1. Shifting the timing of energy demand: A stochastic modelling approach
Selin Yilmaz, Dr. Steven K. Firth and Dr. David Allinson
London-Loughborough Centre for Doctoral Research in Energy Demand.
School of Civil and Building Engineering,
Loughborough University, Loughborough, Leicestershire. UK
Selin Yilmaz

2. Background
‘Demand flexibility’ is referred to as the possibility to change (adapt, deviate, shift) the electricity consumption profile with the aim of better demand and supply match. `
'Demand response' refers to flexible demand where consumers time-shift demand, either behaviour change or through automation, in response to particular conditions within the electricity system.

3. Background End uses The focus: Domestic Sector - Space heating: 65.5 %
Water heating: 14.6 %
Lighting and appliances: 15.4 %
Cooking appliances: 2.6 %
Figure 1 Proportion of final consumption of UK energy products divided into sectors (DECC, 2013a)

4. PROPOSED RESEARCH Aim:
To explore and quantify the potential of demand response in the UK residential sector
1-Develop a residential electricity demand model that is able to capture all the electricity consumption of a household
2- Apply demand response techniques

5. Number of participants
METHODOLOGY
A stochastic bottom-up engineering model development
Using a set of probability functions such as “availability” and “proclivity”.
Data set
Description
Survey period
Number of participants
Data Resolution
Household Electricity Use Survey
Electricity
measurements of household
electricity on individual
appliance level
25 homes for one year
200 homes for one month
250 homes
2 to 10 minute

6. Non-shiftable but curtailable
Categorization of appliances for demand response 2
Time-shiftable 1
Demand Response Action 3
Non-shiftable but curtailable
Power-shiftable 4
Non-shiftable and
non-curtailable

7. Parameters for Washing machine shifting
INITIAL RESULTS:
Washing machine
Parameters for Washing machine shifting
2 hours shift
Start Probabilities
Scenario 1
0 %
Scenario 2
30 %
Scenario 3
60 %
Scenario 4
90 %
Fig 1: Comparison of average load for with demand response (Scenario 3) and without demand response (Scenario 1)
Fig 2 : Hourly net load changes explaining the differences in the load curves in Fig 1

8. Conclusions & Future Work
Development of quantitative metrics to evaluate demand response potential.
Study and evaluate the impact of different electricity price structures towards total residential demand (i.e. power-proportional price).