Shifting the timing of energy demand: Developing a novel modelling framework to quantify the demand response potential of domestic appliances in UK homes
This study aims to present a high-resolution stochastic electricity demand model for evaluating the demand response. This model will simulate a domestic electricity use at a one minute resolution that includes important features of electricity load such as diurnal and seasonal variations, short time fluctuations and diversity betweenhouseholds. This is important as the UK electricity sector undergoes significant changes as part of the transitionto a low-carbon economy. Demand side flexibility, in particular demand response, is seen as an integral part of the system to support the integration of these low carbon technologies enabling better match with the output of intermittent generation capacities. There will be a change from “supply follows a paradigm” to a “load followssupply paradigm”.
There is a common perception that there exists a significant scope for demand shifting to provide benefit to future system operations with low carbon technologies. However by viewing demand response within a domestic context, in previous studies, there is a limited evidence of the degree of the impacts of load shifting on the energy demand profile for the residential sector. Literature shows data on the effects of load shifting, but only a small amount of new technologies and controlling of the appliances were evaluated such as washing machines and air conditioners. The impact of only a small number of individual appliances have been investigated in the peak periods without considering the impact on the whole electricity load profile. This is partly due to lack of easily-accessible to all appliances and high-resolution consumption data.
A holistic approach is needed for the quantification of the extent of the contribution and the side-effects of shifting the load before it will be assumed that demand response techniques will be successful to address the challenges of the future operation systems. There is, as a result, increasing attention being given to the realistic insights to the demand response schemes.
To evaluate the demand response, varietyof appliances are included with their technical and practical usage patterns and operation constraints which arehighly correlated to empirically measured household electricity consumption data. For this, the study uses “TheHousehold Electricity Use Survey” dataset to identify and examine the patterns of energy need in UK homes. The project was funded by the Department for Environment, Food and Rural Affairs, the DECC and Energy SavingTrust. Electrical power demand and energy consumption of 250 households were measured from May 2010 toJuly 2011. The study focuses on understanding the electricity demand at UK homes, how much electricity isconsumed during different periods of days, weeks and seasons in order to assess the potential for time shifting of the demand. With the developed model, the paper evaluates the extent to which the electricity load profile ofhomes can be shaped with shifting the timing of the certain loads or by changing the behaviours to discover opportunities for the demand response. By modelling the peak loads and the aggregated amount of load that isavailable for shifting without harming household’s convenience, the study intends to provide insights to flexibilityand capability of the residential electricity demands for load shifting that could encourage households to changethe time of use of appliances to reduce electricity demand costs.
Shifting the timing of energy demand: A stochastic modelling approach
Smart Appliances will allow households to time-shift certain energy demands enabling better matching of demand and supply as a key part of a smart grid. There is only limited evidence that quantifies this potential and previous studies have focused on a small number of appliance types and only in peak periods. To address this research gap, this paper presents a modelling framework that generates realistic electricity demand profiles for domestic appliance use that are based on measured data. The aim is to use the model to provide insights into the amount of flexible demand that can be available for shifting, when aggregated across a number of homes. A bottom-up modelling approach is taken which is based on measurements of domestic appliances such as switch-on times, operating time (duration), respective power consumption and frequency of use. The method of the framework uses a probabilistic approach, where probabilistic functions representing the likelihood of appliance usage are constructed and then used to generate high temporal resolution, end-use specific household electricity demand profiles that represent random variations between individual homes.