AI Enhances Power Grid Efficiency and Reliability

By Soham NandiReviewed by Susha Cheriyedath, M.Sc.Jun 17 2024

In an article published in the journal Energies, researchers systematically reviewed the use of artificial intelligence (AI) in electric power systems, highlighting advancements due to improved microprocessors and data storage technologies. They categorized AI applications into four areas, discussed data acquisition for AI/machine learning (ML) algorithms, and presented detailed examples of AI applications in wind power forecasting, smart grid security assessment, and automatic fault detection.

Background

Electric power system providers have historically been cautious about adopting new technologies due to the high demands for safety and reliability. However, the advent of advanced computer hardware and data processing technologies has opened new opportunities for implementing AI in power systems.

The discussion around AI's potential in this sector began in the 1970s, with significant milestones like the establishment of the Conseil International des Grands Réseaux Electriques' (CIGRE) Expert System working group in 1986 marking early collaborative efforts. Modern AI applications benefit from enhanced computational power and Big Data technologies, addressing the complexities of decentralized power systems with high renewable energy integration.

ML, a subset of AI, has seen widespread use in various sectors, including energy, where it improves grid flexibility, transmission efficiency, and distribution network control. Techniques like neural networks and fuzzy logic help in recognizing patterns and making predictions regarding energy consumption and grid stability.

Despite these advancements, traditional modeling techniques still fall short in handling the vast and complex data required for modern power grid operations. This paper addressed these gaps by providing a systematic overview of AI applications in the power sector. It categorized AI application areas, discussed data acquisition methods for AI/ML algorithms, and presented detailed case studies on wind power forecasting, smart grid security, and automatic fault detection. Through this comprehensive approach, the paper aimed to enhance the understanding and implementation of AI in the energy sector, promoting more efficient, reliable, and intelligent power system management.

Application of Potential

AI offers significant advantages for electric power systems by processing large data in real-time, identifying complex patterns, and optimizing energy flows. AI applications in this field could be categorized into four main areas.

• Power Grid Operation and Sector Coupling: AI helped manage the complexity of modern power grids, particularly with the integration of renewable energy and electric vehicles. It optimized maintenance schedules and coordinated the charging of electric vehicles to minimize costs and disruptions.
• Distribution Grid Operations: AI-enhanced the efficiency and reliability of electrical distribution by enabling predictive maintenance, real-time fault detection, renewable energy integration, cybersecurity, and load forecasting. It also supported remote monitoring and control of the grid.
• Energy Market Trading: AI improved the forecasting of weather and energy prices, enhancing grid stability and security. Advanced AI algorithms facilitated automated trading, increasing the efficiency and accuracy of energy market transactions.
• Energy Consumer Side: AI aided in demand-side management (DSM) and demand-side response (DSR), helping consumers adjust their energy use based on market conditions. Smart home technologies and smart meters enabled more efficient energy consumption and support grid stability.

AI applications were further classified using a matrix technique into three clusters: general decision-making, maintenance and safety, and sales and consumption services. These classifications helped optimize various aspects of the energy industry, from generation to consumption, enhancing overall system efficiency and reliability.

Overview of Methods

AI aims to create machines with human-like intelligence to solve complex tasks. ML, a key subset of AI, uses algorithms to learn from data and identify patterns without explicit programming. Deep learning, within ML, employs multi-layered neural networks to recognize intricate patterns in large datasets.
ML methods are categorized into four types.

• Unsupervised Learning: Algorithms independently identify patterns and structures in input data, such as clustering and dimension reduction.
• Supervised Learning: Algorithms learn from labeled input/output data to minimize output errors, used in classification and regression.
• Reinforcement Learning: Agents learn optimal actions through rewards or punishments, applied in robotics and game strategies.
• Ensemble Methods: Combines multiple models to improve predictions and robustness, leveraging their collective strengths.

This review offered an introduction to ML, highlighting its various techniques and applications, and emphasizing its pivotal role in technological advancements and innovative solutions.

Application Examples and New Approaches

The use of AI and ML significantly advanced wind generation forecasting, smart grid security, grid stability, and fault detection. In wind generation forecasting, AI methods such as deep learning neural networks, enhanced by hybrid optimization algorithms like hybrid particle swarm optimization (PSO) and bat algorithm (BA) (HPSOBA) and modified hybrid PSO and BA with parameter-inspired acceleration coefficients (MHPSO-BAAC), improved the accuracy of wind speed and power predictions. This enhanced grid planning and reduced operational costs.

For smart grid security, an intrusion detection model based on artificial neural networks (ANN) trained with the whale optimization algorithm (WOA) could classify cyberattacks, improving fault detection and system protection. Grid stability was addressed through reinforcement learning, using multi-agent systems to optimize power balancing and reduce peak energy consumption. This method was effectively implemented in the DEZENT project, which managed energy distribution in a decentralized grid.

In fault detection, an ensemble-learning approach combined with the sine-cosine optimization algorithm (EL-SCOA) enhanced fault diagnosis in wind turbines. This method processed and optimized data to identify turbine faults, reducing downtime and maintenance costs.

Conclusion

In conclusion, this systematic review highlighted the transformative impact of AI in electric power systems, driven by advancements in microprocessors and data storage technologies. AI applications were categorized into four areas: power grid operation, distribution grid operations, energy market trading, and energy consumer side. Detailed examples included wind power forecasting, smart grid security, and automatic fault detection.

Despite challenges like data security, interpretability, and regulatory hurdles, AI promises significant benefits in optimizing energy production, enhancing grid reliability, and integrating renewable energies. Future developments will see AI further advancing predictive maintenance, cybersecurity, and autonomous grid operations, promoting a more efficient and sustainable energy sector.