Energy Control for Household Optimisation
|Duration||Aug 2013 - Jan 2017|
As technology grows, our reliance on electricity goes with it. But it does also provide its own alternative solutions in coping with the greater demands placed on the electricity network. This project focused on assessing Domestic Demand Side Response (DDSR), which is seen as a key solution to managing the future electricity network.
DDSR is the ability to remotely control loads on the network (i.e. electricity use) through direct control, price signals and planned load shifting. Essentially, this means that customers agree to switch the times that they put certain appliances on for an incentive. This technique could provide additional network control at times of peak loading, allowing high loads to be actively managed and potentially reducing the need for network reinforcement.
Working with the Energy Saving Trust, ECHO looked to utilise a number of off-the-shelf interactive plug-in devices that facilitated the scheduling of loads for individual domestic appliances at two hundred premises. A range of incentives were trialled with statistically representative groups and monitored to gauge take-up of demand response events.
Two hundred customers were recruited for the trial through the Energy Saving Trust, allowing several statistically representative groups to be created. To incentivise participation, a range of financial incentives were developed and trialled. Data was collected and analysed allowing changes made to the trial conditions and incentives assessed consumer behaviour in various scenarios.
Each property received a number of smart plugs, which sat between the plug socket and the appliance to be controlled. Each unit collected data on the appliances, while allowing load control signals to be actioned through a broadband link. A software system was used to schedule the load control events and sent signals to the smartplugs to control the loads. A customer web portal was available for customers to monitor their energy usage and manage appliances remotely.
Existing network modelling tools, such as the WS3 Transform Model, were being used extensively for network investment planning purposes. However, there was limited data on the effectiveness of DDSR, which meant that the modelling tools could not adequately account for this. The technology solutions required were unproven and there was limited evidence as to what appliances were best directly controlled. Consumer attitudes and acceptance was also unknown, including the level of incentives required to drive behaviour change.
This study allowed us to learn more about consumer attitudes towards DDSR. The scheme’s intention was to generate an improved knowledge of the scale of domestic load control possible, feeding into future investment planning models. Additional learning was also generated to update parameters in the WS3 Transform Model.