Optimizing Energy Infrastructure with AI Technology: A Literature Review ()
1. Introduction
Global energy systems face unprecedented challenges due to rising energy demand, driven by factors such as industrial growth, technological advancements, and population increases. According to the International Energy Agency [1], global energy demand is projected to increase by 25% by 2040. Currently, buildings account for approximately 30% of global energy usage, underscoring the significant impact AI-driven energy management could have, especially in sectors with high energy demand, like the commercial and residential sectors [2]. China, one of the largest energy consumers globally, has demonstrated that AI integration in its smart grid can save approximately 500 million metric tons of CO2 emissions annually, as AI enhances energy use by optimizing distribution [3]. In response to these pressures, Europe has implemented AI-powered infrastructures that optimize renewable energy deployment, facilitating an eco-friendly urban development strategy that aligns with smart city goals and helps achieve net-zero objectives [4]. Simultaneously, the world’s reliance on renewable energy continues to grow, with renewables accounting for nearly 29% of global electricity generation in 2022, a figure expected to rise to 42% by 2028 [5]. This growing trend is visible in Figure 1 and Figure 2, which show the world’s increasing dependency on renewable energy. While Figure 3 shows the distribution of this dependency across the globe as of 2022.
However, the fluctuating nature of renewable energy sources, such as wind and solar, presents significant challenges. Wind and solar energy, while critical for reducing carbon emissions, are intermittent and can lead to variability in supply. This variability is expected to impact energy demand consistency by 20% - 30%, leading to periods of both surplus and shortage, which destabilizes energy infrastructure and complicates grid management [1] [6]. Smart grid technologies integrated with AI are proving essential for managing this variability. By using predictive modeling, these systems can stabilize energy supply and demand fluctuations, as evidenced during the COVID-19 pandemic when energy consumption patterns shifted dramatically, and AI-based systems enabled grids to adapt effectively [7]. Similarly, the EU has responded to this by integrating AI with smart grids, which facilitates real-time data analysis and optimal distribution, reducing system strain and increasing resilience against fluctuations [4].
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Figure 1. Renewable energy as percentage of annual energy [5] [8]-[11].
Figure 2. Renewable energy as a percentage of annual energy used (World) [5] [10].
Figure 3. Renewable energy consumption (Percentage of total final energy consumption) [12].
Traditional energy grids, which were designed for centralized and consistent power generation, struggle to accommodate these fluctuations. These grids primarily rely on manual processes and limited automation, which are no longer sufficient to manage the complexities of modern energy systems [13]. AI’s predictive analytics capability significantly improves energy infrastructure stability by optimizing real-time distribution, reducing unplanned downtime by up to 40% [14] [15].
Compared to earlier research that focused primarily on traditional grid systems, this study emphasizes the transformative potential of AI in optimizing modern energy systems. Prior works, such as the integration of basic fault detection mechanisms in isolated grids [16], lacked robust cybersecurity measures. This study stresses need to incorporate advanced cryptographic frameworks and AI-enabled fault detection mechanisms to ensure both operational reliability and security.
Despite its promise, AI integration in energy infrastructure remains in its early stages, facing challenges such as high costs, data availability issues, and cybersecurity concerns, which limit its widespread adoption, particularly in developing countries with insufficient infrastructure and resources [7] [17]. Table 1 outlines the timeline and objectives of AI strategies across different nations, showcasing how governments globally are leveraging AI to transform energy systems.
Table 1. Countries and investment timeline in AI strategies [18]-[41].
Year |
Country |
Name of AI Strategy |
Goal |
2017 |
Canada |
Pan-Canadian AI Strategy |
To boost Canada’s AI ecosystem and retain top talent |
2017 |
Japan |
AI Technology Strategy |
To enhance AI R&D and strengthen industry-academic partnerships |
2017 |
China |
Next Generation AI Plan |
To make China a global leader in AI by 2030 |
2017 |
Finland |
Finland’s AI Strategy |
To create a competitive edge for Finnish companies with AI |
2017 |
Singapore |
National AI Strategy |
To integrate AI across sectors like healthcare, transport, and finance |
2018 |
United Kingdom |
AI Sector Deal |
To invest in AI to boost industry and secure the UK’s leadership in
AI innovation |
2018 |
France |
France’s AI Strategy |
To develop an AI ecosystem that strengthens the economy and
addresses ethical challenges |
2018 |
United States |
White House Summit on AI |
To coordinate AI policy and strategies across federal agencies |
2018 |
South Korea |
AI R&D Strategy |
To drive AI innovation with a focus on core technologies and key
industries |
2018 |
India |
National Strategy for AI |
To leverage AI for inclusive growth, particularly in agriculture,
healthcare, and education |
2019 |
Germany |
National AI Strategy |
To position Germany as a global AI leader through innovation and
ethical guidelines |
2019 |
Australia |
AI Roadmap |
To boost AI research, industry applications, and regulatory
frameworks |
2019 |
Brazil |
Brazilian AI Strategy |
To enhance productivity and competitiveness through AI in
various sectors |
2020 |
European Union |
European AI Strategy |
To promote ethical AI use and bolster AI capabilities across
EU countries |
2020 |
United Arab Emirates |
National AI Strategy 2031 |
To position UAE as a global AI hub, focusing on government,
healthcare, and education |
2020 |
New Zealand |
AI for Aotearoa |
To drive economic and social well-being through AI |
2021 |
United States |
National AI Initiative |
To enhance AI R&D, workforce training, and leadership in
trustworthy AI |
2021 |
South Africa |
AI Policy Framework |
To promote AI for social and economic transformation |
2021 |
India |
National AI Portal |
To provide an ecosystem to promote AI-driven innovation and
entrepreneurship |
2022 |
Canada |
AI and Analytics Initiative |
To support AI adoption in business and ensure responsible AI usage |
2022 |
Saudi Arabia |
National Strategy for Data and AI |
To diversify the economy through AI in alignment with Vision 2030 |
2023 |
European Union |
AI Act |
To establish clear regulations for safe and ethical AI use across the EU |
2023 |
Japan |
AI Strategy 2023 |
To focus on AI advancements in robotics, aging population support,
and sustainable growth |
2. Literature Review
2.1. Theoretical Review
The integration of AI into energy systems is heavily informed by several theoretical frameworks, with Systems Theory (ST) being the most prominent. Systems Theory perceives energy grids as complex adaptive systems that require constant monitoring, dynamic adjustment, and balance to maintain stability—particularly in the face of increasing reliance on intermittent renewable energy sources. Systems Theory provides the foundation for understanding how AI can autonomously manage these dynamic energy systems by using real-time data to adjust energy distribution and mitigate fluctuations [42]. It offers a framework to design decentralized AI-driven microgrids that reduce energy dependence on centralized sources and enhance the resilience of urban energy infrastructure [4]. Similarly, research in Control Theory has enabled AI systems to manage energy load balancing autonomously, further optimizing energy distribution during high-demand periods [43].
In addition, the integration of Machine Learning (ML) and Deep Learning (DL) underpins AI’s functionality in optimizing energy systems. ML and DL further enhance this by improving predictive accuracy, enabling AI to forecast energy demands with up to 90% accuracy, aiding grid reliability [44]. ML enables energy systems to process large datasets to identify trends and patterns that assist in making more informed decisions about energy distribution and grid management. Supervised learning models, a subset of ML, are employed to forecast energy demand, enhancing grid reliability by predicting fluctuations in supply and demand [44].
DL, which employs neural networks to process more complex datasets, further enhances AI’s ability to perform real-time optimization. These neural networks can predict energy consumption, adjust energy flows, and adapt to new patterns in energy usage, making it an indispensable tool in the rapidly changing energy landscape. As renewable energy introduces greater unpredictability into grids, DL models provide the responsiveness required to ensure grid stability and efficiency.
While ML and DL play a critical role, Systems Theory remains central to the application of AI in energy systems. The complexity of energy grids—interconnected, decentralized, and increasingly reliant on renewable energy—makes Systems Theory essential in describing how AI autonomously manages grid behavior. Systems Theory ensures that AI responds in real-time to the dynamic interactions of energy production, distribution, and consumption. Reinforcement Learning (RL), a subset of ML, further enhances this by learning from interactions with the energy grid to optimize energy distribution. Studies have shown that reinforcement learning models improve grid efficiency by as much as 15%, highlighting the effectiveness of AI-driven grid optimization [44] [45].
Additionally, cryptographic theories provide essential frameworks for securing AI-driven energy systems. A relevant theory is the Public Key Cryptography Theory, which ensures secure communication between distributed nodes, critical for maintaining data integrity and preventing unauthorized access in decentralized microgrids and IoT-enabled infrastructures [46]. Similarly, Elliptic Curve Theory enables the development of lightweight cryptographic protocols suitable for resource-constrained devices. By ensuring secure and rapid data exchanges, ECC supports real-time optimization and dynamic interactions in complex energy systems, providing high security with lower computational overhead, ideal for IoT devices [47] [48].
Furthermore, Post-Quantum Cryptography (PQC), such as the Supersingular Isogeny Key Encapsulation (SIKE) framework, offers long-term protection against quantum computing threats. SIKE provides advanced encryption capabilities that ensure secure communication and data integrity even as quantum computing evolves, making it crucial for future-proofing energy systems [49]. These cryptographic foundations complement AI technologies like ML and DL by providing the necessary security infrastructure to protect the integrity and confidentiality of real-time data flows.
This combination of AI technologies, systems theory, and cryptographic frameworks, including ECC and SIKE, forms the foundation for advancing energy system resilience, reliability, and security in the face of growing complexity and decentralization.
2.2. Conceptual Framework
The AI-driven optimization of energy infrastructure follows a structured framework consisting of three key stages: data collection, predictive analytics, and real-time decision-making [17]. These stages work together to create a seamless process for energy optimization, allowing AI systems to make accurate predictions and decisions that enhance grid performance. Figure 4 illustrates the conceptual framework of AI in energy optimization, highlighting the interconnected stages of data collection, predictive analysis, and real-time decision-making, supported by advanced cryptographic measures for secure data management and optimization. These stages were crucial during the COVID-19 pandemic, as AI systems adapted to sudden demand shifts, showcasing the flexibility of predictive analytics in managing grid resilience.
Figure 4. Conceptual framework of AI in energy optimization.
The process begins with data collection, aided by IoT and AI, which form the backbone of energy management frameworks, reducing operational costs by up to 30% and decreasing maintenance costs by nearly 50% due to predictive maintenance capabilities [7] [50]. IoT devices and sensors within the energy grid gather live data on energy usage, generation, and environmental factors like weather patterns. Without accurate and comprehensive data, the subsequent stages of optimization cannot function effectively. To secure this data, cryptographic frameworks such as ECC ensure secure data transmission within IoT-enabled energy grids. ECC provides robust encryption with minimal computational overhead, making it ideal for energy systems that rely on resource-constrained devices [47] [48].
The next stage is predictive analysis, which comes into play as AI leverages the gathered data to predict future energy requirements, detect possible equipment malfunctions, and enhance energy distribution. According to [50], predictive analytics has played a key role in lowering operational expenses by as much as 30% by improving energy management efficiency. In addition, AI’s ability to predict has resulted in a 40% drop in equipment failures and a 50% cut in maintenance expenses [51]. Predictive analytics enhances grid reliability by anticipating energy demand and system failures, leading to improved efficiency. To safeguard the integrity of predictive models and prevent unauthorized data manipulation, Public Key Cryptography ensures secure communication between distributed systems. This approach is critical for protecting sensitive operational data, especially in decentralized grid architectures [46].
The last phase involves making decisions in real-time, with AI adjusting energy distribution based on real-time data and forecasts. AI can optimize energy flows by using neural networks and reinforcement learning algorithms to ensure that energy production matches demand. These models have improved energy efficiency by up to 20%, making real-time decision-making a critical component of AI-driven energy management, dynamically balancing production and consumption [13]. In addition, cryptographic solutions like post-quantum cryptography, such as the SIKE framework, provide future-proofing against quantum computing threats, ensuring the long-term security of real-time decision-making processes [49]. These integrated frameworks allow AI to operate securely and efficiently, adapting to the complexities of modern energy systems.
Figure 5 provides a detailed depiction of a typical smart power network for load planning and supply management. It showcases how AI-powered models, such as those used in distributed generation systems like photovoltaics and wind energy, interact with utilities and consumers through a master control operation system, ensuring bi-directional communication and optimized energy management. Countries like Denmark have effectively put this framework into practice. Denmark heavily depends on wind power, with more than half of its electricity coming from wind turbines and it has enhanced its wind energy predictions by 20% through the use of AI and IoT algorithms, resulting in a more steady and dependable energy network [52].
Figure 5. Smart power network for load planning and supply management [13].
2.3. Research Gaps
Despite the potential of AI in energy systems, there are still various research gaps that prevent the widespread use and expansion of these technologies. A major drawback is that a large portion of the studies concentrate on simulations or minor projects. These controlled environments provide valuable insights but do not account for the complexities and unpredictability of real-world’s large-scale energy systems. As a result, findings from these studies are often difficult to generalize to broader applications [15]. Similarly, the energy usage prediction models for residential buildings are often limited by data accessibility and privacy issues, presenting a barrier to AI deployment at scale [2]. Similarly, there is a clear lack of long-term studies that evaluate AI’s effectiveness in diverse energy environments. Most research spans relatively short timeframes, limiting the ability to assess AI’s long-term impact on energy infrastructure, grid stability, and maintenance costs. The absence of comprehensive longitudinal studies makes it difficult for energy policymakers and businesses to make fully informed decisions on AI integration [17].
In addition to these methodological gaps, the socioeconomic impacts of AI integration in energy infrastructure are also underexplored. This is particularly true for developing countries, where the digital literacy and technological infrastructure required for successful AI implementation may be insufficient. For instance, while AI could optimize energy distribution in underserved regions, the lack of basic infrastructure or skills to support AI-driven systems remains a substantial barrier. Addressing this issue requires not only technical innovations but also investments in education and digital literacy [17].
Yet another important deficiency is the cybersecurity dangers linked to AI in energy networks. With the increasing digitization and AI integration of energy systems, the risk of cyberattacks also grows. Nevertheless, there is a notable gap in research investigating ways to enhance the resilience of AI systems against cyber threats [53]. With the increasing amount of attacks on vital infrastructure, this is a serious issue that requires further investigation. Other future research directions could include the following:
Development of lightweight cryptographic algorithms tailored for energy-constrained IoT devices in energy systems
Integration of AI and blockchain technologies for decentralized and tamper-proof grid management
Long-term studies to evaluate the scalability and effectiveness of AI-driven security mechanisms in diverse energy environments
2.4. Case Study: AI in Renewable Energy Integration
The incorporation of AI in renewable energy systems has been revolutionary in enhancing energy production and delivery. Several regions have already implemented AI technologies to enhance their renewable energy infrastructure. These case studies highlight the effectiveness of AI in managing energy variability and improving grid efficiency, including the levels of investment, technological advancements, and the achievements realized so far.
2.4.1. Denmark: Wind Energy Optimization
AI is essential in optimizing wind power forecasting, contributing to almost half of Denmark’s electricity production coming from wind energy. In 2021, Siemens created a set of AI algorithms to improve forecast accuracy by examining current weather data and past energy production trends. With around €90 million investment from the Danish government, this initiative enhanced wind energy predictions by 20%, aiding in managing supply and demand while boosting grid stability [8] [54]. By 2020, this improvement resulted in Denmark producing more than 16.3 terawatt-hours (TWh) of wind power annually, lessening its dependency on fossil fuels and improving the overall reliability of the grid [55]. Furthermore, the use of AI for forecasting has resulted in a 5% - 7% decrease in energy wastage when there is excess production. The Danish Energy Agency has a crucial responsibility in supervising the regulation of these technologies, guaranteeing adherence to the General Data Protection Regulation (GDPR). Looking forward, Denmark aims to integrate predictive maintenance capabilities into its AI framework to further reduce maintenance costs by 15% by 2030.
2.4.2. United States: Google’s DeepMind in Wind Farms
In the US, Google’s DeepMind changed wind energy production by investing $50 million in AI for renewable energy. DeepMind’s enhanced DL models forecast wind power production up to 36 hours ahead, enabling energy operators to make informed choices about energy distribution, leading to a 20% enhancement in the economic worth of wind energy [56]. This ability has led to a rise in wind energy usage in the U.S., with states such as Texas producing more than 92 TWh of wind power each year, accounting for approximately 28% of total electricity output in 2022 [11]. Furthermore, AI-powered optimization tools for solar energy in different U.S. utilities have reduced energy waste by 15%, demonstrating AI’s ability to scale for different types of renewable energy sources [57].
DeepMind’s AI models with live weather and grid information, work to prevent shortages and surpluses by efficiently managing energy dispatch. This has led to a more dependable energy supply during both high-demand and low-demand times by decreasing grid fluctuations by 10% - 15% [58]. Furthermore, the Cybersecurity Framework of the National Institute of Standards and Technology (NIST) offers guidelines for cybersecurity, safeguarding AI-powered energy systems against cyber threats and guaranteeing their security [59].
2.4.3. China: Solar Energy Forecasting
China has made a substantial investment in AI for improving solar energy predictions, with the National Energy Administration (NEA) dedicating ¥1.2 billion (around $180 million) for AI-based solar energy systems by 2020. AI systems processing real-time weather data and satellite imagery have improved forecast accuracy by 15%, enabling operators to effectively handle solar energy fluctuations and reduce grid overload during peak production times [60]. This progress resulted in China producing 305 terawatt-hours of solar energy in 2021, making up 11% of the nation’s overall renewable energy output [61]. The AI models, utilizing neural networks and reinforcement learning, constantly adjust to refresh information, particularly aiding areas experiencing varying levels of solar radiation, like Xinjiang and Tibet. The AI-boosted solar predictions have decreased grid imbalances by 8% - 10%, aiding in the stability of renewable energy supply and decreasing solar power curtailment [62]. Looking ahead, the NEA plans to further expand AI capabilities in solar forecasting by 2030, targeting a 25% increase in efficiency and broader integration with smart grid technologies to better handle demand fluctuations.
2.4.4. Germany: AI in Energy Storage
Germany has also made considerable investments in AI-powered energy storage, backed by the “AI Made in Germany” program, which started in 2020 with €500 million from the Federal Ministry for Economic Affairs and Climate Action. Working with Fraunhofer ISE has resulted in the creation of AI algorithms that improve the storage and utilization of energy, crucial for handling excess renewable energy. These algorithms have enhanced grid reliability by 18% [63]. In 2022, Germany’s renewable energy production exceeded 243 TWh, with 18% of this energy efficiently managed through AI-driven storage solutions [64]. The AI systems also reduced load imbalances by up to 12%, further improving the cost-effectiveness of Germany’s energy grid. Germany’s Federal Network Agency implements rules from the Renewable Energy Act (EEG) to make sure that energy storage systems meet the grid stability and efficiency objectives. Germany plans to increase the usage of AI in predictive maintenance by 2030, with the goal of cutting operational costs by 20% and improving resilience to demand changes.
2.4.5. Japan: AI-Driven Demand Forecasting and Decentralization
In 2019, Japan allocated ¥150 billion (around $1.4 billion) through the New Energy and Industrial Technology Development Organization (NEDO) to enhance energy demand prediction and decentralized grid control by extensively supporting AI technology. AI models in Japan have allowed grid operators to forecast energy demand with more than 90% precision, leading to a 15% decrease in grid instability during peak times [65]. The presence of models has been crucial in managing the equilibrium between supply and demand, especially in heavily populated regions. Japan’s AI-powered decentralized grids ensure reliable energy management through operations that adjust based on real-time data. NEDO aims to enhance resilience against natural disasters by 2035, with a goal of decreasing energy waste and operational costs by 25% to establish Japan as a pioneer in AI-powered energy grid advancement.
2.4.6. European Union: Horizon Europe and the Green Deal
The Horizon Europe program of the European Union, launched in 2021 with a budget of €100 billion, allocates a significant portion to energy projects powered by AI as part of the European Green Deal, with the goal of achieving net-zero emissions by 2050. The projects, focusing on renewable energy optimization, grid stability, and predictive maintenance across EU member states, are overseen by the Directorate-General for Energy [35]. The measures have improved immediate monitoring and decreased energy waste, boosting grid dependability by 15%. The EU aims to expand AI applications throughout member states and achieve a 45% renewable energy share by 2030 in its overall energy mix. This initiative also backs a structure for compatible smart grids to manage the flexible changes needed for integrating renewable energy in different EU energy systems.
This statistical analysis emphasizes AI’s effects on improving renewable energy optimization, providing numerical proof of AI’s potential to improve efficiency, reliability, and energy management. Likewise, Table 2 summarizes the significant progress made by leading countries in AI-managed energy infrastructure and the related impacts and benefits.
Table 2. Key AI applications in energy systems across different countries [8] [63] [65]-[73].
Country |
AI Application |
Description |
Impact/Benefits |
United States |
Predictive Maintenance |
AI models are used to forecast equipment failures in power plants and grid infrastructure. |
Reduces unplanned outages by up to 40%, lowering maintenance costs and enhancing grid reliability. |
China |
Solar Energy Forecasting |
AI-driven systems forecast solar energy production using real-time weather data and satellite imagery. |
Improves solar grid efficiency by 15%, helping balance demand and reduce curtailment. |
Denmark |
Wind Energy Optimization |
AI algorithms enhance wind energy forecasts, allowing optimized scheduling and dispatch. |
Increases wind energy’s contribution to the grid, stabilizing supply and reducing fossil fuel dependency. |
Germany |
Energy Storage Management |
AI systems optimize storage for surplus renewable energy and control the release of stored energy during high demand. |
Enhances grid stability by 18% and enables better management of renewable fluctuations. |
Japan |
Demand Forecasting |
AI algorithms predict electricity demand patterns, facilitating grid adjustments in response to real-time demand. |
Helps prevent power shortages and balance the supply-demand equilibrium, critical in densely populated areas. |
India |
Microgrid Management in Rural Areas |
AI manages decentralized microgrids in rural regions, integrating renewable sources with local grids. |
Provides reliable electricity to remote areas, optimizing energy distribution for off-grid communities. |
Netherlands |
Vehicle-to-Grid (V2G) Systems |
AI coordinates EV charging/discharging, integrating electric vehicles as mobile energy storage units within the grid. |
Reduces grid strain during peak hours by utilizing EVs as a temporary energy source. |
United Kingdom |
Cybersecurity for AI-Driven Grids |
AI-driven cybersecurity measures protect smart grids from cyber threats by detecting anomalies in real-time. |
Reduces the risk of cyberattacks, ensuring continuity and reliability of energy services. |
South Korea |
Real-Time Energy Distribution Optimization |
AI dynamically adjusts energy distribution across the grid based on real-time consumption and supply data. |
Enhances overall grid efficiency, reduces energy losses, and optimizes resource use. |
Australia |
AI in Demand Response Programs |
AI enables demand response programs that adjust consumer energy use in response to grid conditions. |
Reduces peak demand, preventing grid overload, and lowering energy costs for consumers. |
Singapore |
Smart Building Management |
AI optimizes energy usage within smart buildings, integrating HVAC, lighting, and other systems based on occupancy data. |
Cuts energy consumption by 8% - 12% within commercial buildings, improving energy efficiency in urban areas. |
Finland |
District Heating Optimization |
AI manages district heating systems, adjusting based on real-time demand and weather forecasts. |
Increases heating efficiency, reduces waste, and minimizes emissions in urban areas during winter. |
Italy |
Energy Efficiency in Industrial Sectors |
AI tools optimize energy consumption in manufacturing by analyzing operational data. |
Lowers operational costs and enhances energy efficiency by up to 10% in industrial processes. |
2.5. Challenges and Opportunities in AI Integration
Incorporating AI into energy systems shows potential in improving energy efficiency, streamlining grid management, and facilitating the integration of renewable energy sources. Nevertheless, this change comes with many important obstacles that need to be resolved in order to fully unleash the potential of AI. Primary challenges consist of limited data access, cybersecurity threats, and financial limitations. Overcoming these challenges offers significant potential to enhance the sustainability and resilience of worldwide energy structures.
2.5.1. Data Availability
A major obstacle to successful AI integration in energy systems is the lack of access to data. AI’s ability to improve grid management and boost predictive maintenance relies heavily on having access to extensive, top-notch datasets. Regrettably, numerous energy companies face challenges with scattered or inadequate data, hindering the effectiveness of AI. Deloitte’s 2020 report revealed that just 38% of energy companies have the required data infrastructure to effectively utilize AI in their business activities. The lack of comprehensive data hinders AI from performing at its best, especially in crucial areas like predicting energy demand and optimizing grids. Notably, improving data-sharing infrastructure in Europe and the U.S. has enhanced predictive maintenance outcomes, increasing reliability in energy distribution by up to 30% ([7] [15]). Likewise, research conducted by the European Union focusing on energy systems found that increasing data transparency could boost the predictive accuracy of AI by 25%, highlighting the importance of having reliable and easily accessible data [74].
This problem requires, enhanced data accessibility, which necessitates collaboration between private sector and governmental organizations. The United Kingdom and other countries have tackled this problem by creating tools like the National Grid Data Explorer, which aggregates live operational data for different users to access [75]. This is a platform for sharing data, allowing for better predictions and streamlined energy allocation, serving as a blueprint for other countries to emulate. In the United States, the Federal Energy Regulatory Commission (FERC) and the Department of Energy (DOE) are crucial in enabling data sharing among energy companies through the establishment of standardized protocols.
Other industries showcase how enhanced data availability can bring about significant changes. The inclusion of electronic health records (EHRs) in healthcare has greatly improved patient outcomes by allowing for improved data sharing between hospitals [76]. Likewise, the manufacturing sector has experienced significant enhancements in machine productivity thanks to General Electric’s (GE) real-time data-sharing systems, leading to a decrease in downtime of around 30% [77]. The potential of AI-driven data systems in healthcare and manufacturing is showcased for their ability to bring about transformation, with potential efficiency gains of 10% - 15% if fully deployed in energy. These examples suggest similar advancements are possible in the energy sector if data availability challenges can be addressed.
2.5.2. Cybersecurity Risks
The growing digitalization of energy systems leads to increased cybersecurity threats. AI-powered energy systems are connected and depend on up-to-the-minute information, leaving them susceptible to cyber threats. The possibility of cyber threats causing disruptions in grid reliability emphasizes the importance of incorporating real-time threat detection models into energy infrastructures [17]. The susceptibility of AI-enabled grid management systems was highlighted by the 2020 cyberattack in India, causing power outages for millions. This event resulted in considerable financial damages and exposed the vulnerability of contemporary energy systems to cyber-attacks. However, a study from 2023 discovered that implementing AI technology in cybersecurity can lower the likelihood of cyberattacks on energy grids by as much as 60%, highlighting the significance of incorporating comprehensive security systems [78].
Another well-known instance is the cyberattack on Ukraine’s power grid in 2015, causing 230,000 individuals to lose power. The cyberattack revealed weaknesses in Ukraine’s energy infrastructure, leading the country to adopt thorough cybersecurity measures, such as real-time monitoring systems driven by AI [53]. Ukraine greatly decreased the likelihood of future attacks and saved millions of dollars in potential damages by implementing AI for threat detection.
Specific emerging security threats, such as systematic poisoning attacks and side-channel vulnerabilities, pose significant risks to AI-powered energy systems. Energy-efficient cryptographic frameworks are required to counter these evolving threats. For instance, lightweight cryptography enables secure operations in IoT-enabled devices with constrained resources [79]. Furthermore, FPGA-based implementations of the SIKE cryptographic algorithm offer high-speed solutions for protecting post-quantum systems [80]. Additionally, advances in polynomial multiplication accelerators provide faster and more energy-efficient operations for post-quantum cryptography, enhancing grid security and reliability [81]. Addressing these vulnerabilities is paramount to safeguarding critical energy infrastructure from sophisticated cyberattacks.
It is evident that there is a clear requirement for strong cybersecurity frameworks. Governments and energy companies must invest in advanced encryption technologies, secure communication protocols, and AI-driven threat detection systems. Nations such as the United States and the United Kingdom have already implemented AI-driven real-time threat detection systems in their national grids, aiding in averting major disruptions and cutting down on potential economic losses by millions [82]. Utilizing predictive security measures in a proactive manner can help detect and prevent potential threats, ultimately decreasing the chances of expensive cyber-attacks [83].
2.5.3. Financial Constraints
The significant challenge of the financial burden tied to AI incorporation into energy systems continues to be a major obstacle, especially for developing countries. [84] reported that obtaining AI technologies, such as hardware, software, and skilled personnel, can represent 15% - 25% of overall infrastructure expenses. In Europe, COVID-19 slowed energy investments, but recovery efforts include AI-focused projects funded to stabilize grid operations while optimizing costs, demonstrating the economic viability of AI in long-term planning [4]. This makes it difficult for energy companies with limited financial resources to adopt AI technologies at scale. Even though the initial expenses are significant, the lasting financial advantages are substantial [85], for instance AI has the potential to decrease worldwide energy usage by 10%, which could lead to annual savings of approximately $1.6 trillion for the global economy. In the US, the Department of Energy has addressed budget limitations by promoting collaborations between the public and private sectors, such as the Grid Modernization Initiative. This initiative has received $220 million in funding to update energy grids using AI technology [86]. Through leveraging PPPs and support from international funding organizations, countries can speed up the implementation of AI technologies in their energy systems. The lasting advantages, such as lower energy usage, enhanced grid efficiency, and decreased operational expenses, are greater than the initial challenges in investment, making AI integration both environmentally friendly and cost-effective.
2.6. Future Trends
AI’s impact on energy infrastructure is set to significantly increase in the near future, driven by technological advancements, higher interest in renewable energy, and the necessity for more effective energy networks. Multiple significant trends are influencing the future of integrating AI into energy systems, offering significant possibilities for improvement and adaptability.
AI-driven self-healing grids mark a major advancement in grid control. These grids utilize ML algorithms to identify issues in real-time and automatically redirect energy flows, avoiding outages and reducing downtime. [12] stated that self-healing grids have the potential to cut power outages by 50% by the year 2030. The United States has started testing AI-powered self-healing grids, like the Grid Modernization Lab Consortium, which found that initial implementations cut outage time by 40%. Advanced sensor networks and AI algorithms help these grids detect and isolate faults quickly, guaranteeing continuous electricity distribution.
Decentralized Energy Systems: With the movement towards decentralized systems in the energy sector, AI will be crucial in overseeing microgrids. Microgrids are energy systems that operate independently or alongside the main grid, offering increased resilience in case of outages or natural disasters. AI-powered microgrids are expected to increase by 30% by 2030, especially in developing nations scaling up their use of renewable energy sources [87]. India, for instance, has put into place multiple AI-driven microgrid initiatives to oversee its growing solar capacity, essential for supplying consistent energy to rural regions with restricted grid connectivity. AI systems optimize energy storage, distribution, and consumption to ensure efficient energy utilization, even within isolated systems.
The electrification of transportation is another field where AI is anticipated to have a major influence. AI is essential for improving charging schedules for electric vehicles (EVs) and allowing vehicle-to-grid (V2G) technologies to operate, enabling EVs to supply energy back to the grid when needed most. AI algorithms will be used to control the flow of energy in order to charge electric vehicles during times of low demand and discharge energy during times of high demand, in order to lessen the burden on the grid. AI is utilized in the Netherlands to control V2G systems in cities like Utrecht. Electric buses and vehicles are incorporated into the grid, offering extra energy during peak hours and cutting down overall energy usage by 10% [13].
2.7. Key Strategies and Frameworks for AI Integration
Successfully incorporating AI into energy systems necessitates deploying various strategies and frameworks that tackle technical, regulatory, and financial obstacles. These tactics are crucial for increasing AI usage, boosting grid durability, and improving operational productivity.
2.7.1. Development of Smart Grids
An essential tactic for incorporating AI is the creation of intelligent grids. Advanced sensors, IoT devices, and AI algorithms are employed in these grids to oversee and control energy flows in real-time. The incorporation of AI in smart grids allows for predictive analytics to forecast energy usage, enhance distribution, and avoid grid overloads. One instance is the Smart Grid Investment Grant Program by the U.S. Department of Energy, which allocated more than $4 billion to enhance AI-driven smart grid technology, resulting in increased grid reliability and decreased operational expenses in various states [88]. AI-driven technologies in modern energy systems have increased grid efficiency by as much as 20%, showcasing the benefits of smart grids.
2.7.2. Public-Private Partnerships (PPPs)
Collaboration between governments and private companies is essential for expanding AI technologies in the energy sector. Partnerships between public and private sectors enable the pooling of resources and expertise to accelerate AI integration. A notable case is the Energy Efficiency Fund of Germany, which invested over €6 billion in energy projects powered by AI during the time period of 2016 to 2020. These efforts resulted in a significant enhancement in the dissemination of renewable energy and more efficient grid control, leading to an 8% decrease in energy usage over the next ten years [89]. In the United States, the DOE’s Grid Modernization Initiative, which received $220 million in funding from public and private sources, has improved energy grid resilience and efficiency through the integration of AI [86].
2.7.3. Regulatory Frameworks
Strong regulatory frameworks are crucial to ensure the safe and effective integration of AI technologies in energy systems. Governments need to tackle concerns concerning data sharing, cybersecurity, and the compatibility of AI systems among various energy grids. An example would be the European Union’s Digital Strategy, which consists of extensive rules on data protection and cybersecurity for AI-powered energy systems, guaranteeing their scalability and security throughout the European energy sector. Likewise, the NIST Cybersecurity Framework in the United States offers guidelines for protecting AI systems in crucial energy infrastructures, minimizing the chances of cyber assaults [59].
2.7.4. Data Standardization
Data standardization is crucial to combat data fragmentation and guarantee that AI systems have access to top-notch, up-to-date data for peak performance. Energy companies and governments need to work together to create consistent protocols for collecting and sharing data. The IEA promotes worldwide data standards to make it easier for AI-powered energy systems. The National Grid Data Explorer in the UK is a platform that allows for real-time data sharing, helping to improve energy efficiency and distribution [75]. Standardizing data across regions and industries is essential to fully leverage AI in energy systems.
2.7.5. Investment in AI Research and Development (R&D)
It is crucial to continue investing in AI research and development to tackle technical obstacles and enhance AI capacities in energy systems. Governments and private companies need to dedicate resources to create more energy-efficient AI models, boost predictive analytics, and enhance real-time decision-making processes. As an example, Japan’s government made significant investments in AI research and development to enhance their energy grid, leading to a 15% enhancement in grid efficiency overall [17]. Investments like these are essential to ensure that AI technologies continue to be advanced and able to meet the changing requirements of modern energy systems.
2.7.6 Cybersecurity Enhancements
The security of AI-driven energy systems is paramount, especially in the face of sophisticated cyberattacks. In order to reduce cybersecurity risks, AI-driven real-time threat detection systems have been created to oversee energy grids for unusual data patterns signaling possible cyber threats. The National Grid in the United Kingdom improved its capacity to identify threats quickly by implementing an AI-based cybersecurity model, minimizing the chances of significant disruptions [73]. Furthermore, predictive AI models can predict possible cyberattack routes by examining past data and simulating upcoming threat situations. The FERC in the U.S. has implemented predictive security measures that prevent disruptions and infrastructure damage [58].
Recent studies demonstrate the susceptibility of machine learning systems to systematic poisoning attacks, where adversarial inputs compromise model reliability [87]. To address such vulnerabilities, concurrent fault detection mechanisms have proven effective for safeguarding encryption protocols like AES, offering valuable lessons for enhancing AI system defenses in energy grids [88]. Fault detection mechanisms also play a critical role in identifying anomalies in real-time, preventing system disruptions, and enhancing grid resilience.
In addition to fault detection, cryptographic protocols form a cornerstone of cybersecurity in AI-driven energy systems. For example, cryptographic protocols such as Curve448 and Ed448 ensure secure communication within distributed energy grids, particularly for low-power IoT devices critical to grid monitoring and management [90]. Optimized architectures for ECC further improve performance while lowering energy consumption, making them suitable for embedded systems used in energy applications [91] Moreover, emerging post-quantum cryptographic solutions, such as the SIKE framework, provide resistance to quantum attacks, ensuring the long-term security of energy infrastructure [49].
When these cryptographic technologies are integrated with AI-based fault detection systems, they form a robust defense framework. Together, they protect modern energy systems from both operational disruptions and advanced cyber threats, future-proofing critical infrastructure against evolving cybersecurity challenges [92].
2.8. Implementation and Monitoring
Effective implementation and monitoring are crucial to ensuring that AI technologies are deployed efficiently and securely across energy systems. To fully leverage the advantages of AI in energy infrastructure, various crucial factors need to be taken into account.
2.8.1. Standardized Data Collection and Real-Time Monitoring
For AI systems to function optimally, standardized data collection protocols must be implemented across all levels of the energy grid. IoT sensors and AI-driven monitoring systems can track energy production, consumption, and distribution in real time, ensuring efficient energy use and identifying potential faults before they escalate. In Italy, Enel has introduced AI monitoring systems which boosted energy efficiency by 5% in its worldwide activities [15]. The continuous feedback provided by these monitoring systems improves grid stability by adjusting energy flows to meet changes in demand.
2.8.2. Continuous System Updates and Cybersecurity Measures
AI systems must be constantly updated to adjust to changing energy demands and defend against new cybersecurity risks. Governments and energy companies need to focus on creating secure AI systems that can quickly identify and address cyberattacks. National Grid in the UK has put in place AI-powered cybersecurity systems to watch grid activity around the clock, leading to a notable decrease in the likelihood of cyberattacks. These systems are crucial for avoiding widespread disruptions and safeguarding the country’s energy infrastructure [13].
2.8.3. Scaling AI Globally
While several countries have made strides in AI integration, more efforts are needed to scale AI across global energy systems. Countries like Singapore, which has successfully integrated AI into its energy grid, serve as a model for other nations. Singapore’s AI-powered grid management system has reduced energy waste by 8% and improved overall grid reliability [93]. Ensuring global energy stability will require sharing best practices and establishing international cooperation as more countries implement AI-driven systems [17].
2.8.4. Feedback Loops for Continuous Improvement
One benefit of AI is its capacity to improve processes over time by learning from previous performance. Feedback loops, necessary for enhancing energy efficiency and decreasing operational costs, involve AI systems consistently evaluating their performance and making adjustments. By incorporating AI systems with feedback loops, energy companies can guarantee that their operations adapt to different circumstances, enhancing their short- and long-term performance. For example, Google’s DeepMind AI, applied to the UK’s wind farms, utilizes feedback loops to optimize energy outputs, leading to an estimated 20% improvement in the economic value of wind energy [56].
Combining these strategies in the framework depicted in Figure 6, AI-driven energy systems can optimize efficiency, enhance cybersecurity, and reduce operational costs, making energy grids more resilient and adaptive to future challenges.
Figure 6. Implementation and monitoring framework.
3. Material and Methods
This literature review follows a descriptive and explanatory approach, synthesizing data from a range of academic research, industry reports, and real-world case studies. The study focuses on the regions at the forefront of AI adoption in energy systems, particularly North America, Europe, and Asia, where large-scale AI applications in energy infrastructure have already shown significant results [15]. To collect data, key academic databases such as IEEE Xplore, ScienceDirect, and Google Scholar were used, with keywords like “AI in energy infrastructure,” “AI in renewable energy,” and “predictive maintenance.” The review focuses on research released since 2020, showcasing the latest progress in AI applications and the difficulties in adopting these technologies across different areas. [50] analyzed both quantitative and qualitative results to gain a thorough understanding of the current status of AI integration in energy systems.
4. Findings
The results of the research show how AI plays a key role in improving energy infrastructure by enhancing grid reliability, operational efficiency, and the integration of renewable energy sources. One significant outcome is the impact of predictive maintenance in reducing operational disruptions. Research indicates that utilizing AI for predictive maintenance can reduce unexpected equipment downtime by as much as 40%, leading to substantial cost savings and enhanced grid dependability [14] [51]. Throughout the grid, sensors gather information on the performance of equipment, including voltage levels, current consumption, and temperature. AI algorithms analyze this real-time data to identify patterns and predict potential failures, allowing maintenance teams to address issues before they escalate, thus avoiding costly emergency repairs [9]. For instance, in countries like Germany and the United States, where AI has been widely adopted, operational costs in smart grids have been reduced by as much as 25% through the proactive scheduling of equipment maintenance and repairs [15]. This not only minimizes disruptions but also enhances the lifespan and performance of grid infrastructure.
Another key finding relates to AI’s role in energy demand forecasting. AI has increased the precision of forecasting energy demand by as much as 90%, offering vital information to grid operators to enhance energy generation and distribution [13]. In Japan, utility companies use AI-driven forecasting to prevent power shortages and improve grid stability by making proactive adjustments based on data to respond to changing demand. This ability becomes especially important as energy systems become more complicated due to the growing integration of renewable energy sources. In addition, the review emphasizes AI’s role in optimizing energy storage, a crucial aspect in the incorporation of wind and solar power into countrywide grids. AI-powered storage solutions manage when excess energy is stored or released, effectively stabilizing the energy supply despite the unpredictable nature of renewable sources. In Denmark, wind energy makes up almost half of electricity generation, and the use of AI forecasting and storage systems has led to a decrease in blackouts and a boost in grid efficiency by 18% [8] [55]. Similarly, in China, AI has enhanced the management of solar energy storage, leading to an overall grid efficiency improvement of 15% - 20% [17].
These findings also underscore the global shift toward AI in renewable energy management, especially in countries like the United States, where California has employed AI-driven systems to optimize solar energy dispatch. This has led to decreased energy loss and fewer power outages, illustrating how AI can help stabilize fluctuations from renewable energy variability [82]. In Germany, high penetration of renewable energy has led to an 18% increase in grid stability due to the implementation of AI systems for demand forecasting and grid optimization [55] [63].
This review shows that AI greatly improves the stability, efficiency, and resilience of contemporary energy systems. AI is leading innovation in energy infrastructure by being able to forecast energy usage, improve energy distribution, and reduce the unpredictability of renewable energy sources. In the future, it will be crucial to invest continuously in AI technologies and collaborate internationally to scale these benefits worldwide and secure sustainable energy solutions for the future.
5. Conclusions and Recommendations
In conclusion, incorporating AI into worldwide energy systems could revolutionize energy infrastructure through improved efficiency, reliability, and the incorporation of renewable energy sources. AI technologies, like predictive maintenance, energy demand prediction, and energy storage enhancement, have already shown their effectiveness. Anticipatory upkeep, for instance, has decreased unexpected interruptions by up to 40%, with nations like Germany and the United States attaining a 25% cutback in operational expenses via AI-powered intelligent grids [15] [16]. Additionally, AI has increased the accuracy of energy demand predictions by as much as 90%, allowing operators to more effectively handle the fluctuations in renewable sources such as wind and solar [13] [56].
Even though there is potential, there are still major obstacles to overcome. Limited data accessibility is a major hindrance, as inadequate and disjointed data sets impede optimization potential [51]. Cybersecurity threats are also significant, as AI-driven technologies are becoming more susceptible to cyberattacks, like the grid disturbance in India in 2020 [53]. Financial restrictions, especially in developing nations, continue to hinder the adoption of AI, underscoring the importance of working together to find funding solutions.
To tackle these difficulties, it is crucial to implement important strategies and frameworks. Creating intelligent electrical grids with sophisticated sensors, IoT devices, and AI algorithms can improve energy distribution and boost grid durability. Germany’s Energy Efficiency Fund and the U.S. Grid Modernization Initiative have shown that PPPs are successful in improving grid reliability and efficiency by sharing resources and expertise [89] [94]. Regulatory guidelines, like the Digital Strategy of the European Union and the NIST Cybersecurity Framework, play a crucial role in guaranteeing the safe and effective integration of AI, especially when dealing with issues related to data sharing and cybersecurity. To address evolving cybersecurity threats, integrating advanced cryptographic frameworks such as Elliptic Curve Cryptography (ECC) and Post-Quantum Cryptography (PQC) offers a robust defense. ECC provides efficient encryption ideal for IoT-enabled grids, while PQC frameworks like the SIKE algorithm ensure resilience against future quantum computing threats [47]. These solutions safeguard AI systems and protect critical energy infrastructure from unauthorized access and data breaches.
Data standardization and sharing protocols, such as the UK’s National Grid Data Explorer, are crucial for AI systems to access top-notch, real-time data for best performance. Also, continuous funding for AI research and development is crucial to tackling technical hurdles, enhancing predictive analytics, and improving decision-making procedures. Likewise, both effective execution and supervision are vital to fully exploit the advantages of AI technologies in energy infrastructure. Keeping system efficiency and security requires standardized data collection, real-time monitoring via IoT sensors, and ongoing system updates. Continuous system updates must also incorporate emerging cryptographic measures to enhance the integrity and confidentiality of real-time data flows. Cryptographic advances such as Curve448 [95] and optimized architectures for ECC [96] not only ensure secure communication but also support energy-efficient operations in IoT devices critical to grid monitoring and management. Feedback loops, which are essential for continuous improvement, involve AI systems learning and adjusting using performance data, as evidenced by DeepMind’s AI achieving a 20% increase in wind energy outputs [57]. Expanding these strategies internationally while exchanging successful approaches between countries will additionally guarantee the durability and flexibility of AI-powered energy systems.
Through the utilization of these tactics and models and prioritizing successful execution and supervision, AI has the capability to facilitate a durable, productive, and safe energy future, satisfying the needs of a renewable-focused energy environment and tackling international energy obstacles.