1. Customer Profiles – Analysing the customers of today
Peter Davison
Durham University

2. The aims of CLNR
WHY?
So UK Distribution Network Operators (DNOs) can be robust to the challenges and are able to exploit the opportunities presented by decarbonisation
WHAT?
What is the optimum mix of technical, social and commercial interventions to facilitate the transition to a low carbon economy.

3. How do we tackle the problem?
Heat pump cluster
Focussed integrated network technology on rural and urban network:
Enhanced automatic voltage control
Real-time thermal rating
Electrical energy storage
National smart meter data offers baseline electricity profiles
Analysis of new loads and generation
Active customer participation to minimise electricity costs through flexibility
PV generation cluster

4. Learning outcomes
Learning Outcome 1 - Monitoring: What are current, emerging and possible future customer (load and generation) characteristics?
Learning Outcome 2 – Customer flexibility: To what extent are customers flexible in their load and generation, and what is the cost of this flexibility?
Learning Outcome 3 – Network flexibility: To what extent is the network flexible and what is the cost of this flexibility?
Learning Outcome 4 – Optimum solutions: What is the optimum solution to resolve network constraints driven by the transition to a low carbon economy?
Learning Outcome 5 – Effective delivery: What are the most effective means to deliver optimal solutions between customer, supplier and distributor?

5. Data Analysis 14,000 I&C customers, ½ hourly - 2 years
≈4,500 residential customers, ½ hourly - 1 year
9,000 target
1,700 SME , ½ hourly - 1 year
2,250 target
Plus network monitoring LV, RTTR …
Test Cell 1 data alone is comparable to the size of the Irish Trials, we have 20 test cells

6. Industrial and Commercial Customers (CDCM)
Smart meters operate on GMT all year.
Data from the BST period has been converted to GMT to compare ‘like with like’

7. Smart Meter Data (4,500 customers)
Smart meters operate on GMT all year.
Data from the BST period has been converted to GMT to compare ‘like with like’

8. Smart Meter Data (4,500 customers)
Smart meters operate on GMT all year.
Data from the BST period has been converted to GMT to compare ‘like with like’

9. Effect of Sample Size

10. January 2012 Analysis Data for all customers across January 2012
Broken down into analysis of weekday and weekend as well as Sunday consumption

11. Extreme Weather Analysis
Coldest November since 1993
2nd warmest November in 100 years
December 2010 was the coldest December since 1910
Previous coldest December was 0.1 deg C (10x colder)
The average temperature in December since 1910 is 4.2 deg C in 2010 the average was -1 deg C
November 2011 was the 2nd warmest November in 100 years

12. Coldest April for 23 years
Extreme Weather Analysis
Coldest April for 23 years
Warmest April since 1910
April 2011 was the warmest April in Central England for 350 years
April 2012 is also provisionally the wettest April on record across the UK

13. Urban Customers

14. Rural On Gas

15. Tuesday

16. Wednesday

17. Thursday

18. Monday
The graph looks at how consumption in individual weekdays compares with the average weekday consumption
The values are PU loads with the average weekday consumption as the base
Increased loading on Monday and Friday during the day with decreased loading at Friday Tea time with a sharp increase around Midnight

19. Friday
The graph looks at how consumption in individual weekdays compares with the average weekday consumption
The values are PU loads with the average weekday consumption as the base
Increased loading on Monday and Friday during the day with decreased loading at Friday Tea time with a sharp increase around Midnight

20. Weekday
The graph looks at how consumption in individual weekdays compares with the average weekday consumption
The values are PU loads with the average weekday consumption as the base
Increased loading on Monday and Friday during the day with decreased loading at Friday Tea time with a sharp increase around Midnight

21. Weekday / Weekend
This graph shows how weekend consumption compares with weekday load.
Increased load in the early morning against weekday
Decreased early morning load due to less people getting up to go to work
Increased load during the day (people are at home) with an increased load on Sunday in comparison to Saturday
Load on Saturday evening is less than the average during the week (people aren’t at home)
Load on Sunday is much the same as during the week. No more load to switch on.

22. Thank you for listening Any Questions?