How AI Adoption in the Energy Sector is Driving Carbon Emission Reductions in Power Generation?

Highlights:

• Introduction
• Improving the efficiency of smart grids with AI
• Role of AI in energy trading

Since the 1992 Earth Summit in Rio, overall awareness regarding environmental sustainability has increased manifold. In the following years, several multinational agreements have been signed which aim to reduce the pace of global warming and climate change. In 2015, for instance, the Paris Climate Agreement was negotiated between more than 190 countries making it one of the most comprehensively accepted environment deals. As per the agreement, countries are required to submit their intended nationally determined contributions (INDCs) in which these sovereign states announce their targets to reduce pollution and decarbonize their economy.

Almost all countries that are part of the Paris Climate Agreement have committed to reducing the carbon footprint of their energy sector to achieve the goal of net zero emissions in the near future. As a result, governments in these nations have started promoting the use of advanced technologies to reduce power consumption. Many public sector enterprises and private power generation companies have even partnered with technology giants to integrate innovations into their operations and bring down the energy demands of various end-use industries.

Artificial intelligence contributing to the development of smart grids

In the last few years, the investments by major technology companies in advanced technologies such as AI have been increasing exponentially. Almost all sectors have started using AI-powered tools to maximize their productivity and improve their profit margin. The energy and power sector, too, has witnessed a rise in the adoption of AI-powered tools to improve the capabilities of different systems. For instance, many leading power businesses are deploying AI-based solutions and machine learning algorithms to enhance the efficiency and output of smart grids.

Smart grids essentially act as networks that facilitate a two-way flow of energy and digital communication signals between the transmitter and receiver. Compared to traditional grids, these networks help companies to streamline their power generation and distribution capabilities. Also, smart grids minimize the negative impact of external factors such as weather, natural disasters, etc., and reduce the chances of power outages. The main aim behind the integration of AI in these systems is to improve their resilience and agility even further.

In rural areas and villages, the energy demand remains generally constant throughout the year as economic activities remain the same. However, in metropolitan cities and industrial towns, the power consumption pattern changes regularly. In such a scenario, AI tools, through demand-side management, help companies manage the power generation and distribution operations during peak energy usage. For instance, a survey done by the U.S. Federal Energy Regulatory Commission found that with appropriate use of smart technologies, peak loads can be reduced by more than 150GW, thus minimizing the environmental impact of energy generation. Along with this, machine learning algorithms aid in predictive maintenance by forecasting the problems that might arise in the future based on the usage trends in a particular region. This ultimately lowers the chances of grid failure, reduces downtime, and brings down the cost of repair.

The deployment of AI in energy trading creating new investment opportunities in the industry

To help countries achieve their INDCs and net zero goals, recently, several multinational organizations launched innovative schemes such as carbon offset programs and energy trading mechanisms. Energy trading refers to the purchasing and selling of different commodities such as crude oil, petroleum, coal, electricity, solar power, wind energy, etc. Over the years, with the increased transition toward renewables, the scope of energy trading has increased significantly. Companies are using AI tools to increase their profits by trading in the energy industry and optimizing their portfolio based on various parameters such as pricing structure, supply-demand dynamics, etc. Additionally, using predictive analytics methodologies, ML algorithms help traders forecast the price of a commodity based on the information collected from various sources, current industry scenarios, and historical trends in that energy segment.

In December 2023, Powerledger, a technology startup, announced the launch of an AI-powered Hybrid Weighted Energy Forecasting Algorithm (HWEFA). Based on blockchain’s decentralized ledger system, the platform has been designed specifically for peer-to-peer lending and energy trading. Similarly, in November 2024, Hitachi Energy, a Swiss-based technology company, unveiled an AI-based energy forecasting solution, viz., Nostradamus AI. Designed on a cloud-based architecture, the platform provides configurable predictive analytics services to the user for holistically managing their portfolio. Moreover, the ML algorithms embedded in the solution aid the user in predicting future asset prices, thereby helping the trader make informed decisions. Such product launches are expected to expand the footprint of the AI in energy market significantly in the coming period.

Winding up, the growing electricity demand across the globe and the rising need to decarbonize the power generation sector have led to the integration of AI in energy production and distribution processes. In the last few years, AI tools have assisted companies in improving the efficiency of smart grids and helped companies streamline their energy trading portfolios. Additionally, the launch of innovative platforms by leading technology companies is anticipated to create numerous opportunities in the industry.

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