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The indoor agriculture industry: a promising player in demand response services

Javier Penuela, Cecile Ben, Stepan Boldyrev, Laurent Gentzbittel, Henni Ouerdane

TL;DR

This study demonstrates that the rapidly growing indoor farming sector can participate in demand-response programs without compromising vegetative growth of leafy plants. Using a phytotron-embedded vertical farm and a greenhouse-mimicking setup, the authors show that both implicit and explicit DR can yield meaningful economic benefits for the industry and the grid, particularly in Russia, while highlighting the limited potential for carbon-footprint reductions under current energy mix conditions. The work combines detailed experimental design, light-control optimization, and techno-economic modeling to quantify scenarios where DR participation is advantageous. The findings advocate for integrating indoor farming into flexible-grid strategies and call for enhanced data sharing and decarbonization policies to maximize environmental benefits. Overall, DR-enabled indoor farming can enhance grid reliability and local food production, provided efficient energy practices and supportive policy frameworks are in place.

Abstract

Demand response (DR) programs currently cover about 2\% of the average annual global demand, which is far from contributing to the International Energy Agency's ``Net Zero by 2050'' roadmap's 20\% target. While aggregation of many small flexible loads such as individual households can help reaching this target, increasing the participation of industries that are major electricity consumers is certainly a way forward. The indoor agriculture sector currently experiences a significant growth to partake in the sustainable production of high-quality food world-wide. As energy-related costs, up to 40\% of the total expenses, may preclude full maturity of this industry, DR participation can result in a win-win situation. Indeed, the agriculture system must transform and become a sustainable source of food for an increasing number of people worldwide under the constraints of preservation of soils and water, carbon footprint, and energy efficiency. We considered the case of the Russian Federation where indoor farming is burgeoning and already represents a load of several thousand megawatts. To show the viability of the indoor farming industry participation in implicit and explicit DR programs, we built a physical model of a vertical farm inside a phytotron with complete control of environmental parameters including ambient temperature, relative humidity, CO$_2$ concentration, and photosynthetic photon flux density. This phytotron was used as a model greenhouse. We grew different varieties of leafy plants under simulated DR conditions and control conditions on the same setup. Our results show that the indoor farming dedicated to greens can participate in DR without adversely affecting plant production and that this presents an economic advantage.

The indoor agriculture industry: a promising player in demand response services

TL;DR

This study demonstrates that the rapidly growing indoor farming sector can participate in demand-response programs without compromising vegetative growth of leafy plants. Using a phytotron-embedded vertical farm and a greenhouse-mimicking setup, the authors show that both implicit and explicit DR can yield meaningful economic benefits for the industry and the grid, particularly in Russia, while highlighting the limited potential for carbon-footprint reductions under current energy mix conditions. The work combines detailed experimental design, light-control optimization, and techno-economic modeling to quantify scenarios where DR participation is advantageous. The findings advocate for integrating indoor farming into flexible-grid strategies and call for enhanced data sharing and decarbonization policies to maximize environmental benefits. Overall, DR-enabled indoor farming can enhance grid reliability and local food production, provided efficient energy practices and supportive policy frameworks are in place.

Abstract

Demand response (DR) programs currently cover about 2\% of the average annual global demand, which is far from contributing to the International Energy Agency's ``Net Zero by 2050'' roadmap's 20\% target. While aggregation of many small flexible loads such as individual households can help reaching this target, increasing the participation of industries that are major electricity consumers is certainly a way forward. The indoor agriculture sector currently experiences a significant growth to partake in the sustainable production of high-quality food world-wide. As energy-related costs, up to 40\% of the total expenses, may preclude full maturity of this industry, DR participation can result in a win-win situation. Indeed, the agriculture system must transform and become a sustainable source of food for an increasing number of people worldwide under the constraints of preservation of soils and water, carbon footprint, and energy efficiency. We considered the case of the Russian Federation where indoor farming is burgeoning and already represents a load of several thousand megawatts. To show the viability of the indoor farming industry participation in implicit and explicit DR programs, we built a physical model of a vertical farm inside a phytotron with complete control of environmental parameters including ambient temperature, relative humidity, CO concentration, and photosynthetic photon flux density. This phytotron was used as a model greenhouse. We grew different varieties of leafy plants under simulated DR conditions and control conditions on the same setup. Our results show that the indoor farming dedicated to greens can participate in DR without adversely affecting plant production and that this presents an economic advantage.
Paper Structure (33 sections, 10 equations, 10 figures, 7 tables)

This paper contains 33 sections, 10 equations, 10 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Materials and methods
  3. Setup main characteristics
  4. Vertical farm
  5. Phytotron
  6. Plant material and growth conditions
  7. Light control
  8. Sampling and measurements of plant physiological parameters
  9. Environmental parameters measurements
  10. Demand response simulation
  11. Statistical data analysis
  12. Results
  13. Data collection
  14. Statistical data analysis
  15. Evaluation of potential economic impact
  16. ...and 18 more sections

Figures (10)

  • Figure 1: Illustration of the different possible strategies that can be accomplished using DR.
  • Figure 2: Experimental setup. On the left side (a) the general view of the setup for EI and EII simulating a vertical farm; on the right side (b) the general view of the setup for EIII simulating an industrial greenhouse.
  • Figure 3: Illustration of the light control profile with minimized price for a selection of days from 06.07.2018 to 10.08.2018. A level of 100% light intensity is represented in yellow, 33.33% in green, and 0% in dark blue. Note that from 16:00 to 0:59 we have constant 100% light intensity.
  • Figure 4: Daily maximum, minimum and average electricity spot prices in 2018.
  • Figure 5: Comparison of growth curves in replicates EI and EII for DR (in blue) and control (red) condition groups. In the left panel, the measured fresh weight is normalized to 1 g and reported on a $\log_{10}$ scale; in the middle panel, the measured dry weight is normalized to 1 g and reported on $\log_{10}$ scale; and in the right panel, the number of true leaves is reported on a $\ln$ scale. The blue and red lines correspond respectively to the predicted values for control and demand-response from the linear model of equation \ref{['eq:AnovaEI_EII']}. No statistically significant differences were found due to condition, i.e. control or demand response (Table \ref{['tab:ANOVA']}) by the two-way ANOVA.
  • ...and 5 more figures