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At least 235 records · Page 13

Digital Twin + AI: Control Room of the Future [Slides]

The control room functions as the central brain of the grid, essential for balancing supply and demand and ensuring moment-to-moment grid reliability. Like the human brain, which processes sensory data to make decisions, control room operators analyze operational data from power generation, transmission, and distribution to make informed decisions. Currently, decision-making primarily rests with operators due to hardware and software limitations. However, with technological advancements, Digital Twins and AI are becoming high interest points in the control room's decision-making pilot programs. NREL is developing a comprehensive decision-making platform that integrates Digital Twins, AI, and advanced visualization techniques. As this integration progresses, the role of Digital Twins will evolve from conducting automated simulations to serving as a Trustworthy AI enabler, offering verification and validation of AI-generated response for power systems or providing physics-aware synthetic data of AI pre-training.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Considerations for Distributed Energy Resource Integration in Puerto Rico: DOE Multi-Lab Grid Modeling Support for Puerto Rico; Analytical Support for Interconnection and IEEE Std 1547-2018 National Renewable Energy Laboratory (Task 3.0)

To ensure the sustainable long-term recovery of Puerto Rico's electric power grid from hurricanes María and Irma and to build capacity to manage future potential natural disasters in the most secure and resilient way, the U.S. Department of Energy (DOE) convened experts from multiple national laboratories to develop a comprehensive set of data, models, analytic tools, and studies, considering inputs from a wide variety of stakeholder groups, to support technically sound recommendations for Puerto Rico's energy investment decisions. A resilient electric grid is vital to Puerto Rico's security, economy, and way of life, and it will provide the foundation for essential services that people and businesses on the island rely on every day. This report shows progress for a grid modeling task under the DOE-sponsored project that is a collaboration among the National Renewable Energy Laboratory (NREL) and other national laboratories. In Phase 1 of the multilab effort to support Puerto Rico's recovery, NREL provided the utility company Puerto Rico Electric Power Authority (PREPA) recommendations for a new framework of interconnection standards to accelerate the integration of utility-scale, transmission-connected renewable electrical generation and energy storage that ensure cross-technology compatibility and enable high deployment levels without compromising grid reliably, safety, or security. This Phase 2 report focuses on the interconnection of distributed energy resources (DERs) to the electric distribution system in Puerto Rico. This report is intended to familiarize the reader with Puerto Rico's distribution infrastructure and operational practices and procedures that are relevant to DER interconnection. The report also provides considerations for streamlining the interconnection process given the expected increase in deployments resulting from Puerto Rico's renewable portfolio standard (RPS) goal of 100% renewables by 2050. Accordingly, the report identifies considerations and concerns associated with the increase in intermittent generation, strategies for DER interconnection best practices, and the potential use of the latest technological solutions identified in the latest revision of the Institute of Electrical and Electronics Engineers (IEEE) 1547-2018 interconnection standard.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Lessons Learned for Transmission Cost Allocation in U.S. Regional Markets

Expanding electric transmission can facilitate generator interconnection and improve grid reliability. Assigning costs for new transmission infrastructure is highly contentious because these costs can have a direct impact on energy prices and ratepayer bills. In this report, we evaluate what factors influence successful transmission cost allocation agreements. Through a review of legal disputes, existing cost allocation practices, and regional case studies, we identify potential strategies to minimize cost allocation disputes for future projects. The report also highlights the processes by which regions can update their cost allocation methods. While we do not consider cost allocation methods currently under development for compliance with FERC Order 1920, the trends and lessons learned identified in this report can inform discussions on effective cost allocation methods to reduce barriers for transmission development.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Solar Energy CommUnity Resiliency (Final Technical Report)

Final Technical Report for DE-EE0009336 Solar Energy CommUnity Resiliency (SECURE) The Solar Energy CommUnity Resiliency (SECURE) project aimed to support development of resilient community microgrids to improve grid reliability and maintain power during times of crisis. The SECURE project approached this objective by addressing key technical and business challenges impeding implementation of resilient community microgrids. To achieve the goals of this project, we pursued several specific objectives through comprehensive design and requirements development.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Hydropower Operations in the Colorado River Basin: Institutional Analysis of Opportunities and Constraints

The Colorado River Basin is facing an unprecedented drought. In ongoing drought management efforts, limited attention has been paid to hydropower generation. While some studies do exist on hydropower, they are quantitative in nature and focus on calculating the reduction in megawatts generated at dams in the Basin with declining water availability. These studies simplify the complex process of hydropower generation; water availability is but one factor that impacts hydropower generation. At a more fundamental level, formal institutional arrangements, that is, laws, policies, and rules create the framework within which dams are operated and hydropower is generated. This paper conducts a comparative institutional analysis of water, environment, and energy laws and policies and changes therein to understand the constraints and opportunities faced by hydropower generation in the Colorado River Basin. To tease out the nuances in how institutional arrangements affect dam operations and hydropower generation, the comparative analysis focuses on the two largest and strategically important dams in the Basin: Hoover and Glen Canyon. This paper uses Elinor Ostrom’s Institutional Analysis and Development Framework to analyze laws and policies at three levels: constitutional-choice, collective-choice, and operational levels. Constitutional-choice level laws and policies apply to the entire Basin, whereas collective-choice level and operational level laws and policies are dam specific. Hoover and Glen Canyon Dams face similar biophysical challenges by the virtue of their location in the same river basin. Yet, despite the similarity in the biophysical setting, the analysis in this study finds that the differences in the applicability of constitutional-choice level laws along with the differences in dam specific collective-choice and operational level institutional arrangements produce a distinct set of constraints for hydropower generation at Hoover and Glen Canyon Dams. Even without a drought, water and environmental laws at both the constitutionalchoice and collective-choice levels as well as power contracts constrain hydropower generation and limit the flexibility with which Glen Canyon Dam can be operated. Water and environmental laws also impose specific water release requirements that, at times, require off-peak power generation at Glen Canyon Dam. On the other hand, even with a drought, Hoover Dam faces limited hydropower generation constraints and can operate flexibly. This is because constitutional-choice level laws and dam-specific collective-choice and operational level laws pose limited constraints for flexible daily operations at Hoover. The result is that Hoover Dam can generate hydropower at the same level as it did three decades ago and operate flexibly to provide ancillary services and peaking generation. While water and environmental laws and policies pose constraints for hydropower generation, the analysis in this study further finds that specific historic provisions within energyrelated institutional arrangements and recent changes within power contracts have maintained and even enhanced the value of hydropower to power customers. Historic institutional provisions ensure that hydropower is sold ‘at cost’ making this resource economically competitive with wholesale electricity market rates. Recent power contract modifications have resulted in the amendment of an older resale prohibition clause to expand the flexibility available to power customers in using their capacity and/or energy allocation in RTOs, ISOs, and bulk power markets. This amendment has opened up an opportunity for customers, especially Hoover power customers, to use flexible generation and ancillary services in a market environment. In addition, the extension of power contract duration to the legally maximum term has enhanced the reliability and stability of this resource for customers. In the Colorado River Basin, despite the enduring economic responsibility of power customers—where laws require customers to pay for a large portion of construction and O&M costs whether or not they actually receive hydropower— the persistent threat of a drought-induced water shortage, and constraints imposed by water and environmental laws and policies, power customers continue to invest in this resource as energy-related institutional arrangements and power contract provisions protect the reasons why they value hydropower. Lastly, the analysis in this study finds that the consequences of changes in hydropower generation for energy users, irrigators, and environmental programs in the Basin depend on how specific institutional arrangements tie electricity revenues to irrigation aid and environmental programs, and how the power contracts themselves are set up. Collective-choice level institutional arrangements create a higher level of financial dependency of irrigation aid and environmental programs on electricity revenues in the Upper Basin—the legal subdivision of Colorado River where Glen Canyon Dam is located—compared to the Lower Basin—the legal subdivision of Colorado River where Hoover Dam is located. Therefore, changes in hydropower generation or the way its revenue is collected and used will have far reacting detrimental consequences for the Upper Basin. Likewise, differences in the nature of power contracts for Glen Canyon and Hoover Dams also creates differences in the financial impact incurred by energy users when there is a reduction in hydropower generation. While this study identifies the types of impacts on resource users as a result of specific institutional arrangements, the calculation of extent of impact warrants further attention. Hydropower in the United States is in a unique position today. The strategic importance of this resource for the nation’s electricity sector is rapidly growing even as its contribution to overall electricity generation remains fairly small. This strategic importance, however, is built hydropower’s ability to operate flexibly in order to support the integration of intermittent renewable generating sources and the expansion of electricity markets. As this study shows, such flexibility may not be available at certain plants not due to the lack of water availability but because of institutional constraints. Institutional arrangements may also require dam operators to first consider high priority water uses (such as irrigation or environmental needs), which in turn may limit the ability to generate hydropower when it is most valuable or useful. Engineering and quantitative models, such as production cost models, recognize policy constraints for hydropower operations but often inadequately capture or assume away such constraints in the models. A failure to account for policy constraints in these models runs the risk of inaccurate representation of the operational flexibility and capacity available at specific hydropower plants, which can result in over/underestimation of hydropower’s ability to support the integration of variable renewable resources and address grid reliability concerns. Against this background, this paper and the analysis herein serves as an example of how we can systematically identify institutional constraints (and opportunities) that influence the flexibility in not only generating electricity at specific dams but also using this hydropower once it is generated.

13 HYDRO ENERGY↗

Recent Progress in Redox Flow Battery Research and Development

With the increase need to seamlessly integrate the renewable energy with the current grid which itself is evolving into a more intelligent, efficient, and capable electrical power system, it is envisioned that the energy storage system will play a more prominent role in bridging the gap between the current technology and a clean sustainable future in grid reliability and utilization. Redox flow battery technology is leading the way in this perspective in providing a well balanced approach for current challenges. Recent progress in the research and development of redox flow battery technology is reviewed here with a focus on new chemistries and systems.

Wang, Wei↗

Personalized Energy Reduction Cyber-Physical System (PERCS): A gamified end-user platform for energy efficiency and demand response.

The mission of the Personalized Energy Reduction Cyber-physical System (PERCS) is to create new possibilities for improving building operating efficiency, enhancing grid reliability, avoiding costly power interruptions, and mitigating greenhouse gas emissions. PERCS proposes to achieve these outcomes by engaging building occupants as partners in a user-centered smart service platform. Using a non-intrusive load monitoring approach, PERCS uses a single sensing point in each home to capture smart electric meter data in real time. The household energy signal is disaggregated into individual load signatures of common appliances (e.g., air conditioners), yielding near real-time appliance-level energy information. Users interact with PERCS via a mobile phone platform that provides household- and appliance-level energy feedback, tailored recommendations, and a competitive game tied to energy use and behavioral changes. PERCS challenges traditional energy management approaches by directly engaging occupant as key elements in a technological system.

Sintov, Nicole↗

Supercomputing Centers and Electricity Service Providers: A Geographically Distributed Perspective on Demand Management in Europe and the United States, In: ISC High Performance 2016: High Performance Computing

Supercomputing Centers (SCs) have high and variable power demands, which increase the challenges of the Electricity Service Providers (ESPs) with regards to efficient electricity distribution and reliable grid operation. High penetration of renewable energy generation further exacerbates this problem. In order to develop a symbiotic relationship between the SCs and their ESPs and to support effective power management at all levels, it is critical to understand and analyze how the existing relationships were formed and how these are expected to evolve.

Computer Science↗

Energy Storage as an Equity Asset

Abstract Purpose of Review This review offers a discussion on how energy storage deployment advances equitable outcomes for the power system. It catalogues the four tenets of the energy justice concept—distributive, recognition, procedural, and restorative—and shows how they relate to inequities in energy affordability, availability, due process, sustainability, and responsibility. Recent Findings Energy storage systems have been deployed to support grid reliability and renewable resource integration, but there is additional emerging value in considering the connections between energy storage applications and equity challenges in the power system. Through a thorough review of the energy justice and energy transitions literature, this paper offers the equity dimensions of storage project design and implementations. Summary Emerging energy programs and projects are utilizing energy storage in pursuit of improved equity outcomes. Future research and policy design should integrate energy justice principles to align storage penetration with desired equity outcomes.

25 ENERGY STORAGE↗

Evaluation of smart charging for electric vehicle-to-building integration: A case study

Higher electric vehicle (EV) adoption will stress the importance of demand flexibility to achieve more economic, efficient, and reliable grid operation. Charging technologies will be paramount in shifting temporally to better fit the variable generation of wind and solar. As such, analysis is warranted on the benefits of EV charge scheduling with respect to installation cost, operation cost, difficulty of implementation, and grid flexibility. We tackle this by analyzing the cost savings of implementing an EV charge scheduling infrastructure to reduce demand charges and installation costs. In this paper, we analyze a case study for operation of 16 level 2 chargers and 1 fast charger for two different building types. We then evaluate various test phases for controlling building and charging loads using an adaptive charging network (ACN) algorithm to characterize the ACN’s potential to reduce overall project cost.

30 DIRECT ENERGY CONVERSION↗

A surrogate-model-based approach for estimating the first and second-order moments of offshore wind power

Power curve, the functional relationship that governs the process of converting a set of weather variables experienced by a wind turbine into electric power, is widely used in the wind industry to estimate power output for planning and operational purposes. Existing methods for power curve estimation have three main limitations: (i) they mostly rely on wind speed as the sole input, thus ignoring the secondary, yet possibly significant effects of other environmental factors, (ii) they largely overlook the complex marine environment in which offshore turbines operate, potentially compromising their value in offshore wind energy applications, and (ii) they solely focus on the first-order properties of wind power, with little (or null) information about the variation around the mean behavior, which is important for ensuring reliable grid integration, asset health monitoring, and energy storage, among others. In light of that, this study investigates the impact of several wind-and wave-related factors on offshore wind power variability, with the ultimate goal of accurately predicting its first two moments. Further, our approach couples OpenFAST—a multi-physics wind turbine simulator—with Gaussian Process (GP) regression to reveal the underlying relationships governing offshore weather-to-power conversion. We first find that a multi-input power curve which captures the combined impact of wind speed, direction, and air density, can provide double-digit improvements, in terms of prediction accuracy, relative to univariate methods which rely on wind speed as the sole explanatory variable (e.g. the standard method of bins). Wave-related variables are found not important for predicting the average power output, but interestingly, appear to be extremely relevant in describing the fluctuation of the offshore power around its mean. Tested on real-world data collected at the New York/New Jersey bight, our proposed multi-input models demonstrate a high explanatory power in predicting the first two moments of offshore wind generation, testifying their potential value to the offshore wind industry.

17 WIND ENERGY↗

Multiscale Effects Masked the Impact of the COVID-19 Pandemic on Electricity Demand in the United States

Shelter-in-place orders and business closures related to COVID-19 changed the hourly profile of electricity demand and created an unprecedented source of uncertainty for the grid. The potential for continued shifts in electricity profiles has implications for electricity sector investment and operating decisions that maintain reserve margins and provide grid reliability. This study reveals that understanding this uncertainty requires an understanding of the underlying drivers at the customer-class scale. This paper utilizes three datasets to compare the impacts of COVID-19 on electricity consumption across a range of spatiotemporal and customer scales. At the utility/customer-class scale, COVID-19-induced shutdowns in the spring of 2020 shifted weekday residential load profiles to resemble weekend profiles from previous years. Total commercial loads declined, but the commercial diurnal load profile was unchanged. With only total loads available at the balancing authority scale, the apparent impact of COVID-19 was smaller during the summer due in part to phased re-opening and spatial variability in re-opening, but there were still clear variations once total loads were broken down zonally. Monthly data at the state scale showed an increase in state-level residential electricity sales, a decrease in commercial sales, and a small net decrease in total sales in most states from April-August 2020. Analyses that focus on total load or a single scale may miss important changes that become apparent when the load is broken down regionally or by customer class.

COVID-19, electricity demand, multiscale, Commonwe↗

The value of concentrating solar power in ancillary services markets

Ancillary services, such as spinning reserves, can provide grid reliability and contribute to profitability of an energy resource. We exercise an existing dispatch optimization model to estimate the profitability of a concentrating solar power plant by incorporating the sale of spinning reserves in the ancillary service market using the National Renewable Energy Laboratory's System Advisor Model to simulate operations within a 72-h rolling horizon framework. Assuming a price-taker approach with day-ahead energy and spinning reserve prices from both the California Independent System Operator and the Electricity Reliability Council of Texas, we find that selling spinning reserves in addition to electric energy increases plant profitability by up to 7% with perfect knowledge of day-ahead pricing and solar resource availability. Here, this finding suggests that spinning reserve markets provide significant value streams to concentrating solar power plants that can leverage thermal energy storage to offer reliable production in the short-to-medium term.

14 SOLAR ENERGY↗

Demand response event simulator and risk-aware bidding tool for industrial customers

Incentive Based Demand Response (IBDR) program participation delivers financial benefits to the consumers and resiliency benefits to the electricity grid. Effectively participating in these programs as an industrial consumer requires bidding strategies that balance financial risk with operational constraints. Existing bidding tools tend not to fully incorporate stochastic IBDR event modeling, program specific baseline and payment/penalty calculations, or demand reduction process control schemes that account for the cascading impacts of shutdown in complex facilities. Here, this work presents an IBDR event simulator and risk-aware bidding framework tool integrating three key components: a flexible, parameterized demand response event generator that rigorously accounts for program structures and stochasticity, a demand response operational simulation model that generates explicit control strategies for load reduction, and a Monte Carlo simulator to evaluate financial risk for varied capacity bids. A case study at a wastewater treatment plant participating in PG&E's Capacity Bidding Program demonstrates the framework's utility. In the peak capacity price month of August, optimal bidding by the wastewater treatment plant nets a mean IBDR benefit of $101,000 (67% of the August electricity bill) with 0.4% probability of a financial loss. This framework enables industrial operators to make informed bidding decisions, negotiate better program terms with demand response load aggregators, and analyze energy flexibility investments at their facilities. Ultimately, this work reduces participation barriers in IBDR programs and supports the broader goal of enhancing grid reliability and renewable energy integration.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids

As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Bridging semantics, control specifications and assessment: A library for scalable demand flexibility controls

There is growing recognition that Demand Flexibility (DF) can play a major role in enhancing grid reliability, with building control applications emerging as key enablers for DF. However, the traditional approach to deploying new control applications in buildings, including those for DF, remains largely manual and tailored to individual buildings, making it difficult to scale. While research efforts have explored semantics-driven portability, DF controls specification, and assessment approaches, these initiatives are fragmented and limited in scope. This paper proposes a novel methodology, grounded in design science research, to integrate these elements and create a comprehensive DF controls library for both industry and academia. This approach is applied to develop the Demand FLEXibility controls LIBrary using Semantics (DFLEXLIBS), an extensible open-source library that provides DF controls for HVAC systems in Python. DFLEXLIBS enables portable, easy-to-deploy controls that abstract building-specific data points, facilitating assessment across diverse buildings. DFLEXLIBS features nine different control applications, and it is successfully implemented and tested across four virtual and two real buildings, bridging the gap between semantics-driven portability, DF controls specification, and rigorous performance assessment. Its benefits are measured by a reusability ratio greater than 90% and a functional overlap ratio of around 70% for the most common functions used in the library, significantly reducing time for deploying new controls.

Controls library↗

Home energy management (HEM) database: A list with coded attributes of 308 devices commercially available in the US

Policymakers worldwide are currently discussing whether to include home energy management (HEM) products in their portfolio of technologies to reduce carbon emissions and improve grid reliability. However, very little data is available about these products. Here we present the results of an extensive review including 308 HEM products available on the US market in 2015–2016. We gathered these data from publicly available sources such as vendor websites, online marketplaces and other vendor documents. A coding guide was developed iteratively during the data collection and utilized to classify the devices. Each product was coded based on 96 distinct attributes, grouped into 11 categories: Identifying information, Product components, Hardware, Communication, Software, Information - feedback, Information - feedforward, Control, Utility interaction, Additional benefits and Usability. The codes describe product features and functionalities, user interaction and interoperability with other devices. A mix of binary attributes and more descriptive codes allow to sort and group data without losing important qualitative information. The information is stored in a large spreadsheet included with this article, along with an explanatory coding guide. This dataset is analyzed and described in a research article entitled “Categories and functionality of smart home technology for energy management” (Ford et al., 2017) [1].

Home automation↗

Assessing thermal comfort and participation in residential demand flexibility programs

Residential space-conditioning-based demand flexibility (DF) has become an increasingly sought-after method for demand-side load management to enhance grid reliability and facilitate integration of renewable energy generation. However, predicting the effectiveness and flexibility of residential DF resources is challenging due to the variability in household energy use behaviors. Current estimates show that only 50 % of projected savings from DF resources are actualized due to regulatory, technological, and social barriers. From a household perspective, concerns over thermal comfort during space conditioning-based DF events significantly impact participation decisions. Currently, there is a very limited understanding of how thermal comfort during space-conditioning-based DF events in real-world settings impacts household energy use behaviors and, consequently, the success of DF programs in achieving targeted savings. This paper proposes a method to comprehensively assess the thermal comfort implications of DF strategies and presents results of their impacts on DF event participation decisions and demand savings. Here, the proposed method was applied to a heat pump DF field study in Cordova, Alaska. The study’s key findings are: 1) DF event setpoint offsets that maintain indoor operative temperatures between 18 to 22 °C (65 to 71°F) may be preferred in Cordova, Alaska; 2) Household-level thermal comfort is more sensitive to the duration of the DF event than to the degree of temperature offset from baseline conditions; 3) The delayed impact of changes in indoor operative temperature in response to setpoint offsets, both during and after a DF event, influences occupants’ thermal comfort perceptions and willingness to persistently participate in events. The findings from application of the proposed method can help inform future larger-scale occupant-centric DF programs as it can capture information not readily available through utility and device-level energy use data. Thus, it can supplement these sources and help program administrators develop occupant-centric DF strategies, enabling more accurate predictions of participation rates and savings estimates for space-conditioning-based DF programs.

Demand side management↗