Evaluating resource adequacy impacts on energy market prices across wind and solar penetration levels
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Here, we quantify the quantum-to-classical transition of the single-mode Kerr nonlinear dynamics in the presence of loss. We establish three timescales that govern the dynamics, each with distinct characteristics. For times short compared with the Ehrenfest time, the evolution is classical, characterized by Gaussian dynamics. For sufficiently long times, as we increase the initial photon number, unitary Kerr evolution would generate macroscopic superpositions of coherent states (so-called kitten states). However, this is severely restricted in the presence of small photon loss, and the expectation values of observables coincide with their classical values. The intermediate timescale, however, shows resilient quantum behavior in the macroscopic limit. We show that in the mean-field non-Gaussian regime, the Kerr Hamiltonian (with small photon loss) generates a significant amount of Wigner-negativity, and classical flow is recovered only if the loss rate grows with system size. Our results broaden the usual understanding of quantum-to-classical transitions and demonstrate the potential for creating robust nonclassical resources for continuous-variable quantum information processing in the presence of loss.
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This paper presents a novel approach - distributed Real-Time Simulations (RTS) - for innovative wind energy and grid integration research. Distributed RTS is performed with Digital Real-Time Simulators (DRTS) in different geographical locations and connected over the Internet. Geographically distributed RTS supports - higher computation capability for RTS, leveraging distributed assets, novel testing techniques and hybridized smart grid testbeds. This paper discusses some of the basic aspects of distributed RTS with the communication delay as a probabilistic variable. To leverage hardware resources and enable flexible collaboration of national laboratories, the distributed RTS connection between the simulators at Idaho National Laboratory (INL) and National Wind Test Center (NWTC) at National Renewable Energy Laboratory (NREL) is established. The main objective of this distributed lab setup is to perform wind turbine characterization and assessment of wind integration studies in large scale grids. Several systematic steps based on integrating models are carried out to assess the impact of communication delay on distributed RTS.
Integration of renewable resources to meet growing energy demand is becoming a global priority under decarbonization mandates. This study contributes to ongoing efforts on this key subject by assessing the feasibility of using coastal-urban renewable energy resources, namely, offshore wind and rooftop photovoltaic systems, to meet electricity demand of New York City during the intense recent heat wave period of June 2025. A unified modeling framework, based on the urbanized weather research and forecasting model, is used to simulate climate, renewable resources, and energy demand variables. Findings show significant energy load mismatch of approximately 1150 GWh over the month, between the demand and the combined renewable generation outcome. Three storage integration scenarios are analyzed to mitigate the deficits, reducing said deficits by a minimum of approximately 9% over the duration of the month. This study provides a transferable modeling framework tool for evaluating renewable integration in dense urban environments that can be used by grid operators to support grid resilience during extreme heat events.
Deadlocks are notorious bugs in multithreaded programs, causing serious reliability issues. However, they are difficult to be fully expunged before deployment, as their appearances typically depend on specific inputs and thread schedules, which require the assistance of dynamic tools. However, existing deadlock detection tools mainly focus on locks, but cannot detect deadlocks related to condition variables. This paper presents a novel approach to fill this gap. It extends the classic lock dependency to generalized dependency by abstracting the signal for the condition variable as a special resource so that communication deadlocks can be modeled as hold-and-wait cycles as well. It further designs multiple practical mechanisms to record and analyze generalized dependencies. In the end, this paper presents the implementation of the tool, called UnHang. Experimental results on real applications show that UnHang is able to find all known deadlocks and uncover two new deadlocks. Overall, UnHang only imposes around 3% performance overhead and 8% memory overhead, making it a practical tool for the deployment environment.
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Information and communications technology (ICT) has enabled the integration of computer and audio-visual networks, leading to unprecedented exchange of data between various users and entities. By providing the infrastructure to coordinate millions of distributed assets within and across buildings, including variable loads, distributed energy resources, and energy-storage, ICT has the potential to lead to a similar convergence across building and grid domains. This paper illustrates obstacles to a broad application of ICT, lists steps to resolve these barriers, and explains how applying ICT to the building and grid domains, as well as the interface between those two domains, could provide new value to stakeholders by facilitating an exchange of services.
The need for slow dynamics models of renewable resources in cascade modeling essentially arises from the challenges associated with the increased use of solar and wind electric power. Indeed, the main challenge is that the power produced by wind and sunlight is not consistent; thus, renewable energy resources tend to have variable output power on many different timescales, including the timescales that a cascade unfolds.
Solar energy production is subject to variability in the solar resource – clouds and aerosols will reduce the available solar irradiance and inhibit power production. The fact that solar irradiance can vary by large amounts at small timescales and in an unpredictable way means that power utilities are reluctant to assign to their solar plants a large portion of future energy demand – the needed power might be unavailable, forcing the utility to make costly adjustments to its daily portfolio. The availability and predictability of solar radiation therefore represent important research topics for increasing the power produced by renewable sources.
Despite hydropower’s status as a well-established technology, changes in the global energy sector have prompted a variety of necessary hydropower technological innovations. Examples include efficient lowhead turbines, more flexible and dispatchable hydropower and pumped storage systems to complement variable and intermittent renewable resources, and technologies providing higher environmental performance. However, while innovative technologies are currently being proposed to meet these development challenges, small hydropower facility owners do not have sufficient risk-bearing capacity to adopt new, unvalidated technologies. This discourages manufacturers from bringing nascent technologies to market and stalls the technological growth of the sector. To reduce the risks associated with new technologies and promote further innovation, systemic (and sometimes unconventional) validation activities and new testing capabilities for hydropower are highly desired. These testing capabilities must demonstrate the safety, environmental acceptability, reliability, and performance of innovative technologies to quantify their value compared with existing technologies. Establishing these capabilities through dedicated testing facilities will be key to promoting hydropower growth in the United States. Following direction from the House Energy and Water Development Committee, the US Department of Energy’s Water Power Technologies Office (WPTO) has been tasked with understanding the state of hydropower testing in the United States. This scoping report discusses the needs and opportunities of hydropower testing in the United States, with a specific focus on small hydropower. Future developments will likely mostly target low-head sites with less than 30 ft (9.1 m) from new stream-reach developments, non-powered dam retrofits, and rehabilitation/upgrade of existing plants.
The project goal is to commercialize the VOLTTRON-hosted “DER as a Service” (DaaS) application and make it an “out-of-the-box” solution for ecoLong to use as a secure residential energy management system that allows 1.) distributed energy resources (DERs) to participate in wholesale markets and provide services to the grid and 2.) intelligently control the energy in the residential buildings to the desired level while maintaining occupancy comfort. The product will be field deployed onto multiple Solar Liberty sites in New York state to demonstrate wide-scale participation of DERs in wholesale markets to provide services to the grid and validate the effectiveness of the solution. The integrated management of DERs and buildings increases the penetration of solar resources by managing its variability, providing grid services, and solving the evening ramping challenges. Ultimately, the DaaS platform will make buildings and DERs smarter by unlocking grid-interactive efficient buildings.
Uncertainty in PV energy estimates is "one of the most critical areas of lack of understanding" according to independent engineers, financiers, PV model developers, and other industry stakeholders. The primary problem is a lack of rigorous, transparent, widely accepted methods for quantifying uncertainty in energy production estimates. Uncertainty in energy production estimates arises from variability of the solar resource, inexact PV performance models and their parameters, and system reliability considerations. Uncertainty in annual energy production is frequently calculated for larger projects in order to quantify financial risk. Key statistics for energy, such as the P-values "P50" and "P90" (the annual energy values that are exceeded in future years with 50\% and 90\% probability, respectively) are used by financing institutions to calculate the repayment risk for the project. The current methods to estimate these statistics are typically proprietary, specialized, and involve significant post-processing of commercial performance model results. This black-box approach leads to inconsistent P-value estimates from different parties, which reduces investors' confidence in the results. Since the financial community bases its risk assessment on these estimates, reduced confidence increases perceived project risk, and consequently financing costs. The goal of this project was to establish a set of best practices for quantifying uncertainty in energy production estimates, including identifying what sources of uncertainty must be considered with clear definitions and metrics, determining which sources are the biggest drivers of uncertainty, and providing a computationally efficient framework for combining different sources of uncertainty that is flexible enough to accommodate substitutions of data or methods when better information is available. We engaged a wide set of stakeholders to ensure industry endorsement and adoption, and leveraged complementary projects investigating individual sources of uncertainty in great detail, as well as others' work that started down this path.
Solar photovoltaic (PV) cybersecurity is a growing field of research. As deployments of solar PV has increased, cyber risk has also increased. However, utility solar PV installations are not required to comply with North American Electric Reliability Corporation (NERC) Critical Infrastructure Protection (CIP) unless they meet a minimum generation threshold of 75 Megawatts (MW). Individual residential scale solar PV deployments will not meet that generation threshold and are therefore excluded from NERC CIP requirements. With most solar installations below 75MW, solar PV has been deployed with minimal oversight and highly variable cybersecurity maturity. The resources that make up the digital supply chain can include software, code, data, as well as other digital components. However as clean energy technology advances, cybersecurity threats and vulnerabilities continue to evolve and grow in sophistication. Solar PV faces a unique challenge in which it can be deployed in residential buildings and purchased by a consumer directly. This makes the supply chain of PV a unique challenge, where responsible parties for cybersecurity vary widely depending on the type of solar PV being deployed. Supply chain cybersecurity for solar PV represents a critical area for ensuring safe operations as the U.S. moves towards a clean energy future.
The Bioenergy Feedstock Library (BFL), part of the Biomass Feedstock National User Facility (BFNUF) located at Idaho National Laboratory (INL), is a physical sample repository and a web-accessible electronic database. The BFL stores physical and chemical characteristics of biomass and waste carbon sources for energy use, as well as samples generated from U.S. Department of Energy (DOE) Bioenergy Technologies Office (BETO) and U.S. Department of Agriculture-funded projects. The objective of this Bioenergy Feedstock Library Annual Summary Report for 2024, similar to the 2023 Annual Summary Report , is to focus on the updates to: (1) publicly available analytical data and equipment tracked through the BFNUF, (2) significant increases in the physical samples available for request, (3) sample and data archival progress from recent BETO-funded projects, and (4) publicly available data sets created upon request from BETO, INL projects, or outside entities compared to the previous annual summary reports. This report highlights key statistics and available data and information important for INL, BFL users, academics, and industry.
The power grid in the western United States is undergoing a major transformation, driven by technological advancements, power markets, policy shifts, and evolving energy demands. The integration of variable renewable energy (VRE) resources, such as wind and solar, into the power grid has become a major driver of change. Between 2018 and 2023, about 19 gigawatts (GW) of new solar capacity and 14 GW of new wind capacity was built in the Western Interconnection (WI) region. These two VRE resources accounted for the majority of WI capacity additions. The Western Interconnection is expected to host 30 GW of wind, 40 GW of solar, and 14 GW of energy storage by 2030 (Western Electricity Coordinating Council n.d.).
Based on the available geographically dispersed data for the Western U.S. (excluding Alaska), we analyze to what extent the geographic diversity of these resources can offset their variability. Without energy storage and assuming unlimited energy flows between regions, wind and PV can meet up to 80% of loads in Western U.S. while less than 10% of the generated power is curtailed. Limiting hourlyenergy flows by the aggregated transmission line carrying capacities decreases the fraction of the load that can be met with wind and PV generation to approximately 70%.
Drought, variable renewable energy (VRE) resources (primarily wind and photovoltaic technologies), and power markets can all impact hydropower plant operations. We analyze how these three factors collectively may potentially change the future operation and economics of federal dams and hydropower plants located in the Colorado River Basin (CRB). Federal CRB energy and capacity resources, including 4,200 MW of hydropower capacity, are marketed by Western Area Power Administration (WAPA) and sold through long-term Firm Electric Service (FES) contracts to entities located in 15 western and central States that serve millions of electricity consumers. The parameters of FES contracts allow a wide range of scheduling flexibility for FES customers. The FES customers will adjust the timing of energy delivery according to the changing water, power market, and VRE contribution level to the power grid. We simulate and project water and hydropower operations with two models: 1) reservoir operation and 2) power system operation (production cost) with various temporal granularity. We consider two sets of grid scenarios (one representing today's infrastructure, and one representing higher VRE contributions in 2036) and two sets of water scenarios (drought and normal hydrology). We modeled scenarios to understand the power market-energy price levels and patterns, FES customer scheduling, hydropower economics, WAPA finances and hydropower operation for various hydrologic conditions, and VRE contribution level to the power grid. The detailed modeling framework includes: (1) creating ensembles of CRB hydrologic and hydropower futures, (2) selecting representative futures, (3) simulation of FES customer contracts, (4) simulating western U.S. power grid economic scheduling and dispatch under two VRE contribution levels, utilizing information from modeling steps (1)-(4), (5) assessment of reservoir water and hydropower operation, and (6) comparing scenario operations and economics. Our study team comprising WAPA, and an interdisciplinary team of national labs will gain insight into how WAPA can prepare for and adapt its practices in response to an evolving U.S. power grid.