No abstract

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.

This paper presents the first systematic comparison between south-facing monofacial and bifacial photovoltaic (PV) modules, as well as between south-facing bifacial and vertical east-west facing bifacial PV modules in Alaska. The state’s solar industry, driven by the high price of energy and dropping equipment costs, is quickly growing. The challenges posed by extreme sun angles in Alaska’s northern regions also present opportunities for unique system designs. Annual bifacial gains of 21% were observed between side by side south-facing monofacial and bifacial modules. Vertical east-west bifacial modules had virtually the same annual production as south-facing latitude tilt bifacial modules, but with different energy production profiles.

The food-energy-water (FEW) nexus describes interactions among domains that yield gains or tradeoffs when analyzed 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.

Accurate soil heat transfer models are needed to predict and adapt to a warming arctic. A numerical model to accurately predict temperatures and thaw depths in soils, both with depth and with horizontal distance from features such as cliffs, was developed in Matlab using the finite element method. The model was validated against analytical solutions to simple versions of the problem and experimental temperature data from borehole thermistor strings on the north shore of Alaska. The current model is most useful for short term (on the order of days) predictions of thaw depth and near surface temperatures in homogeneous soils with existing data to allow the calibration of soil thermal parameters. These are exactly the time scales and capabilities that would integrate well with erosional models to predict the erosion during storm events and summer thaw conditions. Comparisons with analytical solutions show the model to be fairly accurate in predictions of temperatures thaw-depth and temperatures, within about 0.25 °C and 0.02 m respectively, for reasonable arctic soil parameters. Differences between predicted temperatures and thaw-depth against borehole data from Barter Island, Alaska are within about 1 °C and 0.5 m respectively. Comparison to commercial software, which does not directly track and move the phase change boundary, shows that this moving-mesh model has much better agreement. The model developed in this work is flexible and can be modified to model a wide variety of problems, but is efficiently set up to take a surface and thaw-boundary profile (not necessarily horizontal) and use soil parameters and surface boundary conditions appropriate to Arctic regions. It has been verified to appropriately model cliffs, which are particularly vulnerable to erosion.