Behavioural patterns in aggregated demand response developments for communities targeting renewables. (September 2021)

• Record Type: Journal Article
• Authors: Cruz, Carlos; Palomar, Esther; Bravo, Ignacio; Aleixandre, Manuel
• Is Part Of: Sustainable cities and society. Volume 72(2021)
• Publication Date: 2021-09
• Subjects: Demand response -- Aggregated demand scheduling -- Load patterns -- Flexibility impact -- Clustering analysis -- Automatic demand profiling; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2021.103001
• Languages: English
• ISSNs: 2210-6707

Abstract

Abstract: 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. Highlights: Understanding the consumer community's demand pattern helps successful DR development. Consumer flexibility, demand flow and volume impact the most on the scheduling process. Clustering and PCA throw high accuracy rates at identifying consumer coalitions. Communities benefit from jointly targeting renewables through turn-key aggregated DR.