Alaska Center for Energy and Power

Global catastrophes such as a supervolcanic eruption, asteroid impact, or nuclear winter could cause global agricultural collapse due to reduced sunlight reaching the Earth's surface. The human civilization's food production system is unprepared to respond to such events, but methane single cell protein (SCP) could be a key part of the solution. Current preparedness centers around food stockpiling, an excessively expensive solution given that an abrupt sunlight reduction scenario (ASRS) could hamper conventional agriculture for 5-10 years. Instead, it is more cost-effective to consider resilient food production techniques requiring little to no sunlight. This study analyses the potential of SCP produced from methane (natural gas and biogas) as a resilient food source for global catastrophic food shocks from ASRS. The following are quantified: global production potential of methane SCP, capital costs, material and energy requirements, ramp-up rates, and retail prices. In addition, potential bottlenecks for fast deployment are considered. While providing a more valuable, protein-rich product than its alternatives, the production capacity could be slower to ramp up. Based on 24/7 construction of facilities, 7%-11% of the global protein requirements could be fulfilled at the end of the first year. Despite significant remaining uncertainties, methane SCP shows significant potential to prevent global protein starvation during an ASRS at an affordable price-US$3-5/kg dry.

Abstract Large‐scale accrual of snow and ice on solar arrays in northern latitudes can cause significant power generation losses during winter. Depending on environmental conditions, snow can encompass a wide range in physical characteristics from dry snow (modulus ≈100 kPa and density ≈0.1 g cm −3 ) to bulk ice (modulus ≈8 GPa and density ≈0.9 g cm −3 ). This variation in snow morphology has made the development of a passive, broad‐spectrum, snow and ice‐shedding surface challenging. Here, the authors develop one of the first surfaces that simultaneously possesses both low‐interfacial strength ( τ˄ ice < 50 kPa) and toughness (Γ ice < 0.5 J m −2 ) with ice. These surfaces, fabricated via the addition of mobile polymer chains/oils to a thin polymeric coating, require extremely low detachment forces for ice, enabling its passive shedding at virtually any accretion length scale. Preliminary evidence that the new surfaces can shed different forms of snow and ice from field‐deployed solar arrays, over a range of subzero temperatures for several weeks, leading to significant increases in power generation is provided. The optically transparent surfaces are easily scalable and can be widely deployed by the solar industry in areas that see persistent snow. Other applications include automotive windshields, LIDAR covers for autonomous vehicles, and cold climate optical sensors.

Buildings are responsible for a large portion of global greenhouse gas emissions. While energy efficiency features can significantly reduce the greenhouse gas emissions during a building’s operational stage, extra materials and processes associated with these features typically involve higher greenhouse gas emissions during the construction phase. In order to study this relationship, a case study of a highly energy-efficient house in rural Alaska was performed. For the purposes of this case study, a theoretical counterpart home was designed that has the same interior space, but insulation values close to the code minimum requirements. Using computer simulations, a Life Cycle Assessment (LCA) was performed for the case study home as well as its conventional counterpart. The extra greenhouse gas emissions associated with the construction of the case study home were compared to the annual savings in greenhouse gas emissions achieved thanks to the energy efficiency features, and carbon payback was calculated. The carbon payback was calculated to be just over three years, which is only a small fraction of the life of the building. The results of this study show that despite higher greenhouse gas emissions during the construction phase, highly energy-efficient homes can play an important role in addressing climate change.

A sun-blocking global catastrophic risk (GCR) such as a nuclear winter could completely collapse the agricultural system. Producing alternative foods through methods requiring little to no sunlight has been identified as a cost-effective response to these types of GCRs. This preliminary techno-economic assessment evaluates the potential of acetic acid (AA) derived from carbon dioxide (CO2) via microbial electrosynthesis (MES) as an alternative food source for GCRs. Production and retail costs are estimated using net present value analyses for catastrophe and non-catastrophe scenarios. Based on nonstop (24/7) facility construction, the speed of food production ramp-up is estimated from capital expenditures using a reference class forecasting correlation. Potential production bottlenecks are assessed via a global resource requirement analysis. In regular conditions, the production cost of AA produced via MES is estimated at $1.83–$5.20/kg (dry). MES production ramp-up is expected to fulfill less than 1% of global human caloric requirements by the end of the first year after the catastrophe. The retail cost of AA produced via MES in catastrophe conditions is estimated at $6–$15/kg (dry). Potential bottlenecks to ramp-up include high electricity use and platinum dependency, which could be palliated via alternative processes based on gasification or bioelectrodes. AA from MES is not currently recommended as an alternative food for GCRs, because it is significantly more expensive and resource intensive than alternatives. Future research may change this, and could perhaps even enable MES as a sustainable food production method outside of catastrophes, given its potential for CO2 utilization.

The worsening wildfires due to intensified climate variability increases the risk of both unplanned power outages as well as planned power line de-energizations. It is because wildfires cause thermal stress on overhead conductors, which harms the mechanical properties of overhead distribution lines. This article proposes a proactive strategy for improving the operational efficiency and decision-making capabilities of power distribution networks under progressive wildfire conditions. Dynamic heat balance equations are used to characterize the effect of wildfire on the overhead line conductors. The optimal dynamic reconfiguration of the distribution system and the operation of backup generators are considered as tools to minimize the curtailed loads while maintaining the maximum flow of current through the lines within the thermal rating of the line conductors. A mixed-integer conic programming model is adopted to minimize the operation and load curtailment costs. A higher value of lost load is applied to enhance the continuity of the electricity supply to critical loads. The proposed framework is tested under various environmental conditions and wildfire paths using both a modified 33-node network and the practical 83-node Taiwan Power Company's distribution grid. Results show that the proposed approach enhances proactive decision-making for power distribution system operations and increases the resilience of critical loads to wildfire threats.

Producing sugar from lignocellulosic biomass is a promising resilient food solution to counter the near-total global failure of food production due to the agricultural collapse that would likely follow an abrupt sunlight reduction catastrophe such as a nuclear winter, a supervolcanic eruption, or a large asteroid or comet impact.This study examines how quickly edible sugar production could be ramped up globally by repurposing pulp and paper mills, sugarcane biorefineries, corn biorefineries, and breweries for lignocellulosic sugar production. A sub-unit component comparison to the NREL 2017 Biochemical Sugar Model indicates that 61%, 62%, 85% and 38% of ISBL unit components are present, respectively. Fast construction methods were studied to analyze how this and other industrial foods could be rapidly leveraged in a catastrophe.Results suggest that the world’s current sugar demand could quickly be fulfilled by repurposing pulp and paper mills for lignocellulosic sugar production, given 5 months of production ramp-up and 24/7 construction. This method could reduce construction time to an estimated 32% of the original at an increased labor cost of 1.47 times, resulting in sugar production beginning 5 months after the catastrophe at a retail cost of $0.82 USD/kg. This could not only contribute a significant share of the food requirement after the catastrophe (∼28% within the first year), but also be key to preventing global starvation between the time at which global food storages run dry and other resilient food solutions can scale up significantly.This study aims to serve as the basis for more comprehensive scenario analyses. More research is needed to characterize material and labor constraints to fast response in more depth; repurposing and fast construction pilot studies and food safety studies are recommended.

Energy costs are large and increasing in rural Alaska communities, so communities are turning to renewable energy. While, many of these communities have a mixed subsistence-cash economy, the relationship between renewable energy and subsistence has not been studied. Tanana, Alaska has a biomass program and we conducted interviews with 61 households in 2017 to understand how residents perceive the program and its association with subsistence activities. We analyzed Alaska Department of Fish & Game subsistence surveys from 89 communities to estimate differences in subsistence harvest between households that harvest wood and those that do not. Interviews indicated that people who harvest wood for the biomass program were six times more likely to engage in subsistence. Subsistence harvests were nearly double (184 kg/per capita) in households that harvested wood for personal use versus those that did not (101 kg/per capita). Equipment used for both activities was similar, and 57% respondents combined wood harvesting with other activities (e.g. subsistence, travel, etc.). Higher household incomes and employment were positively associated with subsistence participation (p < 0.001) while only household incomes was positively associated with wood harvest through the biomass program (p < 0.001). Overall, the program was perceived as having a positive effect (69%) for the community because it has created jobs (36%), saved people money (23%), promoted sharing (16%), and reduced fuel use by the community (15%). Our research shows that biomass programs have the potential to complement subsistence activities and enhance the sustainability of communities in rural Alaska that are faced with high energy costs.

Before rotating, fossil fuel-based, synchronous generators (SGs) are phased out, in line with renewable generation goals, grid-forming (GFM) inverters are expected to parallel SGs. Primary droop control allows GFM inverters to share power without communication; however, it is necessary to dispatch GFM inverters and/or SGs with the desired output power for better energy management (e.g., one GFM inverter needs to charge the battery due to a low state of charge). Therefore, this paper develops an analytic approach to dispatching GFM inverters and SGs with the desired output power by shifting the droop intercept up/down while maintaining the same frequency operating point for improved transient stability. This concept is demonstrated through a pure hardware setup with two off-the-shelf inverters and one diesel generator under an islanded microgrid, and we provide insight on the real-world implementation of the proposed concept.

The potential benefits from energy storage deployment are highly location dependent. In market areas, remuneration is largely based on market products and prices. In a vertically integrated utility, the benefits are often monetized through cost savings. This paper quantifies the potential benefits of using an energy storage system to provide spinning reserve within the Cordova Electric Cooperative (CEC) grid. The CEC is a small rural electrical cooperative in Cordova, Alaska. Energy storage is being considered to provide spinning reserve. The cost saving is realized through reduced fossil fuel consumption and run time on the diesel generators. In this paper, the cost savings are used to determine the benefit-to-cost ratio for various energy storage configurations. Additional potential benefits of energy storage for the CEC are also presented.

As solar photovoltaic (PV) power generation continues to grow in popularity, the variability in solar irradiance caused by weather effects such as clouds poses an increasing challenge to maintaining grid stability. Characterizing the variability and trends present in historical PV production data is vital to the development of effective models for predicting rapid changes. This is particularly important at higher latitudes where seasonal changes in PV generation are more extreme. In this paper, we analyse data from a small, grid-scale PV array in Kotzebue, Alaska (66.8969° N, 162.5931° W), located above Arctic Circle. We also successfully validate the variability index (VI), a previously proposed metric which quantifies the volatility of solar PV data over a given time span using a synthetic cloudless (clear-sky) dataset as a reference. We also include an examination of the stationarity of the PV production data at various timescales as well as the efficacy of using clear-sky models as a reference for de-trending solar irradiance data via, showing that better results can be obtained from data closer to solar noon. To the best of our knowledge, this is the first use of VI to assess PV production data from above the Arctic Circle.

The increasing complexity of electric power systems driven by integration of renewable resources requires accurate and computationally efficient models for thorough analysis. Detailed Electromagnetic transient (EMT) models are considered to be very accurate and have the ability to capture the fast transients, but are computationally expensive specially when modeling large microgrid system due to the requirement of a high-resolution simulation step size. On the other hand root mean square (RMS) models under the assumption of quasi-steady state require fewer computational resources but cannot capture the electromagnetic transients. Dynamic phasors (DP) modeling technique provides a balance between accuracy and computational burden. The paper presents the DP model of a synchronous generator connected to a local load. This paper discusses the additional considerations required to interface machines modeled in the rotor reference frame with network elements modeled in the natural reference frame. The accuracy and computational benefits of the model are evaluated by comparing it with its EMT counterpart, and results are presented for balanced and unbalanced resistive and inductive loading conditions.

PM2.5 poses significant health risks and requires accurate monitoring. While the EPA’s high-cost Federal Reference Methods and Federal Equivalent Methods provide reliable data, they are often sparsely distributed, limiting community-scale assessments. Low-cost sensors like PurpleAir (PA) offer a promising alternative but require careful location-specific calibration and correction for environmental influences. Although several correction factors have been developed for use across regions and nationwide, these models often exhibit bias due to the predominance of data from temperate and warmer climates. This study was conducted to evaluate the performance of PA sensors in measuring PM2.5 in extremely cold environments, specifically North Pole, Alaska. Data from PA sensors and a Beta Attenuation Monitoring (BAM) reference sensor were used to develop correction models. The study found that temperature and relative humidity significantly influenced PA sensor accuracy in the region. By comparing various regression models, including Ordinary Least Squares, Lasso, Ridge, and Elastic Net, an optimal model was identified that substantially reduced errors and aligned PA sensor data with BAM measurements. This research highlights the importance of localized calibration models to enhance the reliability of low-cost air quality sensors in diverse environmental conditions, particularly in cold regions.

Vertical bifacial solar photovoltaic (PV) systems oriented directly east/west are garnering interest for their ability to enhance power generation at key times of the day while minimizing land use. The performance of these systems, compared to traditional fixed-tilt, equator-facing PV arrays, varies with local climate factors like the yearly fraction of diffuse sunlight and the duration of high ground reflectivity due to snow. Using the grid simulation software HOMER Pro and NREL's System Advisor Model (SAM), we optimized a PV array that combines these two orientations, along with a battery energy storage system (BESS), under different cost scenarios for the Arctic microgrid community of Kotzebue, Alaska. We also perform a sensitivity analysis assessing the impact of variation in annual diffuse sunlight and ground reflectivity. Results indicate that a system with at least 60% east-west orientation is generally optimal across various fuel costs, and a minimum of 34% east-west orientation remains optimal even when costs are 20% higher than those of south-facing fixed-tilt systems, especially with higher fuel prices. Furthermore, increased annual diffuse sunlight and extended periods of high reflectivity favor a higher proportion of east-west oriented PV.

The 2025 Alaska Railbelt Net Metering Update is a summary of the growth of the installed net metered distributed generation capacity on the Alaska Railbelt during 2024 as reported in utility filings submitted to the Regulatory Commission of Alaska (RCA). The Alaska Railbelt is a 700-mile-long transmission corridor built beside a vital rail line that spans from Fairbanks, through Anchorage and Homer.

The solar photovoltaic (PV) power generation industry has experienced substantial, ongoing growth over the past decades as a clean, cost-effective energy source. As electric grids use ever-larger proportions of solar PV, the technology’s inherent variability—primarily due to clouds—poses a challenge to maintaining grid stability. This is especially true for geographically dense, electrically isolated grids common in rural locations which must maintain substantial reserve generation capacity to account for sudden swings in PV power production. Short-term solar PV forecasting emerges as a solution, allowing excess generation to be kept offline until needed, reducing fuel costs and emissions. Recent studies have utilized networks of light sensors deployed around PV arrays which can preemptively detect incoming fluctuations in light levels caused by clouds. This research examines the potential of such a sensor network in providing short-term forecasting for a 575-kW solar PV array in the arctic community of Kotzebue, Alaska. Data from sensors deployed around the array were transformed into a forecast at a 2-minute time horizon using either long short-term memory (LSTM) or gated recurrent unit (GRU) as base models augmented with various combinations of 1-dimensional convolutional (Conv1D) and fully connected (Dense) model layers. These models were evaluated using a novel combination of statistical and event-based error metrics, including Precision, Recall, and Fβ. It was found that GRU-based models generally outperformed their LSTM-based counterparts along statistical error metrics while showing lower relative event-based forecasting ability. This research demonstrates the potential efficacy of a novel combination of LSTM/GRU-based deep learning models and a distributed sensor network when forecasting the power generation of an actual solar PV array. Performance across the eight evaluated model combinations was mostly comparable to similar methods in the literature and is expected to improve with additional training data.

Marine energy offers a reliable energy solution for island and coastal communities, which often lack traditional local generation, to support their transition to energy independence and reduce reliance on externally imported fuels. Successful deployment of new technologies in these isolated locations requires community acceptance and approval from the outset, as these communities typically lack the financial and technical resources to operate and maintain new systems. This report presents a community-centric microgrid planning framework for remote coastal and island communities. Community engagement is integrated as the first step in the planning process, incorporating community profiles and visions into energy development scenarios. A case study was conducted in St. George, Pribilof Islands, Alaska, which relies entirely on diesel yet has significant wind and wave energy potential. Community engagement revealed a unique history and current economic status, with an interest in adopting advanced energy technologies despite past failures. Various microgrid configurations were optimized, considering different technologies to meet current and future energy needs while balancing cost and energy resilience. Wave energy converters (WECs) were a key component, integrated with other energy sources using the Xendee optimization tool. The Marine Energy Microgrid Toolkit, developed as part of this work, uses commercial power system analysis tools to optimize and analyze microgrid scenarios. The developed framework and toolkit can be applied to island and coastal communities to enhance resilience and support microgrid deployments. Future enhancements will include incorporating new marine resources, developing dynamic models, and automating the integration of Xendee and PowerFactory simulations.

With the sharp increase of heating oil prices in Alaska this past half decade, and with no substantial tax base for many Alaskan school districts to levy from, many districts have no recourse to recoup lost resources for educational delivery without considering a cheaper fuel source for heating their schools. A half dozen districts in Interior and Southeast Alaska have successfully switched to burning wood, which has resulted in cutting fuel costs by at least 50%, and in some cases up toward 75%. What community 'capitals' or assets did these locations that made the switch, have? This interview-based research looks at the resulting benefits of these biomass efforts in light of the Community Capitals Framework, which looks at a utility/heat project from the point of view of community development, sustainability, and resiliency.

Islanded power systems are generally exposed to higher expenses and more grid stability challenges compared to<br> larger interconnected power grids. The interconnection of islanded power systems can lead to numerous advantages.<br> In this work, the techno-economic modeling of the interconnection of two remote communities is presented to ex-<br> plore the feasibility and the economic advantages of an electrical intertie, as well as the challenges from a technical<br> perspective. The unique case study explores two different intertie voltage levels for an AC and a DC intertie. Instal-<br> lation cost estimates were obtained for each intertie case as well as the necessary upgrade costs to keep the system<br> unconnected. These scenarios were simulated using HOMER Pro energy balance models of the system with and<br> without energy storage. The study considers the voltage drop and the power loss along the 25-mile intertie for the<br> power balance modeling process. It is found that with available cost estimates the MVDC intertie economically out-<br> performs the MVAC intertie as well as the current standalone configuration of the modeled islanded power systems.<br> <strong>The MVDC intertie scenario outperforms the standalone configuration even with the inclusion of the energy<br> storage required to increase the reliability of the intertied scenarios.</strong>

A recent expansion in wood energy use at schools in Alaska has resulted in more than a dozen wood energy systems in operation.However, few have been evaluated for fuel efficiency and pollution impacts, both of which can be examined via combustion gas analysis.In this research, we monitored the wood energy system at a public school during winter heating conditions.Wood energy parameters were sampled on three occasions during early, mid, and late winter in northern Alaska.Combustion gas was sampled for a range of parameters that indicated boiler performance, including gas emmissions of oxygen (O 2 ), carbon dioxide (CO 2 ), carbon monoxide (CO), excess air, combustion efficiency, and stack temperature, which were monitored over 6 days.We observed differences in combustion gas composition between seasons as well as the response of combustion efficiency to gas concentrations.Combustion efficiency most strongly correlated with excess air (R 2 = 0.693), but poorly correlated with stack temperature (R 2 = 0.005).The primary combustion gases (O 2 , CO 2 , and CO) were moderately correlated with combustion efficiency (with R 2 values of 0.40, 0.56, and 0.55, respectively).Seasonal differences were found between early, mid, and late winter, with generally less variation in combustion gas contents occurring during late winter.Mean combustion gas concentrations also varied with heating season.In all cases, mid-winter means were significantly different than early and late winter values.This research found that more efficient combustion of wood fuels should lead to cost savings, especially during early and late heating seasons.The findings should also be relevant to those of other wood-energy-using schools (in Alaska and elsewhere) that experience severe mid-winter conditions coupled with milder shoulder seasons.

This study examines the dynamic stability of a decarbonization scenario for the Railbelt, the biggest regional electric system in Alaska, projected for the year 2050. In this future grid, there is a large deployment of inverter-based resources (IBR), increased electrification, and retirement of a majority of the fossil generation fleet. A comparative analysis of grid-forming inverters (GFM), grid-following inverters (GFL), and synchronous condensers is conducted to ascertain their impact on grid strength and system stability using state-of-the-art positive-sequence power system simulation models. The results highlight the role GFM-based batteries play in preserving system stability in an IBR-dominated islanded electric grid.