SEARCH · Search NASA

Results for “variable resources”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 307 records · Page 17

All sources NASA NTRS DOE OSTI

DOE OSTI2014

Ramping and Uncertainty Prediction Tool - Analysis and Visualization of Wind Generation Impact on Electrical Grid

RUT software is designed for use by the Balancing Authorities to predict and display additional requirements caused by the variability and uncertainty in load and generation. The prediction is made for the next operating hours as well as for the next day. The tool predicts possible deficiencies in generation capability and ramping capability. This deficiency of balancing resources can cause serious risks to power system stability and also impact real-time market energy prices. The tool dynamically and adaptively correlates changing system conditions with the additional balancing needs triggered by the interplay between forecasted and actual load and output of variable resources. The assessment is performed using a specially developed probabilistic algorithm incorporating multiple sources of uncertainty including wind, solar and load forecast errors. The tool evaluates required generation for a worst case scenario, with a user-specified confidence level.

Etingov, Pavel↗

DOE OSTI2011

Key Residential Building Equipment Technologies for Control and Grid Support PART I (Residential)

Electrical energy consumption of the residential sector is a crucial area of research that has in the past primarily focused on increasing the efficiency of household devices such as water heaters, dishwashers, air conditioners, and clothes washer and dryer units. However, the focus of this research is shifting as objectives such as developing the smart grid and ensuring that the power system remains reliable come to the fore, along with the increasing need to reduce energy use and costs. Load research has started to focus on mechanisms to support the power system through demand reduction and/or reliability services. The power system relies on matching generation and load, and day-ahead and real-time energy markets capture most of this need. However, a separate set of grid services exist to address the discrepancies in load and generation arising from contingencies and operational mismatches, and to ensure that the transmission system is available for delivery of power from generation to load. Currently, these grid services are mostly provided by generation resources. The addition of renewable resources with their inherent variability can complicate the issue of power system reliability and lead to the increased need for grid services. Using load as a resource, through demand response programs, can fill the additional need for flexible resources and even reduce costly energy peaks. Loads have been shown to have response that is equal to or better than generation in some cases. Furthermore, price-incentivized demand response programs have been shown to reduce the peak energy requirements, thereby affecting the wholesale market efficiency and overall energy prices. The residential sector is not only the largest consumer of electrical energy in the United States, but also has the highest potential to provide demand reduction and power system support, as technological advancements in load control, sensor technologies, and communication are made. The prevailing loads based on the largest electrical energy consumers in the residential sector are space heating and cooling, washer and dryer, water heating, lighting, computers and electronics, dishwasher and range, and refrigeration. As the largest loads, these loads provide the highest potential for delivering demand response and reliability services. Many residential loads have inherent flexibility that is related to the purpose of the load. Depending on the load type, electric power consumption levels can either be ramped, changed in a step-change fashion, or completely removed. Loads with only on-off capability (such as clothes washers and dryers) provide less flexibility than resources that can be ramped or step-changed. Add-on devices may be able to provide extra demand response capabilities. Still, operating residential loads effectively requires awareness of the delicate balance of occupants health and comfort and electrical energy consumption. This report is Phase I of a series of reports aimed at identifying gaps in automated home energy management systems for incorporation of building appliances, vehicles, and renewable adoption into a smart grid, specifically with the intent of examining demand response and load factor control for power system support. The objective is to capture existing gaps in load control, energy management systems, and sensor technology with consideration of PHEV and renewable technologies to establish areas of research for the Department of Energy. In this report, (1) data is collected and examined from state of the art homes to characterize the primary residential loads as well as PHEVs and photovoltaic for potential adoption into energy management control strategies; and (2) demand response rules and requirements across the various demand response programs are examined for potential participation of residential loads. This report will be followed by a Phase II report aimed at identifying the current state of technology of energy management systems, sensors, and communication technologies for demand response and load factor control applications for the residential sector. The purpose is to cover the gaps that exist in the information captured by the sensors for energy management system to be able to provide demand response and load factor control. The vision is the development of an energy management system or other controlling enterprise hardware and software that is not only able to control loads, PHEVs, and renewable generation for demand response and load factor control, but also to do so with consumer comforts in mind and in an optimal fashion.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Comparison of One- and Two-Variable Linear Regression Models and Classic Energy Intensity for Energy Performance Tracking of Two Manufacturing Sectors

We report manufacturing facilities consumed about 32% of total domestic energy in the United States in 2016. To evaluate the energy savings achieved through the implementation of energy conservation projects and to establish powerful arguments for future projects, accurate energy performance tracking methods are necessary. The classic energy intensity method (i.e., the ratio of annual total energy over annual total production) is the means most widely used to measure savings because it can be understood and calculated easily. This method considers the variation in production rates to some extent; however, it fundamentally assumes facilities' base energy consumption (energy consumption at zero production) to be zero, which rarely holds true. Furthermore, this method does not consider variations in other relevant parameters, such as weather conditions. Therefore, the regression models approach is commonly recommended to track energy performance improvements. However, because it requires more data and statistical expertise, the regression models approach has been adopted by only a few facilities, and many others suspect it is not worth the effort for their specific cases. For this reason, the improved accuracy this approach offers needs to be demonstrated. By analyzing 477 monthly energy (electricity and natural gas) data sets, this study quantitatively compared the accuracy of classic energy intensity, one-variable (production only) linear regression models, and two-variable (i.e., production and weather) linear regression models for two manufacturing sectors (primary metal and transportation equipment manufacturing). Results showed that significant improvements in accuracy were achieved with one-variable regression models when compared with the classic energy intensity method and with two-variable regression models when compared with one-variable regression models. These improvements were demonstrated using the p values of intercept, cooling degree days, and heating degree days. Based on these results, to achieve a good balance between accuracy improvements and resources requirements, one-variable (production only) linear regression models for electricity consumption and two-variable (production and heating degree days) linear regression models for natural gas consumption are recommended for both sectors in the case of limited resources. Lastly, facilities can also use the results to decide which approaches fit better in their cases.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch

For decades, electricity customers have been treated as mere recipients of electricity in vertically integrated power systems. However, as customers have widely adopted distributed energy resources and other forms of customer participation in active dispatch (such as demand response) have taken shape, the value of mining knowledge from customer behavior patterns and using it for power system operation is increasing. Further, the variability of renewable energy resources has been considered a liability to the grid. However, electricity consumption has shown the same level of variability and uncertainty, and this is sometimes overlooked. This article investigates data analytics and forecasting methods to identify correlations between electricity consumption behavior and distributed photovoltaic (PV) output. The forecasting results feed into a predictive energy management system that optimizes energy consumption in the near future to balance customer demand and power system needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Achieving an 80% Renewable Portfolio in Alaska's Railbelt: Cost Analysis

This study examines the system-level costs and benefits of increased renewable energy deployment in the Railbelt grid, in the context of a proposed 80% renewable portfolio standard (RPS). This work studies the period from 2024 to 2040 and uses a model that simulates the planning, evolution, and operation of the power system to identify the mix of resources that maintains system reliability at the lowest electricity system cost over the period of analysis. The model tracks several reliability metrics, including the ability to serve demand during all hours of the year, even when normal power system failures occur. The model includes several measures (and associated costs) to address the variable output of renewable resources, including additional operating reserves, fuel storage, cycling of fossil plants, and additional equipment needed to maintain system stability. The Reference (least-cost) scenario results in substantial deployment of renewable energy and cost savings, reaching about 76% of Railbelt generation derived from renewables in 2040. Annual savings average about $105 M/year from 2030 to 2040. About 50% of this generation is from wind by 2040. Enforcing an 80% RPS results in about a 2% cumulative reduction in net savings. Demand is met in all scenarios, relying heavily on use of existing hydropower and fossil-fueled generators during periods of low renewable output. Meeting the increase in variability will require substantial changes in how the system is operated, with inverter-based resources providing nearly 100% of electricity during some periods.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2011

Observation of Hydrological Processes Using Remote Sensing: Volume 2: The Science of Hydrology - Chapter 2.14

Improving water management can make a significant contribution to achieving most of the Millennium Development Goals established by the UN General Assembly in 2000, especially those related to poverty, hunger, and major diseases. The World Summit on Sustainable Development (WSSD) in 2002 recognized this need. Water and sanitation in particular received great attention from the Summit. The Johannesburg Plan of Implementation recommended to improve water resources management and scientific understanding of the water cycle through joint cooperation and research. For this purpose, it is recommended to promote knowledge sharing, provide capacity building, and facilitate the transfer of technology including remote-sensing (RS) and satellite technologies, especially to developing countries and countries with economies in transition, and to support these countries in their efforts to monitor and assess the quantity and quality of water resources, for example, by establishing and/or further developing national monitoring networks and water resources databases and by developing relevant national indicators. The Johannesburg Plan also adopted integrated water resources management as the overarching concept in addressing and solving water-related issues. As a result of the commitments made in the Johannesburg Plan of Implementation, several global and regional initiatives have emerged. Current international initiatives such as the Global Monitoring for Environment and Security (GMES) program of the European Commission and the European Space Agency (ESA), and the Global Earth Observation System of Systems (GEOSS) 10-Year Implementation Plan, have all identified Earth observation (EO) of the water cycle as the key in helping to solve the world s water problems. The availability of spatial information on water quantity and quality will also enable closure of the water budget at river basin and continental scales to the point where effective water management is essential (e.g., as requested by the European Union s Water Framework Directive (WFD), as well as national policies). Geo-information science and EO are vital in achieving a better understanding of the water cycle and better monitoring, analysis, prediction, and management of the world s water resources. The major components of the water cycle of the Earth system and their possible observations are presented. Such observations are essential to understand the global water cycle and its variability, both spatially and temporally, and can only be achieved consistently by means of EOs. Additionally, such observations are essential to advance our understanding of coupling between the terrestrial, atmospheric, and oceanic branches of the water cycle, and how this coupling may influence climate variability and predictability. Water resources management directly interferes with the natural water cycle in the forms of building dams, reservoirs, water transfer systems, and irrigation systems that divert and redistribute part of the water storages and fluxes on land. The water cycle is mainly driven and coupled to the energy cycle in terms of phase changes of water (changes among liquid, water vapor, and solid phases) and transport of water by winds in addition to gravity and diffusion processes. The water-cycle components can be observed with in situ sensors as well as airborne and satellite sensors in terms of radiative quantities. Processing and conversion of these radiative signals are necessary to retrieve the water-cycle components.

Wilder, Peter↗

DOE OSTI2020

Management of Risk and Uncertainty Through Optimized Co-Operation of Transmission Systems and Microgrids With Responsive Loads (Final Report)

The evolution of the power system to the reliable, efficient and sustainable system of the future will involve development of both demand- and supply-side technology and operations. Ambitious national and state-level goals around the decarbonization of electricity relies on the integration of very high levels of renewable resources, most of which are variable and intermittent. The use of demand response is an ideal approach to counterbalance the intermittency of renewable generation and brings the consumer into the spotlight. Though individual consumers are interconnected at the low-voltage distribution system, these resources are typically modeled as variables at the transmission network level. Demand-side participation cannot be leveraged effectively without explicitly including the distribution system dynamics in the optimization-based wholesale market operations. This project grew from a vision for co-optimized interaction of distribution systems, or microgrids, with the high-voltage transmission system. In this framework, microgrids encompass consumers, distributed renewables and storage. The energy management system of the lower voltage system (distribution or microgrid) can also sell (buy) excess (necessary) energy from the transmission system. Until recently, very little research had been conducted on the co-optimization of these two systems due to computational limitations. However, advances in computational capabilities, and the judicious use of decomposition methods and innovative approximation methods for high-dimension dynamic programming made this goal a viable objective for this project, leading to a fundamental shift in the ability to integrate and fully utilize demand-side resources. To this end, the modeling framework developed introduces a novel co-optimization framework, to include the operations of both the transmission and distribution systems (or microgrids) in operational decision making. This framework was used to analyze renewable and distributed generation along with responsive demand and to compare the capability of co-optimized systems to perform with higher levels of variable renewables. An ideal microgrid is defined as an electric entity capable of operating in both interconnected (with the high-voltage grid) and islanded mode. As such, the microgrid should incorporate generating units (traditional units and intermittent) and if needed, exchange power with the high-voltage grid. The interplay between the microgrid and high-voltage grid motivated the development of the co-optimization approach to ensure efficient performance of the interconnected network. Results show that the use of a bi-level optimization approach is an appropriate structure, capable of co-optimizing a transmission system with multiple distribution systems and microgrids. While increasing the number of connected systems provides increasing flexibility for renewables integration this can also the economic benefits to the low-voltage subsystems with each additional system connected. Comparison of a traditional single-level decision structure with the co-optimization approach illustrates a reduction in overall system cost under co-optimization, while specific cost allocations to transmission and distribution systems are changed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2015

Wind and Solar on the Power Grid: Myths and Misperceptions, Greening the Grid

Wind and solar are inherently more variable and uncertain than the traditional dispatchable thermal and hydro generators that have historically provided a majority of grid-supplied electricity. The unique characteristics of variable renewable energy (VRE) resources have resulted in many misperceptions regarding their contribution to a low-cost and reliable power grid. Common areas of concern include: 1) The potential need for increased operating reserves, 2) The impact of variability and uncertainty on operating costs and pollutant emissions of thermal plants, and 3) The technical limits of VRE penetration rates to maintain grid stability and reliability. This fact sheet corrects misperceptions in these areas.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

Current Developments in Retail Rate Design: Implications for Solar and Other Distributed Energy Resources

Retail electricity pricing is evolving in the context of broader shifts in how electricity customers pay for grid services and how they are compensated for customer-sited generation. This evolution has been prompted by a broad set of factors, chief among them being: widespread adoption of advanced metering infrastructure (AMI), increased customer investment in solar and other distributed energy resources (DERs), concerns about utilities’ fixed cost recovery and revenue sufficiency in an era of flat or declining load growth, and significant changes to utilities’ hourly net load profiles and operational needs as greater amounts of variable renewable energy (VRE) resources connect to the grid. This report discusses five current retail rate design trends among residential and commercial customer classes that have emerged, at least in part, in response to those drivers, and uses case studies to illustrate key motivations, variations in rate design, and aspects of implementation experience.

14 SOLAR ENERGY↗

DOE OSTI2024

An overview of the E3SM version 2 large ensemble and comparison to other E3SM and CESM large ensembles

Abstract. This work assesses a recently produced 21-member climate model large ensemble (LE) based on the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) version 2 (E3SM2). The ensemble spans the historical era (1850 to 2014) and 21st century (2015 to 2100), using the SSP370 pathway, allowing for an evaluation of the model's forced response. A companion 500-year preindustrial control simulation is used to initialize the ensemble and estimate drift. Characteristics of the LE are documented and compared against other recently produced ensembles using the E3SM version 1 (E3SM1) and Community Earth System Model (CESM) versions 1 and 2. Simulation drift is found to be smaller, and model agreement with observations is higher in versions 2 of E3SM and CESM versus their version 1 counterparts. Shortcomings in E3SM2 include a lack of warming from the mid to late 20th century, likely due to excessive cooling influence of anthropogenic sulfate aerosols, an issue also evident in E3SM1. Associated impacts on the water cycle and energy budgets are also identified. Considerable model dependence in the response to both aerosols and greenhouse gases is documented and E3SM2's sensitivity to variable prescribed biomass burning emissions is demonstrated. Various E3SM2 and CESM2 model benchmarks are found to be on par with the highest-performing recent generation of climate models, establishing the E3SM2 LE as an important resource for estimating climate variability and responses, though with various caveats as discussed herein. As an illustration of the usefulness of LEs in estimating the potential influence of internal variability, the observed CERES-era trend in net top-of-atmosphere flux is compared to simulated trends and found to be much larger than the forced response in all LEs, with only a few members exhibiting trends as large as observed, thus motivating further study.

58 GEOSCIENCES↗

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 OSTI2014

Comparing Resource Adequacy Metrics and Their Influence on Capacity Value: Preprint

Traditional probabilistic methods have been used to evaluate resource adequacy. The increasing presence of variable renewable generation in power systems presents a challenge to these methods because, unlike thermal units, variable renewable generation levels change over time because they are driven by meteorological events. Thus, capacity value calculations for these resources are often performed to simple rules of thumb. This paper follows the recommendations of the North American Electric Reliability Corporation?s Integration of Variable Generation Task Force to include variable generation in the calculation of resource adequacy and compares different reliability metrics. Examples are provided using the Western Interconnection footprint under different variable generation penetrations.

17 WIND ENERGY↗

DOE OSTI2013

Treatment of Solar Generation in Electric Utility Resource Planning

Today's utility planners have a different market and economic context than their predecessors, including planning for the growth of renewable energy. State and federal support policies, solar photovoltaic (PV) price declines, and the introduction of new business models for solar PV 'ownership' are leading to increasing interest in solar technologies (especially PV); however, solar introduces myriad new variables into the utility resource planning decision. Most, but not all, utility planners have less experience analyzing solar than conventional generation as part of capacity planning, portfolio evaluation, and resource procurement decisions. To begin to build this knowledge, utility staff expressed interest in one effort: utility exchanges regarding data, methods, challenges, and solutions for incorporating solar in the planning process. Through interviews and a questionnaire, this report aims to begin this exchange of information and capture utility-provided information about: 1) how various utilities approach long-range resource planning; 2) methods and tools utilities use to conduct resource planning; and, 3) how solar technologies are considered in the resource planning process.

14 SOLAR ENERGY↗

DOE OSTI2016

Development and Experimental Hardware Validation of Novel Variable Speed Hydropower Control Schemes for Emerging Applications and Water Resource Paradigms

Recent opportunities for new hydropower generation in the United States have often been in non-powered dams and run-of-river type flows occurring in low-impact natural areas and unregulated conduits. At the same time, a changing water resource paradigm is challenging some existing generation in drought stricken areas where supply reservoirs behind many medium and high head units are at historically low levels. The result is a developing market space which is potentially best captured by machines capable of variable speed operation. Variable speed units have a wider range of operating conditions compared to their synchronous counterparts and have already proven their resilience through a 20+ year history in pumped hydro applications. This work develops a control scheme for variable speed hydropower units operating to deliver a set-point power through flow controlling gates. This control scheme increases both the hydrologic operating range of a unit as well as the speed of response to grid contingencies under droop and automatic generator control. Results from simulation are confirmed on hardware.

Starrett, Mike↗

DOE OSTI2018

Assessing variability of wind speed: comparison and validation of 27 methodologies

Because wind resources vary from year to year, the intermonthly and interannual variability (IAV) of wind speed is a key component of the overall uncertainty in the wind resource assessment process, thereby creating challenges for wind farm operators and owners. We present a critical assessment of several common approaches for calculating variability by applying each of the methods to the same 37-year monthly wind-speed and energy-production time series to highlight the differences between these methods. We then assess the accuracy of the variability calculations by correlating the wind-speed variability estimates to the variabilities of actual wind farm energy production. We recommend the robust coefficient of variation (RCoV) for systematically estimating variability, and we underscore its advantages as well as the importance of using a statistically robust and resistant method. Using normalized spread metrics, including RCoV, high variability of monthly mean wind speeds at a location effectively denotes strong fluctuations of monthly total energy generation, and vice versa. Meanwhile, the wind-speed IAVs computed with annual-mean data fail to adequately represent energy-production IAVs of wind farms. Finally, we find that estimates of energy-generation variability require 10±3 years of monthly mean wind-speed records to achieve a 90 % statistical confidence. This paper also provides guidance on the spatial distribution of wind-speed RCoV.

17 WIND ENERGY↗

DOE OSTI2025

Existing Methods for Grid Strength Assessment and Role of Hydropower in Future Grids

The power system is undergoing rapid evolution with increasing penetration of inverter-based resources (IBRs), large loads, microgrids, and power electronic devices. Ensuring reliable and stable operation of the modern power grid is a multi-faceted challenge that requires detailed understanding of this complex system. Dynamic stability is a major concern in maintaining the security of power grids as the generation mixes and large loads transitions to include high shares of power electronic devices. The operation of IBRs in regions with low system strength and higher grid impedances has been found to be the main reason behind many of the power system instabilities that manifest themselves in various types of oscillations and interactions that, if not properly addressed and damped, can jeopardize the reliable operation of the power system. Weak grid conditions compound such stability problems, particularly when many IBRs operate in proximity to and connect to weak power grids. Therefore, it is important to assess grid strength for planning, integration and operation of IBRs for a given power system. In this report, we aim to understand and classify existing methodologies for grid strength assessment, along with their limitations and future needs. Further, we aim to utilize the huge untapped potential in utilizing hydro energy resources in addressing some of the pertinent challenges of grid strength and reliable operation of modern power systems. Hydropower, historically valued for its flexibility and dispatchability, now faces new constraints due to reduced share of synchronous machines in the generation mix and seasonal variability of available water resources. Yet, these same plants present untapped potential beyond energy generation - notably, as providers of critical grid services. This report explores how hydropower plants, particularly through operation as synchronous condensers, can play a pivotal role in strengthening the grid amid evolving system dynamics.

13 HYDRO ENERGY↗

DOE OSTI2024

Generic and ML Workloads in an HPC Datacenter: Node Energy, Job Failures, and Node-Job Analysis

HPC datacenters offer a backbone to the modern digital society. Increasingly, they run Machine Learning (ML) jobs next to generic, compute-intensive workloads, supporting science, business, and other decision-making processes. However, understanding how ML jobs impact the operation of HPC datacenters, relative to generic jobs, remains desirable but understudied. In this work, we leverage long-term operational data, collected from a national-scale production HPC datacenter, and statistically compare how ML and generic jobs can impact the performance, failures, resource utilization, and energy consumption of HPC datacenters. Our study provides key insights, e.g., ML-related power usage causes GPU nodes to run into temperature limitations, median/mean runtime and failure rates are higher for ML jobs than for generic jobs, both ML and generic jobs exhibit highly variable arrival processes and resource demands, significant amounts of energy are spent on unsuccessfully terminating jobs, and concurrent jobs tend to terminate in the same state. We open-source our cleaned-up data traces on Zenodo (https://doi. org/10.5281/zenodo.13685426), and provide our analysis toolkit as software hosted on GitHub (https://github.com/atlarge-research/2024-icpads-hpc-workload-characterization). This study offers multiple benefits for data center administrators, who can improve operational efficiency, and for researchers, who can further improve system designs, scheduling techniques, etc.

crossanalysis↗

DOE OSTI2025

Load Matching Potential of Urban Renewables during Extreme Heat in New York City

Integration of renewable resources to meet growing energy demand is becoming a global priority under decarbonization mandates. This study contributes to ongoing efforts on the matter by assessing the feasibility of using offshore wind and rooftop photovoltaic systems to meet electricity demand during a period of thermal stress in New York City—August 2019. A unified modelling 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 miss-match, ranging from 3 to 5 GW, between the demand and the composite renewable generation outcome. This study provides a transferable framework for evaluating renewable integration in dense urban regions and highlights the need for additional strategies to support grid resilience during extreme heat events, while clarifying the role of local resources in fully managing NYC’s load.

54 ENVIRONMENTAL SCIENCES↗