The Role of Artificial Intelligence in Modern Smart Grids
Unravelling the intricate dance of smart grids and the pivotal role of artificial intelligence in orchestrating data and performance
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The Role of Artificial Intelligence in Modern Smart Grids
Unravelling the intricate dance of smart grids and the pivotal role of artificial intelligence in orchestrating data and performance
Published:
Loading reading time...
The Role of Artificial Intelligence in Modern Smart Grids
Unravelling the intricate dance of smart grids and the pivotal role of artificial intelligence in orchestrating data and performance
Published:
Last updated:
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Artificial intelligence and modern smart grids

Artificial intelligence (AI) in smart grids plays a critical role in the continuing evolution of our society and energy sector.

The transformation of the electric grid into a smart system has led to notable advancements and increased intricacy in the relationship between its components.

  • The energy path is no longer the traditional path from source to consumer
  • The information path is no longer the simple path of consumption and generation to control
  • Control signals are no longer those signals issued by generation, transmission, and distribution stations with their known and specific functions.

The infrastructure of energy generation sources, transmission methods, and distribution networks has changed. We now have an energy sector with intelligent components that provide the control system with information and receive control signals around the clock, even by the second.

Advances in digital technology and access to information have made the processing of data with traditional control models unfeasible, and the use of artificial intelligence systems has become a necessity.

In this article, we discuss the aspects of benefiting from artificial intelligence in various operations on the smart grid environment.

What is artificial intelligence?

Artificial intelligence (AI) is a branch of computer science that aims to create machines capable of performing tasks that typically require human intelligence. These tasks enhance problem-solving, pattern recognition, and the decision making process.

Two subsets of particular relevance to smart grids are

  • Machine learning (ML) applies mathematical models to large datasets to find trends and patterns while mapping inputs and outputs. 
  • Deep learning (DL) is a subset of machine learning that utilises neural networks with many layers (hence “deep”) to analyse various forms of data.

ML and DL in smart electrical grids enable the forecasting of future energy demand patterns and the seamless integration of renewable energy systems.

By analysing vast amounts of data from sensors, these technologies help grid operators and the energy industry make informed decisions. AI is used in rapid anomaly detection, rectification, and dynamic pricing based on real-time conditions.

They also assist with energy management by predicting maintenance needs and operational costs while efficiently balancing energy loads.

Why do we need algorithms to control smart grids?

In smart grids, AI-based systems can process large volumes of data from various sources such as distributed generators, transmission media, and electricity demand-side information. The objective is to provide precise control actions with high accuracy and security measures.

Diagram showing the flow of information in a traditional grid
Traditional grid
Diagram showing the flow of information in an AI smart grid
AI Smart Grid Management

We will examine the role of advanced technologies in smart grids from various component perspectives.

  1. Supply-side
  2. Transmission media
  3. Demand side
  4. Energy market
  5. Security aspects

Supply-side

In smart grid technologies, electricity is generated from distributed generators using renewable energy sources like solar panels, wind, and biomass but also non-renewable sources like fuel thermal stations.

Renewable energy generation needs special control due to the intermittent nature of resources like the sun and wind. Both real-time data and historical data on the generator location are needed to predict power output.

This data can be used for electricity generation forecasting in the short-term, mid-term, and long-term. This predicted data can help in planning the generation to match current consumption and to plan for future grid expansion.

AI in smart grid operation monitoring

The information collected in the smart grid from various sources can be used to monitor the performance of the network as a whole, compare the performance with previous periods, and then discover the presence of deficiencies in any part of the network’s operation.

For example, a defect can be detected in a particular solar energy system when comparing its performance with the performance of another nearby system.

Artificial intelligence algorithms can also analyse and differentiate between the causes of performance deficiencies in a solar system, such as panel cleanliness or the presence of a defective panel affecting the others.

This is achieved by training the artificial intelligence algorithm to perform fault diagnosis. The algorithm is able identify various faults by considering the overall circumstances of the malfunction, and then determine the cause of the new malfunctions.

Grid stability assessment

The AI also contributes to stability assessment and helps prevent power outages. It addresses various stability issues, including transient, frequency, small signal, and voltage instability.

Fault detection

ML algorithms can be used to monitor the smart grid and detect anomalies in the data flow from different meters.

This algorithm is able to differentiate between changes caused by network issues and changes caused by fluctuations in renewable energy production.

Energy transmission

Energy transmission in the smart grid environment is different from traditional grids. Smart grids depend on distributed generation, and the demand is located near the generation. New means of energy transmission, like hydrogen containers and energy storage systems, are used in smart grids.

To manage these distributed resources, AI algorithms are needed to find the optimum transmission means to route the energy for the consumer with the least loss and highest efficiency.

Demand side

Load forecasting

Load forecasting is critical for grid production planning in short-term planning to manage energy usage among different resources and long-term planning for future addition of power plants.

Load shifting

Demand-side management includes IoT to control consumer appliances for load shifting without interference with their own comfort. This load shifting is based on data from energy resources and historical data from the demand side.

Not all appliances can be shifted, only some of them, like the refrigerator and the air conditioner, with small shifts that do not affect the overall performance of the device.

AI-based mobile apps can be used to encourage consumers to shift their electric power consumption to off-peak times with lower electricity bills at off-peak times.

Consumers can operate their washing machines or charge electric vehicles during those low-price periods. This requires a dynamic energy price for smart grid control.

Implementing AI to enhance consumer engagement will lead to more effective load balancing measures, reduced energy waste and cost savings for both utilities and consumers.

Energy market

AI for energy tariffs

The energy price in a smart utility grid is affected by several factors, like solar and wind availability, fuel prices, demand levels, and others. These contradictory factors make it difficult to calculate the energy price using traditional algorithms.

AI and especially ML can use supply and load forecasting along with other historical and real-time data to calculate a dynamic price that leads to better energy supply and demand matching.

Managing the new electricity trading markets

The concept of peer-to-peer energy trading emerged as a result of the shift in energy production and consumption, allowing local energy providers and consumers to trade electricity.

To control such a distributed market, AI can handle the energy and information flow between the two sides of the energy trade.

Artifical intelligence for smart grid security

Data security

This much information flow and the importance of energy at all levels need a strong security structure. AI can enhance smart grid security and provide fast thread detection.

The operation of smart power grids includes physical control of customer devices and billing systems; these types of data and control are very sensitive to hacking and unauthorized access.

For all these aspects, smart grid systems need to adopt a very high level of security to support the data and control signals flowing through the grid.

Blockchain and AI for energy source security

New energy markets make it possible for consumers to buy green energy from a certain electricity supplier. Applying blockchain security aspects along with AI algorithms can assure the consumer that this energy is definitely coming from a trusted energy supplier.

Blockchain and AI can also be applied to ensure the source of hydrogen and to make sure if it is green hydrogen or not.

The integration of blockchain technology has the potential to improve transparency and trust by establishing decentralised energy markets with secure and immutable records of transactions and energy sources.

Bottom line

Smart grids, driven by the evolution of our energy sector, have birthed complex interconnections between their components. With such complexity, traditional control models have become obsolete.

Artificial intelligence, encompassing machine learning and deep learning, has emerged as the linchpin in ensuring optimal energy management, real-time anomaly rectification, and dynamic pricing, all while bolstering grid security and fostering transparent energy markets.

As we navigate this electrifying transformation, AI stands as the maestro, orchestrating a harmonious symphony of operations within the smart grid ecosystem.

Frequently Asked Questions

AI can optimise the use and control of energy storage systems, such as batteries or thermal storage, by predicting the best times to store or release energy, ensuring grid stability and maximising economic benefits.

AI-driven applications can provide consumers with insights into their energy consumption patterns, offer suggestions for efficiency improvements, and give real-time feedback on how behaviour changes affect their energy bills.

While AI may automate certain tasks, leading to a shift in job roles, it’s also poised to create new opportunities in areas like data analysis, system monitoring, and AI-driven solution development for the energy sector.

Some challenges include ensuring data privacy and security, managing the high initial investment costs for AI infrastructure, and the need for continuous training and adaptation of AI models to keep up with the evolving energy landscape.

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Author

Dr Samah Hashim
Dr Samah Hashim is a solar energy teacher and researcher. She shares her knowledge by writing articles and producing YouTube videos.

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