Smart grids enable the collection of substantial amounts of multi-type and high-dimensional data on electric power grid operations by integrating advanced communication technologies, control technologies, and metering infrastructure. However, the conventional control, optimization, and modeling technologies have several limitations in effectively processing the data.
Artificial intelligence (AI) techniques can enable smart grids to integrate renewable energy sources, improve grid reliability, and optimize energy distribution by efficiently processing the power grid operation data. This article discusses the key benefits, applications, and challenges of AI in smart grids.
Benefits of AI in Smart Grids
More Accurate Forecasting: Extensive price variability due to alterations in consumption patterns is a major problem for the utility sector. Predictive analytics models can be employed to predict renewable energy generation and power loads more reliably and accurately compared to conventional approaches by combining data obtained from advanced metering infrastructure with AI
Sophisticated Alerts on Outages: Real-time data of smart meters and predictive capabilities of AI can notify operators of zonal, street, and individual outages in advance.
Optimized Power Yield: AI-powered sensor networks can be utilized in generation stages to optimize power output. Additionally, different AI tools can be employed to increase solar energy generation by predicting solar radiation.
Improved Automated Switching: AI tools can predict grid imbalances and differentiate between a full-on outage and a brief power interruption, which can allow automation of switching protocols and enable utility companies to isolate affected areas or reroute energy before the outage expands to other areas or severely damage to grid infrastructure.
Flexible Demand-side Management: Smart meters and AI can be used for monitoring, executing, planning, and scheduling changes in energy demand to ensure effective and flexible demand-side management, which can significantly reduce peak loads.
Improved Cybersecurity: The rising complexity and number of cyberattack strategies have created a significant risk to power grids. AI tools can reduce this risk by detecting malware, intrusion, and network attack features and providing network security protection for power systems.
Lower Costs and Fewer Outages: AI-driven smart grid management and smart metering allow consumers to obtain hourly power usage assessments and offer personalized suggestions to optimize their daily power consumption patterns to reduce usage during peak times.
Additionally, AI tools can decrease the number of power outages and mitigate their impact on both commercial and residential consumers, which increases consumer confidence in the grid, specifically during extreme temperatures and weather events.
Resilience and Agility: Renewable energy is typically intermittent, increasing the challenge of integrating renewable energy sources into the grid. AI-based automation and sensors can be utilized to identify the vulnerable zones of the grid and respond with automated rerouting, which involves storing surplus energy during the peak generation period and rerouting the stored energy during gaps in the flow.
Applications of AI in Smart Grids
Load Forecasting: Power load forecasting primarily involves the prediction of power demand in the grid and can be divided into mid-term load forecasting (MTLF), which predicts the load from hours to weeks, long-term load forecasting (LTLF) which predicts the load for years, and short-term load forecasting (STLF) which predicts the load from minutes to hours. Artificial neural networks are commonly used for power load forecasting.
Dynamic Bayes network (DBN), recurrent neural network (RNN), ensemble models, convolution neural network (CNN), factored conditional restricted Boltzmann machine (FCRBM), wavelet neural network (WNN), and ANN are the major AI techniques used for STLF.
Deep neural networks (DNN), DBN, support vector regression (SVR), long-short-term-memory (LSTM), CNN, ensemble methods, and ETS can be used for MTLF, while LSTM, ANN, fuzzy methods, RNN, support vector machine (SVM), k-nearest neighbors (KNN), Gaussian process regression (GPR), multivariate adaptive regression spline (MARS), generalized regression neural network (GRNN), gated recurrent unit (GRU), and linear regression (LR) can be employed for LTLF.
Power Grid Stability Assessment: The power grid stability assessment comprises voltage stability, small signal stability, frequency stability, and transient stability assessments, which are crucial to ensure the security and reliability of the power system.
Conventional stability assessment models are complex and require significant computing resources due to their reliance on accurate real-time dynamic power system models. However, several data-driven AI methods have been applied for stability analysis on the power grid owing to the development of the wide-area measurement system (WAMS) and phasor measurement units (PMU).
For instance, ANN, extreme learning machines (ELMs), CNN, stacked autoencoders (SAE), neural network (NN), decision tree (DT), SVM, RNN, LSTM, and DBN can be employed for transient stability assessment (TSA).
Random forest (RF), spectrum estimation method, DT, SVR, SVM, fuzzy logic (FL), and ANN can be utilized for voltage stability assessment (VSA), while ELM can be used for frequency stability assessment (FSA). Particle swarm optimization (PSO), CNN, and multivariate random forest regression (MRFR) can be employed for oscillatory stable assessment (OSA).
Fault Detection: ELM, SVM, and ensemble methods are commonly employed for fault detection. KNN, DT, ANN, probabilistic neural network (PNN), and GPR can be used for fault detection in microgrid and photovoltaic systems.
ANN/ELM, ELM, ANN, and SVM/AE/LSTM can be utilized for high-impedance fault detection, line fault detection, wind turbine fault detection, and line trip fault detection, respectively.
Smart Grid Security: Cyberattacks on smart grids can lead to complete blackouts, cascading failures, high financial damages, power supply interruption, synchronization loss, and operational failures. Several AI techniques can be used to enhance smart grid security.
For instance, FL, reinforcement learning (RL), and domain-adversarial learning can be used for intrusion detection, while ANN, RL, stacked denoising autoencoder (SDAE) NN, KNN, and SVM can be employed for effective attack detection. Moreover, CNN/RF, isolation forest/SVM, and ANN can be utilized for electricity theft detection, covert cyber deception assault detection, and malicious voltage control action detection, respectively.
Challenges of AI in Smart Grids
Preserving Data Privacy and Security: Physical equipment, operating systems, and network protocols used in the current smart grids expose the system to different types of cyberattacks and increase data privacy and security concerns. Although several AI tools for smart grids can improve cybersecurity, a number of them have trade-offs between performance and security.
Big Data Storage: The efficient storage and retrieval of big smart grid data for AI applications is another major challenge of using AI in smart grids.
Explainability and Limitation of AI Techniques: AI algorithms are typically not easily explainable or interpretable, which hinders their application in critical sectors such as smart grids. Additionally, the limitations of every AI method must be considered before applying them to the smart grid to ensure that the most suitable method is used for a specific application.
Insufficient Data Sample Accumulation: In smart grids, the application of mass data analysis is still in the early stages, and the data samples that meet the requirements of different AI application scenarios are insufficient. Thus, realizing AI applications depending on small samples is a significant challenge.
Reliability: Although AI techniques used in power systems have attained a high identification rate for faults and problems, they still cannot meet the levels of reliability required for practical applications.
Need for Improved Infrastructure: The effective use of AI depends on distributed communication collaboration, advanced computing power, and abundant data samples. However, the existing level of infrastructure resources, such as distributed collaboration platform and cloud computing, and supporting capacity are inadequate for efficient and extensive implementation of AI in smart grids and requires substantial improvements.
AI can be integrated with cloud computing to improve system robustness and security to realize a fully self-learning smart grid system. The unavailability of labeled data is a major challenge for smart grid analysis. Transfer learning can be used to reduce the training data requirements and address the issue of insufficient data.
Additionally, the evolution of the 5G network and the advent of fog computing have increased the importance of demand-side management for managing user participation in power systems. Effective AI techniques that can learn consumer behavior and power consumption patterns can be identified to improve the demand response tasks.
References and Further Reading
Omitaomu, O. A., Niu, H. (2021). Artificial Intelligence Techniques in Smart Grid: A Survey. Smart Cities, 4(2), 548-568. https://doi.org/10.3390/smartcities4020029
Jiao, J. (2020). Application and prospect of artificial intelligence in smart grid. IOP Conference Series: Earth and Environmental Science. https://doi.org/10.1088/1755-1315/510/2/022012
The smart grid: How AI is powering today’s energy technologies [Online] Available at https://www.sap.com/india/insights/smart-grid-ai-in-energy-technologies.html (Accessed on 19 November 2023)