Welcome to the third installment in our 6-part series on how artificial intelligence (AI) is continuing to transform today’s increasingly complex and fragmented energy landscape. In part one, we provided an overview of the energy challenges we currently face – and how AI is helping to integrate green power sources for a cleaner and more reliable grid. In part 2, we explored how AI is now being used to smooth out peaks and valleys in renewable power production – often with the aid of on-site battery storage.

This installment continues the battery storage thread, highlighting how artificial intelligence (AI) and machine learning (ML) are being deployed to: 

  • Intelligently charge and discharge batteries for more reliable energy delivery.
  • Unlock new revenue streams across increasingly profitable energy markets.
  • Optimize battery charging schedules to safeguard sensitive electrical circuitry in edge devices.

To fully appreciate the huge potential that exists when pairing AI with energy storage, however, it’s important to understand what problems batteries help to solve.

The Modern Energy Landscape (without Battery Storage)

As discussed in Part 2 of this series, solar and wind currently represent the most promising technologies for weaning society off of dirty fossil fuel. Neither power sources pollute – and both are becoming increasingly affordable.

However, both technologies are intermittent, outputting variable levels of power throughout the day. This creates headaches for utility operators who must constantly scramble to ensure energy supply and demand remain in balance across the grid. Solar and wind’s unpredictability also places unnecessary strain on grid-connected distributed energy resources (DERs) whose circuitry wasn’t designed for sudden spikes in electricity.

Batteries help bridge the gap by creating:

  • A reservoir for accepting excess renewable power when conditions are right (i.e. charging up when there is an oversupply of wind or solar energy).
  • A reservoir from which to draw stored electricity (i.e. discharging batteries whenever grid-wide demand exceeds the current supply).

However, most battery charging and discharging schedules are set manually, requiring advanced knowledge of what grid conditions will be like in the future. This isn’t a problem when trying to mitigate gradual and predictable swings in renewable power output. 

For example, it’s possible to schedule batteries to begin discharging in the late afternoon as solar production starts to decrease with the setting sun. But this balancing act becomes harder when factoring in changing weather patterns – plus all the real-time energy consumption, production, and storage devices that exist across the electricity network. 

Even with real-time visibility and control, there’s often a lag between the moment utility operators notice an energy shortfall or surplus and the moment they can correct this imbalance by topping up or emptying batteries. These lags inevitably manifest as outages, surges, dumping, or device failures.

There are limits to what battery storage can do when actively managed by grid operators. However, these limits disappear when outsourcing the problem to AI-powered tools that are purpose-built for real-time grid management.

The Benefits of AI for Energy Management 

Previous installments of this series already explored how AI and ML are being used to help stabilize the grid as more DERs come online. When combined together, these powerful technologies can autonomously:

  • Analyze reams of historic and real-time data to accurately predict where energy demand, supply, and pricing will be seconds and weeks into the future.
  • Use these forecasts to remotely control grid-connected energy assets to ensure supply and demand remain in balance – whether this requires onboarding more fossil fuel, scaling back solar power production, or selling unused electricity to neighboring utility markets (often at a loss).

Central to this delicate real-time balancing act are:

  • Sensors on edge devices that can collect and send energy production, consumption, and storage stats to the AI algorithm.
  • Receivers that can remotely receive and execute instructions sent by the autonomous algorithm.

The ML component is what allows the AI to evolve. The algorithm’s initial forecasts are matched against actual results, which enables the AI software to refine its predictive modeling and become even more accurate over time.

When expanded to grid-connected storage technology, these iterative improvements in forecasting yield a host of powerful benefits. 

1. AI-Powered Batteries and Dynamic Charging Schedules

Predictive modeling allows for dynamic battery charging with virtually zero lag. Based on current conditions, artificial intelligence can top up or empty batteries to correct grid imbalances in real-time. For example, it might divert a sudden solar power spike into on-site batteries. Alternatively, it could discharge stored wind power from a nearby community’s battery banks to meet an unexpected surge in demand elsewhere in the grid.

Either way, the end result is cleaner and cheaper energy delivery – with far less waste. 

However, these benefits are not limited to grid operators and utility providers. Intelligent, real-time battery management also unlocks opportunities for other energy stakeholders, from independent power producers to energy traders to consumers.

2. AI-Powered Batteries and Independent Power Producers

Many privately owned photovoltaic (PV) farms already use on-site batteries to store daytime solar energy, either to power their operations or to sell back to their local utility provider. But because charging and discharging schedules only shift a couple of times a day, this very blunt approach delivers limited benefits. 

With the help of AI, however, that facility now has the ability to intelligently and strategically control when its batteries charge and discharge throughout the day for optimal profit maximization. Rather than adhere to static schedules, battery charging constantly changes in real-time based on current weather and grid conditions.

In essence, coupling batteries and artificial intelligence allows the facility to better “time the market” and create new revenue streams.

3. AI-Powered Batteries and Energy Traders

Those who buy and sell energy stocks are even more incentivized to “time” the market. And they can use AI-powered battery storage in much the same way – effectively buying (i.e. storing) energy when prices are low and selling (i.e. discharging) when prices are high. 

Note that this type of energy arbitrage is already possible when manually setting charging and discharging schedules. However, the profit potential is much higher when using predictive modeling backed by real-time data from the actual world.

4. AI-Powered Batteries and Business Continuity 

Battery storage is playing an increasingly important role in business continuity plans, especially as the changing climate and severe weather events continue to impact electricity delivery. Although the average company wouldn’t necessarily invest in its own in-house AI infrastructure, connecting its batteries and other DERs to a grid controlled by artificial intelligence helps to make all of the company’s energy assets more valuable and reliable. 

5. AI-Powered Batteries and Society at Large

Regardless of the specific application, all batteries in this connected ecosystem enjoy longer lives since AI can intelligently regulate charge cycles based on each energy storage manufacturer’s technical recommendations. These guidelines help to protect sensitive circuitry, which both extends the lifetimes of these batteries while also increasing their ROI. As a result, homeowners, businesses, and entire communities can more easily justify the upfront cost of investing in sustainable power technologies. And this incentivizes even more people to go green, which is precisely the whole goal of the decarbonization movement.

All of these benefits emerge early on. 

And with continuous feedback to help improve its predictive modeling, artificial intelligence only becomes more accurate with time. This encourages more green investment – and by extension – a healthier planet for everyone.

Ready to Continue the Discussion on the Future of Energy?

Batteries already play a critical role in our quest to build a greener grid. And in fact, it would be impossible to reduce carbon emissions at scale without some type of storage capabilities for storing excess renewable power.

However, fully leveraging battery storage’s potential requires collecting, analyzing, and acting on the petabytes of real-time energy and weather data generated every second of every day. And currently, AI and machine learning are the only technologies capable of intelligently managing this deluge of information.

If you’d like to see what AI-powered storage solutions look like in practice, request a free demo from our energy experts today.