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The Role of Artificial Intelligence in the Renewable Energy Industry

by Marquette Turner Luxury Homes

in Features, Lifestyle, Technology, Variety

Artificial intelligence (AI) programs compute functions that can mimic the human brain—sounds like something out of a sci-fi movie, right? They can observe the environment, process data, make decisions, evaluate results and learn from the results. There are some concerns about AI that revolve around whether these applications will usurp job positions. In reality, AI is merely a tool.

AI processes vast amounts of data and performs tasks more efficiently than humans. AI is used in fields like the financial industry to reduce administrative work and make complex decisions. The same power and efficiency can aid the renewable energy sector.

An Aging Energy Grid

In the 1920s, utility entities decided to collaborate, sharing peak load coverage and backup power. Electric functions were regulated as public services through the Public Utility Holding Company Act in 1935. The energy market diversified after the Energy Policy Act of 1992. 

This legislation forced electric generation companies to share network access with other power generators to encourage market competition. This action ended the era of vertical monopolies on power generation, transmission, and distribution. The advancement of alternative energy production and innovative technologies that avoided greenhouse emissions was accelerated in 2005 through the Energy Policy Act, providing incentives and loan guarantees to institutions.

America generates most of its energy using coal, natural gas, nuclear products, and hydroelectric infrastructure. The energy grid comprises power plants, transmission lines, distribution lines, transformers, and substations. According to some estimates, most transmission lines and transformers are 28 years old. Solar and wind plants are on average 10 years old, while fossil fuel plants are decades older.

In the 2017 Infrastructure Report Card, The American Society of Civil Engineers (ASCE) stated most of the nation’s electric transmission and distribution lines have a 50-year life expectancy, which is sobering considering the majority were constructed in the 1950s and ‘60s. Additionally, 640,000 miles of high-voltage transmission lines in 48 states are at full capacity.

The House Subcommittee on Energy derived similar results on the state of national energy infrastructure. The council concluded the grid is antiquated and needs to be more reliable, dynamic, and integrated.

Problems With Renewable Energy

Right now, there is a concerted effort to add renewable energy sources to the power grid for a variety of reasons, like reducing greenhouse emissions using solutions such as clean electricity providers in Texas and other states. The collaboration between the government and private sector solves several network challenges, including integrating renewable energy sources (wind, solar, wave, and others) with the existing energy grid.

However, more eco-friendly, renewable energy can be unpredictable. Forecasting wind or sun strength and power generation output is challenging. Hence, renewable energy sourcing is more prevalent in states like Texas, where seasonal weather changes are minimal and ideal conditions are more frequent.

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How AI Helps

To replace the entire energy grid in one go is unfeasible. A better plan of action is to leverage existing utilities and expand on current infrastructure. For example, gas power stations can be established on outdated coal plants that use similar infrastructure to generate and transmit energy. 

As energy production moves toward a more distributed system (with new solar and wind fields or energy generated onsite), traditional infrastructure will need to link to new energy sources. AI and machine learning can help with these problems when running a hybrid energy grid that combines traditional and renewable energy sources.

AI and machine learning are adept at analyzing the past, optimizing the present, and predicting the future. Power stations, transmission lines, millions of households, and businesses generate data that AI utilities can collect and analyze. These systems can help manage energy flow, forecast demand, source high-quality energy, and efficiently trade power.

Using AI, the energy grid features smart control centers that better derive insights on operations and give operators flexibility in managing supply and demand. These control centers can link to advanced sensors that monitor the weather to calculate predictions.

Adding small renewable producers to the grid can be beneficial, but it can add complexities to balancing energy flow. An AI-powered integrated microgrid can solve quality and congestion issues.

AI predictive analysis linked to sensors can improve renewable energy machines’ safety and reliability. It can monitor the equipment’s overall health and alert workers when maintenance is needed or identify wear and tear within a wind turbine.

The Grid Can be Better With AI

Power systems must finely balance supply and demand across timescales to efficiently operate and provide consistent service. Every watt of electric power providing light to homes or electricity to offices is generated instantaneously and often in different locations. As grids feature virtually no electricity storage, AI is the answer to a more efficient network. It will help manage both traditional and new forms of energy as America’s renewable market moves forward.

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