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Global Optimisation of Chiller Sequencing and Load Balancing Using Shuffled Complex Evolution
IAIN STEWARTA (MENG)LU AYEA (FAIRAH, FAIE, PHD)
TIM PETERSONB (PHD)
A Renewable Energy and Energy Efficiency Group, Department of Infrastructure Engineering,The University of Melbourne, Vic 3010, AustraliaB Environmental Hydrology and Water Resources Group, Department of Infrastructure Engineering, The University of Melbourne, Vic 3010, Australia
Evolutionary Chiller Optimisation (ECO)
โข The why?
โข A brief history of building controls
โข Chillers and VSDs
โข The how? Improving system efficiency
โข Aims- Optimising equipment loading
โข Model- Structure, inputs & outputs
โข Results- Modelled savings
Why?
โข Energy costs
โข Carbon emissions
โข Peak demand reduction
40%
35%
15%
20%
Typical Commercial Building End Use Energy
HVAC
Lighting
Equipment
Other
92PJ
22MtCO2
A Brief History of Building Controls
Pneumatic controlsโฆ.
Direct Digital Control (DDC)
A Brief History of Building Controls
Building Management System (BMS)
A Brief History of Building Controls
Reciprocating vs Centrifugal Chillers
Chiller COP vs Loading
0
2
4
6
8
10
12
0% 25% 50% 75% 100%
Ch
iller
CO
P
Chiller Load %
New centrifugal variable speed chiller
Old reciprocating chiller
100%300%
Variable Speed Drives (VSDs)
Variable Speed Drives (VSDs)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Ener
gy C
on
sum
pti
on
Pump Speed
Pump Speed vs Energy Consumption
<50%
10% 80%
Technological improvements
โข Capabilities of Building Management Systems
โข Improved chiller part load efficiency
โข Affordability of Variable Speed Drives
Current Control Technique
โข Return water temperature staging
Switch-on thresholds Switch-off thresholds
From 1 chiller to 2 chillers: Trtn > 11.94 ยฐC From 2 chillers to 1 chiller: Trtn < 9.28 ยฐC
From 2 chillers to 3 chillers: Trtn > 11.85 ยฐC From 3 chillers to 2 chillers: Trtn < 10.10 ยฐC
From 3 chillers to 4 chillers: Trtn > 11.81 ยฐC From 4 chillers to 3 chillers: Trtn < 10.51 ยฐC
Trtn = Temperature of return chilled water from the building
The Opportunity
โข Maximize cooling system efficiency [COP]
โ Non linear efficiency curves
โ Multiple pieces of equipment
โ Inherently difficult for humans
Aims
โข Accurately simulate waterside plant equipment
โข Optimise equipment loading for cooling loads
โ Maximise plant COP for all cooling loads
โAll models are wrong but some are usefulโ โ George Box
ECO
1. Simulate waterside plantโ Energy inputs
โข Flow ratesโข Mechanical workโข Efficienciesโข Physical limits
โ Refrigeration delivered2. Optimise equipment staging and loading using genetic algorithm for a range of cooling loads
Modelling assumptions
โข Chiller turn down ratio = 10:1
โข Condenser and chilled water pumps operate at minimum flow rate when chillers are at minimum output (eg. 50% pump speed when chiller is at 10% capacity)
โข Pumps speed increases linearly with chiller energy
โข Maximum temperature difference across chillers = 7ยฐC
โข Fans excluded from simulation
Plant layout
Plant Simulation
Inputs Simulation Output
Simulation of chilled water plant (Chillers &
Pumps)*Fans excluded
Cooling Load [kWr]
Low load chiller energy [kW]
High load chiller 1 energy [kW]
High load chiller 2 energy [kW]
Input energy [kWe]
System COP
Plant simulation
ว๐๐ = ว๐cw๐๐ค(๐๐ โ ๐๐)
โข where, ว๐cw is the mass flow rate of the chilled water [kg s-1]
โข ๐๐คis the specific heat of chilled water [kJ kg-1K-1]
โข ๐๐ and ๐๐ are the inlet and outlet chilled water temperatures [ยฐC] across the chiller.
Regression analysis
ว๐๐ = aP + bP2
where, ว๐๐ is the refriegeration energy [kWr], P is the chiller input power [kWe], and a & b are scalars to be
calculated in the regression analysis
y = -0.0185x2 + 8.8461xRยฒ = 0.9224
0
200
400
600
800
1000
1200
1400
0 50 100 150 200
Co
olin
g ca
pac
ity
[kW
r]
Input Energy [kWe]
Regression of Chiller [kWr vs kWe]
Results of chiller regression analysis
Chiller #Input Power
[kWe]Nominal
Capacity [kWr] a b R2
1 59 289 8.7957 -0.0699 0.9236
2 178 988 8.8461 -0.0185 0.9224
Modelling Structure
InputSimulation & Optimisation
Outputs
Simulation of chilled water plant (Chillers &
Pumps)*Fans excluded
Cooling Load [kWr]
Low load chiller energy [kW]
High load chiller 1 energy [kW]
High load chiller 2 energy [kW]Shuffled complex
evolution algorithmObjective function:
โ Plant equipment energy
Shuffled Complex Evolution
โข Deterministic & probabilistic
โข Competitive evolution
โข Clustering
โข Steepest decent
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 500 1000 1500 2000 2500 3000 3500
Ch
ille
r In
pu
t P
ow
er
[kW
r]
Cooling Load [kWr]
Chiller Input Power [%] vs Cooling Load [kWr]
Low LoadChiller
High LoadChiller 1
High LoadChiller 2
5.9
5.95
6
6.05
6.1
6.15
6.2
6.25
6.3
6.35
0 500 1000 1500 2000 2500 3000 3500
Syst
em
CO
P
Cooling Load [kWr]
System COP vs Cooling Load [kWr]
5.9
5.95
6
6.05
6.1
6.15
6.2
6.25
6.3
6.35
0 500 1000 1500 2000 2500 3000 3500
Syst
em
CO
P
Cooling Load [kWr]
System COP vs Cooling Load [kWr]
Response surface, COP vs Chiller Loading [%]
0
20
40
60
80
100
120
140
0 500 1000 1500 2000 2500 3000 3500
Ene
rgy
Savi
ngs
[kW
e]
Cooling Load [kWr]
Energy Savings [kWe] vs Cooling Load [kWr], aggregated into bins of 100kWr
0
20
40
60
80
100
120
140
0 500 1000 1500 2000 2500 3000 3500
Ene
rgy
savi
ngs
[kW
e]
Cooling Load [kWr]
Energy savings [kWe] vs Cooling load [kWr]
148,269
18,683
166,952
21,579
11,594
33,173
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Chillers Pumps Total
Modelled Energy Savings, 2016 [kWh]
Modelled energy consumption 2016 Energy savings from observed consumption
Results
Chiller 1 Chiller 2 Chiller 3 Pumps Total
2016 actual [kWh] 95,254 71,480 3,114 30,277 200,125
Modelled control strategy [kWh] 82,014 50,877 15,378 18,683 166,952
Energy savings [kWh] 13,240 20,603 (12,264) 11,594 33,173
% Savings 13.9% 28.8% -393.8% 38.3% 16.6%
40%
35%
15%
20%
Typical Commercial Building End Use Electricity
HVAC
Lighting
Equipment
Other
92PJ
22MtCO2
Savings
15PJ
4MtCO2
15-25% Peak
demand reduction
Next stepsโฆ
โข Test ECO in real world
โข Represent chillers and fans using neural networks to increase optimisation opportunities (adaptive control)
โข Incorporate ambient conditions and fan/condenser pump speed modulation (dynamic control)