A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems

Abstract

The integration of renewable energy sources (RES) into smart grids has been considered crucial for advancing towards a sustainable and resilient energy infrastructure. Their integration is vital for achieving energy sustainability among all clean energy sources, including wind, solar, and hydropower. This review paper provides a thoughtful analysis of the current status of the smart grid, focusing on integrating various RES, such as wind and solar, into the smart grid. This review highlights the significant role of RES in reducing greenhouse gas emissions and reducing traditional fossil fuel reliability, thereby contributing to environmental sustainability and empowering energy security. Moreover, key advancements in smart grid technologies, such as Advanced Metering Infrastructure (AMI), Distributed Control Systems (DCS), and Supervisory Control and Data Acquisition (SCADA) systems, are explored to clarify the related topics to the smart grid. The usage of various technologies enhances grid reliability, efficiency, and resilience are introduced. This paper also investigates the application of Machine Learning (ML) techniques in energy management optimization within smart grids with the usage of various optimization techniques. The findings emphasize the transformative impact of integrating RES and advanced smart grid technologies alongside the need for continued innovation and supportive policy frameworks to achieve a sustainable energy future.

1. Introduction

Shifting towards sustainable and resilient energy has become a priority for contemporary countries that have plans to mitigate the challenges related to climate change and energy security. RES integration in the smart grid is the pivotal element in advancing energy sustainability [1]. Utilizing RES, including wind, solar, and hydropower, offers the promise of mitigating the greenhouse footprint and diminishing the hazardous impacts of traditional fossil-fuel energy sources on the environment [2]. However, the unpredictability of these sources has caused problems with their management and integration into the power grid alongside other sources [3].

The smart grid concept brought a significant evolution in the traditional power grid with itself, such as leveraging advanced communication, control mechanisms, and energy management systems for optimization in generation, distribution, and electricity consumption [4]. A pivotal characteristic of the smart grid, which has gathered lots of attention in recent years, is its capability to respond dynamically to changes in energy demand and supply, which leads to improvements in overall grid efficiency and reliability [5,6].

Machine Learning (ML) has emerged as a powerful tool in the enhancement of smart grid functionalities. By enabling predictive analytics, optimization, and automated control, ML techniques can significantly improve the efficiency and reliability of energy management within the smart grid [7]. The applications of ML in this domain include optimizing energy management strategies [8], predicting renewable energy generation (such as wind power) [9], and improving fault detection and system maintenance [10]. These advancements not only facilitate the integration of RES but also contribute to the overall robustness and adaptability of the smart grid [11].

Energy storage technologies play a crucial role in balancing the supply–demand equation within the smart grid. Innovations in battery technology, supercapacitors, and thermal storage systems offer promising solutions for storing excess energy generated during periods of high renewable energy output and releasing it during periods of low generation or high demand [12]. Effective energy storage systems are indispensable for mitigating the intermittency of RES and ensuring a consistent energy supply [13].

This review paper aims to provide a comprehensive analysis of the current state of smart grid technologies, with a particular focus on the integration of RES and the application of ML techniques. It explores the advancements in smart grid components, such as communication infrastructure, control mechanisms, and energy management systems, highlighting their roles in enhancing grid reliability and efficiency [14]. Additionally, this paper examines various energy storage technologies and their contributions to the smart grid’s operational stability [15].

Through an in-depth analysis of recent studies, this work illustrates the successful implementation of smart grid systems integrating wind energy and ML techniques [16]. It discusses future opportunities and challenges associated with large-scale wind energy integration and presents the authors’ solutions to these challenges [17]. Furthermore, this paper identifies emerging trends in using wind energy and ML technologies for smart grid development, providing insights for researchers, stakeholders, and industry practitioners [18]. Ultimately, this synthesis aims to foster advancements towards a more sustainable and efficient use of clean energy, thereby contributing to reducing carbon footprints and promoting environmental sustainability [19]. By highlighting the synergies between renewable energy integration, smart grid technologies, and ML, this review underscores the critical pathways toward achieving a resilient and sustainable energy future [20].

Despite all of the efforts that have been made that are focused on the integration of RES into smart grids and the application of new technologies such as ML and energy storage systems, several critical gaps still remain. Identifying these gaps could provide a clear perspective of this paper’s contribution and set the stage for further research.

• Limited Focus on Holistic Integration: While there are many works on smart grid technologies, such as energy storage or ML applications, they did not provide a comprehensive analysis of the way these technologies could holistically integrate into the grid. This segmented approach often overlooks the complexities and synergies involved in integrating multiple technologies simultaneously, such as how energy storage systems can be optimized using ML within a smart grid.
• Insufficient Attention to Emerging Technologies: Despite the fact that many technological advancements have been made in renewable energy integration and smart grid, many reviewers fail to adequately cover emerging technologies such as blockchain. These technologies have the revolutionized potential to evolve the grid, but their integration into the existing smart grid infrastructure remains unexpected.
• Geographical and Contextual Limitations: The main focus of studies is on developed countries such as North America or Europe, with limited attention to developing countries and the challenges they face because of their environmental conditions or industrial context. This bias limits the applicability and recommendations to a global audience.
• Lack of Longitudinal Studies: Many review papers’ findings are synthesized from short-time studies or simulations that may not have the capability to capture the long-term impacts and sustainability of integrating RES and technologies into smart grids. Using longitudinal studies, could assess the performance of these technologies over time and explore any probable drawbacks of these models.

This paper is structured into eight comprehensive sections that focus on the critical aspects of smart grids and their integration with RES. The Section 1 introduces the smart grid concept by emphasizing dynamic response capabilities. The Section 2 discusses renewable energy resources, including wind, solar, and hydropower, and their importance in mitigating environmental impacts. The Section 3 focuses on the usage of ML in enhancing grid functionalities through predictive analytics and optimization. It is also exploring energy storage technologies by focusing on their role in balancing supply and demand. In the Section 4, a comprehensive analysis of smart grid technologies has been provided, particularly focusing on renewable energy integration and ML application. The Section 4 also offers an in-depth analysis of smart grid systems with wind energy in ML. The Section 5 covers the synthesis and conclusion, which summarize the synergies among renewable energy integration, smart grid technologies, and ML. Finally the Section 6 discusses the future opportunities and challenges associated with the integration of wind energy into smart grids., All of the mentioned information can be seen in Figure 1.

2. Literature Review

The integration of RES into smart grid systems has faced enormous attention from various nations because of the growing concerns over climate change, energy security, and sustainability. Utilizing smart grid technologies in the traditional power grid provides an innovative solution for handling the complexities associated with the unpredictability and variety of produced power related to RES, such as wind, solar, and tidal. Empowering advanced communication and control mechanisms can offer a dynamic and efficient way for the smart grid to optimize energy generation distribution and communication. The following section will provide a comprehensive review of the current state of smart grid technologies, focusing on their role in renewable energy integration and the application of ML techniques.

2.1. Overview of Smart Grid Technologies

The smart grid is the integration of traditional power grids with advanced communication, control, and energy management systems to increase the reliability, efficiency, and sustainability of the grid itself. Unidirectionality was one of the main drawbacks of traditional power grids, where electricity could only flow from the centralized power plants to customers. By revealing the smart grid, a bidirectional flow has been introduced that enables Distributed Energy Resources (DERs) and the active participation of consumers in energy management [1]. This evolution is driven by the need to accommodate the increasing amount of RES and address the challenges that these systems have with their intermittency and unpredictability [6].

2.1.1. Key Components of Smart Grids

As mentioned before, smart grids consist of several components that work synergistically to optimize the generation, distribution, and consumption of electricity. These components are as follows:

• Advanced Metering Infrastructure (AMI): AMI represents smart meters communication networks and data management systems that provide real-time monitoring and control of energy [7]. Utilizing this technology facilitates dynamic pricing, demand response, and efficient load management. For instance, AMI helps reduce peak demand by shifting consumption to off-peak periods through time-of-use tariffs. However, the high deployment cost of AMI systems can be a barrier, making it most suitable for regions that have high energy consumption alongside dynamic pricing structures [8].
• Distributed Energy Resources (DERs): DERs such as solar panels, wind turbines, and energy system management systems are integrated into the grid to provide localized generation and storage. With this method, grid flexibility and reliability of the traditional centralized power plants will be reduced. Alongside that, a higher proportion of renewable energies can be accommodated into the grid, which leads to a lower carbon footprint for the environment. In addition, microgrids, which are small-scale power grids and act independently or in conjunction with the main grid, can be used for the system [4,9].
• Energy Management Systems (EMS): The EMS is central to optimizing the operation of the grid by managing the flow of electricity, balancing supply and demand, and also ensuring efficient energy distribution. EMS uses real-time data from different locations and components to have proper functions such as load forecasting, demand response, and generation scheduling [10]. Machine learning has also been utilized in this system to provide better predictive analytics and automated control that could enhance the grid’s reliability and efficiency. One of the machine learning usability in EMS could be the ability to predict the energy demand based on previous data that this system has been fed and optimize the dispatch of generation resources accordingly [11,12].
• Demand Side Management (DSM): Demand response (DR) programs include methods of demand side management (DSM), which refers to the change in the consumption of customers due to the change in the price of electricity in the market. It should be mentioned that some such programs were also used in the traditional electricity system in the form of multi-tariff meters. DSM involves many strategies to optimize energy consumption among end-users. It can include adjusting consumption patterns in response to pricing signals or implementing energy-efficient technologies [13]. Through DSM, smart grids can reduce peak load, which leads to a reduction in the need for additional capacity in power plants and transmission networks. DSM helps consumers to optimize their energy utilization. This optimization can include shifting energy consumption from peak hours to off-peak hours, using high-efficiency devices, or setting schedules for energy consumption [14].
• Grid Security: Cybersecurity protocols are utilized for grid protection from potential threats and vulnerabilities, ensuring the integrity and reliability of the grid. Since a smart grid highly depends on an interconnected and data-driven network, the probability of being exposed to cyber-attacks, which can disrupt the operation or comprise data privacy, is high [15]. Considering all of the mentioned challenges, grid security’s responsibility is to implement robust encryption, authentication protocols, and real-time monitoring systems for detecting and mitigating cyber threats. It also includes physical security measures to protect critical infrastructure from physical attacks and natural disasters [16,17].

2.1.2. Communication and Control Mechanisms

Having proper communication and control mechanisms is essential as part of the smart grids, which will be explained in the following parts. These technologies of the smart grid also can be seen in Figure 2:

• Communication Infrastructure: This part of the smart grid contains a network of devices and systems that facilitate data exchange among various grid components, such as wireless networks, fiber optics, and power line communication (PLC) systems, to ensure the reliability and efficiency of data transmission [18]. The main responsibility of communication networks is to support advanced functionalities such as remote monitoring, automated meter reading, and integration of DERs [19]. One of the critical parts of this communication, the Internet of Things (IoT), can be named as it allows for seamless communication among various devices and enhancement of operational efficiency in smart grids [20].
• Control Systems: This section of smart grids includes Distributed Control Systems (DCS) and Supervisory Control and Data Acquisition (SCADA) systems. Utilizing these systems provides real-time monitoring and control of grid operations, enabling automation in the field of various processes such as voltage regulation, load balancing, and fault detection [21]. Considering the ML technique’s power in this field by having advanced algorithms, it aids the grid in enhancing its performance and ensuring stability [22]. This goal can be reached by using automated demand response (ADR) systems that control appliances and equipment based on grid conditions [23]. The integration of DCS with an existing grid can be complex and needs significant changes in the traditional infrastructure. Moreover, SCADA systems lack the ability to provide comprehensive oversight of grid operations, which makes them indispensable in the management of intricate dynamics of modern energy grids, mostly in large-scale scenarios.
• Demand Response Management: DCS enables decentralized management of the grid by distributing control tasks among multiple controllers that empower the scalability and resilience of the grid by allowing local decision-making and reducing the dependency on a central control unit [24]. Automation technologies, such as automatic microgrids, can operate independently or collaboratively with the main grid to manage the local energy resources efficiently [25].
• Real-time Monitoring and Analysis: Advanced sensors and metering devices collect data continuously on grid performance, which is then analyzed using big data analytics and ML techniques [26]. This section provides an allowance for predictive maintenance, fault detection in real-time conditions, and optimization of grid operations to forecast potential equipment failures and schedule maintenance proactively [27].

2.2. RES Integration

RES plays a crucial role in the development and functionality of smart grids. Considering RES integration into the power grid is essential for reducing greenhouse gas emissions, enhancing energy security, and promoting sustainable development. RES, such as wind, solar, and hydropower, provide a clean and inexhaustible supply of energy that can help to alleviate environmental impacts associated with traditional fossil fuel-based power generation [3]. This usage of smart grids also supports the transition towards a more decentralized and resilient energy system capable of responding dynamically to fluctuations in energy supply and demand [3].

2.2.1. Types of Renewable Energy Sources

Renewable energies play a crucial role in the transition to renewable energy systems. Providing clean, reliable, and increasingly cost-effective alternatives to fossil fuels could be named as some of their advantages. Among all types of RES, solar, wind, and hydropower are considered the most prominent types. In the upcoming section, these energies will also be discussed briefly for better visualization. Figure 3 illustrates these energies.

• Wind Energy: Wind energy is harnessed using wind turbines that convert kinetic energy from the wind into electrical power. As one of the fastest-paced-growing RES globally, it offers significant potential for large-scale power generation. By considering the offshore and offshore environments, wind farms can generate substantial amounts of electricity, contributing to the overall smart grid system. Dissimilar to the first years of introducing this type of energy, turbine technology advancement in recent years, not only has the cost been reduced, but it also empowers the efficiency of wind energy generation, which makes wind energy more competitive with traditional energy sources [28]. Onshore wind turbines, for example, are favored for their lower installation and maintenance costs, making them an economically viable option for regions with consistent wind patterns. However, they are limited by land availability and the variability in wind patterns, which can affect their overall efficiency. On the other hand, offshore wind turbines benefit from higher wind speeds and greater energy output, particularly in coastal regions. Despite their higher installation and maintenance costs, offshore wind turbines are ideal for maximizing energy generation in areas with robust wind resources [29].
• Solar Energy: Solar energy is captured using Photovoltaic (PV) panels that convert sunlight directly into electricity. The high versatility of this power provides a situation for its deployment on different scales, from small residential rooftops to large-utility solar farms [30]. Similarly, wind turbines, the declining cost of PV panels, and advancements in solar technology have accelerated the adoption pace of solar energy worldwide [31]. Moreover, another usability, such as concentrating sunlight energy to produce steam that could drive turbines, can be named as another aspect of this energy, which absorbs a large amount of attention [32]. Despite the scalability and accessibility of PV panels, their reliance on solar irradiation and cloud coverage hinders their application. On the other hand, Concentrated Solar Panel (CSP) systems can generate electricity even after sunset by utilizing thermal storage, yet they require high investment and significant land area [33].
• Hydropower: As one of the oldest and most established forms of energy conversion, hydropower utilizes the energy from flowing water to generate electricity. Hydropower plants can provide large-scale and reliable power generation that can be maintained as a base-load power source [34]. Small-scale power systems are also used in remote areas to provide localized electricity power supply, especially in locations where traditional electricity generation lines are challenging to maintain [35]. The flexibility and potential for storage capacity of this energy make it a valuable asset in balancing supply and demand within the grid [36]. While large hydropower plants provide stable base-load power, their significant environmental impact on aquatic ecosystems is inevitable. In these cases, small or micro hydropower can be used as they are also capable of implementation in smaller rivers; however, their generated electricity is not sufficient enough to feed large loads [37].

2.2.2. Challenges in Integrating RES into Smart Grids

Among all of the perks that RES could bring for the grid, some challenges may also be raised, and they are explained in the upcoming section alongside Figure 4 for a better recap.

• Intermittency and Variability: The unpredictability of natural resources is one of the primary challenges in integrating RES. Dissimilar to conventual power plants, which can provide a steady output, renewable energy generation is highly related to environmental conditions such as wind speed and sunlight availability, which leads the system to fluctuations in power supply [38]. This variability can cause severe instability in the grid and complicate energy management [20]. However, in some works similar to [39], a two-stage method has been offered to handle the multi-energy microgrids for uncertainties in grids.
• Grid Stability and Reliability: The voltage and frequency fluctuation due to the unprecedented RES could be named as some of the issues that natural energy integration to the power grid would sense [40]. Ensuring that a smart grid can mitigate these challenges requires advanced grid management techniques and real-time monitoring systems [23].
• Energy Storage: Because of the intermittency of RES, effective energy storage solutions are required to address any upcoming problems for the smart grid. Since the amount of energy, most specifically voltage, that RES produce should be constant during different times of the day, various energy storage systems, such as batteries, supercapacitors, and pumped hydro storage, can store the extra generated energy and release it during periods of low generation or high demand [41]. This advancement also imposes high costs and limits the lifespan of some storage technologies, which remain significant challenges [42].
• Infrastructure and Investment: Upgrading existing grid infrastructure to accommodate RES integration is costly. It includes enhancing transmission and distribution networks, utilizing AMI, and system control development that is capable of managing distributed generation. Government support is essential for achieving this upgrade on a large scale [5].
• Regulatory and Market Challenges: Regulatory and market challenges often lag behind technological advancements, which pose some issues when integrating RES. Policies and incentives that could promote the adoption of renewable energies, support grid modernization, and ensure fair market conditions are critical to mitigate the mentioned challenges [18].

2.3. Role of Machine Learning in Smart Grids

2.3.1. Overview of Machine Learning Techniques

ML encompasses various computational techniques that allow the systems to learn from valuable data and enhance their efficiency and performance over time. These techniques can be categorized into supervised learning, unsupervised learning, reinforcement learning, and deep learning [43].

• Supervised Learning: In this training, the model will be trained on a labeled dataset, where the input–output pairs are known, and the main path for learning is to map inputs to outputs. Common algorithms include [44]:

  ○

  Linear Regression: Utilized for a continuous output variable prediction based on one or more input features. The main assumption here is a linear relationship between the inputs and the output of the labeled dataset.

  ○

  Support Vector Machine (SVM): Used for classification and regression tasks. In this way, this model finds the best hyperplane that separates the classes in the feature space.

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  Neural Networks: This technique is composed of layers of interconnected neurons similar to human brains. This method is used for complex pattern recognition tasks, and they can learn non-linear relationships between inputs and outputs.

• Unsupervised Learning: This technique deals with unlabeled data, and output variables do not have any categorization. The main role of this model is to identify patterns or groups within data. It is suitable for exploratory data analysis and feature extraction [45]. Common algorithms for this method include:

  ○

  Clustering Algorithms: Techniques such as K-means where group data points into clusters based on the similarity that each data point has with other ones.

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  Dimensionality Reduction Techniques: Methods such as Principal Component Analysis (PCA) are used to reduce the number of features in data while preserving its essential structure. This aids in visualizing high-dimensional data and also has a decrement in computational complexity, especially for big datasets.

• Reinforcement Learning: This method involves training a model to make a sequence of decisions by rewarding desired behaviors and punishing ones. By this procedure, the model learns to maximize cumulative rewards over time and find valuable information [46]. It includes:

  ○

  Q-learning: A model-free reinforcement learning algorithm that can learn the values of actions in a given state based on reward maximization.

  ○

  Deep Reinforcement Learning: Combines reinforcement learning with deep neural networks to manage big and complex state spaces in applications such as game playing and robotics.

• Deep Learning: It is a subset of ML that involves neural networks with layers that can learn complex patterns in large datasets [47]. Common architectures include:

  ○

  Convolutional Neural Networks (CNNs): Used for image and video recognition tasks. This way, convolutional layers can be detected automatically from raw data.

  ○

  Recurrent Neural Networks (RNNs): These are suggested for sequential data, such as time series or natural language processing. RNNs use the memory of previous inputs to enhance their future predictions.

2.3.2. Data Preprocessing Phase

Data preprocessing is a crucial step in the development of ML models, most importantly in the context of RES integration within smart grids. A proper preprocessing procedure ensures that data used for training and also deployment of ML are clean, consistent, and suitable for analysis [48]. This phase involves several key subtopics:

• Data Collection: Identify and integrate data from various sources such as sensors, smart meters, weather stations, and historical energy consumption records, ensuring a diverse and comprehensive dataset that enhances the model’s ability to learn and generalize a proper model. In addition, with the help of IoT devices and advanced communication systems that can facilitate continuous data flow, real-time data can be gathered for future predictions and grid monitoring.
• Data Cleaning: Missing data can adversely affect model performance. There are various techniques to address this issue, such as filling missing values with mean, median, or mode and interpolation. Rather than missing data, having corrupted and noisy is another aspect of data preprocessing that should be performed. Techniques such as smoothing, filtering, and outlier detection can help maintain the integrity of data. Moreover, since the information is gathered through different sources with various measurement units, it is important to put them within a specific range or distribution to be used. By normalizing or standardizing data, the convergence of the gradient-based algorithm and model performance will improve.
• Data Transformation: Through model training, inputs and outputs are obtained. The inputs are considered features, and by creating new features from raw data or combining existing features, the model can improve its predictive accuracy. In addition, by reducing the number of features while retaining the essential information, computational complexity will be reduced alongside improving the model interpretability since ML models can interact better with numerical format data, methods such as one-hot encoding where categorical data would be useful. The mentioned techniques help to ensure that inputs are in the most suitable format for training and prediction, leading to better performance and more reliable results.
• Data Segmentation: Through the model training, inputs and outputs are obtained. The inputs are considered features, and by creating new features from raw data or combining existing features, the model can improve its predictive accuracy. In addition, by reducing the number of features while retaining the essential information, computational complexity will be reduced alongside improving the model interpretability since ML models can interact better with numerical format data, methods such as one-hot encoding where categorical data would be useful. The mentioned techniques help to ensure that inputs are in the most suitable format for training and prediction, leading to better performance and more reliable results.
• Data Segmentation: Dividing the dataset into training and testing sets to evaluate model performance. A common division method is to allocate 70–80% of data for training and 20–30% for testing. Moreover, these training data were further split to create a validation set used for hyperparameter tuning and model selection. These steps prevent overfitting the model and ensure the model’s generalization for new data.
• Data Augmentation: During the time that data are limited or imbalanced, techniques such as the Synthetic Minority Over-sampling Method (SMOTE) can help the model generate synthetic data to augment an existing dataset. In addition, by aggregating over different periods, new features can be created to improve the model’s robustness and also capture trends and patterns at different granularities.

2.3.3. Evaluation of ML Models

Evaluating the performance of ML models is crucial to ensure their effectiveness and reliability in managing renewable energy integration within smart grids. This section’s main concern is with the key performance metrics used to evaluate ML models and the cross-validation techniques that are suitable for assessing the robustness of these models [49,50].

Table 1. Evaluation parameters and formulas.

Term	Description	Formula
Accuracy	The ratio of total successful prediction using the model’s overall number of samples	${\displaystyle \frac{TP+TN}{TP+TN+FP+FN}}$
Precision	The ratio of True Positives to total positive predictions. Useful when the false positive cost is high.	${\displaystyle \frac{TP}{TP+FP}}$
Recall	The ratio of True Positives to total actual positives. Useful when the false negative cost is high.	${\displaystyle \frac{TP}{TP+FN}}$
F1 Score	The harmonic mean of Precision and Recall. Useful for imbalanced datasets.	$2\times {\displaystyle \frac{Precision\times Recall}{Precision+Recall}}$
MSE	Average squared difference between predicted and actual values. Sensitive to outliers.	${\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n}{(y_i-{\widehat{y}}_i)}^2$
RMSE	Average magnitude of errors in predictions. Less sensitive to outliers than MSE.	$\sqrt{{\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n}{(y_i-{\widehat{y}}_i)}^2}$
R-squared	The proportion of variance in the dependent variable is predictable from the independent variables.	$1-{\displaystyle \frac{\sum _{i=1}^n{(y_i-{\widehat{y}}_i)}^2}{\sum _{i=1}^n{(y_i-\widehat{y})}^2}}$
k-fold Cross-Validation	The dataset is divided into k subsets; the model is trained on k − 1 subsets and tested on the remaining fold. Repeated k times.	Train on k − 1 folds and test on the remaining folds.
Stratified k-fold Cross-Validation	Ensures each fold has a similar distribution of class labels as the original dataset.	Stratified sampling within k-fold.
LOOCV	Each instance is used as a single test case, and a model is trained on the remaining instances. Computationally expensive.	Train on all except one instance and test on the remaining instance.
Time Series Cross-Validation	It preserves the temporal order of data, and the training set always contains data preceding the test set.	Rolling-window or expanding-window techniques.
Nested Cross-Validation	Outer loop is used to evaluate model performance, and the inner loop is used for hyperparameter optimization. Prevents overfitting.	Outer loop: model evaluation; Inner loop: hyperparameter tuning.

depicts a summary of all of these formulas alongside the related information.

• Accuracy: Is the ratio of correctly predicted instances (both True Positives (TP) and True Negatives (TN)) to the total instances in the dataset (sum of True Positives, True Negatives, False Positives (FP), and False Negatives (FN)). It is a straightforward metric for the classification conditions but may not be suitable for imbalanced datasets where the number of instances in different classes varies significantly.

$$Accuracy={\displaystyle \frac{TP+TN}{TP+TN+FP+FN}}$$

(1)

• Precision: This term measures the number of True Positives divided by the total number of positive predictions (True Positives and False Positives). It is mostly useful for scenarios where the cost of false positives is high.

$$Precision={\displaystyle \frac{TP}{TP+FP}}$$

(2)

• Recall: Also known as sensitivity, it is the ratio of True Positive predictions to the total number of actual positive instances (True Positives and False Negatives). It is mostly usable for the times that the cost of False Negative is high, such as in fault detection systems.

$$Recall={\displaystyle \frac{TP}{TP+FN}}$$

(3)

• F1 Score: This is the harmonic mean of two previous metrics, precision, and recall, providing a balance between the two of them. It is commonly practical for imbalanced datasets where neither precision nor recall alone is sufficient to evaluate the model performance.

$$F1Score=2\times {\displaystyle \frac{Precision\times Recall}{Precision+Recall}}$$

(4)

• Mean Squared Error (MSE): It is used primarily in regression tasks to measure the average squared difference between predicted and actual values. It also penalizes larger errors more than smaller ones, making it more sensitive to outliers.

$$MSE={\displaystyle \frac{1}{n}}\sum _{i=1}^n{(y_i-{\widehat{y}}_i)}^2$$

(5)

• Root Mean Squared Error (RMSE): It is the square root of MSE, providing an error metric in the same units as the target variable. The usage of this metric is in energy demand and supply forecasting.

$$RMSE=\sqrt{{\displaystyle \frac{1}{n}}\sum _{i=1}^n{(y_i-{\widehat{y}}_i)}^2}$$

(6)

• Mean Absolute Error (MAE): It measures the average magnitude of error in predictions without considering their direction. It provides a straightforward interpretation of average error and is less sensitive to outliers than the MSE itself.

$$MAE={\displaystyle \frac{1}{n}}\sum _{i=1}^n|y_i-{\widehat{y}}_i|$$

(7)

• R-squared (Coefficient of Determination): It indicates the proportion of variance in the dependent variable that is predictable from the independent variable. It is a number that ranges from 0 to 1, with higher values indicating better performance.

$$R^2=1-{\displaystyle \frac{\sum _{i=1}^n{(y_i-{\widehat{y}}_i)}^2}{\sum _{i=1}^n{(y_i-\widehat{y})}^2}}$$

(8)

• k-fold Cross-Validation: In k-fold cross-validation, the dataset is divided into k subsets, the model is trained on k − 1 subsets, and tested on the remaining fold. This procedure is repeated k times, with each fold serving as the test set once. The average performance across all k iterations provides a robust estimate of the model’s performance. Most choices for k are 5 and 10.
• Stratified k-fold Cross-Validation: This method ensures that each fold has a similar distribution of class labels as the main dataset. For the time that the dealt is with imbalanced datasets, this method is used to ensure the proper representation of the overall class distribution in each fold.
• Leave-On-Out Cross Validation (LOOCV): It is a special case of k-fold cross-validation where k is the number of instances in the dataset. Each instance is used as a single test case, while the model is trained on the remaining instances. It is beneficial from the aspect of unbiased estimation of model performance; however, due to its high computation cost for large datasets, it is not very common.
• Time Series Cross-Validation: This technique is used for data that are highly related to time, such as energy consumption patterns. It preserves the temporal order of data, ensuring that the training set always contains data that precedes the test set.
• Nested Cross Validation: It is used for hyperparameter tuning and model selection. It contains an outer cross-validation loop for evaluating model performance and an inner loop for hyperparameter optimization. Any upcoming overfitting and biased estimation of the model performance can be prevented by the usage of this method.

2.3.4. Applications of ML in Energy Management

ML techniques are revolutionizing energy management in the smart grid by providing predictive analytics, optimization, and automated control. These techniques continuously enhance the efficiency, reliability, and sustainability of the power grid, addressing both traditional challenges and new ones introduced by the integration of RES into the grid.

Predictive analytics in smart grids involves the use of ML models to forecast future events using historical and real-time data. This capability is essential for having management proactively and efficiently over the grid [22].

• Demand Forecasting: ML algorithms such as time series analytics and neural networks can predict both short-term and long-term electricity demand, which can help utilities for better energy supply optimization and reduce costs. Deep learning can accurately forecast demand by analyzing past consumption patterns and considering external factors such as weather conditions [51].
• Renewable Energy Prediction: The main concern of a power grid is about its stability, despite the integration of various energy sources such as wind or solar. By considering features such as weather, historical performances, and other variables, ML can aid the system in adapting itself based on the energy prediction of different renewables, such as wind. Techniques such as Support Vector Regression (SVR), which involves specific adjustments based on different conditions, have been successfully used to predict solar irradiance and wind speeds that are needed for their energy generation [52,53].

2.4. Addressing Challenges in Integrating Renewable Energy Systems into Smart Grids with Machine Learning

ML offers powerful tools that can tackle the mentioned challenges in RES integration with smart grids, using predictive analytics, optimizing energy management, and supporting decision-making.

Table 2. Correlation of RES integration challenges with ML techniques.

Challenge	Description	Relevant ML Technique	Advantages of ML Application
Intermittency and Variability	The unpredictability of natural resources such as wind and solar leads to fluctuations in power supply, causing grid instability.	Time Series Analysis, LSTM	Predicts energy generation based on weather and historical data and enhances grid stability by forecasting supply variations.
Grid Stability and Reliability	Voltage and frequency fluctuations due to the integration of RES pose challenges to grid stability.	SVM and Reinforcement Learning	Optimizes grid control systems for better stability and learns optimal responses to fluctuations in real-time.
Energy Storage	Need for effective energy storage solutions due to the intermittent nature of RES.	Reinforcement Learning and Optimization Algorithms	Optimizes battery charging/discharging cycles and enhances the efficiency of energy storage systems.
Infrastructure and Investment	High costs associated with upgrading grid infrastructure to accommodate RES.	Predictive Analytics, Cost–Benefit Analysis Models	Predicts infrastructure needs based on demand and RES integration and optimizes investment by prioritizing critical upgrades.
Regulatory and Market Challenges	Log in policy and market adaptation to technological advancements in RES integration.	Decision Trees, Predictive Modeling	Analyzes market trends and regulatory impacts and supports policy-making with data-driven insights.

illustrates the correlation between these challenges and the specific ML techniques.

2.5. Energy Storage Systems

Energy storage systems play a crucial role in smart grids as they enable supply and demand balance, grid stability enhancement, and integration of RES facilitation. These systems contain a diverse array of technologies, each of which is tailored to specific contexts and requirements, ranging from Lithium-ion batteries for rapid response to pumped hydro storage for long-term energy storage needs [54]. Each of these technologies will be discussed in the following parts.

2.5.1. Batteries

Batteries are one of smart grids’ most widely used energy storage technologies. They can store electrical energy in the form of chemical energy and convert it back to electrical energy when it is needed [41]. Various types of batteries are used in the industry based on their usage:

• Lithium-ion Batteries: Known for their high energy density, efficiency, and long cycle life, lithium-ion batteries are commonly used in various applications, such as Electrical Vehicles (EVs) and grid storage for renewable energies. Continuous advancements in these kinds of storage systems empower their scalability among industrial and commercial users and decrease their cost to be accessible in various conditions. Despite all of the perks these types of batteries have, they are often limited by the high costs and relatively limited lifespan, which could affect the long-term economic feasibility of large-scale projects [55]. To manage them properly, it is important to utilize a real-time management system. For instance, this work [56] offers a practical solution for real-time battery management, enhancing the overall efficiency of EV integration into smart grids.
• Hydrogen Fuel Cells: As another type of storage system, hydrogen fuel cells operate by converting chemical energy from hydrogen into electricity through its chemical reaction with oxygen. They are common because of their high efficiency and environmental benefits, and they are used in different sections such as EVs, grid storage, and complementary parts of RES. The scalability of this type of fuel cell makes it an attractive option for large-scale energy storage [57].
• Solid-State Batteries: In this type of battery technology, liquid or gel electrolytes in the traditional lithium-ion have been replaced with solid electrolytes. Utilizing this method, energy density increases alongside safety improvement by reducing the risk of leakage and flammability. As some of the forefront companies in developing solid-state batteries, Toyota and QuantumScape, are using that by investing in this new technology, they are trying to promise longer lifespans and faster charging times [58].
• Lithium–Sulfur Batteries: Lithium–sulfur (Li-S) batteries offer a better energy density than Li-ion batteries, which could make them suitable for large-scale energy storage. Recent discoveries in stabilizing the sulfur cathode and improving the electrolyte have mitigated some challenges related to the short lifespan and efficiency of these batteries. This also provides the potential for reducing the cost of energy storage systems significantly [59].
• Flow Batteries: These kinds of batteries, such as vanadium redox flow ones, have gained attention because of their scalability and long discharge durations. By storing the energy in liquid electrolytes contained in an external tank, easy scalability is achieved by simply increasing the size of the tanks. This advancement aids them in being a proper candidate for large-scale renewable energy storage applications by having inherent potential for efficiency improvement and cost reduction in the future [60].

2.5.2. Supercapacitors

Supercapacitors, or ultracapacitors, are a form of storage components that save energy electrostatically rather than chemically. They are characterized by their high power, rapid charging and discharging capabilities, and long cycle life [61].

• Electrochemical Double-Layer Capacitors (EDLCs): EDLCs are the most common type of supercapacitors because of their high-power output and outstanding charge-discharge efficiency. Certain kinds of applications, such as regenerative braking in cars, require quick bursts of energy and are preferred over traditional batteries. Recent developments have focused on improving the energy density of these energy storage systems by the usage of advanced materials such as graphene and carbon nanotubes [62].
• Hybrid Capacitors: In hybrid capacitors, the characteristics of batteries and supercapacitors have been combined, offering both high energy and power densities. The mentioned features make them suitable for applications that require both quick energy bursts and a sustainable power supply. Enhancements in this technology cause a more probable market for them in competition with traditional batteries [61,62].

2.5.3. Thermal Storage Systems

In another type of energy storage system, rather than traditional ones, energy is stored in the form of heat or cold, which can be used to generate electricity or provide heating or cooling later at the time of need. The most common use of these systems is in the balancing of supply and demand in smart grids, particularly for solar thermal power plants and other kinds of RES.

• Sensible Heat Storage: This storage involves storing thermal energy by heating or cooling liquid or solid materials, such as water or rocks. Thermal power plants are the common usable place for this technology as they can store the heat for future utilization in steam generation and turbine driving. Recent advancements have also focused on improving the affordability and efficiency of these technologies by developing new materials to optimize storage designs [62].
• Latent Heat Storage: In these systems, Phase Change Materials (PCMs) that absorb and release heat during phase transitions, such as solid to liquid, have been used. PCMs offer higher energy density than sensible heat storage materials. Improvements in this area focus on enhancing thermal properties in PCMs, longer life spans, and better conductivity in the thermal system [63].

2.5.4. Role of Energy Storage in Balancing Supply and Demand

As mentioned before, energy storage systems play a crucial role in balancing supply and demand in smart grids. By saving energy during off-peak periods or high renewable energy generation and releasing it during periods of high demand or low generation, they help the grid maintain the system’s performance in various operating conditions. These sources provide fast response capabilities to maintain grid frequency within acceptable limits, which is crucial as the share of variable RES increases. In addition, by providing ancillary services such as voltage support and spinning reserve, the energy storage system can empower the reliability and stability of the grid [41].

2.5.5. Advances in Energy Storage Technologies

Significant advancements in energy storage technologies are driving improvements in performance, cost, and scalability, making them more viable for widespread adoption in smart grids. By having innovations in battery chemistry, such as solid-state batteries and advanced lithium–sulfur batteries, energy density, safety, and longevity are being increased while reducing costs. Moreover, combining different types of storage technologies in hybrid energy storage systems leads to both high energy and high power capabilities, and developments in large-scale storage technologies, such as pumped hydro storage and Compressed Air Energy Storage (CAES), provide a condition for the integration of massive amounts of renewable energy into the grid [64]. This integration can come with machine learning and artificial intelligence that can optimize storage operation, improve efficiency, and reduce costs [48].

2.6. Discussion on Machine Learning in Energy Storage System Analysis and Evaluation

ML techniques also play a crucial role in the analysis and evaluation of energy storage systems. With the usage of proper techniques and sufficient datasets, these techniques can significantly enhance the efficiency, reliability, and predictive capability of these kinds of systems [65].

• Predictive Analysis: One main perk of ML in energy storage is predictive analytics, where related models can be trained by the usage of both historical and real-time data to forecast the performance and the remaining useful life of energy storage systems. For instance, algorithms such as ANN or SVM can be useful in detecting any abnormality during the process of charging/discharging EV batteries [66] (known as a thermal runaway) alongside the estimation for battery degradation. This capability allows proactive maintenance, reducing downtime and extending the lifetime of these systems.
• Optimization of Energy Storage Operations: ML is also capable of optimizing the operation of energy storage systems by dynamically adjusting the related performance based on various features such as demand forecasts and grid conditions. Techniques such as reinforcement learning can be used to develop the adaptive capability of the systems and increase their efficiency. These optimized operations can optimize energy during peak demand or enhance the integration of renewables into energy storage systems. By increasing this addition, the grid would be balanced, energy waste would also decrease significantly, and the overall economic viability of energy storage solutions would be improved [67]. In this work [68], the author introduced a robust multistage dispatch model that is developed for energy storage systems that, by the usage of a robust framework, can optimize the storage management in smart grids under uncertainty
• Evaluation and Decision Support: ML techniques can also evaluate different energy storage technologies by simulating various scenarios and analyzing large sets of performance data. This contains comparisons of different battery chemistries, energy storage capacities, and cost–benefit analyses under various types of conditions. Operators can use this perk to choose the best technology for energy storage based on any specific need by also considering some factors such as scalability, efficiency, and cost-effectiveness [69].

2.7. Integration of Renewable Energies and Machine Learning

The integration of RES into modern power grids has become a paramount objective in the quest for sustainable and resilient energy systems. As concerns over climate change and carbon footprint have risen intensely, the transition from fossil fuels to renewables is imperative [70]. Smart grids are characterized by their advanced communication, control mechanisms, and energy management systems and have a vital role in this transition. By utilizing cutting-edge technologies, including ML and sophisticated energy storage systems, smart grids enhance the efficiency, reliability, and sustainability of energy distribution. For instance, in this work [71], the author conducted a comparative analysis of various ML techniques for hour-ahead forecasting of EV states, demonstrating the effectiveness of these methods in enhancing grid reliability and efficiency. Accurate forecasting of renewable power is critical for maintaining grid stability. This paper [72] demonstrated the effectiveness of a solar data-enhanced hybrid ML model, integrating ANN, LSTM, and SVR, in significantly improving wind power forecasting accuracy in Canada

2.7.1. Importance of Renewable Energy Integration in the Power Grid

RES such as wind, solar, and hydro are pivotal in the transition toward sustainable energy infrastructure. The main characteristic of these energies is their abundance, environmentally friendly, and potential to reduce greenhouse gas emissions. This integration promotes energy sustainability and reduces dependency on fossil fuels [2]. The reduction in carbon emissions and other pollutants associated with traditional fossil-fuel-based power generation has an effective impact on climate change and improving air quality. Energy security will also increase as the reliability of imported fuels is reduced, and this matter will promote the development of local energy resources and industries. Moreover, this integration necessitates the modernization of the power grid to handle the variability and distributed nature of renewable energy generation [70].

2.7.2. Machine Learning Techniques for Renewable Energy Prediction

ML integration techniques into solar power generation have significantly enhanced the accuracy of predictive analytics, allowing for more efficient energy management that leads to a stable grid during the procedure. Various methods have been used to forecast solar irradiance and energy output, such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and Gradient Boosting Machines (GBMs). Historical weather data, satellite imagery, and real-time sensor inputs have been leveraged for the short-term prediction of solar power generation with high precision and accuracy. ANNs are particularly effective because of their capability to handle complex models and non-linear relationships and learn from historical data for output prediction. SVMs are useful for regression tasks, working with limited data, and handling high-dimensional input features. GBMs utilized the combination of various weak models to create a strong predictive model for managing different tasks in solar power generation [73].

2.7.3. Optimization of Energy Management Systems Using Machine Learning

Rather than having accurate predictions for output, ML plays a crucial role in optimizing Energy Management Systems (EMS) within smart grids. The main responsibility of EMS is to balance supply and demand, manage distributed energy resources, and enhance the overall reliability of the grid [74]. To recognize energy consumption patterns, adjust operational strategies in real-time, and optimize the distribution of renewable energy, ML algorithms, reinforcement learning, and optimization techniques could be used [75]. By ML incorporation, EMS can dynamically respond to changes in energy availability, forecast peak demand periods, and optimize the use of RES. This leads to reduced energy waste, lower operational costs, and improvement in grid reliability.

2.8. Optimization Techniques

Optimization in mathematics, economics, and management refers to selecting the best member from a set of achievable members. In the simplest form, an attempt is made to obtain the maximum and minimum of a real function by systematically selecting data from an accessible and computable set. Many optimization problems in electrical engineering are naturally more complicated to solve by conventional optimization methods such as mathematical programming. Nowadays, many combinational optimization problems, which are often among non-polynomial degree (NP-Hard) problems, can be solved with existing computers. Among the available solutions in dealing with such problems is the use of approximate or innovative algorithms. These algorithms do not guarantee that the obtained answer is optimal, and only by spending a lot of time can a relatively accurate answer be obtained [76].

Many optimization problems in electrical engineering are naturally more complex and difficult to solve by conventional optimization methods such as mathematical programming. Optimizing electricity consumption is a very important issue in today’s era, as it is important to reduce energy consumption and preserve natural resources.

2.8.1. Optimization Objective Functions

According to the project that you are going to optimize, the objective function is also determined. Some common objective functions in power consumption optimization are:

• Reduce total costs: It includes optimization strategies to reduce the total costs of electricity consumption, such as generation, transmission, and distribution costs.
• Reducing energy losses: These programs in transmission or distribution networks can increase efficiency and reduce the need for additional generation.
• Improving energy efficiency: This aim includes increasing efficiency in energy use, reducing the waste of energy, and improving the efficiency of equipment and processes.
• Reducing Voltage deviation: When the power factor is closer to 1 p.u, an increase in voltage improvement will occur.
• Reducing the emission of greenhouse gases: Reducing the consumption of fossil fuels and consequently reducing the emission of greenhouse gases is one of the main goals of optimizing electricity consumption to protect the environment.
• Load minimization during peak hours: Reducing peak demand through DSM and load-shedding programs can help reduce costs and avoid the need for new infrastructure investments.
• Ensuring system stability: It includes maintaining voltage and frequency stability in the power grid and preventing possible instabilities and outages.

2.8.2. Optimization Problem

• Load Shaping: Often, the optimization techniques focus on the curve of load shaping to match the better supply availability. This can involve shifting demand from peak hours to off-peak hours, reducing overall energy consumption, or smoothing out demand spikes.
• Technological Solutions: Advanced technologies play a significant role in optimization efforts. These include smart meters, home energy management systems, and demand response programs that allow utilities to communicate with consumers in real-time, offering incentives for reducing consumption during peak periods.
• Pricing Strategies: Dynamic pricing strategies, such as time-of-use rates and critical peak pricing, are used to incentivize consumers to shift their electricity usage to off-peak hours. This helps in balancing the load on the grid and reduces the need for peaking power plants.
• Algorithmic Approaches: Various optimization algorithms, including genetic algorithms, machine learning models, and other meta-heuristic techniques, are employed to predict consumer behavior, optimize energy schedules, and manage demand response events efficiently [13,77].

2.8.3. Examples of Optimization Algorithms

Teaching and Learning-Based Optimizing (TLBO) Algorithm

TLBO is a population-based algorithm, similar to other natural-inspired algorithms such as PSO and GA, in which a group of learners is considered the population. This kind of algorithm has two fundamental methods of learning: the teacher phase and the learner phase. The number of learners in a class is called class size and is shown by N and D is the number of courses proposed to the learners. MAXIT is a maximum number of iterations. The population X is randomly produced by a search space bounded. The jth parameter of the ith learner is determined values randomly using the following equation:

$$x_{ij}^0=x_j^{min}+rand\times (x_j^{max}-x_j^{min})$$

(9)

Here, rand is a uniformly distributed random variable within 0 and 1, $x_j^{min}$ and $x_j^{max}$ are the minimum and maximum values for the jth parameter. The parameters of learners for the generation g are:

$$x_i^g=[x_{i,1}^g,x_{i,2}^g,\dots ,x_{ij}^g,\dots ,x_{i,D}^g]$$

(10)

Figure 5 depicts the flowchart for this algorithm.

Genetic Algorithm (GA)

A genetic algorithm is a search technique in computer science that finds approximate solutions to model optimization mathematical and search problems. A genetic algorithm is a special type of evolutionary algorithm that uses evolutionary biology techniques such as heredity, biological mutation, and Darwin’s principles of selection to find the optimal formula for predicting or matching a pattern. The basic idea of this algorithm is to transfer hereditary characteristics through genes.

In the steps of machine design by this algorithm, it is necessary to define the following terms:

• Gene: Genes affect the parameters of the target function.
• Chromosome: Each group of genes for which the objective function is calculated forms a chromosome or DNA. Each chromosome contains n genes; in other words, the length of the chromosomes is n.
• Generation: Chromosomes, in each step of the genetic algorithm, form the generation corresponding to that step.
• Population: The number of chromosomes in each generation.
• Children: The new generation produced from the previous generation (parents) is called children or offspring.
• Parents: The generation that produces the next generation.

The steps taken in the genetic algorithm can be briefly stated as:

• Random generation of first-generation chromosomes
• Calculating the objective function for chromosomes and choosing the best values
• Completion of the algorithm if the specified criteria for completion are met
• Reproducing the new generation from the previous generation and returning to the second stage

• Optimization in Genetic Algorithm

In using the genetic algorithm optimization method, two basic and primary steps are of particular interest: the selection of the objective functions and the selection of the optimization parameters corresponding to them. In the following, we will give a detailed description of the objective function and the parameters involved in the optimization.

• Optimization Objective Function in Genetic Algorithm

According to the project that you are going to optimize, the objective function is also determined; for example, in an electric car, the objective function can be the efficiency of the machine, the reduction in its losses, the reduction in the volume of the machine, etc. Of course, in some cases, it may not be a function of a single objective, and it may be more than one. In most machine simulations, the objective function, the increase in efficiency, is the output because, in this case, more variables change.

• Optimization Parameters Function in Genetic Algorithm

The optimization parameters should be selected in such a way that, first, they have the greatest impact on the target functions, and second, they are not related to each other or are considered to be related to each other so that they do not cause a disturbance in achieving the desired result. The third feature is the appropriate selection of optimization parameters and the definition of an acceptable range for them so that this range is wide enough so that the search space is not limited unnecessarily, and it is also logically limited and does not include unacceptable values of that parameter. Due to the importance of the parameters, we try to consider a general transition first and then select coefficients that can be optimized in line with that transition to reach that transition. The related steps are mentioned in Figure 6.

Shuffled Frog Leaping (SFL) Algorithm

The SLF algorithm is based on metaheuristic development and was designed to seek a global solution by implementing an informed heuristic search space using a heuristic function and is a combination of two GA based on the memetic algorithm and particle swarm optimization (PSO) algorithm. This algorithm is inspired by the life of a group of frogs when they are looking for food. The optimizer can exchange information between local and global search elements. The SF algorithm consists of a set of interacting virtual populations of frogs partitioned into different memeplexes. The virtual frogs are treated as hosts or carriers of memes, where a meme is a unit of cultural evolution. The algorithm simultaneously performs an independent local search in each memeplex. The local exploration is implemented using a particle swarm optimization algorithm that is conformed for discrete problems but emphasizes local exploration. To find global exploration, the virtual frog swarms are alternatively shuffled and reorganized into new memeplexes that are utilized in the shuffled complex evolutionary algorithm. The SFL algorithm is illustrated in the following steps [80]:

• Step 1—Initialization: Select m and n, where m is the number of memeplexes, and n is the number of frogs in each memeplex. Therefore, the total sample size P in the swamp is given by P = m × n.
• Step 2—Generate a virtual population: Sample P virtual frogs U(1), U(2), …, U(P) in the feasible space. The ith frog is represented as a vector of decision variable values U(i) = U_i¹, …, U_i^d, where d is the number of decision variables. Compute the performance value f(i) for each frog U(i).
• Step 3—Rank frogs: Sort the P frogs in order of decreasing performance value.
• Step 4—Partition: Divide frogs into m memeplexes Y¹, Y², …, Y^m, each containing n frog.
• Step 5—Shuffle memeplexes: Replace Y¹, Y², …, Y^m into the array.
• Step 6—Check convergence: If the convergence criteria are assured, stop. Otherwise, return to step 3.
• Step 7—Local search: In each memeplex, we find a frog with the best X_b and worst performance, X_w, and the best frog’s position X_g in the entire population.
• Step 8—Improve the worst frog’s position: The new position for X_w is computed by:

  $$\begin{gathered} D=rand\times (X_b-X_w) \\ {X}_w\left(new\right)=X_w\left(old\right)+Dwhere-D_{max}\le D\le D_{max} \end{gathered}$$

  (11)

  ○

  Where rand is a random number in the range [0, 1], and D_max is the maximum step size by a frog. If the new position X_w(new) is better than the old one, then replace the old position with a new position. If step 7 cannot produce a better result, then a new position is computed, but this time, we put X_g in the above equations instead of X_b.

• Step 9—Replacement of Non-improving Frogs: If the new position is not better than the old position again, the spread of defective meme is stopped by randomly producing a new frog r at a feasible location to replace it with X_w.
• Step 10—Upgrade the Memeplex: To induce performance value.
• Step 11—Convergence Check: If the obtained result is assured, then exit the algorithm; otherwise, go to step 2 and perform the rest of the steps again.

Imperialist Competitive Algorithm (ICA)

In the context of imperialist competition, the power of an empire is defined as the sum of the power of the colonizer country and a percentage of the total power of its colonies. Mathematically, this can be expressed as:

$$Z=f\left(Imp\right)+\mathsf{\epsilon }.mean\left(f\left(col\right)\right)$$

(12)

where Z is the power of the empire, f(Imp) is the objective function value for the colonizer country, mean (f(col)) is the average objective function value for the colony, and ε is the percentage coefficient.

The equation of this formula represents the power dynamics in imperialist competition, where the empire’s power is a combination of the individual power of the colonizer country and a weighted average of the power of its colonies.

The Imperialist Competitive Algorithm (ICA) involves several steps in its optimization process:

• Step 1—Initialization: Select a few random points on the function to form the initial empires.
• Step 2—Homogenization: Move the colonies toward the imperialist country by following a homogenization policy.
• Step 3—Revolution Operation: Apply the revolution operator to introduce diversity and exploration.
• Step 4—Empire Restructuring: If there is a colony in an empire with a lower cost than the imperialist, swap to the colony and imperialist.
• Step 5—Total Cost Calculation: Calculate the total cost of an empire using a specified equation.
• Step 6—Colony Redistribution: Choose one or more colonies from the weakest empire and assign them to the empire with the highest probability.
• Step 7—Elimination of Weak Empires: Remove the weak empires from the competition.
• Step 8—Termination Condition: If only one empire remains, stop the algorithm; otherwise, go back to step 2.

2.8.4. Relationship between Optimization and Machine Learning

Optimization and ML are closely intertwined fields that significantly impact each other, mostly in the context of smart grid technologies and renewable energy management. Optimization involves finding the best solution or outcome from possible options. In the ML field, optimization is fundamental to model training, where different algorithms iteratively adjust parameters to minimize a loss function, increasing the accuracy and efficiency of the related model [81].

• Optimization in Machine Learning: Optimization is used in ML techniques to adjust model parameters to increase its performance and accuracy. For instance, gradient descent is a popular optimization algorithm used in neural networks that iteratively adjusts the weights of the model to minimize the difference between the predicted outputs and actual ones. The optimization role here is vital as it aids the MLs in performing properly on unseen data [82].
• Machine Learning in Optimization: Conversely, ML can also be applied to solve optimization problems, most importantly in smart grid cases. They can optimize energy distribution in a grid by predicting demand patterns, adjusting energy flow in real-time, and balancing the supply and demand. A common type of ML method in these scenarios is reinforcement learning, which is particularly effective in optimization tasks where the system must learn the best action through trial and error [83].

2.8.5. Enhancing Grid Stability and Reliability with Machine Learning

Having a trustworthy predictive system using ML requires other techniques to protect the system against faults and irregularities. Techniques such as anomaly detection and predictive maintenance models analyze data from sensors and smart meters to detect potential failures before they occur. By having this maintenance, probable downtime will be reduced, and continuous grid operation will be ensured. Moreover, with the aid of ML models, other challenges for the grid, such as voltage regulation, load balancing, and frequency control, which are essential for having a stable and reliable power supply, can be satisfied. These dynamic procedures are crucial when the smart grid aims to integrate other renewable energies despite their inherent variability in generated power output [84].

2.8.6. Deployment Phase of ML Models in Renewable Energy Integration

This phase is crucial for transitioning from research and development to real-world application. This phase ensures that the models operate efficiently and effectively within the smart grid infrastructure. There are some key considerations in the phase, such as model integration, data handling, model monitoring, scalability and flexibility, user interface and interaction, compliance, and regulatory considerations. For the model integration, ensuring compatibility with existing systems such as SCADA and DCS is vital. It should also work seamlessly with various data sources and other analytical tools to enhance the overall system efficiency and effectiveness. Developing Application Program Interfaces (APIs) facilitates data exchange and model integration. The data handling section also includes real-time data processing, robust data security measures, and efficient data storage solutions to manage large volumes of data from sensors, smart meters, and IoT devices [27,85].

Monitoring and maintaining a model’s performance requires continuous performance monitoring, periodic retraining with new data, and robust error handling alongside logging mechanisms. To ensure scalability and flexibility, some steps, such as cloud-based solutions and distributed computing frameworks, should be performed in order to handle increasing data volumes, along with modular design and load balancing. To help the user have a simplified and functional interaction with the system, developing a user-friendly interface with intuitive dashboards is a must. In addition, with the help of their feedback, the system must be updated constantly to address issues related to the system. Finally, ensuring compliance with relevant regulations and standards, addressing ethical considerations, and conducting impact assessments are critical for preventing any legal issues and fostering trust among the stakeholders [86,87].

2.9. Case Studies on Machine Learning and Renewable Energy Integration

The integration of ML into REs has been the focus of various studies, exposing the potential for enhancements in grid efficiency, reliability, and sustainability. This section highlights key case studies that illustrate the usage of ML techniques in different aspects of RES integration. The upcoming case studies are also mentioned in Figure 7.

2.9.1. Solar Power Prediction in California

This study [88] is a notable one where it explored the use of an ANN for the prediction of solar power generation in California. In this study, historical weather data, satellite imagery, and real-time sensor inputs have been utilized to forecast solar irradiance and the energy that they generate with high precision. The usage of this ML method in power predictions leads to better energy management and grid stability. This approach helped to mitigate the challenge posed because of the unpredictability of solar energy.

2.9.2. Hydroelectric Power Forecasting in Brazil

In this paper [89], the authors employed Long Short-Term Memory (LSTM) networks for hydroelectric power forecasting in Brazil. This study aimed to predict the power output of hydroelectric plants by analyzing factors such as water flow, reservoir levels, and weather conditions. This method effectively captured long-term dependencies in data, leading to more accurate forecasts that enhanced the reliability of the hydroelectric power system. This case demonstrates the significant value of ML methods in managing the complexities associated with this type of RE source.

2.9.3. Wind Power Prediction in Denmark

As one of the pioneer countries in wind energy integration, Denmark has been at the forefront of implementing advanced ML techniques for the optimization of wind power prediction and grid stability enhancements. In this case study [90], they used SVR to predict wind power output based on historical wind speed data and meteorological information. This ML method successfully handled the non-linear relationship between wind power speed and power output by providing a robust prediction that supported grid stability and reliability.

2.9.4. Battery Energy Storage Management in Australia

In Australia, a study focused on optimizing the management of Battery Energy Storage Systems (BESS) using reinforcement learning algorithms [91]. The main objective was to enhance the storage and distribution of energy generated by solar and wind power. The utilization of ML was to predict energy demand and supply patterns to modify and discharge batteries efficiently. The optimization led to better utilization of renewable energy, reduced energy waste, and improved grid reliability.

2.9.5. Wind Turbine Fault Detection in France

A case study in France investigates the usage of ML methods for detecting faults in wind turbines [92]. In this way, they combined SVMs and ANNs to analyze sensor data from wind turbines. By having the capability of anomaly detection and predicting potential failures, the ML methods could provide proactive maintenance and reduce downtime. This approach consideration not only enhanced the reliability and lifespan of wind turbines but also improved overall grid stability by ensuring consistent power generation from wind farms.

3. Current Status and Advancements

The development of smart grids relies on advancements in various components and innovations. This section provides an overview of the status and recent advancements in smart grid technologies, mainly focusing on infrastructure, communication systems, and control mechanisms.

3.1. Smart Grid Components and Innovations

3.1.1. Recent Developments in Smart Grid Infrastructure

Smart grid infrastructure has evolved considerably, incorporating advanced technologies to enhance grid reliability, efficiency, and resilience. AMI was one of the recent developments that enabled the real-time monitoring and control of energy consumption. They consist of smart meters, communication networks, and data management systems that could facilitate dynamic pricing, demand response, and efficient load management. Moreover, the integration of RES, such as wind and solar systems, caused the transformation in traditional grid systems. Using these DERs in localized generation and storage leads to a reduction in reliance on centralized power plants and empowers the flexibility and reliability of the grid [5].

3.1.2. Advanced Communication Systems

Effective communication is a crucial aspect of the smart grid, and all of the components that reach a seamless outcome have to follow that. Recent developments in this field have caused an enormous improvement in data exchange and real-time monitoring in smart grids. In modern grids, a variety of communications are used. Those similar to wireless networks include fiber optics and Power Line Communications (PLC). The key advancement in this area is the integration of the Internet of Things (IoT), which enables devices to communicate perfectly with each other. These devices contain sensors, meters, and control systems where any abnormality in the connections among them could harm the system’s reliability and efficiency. IoT-based communication systems support advanced functions such as remote monitoring, automated meter reading, and the integration of DERs [93]. Recent developments in high-speed, low-latency communication networks, such as 5G, provide the necessary bandwidth and reliability for real-time grid operations. Accessing these advanced networks supports the deployment of sophisticated grid applications, including predictive maintenance, fault detection, and automated demand response systems [94].

3.1.3. Innovations in Control Mechanisms

A stable and optimized energy distribution requires a proper control mechanism. Recent developments in the area have focused mostly on enhancing the capabilities of DCS and SCADA systems. Dividing the control task among multiple controllers is the main responsibility of DCS. By doing so, the reliance on a central decision-making system will be reduced. In addition, automation technologies, such as Automated Demand Response (ADR), can be used to further enhance grid control by adjusting energy consumption in response to grid conditions [95]. ML methods have guided SCADA systems for better advancements in controlling real-time monitoring and grid operations. These systems have enabled the automation of various processes, such as voltage regulation, load balancing, and fault detection. This collaboration between these two technologies allows the system for predictive analysis, enhancing grid reliability and efficiency by the usage of forecasting potential equipment failures and scheduling further maintenance [96].

3.2. Renewable Energy Integration Techniques

Integration of RES into smart grids is critical for maintaining a sustainable grid system that also reduces its dependency on traditional fuels. Recent advancements, including developing hybrid energy systems and optimizing the role of DERs, have improved the efficiency and reliability of renewables such as solar and wind for the smart grid.

3.2.1. New Methods for Integrating Renewable Energy

Integrating various renewable energies, such as solar and wind, into the grid has its challenges because of the unpredictability of these sources. Recent advancements have announced new methods to address this challenge.

• Advanced Forecasting Techniques: As mentioned before, the ML technique’s emersion presence in smart grids alongside big data analytics behested an accurate prediction of unprecedented renewable energies. By analyzing different impactful factors such as weather data, real-time sensor inputs, and satellite imagery, these models aid the grid in better and more precise management over short and long-term scenarios [97].
• Power Electronics and Inverters: Due to the inherent nature of renewables, achieving a constant voltage of frequency and their integration into the system is another challenge for engineers. While ML techniques helped in the prediction area, having the capability to handle these fluctuations should also be tackled. Modern inverters provide a stable and reliable energy supply that could handle the mentioned obstacles concerning energy contribution to the grid [98].
• Grid-Scale Energy Storage: Variability in energy generation of renewable energies needs to be controlled for peak-shifting reasons. At some times, in a day or season, the amount of generation is high or excess of the needs; this energy should be used for other times of the day when demand is high while generation is not sufficient. To handle this challenge, various energy storage systems, such as Li-ion batteries, have been used to balance supply and demand, mitigate variability in RES, and provide a reliable backup during periods of need [99].

3.2.2. Hybrid Energy Systems

In some cases, when there is the possibility of using more than one renewable energy source, hybrid energy systems can be used in a mixed grid, which faces enhancements in its reliability and efficiency. By leveraging the complementary characteristics of various energy sources, the smart grid can benefit from a more stable and continuous power supply.

• Solar-Wind Hybrid Systems: The complementary factor of solar and wind, because of the availability of at least one of them at a time, encouraged this combination. Solar power is typically available during daylight hours, while wind power is abundant either during the day or night in different months of the year. A hybrid system of these two could help the smart grid decrease its dependency only on one renewable source and enhance overall energy stability [100].
• Renewable-Battery Hybrid Systems: Unpredictability of renewable energies and their usage in the times, other than their rich ones, made a condition for utilizing battery storage and different RES. Advanced energy management systems, by using various powerful software and hardware, can optimize the charging and discharging cycles, improving the overall efficiency of the hybrid systems [99].
• Renewable-Hydrogen Hybrid Systems: Despite using one form of chemical conversion that is used in batteries, another type of storage where hydrogen production is possible by using excess renewable energy could be considered as another form of indirect energy storage. The stored hydrogen, in cells or directly, can be used in various industrial processes. Long-term energy storage and helping decarbonize challenging sectors are some of the ways energy storage benefits [101].

3.2.3. Role of Distributed Energy Resources (DERs)

Localizing, storage, and demand responding are capabilities that are given to smart grids by utilizing DERs. These include solar panels, wind turbines, battery storage systems, and other small-scale energy sources.

• Microgrids: These localized grids can operate independently or in conjunction with the main grid. The main usage of these new technologies is for places where conventional grids cannot provide users with their generation, such as remote or underserved areas. Enhancing grid flexibility, supporting the integration of renewable energies, and improving energy security are some of their benefits [9].
• Virtual Power Plants (VPPs): VPPs aggregate multiple DERs to operate as a single power plant. Coordinating the generation and consumption of distributed resources can aid the system in providing services such as frequency regulation, voltage control, and peak shaving that could enhance the efficiency and stability of the overall power system [102].
• Demand Response Program: Rather than solely relying on the reliability of the smart grid to the generation, programs such as demand response encourage customers to collaborate with energy generation procedures and adjust their usage based on the grid conditions. By leveraging smart meters and advanced communication systems, these programs offer commissions for customers to reduce or shift their energy consumption, mostly during peak periods. This program helps the supply and demand balance, reduces unnecessary stress on the grid, and promotes the efficient use of RES [103].

4. Discussion

In this section, the aim is to synthesize the findings of the comprehensive review of current smart grid technologies and the integration of RES, providing a comparative analysis of various studies that have been mentioned in this paper by delving into the key insights from the literature, exploring the technological advancements and the impact on grid stability, efficiency, and sustainability. Most importantly, practical and policy implications for the future developments of other works on the related topic have been considered. For the final stage of this section, the limitations of this review will be discussed to explore the potential biases and gaps in the scope and coverage of the research.

Figure 8 provides an overview of the current status and advancements in smart grid technologies and renewable energy integration. It highlights all the mentioned information in this paper.

4.1. Key Insights from the Literature

4.1.1. RES Impact on Environmental Health

The literature emphasizes the importance of RES integration into the smart grid. These energies, such as wind and solar power, play a crucial role in advancing sustainability by aiding the reduction in fossil fuel consumption, leading to the reduced production of greenhouse gases, which mitigates climate change and promotes environmental health.

4.1.2. Advancements in Smart Grid Technologies

Developments in smart grid technologies have enhanced the reliability, efficiency, and resilience of power grids. Key advancements that also has been mentioned in this paper are in the following part:

• AMI: These systems, comprising smart meters, communication networks, and data management systems that could enable real-time monitoring and control of energy consumption, are the ones that facilitate dynamic pricing, demand response, and efficient load management. This integration into the current grids leads to more informed energy use and cost savings for the current customers. Data gathered by AMI could also be used by ML technologies to produce precise and accurate grid management predictions.
• DCS: This technology improves grid stability by decentralizing control tasks across multiple controllers, which reduces dependencies on the sole central part for decision-making. This approach allows more flexibility and better adaptive grid operations, which are vital for managing the variability in RES because of the unpredictability of these sources.
• SCADA Systems: These systems are integral to modern grid management, offering comprehensive real-time monitoring alongside control capabilities. These systems support the automation of different grid processes, including voltage regulation, load balancing, and detection of any probable faults. With the integration of ML and SCADA, more capabilities will be enabled to perform precise prediction analyses and enhance grid stability.
• Role of ML in Grid Management: ML techniques have emerged as powerful tools for energy management optimization within smart grids. This application could be named predictive analysis, fault detection, and real-time monitoring. This integration could help the system enhance its ability to forecast energy demand, optimize grid operations, and improve the integration of RES. The usage of large datasets and advanced algorithms could significantly improve the efficiency, reliability, and sustainability of the grids.

4.1.3. Comparative Analysis of Studies

• Technological Advancements: These systems, comprising smart meters, communication networks, and data management systems that could enable real-time monitoring and control of energy consumption, are the ones that facilitate dynamic pricing, demand response, and efficient load management. This integration into the current grids leads to more informed energy use and cost savings for the current customers. Data gathered by AMI could also be used by ML technologies to produce precise and accurate grid management predictions.
• Integration Techniques: There are various and diverse methods for integrating RES into smart grids. Some research advocates their efforts on hybrid energy systems, while others focus on advanced forecasting techniques and power electronics for managing the unpredictability of RES. This variety of approaches reflects the complexity and regional specificity of RES integration into the smart grid.
• Energy Storage and Grid Stability: Research comparing different energy storage technologies highlights trade-offs between cost, lifespan, and efficiency. Most studies agree on the necessity of storage systems for grid stability; recommendations on optimal solutions vary based on the regional and operational contexts. Effective energy storage is vital for creating a balanced environment for supply and demand, particularly in the matter of the intermittent nature of renewable energy resources.

4.1.4. Comparative Analysis of Renewable Energy and Smart Grid Technologies

In this part, a comparative analysis of the various renewable energy and smart grid technologies discussed in this paper will be presented. This analysis focuses on the advantages, disadvantages, and the most suitable scenarios for implementing each technology.

Table 3. Comparison table of technologies.

Category	Technology	Advantages	Disadvantages	Best Use Scenarios
Wind Energy	Onshore Wind Turbines	Lower installation and maintenance costs	Limited by land availability and wind patterns	Suitable for regions with consistent onshore wind patterns
	Offshore Wind Turbines	Higher wind speeds and energy output	Higher installation and maintenance costs	Ideal for coastal regions with high wind speeds
Solar Energy	Photovoltaic (PV) Panels	Scalable from residential to utility-scale	Dependent on sunlight availability, efficiency drops in cloudy weather	Ideal for regions with high solar irradiance
	Concentrated Solar Power (CSP)	Can generate electricity even after sunset using thermal storage	High initial costs and large land requirement	Suitable for desert regions with high direct sunlight
Hydropower	Large Hydropower Plants	Provides stable base-load power	Environmental impact on aquatic ecosystems	Best in regions with large rivers and stable water flow
	Small/Micro Hydropower Systems	Suitable for remote or off-grid areas	Limited power generation capacity	Ideal for rural areas with small rivers
Energy Storage Technologies	Lithium-Ion Batteries	High energy density, scalability	High cost, limited lifespan	Suitable for grid-scale storage, especially for balancing renewable energy
	Flow Batteries	Long lifespan, scalable, good for long-duration storage	Lower energy density, higher upfront costs	Ideal for applications requiring large-scale energy storage over long periods
Smart Grid Technologies	Advanced Metering Infrastructure (AMI)	Real-time energy monitoring supports demand response	High deployment cost	Best for regions with high energy consumption and dynamic pricing
	Distributed Control Systems (DCS)	Reduces reliance on central control, enhances reliability	Complex integration with existing infrastructure	Suitable for grids with diverse and distributed energy sources
	Supervisory Control and Data Acquisition (SCADA)	Enhances real-time monitoring and control	High cost and complexity	Ideal for large, complex grid systems needing detailed monitoring

illustrates this comparison.

4.2. Practical Future Implications for Grid Operators

4.2.1. Enhanced Monitoring and Control Systems

Since the unpredictability of renewable energies causes some difficulties, smart grid operators should invest in advanced monitoring and control systems to manage this obstacle of RES effectively. A combination of technologies, such as real-time data analytics, by the usage of AMI, DCS, and SCADA systems, can aid the grid in making proactive adjustments to maintain grid stability and efficiency. In addition, the usage of ML techniques helps to identify the potential issues before they become severe, reducing downtime and maintenance costs.

4.2.2. Enhanced Energy Storage Systems

Another approach to address the intermittent nature of RES would be the investment in scalable and efficient energy storage solutions. Using technologies such as Li-ion batteries, supercapacitors, and pumped hydro energy storage solutions can provide this necessary buffer to balance supply and demand, ensuring a stable energy supply, most importantly during renewable generation fluctuations. This solution also facilitates peak shaving, reducing the need for expensive and less efficient peaking power plants.

4.2.3. Integration of DERs

Embracing DERs, such as rooftop solar panels, small-scale wind turbines for commercial places, and community energy storage systems, can help the grid enhance its resilience and reliability. Grid operators should allocate their effort to developing frameworks and technologies that can adopt other types of DER integration and leveraging their potential to provide localized generation and storage. VPPs are a technology that can aggregate multiple DERs for operating as a single power plant, providing ancillary services such as frequency and voltage control to compensate for generation fluctuations due to the usage of various DERs.

4.2.4. Adoption of Advanced Communication Technologies

The deployment of high-speed, low-latency communication networks, such as 5G, is the essence of proper communication for the efficient operation of a smart grid. These networks share real-time data between grid components, providing advanced applications such as remote monitoring, automated meter reading, and demand response programs. As a vital part of this procedure, grid operators should prioritize upgrading their communication infrastructure to leverage this technology thoughtfully.

4.3. Policy Recommendations

4.3.1. Support for Technological Innovation

Policymakers should provide an environment for the design of their project to have the capability of accepting different innovations in smart grid technologies and renewable energy integration. The use of grants, subsidies, and tax incentives for research and development activities can support pilot projects and demonstrations to adopt cutting-edge technologies in real-world settings.

4.3.2. Development of Comprehensive Regulatory Frameworks

Effective regulatory frameworks are vital for facilitating the integration of RES into the power grid. By developing regulations that promote the deployment of advanced grid technologies, assure fair market conditions, and incentivize sustainable practices, policymakers can address the need for cybersecurity measures to protect the grid from potential threats.

4.3.3. Promotion of Renewable Energy Adoption

The adoption of more RES into the grid needs policies to ensure the sustainability of the grid is maintained. This includes setting ambitious renewable energy targets, offering feed-in tariffs, and providing financial incentives for the installation of RES. Policymakers also should focus on streamlining permitting processes and reducing bureaucratic hurdles to accelerate the deployment of RES.

4.3.4. Encouragement of Public-Private Partnerships

Collaboration among the public and private sectors is beneficial for advancing smart grid technologies and the integration of RES. Related to this matter, policymakers should foster partnerships that could leverage the strengths of both sectors, facilitating knowledge exchange, resource sharing, and joint investments. This partnership can drive innovation, reduce costs, and enhance the scalability of smart grid solutions.

4.3.5. Investment in Education and Training

Another aspect of the successful implementation of smart grid technologies is a skilled workforce equipped with the necessary knowledge and expertise. Policymakers should enhance their investments in training programs that prepare the next generation of engineers and technicians to mitigate any encounters related to modern grid management. This includes developing curricula that cover emerging technologies, data analytics, and renewable energy systems.

4.4. Technological Limitations

While the integration of ML into RES has lots of benefits, there are some technological limitations that must be considered to ensure successful implementation. These constraints can impact the feasibility, efficiency, and scalability of the proposed solutions [48].

4.4.1. Intermittency and Energy Storage Limitations

• Intermittency of Renewable Energy: The unpredictability of RES poses a challenge to maintaining a stable power supply. After advancements in this area, there are still some limitations in capacity, cost, and efficiency for storage technologies that can hinder the ability to balance supply and demand in real life effectively.
• Energy Density and Lifespan of Storage Solutions: Technologies such as Li-ion batteries, despite their popularity, suffer from low energy density and limited lifespan. These issues can restrict their use to large grid applications.

4.4.2. Computational and Data Processing Challenges

• Scalability of Machine Learning Models: When the datasets become large in real time, implementing ML models for grid management requires extensive computational resources. This problem can be a barrier, especially for regions with limited technological infrastructure.
• Data Privacy and Security Concerns: Relying on real-time data for grid management and optimization can raise some issues related to data privacy and security. Ensuring the confidentiality and integrity of sensitive grid data while maintaining compliance with regulations presents an ongoing challenge.

4.4.3. Integration with Existing Infrastructure

• Legacy Systems and Compatibility Issues: Integrating new technological advancements with outdated grid systems is another challenge against existing grids. This can lead to increased costs and complexity in the implementation process, which needs time and investment to overcome.
• Interoperability of Technologies: As mentioned in this paper, various parts need to be integrated. Each of them has faced advancements, and integrating all of them (energy storage, RES, and ML algorithms) and using them in smart grids is challenging.

4.4.4. Technological Maturity and Adoption Rates

• Early-Stage Technologies: Many of the discussed technologies, such as advanced energy storage systems, are still being tested and experimented on. Their adoption in commercial and large-scale applications needs social acceptance, which is limited by technological maturity, which can have a disadvantage on their reliability and performance.
• High Initial Costs and Long Payback Periods: Utilizing new devices and technologies, such as AMIs, needs high initial investment coupled with long payback periods that can deter widespread adoption, especially for undeveloped regions or smaller markets.

4.5. Limitations of This Review

4.5.1. Potential Biases and Gaps

This review is subject to biases and can have many uncontrolled covariates despite the attempts made to provide a comprehensive analysis. One of the most significant sources of bias is related to the selection of literature, as it may favor or be heavily based on the use or the technologies that are in the favorable light. For example, this review might be significantly biased towards positive results because the researchers have found mostly positive or negative impacts of the considered technologies. However, some studies that were found because they consider technologies with some negative features may not be published, be underrepresented in the field of study, or lack adequate citations. In this way, this review might conclude that the current status of smart grid technologies and their cooperation with renewable energy integration are positive despite some barriers.

Another limitation of this study is the possibility of emphasizing well-documented and recognized practices at the expense of emerging and, thus, less-documented technologies. As seen in the analysis, smart grid technologies are rapidly evolving, and the time gap between their introduction and comprehensive documentation can lead to their underrepresentation. Thus, the analysis may provide a biased or partial view of the current state of practice and overlook innovative solutions and breakthroughs that are still new to the industry or researchers.

Additionally, this review may suffer from a language and accessibility bias, as it primarily includes studies published in English and those accessible through major academic databases. Important research conducted in other languages or published in less accessible journals might be overlooked, further contributing to the potential gaps in the analysis.

4.5.2. Scope and Coverage Limitation

Another limitation is the geographical scope. In this review, we mainly concentrate on smart grid technologies and implementations in North America, Europe, and parts of Asia. Unique challenges and solutions specific to other regions, especially developing countries where grid infrastructures and renewable energy integration confront distinct hurdles, can be overlooked. Many of these regions experience region-specific socioeconomic, environmental, and technological issues that are not well presented in this review.

Furthermore, the interdisciplinary nature of smart grid technologies makes it difficult to integrate viewpoints from different disciplines, including engineering, economics, environmental science, and policy. Despite our best efforts to be as inclusive in such disciplinary areas as possible, they may still be under-represented in this research theme issue. This could lead to a somewhat fragmented view of challenges and issues of the integration of smart grids and renewable energy systems. For example, the economic and policy parts may not be equally detailed with the technical issues, which are crucial to understanding the key factors that influence the adoption and success of these technologies.

Finally, the dynamic and fast-changing characteristics of technology development in smart grids imply that the findings and conclusions of this review paper can rapidly become obsolete shortly after publication. Regular updating and follow-up research are needed to obtain a view that is as up-to-date and current as possible.

Recognizing these limitations identifies the areas on which future research should concentrate to achieve a complete and more balanced picture of smart grid technologies for renewable energy integration. Future reviews should attempt to obtain a more representative sample, expand coverage of emerging technologies, and account for the diverse barriers encountered in different regions.

5. Conclusions

Following this direction, this systematic review has presented in detail the current landscape and future outlook of smart grid technologies with the involvement of RES. For that, we analyzed and reported the current advancements in AMI, DCS, SCADA systems, and ML techniques toward making the energy management of the grid more efficient. The concatenation of these technologies not only enhances the resilience, reliability, and efficiency of the existing grid infrastructure but paves the way for advancing sustainable energy systems. This review provides insights into the transformative nature of the aforementioned technologies. However, the applicability of these findings might be constrained.

Another clear limitation is the literature selection bias, which introduced some positive evidence about the effective smart grid technologies and RES integration but could have potentially excluded some challenges and failures in design, operation, and implementation. Another explicit limitation of this review, which should be noted, is the constant development of smart grid technologies. There might be some new or less well-demonstrated advancements in smart grid technologies that were not included in our review due to the rapid development of such technologies and the time required to capture such advancements. The sub-themes of smart grid technologies and RES integration are quite wide, and the focus of this review is not equally balanced. Some of the technical and design issues from this review are superficially explored, while others are more adequately examined as a result of this widespread phenomenon. The broader focus of this review might have overlooked important issues while addressing other less important issues. This could pose a challenge as we try to analyze data for a developing country context, given the developing country’s unique challenge in grid infrastructure and integration.

6. Future Directions

Due to the limitations of the aspects considered, this work only covered part of the broad topic of smart grid and renewable energy integration. To provide a better understanding of the future path, a few suggestions for future research will be mentioned in this section.

• Advanced Forecasting and Predictive Analytics: Future research should focus on improving forecasting techniques and predictive analytics for renewable energy generation. Implementing important factors such as weather prediction and real-time data from sensors and IoT devices can have an enormous impact on the enhancement of RES and grid operations management.
• Enhanced Energy Storage Solutions: The stored energy, investigating new materials and technologies for energy storage is vital, and research should explore this field to advance battery technologies, supercapacitors, and other innovative storage solutions that will offer higher efficiency, longer lifespan, and lower costs for the grid. Moreover, another aspect of exploration would be the hybrid systems that combine different technologies to provide more flexible and reliable storage options.
• Integration of DERs: To develop efficient frameworks and technologies for integrating DERs into the grid, further research is needed. Optimizing the operation of microgrids and VPPs could be one way to empower grid stability and reliability. In the meantime, studies should also explore the economic and regulatory implications of widespread DER integration.
• Cybersecurity and Data Privacy: As smart grids become more interconnected and reliant on data, ensuring robust cybersecurity measures is vital to protect the system against any cyber-threatening situation. Advanced security protocols should also be considered to address data privacy concerns and establish secure data management practices.
• Grid Modernization and Infrastructure Development: Another aspect of development would be the modernization of grid infrastructure, including the development of high-speed communication networks such as 5G and the integration of advanced control systems. Studies also should explore the potential of new grid architectures that support higher levels of renewables penetration and improve overall grid resilience.
• Policy and Market Mechanisms: Establishing more encouraging and ensuring policy mechanisms that promote the adoption of smart grid technologies and renewables is essential. The impact of different incentives, subsidies, and regulatory measures on the deployment and integration of these technologies should be analyzed for future combinations.
• Quantum Computing for Grid Optimization: The recent advent of quantum computing holds significant potential for complex grid optimization. Dissimilar to previous advancements in this field, quantum computers can perform various computations simultaneously and solve complex optimization problems. By utilizing this technology, the focus could shift over energy distribution optimization, load balancing, and the integration of RES in real time [104].
• AI for Autonomous Grid Management: Similar to autonomous EVs, the usage of AI in any industry, such as grid management, is inevitable. In time, AI-driven systems will automatically monitor, analyze, and control grid operations without human intervention. This capability gives them the power to respond to real-time data, predict potential issues, and optimize grid performance dynamically [85].
• Blockchain Technology for Decentralized Energy Transactions: Recent advancements in cybersecurity led to the use of blockchain technology in the smart grid industry. By providing a secure, transparent, and decentralized platform for energy trading, it can enable peer-to-peer (P2P) energy exchanges among consumers and producers. Customers can also trade energy credits directly with each other through this procedure [105].

The evolution of smart grids and renewable energy integration represents a shift towards a more sustainable and resilient energy future. The fast-paced advancements in this area, alongside the combination of increasing adoption of RES, are transforming the way energy is generated, distributed, and consumed. Significant environmental, economic, and social benefits, including reducing greenhouse gas emissions, are offered by these developments.

As the energy landscape continues to evolve, it is necessary to face the challenges and seize the opportunities presented by these advancements. Proper collaboration between researchers, policymakers, industry stakeholders, and consumers is crucial for driving the progress of smart grids and renewable energy integration. This future lies in creating a dynamic, adaptive, and intelligent environment for the grid systems that can harness the utilization of various RES effectively. By embracing innovative technologies, promoting supportive policies, and promoting sustainable practices, a better energy system for the future can be achievable.