Residential Electricity Demand: Peaks, Sequences of Activities and Markov chains (REDPeAk)

b74768d6-7dc8-49bc-950b-6ad88763655c

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EPSRC
EPSRC
EPSRC
EPSRC
EPSRC

£3,078,905

Feb. 1, 2017

July 1, 2022

Peak electricity demand is becoming an increasingly significant problem for UK 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 (DECC, 2013). During peak demand, electricity prices in wholesale markets could fluctuate from less than 0.04 Euros/kWh to as much as 0.35 Euros/kWh (Torriti, 2015). In the future the peak problem is expected to worsen due to the integration of intermittent renewables in the supply mix as well as high penetration of electric vehicles and electric heat pumps. Understanding what constitutes peaks and identifying areas of effective load shifting intervention becomes vital to the balancing of demand and supply of electricity. Whilst there is information about the aggregate level of consumption of electricity, little is known about residential peak demand and what levels of flexibility might be available. REDPeak will fill this gap.

The overall aim of REDPeak is to analyse the variation in sequences of activities taking place at times of peak electricity demand with a view to identify clusters of users which might provide flexibility for peak shifting intervention.

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 occupancy and synchronisation of activities. Markov chains will be used to model load profiles in combination with appliance-specific parameter data. Since Markov chains have proven effective at generating electricity load profiles except for peak times, REDPeak will develop Hybrid Monte Carlo modelling to account for demand moving in larger steps during peak periods. Sequence analysis will be used to mine activities at periods of peak electricity demand. REDPeak will cluster respondents according to sequences of activities and analyse to what extent appliance-specific control variables explain activities at specific times of the day. Three datasets will be used for direct validation between metered data and time use data. Findings on sequence analysis will feed into algorithms for automated demand management or Demand Side Response.

Potential Impact:
This Impact Summary describes who potential beneficiaries might be, and how REDPeak might impact them.

- Energy Service Companies and specifically energy aggregators (beneficiaries, direct impact) will be provided with an empirical framework which informs about Demand Side Response (DSR) investment options in the residential electricity market. From the findings of the proposed project they will be able to make informed decisions about levels of investment for different segments of demand (based on flexibility indices). An expansion of the DSR market for residential customers will open competition to leading aggregators companies from the U.S. and elsewhere in Europe.

-Residential electricity consumers (beneficiaries, indirect impact) will be able to choose from lower tariffs thanks to a whole range of price-based DSR (from dynamic pricing to time-of-use tariffs to regulatory incentives on reward schemes) which will be recommended as a result of the study. The results of the study will indicate which DSR programmes will suit different clusters of households based on their time-use flexibility. This will be informed by an innovative form of segmentation of end-users for peak shifting purposes.

-If the project stimulates higher penetration of DSR in the UK, then energy suppliers (beneficiaries, indirect impact) may benefit from increases in conservation and shedding, which will prevent costs of additional generation for peak loads and will increase absolute demand for microgeneration renewables across the electricity market.

-Policy-makers (users, indirect impact) may find the findings of the model useful as part of any evidence-based approach to regulating DSR. For instance, findings will highlight policies facilitating different types of DSR activities for the residential sector. In cases where variations in occupancy during peak periods are very high, the priority is the harmonization of domestic appliances through so-called 'smart appliance' solutions. In cases where variations in occupancy are very low during peak periods, national regulators might consider incentive-based forms of DSR, where consumers who receive peak signals through smart meters are rewarded (or penalised) based on their response. In cases where variations in occupancy during off-peak periods are low, it might be possible to facilitate market entrance of discrete demand controllers which determine, for instance, the levels of consumption for heating services. Where variations in occupancy during off-peak periods are high, time of use programmes might be the most adequate form of DSR.

-UK manufacturers (beneficiaries, indirect impact) who provide technological devices for DSR equipment might experience increases in demand as the study points out new market geographies for the application of DSR devices in an integrated smart grid. These manufacturers can be classified as follows:

-The smart metering industry, which at the time of writing this project proposal is often acting through large public projects for the national roll-out of smart meters would experience higher competition. The project results will draw attention to areas specific smart metering functions (e.g. peak signals, price signals, distribution LV network congestion signals), emphasising new needs which a more competitive smart metering industry will be able to address.

-Other manufacturers include smart plug companies, frequency controllers' companies, software providers for automated demand side controllers, etc.