As the global energy sector becomes more complex, utility operators and asset managers are increasingly turning to artificial intelligence (AI) to analyze large data sets and balance energy supply and demand—all with the goal of making electricity delivery greener, cheaper, and more reliable.

This post explores some of the key drivers shaping the modern energy landscape including:

  • What are clean energy’s current challenges?
  • How AI and machine learning apply to the energy industry?
  • How AI is influencing the future of clean energy and renewables? 

What are the current challenges of clean energy?

With the transition away from conventional energy generation technologies like fossil fuel combustion, other alternatives are becoming more abundant such solar power and wind energy. Both are modular, green technologies that continue to become more affordable and efficient thanks to global competition. And once installed, neither wind nor solar power emits CO2 or other greenhouse gases.

Although critical to the world’s decarbonization efforts, widespread adoption of these renewable technologies presents challenges for the larger energy industry, including:

1. Scalability

Even though solar photovoltaic (PV) panels and wind turbines are modular by design, it is difficult to balance energy generation consumption and generation at the macro level—especially as more privately owned distributed energy resources (DERs) come online.

Many of these energy assets are behind-the-meter, placing them outside of the direct control of utility operators. But even those DERs that communicate with the grid sometimes operate in a black-box environment—unable to share production stats in real-time. As a result, utilities cannot properly balance energy supply and demand grid-wide, leading to cost overruns, energy dumping, power shortages, and edge device failures.

2. Intermittency

Even when real-time energy production data are shared, renewable technologies like solar and wind are both intermittent—generating variable power output from minute to minute. This unpredictably forces utilities to constantly react throughout the day as weather and grid conditions continuously evolve. With human decision-makers pulling the levers, however, there is always a lag, leading to energy consumption and generation imbalances, cost overruns, and less reliable power delivery to end-users.

3. Affordability

Solar PV panels are a relatively affordable technology that enables consumers to generate their own clean energy on-site. This allows the cost of greening society to be spread as individual homeowners and businesses invest in their own distributed solar PV installations. Similar trends exist with electric vehicles, wind turbines, solar batteries, smart thermostats, and other DERs targeted towards the consumer market.

However, this rapid onboarding of green assets introduces new costs for those tasked with maintaining the grid and balancing supply and demand across the entire electricity network. In addition to energy imbalances, waste, and shortages, this rapid influx of renewable power capacity also leads to device failures as panels, batteries, and turbines prematurely reaching end of use. 

Because fossil fuel is often used to correct these shortfalls and imbalances, greening the grid has the paradoxical effect of making the grid decidedly less green. However, artificial intelligence continues to show potential at managing these new renewable energy assets—all while making electricity delivery cheaper and more reliable across the network.

Before diving into the role of AI and clean energy, however, it’s important to cover a few key technical terms. 

A refresher on artificial intelligence (and machine learning)

Artificial intelligence (AI) is an all-encompassing term to describe any technology that can solve complex problems or complete tasks without being explicitly programmed to do so. And machine learning (ML) is a subset of AI that allows computer algorithms to continuously improve by learning from historic and real-time data.

Google’s AlphaGo technology and Tesla’s self-driving cars are prime examples of AI and ML in action. In both cases, algorithms are trained on large data sets to quickly and autonomously identify the optimal course of action at any given moment in time—whether this means advancing pieces in a board game or avoiding a squirrel in the middle of the road.

This basic concept can be applied to any discipline plagued by large data sets. For example, it’s possible to use AI and ML to optimize an increasingly complex network of distributed and privately owned intermittent clean energy assets deployed at scale across an entire community, nation, or planet.

How is AI shaping the future of clean energy and renewables?

When working with limited data sets, humans are surprisingly good at spotting patterns and making relatively accurate predictions. In fact, this is precisely what utility operators have historically done—essentially using a mix of meteorological data and grid metrics to forecast where energy supply and demand will be minutes into the future. These predictions are what have allowed grid operators to generate enough electricity capacity for optimal delivery at the lowest cost.

As the amount of data grows, however, this balancing act becomes more difficult. Utility operators today, for example, must parse through many terabytes of meteorological data—all against a backdrop of rising energy demand and newly installed renewable energy capacity to manage. Not surprisingly, most grid operators are overwhelmed by the sheer amount of historic and real-time data they must analyze.

By contrast, AI is trained on large data sets, with the express purpose of identifying hidden patterns so that it can generate forecasts. When coupled specifically with iterative ML, these forecasts become increasingly accurate over time as the AI algorithm refines its own predictions using new incoming information.

When applied to the clean energy dilemma, AI is already proving incredibly effective at collecting large amounts of real-time data from edge device sensors, weather satellites, and utility production stats to accurately forecast energy generation and consumption grid-wide. If those edge devices are also equipped with receivers, AI can use its forecasts to generate and send instructions, whether this means:

  • Relaying power from PV panels into on-site battery energy storage systems (BESS) to take advantage of sudden spikes in solar radiation
  • Slowly discharging a fleet of municipal electric vehicles into the grid to help offset an unexpected dip in wind power generation

This balancing act is what grid operators are currently trying to achieve using conventional tools—like cutting edge distributed energy resource management system (DERMS) technology. But with truly integrated AI helping to analyze and act on terabytes of real-time information, it’s possible to ensure that energy demand and supply always remain in perfect balance across the entire electricity network. In addition to more reliable delivery, managing this green energy with AI ensures that the grid benefits from the cleanest power possible—at the lowest price and with the least amount of waste.

Ready to continue the discussion on AI and renewable energy?

The above is a brief overview of the current green energy landscape and how artificial intelligence is already being deployed to help utility operators and asset managers better migrate this terrain as the world rapidly decarbonizes.

The following chapters delve into various use cases across different aspects of the larger renewable energy system, with the next installment in this series on AI, machine learning, and green energy focusing on:

Chapter 2: AI for Wind and Solar Energy

Solar and wind are two of the most promising technologies in our fight against climate change. But their unpredictability makes them notoriously difficult to manage—especially at scale. Discover how artificial intelligence is helping to mitigate energy peaks and valleys for a greener, cheaper, and more reliable electricity grid.

Chapter 3: AI for Energy Management: How to regulate Battery Storage in Real-Time 

Learn how to intelligently charge and discharge batteries for more reliable energy delivery, optimize battery charging schedules, and unlock new revenue streams across increasingly profitable energy markets.

Chapter 4: The Limitations of Facility Energy Optimization When Using Legacy Tools to Meet Green Targets

Facility energy optimization is foundational to long-term business continuity as the country’s energy mix becomes more fragmented and complex. Learn some of the driving forces behind growing interest in comprehensive facility-wide optimization.

Chapter 5: Grid Resilience (coming soon)

Read about the modern definition of grid resilience, why it’s important, what a resilient grid looks like, and the role AI plays in creating greater reliability.