Search for: All records

Reducing greenhouse gas emissions is an international impetus to transition from vehicles with internal combustion engines (ICE) to electric vehicles (EV). While this transition is happening rapidly in some regions of the world that are mainly urbanized, other predominantly rural and less developed regions are slower to adopt this technology. Rural Alaska serves as an example with its not-road-connected communities, high cost of electricity, extreme environmental conditions, and isolated power grids often powered by diesel. This study used co-production and mixed methods to identify barriers and perceived benefits towards EV adoption and explore EV adoption rates across the Arctic. We conducted community workshops in Bethel, Galena, and Kotzebue, Alaska, and 25 interviews with businesses and local governments. The top five impediments to EV adoption are the inability to maintain vehicles locally, cold weather performance, higher purchase prices compared to ICE vehicles, and the cost of electricity. The successful adoption of EVs in isolated microgrid communities in the Arctic requires investments in appropriate financial incentives, especially for low-income households, expansion of renewable power generation, and climate and culture-relevant proof-of-concept vehicles. Residents acknowledged that EVs generally operate much cleaner than vehicles with ICE, can have lower fuel and maintenance costs, and cause less air and noise pollution. We propose a framework to develop policies to facilitate the adoption of EVs in rural areas. Policy implications for overcoming the challenges related to the transition to EVs in remote rural parts of the globe are discussed. 

The Arctic presents various challenges for a transition to electric vehicles compared to other regions of the world, including environmental conditions such as colder temperatures, differences in infrastructure, and cultural and economic factors. For this study, academic researchers partnered with three rural communities: Kotzebue, Galena, and Bethel, Alaska, USA. The study followed a co-production process that actively involved community partners to identify 21 typical vehicle use cases that were then empirically modeled to determine changes in fueling costs and greenhouse gas emissions related to a switch from an internal combustion engine to an electric vehicle. While most use cases showed decreases in fueling costs and climate emissions from a transition to electric versions of the vehicles, some common use profiles did not. Specifically, the short distances of typical commutes, when combined with low idling and engine block heater use, led to an increase in both fueling costs and emissions. Arctic communities likely need public investment and additional innovation in incentives, vehicle types, and power systems to fully and equitably participate in the transition to electrified transportation. More research on electric vehicle integration, user behavior, and energy demand at the community level is needed. 

Those who rely on electricity-dependent durable medical equipment (DME) often struggle to use their medical devices during prolonged power outages. With the increasing frequency of natural disasters and the growing use of electricity-dependent home medical devices, in addition to the continued integration of home-level renewable energy and mobile storage systems such as electric vehicles with vehicle-to-home (V2H) capabilities, home energy management systems (HEMS) must prioritize life-essential medical loads during extended power outages. This work integrates electricity-dependent DME into home energy management optimization. An oxygen concentrator and a hemodialysis machine are used as examples of medical devices with high power demands and distinct usage patterns. The HEMS model is formulated as a mixed-integer linear program (MILP) to minimize the total weighted load curtailment and thermal discomfort during extended outage scenarios. The results demonstrate that the HEMS is effective in sustaining DME operation. 

We used crowdsourced data in Alaska and the literature to develop a light-duty electric vehicle model to help policymakers, researchers, and consumers understand the trade-offs between internal combustion and electric vehicles. This model forms the engine of a calculator, which was developed in partnership with residents from three partner Alaskan communities. This calculator uses a typical hourly temperature profile for any chosen community in Alaska along with a relationship of energy use vs. temperature while driving or while parked to determine the annual cost and emissions for an electric vehicle. Other user inputs include miles driven per day, electricity rate, and whether the vehicle is parked in a heated space. A database of community power plant emissions per unit of electricity is used to determine emissions based on electricity consumption. This tool was updated according to community input on ease of use, relevance, and usefulness. It could easily be adapted to other regions of the world. The incorporation of climate, social, and economic inputs allow us to holistically capture real world situations and adjust as the physical and social environment changes. 

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. 

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.