Shifting the timing of energy demand: A stochastic modelling approach

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Presentation transcript:

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

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.

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)

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

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

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

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

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).