AI Delivers Scalability for Renewable Energy’s March to Net-Zero


Numerous challenges face the energy industry as it strives to meet the carbon cutback targets associated with 2050 net-zero goals, but they all link back to the question of scalability.


For operators in the rapidly growing renewable energy industry, adding more and more turbines, solar panels, and battery storage assets comes with the responsibility of managing growing streams of data around production and safety concerns. And they have to do so without linear growth in hiring for those tasks.


At SparkCognition’s recent Time Machine Interactive event, a panel of renewable energy industry leaders discussed the ways in which artificial intelligence (AI) technology can help navigate  pricing cutbacks in power purchase agreement contracts, increase the effectiveness of battery technology, and manage the challenge of intermittent power generation from wind and solar power.


AI solves intermittency, workforce challenges

Moderator Rob Budny, vice president of renewable energy sales for SparkCognition, said the doubling of the wind and solar sector since 2015 has turned the former “alternative energy” into a mainstream building block to meet the world’s energy demand. But, he said, producers have neither the flexibility nor the financial resources to double or triple their employee headcounts as their fleets continue to grow.


In fact, the move toward mixed-asset portfolios means operators and their teams have to successfully manage a variety of asset types from a number of different online equipment manufacturers (OEMs). Budny noted how AI solutions that collect and analyze vast amounts of data around production and pricing forecasts, as well as using predictive maintenance to reduce downtime and repair costs, are indispensable in today’s operating environment.


Panelist Carla Holly, director of compliance and control services for BP Wind, said AI tools are essential for properly growing the wind assets that will enable the company to transition from being an oil and gas producer to a broad energy company.


“As we start to grow more aggressively, how do we scale up while maintaining our current business practices and operational excellence?” she said. “We’re really exploring the various uses of AI and how we can create efficiencies not only in our operations and in the field, but also in our remote operation center, so my operator can control more assets and accomplish more in a shorter amount of time.”


Holly added that AI shows promise in handling the complexities of contracts for interconnection and power generation that threaten to overwhelm in-house or contracted legal teams. By analyzing the agreements and helping to account for all of BP’s obligations around O&M programs for their assets, AI technology allows the company to attain top performance while maximizing ROI.


Annette Andersen, principal portfolio manager for BP Wind, said advancements made in the last several years have already decreased the demand for work hours spent manually photographing turbine blades to study their structural degradation. Drones operating with AI capabilities now handle those responsibilities, she said, with those personnel resources now used for more impactful work.


“Now that data is being fed automatically into large databases, there’s some automated analysis going on before it gets to a human. And that has driven a lot of efficiencies in how the engineering team works,” Andersen said.


Powerful advancements in battery storage

One of the most significant challenges slowing renewable energy projects’ move to replace traditional carbon-based energy production is the intermittency of wind and solar, especially the mismatch of demand versus peak evening production for wind turbines. 


Improving battery storage is the answer to that problem, with producers moving stored energy into the marketplace when demand is higher, increasing the profitability and financial viability of new projects.


Panelist Jae Choi, head of the North America region for Trina Storage, said improved data and analysis of how batteries are used in different situations will allow operators to make the best decisions about what assets to purchase, and how to best maintain them over their lifespan


Choi said as the reliability of battery technology allows for storage projects to grow larger and become more flexible, there will be a move to reduce O&M costs by scheduling fewer maintenance and repair activities—another opportunity for AI technology to step in and ensure the “value engineering” doesn’t go too far in the direction of sacrificing safety and reliability.


“Some compromise is good, and has a really good name; value engineering, right? Value engineering is absolutely necessary. But at what cost?” said Choi. “This is where AI becomes really important to visualizing the overall impact and quantifying those impacts, allowing decision-makers on the customer side and vendor side, as well as the system integration and engineering sides to make everyone aware so they can talk about what are they compromising on, and what’s the real cost?”


The panelists agreed that the list of possible uses for AI technology in the renewable energy sphere goes on and on, including:

  • AI-controlled drones capturing photos of turbine blades to document degradation
  • Health tracking of maintenance workers’ sleep patterns and hydration to select the best personnel for important tasks
  • Forecasting energy market demand to capture extra percentage points of production, which can have drastic positive impacts on profitability.


At the end of the spirited panel discussion, the consensus message emerged that AI offers the chance for the renewable energy sector to deliver on the decades of promises around what’s possible with wind, solar, and other no-carbon production methods. With the demand for sustainable power supplies growing stronger by the year, it is clear that AI will become more crucial in reaching the net-zero goals needed to ensure environmental and climate stability for decades to come.

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