Overview of HVAC Systems
Topic: HVAC
Truong Nghiem
ESE, University of Pennsylvania
nghiem@seas.upenn.edu
January 24, 2011
Outline
I Part I: HVAC Basics (Bin Yan)
I Part II: Conventional Control of HVAC Systems (Truong Nghiem)
T. Nghiem HVAC Overview 2
Part I
HVAC Basics
T. Nghiem HVAC Overview 3
Part II
Conventional Control of HVAC Systems
Overview
Local Control Strategies
Supervisory Control
T. Nghiem HVAC Overview 4
HVAC Local & Supervisory Control
VAV System:
Exhaust Air Return Air
ZONE
Solar
Circulated Air
Ambient
VAV Box Air
Cooling Coil Supply Air Reheat Coil
Filter Internal heat gain
Fresh Air
CHWS/R
Cooling NEIGHBOR
Tower Chiller ZONE
To Other Zones
I Local control loops: thermostats, supply air controllers, etc.
I Supervisory control: set-points and modes for local control loops.
T. Nghiem HVAC Overview 5
Local Control Loops
Zone temperature control loop (thermostat)
supply air heat gain
set-point reheat
Thermostat VAV Zone
damper
zone temperature
Sensor
Supply Air Temperature (SAT) control loop
Air flow CHW
SAT set-point
Controller Valve HVAC Coil
SAT
Sensor
T. Nghiem HVAC Overview 6
Local Control: On/Off Control
Simplest and common control is on/off control.
I Upper threshold tu , lower threshold tl , differential = tu tl .
I Switch off when t tu and on when t tl .
I Time lag may cause larger operating differential.
I Suitable for thermostats (slow dynamics) but not for supply-air fan
control.
System; Sensor;
Actual
differential
tu
Temperature
Design
differential
tl
time
T. Nghiem HVAC Overview 7
Local Control: P/PI/PID Control
Z t
d
u(t) = KP e(t) + KI e(t)dt + KD y (t), e(t) = SP y (t)
0 dt
I y (t): process value, u(t): control, SP: set-point, e(t): error.
I Popular linear feedback controllers.
I Often requires a driver to convert u(t) to actual action of the
actuator (e.g., to drive a valve motor).
Issues:
I Derivative part is sensitive to noise.
I Prevent derivative kick when SP changes: use y (t) for D.
I Prevent Integral wind-up: limit integral part, temporarily disable
integral part when e(t) is large, change SP gradually, etc.
I Mechanical wear leads to control degradation: reduce frequency of
control action u(t) using deadband.
T. Nghiem HVAC Overview 8
Local Control: P/PI/PID Control
Direct Digital Control (DDC): controllers are implemented in computers.
Digital to analog drivers are required.
Tuning PID controllers
I Tune KP , KI , KD for stability and performance (overshoot, rise time,
steady-state error, etc.).
I Manual tuning: trial-and-error experiments, requires expecience.
I Ziegler-Nichols method.
I Automated tuning with software:
I System identification to obtain plant/sensor/actuator model.
I Calculate PID parameters.
I Simulation.
T. Nghiem HVAC Overview 9
Local Control: More References
I Control books and textbooks.
I HVAC design & control books.
I G. J. Levermore, Building Energy Management Systems:
Applications to low-energy HVAC and natural ventilation control.
E & FN Spon, 2 ed., 2000.
I B. Li and A. G. Alleyne, Optimal on-off control of an air
conditioning and refrigeration system, in Proceedings of the 2010
American Control Conference, pp. 58925897, 2010.
T. Nghiem HVAC Overview 10
Supervisory Control
Building Management System (BMS) is a system that monitors,
controls and optimizes most aspects of a building, including HVAC,
lighting system, security system, etc.
Supervisory control is a part of BMS: computes set-points for local
control loops, sets modes, turns on/off devices, etc.
I Can be manual by human operators, automatic by computers, or
combination.
I Strategies: operation schedules, logical rules, optimization &
adaptation, intelligent control (e.g., neural networks, machine
learning).
I Purposes:
I Safety.
I Comfort.
I Efficiency: reduces energy usage or energy cost, etc.
T. Nghiem HVAC Overview 11
How Is Electric Bill Calculated?
I Billing period: often 1 month.
I Energy usage: amount of energy used during billing period (kWh).
I Energy demand: power demanded by the consumer (kW), averaged
for every time interval of half an hour (or 15 minutes, or an hour).
I Peak periods: the hours during which energy demand by consumers
is significantly higher than average, e.g., noon to 6 PM on weekdays.
I Peak demand: the maximum power demand by consumer during
peak periods.
I Bill = Basic charge + Usage charge + Demand charge.
I Usage charge = Total energy usage Usage price.
I Demand charge = Peak demand Demand price.
I Demand charge is significant (40% of bill).
I Why demand charge? Because peak demand is expensive and
difficult for utility companies.
T. Nghiem HVAC Overview 12
Demand Control
I Demand control (demand limiting): to control peak demand and
reduce demand cost.
I Strategies:
I Load shedding: turn off devices or reduce their powers (e.g., turn off
or dim lights).
I Load shifting: move part of load from peak periods to off-peak
periods.
I Pre-heat or pre-cool a building before peak periods.
I Store energy at night (low-price time) to use in daytime.
I More sophisticated strategies, e.g., look-ahead control with
prediction model (next lecture).
T. Nghiem HVAC Overview 13
Demand Control: Temperature Set-point Schedule
Consider a cooling system.
I Idea: pre-cool the building/zones before peak period by setting a low
set-point, then raise the set-point during peak period (load shifting).
I Change temperature set-points according to a fixed schedule
(pre-computed for each month or season).
I Different schedules:
I Jump/Step-up: reset set-point from low to high at the beginning of
peak period.
I Linear: linearly increase set-point from low to high during peak
period.
I Analytical: use an analytical building model to optimize (off-line) the
set-point schedule. Can be approximated as exponential schedule.
T. Nghiem HVAC Overview 14
ing afternoon periods that are characteristic
Demand Control: Temperature Set-point
Fig. 2.Schedule
here the demand-limiting will be applied. Schematic illustration of SA metho
80F
(26.7C)
temperature
Step-up
Linear-rise
Setpoint
78F
(25.6C) 74F
(23.3C)
70F (21.1C)
Precooling On-peak
Occupied period Time
(From [ho controls.
Fig. 1. Example demand-limiting building setpoint temperature Lee & Braun, 2008])
References:
[ho Lee & Braun, 2004, ho Lee & Braun, 2006, ho Lee & Braun, 2008]
T. Nghiem HVAC Overview 15
Demand Control: Temperature Set-point Schedule
Simulation with Simulink and MLE+ (cosimulation with EnergyPlus).
4
x 10
31 2
30 1.8
1.6
29
1.4
28
1.2
27
1
26
0.8
25
0.6
24
0.4
23 0.2
22 0
6 8 10 12 14 16 18 6 8 10 12 14 16 18
Set-point schedule ( C) HVAC power (W)
T. Nghiem HVAC Overview 16
References
ho Lee, Kyoung, & Braun, James E. 2004.
Development and application of an inverse building model for demand response in small
commercial buildings.
In: Proceedings of SimBuild.
ho Lee, Kyoung, & Braun, James E. 2006.
Evaluation of Methods for Determining Demand-Limiting Setpoint Trajectories in
Commercial Buildings Using Short-Term Data Analysis.
Pages 107114 of: Proceedings of SimBuild.
ho Lee, Kyoung, & Braun, James E. 2008.
Development of methods for determining demand-limiting setpoint trajectories in buildings
using short-term measurements.
Building and Environment, 43(10), 1755 1768.
T. Nghiem HVAC Overview 17
Thank You!
Q&A
