Distributional Effects of Dynamic Pricing for Responsive Electricity Demand (DEePRED)

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Peak electricity demand is becoming an increasingly significant problem for UK electricity networks as it causes imbalances between demand and supply with negative impacts on system costs and the environment. The residential sector is responsible for about one third of overall electricity demand and up to 40% of peak demand. During peak demand, electricity prices in wholesale markets could fluctuate from less than £0.03/kWh to as much as £0.29/kWh. Time of Use tariffs offer significant potential benefits to the system by enabling responsive electricity demand and reducing peaks. For example, this could reduce the need for new generation and network capacity. However, the impact of more cost-reflective pricing will vary between consumers. In particular, those who consume electricity at more expensive peak periods, and who are unable to change their consumption patterns, could end up paying significantly more. Understanding the distributional effects of Time of Use tariffs becomes vital to ensuring affordability of energy bills, whilst making demand more flexible. Whilst there is research on fuel poverty in relation to aggregate level of consumption of electricity, little is known about the effects of dynamic tariffs on different socio-demographic groups. DEePRED will fill this gap.The overall aim of DEePRED is to analyse the distributional effects of Time of Use tariffs with a view to identify clusters of users which might significantly benefit or be disadvantaged through the provision of demand flexibility. The project will analyse 10-minute resolution time use activity data from the UK Office for National Statistics Time Use Survey with a view to derive information about times of the day in which different groups of people occupy households and carry out energy-related activities. The time use data will be combined with parameter data on temperatures, sunlight, number and typical consumption of household appliances and dwelling types to derive load profiles. This will take place thanks to the implementation of activity schemes. Load profiles data will then be used to calculate how consumer bills may change for different groups of consumers on stylised Time of Use tariffs.