Remote communities in Alaska pay some of the highest prices for electricity in the United States, often in excess of $
]. These high costs are due to the communities’ isolation from Alaska’s electric grid and its road system. Communities must instead operate and maintain their own diesel generators to provide electricity via a self-contained electric grid, also known as an islanded microgrid. Diesel fuel must be imported over a long distance by plane or boat. Electricity is subsidized for residential use in many communities, but subsidies are not available for commercial operations including for agriculture [1
Alaskan remote communities are therefore more similar to energy-insecure areas worldwide than they are to the rest of the United States [2
]. Most people worldwide facing energy insecurity live in rural areas. For over 70% of them, microgrids provide the best solution to addressing energy insecurity due to the logistical challenges of extending a centralized grid [2
]. Microgrids can also help to reduce climate change damage, air pollution, and land degradation [4
Harnessing solar energy for electricity may provide an opportunity to reduce energy costs while increasing reliability and decreasing maintenance costs. A third of Alaska’s ~200 microgrids already have some renewable electricity generators installed [1
]. However, renewable electricity systems such as solar photovoltaic (PV) arrays require substantial upfront capital investment. Diesel generators have a lower capital cost but a continuous fuel cost throughout their lifetimes. Thus, in order to make solar PV economically advantageous over a diesel generator, the size of the PV system should be optimized and its output should be used as much as possible.
Solar PV electricity generation is also intermittent diurnally and seasonally, especially at high latitudes. In order to provide stable, or firm, electricity production from renewables, battery storage is often installed to balance times of both excess and low PV supply. However, batteries also require substantial capital investment, especially in remote areas. A potentially more cost-effective method of integrating renewable electricity involves scheduling electric loads to operate at higher levels, or even exclusively, during times of otherwise excess renewable electricity output [5
]. These methods, including demand flexibility and demand response, are collectively known as demand-side management (DSM) strategies [6
Analogous to energy, food is also expensive in remote Alaskan villages given the high cost of freight by barge or plane. Food prices may be 2.5 times higher in rural Alaska than in cities in the contiguous U.S. and sometimes up to 10 times higher [8
]. Indigenous communities have used subsistence practices to harvest meat and plants locally for millennia; however, there has been an increased reliance on market food imports in recent decades, partly due to declining harvests threatened by climate change [8
]. Given the lengthy and difficult supply chains, most imported foods are processed and shelf-stable, resulting in a lack of access to quality produce. Some communities have begun gardening in outdoor beds or greenhouses to complement subsistence diets and expensive imports. However, the growing season is short and even greenhouses cannot extend the season through a cold Arctic winter. This limits the ability to grow food with a sufficient number of calories, and most food grown, such as herbs and greens, is used only as nutrients or flavor supplements.
There is an increased interest in container farming in the Arctic in order to grow more food locally, more intensively, and year-round [9
]. Given constraints on hydroponic growing, production is generally limited to basil and lettuce. Test-case container farms have demonstrated yields equivalent to an acre of farmland in less than 1% of the area, or in 15% of the area of a typical greenhouse [10
]. To grow food more intensively, however, significant energy is used by container farms, leading to expensive operational costs. Therefore, while container farms may provide additional time over a year to grow food and more concentrated means of doing so, their effectiveness in affordably addressing food security is still an open question.
Using local renewable electricity generation may reduce the energy cost of container farms. However, there are challenges in properly balancing and integrating intermittent renewable electricity sources, such as solar PV, with container farming. The focus in this study is to optimize the nameplate capacities and operations of solar PV, batteries, and farm loads when all are connected to the community microgrid.
Controlled environmental agriculture (CEA), of which container farming is a subset, has been noted for its potential to flexibly adopt loads to integrate renewable energy, however, no study has specifically analyzed such a prospect [11
]. Studies have analyzed how to best retrofit a container farm with the optimal growing equipment, but have assumed fixed energy requirements of plants without the ability to incorporate DSM [12
]. For example, DSM of specific farm electric loads, such as lighting, ventilation, and dehumidification, can allow for optimal demand dispatch coincident with available solar energy. This can reduce cycling of a battery system, all while maintaining indoor plant growing constraints.
There are numerous computer models in the literature for optimizing renewable energy systems around a specific load within a microgrid [14
]. There is generally a tradeoff between computational speed and model complexity. Mixed-integer linear programming (MILP) methods have often been used, given their ability to solve problems at speed with necessary detail, as state-of-the-art in energy system modeling [16
Commercial tools have been built to bridge this gap of complexity and performance with speed and accessibility in designing renewable energy microgrids. For example, the default tool for analyzing microgrids is HOMER (Hybrid Optimization of Multiple Energy Resources), originally developed at the National Renewable Energy Laboratory (NREL). HOMER has been used widely for modeling remote islanded microgrid systems [17
]. Singh et al. [18
] compares the hybrid PV–wind–biomass–storage optimization in HOMER with an evolutionary algorithm approach, which has better performance. Shoeb and Shafiullah [19
] use HOMER to optimize operation of water pumps in a microgrid with solar PV. Tapia [20
] implemented a HOMER model to study hybrid renewable energy optimization of a container farm; however, the load profile was assumed fixed and no load flexibility was implemented in this analysis.
Still, other microgrid design tools have been developed by government labs and organizations. NREL has developed ReOpt (Renewable Energy Integration and Optimization) Lite as an open-source tool for optimizing solar and storage capacity for a specific load, and is based on the more complex ReOpt software; however, it is not possible to manipulate the load profile to analyze DSM of specific loads [21
]. NREL has also developed RPM (Resource Planning Model) to analyze the value of flexibility of battery storage and interruptible loads, but it is used at a large-scale for capacity expansion modeling [22
Numerous microgrid models have also been developed that use demand-side management. Neves et al. compare a self-built economic dispatch model that uses genetic algorithms with the capabilities of HOMER for modeling demand response [23
]. They emphasize that there is a significant challenge in using commercial tools for demand response modeling and have found that most models using DSM are self-built. An optimization framework must also balance long-term investment planning with short-term dispatch strategies [24
]. HOMER cannot schedule specific loads as part of an overall demand profile in hourly increments with custom dispatch strategies, and instead distributes flexible load operations evenly across a day [23
A model has not yet been developed that optimizes the capacity and dispatch of novel food–energy–water controllable loads for demand-side management as part of a container farm in islanded renewable microgrids. Most models also do not account for thermal energy requirements or optimization, which is more typically suited for energy models for buildings. Numerical models for operating container farms have been developed, though these do not include energy optimization strategies with renewable energy systems [25
]. Other tools have been designed around the food–energy–water nexus, but optimize the size of a greenhouse—not a container farm or individual loads—with a solar PV array [26
This paper’s contribution, then, is the development of a tool, FEWMORE: Food–Energy–Water Microgrid Optimization with Renewable Energy, to optimize the capacity and operations of a solar PV and battery system in order to power optimally-scheduled loads of a container farm within a remote islanded microgrid. Additionally, for modeling flexible loads, it is critical to have a tool tailored to specific constraints and system dynamics of which no energy optimization model is currently articulated for container farms. The model also balances electric and thermal (both sensible and latent) energy requirements.
The rest of the paper is structured around container farming description, modeling methodology, and energy simulation results and conclusions.
2. Container Farming
Container farms have been introduced in the Arctic due to their modular nature, ease of transport and siting, and ability to grow food year-round indoors in a well-insulated unit [27
]. Shipping containers are already a relatively common method of transporting goods to Alaskan villages. These containers are generally retrofitted with hydroponic growing systems, in which plants are grown in trays of circulated nutrient-rich water. In addition to being highly productive, this system does not use soil, which can be advantageous in Arctic areas without access to fertile soil [28
]. An example of a container farm is a CropBox unit, which can produce 5400 kg (12,000 lbs) per year of herbs and greens [10
]. Based on an analysis of a CropBox, the cost of growing this produce (in a typical location) is $
2.88/lb) including all energy, labor, and miscellaneous costs [12
provides the typical characteristics of a hydroponic growing container used for this analysis [29
]. All heating, ventilation, and air-conditioning (HVAC) are assumed to be electric. There are also accessory loads including those for carbon dioxide (CO2
) pumping, nutrient pumping, and sensors that have negligible electric requirements, and therefore are not included. The loads are assumed to be well optimized in size within an energy-efficient container.
Loads that are on at all times or baseloads include air circulation fans and water pumps for the hydroponic growing trays. These total approximately 1 kW of instantaneous power. They can, in theory, be shut off for a period if there is a lack of renewable power; however, in reality, this may lead to a decline in produce quality, so were not considered for the DSM. All equipment results in heat gain, which must be removed via cooling if the unit exceeds its interior temperature range.
Due to its opaque envelope, a container farm substitutes natural sunlight with LED grow lights at a relatively high energy demand. LED arrays provide lighting for 18 h per day at 4.5 kW (or 81 kWh per day). While LED lighting is relatively efficient, this still implies that nearly all of the 4.5 kW of electricity is dissipated as heat to the indoor environment, leading to a potentially high cooling load. Lights are assumed to operate continuously during the 18-h block, but can be scheduled to be on during any time of day.
Dehumidification is also a significant load. Plants transpire, resulting in an accumulation of humidity in the indoor space, or latent heating load. Plants also reduce the sensible heat load due to the evaporative cooling effect of evapotranspiration, thus offsetting some heat gains from lighting and other equipment. These effects occur primarily during lighting hours; it was assumed that the ratio of transpiration during the plants’ day to night was 3:1 [30
]. The container farm operates at an optimal 65–75% relative humidity (RH); we assumed the RH was fixed at 70% [29
]. If this level is exceeded and moisture is not removed, then fungal growth and disease may occur; alternatively, if too much moisture is removed, plants may desiccate and reduce yield. A dehumidifier, rated at 1.5 kW, was assumed to operate continuously during the baseline operation of a container farm [31
] (See Appendix C.3
. for additional energy modeling detail). However, this load may be dispatched accordingly, as long as the RH is managed using ventilation.
Ventilation can be used, especially in an Arctic climate to balance sensible and latent thermal loads by exhausting warm, moist indoor air and replacing it with cold, dry ambient air, respectively. The cost of carbon dioxide pumping must also be accounted for when considering ventilation, given that plants have higher yields at elevated CO2
levels and excessive ventilation would increase costs due to the high price of replacing compressed CO2
]. However, during winter, when there is significant potential to use ventilation for cooling due to the high temperature and humidity difference between the indoor and ambient air, no CO2
pumping is used given the reliance on outdoor air exchanges in the baseline operation. There is also a minimum number of continuous air exchanges in the container farm, assumed to be 0.5 air changes per hour, due to the combined effects of infiltration and ventilation [33
] (See Appendix C.2
Heating and cooling are provided via an electric fan-coil unit. This unit cycles throughout the day and requires ~2 kW. In the baseline case, it is assumed to operate for half the time in a given day (See Appendix A
for technology modeling parameters). Thermal energy flows include sensible and latent heat balances. During the vast majority of a year in the Arctic, the ambient temperature is colder outside than the balance point temperature set by the thermostat (20 °C). Sensible heat loss occurs via conduction through the container envelope, infiltration, and ventilation of outside air, and evaporative cooling from plants. Sensible heat gains result from mechanical equipment, and from exterior conduction and infiltration on warm summer days (See Appendix C.1
). Latent heat must also be balanced due to the plants’ emitted moisture as well as the latent component of infiltration and ventilation air flows (See Appendix C.3
The FEWMORE model is applied to a community in Interior Alaska that is interested in local food production and renewable energy, and does not currently have a container farm. This analysis uses collected data from an operating CropBox container farm in Whitehorse, Yukon. These data are used within the FEWMORE model to analyze a control case, named the Base Case, of optimizing solar PV and battery storage with container farm loads in the status quo. Then, the FEWMORE model is used to optimize solar and storage while allowing the prior container farm load profile to be flexible and optimally dispatched, named the Dispatchability Case. In this section, the collected data are analyzed, the FEWMORE model is summarized, and a model simulation procedure is presented.
3.1. Container Farm Load Data
A year of power consumption data was collected for the CropBox unit operating in Whitehorse, Yukon, Canada. The data contains the total energy consumption values at 5-min temporal resolution from November 2018 to October 2019, which have been processed to align with a calendar year and averaged to hourly resolution. Averaged diurnal load profiles for each season are shown in Figure 1
Electricity use in the container farm follows a relatively similar daily profile from season to season. Consumption is fairly constant over a day, peaking at 8 kW when all loads are operating, typically overnight and in the middle of the day. During the late afternoon and early evening, approximately 3 pm–9 pm, lighting is shut off. In the mid-morning hours, there is also a reduction in energy use due to less HVAC and dehumidification operation. The profile is slightly shifted later in the day during the fall and winter, presumably due to daylight savings time and later sunrises. In November 2018, during system initialization, there was slightly less energy use, with a peak power demand of 6.5 kW.
3.2. Model Summary
The FEWMORE model is a mixed-integer linear program built in the Julia/JuMP optimization language. The modeling framework has been chosen to reduce computational expense while preserving necessary modeling complexity. Optimization is performed hourly for energy operations and DSM, over an annual capacity planning horizon. The single-year output was extrapolated with perfect foresight to a 20-year project lifetime, subject to a discounted cash flow analysis. The FEWMORE model output was also ultimately compared with HOMER.
The objective of FEWMORE is to minimize the total net present cost of the project, namely capital and lifetime operational costs of powering a container farm (See Appendix B
for model form detail). The final objective cost is also divided by the total amount of greens grown over the lifetime to estimate an energy cost per unit of crop production. Assuming the container itself is already purchased, the total project cost includes purchasing, installing, and maintaining the solar PV, battery storage, and power conversion infrastructure to couple directly with the container, in addition to buying electricity from the community microgrid. Grid electricity is purchased at an unsubsidized rate of $
0.67/kWh, and prices are assumed to escalate annually at a real rate of 3% [34
]. Capital costs include the solar array, with a 20-year lifetime, and a battery and inverter with a 10-year lifetime, replaced at a real discount rate of 3%. Operational costs include a solar PV maintenance cost of $
50/kW/yr and a variable cost of battery system maintenance of 0.5 cents per kWh throughput.
The model inputs included the container farm electric load profile and solar yield profile. To determine a synthetic thermal load profile, the ambient temperature and humidity were also used for the specific Interior Alaska climate. The model output the optimal capacity of additional infrastructure (solar PV, battery storage, and inverter) as well as an hourly dispatch of all technologies (battery charging/discharging and DSM of select loads) (See Appendix A
for all model inputs and outputs). The dispatch of flexible loads including ventilation fans and dehumidifiers were constrained by physical requirements and optimized for lowest operating cost (See Appendix B
3.3. List of Model Simulation Cases
Two sets of simulations were performed, one with the collected electric load data named the Base Case, and the other with a flexible, synthetic electric load profile known as the Dispatchability Case, as shown in Table 2
. The synthetic load profile was derived from typical patterns in the collected electric load data in order to disaggregate specific loads and optimally dispatch them as part of DSM strategies. Lighting was fixed in the synthetic profile to operate coincident with the solar noon. The synthetic load profile also included a sensible heat and latent heat thermal load profile, derived from profiles of ambient temperature and ambient humidity, respectively, to model the optimal operation of the HVAC and dehumidifier units.
For each of the two sets of simulation cases, the Base Case and Dispatchability Case, the same three simulations were initially performed. The first simulation (named Baseline simulation) analyzed the container farm operations when all loads were powered by the community microgrid at the unsubsidized electricity rate. In the next simulation (Solar simulation), the amount of solar PV capacity to be added to the container farm was optimized. The solar array generated electricity to be used directly by the container farm, thus potentially reducing the amount of energy purchased from the microgrid, and any excess solar generation beyond the farm load was curtailed. In the Solar & Storage simulation, the amount of battery storage capacity and inverter power capacity were optimized including hourly charging and discharging strategies, in addition to solar PV optimization.
The next simulations (Lighting with Solar, and Lighting with Solar & Storage) were performed only for the Base Case. The lighting schedule was modified from the collected load data, which included lighting operating for an 18-h block somewhat parallel with solar daylight hours (approximately two hours misaligned from solar noon), to a schedule that was perfectly centered around peak solar PV output. The rest of the load profile was assumed to be the same. Then, solar PV capacity and solar PV plus battery storage capacities were optimized for the two respective cases.
The final simulations were performed only for the Dispatchability Case, using a synthetic load profile to analyze DSM strategies of specific loads. The first simulation, named Ventilation, optimized the operation of the ventilation system by determining the number of air changes per hour to perform, given constraints on replacing CO2 and thermal load requirements. The next simulation, named Dehumidification, optimized how the dehumidifier should operate, in addition to the ventilation system. The dehumidifier was optimized to turn on and off, given that it was assumed to run at a single maximum power setting when operating.
This paper provides an initial planning tool for Arctic communities interested in container farms to understand their overall energy use, as well as strategies to modify them appropriately for islanded renewable microgrids. A tool (FEWMORE) has been developed specifically to optimize container farm loads together with solar and battery nameplate capacities when all three are connected to the existing local microgrid.
Adding approximately 15–17 kW of solar PV nameplate capacity to power the container farm was optimal based on a collected electric load profile of an experimental farm. Battery energy storage did not provide substantial benefits, and is not justified. FEWMORE recommended adding 17.1 kW of solar PV, slightly higher than the optimal result of 15.6 kW from the HOMER model. Using a synthetically-derived load to allow for optimizing demand-side management of loads, namely ventilation fans and a dehumidifier, resulted in reductions in energy cost of up to 18% from the baseline. The subsequent cost of energy delivered per unit of crop production was reduced from $7.55 to $6.18/kg ($3.43–$2.81/lb). Analyzing other forms of energy storage, modeling at a higher temporal resolution, and studying additional integration with a community microgrid, such as modeling benefits in frequency regulation or export of excess energy, are left to future work.
This paper presents an initial modeling framework, and the control strategy and assumptions on container farm operations and specific loads have not been experimentally validated. The study builds on early stages of installing a CropBox container farm at the Kluane Lake Research Station (KLRS) in Yukon Territory, Canada. Future work will utilize the KLRS system year-round for expanded data analysis and experimental operation of the strategies recommended here. There are numerous other demand-side management techniques, such as shifting baseload and thermal storage, that can also be incorporated in the future.