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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.

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DOE OSTI2020

Synthetic Ancillary Service Generator (SynAS) v1

The balance of demand and supply in the electric power grid is a crucial element to grid reliability. While balancing is typically provided by conventional power plants, with spinning generators, distributed energy resources (DER) and especially electric vehicles (EVs) pose a great potential to provide such service. This project conducted a field demonstration at the Los Angeles Air Force Base (LA-AFB) where a fleet of mixed-use bi-directional EVs were actively participating in the California Independent System Operator (CAISO) market for frequency regulation. While participation in regulation markets is promising, a major drawback in designing control systems and assessing potential revenue is the lack of example frequency regulation signals. This project introduces an open-source dataset for 143 days of four-second regulation data, and the SynAS Python module to synthetically generate frequency regulation signals of arbitrary length.

Black, DouglasR↗

DOE OSTI2019

SPC-2734 Chromalox Oil Heater System Procurement Specification

This specification will supply the necessary technical information to the vendor to allow them to provide the system we need for the Thermal Energy Delivery System (TEDS) construction. TEDS is being constructed within the dynamic energy transport and integration laboratory (DETAIL) facility to test heat transfer components, distribution systems, instruments, and controls that can be monitored and controlled for hybrid generation of electrical power and/or non-electrical products. TEDS is designed to operate as a part of an integrated system or independently to demonstrate component operations, to develop and validate thermal energy transport models and control systems, and to study thermal energy inertia and storage. Within the integrated system, TEDS will be connected to the INL Real Time Digital Simulator (RTDS) test platform to develop and demonstrate monitoring and control systems and to investigate real-time, hardware-in-the-loop response characteristics relative to grid operations. As the basis of the TEDS, Chromalox (the vendor) has been chosen to design and supply a heater, pump, storage tanks, and pressure vessel due to their experience with industrial-scale heat transfer oil systems.

25 ENERGY STORAGE↗

DOE OSTI2019

Optimal Variable Switching Frequency Scheme to Reduce Loss of Single-Phase Grid-Connected Inverter With Unipolar and Bipolar PWM

Reducing the power loss of a converter without increasing its volume or cost in hardware has always been a much sought-after but challenging goal. This article proposes a comprehensive variable switching frequency (VSF) scheme to improve the overall system efficiency while still meeting a given total harmonic distortion (THD) requirement without any hardware changes, achieved only by changes to the controller. In this article, it is analyzed that for the H-bridge inverter, the inductor copper loss and the switch conduction loss are independent of the switching frequency (SF), while the device switching loss and the inductor core loss are functions of the SF. This article aims at weighting the combined switching loss and inductor core loss for both the unipolar and bipolar modulation techniques, with the constraint on the output THD, which, for fixed passive components, is a function of inductor ripple current that in turn depends on the SF. An optimal VSF scheme is devised through rigorous mathematical analysis of the output ripple component and the SF-dependent losses. In each switching cycle, the inductor ripple current is predicted with the circuit parameters, such as duty cycle, switching period, inductance, and dc voltage, and by implementing the VSF scheme, the current ripple is optimally varied such as to satisfy the THD requirement while reducing the SF-dependent losses. A generalized method to design VSF schemes for any topology and specifications is also presented, which would help future users in designing the VSF controller. A 1-kW H-bridge inverter using SiC MOSFETs and powdered iron/ferrite core has been built to validate the theoretical analysis. Both constant SF (CSF) scheme (200 kHz) and optimal VSF scheme with simple controller modification have been implemented in the experimental prototype using DSP TMS320f28335. Compared with the CSF scheme, the optimal VSF scheme shows a significant improvement in the system efficiency at rated power from 94.7%...

conduction loss↗

DOE OSTI2022

Advanced Hydropower and PSH Capacity Expansion Modeling (Final Report on HydroWIRES D1 Improvements to Capacity Expansion Modeling)

Hydropower and pumped-storage hydropower (PSH) have played a key role in providing flexible, low-carbon electricity to the U.S. electricity system for over a century. As variable generation (VG) deployment increases the demand for flexible, dispatchable generation, it is important to use all available methods for understanding how hydropower and PSH interfaces with VG in a future low-carbon grid. Capacity expansion models (CEMs) of electricity systems are often used to study future electricity scenarios, but these tools often have difficulty representing site- and technology-details of hydropower and PSH due to limited spatial, temporal, or process resolution. This report demonstrates a set of model advancements to improve hydropower and PSH representations in CEMs and other models that consider hydropower and PSH's role in electricity systems. Model advancements include new data integration to define closed-loop PSH resource availability and cost, along with site-level parameterizations of existing PSH capacity and energy storage specifications. Shifting energy across seasons for both hydropower and PSH is explored to demonstrate the potential value of long-duration storage. New representations of hydropower upgrades offer opportunities to increase dispatchability, add pumps, or independently add capacity or energy depending on what is most valuable. New model structures and data are demonstrated individually and in combination to observe their impact on model results and provide initial insights into what is most important for analysts, electricity system planners, and hydropower decision-makers to consider when assessing future roles of hydropower. Improving flexibility of existing hydropower assets has the potential to reduce CO 2 emissions through complementing VG technologies and/or improve electricity system economics by reducing the need to deploy higher-cost systems. Large-scale energy storage is also shown to provide opportunities for balancing seasonal differences in VG, particularly solar photovoltaics (PV). Initial analysis with a new closed-loop PSH resource and cost dataset also demonstrate the potential for new PSH deployment to offer new grid flexibility opportunities. Future analysis and modeling of hydropower's role in the U.S. and other electricity systems could include methodological improvements based on those discussed here along with sensitivity analysis to better represent current and future potential hydropower and PSH flexibility. Improvements to geospatial and site-level data can further improve the understanding of how hydropower and PSH can be upgraded and operated in response to future electricity system needs. This work lays the groundwork to use advanced planning models to explore the range of roles hydropower and PSH can play in the grid of the future.

13 HYDRO ENERGY↗

DOE OSTI2023

Valuation of Distributed Wind Turbines Providing Multiple Market Services

The role of wind turbines has traditionally been limited to providing energy capacity to the grid, but the availability of smart inverters and recent regulatory changes provide the technical and policy capability for wind turbines to also provide ancillary services. However, in contrast to the technical and policy aspects, the valuation of distributed wind turbines providing such services has not been thoroughly studied. This paper presents an optimal market-participation method for distributed wind turbines and valuates different strategies in California Independent System Operator’s balancing area. The services include energy capacity, regulation up and down, and reserves. An optimization problem is formulated to determine optimal power output for each service and demonstrated using historical data for one complete year. The revenues from multiple services are quantified, and a sensitivity analysis is performed to relate market prices with revenues. It is found that the optimal strategy generates 6% more revenue compared to the revenue from participating in the energy market only. Also, the reduced energy prices in future scenarios increase the relative importance of market participation in ancillary services.

Bhatti, Bilal Ahmad↗

DOE OSTI2022

Stochastic Strategic Participation of Active Distribution Networks With High-Penetration DERs in Wholesale Electricity Markets

With the increasing penetration of distributed energy resources (DERs), traditional distribution networks as load-serving entities in wholesale electricity markets, now evolve towards active distribution networks (ADNs) which can proactively participate in wholesale markets by optimally controlling the DERs in their networks. A stochastic bilevel optimization model is proposed in this paper for the strategic participation of ADNs and DERs to provide energy and grid services in wholesale electricity markets. The bilevel optimization model can capture the interactions between the ADN and the wholesale energy and ancillary service markets, considering the uncertainties of DERs in the ADN. In the upper-level model, the ADN makes optimal decisions on energy and reserve bidding considering the availability, uncertainties, and flexibility of DERs. The joint energy and reserve market-clearing of the independent system operator (ISO) is modeled as the lower-level problem. Using strong duality theory and Karush-Kuhn Tucker (KKT) conditions, the proposed bilevel optimization problem is reformulated as mathematical programming with equilibrium constraints (MPEC) problem and further converted into a computationally-solvable mixed-integer second-order-cone programming (MISOCP) model. The simulation results demonstrate the effectiveness of the model and the interactions between an ADN and wholesale electricity markets.

active distribution network↗

DOE OSTI2023

Optimization of Energy Storage System Economics and Controls by Incorporating Battery Degradation Costs in REopt

The use of stationary electrochemical energy storage systems utilizing lithium-ion batteries has increased rapidly as the production scale and price for lithium-ion batteries has decreased. These energy storage systems are crucial for maintaining grid resiliency, especially for grids operating with high penetration of renewable energy generation assets or for with a variety of distributed energy generation and storage systems. One challenging factor for the development of battery energy storage systems is estimating the proper sizing, in terms of both power and energy, that minimizes total costs over the lifetime of the systems; this calculation is difficult in simple cases, where a battery is costed independently, but is extremely challenging when building loads and electrical generation by photovoltaic resources are also considered. REopt is a techoeconomic optimization tool developed by NREL to address these challenges. Previously, battery degradation has been priced by simply assuming a 10-year replacement schedule for battery systems. However, this does not account for varying degradation trends observed across real-world batteries, or allow for batteries to be operated in a degradation-aware manner that optimizes battery dispatch based on operating costs. This work incorporates a battery life model into REopt. This battery life model is simple, so that it may be solvable within the constrains of a mixed-integer linear optimization problem, but is fit to accelerated aging data recorded in the lab. To achieve the best possible accuracy for lifetime estimates given these constraints, parameters for the battery life model in REopt are estimated by fitting 20-year simulations of battery life after identifying state-space battery degradation model from accelerated aging data. Comparisons of battery life predicted in REopt and from the state-space battery degradation model to ensure validity of lifetime estimates made by REopt. Battery life and cost is optimized by controlling three decision to minimize system life cost: battery sizing, daily state-of-charge, and daily energy-throughput. The cost of battery degradation as a function of these control variables is then estimated assuming two possible maintenance strategies: replacement, where the entire battery system is replaced if cell reach an end-of-life capacity threshold; and augmentation, which establishes a fund to pay for continual purchase of new batteries to maintain the initial energy capacity of the system. These two strategies offer conservative (for replacement) and optimistic (for augmentation) bounds for total system cost. The degradation cost incurred by these strategies is then used to control battery dispatch decisions, operating the battery in a degradation-aware manner that maximizes battery lifetime while also providing energy when favorable. Because the mixed-integer linear program has perfect foresight of future energy needs, batteries with degradation costs are always operated using 'just-in-time' charging, which is unrealistic, as no energy is left in the storage system to perform other energy services or to serve as emergency back-up power. To combat this, an inequality constraint on the average annual state-of-charge is imposed, and the sensitivity of system cost to average stored energy, e.g., the cost of system resiliency, can be quantified. Analysis of results has several conclusions, for instance, oversizing of battery storage systems is not a cost burden when battery storage is an optimal solution, as any additional battery capacity can simply be utilized to avoid costs of purchasing energy from a utility.

battery↗

DOE OSTI2020

Real-Time Coupling of Geographically Distributed Research Infrastructures: Taxonomy, Overview, and Real-World Smart Grid Applications

Novel concepts enabling a resilient future power system and their subsequent experimental evaluation are experiencing a steadily growing challenge: large scale complexity and questionable scalability. The requirements on a research infrastructure (RI) to cope with the trends of such a dynamic system therefore grow in size, diversity and costs, making the feasibility of rigorous advancements questionable by a single RI. Analysis of large scale system complexity has been made possible by the real-time coupling of geographically separated RIs undertaking geographically distributed simulations (GDS), the concept of which brings the equipment, models and expertise of independent RIs, in combination, to optimally address the challenge. This article presents the outputs of IEEE PES Task Force on Interfacing Techniques for Simulation Tools towards standardization of GDS as a concept. First, the taxonomy for setups utilized for GDS is established followed by a comprehensive overview of the advancements in real-time couplings reported in literature. The overview encompasses fundamental technological design considerations for GDS. The article further presents four application oriented case studies (real-world implementations) where GDS setups have been utilized, demonstrating their practicality and potential in enabling the analysis of future complex power systems.

distributed laboratories↗

DOE OSTI2022

A Cartesian-diffusion Langevin method for hybrid kinetic-fluid Coulomb scattering in particle-in-cell plasma simulations

A novel, drag-diffusion Langevin method of hybrid, kinetic-fluid Coulomb scattering in plasmas is presented. Unlike previous methods, the frictional drag is always applied in the simulation frame of reference. The velocity-space diffusion is performed in the stationary-fluid frame of reference when anisotropic, and in the laboratory frame when isotropic. While the general method is mass-ratio independent, we focus on interactions of kinetic-ions and fluid-electrons to show first-order modifications to the electron velocity distribution function that are an important correction for the accurate calculation of electric resistivity. Inclusion of sub-cycling and a limit to the maximum collision frequency is shown to allow for arbitrarily large timesteps without numerical failure. Here the Langevin method is compared with a grid-based binary method and found to require a much less restrictive timestep in cases of ion–electron slowing and temperature equilibration; this finding differs from previous work and is dependent on the mass ratio.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Machine Learning Derived Dynamic Operating Reserve Requirements in High-Renewable Power Systems

Accurately forecasting wind and solar power output poses challenges for deeply decarbonized electricity systems. Grid operators must commit resources to provide reserves to ensure reliable operations in the face of forecast errors, a process which can increase fuel consumption and emissions. We apply neural network-based machine learning to expand the usefulness of median point forecast data by creating probabilistic distributions of short-term uncertainty in demand, wind, and solar forecasts that adapt to prevailing grid conditions. Machine learning derived estimates of forecast errors compare favorably to estimates based on incumbent methods. Reserves derived from machine learning are usually smaller than values derived using incumbent methods, which enables fuel savings during most hours. Machine learning reserves are generally larger than incumbent reserves during times of higher forecast error, potentially improving system reliability. Performance is tested using multi-stage production simulation modeling of the California Independent System Operator (CAISO) system. Machine learning reserves provide production cost and greenhouse gas (GHG) emission reductions of approximately 0.3% relative to historical 2019 requirements. Savings in the 2030 timeframe are highly dependent on battery storage capacity. At lower levels of battery capacity, savings of 0.4% from machine learning reserves are shown. Significant quantities of battery storage are expected to be added to meet California's resource adequacy needs and GHG reduction targets. Addition of these batteries saturate reserve needs and results in minimal within-hour balancing costs in 2030.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Signal Decomposition for Intrusion Detection in Reliability Assessment in Cyber Resilience (Summary Report)

The complexity of assuring cyber resilience for physical process interactions in connected systems such as energy grids increases dramatically as the coupling between processes becomes more direct and responsive. An example of this growing complexity is provided by Integrated Energy Systems (IES), in which various processes such as nuclear heat generation and commodity production are being directly coupled for increased responsiveness to highly variable signals such as market pricing or electricity demand. As such, the potential attack surface of the coupled processes is larger than the two processes independently. Securing these complex systems requires two-fold monitoring: cybersecure monitoring for potential malicious incursion, and physics monitoring for system tampering. Physics monitoring includes analyzing the behavior of the signals within the system for anomalous behavior. This analysis has been shown to be insufficient if approached by only data-driven machine learning and artificial intelligence (MLAI) techniques or only low-level model comparison. Previous efforts at Purdue University suggested combining high-fidelity models with MLAI algorithms as a basis for a software tool for detecting anomalies in physical processes. This work built on that suggestion, developing an advanced library for signal decomposition and analysis using both MLAI and high-fidelity physics algorithms for greatly improved anomaly detection, especially false data injection. This software can be used as part of a secure imbedded intelligence (SEI) system designed under Consequence-driven Cyber-informed Engineering (CCE) for complex coupled systems. This library established a foundation for online and posteriori analysis of digital signals for the purpose of detecting potential malicious tampering in digital signals representing physical processes. Demonstrations carried out throughout the development highlight the effective use of characterization algorithms to detect signal perturbations, particularly triangle attack-style perturbations, in three wide-ranging applications: seismic monitoring, nuclear thermal hydraulics system simulation, and custom manufacturing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Aircraft-based inversions quantify the importance of wetlands and livestock for Upper Midwest methane emissions

Abstract. We apply airborne measurements across three seasons (summer, winter and spring 2017–2018) in a multi-inversion framework to quantify methane emissions from the US Corn Belt and Upper Midwest, a key agricultural and wetland source region. Combing our seasonal results with prior fall values we find that wetlands are the largest regional methane source (32 %, 20 [16–23] Gg/d), while livestock (enteric/manure; 25 %, 15 [14–17] Gg/d) are the largest anthropogenic source. Natural gas/petroleum, waste/landfills, and coal mines collectively make up the remainder. Optimized fluxes improve model agreement with independent datasets within and beyond the study timeframe. Inversions reveal coherent and seasonally dependent spatial errors in the WetCHARTs ensemble mean wetland emissions, with an underestimate for the Prairie Pothole region but an overestimate for Great Lakes coastal wetlands. Wetland extent and emission temperature dependence have the largest influence on prediction accuracy; better representation of coupled soil temperature–hydrology effects is therefore needed. Our optimized regional livestock emissions agree well with the Gridded EPA estimates during spring (to within 7 %) but are ∼ 25 % higher during summer and winter. Spatial analysis further shows good top-down and bottom-up agreement for beef facilities (with mainly enteric emissions) but larger (∼ 30 %) seasonal discrepancies for dairies and hog farms (with > 40 % manure emissions). Findings thus support bottom-up enteric emission estimates but suggest errors for manure; we propose that the latter reflects inadequate treatment of management factors including field application. Overall, our results confirm the importance of intensive animal agriculture for regional methane emissions, implying substantial mitigation opportunities through improved management.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Improving Prediction of Surface Solar Irradiance Variability by Integrating Observed Cloud Characteristics and Machine Learning

A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.

Riihimaki, Laura↗

DOE OSTI2021

Tropical Cyclone Frequency

Abstract The frequency with which tropical cyclones (TCs) occur controls all other aspects of tropical cyclone risk since a storm that does not occur can do no harm. Yet this frequency is poorly understood. There is no accepted theory that explains the average number of TCs that occur each year on the Earth, nor how that number will change with global warming. Arguments based on global budgets of heat or moisture do not yet appear helpful, nor does a detailed understanding of the physical processes of TC genesis. Empirical indices that predict TC frequency as a function of large‐scale environmental variables can explain some of its relative variations in space and time, but not its absolute value. Global numerical models with horizontal grid spacings on the order of 25–50 km have allowed much improved simulations of TC activity, however. Many such models project a decrease in frequency with warming, but some project an increase. Idealized simulations, including those at higher resolutions, offer promise by allowing a systematic, deductive investigation of the roles of individual environmental factors. In addition to the larger‐scale environmental modulation of genesis likelihood, precursor disturbances, or “seeds”, may exert an independent influence on TC frequency.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

On decoupling the integrals of cosmological perturbation theory

ABSTRACT Perturbation theory (PT) is often used to model statistical observables capturing the translation and rotation-invariant information in cosmological density fields. PT produces higher order corrections by integration over linear statistics of the density fields weighted by kernels resulting from recursive solution of the fluid equations. These integrals quickly become high dimensional and naively require increasing computational resources the higher the order of the corrections. Here, we show how to decouple the integrands that often produce this issue, enabling PT corrections to be computed as a sum of products of independent 1D integrals. Our approach is related to a commonly used method for calculating multiloop Feynman integrals in quantum field theory, the Gegenbauer Polynomial x-Space Technique. We explicitly reduce the three terms entering the 2-loop power spectrum, formally requiring 9D integrations, to sums over successive 1D radial integrals. These 1D integrals can further be performed as convolutions, rendering the scaling of this method Nglog Ng with Ng the number of grid points used for each fast Fourier transform. This method should be highly enabling for upcoming large-scale structure redshift surveys where model predictions at an enormous number of cosmological parameter combinations will be required by Monte Carlo Markov Chain searches for the best-fitting values.

Slepian, Zachary↗

DOE OSTI2022

The Essential Role of Nuclear Energy in Achieving Economy-wide Net-Zero Solutions

Governments and private industry around the world have established aggressive goals to achieve net-zero emissions for the power, industrial, and transportation sectors by 2050. These aggressive goals demand immediate action if we are to be successful, and they require us to think more holistically about our clean energy options. Programs within the U.S. Department of Energy (DOE) are addressing these holistic solutions. Traditionally, electricity generation and management and meeting energy demands for industry and transportation are considered independently. As we seek to achieve net-zero, we need to reassess energy demands. When we consider overall energy use, only one-third is in the form of electricity. Additional energy demands are in the form of heat or steam for industrial processes, as well as transportation. These sectors are much harder to abate, and electrification may not be the best option. Reducing environmental emissions at an affordable cost, while maintaining grid reliability and resilience, will require us to use all of the clean energy resources that we have available. That means coordinating the use of nuclear, renewables, and fossil fuels with carbon capture to meet growing demands for electricity, industrial applications, and mobility. The DOE Office of Nuclear Energy (DOE-NE) program on Integrated Energy Systems (IES) is led by researchers at Idaho National Laboratory (INL) and work is conducted in partnership with an array of other DOE laboratories, industry, and academia. The primary focus of IES research is to assess the technical and economic potential of IES to enhance the flexibility and utilization of nuclear reactors working alongside renewable generators to meet an array of energy demands—thereby maximizing the utilization of clean energy resources across all energy sectors. Various energy applications and product streams beyond electricity are being evaluated, ranging from generation of potable water to production of hydrogen, fertilizers, synthetic fuels, and chemicals. The DOE-NE program additionally partners with the Hydrogen and Fuel Cell Technologies Office under the DOE Office of Energy Efficiency and Renewable Energy to jointly fund the development of analysis tools, technologies, and nuclear-integrated hydrogen demonstration projects. This presentation highlights the wide array of R&D being conducted across multiple DOE-funded programs to develop and deploy nuclear-based IES that will be key to achieving our net-zero goals. By working with key collaborators in the nuclear industry, analytical studies are now becoming a reality in demonstration projects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Advancing Nuclear Energy to Support Economy-wide Net-zero Solutions: Challenges and Opportunities

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Bishop Paiute's Residential Solar Program Phase III

The project consisted of the design, installation, inspection, interconnection and monitoring of 40 grid-tied solar electric systems, totaling up to 108 kW rated capacity, on qualified existing low-income single-family homes located within the Bishop Paiute Reservation. The systems are to provide at least 30-75% savings in displaced electricity totaling 175,000 kWh/year. After the DOE grant contract was signed, the Tribal Employment Rights Ordinance (TERO) board voted to reduce the tribal tax to work on the reservation for these low-income projects from 4% to 1%. This generous reduction was applied back into the community solar project and two additional tribal homes were added for a new total of 40 grid-tied solar electric systems, totaling up to 113 kW rated capacity (two more than originally planned) projects. Additionally, each homeowner was educated on energy efficiency and how solar works and saves them money. The estimated 75% savings on monthly electric bills brings financial relief to tribal homeowners and makes a significant difference on the reservation; giving low-income families money to spend on other essential items, while reducing their carbon footprint in this remote tribal reservation. The GRID IE program and this tribal project had a training component for tribal members to get free hands-on job training on 8 of the 40 homes. Those tribal members that came out to train with GRID IE could then be eligible for the “train to hire” portion of the program, on the remaining 32 homes. Overall, the triple impact of the Bishop Paiute Tribe Residential Solar Program Phase III—affordable energy for low-income families, on-site clean energy production, and hands-on solar installation jobs for local workers—these all help build the Tribe’s energy, economic, environmental goals and offers local self-sufficiency while supporting energy independence to the neediest on the Reservation.

14 SOLAR ENERGY↗