Prof. Furong Li, Director, Centre for Sustainable Power Distribution, University of Bath, opened with an informative discussion entitled ‘From Economy of Scale to Economy of Flexibility – Low Carbon Opportunities’.

1. From Economy of Scale to Economy of Flexibility
- Low Carbon Opportunities
Professor Furong Li,
Director, Centre for Sustainable
Power Distribution
University of Bath
12 July 2018

2. High Assets Low Intelligence
Local Energy System – Status Quo
Centralised System• Passive components
• Passive network, fat investment
thin intelligence
• Slow in reacting to changes in
energy landscape

3. Low Assets High Intelligence
Local Energy System – Vision
• Passive components + active
components
• High operational intelligence –
technical and commercial
• Quick in reacting to changes in
energy landscape
Decentralised,
highly connected,
Open System

4. • Low cost renewable technologies
• High integration cost (to grid)
- incompatible with customer energy needs/local network
- cease renewable integration (Southwest England)
- huge renewable curtailment (Scotland)
• Low value, requires subsidies to grow
Economy of Scale
How to ensure renewables thrive in a
subside-free environment?

5. Integrate renewables with local flexibility
• Make demand more compatible with renewables
• Increase demand for renewables, reduce uncertainty
• Create value for renewable and flexibility, enable renewable to
thrive in a subsidy-free environment.
Economy of Flexibility

6. Economy of Flexibility – Functions
Local Network
Components (EVs, Storage)
2
4
Local Energy (PVs, CHPs)
Energy
Market
Network
Services
Energy
Management
System
ICT
Visibility, predictability
controllability
1
Deliver value to
renewable and
flexibility

7. Opportunity - Visibility
National Demand
Regional Demand
Household Demand
0.00.20.40.60.81.0
Power within a day
Time
NormalisedLoad
00:00 04:00 08:00 12:00 16:00 20:00

8. National Demand
Household Demand
Regional Demand
£200bn
£19bn
1. Extensive
2. Expensive
3. Low information
content
4. Huge resources in
processing and
storage
5. Low cost solution
Opportunity - Visibility

9. 415V system Homes
11kV system
£2 Billions for full
visibility
1,000,000 LV Substations
£2,000 for Each Monitoring
Last Miles
Opportunity - Visibility

10. 800 HV/ LV substations
Less than 1%
1. Do we need to monitor every single substation?
2. Are there common patterns?
3. Can we use the common patterns to estimate a
substation load without expensive monitoring
Low Voltage Network Templates - £32m
Gu, Li (R), Yan, Zhao,
Martin, Shaddick, Walker
Opportunity - Visibility

11. 0.00.20.40.60.81.0
Power within a day
Time
NormalisedLoad
00:00 04:00 08:00 12:00 16:00 20:00
Opportunity - Visibility

12. 0.00.20.40.60.81.0
Power within a day
Time
NormalisedLoad
00:00 04:00 08:00 12:00 16:00 20:00
Estimating un-
monitored substation
achieved 85% accuracy
£32m at 85%
£2bn at 100%
Opportunity - Visibility

13. •Daily profiles from smart metering
data in July 2012 (grey)
•Decompose daily profiles into
components on each frequency level
(right)
Spectral Analysis of smart metering data
Opportunity - Visibility

14. Opportunity - Visibility
Customer Classification
•Similar to gene expression profile: each question (column) is a gene, each cluster (row) is a
chromosome
•Recessive Questions: Dark blue indicate mix of answers, which cannot be used to classify
customers (recessive gene)
•Dominant Questions: Other colors (except dark blue) indicate over 90% customers give the
same answers on the question (dominant gene)

15. Solar Bristol Project
Bristol City Council
Deploying technology at their sites, engaging
with schools and offices
Knowle West Media
Centre
Leading the domestic properties engagement
Siemens
Designing and providing technology for the
project
University of Bath
Academic partner, Knowledge dissemination,
tariffs and design
Project Partners
Enable Smart Home/Businesses
Energy Management Systems

16. Three Methods:
•Batteries with
Integrated Energy
Management
•DC Network.
•Tariffs.
Four Outcomes:
•Permitted PV
Connections.
•Increase efficiency
•Increase value for local
storage
(whole-sale energy cost).
•Defer network investment.
Energy Management Systems
First project to demonstrate the inter-play between
smart home/businesses and smart distribution grid
in scale

17. P V B a tte ry
D C lo a d
A C lo a d
B i-d ire c tio n a l
c o n v e rte r
D C B u s
A C B u s
M a in g rid S m a rt h o m e
)(tP lo a dA C 
)( tPB
)(tP lo a dD C 
)(tPP V
)(tP )(tP D CA C 
Local DC
network
AC network
DC Micro-grids – Domestic
Energy Management Systems

18. State of
Charge
AC Load
PV
Real-time EMSOn-line Tariffs
Predictive
EMS
Target State
of Charge
Off-lineTariffs
AC Load
Network Stress
DC Load/PV
DC Load
Energy Management Systems

19. Optimisation considering charging and discharging
efficiencies
Efficiency loss versus financial gain - Price elasticity
Energy Management Systems

20. •Daily profiles from smart metering
data in July 2012 (grey)
•Decompose daily profiles into
components on each frequency level
(right)
Spectral analysis of smart metering data
Energy Management Systems

21. 0
1000
2000
3000
4000
5000
6000
Demand(W)
Time
Normal Homes
0
1000
2000
3000
4000
5000
6000
Demand(W)
Time
High Load
0
2000
4000
6000
8000
10000
12000
Demand(W)
Time
Economy 7
10
20
30
40
50
60
70
80
90
100
Demand(KW)
Time
Substation
Energy Management Systems
Different household have energy management strategies

22. • Impact to house demand • Impact to network demand
• Trial dates are 20th, 22nd and 28th April2015
• Between 14:30-16:29 and 18:15-18:59 (forced charge), the maximum difference in power
consumption introduced from the trial is more than 1kW (83% of the peak demand)
• Between 17:00-18:14 (forced discharge), the average exporting power on trial days increased 50%
over the normal days
Network Impact from Smart homes
-1.1
-0.6
-0.1
0.4
0.9
1.4
00:15
01:15
02:15
03:15
04:15
05:15
06:15
07:15
08:15
09:15
10:15
11:15
12:15
13:15
14:15
15:15
16:15
17:15
18:15
19:15
20:15
21:15
22:15
23:15
Power(kW)
House non-trial average House trial average
-5
15
35
55
75
95
115
00:15
01:15
02:15
03:15
04:15
05:15
06:15
07:15
08:15
09:15
10:15
11:15
12:15
13:15
14:15
15:15
16:15
17:15
18:15
19:15
20:15
21:15
22:15
23:15
Power(kW)
Network-non trial average Network- trial average
Energy Management Systems

23. Weather Effects on Demand
-0.2
0
0.2
0.4
0.6
0.8
1
00:15
01:15
02:15
03:15
04:15
05:15
06:15
07:15
08:15
09:15
10:15
11:15
12:15
13:15
14:15
15:15
16:15
17:15
18:15
19:15
20:15
21:15
22:15
23:15
Power(kW)
Average house demand on sunny day
Average house demand on non sunny day
• Network demand reduction
0
20
40
60
80
100
120
00:1…
01:1…
02:1…
03:1…
04:1…
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Power(kW)
Network demand with high PV
output
Network demand with low PV
output
• Dates are 3rd -24th October 2014.
• An average of 10kW demand from substation can be observed on sunny days
• Maximum demand reduction can be introduced from 8 homes’ Solar Solution is around 5kW
• Observation: Less energy consumed on sunny days
• House demand reduction
Finding - 4
Energy Management Systems

24. Race - Macro/Micro Systems
Macro System - fossil
•High cost
•High reliability
•High carbon
•Low uncertainty
•Energy management system will
impact in large areas, result in
conflicts between areas and over
time
•Low visibility of distributed
energy resources
Economy of Scale
Micro System - renewable
•Low cost
•Low reliability
•Low carbon
•High uncertainty
•Energy management system can
fully exploit local availability
•High visibility of distributed
energy resources
Economy of Flexibility