Presentation Supermarket Tasks and Strategies

Report
Dr. Salvador Acha – Imperial College Research Associate
Supermarket Tasks and Strategies in
Reducing Energy Demand and CO2 Emissions
9th January 2012
Contents
Sections
1
Sainsbury’s supermarkets
Collaboration agreement / Devising a low carbon roadmap
2
Engineering project: Hythe
•
•
•
•
3
A day in the life of Hythe
Supermarket energy systems
Case Studies
Going Forward
Q&A
Who is Sainsbury’s?
• Supermarket chain was founded in 1869.
• Currently has16% share of UK market (3rd place), tallying
over 600 supermarkets and 400 convenience stores.
• Approximate labour force of 200,000 workers.
• Annual sales of £20,000 m and £600 m profits (3%)
• Annual energy demand (only supermarkets) – 77% power
and 23% natural gas.
Electricity Use
(MWh)
1,554,082
Electricity CO2
(tonnes)1
860,961
Electricity Bill
(£)3
119,664,314
$2,393,286,280
Notas:
1Electricas Emisiones UK: 0.554 kgCO /kWh
2
2Natural Gas Emisiones UK: 0.184 kgCO /kWh
2
3Electricity cost: 0.077 p/kWh = $1.54 pesos/kWh
4Natural gas cost: 0.02 p/kWh = $0.40 pesos/kWh
Natural Gas
Use (MWh)
462,132
Natural Gas
CO2 (tonnes)2
85,032
Natural Gas
Bill (£)4
9,242,648
$184,852,960
Strategic Collaboration
Background
March 2010, Sainsbury’s announced a 5 year business partnership with Imperial College
London and Grantham Institute.
Covers Climate Change, Supply Chain Management, Energy Efficiency and Intelligent
System Operation and Low Carbon Construction.
Vision
• Develop low carbon pathways to create a store fit for 2020 and beyond ahead of sector
• Scenario planning – identify future performance requirements + key policy challenges,
opportunities and constraints.
Objectives:
-Decarbonisation
-Energy efficiency and cost savings
- Wider sustainability
-Education and training
Sustainability
Waste
Cost
Energy Security
Carbon Footprint
2020 Store
and beyond
Engineering Design
Smart Systems
Modelling/Integration
Climate Policy
Optimisation
Low Carbon Roadmap
• Has the purpose to illustrate the factors Sainsbury’s should
consider when defining their long term energy strategy
Hythe is our Test Bed
• The “smart-grid supermarket” concept focuses on
managing the different processes occurring within a store
with the objective of enhancing its energy consumption for
mutual cost and environmental benefits while not forsaking
the appeal and daily operations of supermarkets.
•
•
It will be a learning exercise
– Practical implementation or use of existing smart controls to enhance
supermarket energy demand.
Management of in-store processes
– Enhancing energy efficiency strategies and ‘load flexibility’ in order to
calculate the capacity available for devising ‘demand response’ programs.
Hythe Deliverables
• Objectives of the project include the following:
–
–
–
–
–
Conduct a thorough monitoring of energy demand
Understand demand characteristics of services
Identify operational constraints of systems
Perform system trials that either save/shift energy use
Define best practices of energy demand per system
and reduce carbon emissions
– Trials will serve to validate Imperial College models
– Assess potential of adopting flexible tariff schemes and
offering demand response services to the grid
– If trials are successful adopt roll back/roll fwd plan
Hythe Specs – A Carbon Step Store
• Store specifications and what's innovative?
– Opened in late February 2011
– Sales floor area of 35,759 sq. ft. with reflective ceiling,
light tubes and considerable windows in the shop front
and GM areas
– Thorough energy monitoring with over 50 sensors
– Rain water harvesting unit reduces water demand
– Biomass boiler supplies low carbon heat with a
maximum 700 kW capacity
– Generator supplies low carbon electricity with a
maximum 140 kW capacity (installed recently)
– 50 kW PV panels in the roof
A day in the life of Hythe
• About 5000 kWh/day = £385/day = 2.7 tCO2
– 272 kW peak, 130 kW base, 205 kW average
Hythe Energy Profile on 15/03/11 - Overlayed Chart
52
Refrigeration systems begin consuming more energy during trade hours due
to higher occupancy levels and ambient temperatures.
Lighting systems go on 15 minutes before store opens and then smart
controls manage the sales floor lighting while trading.
39
Lighting
kWh
Refrigeration
HVAC system consume most of its energy through AHU’s during trading
hours; weather conditions, occupation and set-points drive this demand.
26
HVAC
Bakery
Hot Food
Others
13
Hot food activities begin at 5 am when prepping the
Bakery activities begin at 4 am and are fish and meat counters, energy peaks indicate oven
usually over by lunch time.
use for chickens, pastries, pizzas and staff food.
0
00:30
02:30
04:30
06:30
08:30
10:30
12:30
Time
14:30
16:30
18:30
20:30
22:30
A day in the life of Hythe
• Load composition
– Lighting 32%, Refrigeration 39%, HVAC 10%, Hot Food 8%,
Bakery 6%, Others 4%
Hythe Energy Profile on 15/03/11 - Stacked Chart
150
125
100
Others
kWh
Hot Food
Bakery
75
HVAC
Refrigeration
Lighting
50
25
0
00:30
02:30
04:30
06:30
08:30
10:30
12:30
Time
14:30
16:30
18:30
20:30
22:30
A day in the life of Hythe
• League Table Analysis
– Ability to monitor thoroughly improves the level of analysis
Top users
of energy
A day in the life of Hythe
• Carbon emissions
– Having a biomass boiler shifts almost entirely the carbon emissions
to the use of electricity
Hythe Carbon Emissions Profile on 28/07/11 - Stacked Chart
80
CO2 (kg)
60
Electricity CO2
40
Heat CO2
20
0
00:30
02:30
04:30
06:30
08:30
10:30
12:30
Tim e
14:30
16:30
18:30
20:30
22:30
Hythe Monitoring
• Opened in February 2011, Hythe allows the partnership to
learn the characteristics of each system’s energy demand.
– Trials of different systems for energy efficiency and demand
response strategies which will benefit SSL are in progress
– Desired outcome will be to better understand how to optimally
integrate the systems
• Benchmarking:
– Hythe per week: 1st Q = 0.97 kWh/ft2 and 2nd Q = 1.01 kWh/ft2
– Baselines per week 05/06 = 2 kWh/ft2 and 09/10 = 1.40 kWh/ft2
1st Quarter Electricity Load Distribution
(kWh)
18,277
7,171
Mains vs Max Daily Temperature - Monday to Saturday
2nd Quarter Electricity Load Distribution
(kWh)
19,224
5800
6,798
27,704
29,599
Ref rigeration
Ref rigeration
Lighting
Lighting
5400
HVAC
Hot Food
45,836
HVAC
46,185
Bakery
Others
138,150
Unmetered
Hot Food
Bakery
122,130
Others
kWh
36,153
38,370
5600
209,171
173,785
y = 0.5313x2 + 13.766x + 4727.3
5200
5000
4800
Unmetered
4600
0
5
10
15
Daily Max Temperature
20
25
Hythe Load Profile Analysis
• Thorough monitoring allows us to build virtual store
models and hence assess trade-offs in energy design
strategies when the framework is strengthened
• Consequently, impact of low carbon technologies can be
estimated (e.g. biomass boiler, bio fuel generator)
• At Hythe new technologies can abate emissions 20-45% and
imported electricity from 1-25%
Weekly Heat Demands
2nd Quarter
Weekly Energy Demands
1st and 2nd Quarters
28
39,000
30
24
38,000
25
20
37,000
16
36,000
16,000
14,000
12
35,000
8
34,000
Celsius
Celsius
20
12,000
15
10,000
10
Max Avg Temp (C)
4
Min Avg Temp (C)
33,000
5
32,000
0
Min Avg Temp (C)
Total Main (kWh)
0
8
10
12
14
16
18
20
22
Week
24
26
28
30
32
8,000
Max Avg Temp (C)
Boiler (kWh)
21
23
6,000
25
27
Week
29
31
33
Quarterly Report
• Refrigeration relationship results (spring 2011)
– Temperature variations have an impact on pack performance
however this is not the case for cabinets
Packs vs Max Daily Temperature - Monday to Saturday
2,500
2,000
kWh
1,500
1,000
2
y = 0.5699x + 21.267x + 933.13
Cabinets vs Max Daily Temperature - Monday to Saturday
500
800
0
5
10
15
20
25
Daily Max Temperature
600
kWh
0
400
2
y = -0.026x + 1.9922x + 515.13
200
0
0
5
10
15
Daily Max Temperature
20
25
Quarterly Report
• HVAC relationship results (spring 2011)
– Temperature variations have a slight impact on air handling unit
performance but over 15 degrees demand more or less stagnates
AHUs vs Max Daily Temperature - Monday to Saturday
500
400
kWh
300
200
y = -0.3337x2 + 14.289x + 163.1
100
0
0
5
10
15
Daily Max Temperature
20
25
Quarterly Report
• Lighting relationship results (spring 2011)
– As sunlight hours increase the energy demand is reduced
indicating that natural light is well used by light tubes, windows and
reflective ceiling
Sales Floor Area vs Sunlight Hours - Monday to Saturday
1500
1200
kWh
900
2
y = 0.1153x - 9.2016x + 1166.1
600
300
0
0
3
6
9
Daily Sunlight Hours
12
15
Quarterly Report
• Daily profile results (spring 2011)
– Logging energy data every half hour allows us to visualise daily
load profiles with great detail, this will help us build energy models
– This figure displays daily refrigeration demand, clearly showing
how the system behaves differently due to weather conditions
Hythe Energy Profile - Refrigeration > max 22 Celsius, < min 6 Celsius, = 13 Celsius
80
kWh
60
40
20
0
00:30
02:30
04:30
06:30
08:30
10:30
12:30
Time
14:30
16:30
18:30
20:30
22:30
Case study: Lighting System
• Research and trials
– Through the Clipsal system lux intensity can be regulated and
there is an opportunity to question current settings (900 lux) and
re-define best practices when commissioning new projects
– Projected savings at Hythe under current settings are of 50 tCO2
DSI %
DSI Control, Lux, and Power Relationship
100
100
80
80
60
60
40
40
20
20
0
DSI %
Power
0
189
277
415
571
654
756
869
967
1059
Illuminance (Lux)
Pre-Clipsal Annual Projections
kWh
424,320
Bill
32,673
kgCO2
229,557
DSI 96 Annual Projections
kWh
410,046
Bill
31,574
kgCO2
221,835
DSI 93 Annual Projections
kWh
353,132
Bill
27,191
kgCO2
191,044
DSI 90 Annual Projections
kWh
335,855
Bill
25,861
kgCO2
181,698
DSI 87 Annual Projections
kWh
320,242
Bill
24,659
kgCO2
173,251
Hythe Lighting (September 2011)
Granularity is key in analysing performance
Diming in the shop front area
is not being achieved
Dimming is being achieved efficiently in GM, shop front,
and car park areas when natural day is abundant
Car park load impact
Case study: Lighting System
• Further work
– Revise final commissioning and ideal sensor location
– There are 100 Sainsbury’s sites with Clipsal – roll back/fwd
program would be an easy win (10 store pilot approved!!!)
– Each store is unique in itself, however new standard settings can
be established based on store characteristics
Peak Reduction
93% DSI
90% DSI
Sensor 1
Next to a duct
Case study: Lighting System
• Early learning’s from trials are:
– Customers and staff have not perceived changes and these have
not impacted trade
– Trials have been successful due to the robust lighting system
control, thorough study of how the system works, and good
collaboration between the partners
– Lighting system at Hythe has benefited greatly from light tubes,
shop front windows, and reflective ceilings and floor
– Flexible energy demand in the lighting system is non-intrusive with
Sainsbury’s business and has potential for further uses that can
generate value for the company
– Dial-in system is required to easily modify settings
HVAC + Boiler Energy Consumption Distribution
Biomass Boiler
Case study: HVAC System
Biomass Panel
H&V Panel
AHU 3 D
AHU 2 SA
• Research and trials
AHU 1 SF-GM
0%
10%
20%
30%
40%
50%
60%
70%
Percentage
– The BMS Trend system controls the heating and cooling of the
building through AHUs and biomass boiler
– Although energy use in this system is efficient it was noted
temperature set-points in the dry goods area are not being met
– AHUs are low energy consumers, the boiler constitutes most of the
demand (excessive use of the boiler during summer months)
Hythe Total Energy Profile Profile on 28/07/11 - Stacked Chart
200
Energy (kWh)
160
HVAC + Boiler
Unmetered
Others
Hot Food
Bakery
Refrigeration
Lighting
120
80
40
0
00:30
02:30
04:30
06:30
08:30
10:30
12:30
Time
14:30
16:30
18:30
20:30
22:30
80%
Weekly Heat Demands
2nd Quarter
30
25
14,000
20
Celsius
Case study: HVAC System
16,000
12,000
15
10,000
10
8,000
Max Avg Temp (C)
5
Min Avg Temp (C)
• Research and trials
Boiler (kWh)
0
21
23
6,000
25
27
29
31
33
Week
– Called Trend bureau to reduce set-points, changes have achieved
less wood pellet use (20%) and ambient temperatures are closer to
set point, savings are estimated to be 3 tCO2
– However, boiler efficiency also appears to give poor performance
25.5C
Set-point
22.7C
Old 19C
Set-point
Case study: HVAC System
• Early learning’s from trial and further work:
– The HVAC system is quite efficient at Hythe and hence it’s demand
is not as flexible as for lighting
– Biggest area of opportunity is to save operating costs by using less
wood chips to fuel the boiler and optimising temperature set points,
and VSD of AHUs; proper post-comission plan is needed!!!
– Relationship between HVAC and refrigeration needs work (waste
heat recovery) and hence data needs to be logged
Case study: HVAC System
• Data logging via Trend Energy Manager is now possible:
– Trend BMS is now able to log its data and display it in its interface
for Sainsbury’s and Imperial’s benefit
– Level of detail in comparing metrics will allow us to find easier the
relationship between HVAC, refrigeration, and ambient store
temperatures.
Case study: Bakery/Hot Food System
• Research and trials
– Dr Acha shadowed bakery and hot food counter activities in order
to learn the reasoning behind energy demand patterns
– After studying the equipment and the manner in which staff employ
them tips are advisable to save energy – if followed they could
save 22 tCO2 without negatively impacting trade
– Hythe management is aware of the potential benefits and are keen
on applying these tips to reduce operating costs; plan is needed!!!
Hythe Hot Food Profile - 3 Typical Days
Hythe Bakery Profile - 3 Typical Days
50
30
40
25
20
kWh
kWh
30
20
10
0
00:30
15
10
5
04:30
08:30
12:30
Time
16:30
20:30
0
00:30
04:30
08:30
12:30
Time
16:30
20:30
Case study: Refrigeration System
• Research and trials
– Danfoss and Parasense monitor and control this system
– Thorough investigation is in progress, hence only energy saving
trials have taken place by covering frozen and chilled cabinets
during ‘non-trading’ with exciting results!
– Management needs to reinforce this practice when possible
– After sufficient data is collected and approval is given a pre-cooling
approach of frozen products would like to be trialled
Covering frozen and chilled cabinets during non-trading has very attractive savings
Case study: Refrigeration System
• Early learning’s from the trial and further work:
– Working closely with Danfoss/Sainsbury’s/Arcus FM etc is key to
successfully progress in this area
– Covers were done last Sunday and compared to a similar day
<Max> 18C <Min> 13C
– Average savings over 29 tCO2
Hythe Energy Profiles on Sundays
75
Lighting October
60
Lighting July
Energy (kWh)
45
Refrigeration
October
Refrigeration July
30
HVAC October
HVAC July
15
0
00:30
04:30
08:30
12:30
16:30
20:30
Time
00:30
04:30
Case study: Refrigeration System
• Covering frozen cabinets make much sense since:
– Cooling of products is more efficient and packs are not worked that
hard (good opportunity to seriously reduce base load)
– Talks with suppliers, retail and refrigeration team to make cover
trials a best practice
Case study: Refrigeration System
• Imperial College recommends a pre-cooling approach of
frozen products to assess energy shifting possibilities:
– It would be useful to identify how much energy consumption can
be consumed at night time and avoided during the day.
– Furthermore, load shedding alternatives such as fan and defrost
dispatch will also be considered
– Close monitoring of product temperature is paramount
Hythe (S2167) : 200 - CS - Coldstore
Product Temp. 1 (24 hours)
Pre-cooling begins
Product Temp. 2 (24 hours)
Pre-cooling ends
Max Temp.
Min Temp.
20
Temperature (C)
10
0
0
2
4
6
8
10
12
-10
-20
-30
-40
Time (hr)
14
16
18
20
22
What trade-offs does
on-site generation bring?
Generation Trials
• Research
– Operating strategies to be tested will first serve to conduct tests
that help comprehend the real performance of the bio fuel unit
while later trials will focus on optimising particular variables
– The operating strategies being considered are:
1. Basic response (efficiency and response times)
2. Peak shaving (reduce maximum demand times)
3. Partial off-grid scenarios (reduce energy imports)
4. Cost reduction scenarios (simulate flexible tariff schemes)
Hythe Energy Profile on 15/03/11
Hythe Energy Profile on 15/03/11
280
280
240
240
200
200
Status Quo
160
Status Quo
kW
kW
160
Off-grid
Peak Shaving 2
120
120
80
80
40
40
0
0
00:30
02:30
04:30
06:30
08:30
10:30
12:30
Time
14:30
16:30
18:30
20:30
22:30
00:30
02:30
04:30
06:30
08:30
10:30
12:30
Time
14:30
16:30
18:30
20:30
22:30
Generation Trials
• Research
– Operating strategies to be tested will first serve to conduct basic
tests that help comprehend the real performance of the bio fuel unit
while later trials will focus on optimising particular variables
– The operating strategies being considered are:
1. Basic response (efficiency and response times)
2. Peak shaving (reduce local grid max demand)
3. Partial off-grid scenarios (reduce energy imports/emissions)
4. Cost reduction scenarios (simulate flexible tariff schemes)
Hythe Energy Profile on 15/03/11
Hythe Energy Profile on 15/03/11
280
280
240
240
200
200
Status Quo
160
Status Quo
kW
kW
160
Off-grid
Peak Shaving 2
120
120
80
80
40
40
0
0
00:30
02:30
04:30
06:30
08:30
10:30
12:30
Time
14:30
16:30
18:30
20:30
22:30
00:30
02:30
04:30
06:30
08:30
10:30
12:30
Time
14:30
16:30
18:30
20:30
22:30
Hythe Savings Summary
• Hythe estimated bills are £165k/year with a carbon
footprint of 985 tonnes
• Potential savings accumulated thus far are £19k (11%)
and 104 tonnes of carbon (10%) – easy wins that can
have immediate impact – winter quarter of 0.90 kWh/ft2?
• If we just extrapolate the electrical power savings to 500
Sainsbury’s stores an attractive benefit of 50,000 tCO2
can be achieved
• Sainsbury’s needs to make the most out of these findings.
How can we effectively translate this learning's to other
sites?
Going Forward
• Tasks
– Continue thorough monitoring of Hythe to learn seasonal variations
and adopt control strategies accordingly; produce quarterly reports
– Address technical issues required to conduct refrigeration trial
– Assess trials conducted thus far until capabilities have been fully
explored (e.g. lighting, HVAC, refrigeration, etc.)
– Implement regular operating schedule of bio fuel generator
– Use data to develop energy models that will serve to evaluate
other Sainsbury supermarkets (e.g. Supervision of Sainsbury’s
MSc projects; strategic planning and virtual store projects)
– Contribute in developing a single interface that monitors and
analyses the energy consumption of the store (GE partner)
– Key learning's should continue to be explored and when ready
transferred to SSL for roll back/forward that enrich the companies
position in its business sector
Thank you for your
attention!

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