Dynamic Chiller Plant Optimization€¦ · Utilization of BMS Big Data is a Way Out for RCx...
Transcript of Dynamic Chiller Plant Optimization€¦ · Utilization of BMS Big Data is a Way Out for RCx...
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Dynamic Chiller Plant Optimization
Evan Cheng
ATAL Building Services Engineering Ltd
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Understanding of Retro-Commissioning
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Overview of HKSAR Gov’t Policy on Building Energy Use
Energy Reduction Target:
• In COP21 held in Paris in 2015, China
committed to lower carbon emission
intensity (emissions per unit of GDP) by
60% to 65% from the 2005 level by
2030
• Hong Kong has set the target of
achieving energy intensity reduction
by 40% by 2025 using 2005 as the
base
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Technical Guidelines on RCx
• Key Objective: Enhance the building energy
efficiency
• Focus on energy consuming equipment/systems
that operate properly as design or users’ requirement
and to identify some area of improvements (e.g.
shifting of system control settings, inaccuracy of
sensors, improper operational schedules and
improper air & water balancing, etc.)
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Chiller plants are one of the largest energy saving opportunities
HVAC55%
Lighting14%
Office Equipment
12%
Others19%
Total Building Energy Use(Office Segment)
Chiller Plant
System60%
Airside Syste
m25%
Others15%
Total HVAC Energy Use
Key Facts:
• More than 50,000 buildings in Hong
Kong
• Buildings account for 90% of total
electricity consumption in Hong Kong
• Among different building services(BS)
systems, HVAC consumes the most
energy consumption (55%) in office
buildings
Implications:
• Reducing energy use by HVAC in
commercial buildings is a key measure
to take to help realize HK’s energy
intensity reduction target.
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How to further reduce HVAC energy consumption
Apart from the traditional approach
• using energy efficient equipment (e.g. VSD chiller, pumps, cooling towers)
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Utilization of BMS Big Data is a Way Out for RCx
Air-handling equipment
Chillers
VS PLP
Ch.W.S.
Ch.W.R.FM
Typical Chiller Plant
Sensors
Big Data
HVAC System
Performance
How
Who
Why
WhenWhat
Which
Where
BMS Data enables us to :
• Know how the current performance is
• Understand when and where the best
performance occurs
• Identify Energy Waste (due to presence
of unnoticed faults)
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Overview of Chiller Plant Control
Dynamic Optimization
Model
Optimization Control
Traditional Chiller Plant Control
1st Generation
2nd Generation
3rd Generation
Features
• Match cooling load demand
• Balance equipment running hours
• Meeting design temperature set points
Features
• Determine chiller sequencing based on
energy use & equipment design data
• Temperature reset (summer/winter mode)
• Mainly focus on chiller energy use
Features
• Real time optimization for the whole chiller plant
• Evaluate all possible combinations of equipment for optimization
• Use actual BMS data for system model development
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Advanced Dynamic Chiller Plant Control Optimization Technology
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Dynamic Chiller Plant Control Optimization
Big Data
Data Quality
Diagnosis
Cooling Load
Analysis
Chiller Plant
Modelling
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Data Quality Diagnosis - Flow Rate Sensors
Comparison between header CHW flow rate and individual chiller flow rate
Chiller 1 Chiller 2
Chiller 3 Chiller 4
Note: The data period is from 01 Jan 2016 to 31 Dec 2016
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Data Quality Diagnosis -Temperature Sensors
Comparison between header CHWR temp and individual chiller CHWR temp
Chiller 1 Chiller 2
Chiller 3 Chiller 4
Note: The data period is from 01 Jan 2016 to 31 Dec 2016
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Annual Peak Cooling Load Profile:
Year 2016 Jan Feb Mar Apr May Jun
Peak Cooling
Load (kW)724 799 741 1033 1377 1556
1/2/2016
8:00
2/13/2016
8:00
3/30/2016
15:00
4/25/2016
13:00
5/30/2016
14:00
6/27/2016
15:00
Year 2016 Jul Aug Sep Oct Nov Dec
Peak Cooling
Load (kW)1533 1648 1420 1391 1149 879
7/4/2016
12:00
8/8/2016
12:00
9/26/2016
13:00
10/3/2016
8:00
11/21/2016
8:00
12/10/2016
8:00
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Daily cooling load profile (Summer vs Winter):
2016-08-08 0 1 2 3 4 5 6 7 8 9 10 11
Chiller Plant
Cooling Load (kW)179 188 119 182 153 156 163 226 1361 1341 1445 1413
12 13 14 15 16 17 18 19 20 21 22 23
Chiller Plant
Cooling Load (kW1648 1599 1568 1581 1555 1469 1304 402 310 269 253 127
2016-02-13 0 1 2 3 4 5 6 7 8 9 10 11
Chiller Plant
Cooling Load (kW)69 84 101 126 150 143 84 64 799 415 437 507
12 13 14 15 16 17 18 19 20 21 22 23
Chiller Plant
Cooling Load (kW512 463 487 133 152 147 89 90 96 78 120 143
Summer Winter
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Cooling Load AnalysisWeekday Weekend and Public Holiday
21oC
~700kW
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Actual Chiller Model
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Continuous Building Optimization
BMS
Cloud
Optimization
System
Local Database
Server
Collect Trend log
from BMS
Calculate the optimal
operation point
Command the
BMS for action
Data processing and
cleaning
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Summary
Features and Benefits:
• Real-time continuous, reliable, automated
optimization control
• Whole chiller plant optimization
• Determine the most efficient combination of
chillers, pumps and cooling towers for different
control setting
• Ensure that the chiller plant is operating in its
most efficient state year-round, regardless of
load
• Considered the real-time weather condition
Dynamic Chiller Plant Optimization
Chiller Plant
Modelling
Data Quality
Diagnosis
Cooling Load
Analysis
Big Data
Proactive Mains.
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THANK YOU