loss of confidentiality, integrity, and/or availability is specified. In addition, the suitability of QKD is assessed for each use case with respect to multiple factors.
The changing landscape of the electricity industry, characterized by a surge in distributed energy resources (DERs) and proactive customers, necessitates practical solutions for coordinated operations especially at the distribution-level. This paper introduces a community-based transactive coordination mechanism designed to incentivize customers for providing localized and system-level services reflected through real-time prices. The work presents a bidding approach for communities with DERs, such as solar photovoltaic (PV) and battery energy storage systems (BESS), to formulate their price-responsiveness for retail energy coordination, emphasizing a community-centric model. By sending bidding curves to a third-party, the mechanism enables customers with DER assets to actively participate in localized coordination with the load serving entity (LSE), thereby supplementing each other’s and even the utilities needs through a shared energy economy. The proposed transactive mechanism is implemented leveraging a co-simulation framework that integrates a distribution grid simulator and Python-based agents for performance evaluation. Collaboration with a local utility to access real distribution feeder models and consumption profiles yields simulation results demonstrating the potential to reduce costs by 12\% for communities with DERs like PV and BESS.