0
Technical Brief

Cost-Optimal Coordination of Interacting HVAC Loads in Buildings

[+] Author and Article Information
Saeid Bashash

Mem. ASME
Department of Mechanical Engineering,
San Jose State University,
One Washington Square,
San Jose, CA 95192-0087
e-mail: saeid.bashash@sjsu.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received March 29, 2016; final manuscript received October 31, 2017; published online December 14, 2017. Assoc. Editor: Srinivasa M. Salapaka.

J. Dyn. Sys., Meas., Control 140(4), 044501 (Dec 14, 2017) (4 pages) Paper No: DS-16-1162; doi: 10.1115/1.4038390 History: Received March 29, 2016; Revised October 31, 2017

This paper investigates the optimal coordination of multiple interacting heating, ventilation, and air conditioning (HVAC) appliances in buildings such as air conditioners and refrigerators, in time-varying electricity pricing environments. Each load is modeled as a first-order differential equation with a binary (ON–OFF) switching control function. An energy cost minimization problem is then formulated with weighted penalties on the temperature deviation from the desired setpoint and the control input fluctuation. Using the dynamic programming (DP) method, the cost-optimal trajectories are computed, which indicate precooling of the loads in anticipation of higher electricity prices. Moreover, the loads are desynchronized in the presence of local renewable generation to maximize the on-site consumption of the local energy. The presented results provide useful insights for the development of predictive control policies for optimal energy management in buildings.

FIGURES IN THIS ARTICLE
<>
Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.

References

EIA, 2017, “ Monthly Energy Review,” United States Energy Information Administration, Washington, DC, accessed Nov. 14, 2017, http://www.eia.gov/totalenergy/data/monthly
EIA, 2009, “Residential Energy Consumption Survey,” United States Energy Information Administration, Washington, DC, accessed Nov. 14, 2017, https://www.eia.gov/energyexplained/index.cfm?page=us_energy_homes
Callaway, D. S. , 2009, “ Tapping the Energy Storage Potential in Electric Loads to Deliver Load Following and Regulation, With Application to Wind Energy,” Energy Convers. Manage., 50(5), pp. 1389–1400. [CrossRef]
Bashash, S. , and Fathy, H. K. , 2013, “ Modeling and Control of Aggregate Air Conditioning Loads for Robust Renewable Power Management,” IEEE Trans. Control Syst. Technol., 21(4), pp. 1318–1327. [CrossRef]
Short, J. , Infield, D. G. , and Freris, L. L. , 2007, “ Stabilization of Grid Frequency Through Dynamic Demand Control,” IEEE Trans. Power Syst., 22(3), pp. 1284–1293. [CrossRef]
Bashash, S. , and Fathy, H. K. , 2012, “ Power Grid Stabilization Through Setpoint Temperature Control of Frequency-Responsive Air Conditioning Loads,” ASME Paper No. DSCC2012-MOVIC2012-8792.
Braun, J. E. , 1990, “ Reducing Energy Costs and Peak Electrical Demand Through Optimal Control of Building Thermal Storage,” ASHRAE Trans., 96(2), pp. 876–888. https://drrc.lbl.gov/publications/reducing-energy-costs-and-peak
Ma, Y. , Anderson, G. , and Borrelli, F. , 2011, “ A Distributed Predictive Control Approach to Building Temperature Regulation,” American Control Conference (ACC), San Francisco, CA, June 29–July 1, pp. 2089–2094.
Missaoui, R. , Joumaa, H. , Ploix, S. , and Bacha, S. , 2014, “ Managing Energy Smart Homes According to Energy Prices: Analysis of a Building Energy Management System,” Energy Build., 71, pp. 155–167. [CrossRef]
Kashima, T. , and Boyd, S. P. , 2013, “ Cost Optimal Operation of Thermal Storage System With Real-Time Prices,” International Conference on Control, Automation, and Information Sciences (ICCAIS), Nha Trang, Vietnam, Nov. 25–28, pp. 233–237.
Li, J. , Platt, G. , and James, G. , 2014, “ Demand Management of Distributed Energy Loads Based on Genetic Algorithm Optimization,” ASME J. Dyn. Syst. Meas. Control, 136(2), p. 021014. [CrossRef]
Alcott, H. , 2011, “ Rethinking Real-Time Electricity Pricing,” Resour. Energy Econ., 33(4), pp. 820–842. [CrossRef]
Mohsenian-Rad, A. , and Leon-Garcia, A. , 2010, “ Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments,” IEEE Trans. Smart Grid, 1(2), pp. 120–133. [CrossRef]
Bashash, S. , 2017, “ Energy Cost Optimization of HVAC Loads Under Time-Varying Electricity Price Signals,” ASME Paper No. DSCC2016-9800.
DOE, 2015, “ Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) Programs, Topics FY2016, Phase I, Release 2,” United States Department of Energy, Washington, DC, accessed Nov. 14, 2017, https://science.energy.gov/~/media/sbir/pdf/TechnicalTopics/FY2016_Phase_1_Release_2_Topics_Combined.pdf
LCG, 2016, “ Industry Data, CAISO (California ISO),” LCG Consulting, Los Altos, CA, accessed Nov. 14, 2017, www.energyonline.com/Data

Figures

Grahic Jump Location
Fig. 1

Numerical simulation of a thermostatically controlled cooling system: (a) ambient and indoor temperatures and (b) consumed electrical power

Grahic Jump Location
Fig. 2

Energy cost optimization of a single AC system: (a) generated electricity price, (b) optimal control input, and (c) optimal indoor temperature trajectory

Grahic Jump Location
Fig. 3

Optimal AC temperature trajectories for (a) different values of w1 while w2 = 0 and (b) different values of w2 when w1 = 0.03 ($/°C2 h)

Grahic Jump Location
Fig. 4

Optimal trajectories of (a) AC temperature, (b) refrigerator temperature, and (c) aggregate power demand in the absence of power surplus

Grahic Jump Location
Fig. 5

Optimal trajectories of (a) AC temperature, (b) refrigerator temperature, and (c) aggregate power demand in the presence of local generation

Grahic Jump Location
Fig. 6

(a) AC, refrigerator, and ambient temperature, and (b) aggregate power demand, solar power, and scaled electricity price trajectories

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In