The Smart Grid: Comparison
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The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies.

  • Smart Grid

T和  1. Introduction

The concept of the smart grid is transitioning the traditional electric power grid from an electromechanically controlled system to an electronically controlled network. According to the US Department of Energy’s Smart Grid System Report [1], the smart grid systems consist of information management, control technologies, digitally based sensing, communication technologies, and field devices that function to coordinate multiple electric processes. These smart grid technologies have changed the conventional grid planning and operation problems in at least three main areas, primarily in the ability to (1) monitor or measure processes, communicate data back to operation centers, and often respond automatically to adjust a process; (2) share data among devices and systems; and (3) process, analyze, and help operators access and apply the data coming from digital technologies throughout the grid. Some of the related problem space in smart grids include load forecasting (LF), power grid stability assessment, fault detection (FD), and smart grid security. These key elements are allowing massive amounts of high-dimensional and multitype data to be collected about the electric power grid operations. However, the traditional modeling, optimization, and control technologies have many limitations in processing these datasets; thus, the applications of artificial intelligence (AI) techniques in the smart grid become more apparent.
AI techniques use massive amounts of data to create intelligent machines that can handle tasks that require human intelligence. Machine learning (ML) is a branch of AI, and the term ML is sometimes used interchangeably with AI. However, ML is just one way to achieve AI systems. Other broader ways to achieve AI systems are neural networks, robotics, expert systems (ES), fuzzy logic (FL), and natural language processing. Overall, AI techniques enable decision making with speed and accuracy. In smart grid applications, AI can be defined as the mimicking of grid operators’ cognitive functions by computers to achieve self-healing capabilities. However, AI might not be able to replace grid operators in some cases. Although AI systems can be more precise, reliable, and comprehensive, there are still many challenges in applying AI techniques to the smart grid. Two types of AI systems are possible in the smart grid: virtual AI and physical AI. Virtual AI systems include informatics that can help grid operators perform their jobs. Physical AI systems include self-aware AI systems that can optimize and control specific grid operations with or without human intervention. AI systems in the smart grid can be further divided into two categories: artificial narrow intelligence (ANI) and artificial general intelligence (AGI). ANI refers to AI systems developed for specific tasks with applicable requirements and constraints, such as an AI system that performs load forecasting via different datasets.AGI refers to AI systems developed to learn and evolve autonomously, just like humans. Developing AGI systems could help realize true smart grid systems in the future.
The amount of AI research for smart grid applications has increased in the last decade. Similarly, in the last 4 years, some of these studies were surveyed in recent papers [2,3,4,5][2][3][4][5]. The authors recognize that one article cannot provide a comprehensive review of all the AI techniques for smart grid applications in load forecasting, power grid stability assessment, faults detection, and security problems; thus, this survey paper presents some present AI applications in some of the areas not covered by these existing reviews, discusses some challenges of applying AI to smart grid problems, and highlights some future potential applications of AI techniques to the smart grid. The references included in this survey should help researchers interested in this exciting area. The findings and related contributions are threefold. First, based on a systematic and structured survey, the authors developed a smart grid review map that inductively categorizes and describes the existing body of research. Second, the authors contributed to the advancement of this field by elaborating on challenges inherent to the smart grid and opportunities for future research. Third, in presenting the review in this paper, the authors strengthened the collation of resources. In this way, the authors hope to stimulate discussions that could further strengthen the exchange of ideas.

2. Artificial Intelligence Techniques in Smart Grids

This section presents a review of AI techniques in smart grids.

2.1. Research Methodology

In line with the objective of our research, the authors adopted an inductive approach and conducted a systematic literature review, following Tranfield, Denyer, and Smart [70][6]. Specifically, the review scope was defined, the related literature was searched, the representative methods were selected, and the collected materials were analyzed.
Several queries were run against Google Scholar databases to gain an overall understanding of the coverage offered by literature under the disciplines. We focused on peer-reviewed sources from top academic journals and conferences. For each criterion, searches were performed by using combinations of keywords containing the term of each criterion, “AI,” and “smart grid” (e.g., “Short-Term Load Forecasting AI smart grid” for “Short-Term Load Forecasting”). The authors also opted to exclude studies in progress and tutorial literature from the search results. The search generated 148 peer-reviewed studies between 2015 and 2021. Figure 51 presents the yearly count of the 148 studies. All 148 studies are reviewed in this paper; however, 75 of the 148 studies are listed in Table 1, Table 2, Table 3 and Table 4.
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Figure 51.
Frequency of peer-reviewed papers in the search results.
The remainder of this section discusses the applications of AI techniques to (1) load forecasting, which is further divided into short-term load forecasting, mid-term load forecasting, and long-term load forecasting; (2) power grid stability assessments, which contain transient stability assessments, frequency stability assessments, small-signal stability assessments, and voltage stability assessments; (3) faults detection; and (4) smart grid security.

2.2. Load Forecasting

With the high integration of renewable energy—such as solar, wind, and tide power—the uncertainty of the scheduling and operation of the smart grid are becoming increasingly challenging. LF, as one of the key components to keep the power system stable and smart, is critical for planning and operation in modern power systems. Accurate forecasting, which is beneficial for reducing production costs and saving electric power [71][7], is very challenging if the load is nonstationary. According to the time that must be forecasted, LF can be classified into three levels [72][8]: (1) short-term LF (STLF), which predicts the load from minutes to hours; (2) mid-term LF (MTLF), which predicts the load from hours to weeks; and (3) long-term LF (LTLF), which predicts the load for years. Moreover, LF can also be affected by various other features, such as weather, time, season, event, type of customer, and academic schedule. Generally, MTLF and LTLF forecasting are modeled as functions of historical data for power consumption, along with other factors, such as weather, customers, and demographic data [73][9]. STLF has mostly been studied in different applications, such as real-time control, energy transfer scheduling, and demand response [74][10]. MTLF and LTLF can be used to plan for future power plants and show the dynamics of the power system [73][9]. Based on the data provided by smart meters, many techniques are proposed and applied for power system LF.

2.2.1. Short-Term Load Forecasting

Qiu et al. [75][11] propose a hybrid incremental learning approach that comprised discrete wavelet transform, empirical mode decomposition, and random vector functional link network. By using the ensemble method, the efficiency and accuracy of STLF can be improved. Li et al. [76][12] present a model with an ensemble approach that integrates three base methods for STLF in which the experiments show the model’s effectiveness for STLF. However, the choice of base methods in the ensemble approach needs further validation. Many DL-based methods are used to solve LF problems. In recent years, DNNs have been used to obtain the potential knowledge for a forecasting model. However, the ANN method is often trapped in local minima [77][13] and over-fitting problems. Shi et al. [78][14] proposed a pooling-based deep RNN for STLF to address the over-fitting issue by increasing data diversity and volume. To address the time-consuming procedure of building a optimal DNN, which determines the number of hidden layers in the DNN model, Moon et al. [67][15] used an ensemble method that combines multiple DNN models with different numbers of hidden layers to achieve overall better performance by eliminating the poorly performed models. However, the computing overhead is a limitation, because several CNNs are included. In He, Deng, and Li [79][16], a DBN embedded with parametric Copula models, is proposed to forecast the hourly load of a power grid of an urban area in Texas, and the results reflect the effectiveness of the method by comparing it with neural networks, SVR, and ELM. Hafeez et al. [43][17] propose a hybrid algorithm using factored conditional restricted Boltzmann machine (FCRBM) as a training module and genetic wind-driven (GWDO) as an optimization algorithm. The model is validated by outperforming the state-of-the-art algorithm. Aly [80][18] built a hybrid clustering method based on wavelet neural network (WNN) and ANN schemes and showed the higher performance of the proposed model, comparing it with other clustering methods.

2.2.2. Mid-Term Load Forecasting

Even though the majority of LF problems fall into STLF, MTLF and LTLF are also very crucial for stable and smooth power system operation. MTLF is used to coordinate load dispatch, maintenance scheduling, and balance demand and generation [81][19]. Unlike STLF, which fit data to a model, MTLF and LTLF have different problems that are often ignored due to their complications [82][20] and randomness [83][21]. The MTLF and LTLF are not only affected by some explicit factors, such as historical load and weather data, but are also affected by local economy and demographic data, such as population and appliances in use [81][19]. Unlike STLF, which treats all weather variables with equal importance, the weather indicators for MTLF and LTLF follow a decreasing order of importance from temperature, humidity, wind, and precipitation [84][22]. Jiang et al. [85][23] proposed a dynamic Bayes network (DBN)-based MTLF model to forecast the peak power load for the following year. In Askari and Keynia [86][24], the authors deployed a DNN model with an optimized training algorithm that comprises two search algorithms for MTLF in power systems and presented the effectiveness of the model. Liu et al. [87][25] also provided a neural network-based model with particle swarm optimization (PSO) and showed the feasibility and validity of the model. Rai and De [88][26] improved a support vector regression model for MTLF with an average minimum mean absolute percentage error (MAPE) of 3.60. Gul et al. [89][27] provide a solution based on CNN and LSTM methods. Dudek et al. [90][28] propose a hybrid DL model for MTLF that combines exponential smoothing, advanced LSTM, and the ensemble method. This is a competitive method that also uses the ensemble approach.

2.2.3. Long-Term Load Forecasting

LTLF is used to predict the power consumption, system planning, and scheduling of generation units expansion in power systems. Generally, it spans from a few years to a couple decades. Because it needs a huge investment to construct new power generation, it requires accurate and effective forecasting for power systems. There are many ML and AI techniques developed for the problem. Nalcaci et al. [37][29] show that the MARS method gives more accurate and stable results than ANN and LR models when predicting the relationship between load demand and several environmental variables. Ali et al. [91][30] applied a novel hybrid fuzzy-neuro model for LTLF. LSTM is also well used in the domain. In 2017, Zheng et al. [72][8] exploited the LSTM-based RNN for the long-term dependencies in the electric load time series for LTLF, in which the method had a promising performance. Agrawal et al. [92][31] also propose an LTLF model with hourly granularity by using the LSTM network with high accuracy. To solve the vanishing and exploding gradient problems of LSTM, Dong et al. [93][32] present a hybrid method based on LSTM and gated recurrent unit (GRU) with a good performance for LTLF. In Kumar et al. [94][33], Apache Sparks was used to deploy a hybrid model that comprises LSTM and GRU for hyperparameter tuning purposes. Bouktif et al. [95][34] also proposes an LSTM-RNN model for this task. Sangrody et al. [96][35] compared six commonly used ML technologies: ANN, SVM, RNN, KNN, GPR, and generalized regression neural network (GRNN). ANN showed better performance than the other five methods for LTLF. Table 1 summarizes the AI techniques for LF.

2.3. Power Grid Stability Assessment

The power grid stability assessment—which comprises transient stability, frequency stability, small signal stability, and voltage stability [97,98][36][37]—is fundamental for ensuring the reliability and security of the power system. Power system stability is the ability to stay at an equilibrium operation state or quickly reach a new equilibrium state of operation after a perturbation [99][38]. Traditional models [92,100,101,102][31][39][40][41] for stability assessments are complex and require significant computing resources because they heavily rely on accurate real-time dynamic power system models [98][37]. Because of the development of phasor measurement units (PMU) and the wide area measurement system (WAMS), many data-driven AI methods for stability analysis have been applied on power grid stability analysis.
Table 1.
Summary of approaches for LF.
Author (Ref.) Year Objective Techniques
Shi et al. [78][14] 2017 STLF RNN
He et al. [79][16] 2017
136][76] used a PNN classifier for FD and fault diagnosis in the DC side of a PV system. In 2020, Hussain et al. [137][77] proposed a fault detection algorithm for PV based on ANN with 97% overall accuracy. Condition monitoring in wind turbines is also important for improving maintenance by detecting faults at an early stage. Baghaee et al. [138][78] evaluate the effectiveness of deep ANNs in wind turbine FD. Gunturi and Sarkar [139][79] present the effectiveness to apply the ensemble method for energy theft detection. Table 3 summarizes the AI techniques for power system FD.
Table 3.
Summary of approaches for power system FD.
Author (Ref.) Year Objective Techniques
Shafiullah et al. [123][63] 2017 FD ELM
Abdelgayed et al. [134]STLF [74DBN
]
Kosek [148][88]2017 Microgrid FD KNN, DT Zheng et al.
2016 Detect malicious voltage control actions ANN[72][8] 2017 LTLF LSTM
Garoudja et al. [136][76] 2017 PV FD PNN
Ozay et al. [154][94] 2016 Attack detection KNN, SVM Qiu et al. [75][11] 2018 STLF Ensemble, statistic models
Zhang et al. [129][69] 2017 Line trip FD
Tan et al. [143LSTM, SVM
][83] 2016 Survey Data-driven approach Agrawal et al. [92][31] 2018 Sirojan et al. LTLF [127]LSTM
[67] 2018 HIFD ANN
Zhou et al. [146][86] 2018 Attacks detection SDAE Ali et al. [91][30] 2018 LTLF Fuzzy, ANN
Wang et al. [131][71] 2018
Ahmed et al. [152][92Line trip FD ]AE, SVM
2018 Detect covert cyber deception assault SVM Sangrody et al. [96][35] 2018 LTLF ANN, SVM, RNN, KNN, GPR, GRNN
Shafiullah et al. [132][72]
Zhang et al. [22][2018 Microgrid FD ANN
96] 2018 Survey DL, RL Kumar et al. [94][33] 2018 LTLF LSTM, GRU
Helbing et al. [138][78] 2018 WT FD ANN
Ni et al. [150][90] 2019 Attacks detection RL Jiang et al. [85][23] 2019 MTLF DBN
Baghaee et al. [135][75] 2019
Hossain et al. [144FD ][84] 2019SVM
Survey Big data, ML Askari et al. [86][24] 2019 MTLF DNN
Govar et al. [128][68] 2019 HIFD ELM
Ahmed et al. [153][93] 2019 Detect covert cyber deception assault Isolation forest Liu et al. [87][25] 2019 MTLF DNN
Jayamaha et al. [133][73] 2019 Microgrid FD ANN
Li et al. [155][95] 2019 Electricity theft detection Nalcaci et al. [37][29] Fazai et al. 2019 LTLF MARS, ANN, LR
[124][64] 2019 PV FD GPR
CNN, random forests
Cui et al. [145][85] 2020 Survey ML Li et al. [76][12] 2020 Ashrafuzzaman et al. STLF [125Ensemble
][65] 2020 FD
Ali et al. [4Ensemble
] 2020 Survey AI Moon et al. [67][15] 2020 Haq et al. [130][70]STLF 2020CNN, Ensemble
Line FD ELM Hafeez et al. [43][17] 2020 STLF FCRBM
Aly [80][18] 2020 STLF WNN, ANN
Dong et al. [93][32] 2020 LTLF LSTM, GRU
Bouktif et al. [95][34] 2020 LTLF LSTM, RNN
Rai and De [88][26] 2021 MTLF SVR
Gul et al. [89][27] 2021 MTLF CNN, LSTM
Dudek et al. [90][28] 2021 MTLF LSTM, ETS, Ensemble

2.3.1. Transient Stability Assessment

Transient stability assessment (TSA) is the ability to determine whether a system will remain synchronised after a huge perturbation. The two most commonly used traditional methods for TSA are time domain simulations and direct methods. However, the increasingly complex power systems result in great challenges in making reliable decisions based on traditional TSA methods.
Fortunately, the development of AI technologies provides the new prospective methods to this issue by using the large volume of data collected by PMU and WAMS. In Baltas et al. [99][38], three ML algorithms—decision trees, SVMs, and ANNs, which are for online TSA—were compared by using two datasets. The results show similar performance for the methods, and performance varies according to dataset quality. Mahdi et al. [103][42] also used a trained ANN model for online TSA prediction with promising performance. Hu et al. [104][43] developed two improved SVM methods to solve the traditional SVM limitation that reduces the false and missed alarms. Mosavi et al. [105][44] present a deep neuro-classifier for TSA and showed the high-generalization capacity of the model. Tang et al. [106][45] propose a TSA method that combined trajectory fitting (TF) and ELM, and the hybrid method showed effectiveness and reliability. Yu et al. [107][46] propose an RNN-LSTM model that better learns from the temporal data dependencies of the input data. Tan et al. [108][47] built a supervised classifier that consists of CNN and stacked autoencoders (SAE) for TSA problems with high accuracy. Liu et al. [109][48] used an intelligent system that comprised an ensemble of neural networks based on ELMs with 100% accuracy. In 2020, the study [110][49] applied a deep belief network (DBN) for TSA with great accuracy improvement. Shi et al. [111][50] trained a CNN model to provide a solution for online TSA for power system control.

2.3.2. Frequency Stability Assessment

Power grid frequency stability assessments (FSAs) can be defined as the ability of a system to maintain a steady range of frequency following a severe system upset or perturbation that results in an imbalance between generation and load [98][37]. A large frequency deviation causes generation units to trip, and the system stability can eventually be influenced. A few studies focused on this area by using AI technologies. In 2019, Wang et al. [14][51] proposed a hybrid model that integrated a frequency response model with an extreme learning ML model for FSA.

2.3.3. Small-Signal Stability Assessment

Small-signal stability is defined as the ability of the system to maintain synchronism when it is under small disturbances [112][52]. The term “small-signal stability assessment” is interchangeable with the term “oscillatory stable assessment” (OSA). A CNN-based method [111][50] was also developed for OSA, and the results show that the model is robust to PMU noise and that algorithm performance will not be reduced as the system grows in scale. Xiao et al. [113][53] used a multivariate random forest regression (MRFR) algorithm for OSA on an 18 bus test system, and the results presented high accuracy and robustness. Kamari et al. [114][54] deployed a PSO scheme to accelerate the determination of OSA.

2.3.4. Voltage Stability Assessment

Voltage collapse can significantly influence the stability of power systems. Thus, a voltage stability assessment (VSA) model, which can evaluate the voltage stability of the system in a timely fashion, would be a prevention. Numerous AI-based models are proposed in VSA, such as ANN [115][55], SVM [116][56], decision trees [117][57], and FL [118][58]. Ashraf et al. [115][55] used an ANN model to estimate the loading margin of power systems and testified to the effectiveness on Institute of Electrical and Electronics Engineers 14 bus and 118 bus test systems. Amroune et al. [119][59] used a hybrid model by using dragonfly optimization and SVR for online VSA. Mohammadi et al. [116][56] proposes a method for VSA by using an SVM. The results showed that the misclassification rates of the SVMs are as low as 2% for real power grids. Yang et al. [120][60] built a moment-based spectrum estimation method to gain insight into changes of voltage magnitudes for real-time static VSA. In Meng et al. [117][57], a decision tree model was used for online VSA. Liu et al. [121][61] built a feature selection model using partial mutual information (PMI) on an iterated random forest (IRF) model. An in-depth review is also found in Amroune [122][62]. Table 2 summarizes the AI techniques for the power system stability assessment.

2.4. Faults Detection

Fazai et al. [123][63] used an ELM-based method for the fault location detection of the system after extracting features by using wavelet transform (WT) and compared it with SVR and ANN models. Miraftabzadeh et al. [124][64] presented a GPR-based generalized likelihood ratio test to enhance FD performance in photovoltaic (PV) systems. In Ashrafuzzaman et al. [125][65], two ensembles are used to detect stealthy false data injection with a supervised classifier and an unsupervised classifier. Niu et al. [126][66] built an ensemble framework that combined five ML algorithms for power grid frequency disturbances analysis. The model can detect faults with three levels of degree of severity. Sirojan et al. [127][67] focused on high-impedance FD (HIFD) in power systems and proposed an ANN-based method for solving the problem with high accuracy (98.67%). ELM is also used for HIFD and is normally based on wavelet packet transform [128][68]. Sirojan et al. [129][69] proposes a method for line trip fault prediction in power systems that use LSTM networks and SVM. In Haq et al. [130][70], the ML-based discrete wavelet transform and double channel extreme learning machine method are proposed to locate and classify the faults in transmission lines. To improve the accuracy of line trip fault prediction, Wang et al. [131][71] proposed a stacked sparse autoencoder-based network with SVM and PCA to demonstrate its application to real-world data.
Table 2.
Summary of approaches for the power system stability assessment.
Author (Ref.) Year Objective Techniques
Mahdi et al. [103][42] 2017 TSA ANN
Tang et al. [106][45] 2017 TSA ELM, TF
Tan et al. [108][47] 2017 TSA CNN, SAEs
Liu et al. [109][48] 2017 TSA Ensemble, NN, ELM
Ashraf et al. [115][55] 2017 VSA ANN
Amroune et al. [118][58] 2017 VSA SVR, FL
Baltas et al. [99][38] 2018 TSA Decision tree, SVM, ANN
Mosavi et al. [105][44] 2018 TSA ANN
Yu et al. [107][46] 2018 TSA RNN, LSTM
Amroune et al. [119][59] 2018 VSA SVR
Mohammadi et al. [116][56] 2018 VSA SVM
Hu et al. [104][43] 2019 TSA SVM
Wang et al. [14][51] 2019 FSA ELM
Kamari et al. [114][54] 2019 OSA PSO
Amroune et al. [122][62] 2019
Hussain et al. [137][77]VSA Survey
2020 PV FD ANN Wang et al. [110][49] 2020 TSA DBN
Shi et al.
Niu et al. [126][66] 2021 FD Ensemble [111][50] 2020
Gunturi and Sarkar [139TSA ][79CNN
] 2021 Energy theft Shi et al. [111][50] 2020 OSA CNN
Ensemble Xiao et al. [113][53] 2020 OSA MRFR
Yang et al. [120][60] 2020 VSA Spectrum estimation method
Meng et al. [117][57] 2020 VSA Decision tree
Liu et al. [121][61] 2021 VSA Random Forest
With the development of microgrids, which present an effective power solution for the increased integration of renewable sources, FD for microgrids remains a challenge. Shafiullah et al. [132][72] used a hybrid approach that combines S-transform and feedforward neural networks for the distribution grid FD. Wang et al. [133][73] also evaluate ANN-based methods, and the results demonstrate the effectiveness of the model when detecting the time and location of faults. To handle labeled and unlabeled data, Shafiullah and Abido [134][74] propose a semisupervised ML model, which consists of a KNN model and a decision tree model, for FD on the transmission and distribution of microgrid systems. Jayamaha, Lidula, and Rajapakse [135][75] built an SVM-based algorithm to solve the problem of islanding and grid FD, and the results showed better performance than traditional methods based on the experiment of a PV plant. In 2017, Abdelgayed, Morsi, and Sidhu [

2.5. Smart Grid Security

With the integration of advanced computing and communication technologies, the smart grid integrates distributed and green energy with the power grid by adding a cyber layer to the power grid and providing two-way energy flow and data communication. However, this has exposed the smart grid to numerous security issues due to the complexity of smart grid systems and the inherent weakness of communication technology. The most probable outcomes of smart grid cyberattacks are operational failures, synchronization loss, power supply interruption, synchronization loss, power supply interruption, high financial damages, social welfare damages, data theft, cascading failures, and complete blackouts [140][80]. The attacks that are commonly used include false data injection attacks (FDIA) and distributed denial of service. The objective of FDIA is an attempt to mislead the system operators by altering the original data. Accurate and fast detection of the security issues or attacks is a prerequisite for stable grid systems operation. In recent years, many approaches have been proposed to improve the overall security of smart grid systems from the academic area and the industry domain. Several research papers were published that provided an overview of the prevailing problems related to security in smart grid systems from a different perspective [4,141,142,143,144,145][4][81][82][83][84][85]. This section summarizes the state-of-the-art AI technologies that are used to improve smart grid security.
ANNs and SVMs were used previously to detect FDIA. Zhou et al. [146][86] built a stacked denoising autoencoder (SDAE) neural network model to identify and classify four attacks in the smart grid with an accuracy as high as 96%. Cui et al. [147][87] used an intrusion detection model for smart grid intrusion detection, which is based on a whale optimization-trained ANN algorithm with one hidden layer. Kosek [148][88] also used a ANN-based model to discover malicious voltage control actions in the low-voltage distribution grid. Wu et al. [149][89] used an awareness mechanism that integrated fuzzy cluster, game theory, and RL algorithms to perform the security situational analysis for the smart grid. Ni et al. [150][90] used an RL method for attacks detection. Zhang et al. [151][91] demonstrated the superiority of a semisupervised framework based on domain-adversarial training to transfer the knowledge of known attack incidences to detect returning threats at different hours and load patterns. The SVM method was also used for the detection. Ahmed et al. [152][92] used an SVM-based algorithm to detect a new type of assault in the smart grid called covert cyber deception assault. Ahmed et al. [153][93] also used an isolation forest method to detect the assault with better performance in 2019. Ozay et al. [154][94] compared several ML-based methods for smart grid security. Li et al. [155][95] demonstrated a novel hybrid CNN-random forest model for automatic electricity theft detection, which significantly influences power supply quality and operating profits. Table 4 summarizes the AI techniques for smart grid security.
Table 4.
Summary of approaches for smart grid security.
Author (Ref.) Year Objective Techniques
Wu et al. [149][89] 2016 Intrusion detection FL, game theory, RL
Haghnegahdar et al.
[
147
]
[
87
]
2020
Attacks detection
ANN
Zhang et al. [151][91] 2020 Intrusion detection Domain-Adversarial Learning

3. Challenges of Artificial Intelligence in Smart Grids

Traditional power systems are very complex, and their analysis and control primarily depend on physical modeling and numerical calculations. With the development of smart grids with the high penetration of environmentally friendly renewable energy and microgrids, the transition of the traditional power grid to smart grid systems exposed more uncertainties and problems of the complex environment. Meanwhile, the current power system uses old infrastructure, which adds more uncertainties to the modern smart grid systems. Because the communication network builds on power systems, very large volumes of data with high variability must be handled; this is still a challenge of smart grids. Additionally, researchers are still working on the robustness, adaptiveness, and online processing of AI algorithms [156][97]. Although numerous data-driven methods have been proposed to deal with the problems of smart grids, there are still many severe challenges, including the following.
  • Integration of renewable energy. Highly integrated renewable energy is a key characteristic of smart grids. However, it presents several significant challenges due to the variability and unpredictability of renewable energy in which the power output can vary abruptly and frequently [157][98].
  • Preserving data security and privacy: Taking into account the employment of massive different devices and two-way communication on smart grid systems, it is more prone to cyberattacks because it is directly exposed to malicious users compared with the traditional power systems. The previous section showed that many novel security techniques were developed to offer fast identifications of cyber risks, false data injection, systems data theft, electricity theft, and so on. However, network protocols, operating systems, and physical equipment in the current smart grid are still exposing the system to a wide variety of attacks. The current AI solutions for smart grid cybersecurity also have trade-offs between security and performance.
  • Big data fast storage and analysis: Another significant challenge is how to continue improving the performance of storing and retrieving big smart grid data for AI applications robustly.
  • Explainability of AI algorithms: Generally, AI algorithms have the black box problem, and they are not interpretable or explainable. This is a barrier that AI algorithms currently face. Ibrahim, Dong, and Yang [158][99] provide a comprehensive discussion about this topic.
  • Limitations of AI algorithms: The development of AI technologies greatly influences the deployment of AI to smart grid systems. However, every method limitation should be considered before applying them to the smart grid.

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