Agent-Based Models for Electricity Markets Accounting for Smart Grid Participation

Lupo, Sara; Kiprakis, Aristides

doi:10.1007/978-3-319-25479-1_4

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…2010-2019: There are a total of 1,142 works in this period. These include energy consumption in wireless sensor networks (Ebadi et al, 2010), reliability and efficiency of smart grids (Rosic et al, 2013), simulation of electric power markets (Vale et al, 2011), social responsibility for low energy consumption and public building energy management (Egging, 2013), smart grid IT governance (Parra et al, 2014), energy markets agent-based modelling (Lupo and Kiprakis, 2015), cybersecurity for smart grids (Yardley et al, 2015), solar power prediction (Cabrera et al, 2016), pricing systems in smart grids (Bandyopadhyay et al, 2016), smart grid congestion management (MacDougall et al, 2017), electricity theft in smart grids (Yeckle and Tang, 2018), and load scheduling in smart grids (Senevirathne et al, 2019). 2020-2022: There are a total of 2,180 works during this less than a 3-year period.…”

Section: Research In Ai and Energy Systems (1967-2022)mentioning

confidence: 99%

AI explainability and governance in smart energy systems: A review

2023

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Traditional electrical power grids have long suffered from operational unreliability, instability, inflexibility, and inefficiency. Smart grids (or smart energy systems) continue to transform the energy sector with emerging technologies, renewable energy sources, and other trends. Artificial intelligence (AI) is being applied to smart energy systems to process massive and complex data in this sector and make smart and timely decisions. However, the lack of explainability and governability of AI is a major concern for stakeholders hindering a fast uptake of AI in the energy sector. This paper provides a review of AI explainability and governance in smart energy systems. We collect 3,568 relevant papers from the Scopus database, automatically discover 15 parameters or themes for AI governance in energy and elaborate the research landscape by reviewing over 150 papers and providing temporal progressions of the research. The methodology for discovering parameters or themes is based on “deep journalism,” our data-driven deep learning-based big data analytics approach to automatically discover and analyse cross-sectional multi-perspective information to enable better decision-making and develop better instruments for governance. The findings show that research on AI explainability in energy systems is segmented and narrowly focussed on a few AI traits and energy system problems. This paper deepens our knowledge of AI governance in energy and is expected to help governments, industry, academics, energy prosumers, and other stakeholders to understand the landscape of AI in the energy sector, leading to better design, operations, utilisation, and risk management of energy systems.

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