Welcome to the final installment in our 5-part series on Smart Grid Technology . Each article so far has focused on different components of intelligent grid architecture, including:
- An overview of what smart grids are , how they work, and why they represent the future of energy delivery.
- How distributed energy resource management systems (DERMS’s ) are used to send and receive data from edge devices, including behind-the-meter batteries and intermittent renewable power sources like solar and wind.
- The growing use of autonomous microgrids as localized power stations that can both support and work independently of the main energy grid.
- The importance of grid resilience , which includes the full spectrum of tools and regulations designed to ensure continuous electricity delivery – even when natural or man-made disruptions to the power network occur.
This final installment looks at how artificial intelligence (AI) and machine learning are being used to optimize energy generation and delivery faster, cheaper, and more accurately than when real-time grid management decisions are left to human actors.
In fact, intelligent automation is the key ingredient that makes a smart grid “smart.”
The Limitations of Current Smart Grid Technology
Microgrids, DERMS software, and grid resilience form the backbone of smart grids – enabling cheaper and more reliable power for everyone. But each of these components also creates many terabytes of data, making it impossible for grid operators and asset managers to effectively balance energy supply, price, and demand as network conditions change in real time.
Something as simple as a passing cloud could temporarily cut a municipality’s solar production in half. And utility providers would normally have to scramble to compensate for the shortfall, either by:
- Relaying stored power from elsewhere in the grid
- Generating more load capacity on-site (from fossil fuels)
But even with highly responsive operators, lags are inevitable at every stage. This leads to shortages, waste, and unnecessary stress to the network, which in turn can create unsafe conditions and the potential for outages.
Of course, asset managers and grid operators deal with a lot more than passing cloud cover. They must also manage:
- Distributed energy resources outside their direct control
- Storms and outages (made worse by the changing climate)
- Renewable energy technologies that are often intermittent
And in nearly all cases, the response is reactive, leading to delays and inefficiencies.
By contrast, artificial intelligence can better balance the two-way flow of information and electricity to produce far superior outcomes – not only for grid-connected customers but for all stakeholders in the larger energy ecosystem.
How Is AI Used in Energy Management?
To understand the potential impact of AI on grid management, it’s important we define a couple of terms:
- “Artificial intelligence” is a branch of computer science focused on designing algorithms that can closely mimic human thought. Although modern AI tools remain narrowly focused within specific domains like chess, traffic lights, or energy, they excel at processing extremely large data sets in real-time.
- “Machine learning” is a subset of AI in which algorithms analyze both real-time and historic data sets to make incredibly accurate predictions about the future. These forecasts are generated by spotting patterns in the data that would be impossible for human actors to identify. Based on these patterns, AI makes educated guesses that are fed back into the algorithm, allowing the machine to learn and improve exponentially over time.
Given AI’s pattern recognition and predictive capabilities, there are many potential use cases for the technology in nearly every industry. Below are 3 immediate applications in the energy sector that are already bearing fruit.
1. Artificial Intelligence Power Grids (“Smart Grids”)
At any given moment, grid operators and asset managers strive to determine the optimal energy output needed to meet system load at the lowest possible cost – subject to transmission and operational constraints. Known as optimal economic dispatch, this is an ongoing process that requires constant recalibration as weather and grid conditions change.
However, AI can perform the same function automatically and in real-time to ensure reliable energy delivery at an even lower price. Below are 2 hypothetical examples of autonomous optimal economic dispatch – powered by artificial intelligence:
- By analyzing real-time weather data, AI determines that passing cloud cover will reduce rooftop solar production by 75% in a certain quadrant of the city. Rather than increase load capacity after the fact, AI automatically initiates slow-charging for the city’s electric vehicle fleet. It also starts drawing power from the school district’s distributed solar batteries.
- In another scenario, a bad summer storm might down power lines in a residential neighborhood. AI instantly initiates grid resilience protocols by relaying emergency backup power from 2 nearby microgrids. As a result, the lights stay on – sparing local residents from a blackout.
As artificial intelligence gathers more data about past and present weather conditions and the supply, demand, and price of energy, its predictive capabilities become sharper – and its response times become even faster.
2. Energy Trading
Artificial intelligence’s ability to predict the supply and price of energy is obviously useful for grid management. But these same benefits also apply to energy trading, with machines instantly processing terabytes of past and present data that would take human analysts millennia to comb through.
When deployed correctly, AI has the ability to let energy traders know the price of power minutes ahead or far into the future – giving them a decisive advantage in major exchanges. In fact, this knowledge even has national security implications, with the US Department of Energy already turning to AI’s predictive capabilities to safeguard energy reserves and infrastructure.
3. Energy Operations and Maintenance
Artificial intelligence can help the energy operations and maintenance (O&M) sector better maintain assets like large solar PV or wind farms. When paired with thermal drone technology, for example, AI can alert field technicians the moment an individual solar panel needs servicing. Predictive maintenance could save PV farms many thousands of dollars by eliminating the need to isolate underperforming modules using traditional trial and error.
The technology can also be used to boost solar power system production by simply repositioning arrays throughout the year to track the sun’s trajectory across the sky. AI in power distribution is another consideration, with machines deciding the optimal and most profitable balance between storing excess solar energy versus feeding it into the grid.
Whereas human decision-makers normally pull the levers, AI can automate the above processes, helping to increase the ROI of clean energy investments – from rooftop solar installations to utility-scale wind farms.
Remaining Hurdles to an Artificial Intelligence Power Grid
In every industry where it is deployed, artificial intelligence consistently delivers faster, cheaper, and more accurate results than human actors can. And the energy sector is no exception . But in terms of truly holistic smart grid management to power an entire nation’s energy network, several challenges remain.
Challenge 1: Technology
The building blocks for smart grid technology must already be in place to necessitate artificial intelligence. At present, however:
- Not every edge device relays sensor data or comes with a receiver for executing instructions
- Not every utility operator uses advanced distribution management systems (ADMS) or DERMS technology
- Not every microgrid can assume local and autonomous grid operations by disconnecting from the main utility network
This is all changing very rapidly. But the evolution is piecemeal, with microgrid developers and independent power producers being the earliest adopters to see traction when automating predictive control over microgrid DERs.
Challenge 2: Cost
Many newer distributed energy resources now ship with intelligent controllers, allowing for seamless integration with the electricity grid and DERMS technology. But retrofitting older devices with the requisite sensors and receivers is costly, with the burden falling solely on edge asset owners.
Fortunately, the investment is one that pays for itself – both for that individual and for the grid as a whole. However, better mechanism should be in place to incentivize distributed energy resource owners to make the necessary improvements upfront.
Challenge 3: Familiarity
People are naturally resistant to change – particularly when that change involves handing over more decision-making control to machines. Hollywood and sci-fi writers have done a great job highlighting the potential dangers of intelligent robots taking over the planet.
In reality, AI helps humans do their jobs better.
From air traffic control to traffic light management, intelligent machines consistently make us more productive while simultaneously reducing errors, cutting costs, and saving lives. The benefits of AI’s predictive capabilities are even on display at the consumer level, with voice-activated assistants like Echo and Siri seemingly knowing a user’s next question before they have a chance to ask it.
Many of these same benefits emerge with an AI-powered energy grid. Machines are faster, cheaper, and far more accurate than the best humanity has to offer. The end result is more reliable and predictable energy at a much lower cost.
The Future of AI in Power Grids
Although the challenges above are real, the general trend is very clear. According to the US Energy Information Administration (EIA ), intermittent renewables like solar and wind already represent 20% of domestic electricity generation. As this percentage grows, utility operators and asset managers will need increasingly intelligent tools to ensure reliable power delivery.
At Veritone Energy , our AI technology has already proven adept at this task, thanks to 4 interconnected pillars that work in concert to give utility operators and asset managers unprecedented control over the grid and distributed energy resources:
- Forecaster , which analyzes large data sets collected from weather satellites, edge device receivers, power generation levels, battery storage capacities, and real-time energy pricing to make accurate predictions about where prices and supply are heading into the future.
- Optimizer and Controller , which send instructions to edge devices to ensure grid-wide stability.
All this data gets fed back into the AI, ensuring that future forecasts and instructions become even more accurate. In fact, it’s best to think of smart grid technology as an ongoing process instead of as a destination. With Veritone Energy’s AI, the grid actually becomes more intelligent as DERMS technology, autonomous microgrids, and sensor-enabled edge devices become more ubiquitous.
To see what a truly autonomous machine learning power grid looks like in action, request a demo today.
Artificial Intelligence Power Grid: Further Reading
We hope you’ve enjoyed this 5-part series on smart grid design . You should now have a better understanding of the challenges around building an artificial intelligence power grid – and how emerging technologies are helping to reshape the global energy landscape as we transition away from fossil fuel towards more sustainable power sources.
If you have additional questions about smart grid technology, be sure to read the other installments from this series, including:
- An Overview of Smart Grid Design
- Distributed Energy Management Systems
- Autonomous Microgrid Technology
- Grid Resilience & Continuity
- Artificial Intelligence
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