Optimizing renewable energy systems through artificial intelligence: Review and future prospects

For their merit, support vector machines (SVM) and ANN are examples of machine learning algorithms that has excel in predictive modeling and pattern recognition tasks. They offer flexibility and scalability, enabling them to be utilized for an extensive range of RETs applications, including forecasting, optimization, and fault detection. However, it does have drawbacks. Machine learning algorithms need huge datasets for training and may suffer from overfitting or bias if not properly tuned. It could also be difficult to comprehend the underlying decision-making process due to its potential lack of interpretability.

Deep learning methods, like convolutional and recurrent neural networks, are highly effective at handling complex, high-dimensional data, including time series and picture data. They can attain state-of-the-art performance in tasks like image recognition, speech recognition, and time-series forecasting. However, the demerits are scanty. Deep learning models often need sufficient computational resources for training and inference, limiting their scalability in resource-constrained environments. They also suffer from the “black-box” challenges, where the internal workings of the model are not easily interpretable.

Optimization algorithms, like particle swarm optimization and genetic algorithms, excel in finding optimal solutions to complex optimization problems. They can be utilized to maximize energy generation, storage, and distribution in RETs systems. Optimization algorithms may struggle with high-dimensional or nonconvex optimization problems, leading to suboptimal solutions. They also require careful parameter tuning and may be computationally intensive, especially for large-scale optimization problems.

There is no one-size-fits-all “best” AI tool for RETs development. The best tool to use will depend on the needs and limitations of each project, weighing the merits and demerits of different AI techniques to achieve the desired outcomes effectively and efficiently. AI has significantly contributed to the development of RETs, offering various advantages and challenges. AI optimizes industrial structures, enhances energy storage technologies, and improves energy transmission efficiency, leading to reduced CO₂ emissions.⁶³ In smart cities, AI automates energy systems, enabling precise management and control of power systems.⁵⁰ AI acts as a catalyst for environmental sustainability and achieving net-zero goals by enhancing energy efficiency and lowering greenhouse gas releases.³⁷ However, integrating AI into RETs development poses challenges. One drawback is the potential risk of reduced creativity and displacement of human capital by AI and other Industry 4.0 technologies, impacting the workforce and production technology.⁶⁴ Additionally, successful integration of AI in the energy sector needs careful consideration of user expectations and benefits.⁶⁵ When selecting AI tools for RETs development, project-specific needs and objectives must be considered. Different AI tools offer unique capabilities that can be leveraged based on project requirements. AI algorithms and techniques are increasingly used in energy and renewable research to address engineering challenges.⁴⁹ Machine learning models are widely applied in energy systems for modeling, design, and prediction, showcasing the versatility of AI tools in the renewable energy sector.⁶⁶ While AI offers benefits such as optimization, automation, and sustainability for RETs development, potential drawbacks like workforce implications and the importance of user-centric implementation must be carefully evaluated. The choice of the best AI tool for RETs development should align with project requirements and desired outcomes to optimize the advantages of AI in advancing renewable energy technologies.

One notable example of successful integration between RETs and AI is evident in solar PV systems. AI has revolutionized various aspects of solar PV, significantly enhancing energy production, efficiency, and cost-effectiveness. AI algorithms are instrumental in optimizing system design for solar energy installations. By utilizing geographical and meteorological data, these algorithms can adjust panel orientation, tilt angle, and array configuration to maximize energy yield. This optimization has been shown to result in significant improvements in energy production, with studies indicating enhancements of up to 10–20%.⁶⁷ Moreover, the utilization of AI-enabled drones for site assessments can expedite project timelines by 30–40%, leading to substantial cost savings ranging from $20,000 to $50,000 per megawatt installed.⁶⁸ This integration has been supported by research that emphasizes the importance of deriving more efficient algorithms for optimizing solar PV parameters.⁶⁹ Furthermore, it highlighted the successful application of ANN and SVM in forecasting generation in PV solar plants, showcasing the advancements in AI applications in the field.⁷⁰

Moreover, the study introduced an AI-based framework to fast-track data-driven policies promoting solar photovoltaics, emphasizing the role of AI resources in policy-making and stakeholder participation.⁷¹ This approach aligns with the need for innovative strategies to enhance the incorporation of RETs and AI in RES. The synergy between RETs and AI, particularly in solar PV systems, has demonstrated significant developments in energy efficiency, cost-effectiveness, and project timelines. The research and advancements in AI techniques for PV applications underscore the potential for further development and optimization in the renewable energy sector.

In performance optimization, AI-driven predictive analytics and monitoring systems increase system uptime by 15–20% through early fault detection and proactive maintenance. This translates into annual cost savings of $10,000 to $30,000 per MW due to reduced downtime and maintenance expenses. Despite these achievements, challenges such as data quality, interoperability, and regulatory constraints persist. Nonetheless, the success of AI in solar PV systems underscores its transformative potential in driving efficiency, reliability, and profitability. As AI technologies continue to develop and become widely accessible, their integration into RETs like solar PV is expected to accelerate further, contributing to the global transition toward a sustainable energy future. Through continued innovation and overcoming existing challenges, AI promises to play a critical role in optimizing RES and advancing the adoption of clean energy technologies.

Predicting the availability of solar and wind power is crucial for grid effective energy management and process stability. AI algorithms trained on a large volume of weather data, historical generation trends, and real-time situations can now forecast renewable energy output with impressive accuracy.⁸⁵ This enables utilities to optimize energy storage, dispatch other sources, and ensure a smooth and reliable power supply. These tools enhance the accuracy of resource assessment and energy forecasting, enabling better planning and management of RES.

Renewable energy forecasting is a crucial area of investigation and development that seeks to enhance the accuracy of predicting energy generation from renewable origins, like wind, bioenergy, and solar wind.⁸⁶ For energy trading, effective grid management, and integrating renewable energy into current power systems, accurate projections are vital. Previous studies often focused on the incorporation of meteorological data like wind speed, solar radiation, and temperature, into forecasting models.^86–88 Advanced techniques, including numerical weather prediction models, are employed to improve the understanding of weather patterns and their impact on renewable energy production. Machine learning approaches, including regression models, SVM, and neural networks, have been widely utilized for renewable energy prediction.^89,90 These models leverage historical data to learn trends and relationships, improving the accuracy of predictions. Data analytics, including feature engineering and pattern recognition, play a key role in extracting meaningful insights from diverse datasets. Ensemble forecasting involves combining multiple individual forecasts to improve overall accuracy. Previous work explores ensemble methods for renewable energy forecasting, including combining predictions from different models, incorporating multiple weather scenarios, and considering various input features.^89,91,92 Recognizing the inherent uncertainty in renewable energy production, some studies focus on probabilistic forecasting. These approaches provide not only a point estimate of expected energy production but also a range of possible outcomes, helping decision-makers account for uncertainties in their planning and operations.⁹³ Hybrid models combine the strengths of different forecasting techniques. For instance, integrating statistical time series models with machine learning algorithms or combining numerical weather prediction models with AI methods.⁹⁴ These hybrid approaches aim to capture a broader range of factors influencing renewable energy production.

Research often addresses the spatial and temporal aspects of renewable energy forecasting. Spatial forecasting involves predicting energy production across multiple locations, while temporal forecasting focuses on predicting energy production over varying time zones, ranging from short-term to long-term predictions.⁹⁵ Remote sensing technology, such as satellite imaging and light detection and ranging (LiDAR), is integrated into certain studies, to enhance the spatial resolution and accuracy of renewable energy forecasting.^96,97 Data from remote sensing is a useful tool for learning about the physical characteristics of renewable energy sources. Recognizing the influence of renewable energy variability on grid stability, research often delves into forecasting methods tailored for grid integration. This includes predicting ramp events, which are rapid changes in energy production, to facilitate smoother integration into the power grid. Previous work explores operational forecasting tailored for energy markets.^91,98–101 Accurate predictions are crucial for market operators and energy traders to make informed decisions regarding energy pricing, scheduling, and market participation. With the rise of hybrid RES that combine multiple origins, like solar and wind, research focuses on forecasting methodologies specifically designed for these integrated systems.¹⁰² This includes optimizing the operation of hybrid systems based on accurate forecasts of each energy source’s contribution. Overall, the body of previous work on renewable energy forecasting reflects a diverse range of methodologies and applications. As technology develops and more data becomes accessible, ongoing research continues to refine and innovate in this critical field. Figure 6 shows the schematic of a hybrid system configuration involving wind energy, solar Photovoltaics (PV), and biogen.

Integrating intermittent renewable energy sources into the grid requires intelligent management systems. AI-powered smart grids can monitor and analyze energy flows in real-time, predict outages, and automatically adjust power distribution to maintain grid stability and optimize energy use.¹⁰³ This leads to a more adaptable and robust grid that can accommodate the increasing share of renewables.

Smart grids powered by AI facilitate real-time monitoring and control of energy distribution. AI algorithms analyze data from sensors and devices across the grid, predicting and mitigating issues such as fluctuations in power supply, grid imbalances, and voltage variations. This enhances grid stability and accommodates the intermittent nature of renewable energy sources.¹⁰³ A typical smart grid management system, is illustrated in Figure 7.

Smart grid management is a dynamic field that entails incorporating cutting-edge technologies to enhance the efficiency, dependability, and sustainability of power distribution systems. Numerous previous investigations have contributed to the understanding and development of smart grid management. Previous studies often focused on communication technologies that allow the exchange of information within various components of the smart grid.^105,106 This includes studies on the application of wired and wireless communication protocols, such as Zigbee, Wi-Fi, and cellular networks, to facilitate real-time data exchange for efficient grid operation. The deployment and effectiveness of advanced metering infrastructure (AMI), which includes smart meters, form a significant focus. Research has examined the ways in which AMI facilitates bidirectional communication between utilities and customers, offering up-to-date information on energy usage.^107–109 This facilitates DR programs, enhances billing accuracy, and supports grid monitoring. Smart grid management involves real-time monitoring and control of the grid to ensure stability and reliability. Previous studies delved into the development and application of supervisory control and data acquisition (SCADA) systems, phasor measurement units, and other monitoring techniques to enable timely decision-making and efficient grid operation. The integration of distributed energy resources (DERs) into the smart grid has been the subject of previous research due to the increasing popularity of renewable energy sources and distributed generation. This includes research on the optimal integration of wind turbines, solar panels, and ESS to improve grid resilience and accommodate fluctuating renewable energy generation. DR programs are explored to balance demand and supply in real-time. Previous studies investigated the effectiveness of DR strategies in optimizing energy consumption, reducing peak loads, and enhancing grid stability during times of high demand or supply variability.^110–112 The increase in the adoption of electric vehicles (EVs) introduces new challenges and possibilities for smart grid management. Studies assess the effects of EV charging on the grid, and research explores intelligent charging solutions, vehicle-to-grid integration, and the potential for using EVs as distributed energy storage resources. As smart grids rely heavily on information and communication technologies, cybersecurity is a critical consideration. Previous studies investigated cybersecurity threats, vulnerabilities, and measures to enhance the resilience of smart grid infrastructure against cyberattacks, ensuring the integrity and reliability of the grid.^113–115 Smart grid management benefits from the application of machine learning and predictive analytics. Previous studies explore the use of these technologies for fault detection, predictive maintenance, and optimizing grid operations.^116–118 Machine learning models are applied to analyze large datasets and make real-time decisions for grid optimization. Research often delves into the regulatory and policy frameworks that support the deployment and operation of smart grids. Studies assess the influence of regulatory measures on the adoption of smart grid technologies and investigate how policy frameworks can incentivize investments in smart grid infrastructure.¹¹⁹ Studies emphasize the importance of consumer engagement and empowerment in smart grid management. Previous research explores ways to educate and involve consumers in demand-side management, encourage energy efficiency, and foster a more interactive and responsive relationship between utilities and end-users.^120,121

In summary, previous studies on smart grid management have contributed valuable insights into the technological, operational, and regulatory aspects of implementing intelligent and responsive power distribution systems. These findings form a foundation for ongoing research and the continued evolution of smart grid technologies toward more sustainable and resilient energy infrastructure.

AI-driven predictive maintenance models leverage sensor data and historical performance metrics to anticipate equipment failures in wind turbines and solar panels. This proactive measure reduces downtime, increases the lifespan of assets, and optimizes the overall operational efficiency of renewable energy installations.¹²² Wind turbines, solar panels, and other renewable energy infrastructure are constantly exposed to harsh elements. AI algorithms can analyze sensor data and maintenance logs to identify early signs of equipment failure.¹²³ This allows for proactive maintenance, preventing costly breakdowns and ensuring optimal performance of RES.

AI-driven predictive maintenance has gained prominence as a transformative method to optimize the performance and reliability of industrial systems. Previous studies on AI-driven predictive maintenance have explored various aspects of this technology, revealing valuable insights into its applications, benefits, and challenges.^124,125 Previous studies emphasize the significance of data-driven approaches in AI-driven predictive maintenance. Machine learning algorithms, such as regression models, decision trees, and neural networks, were used for historical and real-time data to identify patterns indicative of potential equipment failures. The quantity and quality of data play an important role in the accuracy of predictive maintenance models. Research often focuses on the integration of condition monitoring technologies, such as sensors and IoT devices, to gather real-time data on equipment health.⁷⁴ Studies explore the effectiveness of different sensor types and configurations for monitoring factors like temperature, vibration, pressure, and fluid levels. AI-driven predictive maintenance aims not only to predict impending failures but also to provide prognostics, estimating the remaining useful life of equipment. Previous research investigated various models and algorithms for accurately forecasting when equipment is likely to fail, enabling proactive maintenance interventions. Feature engineering is a crucial aspect of developing effective predictive maintenance models. Studies delve into the identification and selection of relevant features from diverse datasets, ensuring that the input variables fed into machine learning models capture the essential information for accurate failure predictions. Previous research includes case studies across diverse industries, like manufacturing, energy, transportation, and healthcare. These case studies illustrate the utilization of AI-driven predictive maintenance in specific contexts, showcasing its effectiveness in improving asset reliability, reducing downtime, and optimizing maintenance costs. Previous studies highlight challenges and limitations associated with AI-driven predictive maintenance, including issues related to data quality, interpretability of models, and the need for continuous model retraining.^126,127 Researchers acknowledge that addressing these limitations is important for the successful deployment and sustainability of predictive maintenance systems. Some studies explore the role of human–machine collaboration in predictive maintenance. The integration of AI-driven insights with human expertise is investigated, emphasizing the importance of creating collaborative environments where maintenance teams can leverage AI predictions to make informed decisions. The implications of AI-driven predictive maintenance on regulations and ethical considerations are discussed in certain studies. Researchers explore how regulatory frameworks should adapt to accommodate these technologies while ensuring privacy, security, and the ethical use of data.¹²⁸ Previous studies on AI-driven predictive maintenance provide a detailed understanding of the technology's applications, methodologies, and challenges. The insights gained from this body of research contribute to the ongoing evolution of predictive maintenance practices across industries, paving the way for more resilient, efficient, and cost-effective maintenance strategies.

Batteries and other energy storage devices have their charging and discharging cycles optimized using AI algorithms. By analyzing historical data and considering real-time conditions, these systems maximize the efficiency of energy storage, ensuring the availability of stored energy when renewable resources are not actively generating power.¹²⁹ Storing excess renewable energy for later use is critical for balancing supply and demand. AI can optimize battery storage systems, determining the most efficient times to charge and discharge batteries based on real-time energy needs and market prices.¹³⁰ This optimizes the use of renewable energy and lowers the dependence on fossil fuels during peak demand periods.

Energy storage optimization is a vital aspect of modern energy systems, providing flexibility, stability, and efficiency. Previous studies on energy storage optimization have explored various dimensions of this field, seeking to enhance the performance and economic viability of ESS.^78,131 Studies often address the challenge of determining the optimum size and placement of ESS within power networks. Optimization models and algorithms are employed to analyze factors like grid requirements, renewable energy generation patterns, and load profiles, to determine the most effective locations and capacities for energy storage installations. This includes investigations into charge and discharge scheduling, peak shaving, and load leveling techniques to maximize the use of stored energy while minimizing costs and grid stress. Integration of energy storage with renewable energy sources, such as solar and wind, is the subject of many studies. Optimization models explore how energy storage can mitigate the intermittency and variability of renewables, providing grid support through services like frequency regulation, voltage control, and smoothing of renewable energy output.¹³² The optimization of hybrid ESS, which integrate several storage technologies such as flywheels, supercapacitors, and batteries, is being studied. Studies investigate how the complementary strengths of different storage technologies can be leveraged to enhance overall system performance and efficiency.^133,134 Optimization studies often consider the economic aspects of energy storage, analyze market participation strategies, and identify potential revenue streams. This includes participation in energy markets, ancillary services, and DR programs to maximize the economic benefits of energy storage assets. Researchers conduct techno-economic analyses to evaluate the cost-effectiveness and financial viability of energy storage projects. These analyses consider factors like investment costs, operation and maintenance expenses, and revenue generation, providing insights into the economic feasibility of implementing energy storage solutions. Some studies incorporate forecasting models and predictive analytics to optimize energy storage operation. This involves predicting energy demand, renewable energy generation, and market prices to inform real-time decision-making and improve the efficiency of energy storage utilization.

Life cycle assessment studies examine the environmental impact of ESS.¹³⁵ Researchers investigate the environmental benefits and trade-offs associated with different storage technologies, considering factors such as manufacturing, operation, and end-of-life disposal. Optimization models are developed to enhance the durability and resilience of ESS. This includes assessing the impact of system failures, developing fault-tolerant strategies, and optimizing maintenance schedules to ensure continuous and reliable energy storage operation. Some studies address regulatory and policy frameworks that impact energy storage optimization.¹³⁶ Researchers explore how existing regulations and policies either facilitate or hinder the deployment and optimal operation of ESS, offering perceptions to industry stakeholders and policymakers.

Previous studies on energy storage optimization offer a comprehensive understanding of the technological, environmental, and economic indices of deploying and managing ESS.¹³⁷ These insights contribute to the ongoing development of optimized energy storage solutions that play a critical role in the shift to energy systems that are more resilient and sustainable.

AI can assist in fine-tuning the operation of RES for maximum output. For example, AI algorithms can assist with the tilt angles of solar panels to track the sun's movement or optimize the pitch of wind turbine blades to capture more wind energy.¹³⁸ This leads to improved energy production and cost-effectiveness of renewable energy projects.

Optimizing renewable energy production is a multifaceted challenge that involves maximizing the efficiency and reliability of RES. Previous studies on this topic have explored different strategies, techniques, and methodologies to enhance the performance of renewable energy sources. Previous investigations often targeted the development and enhancement of advanced prediction methods for renewable energy sources. This includes accurate prediction models for solar irradiance, wind speed, and other weather parameters to anticipate variations in energy production and optimize the incorporation of renewable energy into the grid. Research investigates the optimization of hybrid RES, which combine several sources such as solar, wind, and hydropower. Studies explore how the complementary nature of different renewable sources can be leveraged to achieve a more consistent and reliable energy output.¹³⁹ One common goal is to maximize the integration of energy storage devices, like batteries, into RES. Research examines how energy storage can help maintain grid stability and dependability by storing excess energy during times of peak production and releasing it during times of low production. Machine learning and AI are applied to optimize renewable energy production. These technologies are used for real-time monitoring, adaptive control plans, and predictive maintenance, enhancing the overall efficiency and dependability of RES. Research explores grid-friendly operation strategies for RES, considering factors like grid stability, voltage control, and frequency regulation. Optimization models aim to align renewable energy production with grid requirements, ensuring smooth and reliable integration into the existing power infrastructure.¹³²

Studies often delve into the spatial and temporal analysis of renewable energy resources. This includes optimizing the placement of renewable energy installations based on geographical variations and considering the temporal patterns of energy production to enhance overall system efficiency. Optimizing the siting of renewable energy projects is an important aspect of maximizing energy production. Studies employ geographic information systems and optimization algorithms to determine the optimal locations for solar and wind installations, considering factors like available resources, land use, and environmental impact.¹⁴⁰ Efficient operational and maintenance strategies contribute to optimizing renewable energy production. Research investigates best practices for maintenance scheduling, equipment monitoring, and performance optimization to ensure the durability and dependability of sustainable energy infrastructure. Economic and financial considerations are explored in studies that conduct cost-benefit analyses, evaluate different financing models, and assess the economic viability of renewable energy projects. Optimization models assist in identifying the most economical approaches to maximize the return on investment in renewable energy production.¹⁴¹ Some studies address the influence of regulatory and policy frameworks on optimizing renewable energy generation. Researchers investigate how supportive policies, incentives, and regulatory structures can facilitate the deployment and efficient operation of renewable energy projects.

Previous studies on optimizing renewable energy production cover a wide range of topics, from advanced forecasting techniques and energy storage integration to machine learning applications and economic considerations.^89,129 The collective insights from these studies contribute to the ongoing efforts to maximize the benefits of renewable energy origins in the world energy mix.

AI is employed to forecast energy demand trends and optimize load distribution. By analyzing historical consumption data, weather forecasts, and other important factors, AI systems enable utilities to implement DR strategies, adjusting energy production and usage to match fluctuations in renewable energy availability.⁸¹

DR and load forecasting are crucial components of modern energy management systems, enabling utilities to balance demand and supply, optimize grid operations, and enhance overall system reliability.¹⁴² Previous studies have extensively explored these topics, providing insights into methodologies, technologies, and strategies. Numerous studies have focused on the development and enhancement of load predicting models. These models consist of ANN, regression models, time series analysis, machine learning algorithms, and hybrid approaches.¹⁴³ The goal is to accurately predict future electricity consumption patterns at various temporal resolutions, such as short-term, medium-term, and long-term forecasts. With the increasing penetration of renewable energy sources, studies explore load forecasting techniques that consider the intermittent and variable nature of renewables.¹⁴⁴ Integrating renewable energy forecasts into load forecasting models is crucial for effectively managing the variation and uncertainty related to renewable production. Research mostly emphasizes the application of smart grid technologies on enhance load forecasting and DR. The integration of AMI, smart meters, and real-time data analytics enables more accurate load predictions and facilitates responsive demand-side management. Studies investigate different DR strategies, including time-of-use pricing, incentive-based programs, and automated DR. The effectiveness of these strategies in influencing consumer behavior, reducing peak demand, and enhancing grid reliability is a common focus. The use of data analytics and machine learning techniques is common in load forecasting and DR studies. Researchers leverage these technologies to analyze historical consumption data, weather patterns, and other important factors to improve the accuracy of predictions and optimize DR programs. Understanding the behavioral aspects of energy consumers is an important consideration in DR studies. Research explores how consumers respond to different DR initiatives, the impact of communication strategies, and the factors influencing participation in DR programs. The deployment of advanced metering infrastructure, including smart meters, is a key focus in load forecasting and DR research. AMI enables real-time monitoring of energy consumption patterns, enhances data granularity, and facilitates more responsive demand-side management strategies.¹⁴⁵

Recognizing the inherent uncertainty in load forecasting, studies investigate probabilistic forecasting models. These models provide not only point estimates but also probability distributions, allowing utilities to make specified decisions considering the range of potential results. With the rise in the adoption of EVs, studies explore the impact of EV charging patterns on load forecasting and DR. Research investigates strategies to optimize EV charging schedules, manage increased demand, and leverage EVs as distributed energy resources. Some studies delve into the impact of policy and regulatory frameworks on DR and load forecasting. Researchers assess the effectiveness of regulatory measures, incentives, and market structures in promoting DR initiatives and shaping load forecasting practices.¹⁴⁶

Previous studies on DR and load forecasting cover a diverse range of topics, from forecasting models and smart grid technologies to behavioral aspects and policy considerations.^92,147 The collective findings assist in the development of more accurate, responsive, and sustainable energy management practices in evolving energy landscapes.

In decentralized energy systems, AI aids in the efficient operation of microgrids. These AI-based systems optimize the balance between local energy production, storage, and usage, enhancing the resilience and reliability of microgrid networks in both urban and remote settings.¹⁴⁸

Efficient operation of microgrids is a critical aspect of modern energy systems, especially in decentralized and distributed energy resources. Numerous previous studies have explored various aspects of microgrid operation, aiming to enhance efficiency, reliability, and sustainability. Many studies focus on the development and application of optimization algorithms and control strategies to achieve efficient microgrid operation. These include algorithms for economic dispatch, energy management, and optimal power flow to lower costs and enhance the utilization of available resources. The integration of renewable energy sources, like solar and wind, into microgrids is mostly focused on. Previous research explores strategies to efficiently manage the variability and intermittent nature of renewable generation, considering factors like energy storage, DR, and predictive analytics.

The role of ESS in microgrid efficiency has been extensively studied. Researchers investigate optimal sizing, placement, and operation strategies for energy storage to balance supply and demand, enhance grid stability, and support reliable microgrid operation.¹⁴⁹ Studies explore the integration of DR strategies in microgrids to optimize load profiles, reduce peak demand, and enhance overall system efficiency.¹⁵⁰ Smart DR initiatives and real-time communication technologies are often investigated for their effectiveness in microgrid contexts. Efficient control of distributed energy resources, including generators, storage systems, and flexible loads, is crucial. Research examines advanced control algorithms and coordination strategies to manage DERs optimally, considering factors such as grid constraints, voltage stability, and power quality.¹⁵¹

Resilience and reliability studies focus on ensuring uninterrupted and reliable operation during both normal and challenging conditions. Previous research explores strategies for enhancing microgrid resilience against disturbances, faults, and external threats through adaptive control and robust operation.^152,153 Economic and environmental assessments play a significant role in optimizing microgrid operations. Studies conduct cost-benefit analyses, life cycle assessments, and techno-economic evaluations to ascertain the economic viability and environmental impact of microgrid projects. Efficient operation begins with the planning and design phases. Research investigates optimal microgrid architectures, components, and configurations, considering factors such as resource availability, load profiles, and grid connectivity to ensure efficiency from the outset.¹⁵⁴ Microgrids can operate either connected to the main grid or in islanded mode. Studies explore strategies for seamless transitions between these modes, considering factors like islanding detection, grid synchronization, and the reconnection process to ensure continuity of service and efficient operation.¹⁵⁵

Some studies focus on the engagement of communities and prosumers (consumers who also produce energy) in microgrid operation. Research explores how community involvement and prosumer participation can contribute to overall system efficiency, demand management, and a sense of ownership. Previous studies on the efficient operation of microgrids cover a wide range of topics, from optimization algorithms and renewable energy integration to resilience strategies and community engagement.^156,157 The findings from this body of research contribute to the ongoing development and deployment of microgrid solutions that are efficient, reliable, and adaptable to evolving energy landscapes.

AI algorithms are designed to increase the amount of sunshine that solar panels receive throughout the day by optimizing their tracking and orientation. AI ensures that solar panels are positioned for the best energy capture by monitoring the sun’s position and current weather conditions. This improves overall solar energy conversion efficiency.⁷⁵

Numerous studies have investigated the optimal orientation and tracking strategies for solar panels to optimize energy capture and enhance the efficiency of solar power generation.¹⁵⁸ Some key themes and findings from prior research on solar panel orientation and tracking are presented. Studies compare the energy yield of fixed-tilt solar panels with that of tracking systems. These investigations assess the impact of various tracking technologies, such as single-axis and dual-axis tracking, on energy production under different climatic conditions and geographical locations. Research considers the geographical location and climatic conditions when evaluating the performance of solar panel orientation and tracking.¹⁵⁹ Studies often consider factors like latitude, solar irradiance, and the angle of incidence of sunlight to maximize the tilt and orientation of solar panels. Mathematical models are developed to simulate the energy yield of solar panels with different orientations and tracking systems.¹⁶⁰ Researchers employ tools like solar radiation models, weather data, and simulation software to predict the energy output under varying configurations. Many studies conduct economic analyses to determine the cost-effectiveness of solar panel orientation and tracking systems.^161–163 This includes evaluating the additional costs associated with tracking mechanisms against the increased energy production and financial benefits over the system’s lifetime. Investigations into dual-axis tracking systems are common, focusing on their capacity to follow the sun’s path in both azimuth and elevation. Studies assess the advantages of dual-axis tracking in capturing more sunlight throughout the day and optimizing energy production, especially in locations with variable sunlight angles.^164–166

Some studies leverage machine learning algorithms to optimize solar panel orientation and tracking. These models analyze historical performance data, weather patterns, and solar irradiance information to develop predictive algorithms that dynamically adjust panel angles for maximum energy capture.¹⁶⁷ Research examines how cloud cover and diffuse radiation affect the performance of solar panels with different orientations and tracking systems. Studies aim to develop strategies that adapt solar panel angles to changing sky conditions, optimizing energy capture even during periods of partial shading. Sensitivity analyses are conducted to determine the most important parameters affecting the efficiency of solar panels. Researchers explore how changes in factors such as panel tilt, azimuth angle, and tracking accuracy impact energy yield, guiding the design of efficient solar energy systems. Investigations into bifacial solar panels have been common in recent studies.^168,169 Bifacial panels can capture sunlight from both the front and rear sides, and research assesses the impact of different orientations and tracking strategies on the energy yield of bifacial modules. Some studies involve real-world performance monitoring of solar installations with varying orientations and tracking configurations. This field data supplies valuable information about the actual energy production and efficiency of solar panels under different operational conditions.

Previous studies on solar panel orientation and tracking encompass a wide range of topics, from theoretical modeling and economic analysis to real-world performance monitoring.^170,171 The findings contribute to the ongoing optimization of solar energy systems, providing guidance for the design and deployment of efficient solar installations.

AI-enhanced control strategies are implemented in renewable energy power plants to adapt to changing environmental conditions.¹⁷² Whether it’s adjusting the pitch of wind turbine blades or optimizing the operation of concentrated solar power systems, AI algorithms enhance the overall performance and efficiency of renewable energy facilities.

Advanced control strategies for power plants have been extensively studied to enhance the stability, efficiency, and overall performance of power production facilities. These studies cover a range of power plant types, including thermal, nuclear, and renewable energy power plants. The key themes and findings from prior research on advanced control strategies for power plants are presented here. Research explores the application of Model predictive control (MPC) in power plants. MPC uses dynamic models to predict the behavior of the power plant and optimize control inputs over a specified time horizon. This approach is employed for improved setpoint tracking, disturbance rejection, and optimization of various performance parameters. Adaptive control strategies are investigated to address uncertainties and parameter variations in power plant systems. Robust control techniques are also explored to ensure stability and performance in the presence of disturbances, changes in operating conditions, or component failures. Studies focus on optimizing power dispatch in multiunit power plants. Advanced optimization algorithms, including linear programming, nonlinear optimization, and genetic algorithms, are used to determine the optimum allocation of power generation among different units to minimize costs or meet specific objectives.¹⁷³ Advanced control strategies are developed to regulate frequency and voltage in power systems. Frequency control is critical for maintaining grid stability, and studies investigate strategies like droop control and load shedding. Voltage control techniques, including reactive power compensation and voltage regulators, aim to ensure grid voltage is within acceptable limits.

Research explores decentralized and distributed control strategies for power plants, particularly in the context of distributed energy resources and microgrids. These strategies aim to improve system resilience, reliability, and responsiveness by distributing control functions across multiple units or nodes. Advanced control strategies include fault detection and diagnosis methods for early identification of equipment malfunctions or anomalies. Techniques such as model-based fault detection, signal processing, and machine learning are applied to improve the reliability and availability of power plants. Studies focus on the development and improvement of SCADA systems for comprehensive monitoring and control of power plants.¹⁷ Advanced SCADA systems enable real-time data acquisition, visualization, and control, facilitating efficient plant operation and maintenance. With the increasing integration of digital technologies, research delves into cyber-physical systems for power plants. Strategies to enhance cybersecurity, secure communication protocols, and protect power plant control systems from cyber threats are investigated to ascertain the integrity and reliability of power generation.¹⁷⁴ Control strategies are explored to integrate renewable energy sources into power plants seamlessly. This includes the development of control algorithms for grid-forming and grid-following converters, ESS, and hybrid power plants to manage the variability and intermittency of renewable sources. Human–machine interface (HMIs) design is studied to improve the interaction between operators and control systems. Advanced HMIs incorporate data visualization, alarm management, and decision support tools to enhance situational awareness and operator response in power plant control rooms.

Previous studies on advanced control strategies for power plants covered a diverse range of topics, from MPC and optimization algorithms to fault detection, cybersecurity, and human-machine interface design.^175–177 The findings contribute to the ongoing evolution of control systems, making power plants more efficient, reliable, and adaptable to changing operational conditions.

AI applications extend beyond power generation to optimize energy usage in buildings. To automate and optimize lighting, heating, ventilation, air conditioning (HVAC), and other energy-intensive activities, AI-driven building management systems examine occupancy trends, meteorological predictions, and data on energy usage.¹⁷⁸

Energy efficiency in buildings has been a prominent area of research, with numerous studies exploring strategies, technologies, and policies to minimize energy consumption and improve sustainability in the built environment. Studies often focus on optimizing the building envelope through improved insulation materials, window design, and construction techniques. Enhanced insulation helps reduce heating and cooling loads, leading to lower energy consumption. Research explores the development and implementation of energy-efficient HVAC systems. This includes advanced control strategies, variable speed technologies, and the integration of smart sensors for adaptive climate control.

The integration of smart technologies for building management is a common theme. Studies investigate the use of building automation systems, IoT devices, and data analytics to optimize energy usage, monitor equipment performance, and enhance occupant comfort.^100,179,180 Understanding occupant behavior and its impact on energy consumption is a key focus. Studies analyze how occupants use energy in buildings, exploring the effectiveness of behavioral interventions, feedback systems, and educational programs to promote energy-efficient practices. Researchers examine how renewable energy sources, like wind turbines and solar panels, could be incorporated into building designs. Studies explore the feasibility of on-site renewable energy generation and assess the impact on overall energy efficiency and sustainability. Building energy simulation models are employed to assess the energy performance of different design scenarios. Researchers use tools like EnergyPlus and DesignBuilder to simulate building behavior, optimize design parameters, and examine the effectiveness of energy-efficient measures. Passive design strategies, such as natural ventilation, daylighting, and passive solar heating, are explored in various studies.^181,182 These strategies aim to minimize the need for mechanical systems and reduce energy consumption by leveraging natural elements. The effectiveness of energy labeling and certification programs, such as ENERGY STAR and LEED, is examined. Studies assess the influence of these programs on building energy performance, market adoption, and occupant awareness.

Retrofitting existing buildings to improve energy efficiency is a common focus. Studies explore cost-effective retrofit measures, assess the energy savings achieved through upgrades, and consider the environmental impact of retrofitting projects. Many studies investigate the impact of policies and regulatory frameworks on building energy efficiency. This includes building codes, energy standards, and incentives aimed at promoting energy-efficient construction and renovation practices. Life cycle assessment studies evaluate the environmental impact of buildings from construction to demolition. Researchers analyze the embodied energy of materials, energy consumption during operation, and end-of-life considerations to assess the overall sustainability of building projects. Demand-side management strategies are explored to optimize energy consumption patterns. Studies investigate load-shifting techniques, DR programs, and the utilization of energy storage to minimize peak demand and enhance grid reliability.^183,184 Urban planning and design strategies are considered to create energy-efficient, sustainable communities. Research explores concepts like mixed land use, transit-oriented development, and green building clusters to minimize energy demand and promote sustainable urban living.

In summary, previous studies on energy efficiency in buildings cover a wide array of topics, reflecting a comprehensive approach to creating sustainable, energy-efficient built environments. The insights gained from these investigations contribute to the ongoing development of best practices, technologies, and policies for energy-efficient buildings. AI tools contribute to assessing the environmental effects of RES through life cycle assessments. These assessments consider the total life span of renewable energy infrastructure, aiding in the reduction of carbon footprints and informing sustainable decision-making. The current usage of AI in renewable energy spans a broad spectrum, from resource assessment and predictive maintenance to grid management and energy storage optimization. The integration of AI into RES is anticipated to be a pivotal factor in defining a future where energy is more efficiently and sustainably produced, given the ongoing advancements in technology.

In 2018, DeepMind, Google’s AI subsidiary, collaborated with the United Kingdom’s National Grid to optimize the efficiency of wind energy production. Using machine learning algorithms, DeepMind analyzed weather forecasts and historical turbine data to predict wind patterns. The AI system provided accurate forecasts, allowing the National Grid to schedule electricity production more efficiently, resulting in a 20% increase in energy output compared to traditional methods.¹⁹²

IBM Research Ireland implemented a project that utilized AI for accurate solar forecasting.¹⁹³ The system integrated machine learning algorithms with advanced weather modeling to predict solar power generation. By improving forecasting accuracy, the AI model enabled better grid management and integration of solar power, helping to mitigate the intermittent nature associated with solar energy.¹⁹⁴

In South Australia, an autonomous microgrid project implemented by SIMEC Zen Energy leverages AI to optimize the operation of diverse energy resources, including solar, wind, and energy storage. The AI system continuously analyzes data on energy demand, weather conditions, and equipment status to make real-time decisions on energy dispatch and storage, maximizing the microgrid’s efficiency and resilience.¹⁹⁵

Microsoft's underwater data center project, known as Project Natick, used AI for energy management. The project involved deploying a submerged data center powered by renewable energy sources. AI algorithms optimized cooling, power usage, and energy storage, contributing to energy efficiency gains. The submerged data center concept showcases how AI can be applied to optimize energy usage in unconventional settings.¹⁹⁶

GE Renewable Energy employs AI for predictive maintenance on wind farms. By analyzing data from sensors on wind turbines, machine learning algorithms predict potential equipment breakdowns before they happen. This enables proactive maintenance, minimizes downtime, and increases the overall reliability and efficiency of wind turbines.⁴⁶

Tesla, known for its energy storage solutions, incorporates AI into its Powerpack and Powerwall systems. AI algorithms optimize the charging and discharging cycles of energy storage units based on electricity prices, demand patterns, and renewable energy availability. This results in improved efficiency in storing and utilizing energy, making the integration of renewable sources more effective.¹⁹⁷

In Germany, the grid operator 50 Hz uses AI to regulate the integration of renewable energy into the grid.¹⁹⁸ The AI system analyzes data from sensors, weather forecasts, and grid parameters to make real-time decisions on energy flow and grid stability. This helps accommodate the fluctuating nature of renewable energy origins and enhances the general efficiency of the electricity grid.

Voith Hydro, a provider of hydroelectric power solutions, incorporates AI to optimize the operation of hydropower plants.¹⁹⁹ AI algorithms analyze data on water flow, turbine efficiency, and grid demand to adjust the operation of turbines for maximum energy output. This approach improves the overall efficiency of hydroelectric power generation. These case studies illustrate the diverse applications of AI in optimizing RES, from solar and wind forecasting to grid management and energy storage. The application of AI in the renewable energy sector is anticipated to increase as technology develops, supporting the creation of more efficient and sustainable energy landscape.¹⁹²

RL, a subset of machine learning, is gaining traction for optimizing the operation and control of RES.²⁰² RL algorithms, such as deep RL, are being applied to address complex decision-making problems. In the context of renewable energy, RL is used to optimize energy production, storage, and distribution by learning from interactions with the environment. For example, RL can be applied to manage the charging and discharging cycles of ESS in response to variable renewable generation and dynamic electricity demand.

Machine learning techniques, including RL, are being employed for more accurate and dynamic energy forecasting in RES. Improved forecasting of renewable energy generation, such as solar and wind, enables better grid integration and operational planning.⁹¹ By leveraging historical data and real-time information, RL models can adapt to changing conditions and enhance the accuracy of energy production predictions.

XAI is becoming increasingly important, especially in critical systems like renewable energy, where transparency and interpretability are essential.²⁰³ XAI techniques aim to make AI models more understandable and interpretable for decision-makers. In RES, XAI can provide insights into how AI models make predictions or decisions, helping operators and stakeholders understand the rationale behind actions taken by AI algorithms.⁵⁷ This transparency is crucial for gaining trust and facilitating effective collaboration between AI systems and human operators.

Predictive maintenance is a key application of AI in the renewable energy sector. RL and other AI techniques are used to analyze sensor data from renewable energy assets, such as wind turbines and solar panels, to predict and prevent equipment failures.²⁰⁴ This proactive maintenance strategy reduces downtime, increases asset durability, and enhances system reliability.

Microgrids, which often incorporate renewable energy sources, can benefit from AI-based optimization. RL algorithms can optimize the energy dispatch, storage, and DR within microgrids, considering the intermittent nature of renewable generation.²⁰⁵ AI-driven optimization enhances the resilience and efficiency of microgrid operations, making them more adaptable to local energy conditions.

RL is applied to improve grid management and stability in the presence of renewable energy sources. AI algorithms can learn optimal control strategies for grid-connected devices, such as ESS and DR units, to balance supply and demand, regulate voltage, and enhance overall grid stability.

The performance of ESS can be greatly enhanced by using AI technologies, such as RL. These devices retain extra energy produced by renewable resources and release it when required. RL can adaptively control energy storage based on real-time conditions, grid requirements, and economic factors, maximizing the efficiency of energy storage operations.²⁰⁶ AI technologies are being applied to facilitate collaborative decision-making in energy communities. RL can help optimize energy sharing and distribution among community members with diverse energy assets, fostering a more decentralized and resilient energy landscape. The integration of RL and XAI into RES holds the promise of optimizing operations, enhancing reliability, and facilitating the seamless integration of renewable energy sources into the broader energy infrastructure. These trends contribute to the ongoing transformation of the energy sector toward sustainability and resilience.