AI-Driven Energy Trading Platforms: Market Dynamics and Challenges

Abstract

The rapid evolution of the energy sector is significantly influenced by the integration of Artificial Intelligence (AI) technologies. This paper reviews the work in the areas of AI applications in energy trading platforms, focusing on three broad domains. Firstly, the energy industry is undergoing a transformative phase, where AI-driven digitalization is optimizing energy supply, trade, and consumption. Emphasis is laid on AI’s role in integrating solar and hydrogen power generation, supply-demand management, and the latest advancements in AI technology. These techniques have shown superior performance in areas like big data handling, cyberattack prevention, and energy efficiency optimization. Secondly, the manufacturing sector is witnessing a shift towards smart factories, where AI is enhancing value-added manufacturing by integrating various information communication technologies. The characteristics of these factories include operations optimization and intelligent decision-making, with AI technologies enabling systems to adapt to external needs. Lastly, while AI promises transformative changes in the energy sector, it also brings forth challenges. A multidisciplinary approach identifies these challenges, offering insights and recommendations for successful AI integration in the energy sector.

Full text

AI-Driven Energy Trading Platforms: Market
Dynamics and Challenges
1A. H. Alkkhayat, 2Jaisudha J, 3Ishbayeva Nazira, 4Neeti Misra, 5Dr. G. Durgadevi,
6R.Senthil Kumar, and 7Dr. Subhash Gadhave subhash
*
College of technical engineering, The Islamic university, Najaf
†
Department of ECE, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127,
‡
Tashkent State Pedagogical University, Tashkent, Uzbekistan
§
Department of Management, Uttaranchal Institute of Management, Uttaranchal University,
Dehradun 248007, India
**
New Prince Shri Bhavani college of Engineering and Technology, Anna University.
6Associate Professor, AAA College of Engineering and Technology, Virdhunagar India.
7Associate Professor, Dr. D. Y. Patil Institute of Technology, Pimpri,
Abstract.The rapid evolution of the energy sector is significantly
influenced by the integration of Artificial Intelligence (AI) technologies.
This paper reviews the work in the areas of AI applications in energy
trading platforms, focusing on three broad domains. Firstly, the energy
industry is undergoing a transformative phase, where AI-driven
digitalization is optimizing energy supply, trade, and consumption.
Emphasis is laid on AI's role in integrating solar and hydrogen power
generation, supply-demand management, and the latest advancements in
AI technology. These techniques have shown superior performance in
areas like big data handling, cyberattack prevention, and energy efficiency
optimization. Secondly, the manufacturing sector is witnessing a shift
towards smart factories, where AI is enhancing value-added manufacturing
by integrating various information communication technologies. The
characteristics of these factories include operations optimization and
intelligent decision-making, with AI technologies enabling systems to
adapt to external needs. Lastly, while AI promises transformative changes
in the energy sector, it also brings forth challenges. A multidisciplinary
approach identifies these challenges, offering insights and
recommendations for successful AI integration in the energy sector.
Corresponding Authour:
*
Iraq.ahmedhussienradie@iunajaf.edu.iq,
†
j.jaisudha_ece@psvpec.in
‡
naziraabduraimovna1973@gmail.com
§
neeti.cm@gmail.com
**
hodece@newprinceshribhavani.com
,070 (2024)
E3S Web of Conferences https://doi.org/10.1051/e3sconf/202454007001
540
ICPES 2023
01
© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative
Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).

1 Introduction
The Significance of AI in Energy Sector Transformation
The energy sector is at a pivotal juncture, with the increasing need for sustainable, efficient,
and secure energy systems. The advent of Artificial Intelligence (AI) technologies has the
potential to revolutionize this sector, offering new avenues for optimization and reliability.
AI's capabilities in big data handling, machine learning, and deep learning algorithms are
not just theoretical constructs but have shown practical benefits. For instance, Google has
leveraged deep-mind AI technology to reduce its field device management bill by a
staggering 40%. This highlights the transformative power of AI in optimizing operations
and reducing costs, making it an indispensable tool for the future of the energy sector.
The Challenge of Integrating Renewable Energy Sources
The conventional power grid systems were not designed to accommodate the variable loads
introduced by Renewable Energy Sources (RES) like wind, solar, and hydrogen. This poses
challenges in energy distribution and management. AI technologies can play a crucial role
in addressing these issues. Through machine learning and predictive analytics, AI can
efficiently manage the inverter control of photovoltaic systems, maximize power tracking
points, and even favor particle swarm optimization for maximum power point tracking.
These capabilities make AI an essential component in the integration and management of
RES, thereby aligning with the global push towards sustainable energy.
Cybersecurity and AI in Energy Systems
As the energy sector increasingly adopts Internet of Things (IoT) and smart grid
technologies, the risk of cyber-attacks becomes a growing concern. These attacks not only
jeopardize the infrastructure but also have far-reaching environmental and economic
implications. AI's prowess in data analytics and automated threat detection algorithms can
significantly enhance the cybersecurity measures of the energy systems. This is particularly
important in an era where the smart grid is becoming more integrated with bi-directional
communication technologies, supercomputers, and modern infrastructures.
AI-Driven Customized Manufacturing and Its Relevance
The manufacturing sector is also undergoing a transformation, moving towards smart
factories enabled by AI technologies. These smart factories are characterized by operations
optimization, dynamic reconfiguration, and intelligent decision-making. AI's role in
customized manufacturing (CM) is particularly noteworthy, as it allows for a more flexible
and efficient production line. This is relevant to the energy sector as it opens up
possibilities for the customized manufacturing of energy-efficient devices and systems,
thereby contributing to the overall sustainability and efficiency of the energy sector.
In summary, this review article aims to provide a comprehensive understanding of the role
of AI in transforming the energy sector. It focuses on three key areas: the integration of AI
in renewable energy sources and energy generation, the role of AI in supply and demand
management, and the recent advances in AI technologies that contribute to cybersecurity
and customized manufacturing. These areas are not only interrelated but also crucial in
addressing the complex challenges facing the energy sector today.
2 Review and discussion
In a comprehensive study by Ahmad et al. (2021), the authors meticulously examined the
changing dynamics of global energy scenarios, providing a deep understanding of the
,070 (2024)
E3S Web of Conferences https://doi.org/10.1051/e3sconf/202454007001
540
ICPES 2023
01
2

transformative shifts occurring within the energy sector [1]. This study not only highlighted
the pace at which innovations are taking place but also emphasized the intricate interplay of
various components that make up the modern energy landscape. The research delved into
various facets of the energy industry, from business models and policies to energy
technologies and their intersection with artificial intelligence (AI). By doing so, Ahmad et
al. (2021) have offered a holistic view, emphasizing the pivotal role AI is set to play in
shaping the future of energy, making it an indispensable reference for stakeholders and
researchers alike.
The tables 1,2,3, & 4 below encapsulate the pivotal findings from the study on the
intersections between global energy scenarios and the integration of artificial intelligence
(AI) [4-8].
Table 1: AI's Role in Global Energy Innovations
Aspect
Details
Examples/Techniques
Business Models
Role of AI in shaping new
business strategies
Predictive maintenance, automated
contracting, supply chain optimization
Policy
Influence of AI on energy policies
-
Energy
Technologies
AI's integration into energy tech
System size optimization
Social
Innovations
How AI is driving societal
changes in energy consumption
-
Achievements in
AI
Major advancements in AI over
the past 70 years
ML models, DL models, statistical analysis,
new search techniques
Table 2: AI Applications in Energy Systems
Application
Area
Purpose
Techniques/Models
Utilities
Use of AI for energy planning
Load forecasting, economic load dispatch
Optimization
Enhancing energy production
efficiency
Real-time asset adjustment
Infrastructure
Replacing manual inspection
Predictive maintenance control
Demand-Supply
Modern management of
energy demand and supply
ML and DL models
Energy Theft
Detection
Addressing challenges in
developing countries
Support vector machine, DL, Bayesian
regularization, decision trees, random forest
,070 (2024)
E3S Web of Conferences https://doi.org/10.1051/e3sconf/202454007001
540
ICPES 2023
01
3

Table 3: AI and Big Data in Energy – the 5 Vs of Big Data [7]
Big Data Aspect
Description
AI's Role
Volume
Amount of data produced
Data analysis and scanning
Velocity
Speed of data creation
-
Variety
Different data collection formats
Handling diverse data types
Veracity
Trustworthiness of data
Ensuring data accuracy
Value
Significance in the dataset
Automated data interpretation
Table 4: AI Challenges and Future Opportunities
Aspect
Challenges
Opportunities
Adoption
Bottlenecks
Efficient tuning, data quality, lack of
experts, technical infrastructure, legal
concerns
Simplifying and making AI models cost-
effective, improving efficiency with IoT
and AI
Data Security
Vulnerability to data theft and breaches
Enhanced cybersecurity measures, AI-
driven data protection
AI Progress
Uncertainty in sustained progress
Continued research and development
Fromstudying the findings of Ahmad et al. (2021) in the light of ourreview article, it is
evident that the intersection of AI and the energy sector is not just imminent but crucial.
The comprehensive insights provided by the study underscore the transformative potential
of AI in reshaping the energy landscape. From optimizing energy production to ensuring
cybersecurity, AI's capabilities are vast and varied. As we navigate the complexities of the
modern energy industry, integrating AI's advancements will be pivotal in ensuring
efficiency, sustainability, and innovation. The challenges and recommendations highlighted
offer a roadmap for stakeholders, emphasizing the need for collaboration, research, and
ethical considerations. In essence, the fusion of AI and energy is not just a trend but a
necessity for a sustainable future.
Another study by Wan et al. (2020) delves into the architecture of an AI-driven customized
smart factory, focusing on the integration of artificial intelligence (AI) in customized
manufacturing (CM) [2]. The key findings from the study are summarized as follows [9-
12]:
 AI-Driven Customized Manufacturing: The study presents the architecture of an
AI-driven customized smart factory, emphasizing the importance of intelligent
,070 (2024)
E3S Web of Conferences https://doi.org/10.1051/e3sconf/202454007001
540
ICPES 2023
01
4

manufacturing devices, intelligent information interaction, and the construction of
a flexible manufacturing line.
 Shift from Traditional to Smart Factories: Traditional batch production lacks
flexibility in meeting individual customer requirements. The emergence of AI has
enabled higher value-added manufacturing, integrating manufacturing with
information communication technologies.
 Characteristics of a Customized Smart Factory: Such factories are
characterized by self-perception, operations optimization, dynamic
reconfiguration, and intelligent decision-making. They can adapt to external needs,
perceive the environment, and extract process knowledge.
 AI Technologies in CM: The study surveys state-of-the-art AI technologies that
can be applied to CM, such as machine learning, multiagent systems, Internet of
Things, big data, and cloud-edge computing.
 Benefits of AI in CM: AI-assisted CM offers increased production flexibility and
efficiency. The study validates these benefits with a case study on customized
packaging.
 Challenges and Solutions: The article also discusses the challenges and potential
solutions related to the implementation of AI in CM.
 Industry 4.0 and Smart Manufacturing: The Industry 4.0 initiative promotes
smart manufacturing as a key driver of global economic growth. The study
highlights the transformation from the digital era to the intelligent era in
manufacturing.
 AI Technologies Overview: AI encompasses various techniques and applications,
from perception and machine learning to computer vision and intelligent robots.
The progress in big data analysis and deep learning has ushered in the AI 2.0 era.
 Advantages of AI-Driven CM: Incorporating AI in CM can lead to improved
production efficiency, predictive maintenance, and the development of smart
supply chains.
This study provides a comprehensive overview of the potential and challenges of
integrating AI into the manufacturing landscape, emphasizing the transformative power of
AI in revolutionizing traditional manufacturing processes.
In the context of our review article, the findings from Wan et al. (2020) offer invaluable
insights into the transformative role of artificial intelligence (AI) in modernising the
manufacturing sector. Their exploration into AI-driven customised smart factories aligns
seamlessly with our discussion on the integration of AI technologies across various
industries. The study's focus on the shift from traditional to smart factories, enabled by AI,
resonates with our own examination of how AI is revolutionising business models and
operational efficiencies. Moreover, their identification of the benefits and challenges of AI
implementation provides a balanced perspective that enriches our review. Thus, Wan et al.'s
work serves as a pertinent case study that substantiates and complements our overarching
narrative on the pervasive impact of AI.
Another study by Danish (2023) delves into the challenges and intricacies of integrating
artificial intelligence (AI) into the energy sector [3]. This comprehensive research offers a
deep dive into the transformative potential of AI within the energy landscape, examining
both its promising advantages and the unforeseen challenges that come with its adoption.
By tracing the historical evolution of AI in energy and proposing frameworks for its future
integration, the study provides a roadmap for the sector's next steps.The key findings from
the study are [13-17]:
,070 (2024)
E3S Web of Conferences https://doi.org/10.1051/e3sconf/202454007001
540
ICPES 2023
01
5

 AI's Role in Energy Transformation: AI is poised to drive the transformation of
the energy sector, offering innovative methods to enhance the operation and
reliability of energy systems. This ensures both technical and economic
advantages.
 Unforeseen Challenges: The integration of AI into the energy sector, while
promising, comes with unexpected challenges. These challenges have been
identified and categorised based on common dependency attributes. The research
provides insights and recommendations on how to navigate these challenges,
emphasising the potential benefits of successful AI integration.
 Historical Perspective: The research traces the evolution of AI, highlighting
significant milestones such as the development of expert systems for energy
management in the 1960s and the integration of AI in renewable energy systems in
the 1990s.
 Integration of AI into Energy Policies: The study underscores the importance of
incorporating AI into energy policies. It introduces a framework that focuses on
the techno-economic aspects, organising them into eight key knowledge areas,
ensuring long-term sustainability in the energy sector.
 Objective of the Study: The primary aim is to pinpoint the main challenges the
energy sector faces when adopting intelligent and smart technologies. Through a
comprehensive literature review, the study derives expert insights regarding
challenge exploration from multiple perspectives.
In the context of our review article, Danish's findings are particularly pertinent. They not
only reinforce the transformative potential of AI in the energy sector but also highlight the
importance of being cognisant of the challenges. This balanced perspective ensures that as
we navigate the future of energy, we do so with both optimism and caution, making
informed decisions that benefit the sector and society at large.
3 Future Scope of Research
The ever-evolving landscape of the energy sector, intertwined with the rapid advancements
in artificial intelligence, presents a myriad of opportunities for future research. As we delve
deeper into the integration of AI within the energy domain, it becomes imperative to chart
out potential avenues that can further enhance our understanding and application of this
symbiotic relationship. Here are some pointers for the future scope of research:
 Deep Learning in Renewable Energy: Investigate the potential of deep learning
algorithms in predicting and optimising renewable energy outputs, especially in
fluctuating sources like wind and solar.
 AI in Energy Storage: Explore how AI can revolutionise energy storage
solutions, ensuring efficient and timely distribution of stored energy.
 Ethical Implications of AI in Energy: A comprehensive study on the ethical
considerations and potential biases in AI algorithms when applied to energy
distribution and consumption.
 AI-Driven Energy Conservation: Research on how AI can be utilised to drive
energy conservation initiatives, both at the industrial and consumer levels.
 Integration of IoT with AI in Energy: Delve into the potential of integrating
Internet of Things (IoT) devices with AI to create smarter, more responsive energy
grids.
4 Knowledge Gaps
,070 (2024)
E3S Web of Conferences https://doi.org/10.1051/e3sconf/202454007001
540
ICPES 2023
01
6

The integration of AI into the energy sector, while promising, is still in its nascent stages.
As with any emerging interdisciplinary field, there exist knowledge gaps that need
addressing to ensure a seamless and effective amalgamation of AI and energy. Here are
some areas where further clarity is required:
 Standardisation of AI Algorithms: There's a pressing need for standardised
protocols and benchmarks for AI algorithms in the energy sector to ensure
consistency and reliability.
 Transparency in AI Decision Making: The 'black box' nature of certain AI
models poses challenges in understanding their decision-making processes,
especially in critical energy management scenarios.
 Economic Implications: A deeper understanding of the economic implications of
integrating AI into the energy sector, both in terms of investment and potential
returns.
 Training and Skill Development: Addressing the gap in skills required to
manage and operate AI-driven energy systems. This includes both technical
training and ethical considerations.
 Data Privacy and Security: As AI relies heavily on data, there's a need to address
concerns related to data privacy, especially when dealing with consumer energy
consumption patterns.
In conclusion, while the fusion of AI and the energy sector holds immense promise, it's
crucial to address these future research avenues and knowledge gaps. Doing so will not
only ensure the sustainable growth of this interdisciplinary field but also maximise its
potential benefits for society at large.
5 Conclusion
The integration of artificial intelligence within the energy sector is a transformative
endeavour, reshaping the way we perceive and manage energy resources. As we culminate
our review, it's essential to reflect upon the key findings that have emerged from our
exploration of the three seminal articles:
 AI's Pervasive Role: The energy landscape is witnessing a paradigm shift, with
AI emerging as a pivotal driver, influencing business models, policy-making, and
energy technologies. Its applications, ranging from predictive maintenance to
supply chain optimisation, are revolutionising the sector.
 Enhanced Decision-making: AI's contribution to the energy sector isn't just about
automation; it's about making more reliable and efficient decisions. The
integration of machine learning and deep learning models has facilitated this
enhanced decision-making process.
 Big Data Management: The energy industry's generation of vast amounts of data
necessitates sophisticated management solutions. AI, with its capabilities to handle
the "five Vs" of big data, has proven indispensable in this regard.
 Challenges and Bottlenecks: While AI offers immense potential, its integration
into the energy sector isn't without challenges. Issues like network hyperparameter
tuning, data quality, and lack of expertise underscore the need for continued
research and development.
,070 (2024)
E3S Web of Conferences https://doi.org/10.1051/e3sconf/202454007001
540
ICPES 2023
01
7

 Ethical and Security Implications: The ethical dimensions of AI in energy,
especially concerning data privacy and algorithmic transparency, are areas that
warrant attention. Moreover, the security of AI-driven energy systems, especially
in the face of cyber threats, is of paramount importance.
 Future Research Avenues: The energy sector's tryst with AI is just beginning.
There's a vast expanse of uncharted territory, from exploring AI's role in
renewable energy to its integration with IoT devices, that beckons future research
endeavours.
In light of our abstract, these findings reiterate the transformative potential of AI in the
energy sector. As we stand at the cusp of this technological revolution, it's imperative to
navigate this journey with a blend of optimism, caution, and an unwavering commitment to
sustainable and ethical practices.
References
1. Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021).
Artificial intelligence in sustainable energy industry: Status Quo, challenges and
opportunities. Journal of Cleaner Production, 289, 125834.
2. Wan, J., Li, X., Dai, H. N., Kusiak, A., Martinez-Garcia, M., & Li, D. (2020).
Artificial-intelligence-driven customized manufacturing factory: key technologies,
applications, and challenges. Proceedings of the IEEE, 109(4), 377-398.
3. Danish, M. S. S. (2023). AI in Energy: Overcoming Unforeseen Obstacles. AI, 4(2),
406-425.
4. Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. Is your business ready for
artificial intelligence? Jt. BCG-MIT sloanmanag. Rev. Surv. impact Artif. Intell.
Bus.(2017).
5. Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with
artificial intelligence: Closing the gap between ambition and action. MIT Sloan
Management Review, 59(1).
6. Gerbert, P., Reeves, M., Steinhäuser, S., &Ruwolt, P. (2017). Is Your Business Ready
for Artificial Intelligence?. Accessed, 6, 2020.
7. Nguyen, T. L. (2018, December). A framework for five big v’s of big data and
organizational culture in firms. In 2018 IEEE International Conference on Big Data
(Big Data) (pp. 5411-5413). IEEE.
8. Elsevier. (2018). Artifical Intelligence: How Knowledge is Created, Transferred, and
Used. Elsevier.
9. Gopinath S., Suresh Kumar N., Madhumitha S.& Natraj N.A.(2020),"TSRP: A trust
based secure routing protocol for authentication and load balancing in
MANET",International Journal of Advanced Science and Technology,29(1).
10. Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., & Vasilakos, A. V. (2017). A
manufacturing big data solution for active preventive maintenance. IEEE Transactions
on Industrial Informatics, 13(4), 2039-2047.
11. Wang, T., Guo, S., & Lee, C. G. (2014). Manufacturing task semantic modeling and
description in cloud manufacturing system. The International Journal of Advanced
Manufacturing Technology, 71, 2017-2031.
,070 (2024)
E3S Web of Conferences https://doi.org/10.1051/e3sconf/202454007001
540
ICPES 2023
01
8

12. Valente, A. (2016). Reconfigurable industrial robots—An integrated approach to
design the joint and link modules and configure the robot manipulator. In Advances in
Reconfigurable Mechanisms and Robots II (pp. 779-794). Springer International
Publishing.
13. Harrison, R., Vera, D., & Ahmad, B. (2021). Towards the realization of dynamically
adaptable manufacturing automation systems. Philosophical Transactions of the Royal
Society A, 379(2207), 20200365.
14. Ghobakhloo, M., Iranmanesh, M., Morales, M. E., Nilashi, M., & Amran, A. (2023).
Actions and approaches for enabling Industry 5.0‐driven sustainable industrial
transformation: A strategy roadmap. Corporate Social Responsibility and
Environmental Management, 30(3), 1473-1494.
15. Samuel, O., Javaid, N., Khalid, A., Khan, W. Z., Aalsalem, M. Y., Afzal, M. K., &
Kim, B. S. (2020). Towards real-time energy management of multi-microgrid using a
deep convolution neural network and cooperative game approach. IEEE access, 8,
161377-161395.
16. Preetha M., Anil Kumar N., Elavarasi K., Vignesh T., &Nagaraju V.,(2022)"A Hybrid
Clustering Approach Based Q-Leach in TDMA to Optimize QOS-
parameters",Wireless Personal Communications,123(2).
17. Danish, M. S. S., Senjyu, T., Zaheb, H., Sabory, N. R., Ibrahimi, A. M., & Mata yoshi,
H. (2019). A novel transdisciplinary paradigm for municipal solid waste to energy.
Journal of Cleaner Production, 233, 880-892.
,070 (2024)
E3S Web of Conferences https://doi.org/10.1051/e3sconf/202454007001
540
ICPES 2023
01
9