This study explores whether repowering onshore wind farms will help the United States (US) meet a much larger share of its current electricity demand with wind. Repowering upgrades existing wind farms with new turbines, increasing wind-farm capacity and electricity generation, with less new land requirements than greenfield projects. We analyze the US Wind Turbine Database and project the potential of US wind repowering under different scenarios. Results show that repowering alone could more than double the current US onshore wind nameplate capacity, from 153 GW to ~314 GW, at existing farms. The resulting annual onshore wind electricity generation could increase from 453 TWh in 2024 to ~911 TWh for the same weather year after repowering the fleet. Repowering could allow onshore wind to meet 21%, instead of 10.5%, of 2024 US electricity demand. Repowering is a potential multi-billion-dollar industry in the United States.

Enhanced geothermal systems (EGSs) involve advanced drilling methods for extracting heat from deep in the Earth to generate clean, renewable electricity and/or district heat. Here, we model transitioning 150 countries to 100% wind-water-solar (WWS) systems across all energy sectors after near-full electrification of all sectors and using conventional geothermal and solar heat for the remaining energy, when EGS electricity is excluded versus included as a WWS technology. In low-, mid-, and high-cost-EGS cases, where EGS provides 10% of electricity supply as baseload, WWS-system private energy and social energy costs, respectively, are lower than, similar to, and higher than costs without EGS. Thus, including baseload electricity appears to have little impact on 100%-WWS-system costs. With EGS, net nameplate capacities, land needs, and jobs decrease. Less land benefits small countries the most. With or without EGS, 100% WWS reduces annual private-and social energy costs '60% and '90%, respectively, versus business-as-usual. Thus, EGS helps a worldwide energy transition.

Estimating building performance is important for determining energy demand and optimally sizing heating and cooling equipment. This is especially true for buildings connected to a microgrid in a remote community, disaster relief zone, or military forward operating base, where energy availability may be limited and high energy resilience is necessary. In this study, a standardized modular military structure has been deployed in an off-grid microgrid research station to collect temperature data within the building envelope with no loads operating. A Passive Heat Transfer Model (PHTM) has been developed with a gray-box modeling approach using both physical equations and data science techniques to predict the structure's indoor temperature response to ambient weather conditions using on-site weather data. The model is compared with the well-known EnergyPlus (EP) model using the same building envelope parameters for the six-month period from January 25-July 23, 2024. PHTM simulation results demonstrate higher accuracy (CVRMSE (coefficient of variation of the root mean squared error) 3.06%) compared with EP (CVRMSE = 10.20%) of indoor temperature predictions. The PHTM is the first model to predict performance of this type of military structure, and the developed methodology can be used to estimate energy demand of similar structures in other locations.

Air pollution, global warming, and energy insecurity are three major problems facing the world. This study first examines whether 149 countries can transition 100% of their business-as-usual (BAU) all-sector energy to electricity and heat obtained from 100% wind-water-solar (WWS) sources to solve these problems. WWS eliminates energy-related air pollution deaths and CO2-equivalent emissions while reducing end-use energy needs by ∼54.4%, annual energy costs by ∼59.6%, and annual social (energy plus health plus climate) costs by ∼91.8% among nations, giving energy- and social-cost payback times of 5.9 and 0.78 years, respectively. Conversely, "all-of-the-above" policies promoting carbon capture (CC) and/or synthetic (as opposed to natural) direct air carbon capture (SDACC) to reduce or offset CO2 emissions trigger, with full penetration of CC/SDACC across 149 countries, $60-80 trillion/y in social cost, or 9.1-12.1 times the WWS social cost and only 1.1-25.6% lower social cost than BAU. Even when all CO2 is stored, CC and SDACC increase air pollution, CO2-equivalent emissions (due to capture inefficiencies and not capturing non-CO2 greenhouse gases), energy needs, and equipment costs relative to WWS. Sensitivity tests reinforce this finding. Although full penetration is extreme, any CC/SDACC level increases social cost and emissions substantially versus WWS. Thus, policies promoting CC and SDACC should be abandoned.

The aim of this study is to minimize the cost of developing a renewable energy islanded microgrid that provides reliable electricity and thermal comfort for a small building over multiple decades. The study is carried out with a model that minimizes the total cost of energy system components. Four different system configurations considering solar photovoltaics, electric heat pumps for heating and cooling, and a subset of battery-electricity storage, hydrogen-fuel-cell-electricity storage, and thermal-energy storage with phase-change materials are modeled. The objective is to minimize total lifecycle costs (capacity and operational costs) while ensuring reliable electricity as well as heat and cold supply. Over five climate zones, four system configurations, and 25 weather years, the annual costs of 100 % renewable microgrids for residential-type loads and structures are at least 67 % lower than the same microgrid powered with diesel generators for 75 % of the cases. On average, using renewable energy instead of diesel reduces the annual cost of islanded microgrids by 72 %. Systems with higher technology diversity, such as batteries combined with hydrogen-fuel-cell-electricity storage, result in even lower average-cost solutions (75 % lower than diesel); however, they may increase the risk of loss of load events over the project lifetime if the systems are optimized for only one weather year. Despite the higher risk, the incremental cost to reduce one kWh loss of load over multiple weather years is estimated to be 89 % lower with both batteries and hydrogen than with batteries alone, highlighting the value of diverse technology portfolios in microgrid planning.

Like most northern settlements, Kluane Lake Research Station (KLRS) in Yukon Territory, Canada, is an islanded microgrid dependent on diesel generation and subject to high fuel costs. To reduce diesel costs, the station has a 48 kW solar photovoltaic (PV) array alongside a 27 kW/171 kWh lead-acid battery system to store solar energy for nighttime use, primarily during summer. However, substantial solar energy is often curtailed when the battery becomes full due to prior charging with diesel-generated electricity. The goal of this analysis is to determine how to best operate the diesel generator to maximize solar PV generation, and thus minimize diesel costs. On a monthly basis, solar PV plus batteries can meet 96% of load during June, but only 3% during December, and 67% year-round. This study also analyzes how demand-side management of new food and water infrastructure can aid this objective while providing a constant source of electricity, locally-grown food, and clean water. Findings demonstrate that optimizing the KLRS diesel generator, battery management, and solar energy conversion may reduce diesel generation by up to 100% during June, 31% during the field season (mid-April to early October), and approximately 31% year-round (due to limited solar PV generation during the winter), compared with past operational data.

Many Alaska communities rely on heating oil for heat and diesel fuel for electricity. For remote communities, fuel must be barged or flown in, leading to high costs. While renewable energy resources may be available, the variability of wind and solar energy limits the amount that can be used coincidentally without adequate storage. This study developed a decision-making method to evaluate beneficial matches between excess renewable generation and non-electric dispatchable loads, specifically heat loads such as space heating, water heating and treatment, and clothes drying in three partner communities. Hybrid Optimization Model for Multiple Electric Renewables (HOMER) Pro was used to model potential excess renewable generation based on current generation infrastructure, renewable resource data, and community load. The method then used these excess generation profiles to quantify how closely they align with modeled or actual heat loads, which have inherent thermal storage capacity. Of 236 possible combinations of solar and wind capacity investigated in the three communities, the best matches were seen between excess electricity from high-penetration wind generation and heat loads for clothes drying and space heating. The worst matches from this study were from low penetrations of solar (25% of peak load) with all heat loads.

Reliance on imported diesel fuel, with high transportation costs, has made power and water treatment expensive in remote diesel microgrids in the Arctic. Past attempts at implementing piped water in these areas have proven difficult due to the high cost of energy to pump, transport, and heat water with imported diesel fuel. A modular Water Reuse (WR) system has been developed to provide more affordable, distributed water service for an individual home lacking running water. However, these WR systems still consume substantial electricity and can burden a household with high energy costs, if powered by the community diesel microgrid. Here we expand a mixed-integer linear optimization model - Food-Energy-Water Microgrid Optimization with Renewable Energy (FEWMORE) - to treat the effects of operating WR systems as dispatchable loads connected to a microgrid. We apply the model to a western Alaska community without piped water to analyze demand response (DR) of WR systems with solar and wind energy. Such an analysis has not yet been articulated by current energy optimization, water treatment, and demand response models for modular water service in microgrids. Integrating a solar photovoltaics (PV) array to power a WR system, as opposed to operating solely off of diesel generation, results in a 3% decrease in total project costs (installing and maintaining solar PV, and electricity purchases from the diesel microgrid) over a 20-year lifetime. Optimally dispatching the water treatment processes results in more savings: a 13% decrease in total project costs and a 37% reduction in diesel use.

With the increasing effects of climate change and high costs of energy, many rural Alaska communities are working to implement local alternative energy solutions to improve energy security. Integrating renewable energy systems can reduce reliance on fossil fuels and subsequently improve food, energy, and water (FEW) security. In this study, wind energy modeling techniques using local airport meteorological data were convolved with community loads to determine the most cost-effective combinations of wind turbine technology and dispatchable loads for improving FEW security in a southwestern Alaska village. This approach is different from wind assessments that exclusively analyze wind resources. A 100 kW wind turbine was determined to be suitable for the community, resulting in a capacity factor of 16.7% and levelized cost of energy (LCOE) of $1.15/kWh, with diminishing returns for higher wind turbine capacity. The results from the dispatchability study indicated that dispatchable loads could handle the intermittency of the wind resource with up to 86% of their annual load met. More work is needed to understand the impact of integrating and scheduling dispatchable loads into the grid in practice. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Food, energy, and water (FEW) are essential for human health and economic development. FEW systems are inextricably interlinked, yet individualized and variable. Consequently, an accurate assessment must include all available and proposed FEW components and their interconnections and consider scale, location, and scope. Remote Alaska locations are examples of isolated communities with limited infrastructure, accessibility, and extreme climate conditions. The resulting challenges for FEW reliability and sustainability create opportunities to obtain practical insights that may apply to other remote communities facing similar challenges. By creating energy distribution models (EDMs), a methodology is proposed, and a tool is developed to measure the impacts of renewable energy (RE) on small FEW systems connected to the microgrids of several Alaska communities. Observing the community FEW systems through an energy lens, three indices are used to measure FEW security: Energy–Water (EW), Energy–Food (EF), and Sustainable Energy (SE). The results indicate the impacts of RE on FEW infrastructure systems are highly seasonal, primarily because of the natural intermittence and seasonality of renewable resources. Overall, there is a large potential for RE integration to increase FEW security as well as a need for additional analysis and methods to further improve the resiliency of FEW systems in remote communities.

The food–energy–water (FEW) nexus describes interactions among domains that yield gains or trade-offs when analysed together rather than independently. In a project about renewable energy in rural Alaska communities, we applied this concept to examine the implications for sustainability and resilience. The FEW nexus provided a useful framework for identifying the cross-domain benefits of renewable energy, including gains in FEW security. However, other factors such as transportation and governance also play a major role in determining FEW security outcomes in rural Alaska. Here, we show the implications of our findings for theory and practice. The precise configurations of and relationships among FEW nexus components vary by place and time, and the range of factors involved further complicates the ability to develop a functional, systematic FEW model. Instead, we suggest how the FEW nexus may be applied conceptually to identify and understand cross-domain interactions that contribute to long-term sustainability and resilience. While the food–energy–water nexus has become a focal point for inter- and cross-disciplinary studies in recent years, this analysis of rural communities contextualizes how effective the nexus is for describing and studying interactions.

High transportation costs make energy and food expensive in remote communities worldwide, especially in high-latitude Arctic climates. Past attempts to grow food indoors in these remote areas have proven uneconomical due to the need for expensive imported diesel for heating and electricity. This study aims to determine whether solar photovoltaic (PV) electricity can be used affordably to power container farms integrated with a remote Arctic community microgrid. A mixed-integer linear optimization model (FEWMORE: Food–Energy–Water Microgrid Optimization with Renewable Energy) has been developed to minimize the capital and maintenance costs of installing solar photovoltaics (PV) plus electricity storage and the operational costs of purchasing electricity from the community microgrid to power a container farm. FEWMORE expands upon previous models by simulating demand-side management of container farm loads. Its results are compared with those of another model (HOMER) for a test case. FEWMORE determined that 17 kW of solar PV was optimal to power the farm loads, resulting in a total annual cost decline of ~14% compared with a container farm currently operating in the Yukon. Managing specific loads appropriately can reduce total costs by ~18%. Thus, even in an Arctic climate, where the solar PV system supplies only ~7% of total load during the winter and ~25% of the load during the entire year, investing in solar PV reduces costs.