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Stecuła, K.; Wolniak, R.; Grebski, W.W. Urban Infrastructure Integration of AI-Driven Energy Solutions. Encyclopedia. Available online: https://encyclopedia.pub/entry/53428 (accessed on 18 May 2024).
Stecuła K, Wolniak R, Grebski WW. Urban Infrastructure Integration of AI-Driven Energy Solutions. Encyclopedia. Available at: https://encyclopedia.pub/entry/53428. Accessed May 18, 2024.
Stecuła, Kinga, Radosław Wolniak, Wieslaw Wes Grebski. "Urban Infrastructure Integration of AI-Driven Energy Solutions" Encyclopedia, https://encyclopedia.pub/entry/53428 (accessed May 18, 2024).
Stecuła, K., Wolniak, R., & Grebski, W.W. (2024, January 04). Urban Infrastructure Integration of AI-Driven Energy Solutions. In Encyclopedia. https://encyclopedia.pub/entry/53428
Stecuła, Kinga, et al. "Urban Infrastructure Integration of AI-Driven Energy Solutions." Encyclopedia. Web. 04 January, 2024.
Urban Infrastructure Integration of AI-Driven Energy Solutions
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This entry is adapted from the peer-reviewed paper 10.3390/en16247988

In a rapidly evolving urban landscape, the challenges of energy consumption, sustainability, and efficiency remain critical concerns. Artificial intelligence is a transformative force fundamentally reshaping the way we live, work, and interact with our environment. AI uses advanced algorithms, machine learning, and data analysis to mimic human cognitive functions, enabling machines to perceive, reason, and make decisions. In cities, AI can be used to optimize energy infrastructure and create a more sustainable and resilient urban future.

energy artificial intelligence smart grid electric vehicle charging infrastructure vehicle emission reduction energy storage

1. Introduction

In a rapidly evolving urban landscape, the challenges of energy consumption, sustainability, and efficiency remain critical concerns. The need for energy in cities is still growing and is related to the growing activities of residents, the need for advanced services, and the use of technical means in the city infrastructure. The last ones are connected with the Internet of Things and innovative solutions connected with energy in cities [1][2][3]. The Internet of Things (IoT) has ushered in a new era of urban connectivity and intelligence. In cities around the world, IoT technologies are revolutionizing the way urban infrastructure operates, providing an interconnected web of smart devices and sensors that collect and share data in real time [4]. These IoT systems allow city planners and administrators to gain unprecedented insight into various aspects of urban life, from traffic patterns [5], the use of different sources of energy [6][7][8] and energy consumption [9][10][11] to waste management [12] and air quality [13]. The pursuit of a ubiquitous Internet and the development of urban infrastructure means that the achievements of Industry 4.0 are being used on an increasingly larger scale, leading to more and more solutions based on smart elements [14][15] and neural networks [16]. One of the rapidly developing elements of the fourth industrial revolution is artificial intelligence (AI). Artificial intelligence is a transformative force fundamentally reshaping the way we live, work, and interact with our environment. It is important in the context of Industry 4.0 and the broader use of a ubiquitous Internet not only for entertainment [17] but also for regular everyday life. AI uses advanced algorithms, machine learning, and data analysis to mimic human cognitive functions, enabling machines to perceive, reason, and make decisions [18]. In cities, AI can be used to optimize energy infrastructure and create a more sustainable and resilient urban future. Artificial intelligence offers many solutions in various areas of human activity, including those used in the energy sector [19][20]. Some solutions are aimed at an individual resident, and others at the whole society. Nevertheless, AI-based solutions are the driving force towards a new intelligent society living in cities called smart cities.

2. Electric Vehicle Charging Infrastructure

Artificial intelligence plays an increasingly important role in electric vehicle charging infrastructure to enhance its efficiency, reliability, and user experience [21]. AI algorithms can analyze various factors such as the grid load, energy prices, and individual user preferences to create optimal charging schedules for EVs [22]. This ensures that charging occurs during periods of lower energy demand or when renewable energy sources are abundant, reducing costs and the environmental impact [23]. Also, AI can facilitate demand response programs, allowing EV owners to participate in load-shifting initiatives. During peak demand periods, AI can coordinate with users to temporarily reduce their charging rates, alleviating stress on the grid and helping to prevent blackouts [24].
El Husseini et al. [25] discuss the integration of blockchain and AI technologies as a solution to address these challenges. It suggests that combining these technologies can lead to a more secure, efficient, and decentralized charging ecosystem. They discuss a couple of use cases where AI and blockchain technologies complement each other to enhance the charging infrastructure for EVs [22][26]. These use cases likely illustrate scenarios where technologies work together to improve security and optimize charging schedules. According to this research, it is intended to help stakeholders identify potential directions and implementations for better charging systems for EVs. This implies that the research aims to inform decision-makers about the possibilities and advantages of integrating AI and blockchain in the EV charging infrastructure [27].
Chaihoie et al. [28] points out that to prepare an appropriate predictive model for charger planning, AI usage is very useful. They described the “predict-then-optimize” approach, where AI is used to predict the EV charging demand. This prediction is made using a multi-relation graph convolutional network (GCN)-based encoder–decoder deep architecture. This predictive model allows for more data-driven planning and allocation of resources. In the describe approach, AI is also utilized to optimize the competitive resource allocation strategy for charger planning. This likely involves determining where to place charging stations and how to distribute them effectively to meet anticipated demand. AI can be used to address the optimal size of EV chargers, determining the number of chargers that each service provider should deploy in various areas of the city.
Another complex analysis on AI usage in context of electric vehicle charging infrastructure was described by Qin and Folly [29]. They pointed out that AI, particularly deep learning methods like LSTM and GRUs, can be useful for forecasting EV charging and discharging patterns. The research highlights the importance of accuracy in forecasting models, considering the stochastic and unpredictable nature of EV charging patterns. It also mentions the use of hybrid and ensemble techniques to improve forecast accuracy.
Dynamic pricing strategies are crucial to influence EV owners’ charging and discharging behaviors. The research discusses the challenges of existing dynamic pricing models, such as undervaluing or overvaluing stored battery power. It suggests the need for pricing models that reflect real-time power system conditions and balance the interests of system operators and EV owners. The AI can be used to achieve this dynamic pricing approach.
Also, the AI can be useful in the development phase of vehicle-to-grid (V2G). The development of V2G can be categorized into three phases [30]. In the first phase, the EV charging load is a small proportion of the power grids, mainly using uncontrolled and controlled charging strategies. The second phase sees an increased EV charging load with the high penetration of EVs, necessitating smart charging/discharging control strategies and aggregator coordination. The third phase envisions a mature state in which many EVs provide ancillary services to power grids [31].
Another possible application of AI in vehicles charging infrastructure was described by Mosayebi et al. [32]. The researchers describe the need for improved charging infrastructure for electric vehicles as a result of their increasing numbers worldwide. It introduces the concept of a smart extreme fast portable charger (SEFPC) for EVs with multiple input sources, including the power grid and renewable energy sources such as an energy storage system (ESS). The SEFPC is designed to optimize the charging process by considering available power sources and the condition of the EV battery to save energy and time. A machine learning algorithm, based on IT, specifically a model-free sliding mode controller, is applied to determine the optimal charging operation mode based on the state of the battery and power source conditions. This approach aims to enhance battery life and overall system efficiency. The text concludes by mentioning real-time results obtained using the OPAL-RT platform to validate the effectiveness and feasibility of the SEFPC and the model-free sliding mode controller.
The very important problem in the case of charging infrastructure is connected with the importance of effectively placing charging stations to support the growth of EVs and enhance the traffic network’s efficiency. Existing research often focuses on EV users’ mileage anxiety but overlooks their strategic and competitive charging behaviors. According to Lazari and Chassiakos et al. [33], to address this issue, the concept of charging cost for an EV user can be introduced, considering factors such as the cost of traveling to access charging stations and the cost of queuing at charging stations. The problem can be formulated as the charging station placement problem (CSPP), initially as a bilevel optimization problem. It then leverages the equilibrium of the EV charging game to convert the problem into a single-level optimization task, proposing the “Optimizing eleCtric vEhicle chArging statioN” (OCEAN) algorithm for optimal charging station allocation. Recognizing OCEAN’s scalability limitations, a heuristic algorithm based on AI called OCEAN can be used with continuous variables to handle large-scale real-world scenarios. The results of extensive experiments demonstrate that their approach significantly outperforms baseline methods in addressing the competitive and strategic charging behaviors of EV users.
Artificial intelligence profoundly impacts EV charging infrastructure, optimizing charging schedules based on grid load, energy prices, and user preferences. Studies propose integrating AI with the blockchain to enhance the security, efficiency, and decentralization of EV charging ecosystems. Predictive AI models aid in anticipating EV charging demand, facilitating data-driven planning, resource allocation, and optimal sizing of charging stations. AI-driven deep learning methods like LSTM and GRUs enhance accuracy in forecasting EV charging patterns, which is crucial for dynamic pricing strategies and vehicle-to-grid development. Additionally, the concept of a smart extremely fast portable charger utilizes AI to optimize charging considering various input sources and EV battery conditions.

3. Vehicle Emission Reduction

One of the promising technological advances in this effort is the application of artificial intelligence to mitigate vehicle emissions. AI is revolutionizing the automotive industry by enhancing the efficiency of conventional vehicles, accelerating the adoption of electric and hybrid vehicles, and optimizing traffic management. This two-page essay explores the multifaceted role of AI in vehicle emissions reduction [34].
One of the primary ways that AI contributes to vehicle emission reduction is by optimizing the performance of traditional internal combustion engines. AI algorithms are integrated into the engine control systems, enabling real-time monitoring and the adjustment of parameters to minimize emissions while maintaining efficiency [35]. These AI-driven systems take into account factors such as engine temperature, load, and fuel–air mixture to ensure that combustion is as clean and efficient as possible. By constantly adapting to changing driving conditions, these AI systems can significantly reduce harmful emissions.
According to Zhao et al. [36], the research of electric vehicles equipped with artificial intelligence has the capacity to significantly mitigate air pollution and carbon emissions. AI assistance enables these vehicles to operate more efficiently and make real-time decisions, contributing to a cleaner environment. By optimizing energy consumption and reducing the carbon footprint, AI-assisted electric vehicles offer a promising solution to combat the environmental challenges associated with conventional vehicles.
The rapid growth of electric and hybrid vehicles is a key strategy for reducing emissions [37]. AI plays an important role in the development and operation of these cleaner alternatives. For instance, AI is used to manage the power distribution in hybrid vehicles, deciding when to use electric or gasoline power based on driving conditions. It optimizes battery performance, expanding the range of electric vehicles [38].
Abduljabbar et al. [39] state that artificial intelligence is seen as a well-suited solution to address the complex challenges faced by transportation systems, including growing travel demands, rising CO2 emissions, safety issues, and environmental degradation. These challenges are a direct result of the continuous expansion of traffic, both in rural and urban areas, driven by population growth, especially in developing countries. For instance, in Australia, the cost of congestion is projected to rise to 53.3 billion as the population increases to 30 million by 2031. In Melbourne, Australia, alone, over 640 km of arterial roads experience congestion during peak hours, leading to an annual CO2 emission of 2.9 tons.
AI-driven eco-driving assistants provide real-time feedback to drivers on how to optimize their driving habits for better fuel efficiency and lower emissions [40]. These systems analyze data from various vehicle sensors, including engine performance, speed, and fuel consumption, to advise drivers on the most fuel-efficient speeds, optimal gear shifting points, and efficient acceleration and braking patterns [41]. By following these recommendations, drivers can significantly reduce their carbon footprint, and over time, this has a collective impact on emissions reduction and ensures a seamless transition between electric and internal combustion modes [42]. These assistants also incorporate route optimization, suggesting the most efficient routes to reach a destination. By avoiding traffic congestion and stop-and-go driving, vehicles can operate more efficiently, resulting in reduced emissions [43].
Delnevo et al. [44] explored the integration of big data and machine learning to forecast when the friction brake will be activated. The objective is to enhance energy efficiency in electric vehicles, raise driver awareness, and alleviate concerns related to ‘range anxiety.’ Subsequently, the in-vehicle human–machine interface can take advantage of these real-time predictions to provide drivers with more precise and comprehensive insights into their braking habits, ultimately promoting eco-friendly driving practices.
Also, an AI-based solution can be useful in optimizing traffic flow and reducing congestion, which, in turn, can lead to emissions reduction. AI-powered traffic management systems use real-time data from various sources, including traffic cameras, sensors, and smartphones, to analyze traffic patterns [45]. These systems can adjust traffic signals, suggest alternate routes, and even implement dynamic toll pricing to reduce congestion during peak hours [46].
In the 21st century, numerous researchers [34][37][46][47][48] are striving to establish a more reliable transportation system that minimizes its impact on people and the environment, while remaining cost-effective and efficient through the application of AI techniques. AI holds significant promise for enhancing various aspects of transportation, including road infrastructure, driver assistance, road user experience, and vehicle operation.
Summing up, artificial intelligence demonstrates a pivotal role in curbing vehicle emissions by optimizing traditional combustion engines in real time. These AI-integrated systems meticulously adjust parameters, like engine temperature and fuel–air mixtures, to substantially minimize harmful emissions while ensuring efficiency, thereby showcasing promising results in emission reduction. Additionally, AI plays a vital role in enhancing electric and hybrid vehicles’ efficiency by managing power distribution, extending battery range, and offering real-time decision-making capabilities, thereby significantly reducing the environmental impact. AI-enabled eco-driving assistants provide personalized feedback to drivers, optimizing driving habits for fuel efficiency and lower emissions by advising on optimal speed, gear shifting points, and efficient acceleration patterns. Furthermore, AI-based traffic management systems leverage real-time data to optimize traffic flow, reduce congestion, and subsequently cut down on emissions during peak hours. These implementations underscore AI’s effectiveness in mitigating emissions and optimizing transportation systems.

4. Smart Grid

Artificial intelligence is a very useful solution in enhancing the efficiency, reliability, and sustainability of smart grids, which are modernized electrical grids that use digital technology to monitor and manage electricity generation, distribution, and consumption. AI is applied in various ways within smart grids to optimize operations, improve energy management, and enhance overall grid performance [49].
The utilization of AI in the smart grid offers a digital framework that harnesses advanced technological capabilities. AI strategies within the smart grid encompass various aspects such as power management, automation of the power system, analysis of energy usage trends, and the detection of faults [50]. The ultimate objective of an intelligent grid is to substitute manual procedures with AI-driven solutions, resulting in enhanced efficiency, stability, and cost savings [51]. This covers every facet of an electrical network [49] including power generation [52], energy transmission [53], power conversion [54], electricity distribution [55], and energy consumption [56].
AI can analyze data from sensors and other sources to predict when grid equipment, such as transformers or circuit breakers, might fail. This proactive approach to maintenance reduces downtime and prevents costly outages [57]. AI can forecast electricity demand and adapt the grid’s operation accordingly. It can communicate with smart appliances, thermostats, and electric vehicles to optimize energy consumption during periods of high demand or low supply [58], reducing peak load and managing grid stress [59].
Omitaomu and Niu [60] have described main artificial intelligence techniques which can be used in smart grids. The first important method is load forecasting. The increasing complexity of load forecasting is due to the integration of renewable energy sources in smart grids. Load forecasting is categorized into three levels: short-term load forecasting (STLF), mid-term load forecasting (MTLF), and long-term load forecasting (LTLF). Various AI techniques, including deep learning, are explored to enhance forecasting accuracy. Another area where AI techniques can be useful is power grid stability assessment. Power grid stability, comprising transient stability, frequency stability, small-signal stability, and voltage stability are crucial to ensure the reliability and security of the power system. Traditional stability assessment models are complex and computationally intensive. Data-driven AI methods are useful for stability analysis, leveraging technologies such as phasor measurement units (PMUs) and wide area measurement systems (WAMSs).
Another potential of AI in smart grids is the usage of AI methods for fault detection in power systems. Various techniques, such as extreme learning machines (ELMs), support vector machines (SVMs), and ensemble models, are employed to detect and locate faults in power grids, including high-impedance faults in micro grids and line trip faults [61].
The AI can also be useful in the case of smart grid security. AI technologies, such as artificial neural networks (ANNs), support vector machines (SVMs), and reinforcement learning (RL), are employed to enhance smart grid security by detecting and preventing cyberattacks [49][62].
AI systems continuously monitor the grid for abnormal conditions or disturbances [63]. They can quickly identify and respond to issues such as power outages [63], equipment failures [64], or cybersecurity threats [49]. Its use can help in integrating variable energy sources like solar and wind into the grid by predicting their output based on weather conditions and adjusting grid operations to accommodate fluctuations in generation [65][66].
According to Seyd and Bong [49], AI techniques have revolutionized the energy market by providing efficient solutions for real-time demand response and decision-making. This enables grid operators to optimize all aspects of the power grid, from relay switching to large generator controls, and mitigate unwanted harmonics through sensor networks. Those techniques play a crucial role in coordinating distributed energy resources, enhancing the acceptability of renewable energy sources, and increasing grid reliability. It allows for the efficient management of distributed generation and storage capacity, automatic regulation and optimization, bidirectional energy flow, and the integration of plug-in hybrid electric vehicles.
Distributed grid management requires real-time optimization for large-scale systems with renewable generators and controllable loads. AI techniques, such as consensus-based distributed computational intelligence, offer solutions to address the challenges of rapidly changing conditions, computation, and communication bottlenecks [67]. AI has driven the development of decentralized and intelligent controllers, improving processing speed, reliability, and efficacy. These controllers distribute operations among distributed units, reducing the burden on centralized controllers and improving the resilience of the system [68].
The traditional approach of using supervisory control and data acquisition (SCADA) systems has become impractical due to the complexity of modern grids. AI-driven distributed load balancing algorithms have emerged as effective solutions to optimize loads in distributed systems. AI and blockchain technologies have played a significant role in enhancing security and data management in smart grids, particularly in the context of distributed data storage and local energy trading [69].
According Sulaiman et al. [59], the integration of artificial intelligence into the smart grid presents significant opportunities and challenges. AI can enhance grid security by continuously monitoring, analyzing, and predicting potential threats and vulnerabilities. It enables proactive responses to security incidents, automates decision-making, and promotes collaboration among various infrastructure components in a smart city. However, there are several challenges that need to be addressed.
According to Zambrano and Giraldo [70], predictive models based on AI for renewable energy hold the promise of revealing valuable glimpses into the expected energy enhancements in the near future. Ruhnau et al. [71] believe that combining various approaches can refine these forecasts by making the most of the disparities in individual prediction models. These approaches encompass both standalone and integrated technologies that generate predictions based on distinct time series data derived from specific sources such as weather stations, wind turbines, or solar panels [72]. To enhance forecast precision, the incorporation of information from nearby areas to the location of interest has become increasingly popular, particularly in recent years [40].
AI has enabled the emergence of “prosumers”, allowing domestic energy users to both produce and consume electricity and share it with others. This shift from centralized, fossil-fueled generation to a decentralized, intelligent system enhances economic benefits for consumers, fostering energy sharing and trade [73].
Sami [74] described how to use AI in prosumers management. He pointed out that machine learning within the realm of artificial intelligence has the capability to assess and anticipate energy demand patterns and categorize irregular energy usage. By leveraging data collected through smart meters and subjecting it to AI analysis and data mining, it becomes feasible to discern various customer segments’ electricity consumption behaviors. Subsequently, this data can be employed to enhance statistical precision, facilitating the targeted delivery of advertisements and services. Fluctuations in the environment, such as variations in weather conditions, alterations in electrical appliance usage, and changes in consumer behavior, can impact the accuracy of anomaly detection results. Consequently, it is imperative to emphasize potential adverse aspects within the power grid that could influence the equitable distribution of power among consumers. The analysis of energy consumption is intrinsically linked to human characteristics, which can be addressed by extracting or taxonomy features. In this context, the development of deep learning models, particularly multilayered hidden neural networks, augments the predictive performance of energy demand and consumption.
Rodgers et al. [75], in his study, underscores the significance of AI in smart grids, aligning goals with global sustainability objectives, emphasizing the role of ICT, and outlining practical requirements for smart grids. The study delves into the decision-making processes of experts and their knowledge transfer apparatus. It highlights the importance of information and communication technology (ICT) and AI usage in facilitating knowledge transfer for a greener environment. The researchers have identified three key goals for smart grids: universal access to electricity, environmental protection, and efficiency. These goals align with global sustainability objectives, such as those set by the United Nations Conference on Sustainable Development (Rio + 20). The AI-based solutions can be useful to realize them.
The AI can be also used to enable smart grid stability prediction. This possible usage was described by Ucar [76]. He proposes an enhanced model using explainable AI and feature engineering for predicting the stability of the smart grid (SG). This model approaches the problem with both classification and regression, offering a holistic perspective on existing studies and proposing a novel structure to address their limitations. The GBM (gradient boosting machine) and deep learning models are introduced as effective tools for prediction, despite their drawbacks. The flexibility and practicality of GBMs make them valuable tools for model design and customization. The text concludes by emphasizing the importance of combining data analytics with smart grid research for future studies.
Summing up, artificial intelligence plays a pivotal role in optimizing the performance of smart grids by efficiently managing various grid operations and energy consumption. These AI-driven systems utilize load-forecasting techniques and predictive analysis to enhance stability, minimize downtime, and proactively maintain grid equipment, preventing costly outages. Moreover, AI facilitates real-time adjustments in energy consumption by communicating with smart appliances and electric vehicles, ensuring optimization during peak demand periods, and consequently reducing stress on the grid. AI’s integration within the energy market enables grid operators to coordinate distributed energy resources efficiently, enhancing grid reliability and managing distributed generation and storage capacity effectively. Furthermore, AI-driven decentralized controllers enhance system resilience and processing speed, optimizing operations among distributed units, and reducing the dependency on centralized controllers. Moreover, AI’s role extends to improving grid security by continuously monitoring and predicting potential threats, automating decision-making, and fostering collaboration among infrastructure components in smart cities.

5. Energy Storages

With the global shift toward renewable energy sources like solar and wind, the need for efficient and reliable energy storage solutions has become increasingly critical. AI plays an important role in addressing the challenges associated with energy storage, making it smarter, more cost-effective, and environmentally friendly [77].
Energy storage technology has a role to play in enhancing the capabilities for utilizing new energy sources, ensuring the reliable and cost-effective power systems operation, and advancing the extensive adoption of renewable energy technologies [78]. Various fresh innovations, concepts, methodologies, and technologies have been introduced in this domain, stemming from disciplines such as materials science, knowledge management, electrical engineering, control systems, and artificial intelligence [79].
AI algorithms are being used to enhance the performance of energy storage systems, particularly lithium-ion batteries. By continuously monitoring and analyzing data from these batteries, AI can optimize their charging and discharging cycles, extending their lifespan and improving their efficiency [54]. This not only reduces maintenance costs, but also reduces the environmental impact of battery disposal. This solution is employed to predict potential issues in energy storage systems before they lead to costly breakdowns [80]. Through real-time data analysis and machine learning models, AI can detect anomalies in system behavior, enabling operators to perform timely maintenance and prevent unexpected downtime [81].
Energy storage systems equipped with AI can respond rapidly to fluctuations in the grid. When the supply of renewable energy is inconsistent, AI can instantly adjust the flow of stored energy, stabilizing the grid and ensuring a consistent power supply [82].
In the literature, many techniques of AI usage in energy storage can be found. Ahmed and Abdallia [83] proposed hybrid differential evolution optimization of AI. The efficiency of the proposed controller is confirmed in an electrical grid that includes a synchronous generator, a photovoltaic power source, and a battery energy storage system. The controller’s parameters are adaptively tuned in real-time by training the artificial neural network (ANN) with datasets generated during the optimization phase of both controllers using the hybrid differential evolution optimization method under varying levels of disturbance, ranging from low to high. Athari and Ardehali [84] used the fuzzy logic controller-based approach. The membership features of the fuzzy logic controller (FLC) are tailored to reduce operational costs in green energy hybrid systems. This reduction is achieved by utilizing weekly and periodic data predictions for factors such as water availability, electricity demand, and environmental conditions like wind speed, sunlight, and air temperature. This optimization process employs algorithms inspired by frog-spring shuffling. It is worth noting that accurate accounting of power grid costs plays a significant role in enhancing the efficiency of energy storage components for the hybrid renewable energy systems (HRESs) when connected to the grid. This efficiency improvement is achieved because the configured weekly and periodic FLCs help minimize the operating hours of fuel cells and gas-based generators while reducing state-of-charge (SOC) variability in the battery stack [83].
Zahedi and Ardehali [85] described the situation when a novel energy storage system (ESS) control system employing a multi-agent setup was implemented for a 100-megawatt system. The system’s control performance was verified through simulation analysis and practical testing. The AI-driven solution based on hierarchical control was described by Yunhao et al. [86]. By employing balance regulation, the simulated impedance is dynamically adjusted to eliminate the impact of inaccurate line impedance on the precision of the current distribution. Subsequently, each power storage unit can fine-tune its current based on state-of-charge (SoC) balance control, taking into account its capacity and charging status. This helps reduce SoC discrepancies and facilitates a gradual state of charge (SoC) balance during both charging and discharging operations.
Summing up, artificial intelligence is revolutionizing energy storage solutions by optimizing the performance and longevity of storage systems. In energy storage, AI algorithms continuously analyze and fine-tune the charging and discharging cycles, notably enhancing the efficiency of lithium-ion batteries. By leveraging real-time data and predictive analytics, AI predicts potential system issues, enabling proactive maintenance, reducing downtime, and mitigating the environmental impact associated with battery disposal. These AI-driven solutions in energy storage effectively stabilize the grid by swiftly responding to fluctuations in renewable energy supply, ensuring consistent power flow and minimizing interruptions. Additionally, diverse AI-based approaches, such as hybrid optimization and fuzzy logic controllers, significantly improve system efficiency and reduce operational costs in hybrid renewable energy systems. These advancements underscore AI’s role in enhancing the reliability, efficiency, and sustainability of energy storage systems, offering promising avenues for smarter and more eco-friendly energy management.

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Kinga Stecuła
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Radosław Wolniak
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Wieslaw Wes Grebski
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Update Date: 08 Jan 2024
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