A review of current analytical methods, modelling tools and development frameworks applicable for future retail electricity market design

Harrison Hampton and Aoife Foley

Energy, 2022, vol. 260, issue C

Abstract: Retail electricity markets require development to become transparently functioning, operate in near real-time, permit the integration of smart technology, improve customer engagement, better reflect wholesale prices, be driven by optimal sustainability metrics and mitigate against unexpected price spikes, so that society transitions to net zero in a just manner. This study postulates that it is essential that future retail electricity markets are designed by cross-linking current analytical approaches and integrating proprietary and open-source modelling tools, by adopting the latest development frameworks across data science, software development, cloud computing and Internet of Things interconnectivity. Current analytical methods applicable for retail electricity market design are reviewed and multi-criteria decision analyses are applied to assess modelling tool and development framework effectiveness. A key finding of this study is that current analytical methods applied in the research domain are typically segregated or siloed. As current analytical methods tend to focus on specific clusters, such as electricity customer profiling, demand response and dynamic pricing, peer-to-peer electricity trading, interconnectivity of Internet of Things devices and distribution-level power dynamics, rather than the integration of these clusters and how each analytical method interacts with each other. Similarly, this study finds that modelling tools are also typically segregated or siloed, focusing on energy system planning, electricity market simulation, power system simulation and energy consumption analysis. To provide the necessary tools to design future retail electricity markets, this study suggests that robust modelling tool integration is required.

Keywords: Retail electricity market; Smart grid; Power system modelling; Customer engagement; Demand response; Peer-to-peer; Big data; Digital transformation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222017649
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:260:y:2022:i:c:s0360544222017649

DOI: 10.1016/j.energy.2022.124861

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().