Behavioural patterns in aggregated demand response developments for communities targeting renewables

Encouraging consumers to embrace renewable energies and energy-efficient technologies is at stake, and so the energy players such as utilities and policy-makers are opening up a range of new value propositions towards more sustainable communities. For instance, developments of turn-key demand response aggregation and optimisation of distributed loads are rapidly emerging across the globe in a variety of business models focused on maximising the inherent flexibility and diversity of the behind-the-meter assets. However, even though these developments' added value is understood and of wide interest, measurement of the desired levels of consumer engagement is still on demonstration stages and assessment of technology readiness. In this paper, we analyse the characteristics of the loads, the behaviour of parameters, and in a final extent, the behaviour of each kind of consumer participating in aggregated demand scheduling. We apply both non-automatic and machine learning methods to extract the relevant factors and to recognise the potential consumer behaviour on a series of scenarios that are drawn using both synthetic data and living labs datasets. Our experimentation showcases a number of three patterns in which factors like the community's demand volume and the consumer's flexibility dominate and impact the performance of the tested development. The experimentation also makes current limitations arise within the existing electricity consumption datasets and their potential for inference and forecasting demand flexibility analytics.

Automated summary

Encouraging consumers to adopt renewable energy and energy-efficient technologies is crucial, leading energy players to propose new value propositions for more sustainable communities. Despite the understood value of these developments, measurement of consumer engagement levels is still in the demonstration stages and requires assessment of technology readiness. The analysis in this paper focuses on load characteristics, parameter behavior, and consumer participation behavior in aggregated demand scheduling, utilizing non-automatic and machine learning methods to identify relevant factors and potential consumer behavior in various scenarios.