Applied Artificial Intelligence

AI and Electricity Generation: Aligning the New and the Established

The concept of AI has been around for some years, some say since 1950; although it has really accelerated in the last few years. Power generation has been around since the late 1800s and has gone through changes, but the centralised power grid remains. The current energy transition sees more and more pressure for the decarbonisation of electricity supply, the emergence of community-based localised grids (Distributed Energy Resources, DER) is one viable way to reduce greenhouse gas emissions; AI can have a major role in the operation of these grids and should be seen as a building block of the clean energy transition.

This topic is important as AI can help to drive the energy transition by optimising DERs, community-based girds have the potential for combining renewable energy resources, battery storage, electric vehicles to alleviate the demands placed on the traditional centralised grid. AI offers such DERs greater automation, lower costs for electricity, better system operational efficiencies and resilience, new business models, and reductions in greenhouse gas emissions. AI has the potential to assist responses to flexible loads (e.g., from data centres), ensuring grid stability, flexibility and reliability.

The current energy transition evolves in part from increased pressure for lowering global greenhouse gas emissions quickly, mitigation actions to date have not been sufficient. Community-based power systems utilising AI to co-ordinate operations of these Distributed Energy Systems are viable for decarbonising a nation. For example, in Europe there are community-based power systems; known as Citizen Energy Communities (CEC) which enhance social cohesion and disaster resilience.

This Article Collection welcomes original research, review articles, comparative evaluations, benchmark studies, case studies, and application focused contributions, subject to the article types accepted by the journal. Topics of interest include:

• AI, Deep Learning and Machine Learning for decision systems in community-based power systems
• How AI data analysis can determine the optimal locations for low / zero carbon technologies by predicting the ongoing impact on network capacity
• How might AI inspect, monitor, and control community-based power systems
• How could individuals such as prosumers, nonsumers, or prosumagers benefit from using AI, digitalisation and connectivity
• Can AI and Machine Learning systems help to alleviate the need for additional generation, transmission and distribution assets to the network
• Are there examples of exemplar CECs; what has worked or not? What are the lessons learnt?
• Will AI ensure community-based power systems are trustworthy, fair, just, and free of cyber security issues

Keywords: Artificial Intelligence, Decarbonisation, Electricity Supply, Distributed Energy Resources, Communities

Manuscript Submissions:

Manuscript submission is open until 30th April 2027.

All manuscripts submitted to this Article Collection will undergo desk assessment and peer-review as part of our standard editorial process. Manuscripts which do not fall within the scope of the journal will be rejected.

This journal applies the Taylor & Francis' Open Data Sharing Policy. All authors will be required to make their data and materials supporting their results or analyses presented in their paper freely available.

To submit your papers to this Article Collection, please:

1. Check "Yes" for the question, "Are you submitting your paper for a specific special issue or article collection?"
2. Select the relevant Article Collection from the drop-down menu under the question, "AI and Electricity Generation: Aligning the New and the Established"
3. Enter your Discount Code on the Final Page (Services) in the “Apply Discount Code” box on the right-hand side of the screen, otherwise this will not be applied for their submission, and you would be subject to the journal’s Full APC Fee

We are able to offer a 10% Discount to all authors, and have a limited number of 20% Discount codes only available for early submissions. It should be noted that discount codes must be entered in at the point of submission as they cannot be applied retroactively, nor can these be combined as only the higher valued discount would be applicable.

Please contact Christopher Montgomery, Commissioning Editor regarding details on obtaining your discount codes, and with any other queries for this Article Collection.

Article Collection Guest Advisors

Dr. Lucas Skoufa is a Fellow of Engineers Australia and Chartered Engineer who has an interesting background covering military (naval), energy / power generation and university roles. Lucas’ interests have been in decarbonising power generation, hard-to-abate sectors (e.g., using hydrogen and other low/zero-carbon fuels) and how to implement decentralised/community-based power systems. He sees that the goal of Net-Zero Greenhouse Gas Emissions is achievable and requires a suite of low/zero-carbon energy technologies, a focus on energy efficiency and consumer education / knowledge on how they are part of the current energy and emissions transition.

Dr. Nancy Spencer is an econometrician with a passion for governance, evidence-based strategy and policy; both developing and then implementing these in government agencies and non-government organisations. She is currently the Secretariat and researcher for the Centre in Applied Energy Economics and Policy Research at Griffith University. Nancy worked for Queensland Government in policy areas for almost 20 years and has developed policy and regulations for industry including timber, racing and disaster management. Through her array of experiences, she has developed the political savvy and emotional intelligence needed to address complex, multi-dimensional social and economic policy issues to deliver pragmatic solutions to government.