Spectroscopic Study of Catalytic Nickel Nitride Structures for Plasma-Assisted Ammonia Synthesis

Research Interests: Model Predictive Control, Energy Management, Building-to-Grid Integration, HVAC Systems, Control Systems

In 2020, residential and commercial buildings were responsible for about 40% of total energy consumption in the United States, with space and water heating attributing to nearly 87% of building-generated greenhouse gas (GHG) emissions. To mitigate GHG emissions from buildings and make significant progress towards meeting 2030 and 2050 GHG emission reduction targets, electrification of space and water heating, coupled with load shifting strategies, is crucial [1]. Load shifting is necessary to utilize variable renewable electricity and alleviate stress on the grid. Electric heat pump water heaters (HPWHs) are more energy efficient than electric resistance water heaters. HPWHs also possess load shifting capabilities through their storage tank, storing water heated with low-cost, emissions-free renewables for later use [2]. However, HPWHs are typically controlled by setpoint-tracking rule-based control (RBC) strategies [3] that do not provide any flexibility as to when the HPWH operates [4].

Economic model predictive control (MPC) is an advanced control strategy that integrates dynamic economic optimization and predictive control [5]. MPC is ideal for providing automated HPWH load flexibility as it can account for grid/utility signals and predictions of tank temperature evolution and hot water draw events to determine how to optimally operate the HPWH. Ref. [6] develops an MPC to minimize energy costs while ensuring user comfort by utilizing an electricity price signal. However, electricity price signals, like TOU rates, may not be reflective of which power plants are being utilized to meet electricity demand. Studies have also explored using MPC to promote renewable energy consumption. Starke et. al ([7], [8]) developed an MPC to optimize the heating, ventilation, and air conditioning (HVAC) and HPWH loads for individual households within a neighborhood equipped with a microgrid. The microgrid consists of large-solar, energy storage, and a natural gas generator. The MPC responds to the price signals computed by a microgrid controller to achieve desired HVAC and HPWH load profiles that support grid needs, such as utilizing renewable energy. Wanjiru et. al [9] designed an MPC for a residential household that incorporates on-site solar and wind energy, a HPWH and an instantaneous shower for water heating. Grid energy use is penalized to promote consumption of on-site renewables. In our previous work, we adopted a broader approach to maximize the utilization of clean energy sources by incorporating marginal grid GHG emissions rate signal, in addition to electricity price signal, into a multi-objective MPC cost function [10]. This enables the MPC to shift HPWH loads to times when electricity is cheaper and cleaner.

This work aims to highlight how the proposed multi-objective MPC can be optimally tuned to minimize GHG emissions associated with HPWH operation, with minimal tradeoff on electricity cost and comfort. This tuning strategy provides insight on how to select the cost function weights that achieve the desired MPC performance with minimal trial and error. The MPC uses a thermal model of the tank to predict future tank temperature evolution, with laboratory data used to validate the model parameters. The effect of model mismatch is also discussed, addressing the potential overheating of the tank under MPC. The closed-loop behavior of a simulated HPWH under the proposed multi-objective MPC is compared against a typical RBC strategy for HPWHs and a cost-only MPC.

References

[1] S. Memory, T. Rooney, and J. Yin, “Electrification of Water and Space Heating in Buildings”, A.O. Smith Corporation, Tech. Rep., Sept. 2021.

[2] P. Delforge. (2020) Heat pump water heaters as clean-energy batteries. Natural Resources Defense Council. [Online]. Available: https://www.nrdc.org/experts/pierre-delforge/heat-pump-water-heaters- clean-energy-batteries

[3] P. Lissa, M. Schukat, M. Keane, and E. Barrett, “Transfer learning applied to DRL-Based heat pump control to leverage microgrid energy efficiency”, Smart Energy, vol. 3, pp.100044, 2021.

[4] T. Pean, J. Salmon, and R. Costa-Castello, “Review of control strategies for improving the energy flexibility provided by heat pump systems in buildings,” Journal of Process Control, vol. 74, pp. 35–49, 2019.

[5] M. Ellis, H. Durand, and D. Christofides, “A tutorial review of economic model predictive control methods,” Journal of Process Control, vol. 24, pp. 1156–1178, 2014.

[6] X. Jin, J. Maguire, and D. Christensen, “Model predictive control of heat pump water heaters for energy efficiency,” in Proceedings of the 18th ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, Aug. 2014.

[7] M. Starke, J. Munk, H. Zandi, T. Kuruganti, H. Buckberry, and J. Hall, “Agent-based system for transactive control of smart residential neighborhoods,” in Proceedings of the 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, 2019, pp. 1–5.

[8] M. Starke, J. M. ad H. Zandi, T. Kuruganti, H. Buckberry, J. Hall, and J. Leverette, “Real-time MPC for residential building water heater systems to support the electric grid,” in Proceedings of the 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference, Washington, DC, 2020, pp. 1–5.

[9] E. Wanjiru, S. Sichilalu, and X. Xia, “Model predictive control of heat pump water heater-instantaneous shower powered by integrated renewable-grid energy systems,” Applied Energy, vol. 204, pp. 1333–1346, 2017.

[10] L. dela Rosa, C. Mande, H. Richardson, and M. Ellis, “Integrating greenhouse gas emissions into model predictive control of heat pump water heaters”, In Proceedings of the American Control Conference, San Diego, CA, May 2023, accepted.