The large-scale deployment of renewable energy poses considerable challenges to the task of balancing electricity supply and demand, including the risks of periods of surplus generation, unmet peak demand, and flexible thermal generation operating at uneconomically low capacity factors. To address this challenge, there is considerable interest in demand-side engagement and the potential to support system balancing by securing demand response – encouraging consumers to shift their electricity demand in time, usually in response to a variable electricity price signal.
Cost reductions in the field of information and communication technology have meant that it is increasingly feasible to deploy technology to enable demand response in the domestic sector, such as smart meters that can support the wide-spread introduction of dynamic electricity pricing. The paradigm-case for technology-enabled demand response is that of the ‘smart grid’, consisting of smart appliances, electric vehicles, distributed energy generation and storage technologies and automated smart control systems managing the complex task of scheduling the operation of devices to meet the calculated demands of individual lifestyles while providing support to the grid.
While there is considerable value in assessing the role of technology in enabling demand response, the demand-side engagement team is exploring a bigger socio-technical space of demand response potential. This conceptual ‘demand response space’ (shown below) consists of three dimensions which can, in various combinations, provide demand response. The ‘technology change’ dimension stands for demand response that is provided solely by technology change, such as replacing a conventional fridge with a smart frequency-responsive one. The ‘service expectation change’ dimension represents people being flexible in their service expectations, such as altering their thermostat settings, or eating a cold meal rather than a hot one. Finally, the ‘activity change’ dimension represents the potential for people to be flexible in the timing of their activities eg postponing the laundry. The fundamental point is that to achieve the full potential capacity for demand response (towards the top-right hand corner of the space), in a given population, there is a value in considering the capacity for flexibility in people’s service expectations and activities, alongside that of technology change.
‘Demand response space’ showing the volume of possible demand response scenarios enabled by all hypothetical combinations of technology, activity and service expectation change.
To address this challenge the demand-side engagement team is taking an interdisciplinary approach and using quantitative data-driven engineering models of energy demand and integrating these with qualitative insights from social sciences. The overall aim of this work is to develop an effective unified framework for analysing the potential, performance, cost, and interaction of demand response and storage for the secure integration of renewable energy into power systems.