ARKANSAS, Nov 05 (Future Headlines)- As global energy systems face increasing complexity and the imperative of decarbonization, the role of artificial intelligence (AI) is emerging as a transformative force. AI applications are rapidly progressing, with the computational power required to develop advanced machine learning models doubling every five to six months since 2010. This technological advancement is coinciding with a shifting energy landscape, where the traditional flow of energy from centralized power stations to consumers is evolving into a multi-directional, data-intensive system. From managing renewable energy unpredictability to enabling predictive maintenance and grid reliability, AI is poised to drive efficiency and innovation in the energy sector.

  • The Changing Energy Landscape and the Need for AI

The modern power system has evolved from one that directs energy from central power stations to one that supports multi-directional flows of electricity. This shift is driven by the rise of distributed generators, including solar installations, wind turbines, and electric vehicle (EV) charging stations. However, the increasing number of grid-connected devices makes electricity flows less predictable. Furthermore, energy systems are now intertwined with transportation, industry, and buildings, creating an intricate web of connections. As a result, the need for extensive information exchange and sophisticated tools to plan and operate power systems efficiently is growing.

Simultaneously, the capabilities of AI applications are advancing at an unprecedented rate. AI can analyze data and inputs, generating outputs faster and at a greater volume than a human operator. It can also mimic aspects of human intelligence. These advancements have made AI a crucial resource in the energy sector, capable of addressing numerous challenges and opportunities.

  • AI for Predicting Supply and Demand

One of the primary applications of AI in the energy sector is improving predictions of supply and demand. This is especially critical for power systems that rely on renewable energy sources, as their availability fluctuates. AI, particularly machine learning, plays a significant role in matching variable supply with fluctuating demand, thereby maximizing the financial value of renewable energy. For instance, machine learning models can forecast wind power output by using weather models and turbine locations. Google and its subsidiary, DeepMind, developed a neural network that predicts wind power output with exceptional accuracy, enabling the sale of power in advance, resulting in a 20% increase in financial value.

AI-powered forecasting also allows for better synchronization of peak energy consumption with peak energy production. This has the potential to reduce the need for buying additional power from the market, contributing to load shifting and peak shaving. Companies like Swiss manufacturer ABB and Oracle Utilities use AI to develop energy demand forecasting applications, helping commercial buildings avoid peak charges and benefit from time-of-use tariffs.

  • AI for Predictive Maintenance

Predictive maintenance is another key application of AI in the energy sector. It involves continuous monitoring and analysis of energy assets’ performance to identify potential faults before they cause outages. Traditional maintenance schedules, which are often based on predefined periods, can lead to inefficiencies if they occur too early or too late.

Utilities and energy companies are leveraging AI to monitor physical assets and use historical performance and outage data to predict when maintenance is required. E.ON, a utility company, uses machine learning algorithms to predict when medium voltage cables need replacement, potentially reducing grid outages by up to 30%. Italy-based utility Enel has installed sensors on power lines to monitor vibrations and uses machine learning to identify potential issues, leading to a 15% reduction in power outages. Estonia’s Hepta Airborne employs machine learning to identify defects in transmission lines using drone footage. State Grid Corporation of China relies on AI to analyze data from smart meters and identify equipment problems.

  • AI for Grid Management and Control

AI’s role extends to managing and controlling power grids by collecting data from various sources, such as sensors and smart meters, to observe and manage the flow of power within the network. This is particularly critical at the distribution level, where multi-directional flows are common.

  • AI for Demand Response

Demand response is facilitated by AI through processes like forecasting electricity prices, scheduling and controlling response loads, and setting dynamic pricing. Predictive algorithms enable utilities to adjust energy consumption based on real-time pricing, potentially reducing peak demand and mitigating grid stress.

  • AI for Consumer Services

AI and machine learning applications are improving consumer services in the energy sector. Companies like Octopus Energy and Oracle Utilities are implementing AI and machine learning processes in apps and online chatbots to enhance customer billing experiences and provide more personalized services.

  • Challenges and Considerations

While AI offers significant advantages for the energy sector, challenges and considerations must be addressed:

Cybersecurity and Privacy: AI systems may introduce cybersecurity vulnerabilities, and privacy concerns must be addressed, especially when handling vast amounts of consumer data.

Bias and Errors: Biases or errors in data can influence AI outcomes, leading to unintended consequences.

Training and Skills: A shortage of AI and machine learning specialists poses a challenge, requiring recruitment, upskilling, and reskilling of the workforce.

Energy Consumption: AI uses more energy than traditional computing, and its energy consumption should be managed to avoid offsetting efficiency gains.

Accountability: Decision-making based on AI models can raise questions of accountability, necessitating frameworks for responsible AI deployment.

  • Global Frameworks and Collaboration

Addressing these challenges and realizing the full potential of AI in the energy sector requires the development of national, regional, and international frameworks. The OECD AI Principles and initiatives, like the European Union’s AI Act, aim to provide guidance and regulation to ensure a human-centric and environmentally responsible approach to AI.

As the energy sector underpins the global economy and plays a critical role in achieving climate goals, governments and stakeholders must work together to develop mechanisms for data sharing and governance. A coordinated global approach can facilitate knowledge transfer, expedite the energy transition, and reduce its costs while ensuring the responsible and sustainable use of AI. In a rapidly evolving energy landscape, AI is poised to be a powerful ally in building efficient, decarbonized, and resilient power systems.

Editing by Sarah White