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4 ways artificial intelligence AI can revitalize the energy industry

4 ways artificial intelligence AI can revitalize the energy industry

4 ways artificial intelligence AI  can revitalize the energy industry

The application of artificial intelligence AI in the energy industry is driving new innovations and helping to develop future smart grids. Kolabtree freelancer Christopher Frye details how artificial intelligence AI  can be used in power plants with four examples.

AI is a field with great potential in many fields, not only the value that AI creates, but also the value that can be gained by realizing previously untouched possibilities. Computer performance. With improved access to data storage, AI is now able to analyze datasets in a much more powerful and elegant way than ever before.

More simply, AI extends the scope of work traditionally done by humans, and in some cases is superior to humans. In this blog, I'd like to delve into the impact of this potential on the energy industry, especially how artificial intelligence AI  in power plants creates opportunities and value for power systems.

Fusion of AI and energy

Interestingly , last year's college student blog post, Microsoft founder Bill Gates, said that if we could go back in time and make an impact in new ways, we would consider three areas.

The way artificial intelligence AI  makes people's lives more productive and creative is still in its infancy. The second is energy. A clean, cheap and reliable energy supply is essential to combat poverty and climate change. The third is bioscience, where there are plenty of opportunities for people to lead longer and healthier lives.

Bioscience is a very noble discipline, but what makes this quote even more intriguing is the fact that AI and energy are merging in many ways. The rest of this article provides insights into how AI-related innovations are impacting the energy industry, including some case studies with concrete examples.

Grid basics

Here, we will explain the contents of the electric power business so that you can understand the relationship between AI and the electric power business. The electric power business consists of three functions: power generation, power transmission, and distribution. Power generation includes all sources of electricity, including fossil fuels and renewables, transmission includes high-voltage power lines to bring electricity from the sources to where they are needed, and distribution is lined with utility poles. is.

The entire system is called a "grid", but in recent years, "smarter" and "improved responsiveness" of this grid have become a big topic. There are many expressions for how to define a smart grid, but in essence, it is thought to be a combination of electrical systems and advanced innovations in information technology and communications. The smart grid is not static. It is a system like the one below. Continues to evolve. Is repeatedly tested and completed. The role of AI may be the brains behind the smart grid of the future, the control center behind millions of sensors, and the ability to synthesize and act on an overwhelming amount of data. .. Here are some case studies that have already achieved these things.

Artificial intelligence AI  in power plants. 

1. AI + Power Storage = Athena

Founded in San Francisco in 2009, Stem brings together the power of AI and energy storage to "optimize the timing of energy use."  with a combination of machine learning, predictive analytics and energy storage this system, called

 By analyzing 400 megabytes of data per minute, we continuously evaluate the time value of energy and optimally determine when to purchase energy. The process of aggregating across multiple points of storage capacity is described as a "virtual power plant." The spread of such distributed resources is being increasingly promoted by the increase in what are called distributed energy resources (DERs) on the power grid-mainly rooftop solar power has grown significantly over the last decade. .

The image below is a visual representation of this concept.

In this example, EES stands for Electrical Energy Storage, DG stands for Distributed Generation, and MV and LV stand for Medium Voltage and Low Voltage, respectively.

This virtual power plant aggregation process leverages AI to perform predictive analytics on various variables such as weather, energy consumption, and tariff options, automating the real-time calculation process and continuing. Will be done in. As a result, the amount of load reduction that can be spent with peace of mind even in the unprecedented heat wave (although it is no longer the unprecedented heat heat due to the influence of climate change) is totaled. This is more than 600 times in the wholesale market in California in 2017 when the Stem system was introduced .

2. AI that facilitates the management of renewable energy

The effects of climate change and the continued use of fossil fuels have fueled the growth of renewable energy, which now accounts for one-fifth of the world's electricity production.However, there are aspects of this growth that many are unaware of. As renewable energy grows, system operators face the challenge of integrating renewable energy into their existing power grids.

Intermittent renewable energy is a daunting task as it affects the routine operational planning of traditional power grids. Renewable power fluctuates across multiple timelines, so grid operators must adjust day-ahead, time-ahead, and real-time operations.

Solar power and wind power can also be affected when cloud conditions and wind directions are difficult to predict, so it takes 1 minute and 1 second to ensure a stable power grid supply. You will need to work with. This is another area where AI is active. Here are two such innovations. The first is related to PV resource management, and the second is to aggregate multiple data streams and combine weather forecasting with machine learning to optimize renewable energy operations.

3.VADER-Visualization of distributed energy networks

Vader here is not a dark lord, but a platform that combines data from a solar power generation system and a smart meter, and consumes power from distributed energy resources such as solar power generation installed on the roof or on the ground. And continuously scrutinize the data to model behavior. VADER distributes Analytics D , a distribution system with V visualization and deep penetration of A. E Nagy R resource (or DER).

The core engines of these innovations are machine learning and AI-based algorithms. This platform "models potential changes in connectivity and DER behavior in the grid to optimize real-time distribution planning and operational decisions for utilities. We aim to enable automation and automation. " Below are some of the platform's application screens.

4.Nnergix-The intersection of meteorology, analytics, and energy

Founded in Barcelona in 2013, Nnergix collects large amounts of data and uses AI-based algorithms and analytical models to manage renewable energy and optimize rotation readiness. Spinning reserves are simply power generation resources that are unloaded but online, and can respond quickly in the event of a generator or transmission resource outage.

The future of AI and energy

The above example is just one example of what will eventually be possible. There are many other examples of how the use of AI is affecting energy, including: Short- term load prediction It is also a virtual assistant to improve predictive maintenance (before eventually failure and costly repairs) and customer experience to replace aging resources . Know your electricity bill using AI .

Of course, these innovations are not without risks (data privacy and reliance on internet-connected devices ), but where computing power and data availability benefit artificial intelligence AI in power plants. There may be others. Creativity is the only additional element needed.