Digitalization is increasingly becoming crucial for innovation in the energy sector. Emerging digital technologies, such as the Internet of Things (IoTs), blockchain, big data, and artificial intelligence (AI), are creating new and massive opportunities for energy companies to accelerate growth, optimize performance, drive innovation, and improve efficiency. Specifically, AI plays a crucial role in enabling the digital transformation of the energy sector towards distributed energy integration, intelligent energy management, and smart grids. This article discusses the importance and applications of AI in the energy sector.
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Importance of AI in the Energy Sector
AI is increasingly gaining attention in the energy sector as an effective tool to optimize and manage future electricity systems. Moreover, the application of AI in energy can significantly reduce global emissions while increasing gross domestic product (GDP).
AI can manage the energy flows between microgrids, storage batteries, renewable energy sources, businesses, homes, and the power grid to optimize the energy grid, which reduces energy wastage. Renewable energy sources, such as solar and wind, are intermittent in nature, which can create issues for the energy grid as the generated energy must be managed in real-time. AI and machine learning (ML) can predict the availability of renewable energy, allowing energy companies to manage power grids efficiently.
AI can play a critical role in increasing the efficiency of energy trading by accurately predicting energy demand and providing real-time information to traders about energy prices. Thus, real-time information is extremely beneficial for traders as energy must be delivered immediately during energy trading. Additionally, the information enables traders to make more informed decisions during energy selling/buying.
AI can be integrated with smart energy storage systems to increase energy management efficiency. Energy storage is increasingly being used to develop virtual power plants, which allow energy firms to supply energy when electricity generation is insufficient.
Predictive analytics can be employed to predict the changes in future energy demand. The information obtained using predictive analytics can be utilized for future planning and building the infrastructure necessary to meet future energy demand.
Energy companies can also use predictive analytics to predict machine/equipment failures to prevent unexpected outages. Companies can reduce expenses by planning the replacement of expensive and critical energy assets and avoiding unplanned maintenance operations.
Energy companies can use AI and ML for customer engagement. Customers can be provided with information about the changes they can implement to reduce overall energy consumption in their existing energy usage habits. AI provides such information by understanding customer energy consumption using data analytics.
Applications of AI Techniques in Energy
Power Theft and Cybersecurity: Fraud and theft of electricity lead to substantial financial losses yearly for energy utilities worldwide. AI and ML can automatically detect anomalies in the grid due to power theft, enabling energy companies to secure their assets, prevent financial losses, and reduce energy wastage.
Modern electrical power systems have several crucial cybersecurity requirements as these systems consist of an information/cyber layer, along with a physical layer, which makes them extremely vulnerable to cyberattacks.
AI can improve energy grid security by preventing such cyberattacks. For instance, data analytics can be used to identify energy data patterns that are indicative of cyberattacks, and AI and ML can be used to respond to such attacks.
K-means clustering, random forest (RF) and weighted voting method, RF and extreme gradient boosting (XGBoost) can be used for cyber-physical anomaly detection, cyberattack and power grid disturbance detection, and energy Internet of Things (eIoTs) cybersecurity, respectively.
Similarly, ensemble ML, fuzzy-based method of analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS), and support vector machine (SVM) can be utilized for energy theft detection, energy system cybersecurity, and connected electric vehicle cybersecurity, respectively.
Power Loss Reduction and Energy Savings: Reducing power loss and optimizing energy savings are the major challenges in energy systems that must be addressed to realize ecological and economic benefits. AI can be used to successfully realize these objectives.
For instance, bat algorithm (BA), simulated expression algorithm, decision trees (DTs), long short-term memory network (LSTM), and RF can be used for reducing power losses in electricity distribution, energy path planning, smart homes, energy efficiency in fifth-generation (5G) networks, and responding to demand in energy grids, respectively.
Smart Grid Management: AI can be employed to address several engineering problems in smart grid management, such as energy management in real-time, controlling transformers and the energy grid, implementing intelligent agents, and ensuring grid stability.
For instance, reinforcement learning (RL) and deep Q networks (DQN), DTs, and deep deterministic policy gradient (DDPG)/LSTM/safety guided networks can be employed for power flow management, ensuring smart grid stability, and multi-energy systems management, respectively. Markov decision process (MDP) and fuzzy Q-learning can be used to address the decision problems in peer-to-peer energy trading.
Electricity Load Forecasting: Electricity load forecasting ensures power grid stability. AI can play a key role in overcoming several challenges in building load forecasting, power generation resource optimization, microgrid load estimation, and regional energy load planning.
For instance, self-organizing map (SOM), adaptive neuro-fuzzy inference system (ANFIS), deep-learning regression (DLR), and multi-variable LSTM (MV-LSTM) can be utilized for load estimation for microgrid planning, regional electric load forecasting, short-term load forecasting in smart grids, and building load forecasting, respectively.
Energy System Fault Diagnosis: Fault diagnostics are crucial for ensuring the stability, efficiency, and reliability of energy systems, as these complex cyber-physical architectures are extremely vulnerable to faults. AI can be utilized for fault diagnostics in photovoltaic power plants, wind power plants, hydropower plants, thermal power plants, electric power lines, and power transformers.
For instance, genetic algorithm (GA) and SVM/modified evolutionary particle swarm optimization algorithm with a time-varying acceleration coefficient (MEPSO-TVAC) and artificial neural networks (ANNs) can be used for transformer fault diagnosis/to detect power transformer failures.
Similarly, a feed-forward neural network and backpropagation algorithm, a Bayesian network and the moving window principal component analysis method (MWPCA), hidden Markov model (HMM), and regression and classification based on XGBoost can be employed for fault detection and classification in three-phase power line system, fault diagnosis of the Kaplan propeller turbine in hydroelectric generator systems used in hydropower plants, intelligent fault diagnosis in wind energy systems, and fault diagnosis system architecture for photovoltaic systems, respectively.
Challenges in Implementing AI
Although the integration of AI will drive transformation in the energy sector and lead to techno-economic benefits, it will also result in several multidimensional challenges. These include security and privacy challenges, technical challenges, ethical and social challenges, and cost and performance challenges.
Data security and privacy, compliance, and cybersecurity are the major security and privacy challenges. Technical challenges include data quality and availability issues, integration with existing systems, scalability, interoperability, robustness, and adaptability.
Explainability, transparency, ethical concerns, and human-AI collaboration are key ethical and social challenges, while cost, reliability, regulation, real-time decision-making, energy system management flexibility, and performance evaluation are the primary cost and performance challenges.
Recent Developments
Autogrid is using ML to process data from millions of energy assets and predict demand patterns to assist utilities in the United States (US) to automate distributed energy management. Similarly, National Grid Electricity System Operator focuses on using AI and data science to improve grid management.
IBM collaborated with Western Power to utilize cognitive analysis and ML to identify low-carbon technologies to support network investment and planning strategies. Open Climate Fix is studying the feasibility of using ML to improve solar photovoltaic forecasting.
Centrica is employing AI to manage fluctuating power supply and is investing in AI and automated trading systems to optimize grid balancing and market price forecasting.
The Kraken Technology developed by Octopus Energy uses ML and advanced data to support millions of customer accounts. The technology has been used to roll out the Agile Tariff solution, which indicates the effective functioning of flexible price signals.
References and Further Reading
Lyu, W., Liu, J. (2021). Artificial Intelligence and emerging digital technologies in the energy sector. Applied Energy, 303, 117615. https://doi.org/10.1016/j.apenergy.2021.117615
Szczepaniuk, H.; Szczepaniuk, E.K. (2023) Applications of Artificial Intelligence Algorithms in the Energy Sector. Energies, 16, 347. https://doi.org/10.3390/en16010347
Horner, M. (2022). Top 10 applications of AI and Robotics in Energy Sector. [Online] Available at https://energydigital.com/top10/top-10-applications-of-AI-and-Robotics-in-Energy-Sector (Accessed on 04 July 2023)
Danish, M. S. (2023). AI in Energy: Overcoming Unforeseen Obstacles. AI, 4(2), 406-425. https://doi.org/10.3390/ai4020022
AI for energy [Online] Available at https://pixl8-cloud-techuk.s3.eu-west-2.amazonaws.com/prod/public/fabcade6-c02b-4e45-a6ad63d8e01b05b7/AI-for-Energy.pdf (Accessed on 04 July 2023)