Technical Brief

Cost-Optimal Coordination of Interacting HVAC Loads in Buildings

[+] Author and Article Information
Saeid Bashash

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.

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Fig. 2

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

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Fig. 1

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

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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)

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Fig. 4

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

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Fig. 5

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

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Fig. 6

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



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