We’ve all heard the buzz about Artificial Intelligence (AI) but unlike some other media hype (ahem, the metaverse), the excitement surrounding AI is not without merit. Today’s intelligent technologies have the potential to revolutionize nearly every industry, including renewable energy.
The World Economic Forum’s 2021 publication Harnessing Artificial Intelligence to Accelerate the Energy Transition explored AI’s role in the global transition to clean power, covering topics from energy system governance to management of operational risk. Just two years later, AI is already being applied to support many aspects of solar energy expansion and production.
Currently, solar power is among the fastest growing sources of renewable energy, but widespread adoption and rapid expansion of solar energy is hindered by significant challenges in terms of build efficiency, interconnection with existing power systems, grid scaling, and cost. Still, at even intermediate efficiency, installation of photovoltaic modules over even 0.6 percent of U.S. land space could easily meet electricity demand for the entire country.
Scaling physical growth is one way AI can serve the solar energy sector.
The high costs of constructing solar energy systems and farms have historically been a deterrent, significantly limiting adoption of this renewable energy source. Because environmental factors weigh heavily in the equation, each project presents a range of challenges unique to its physical location. And interconnection stagnancies, permitting delays, and challenges posed by transmission congestion are common and can further stifle projects.
Throw in a few complex permitting and operating requirements (which can vary drastically, due to local regulation), intense environmental and aesthetic analysis, the installation and oversight of specialized equipment, and the unique governmental and regulatory considerations associated with large-scale utility infrastructures, and the expenses associated with the construction of solar energy systems begin to stack up.
Using AI, these complexities can be managed more quickly and efficiently, while minimizing project costs. Here are a few examples:
Solar site selection. The selection and analysis of potential solar farm locations is crucial, as environmental conditions critically affect production and storage capabilities. Because of its capacity to analyze large amounts of geographical and environmental data, many companies are using AI to identify sites and locations with optimal solar resources and conditions, as well as to verify ease of access for connection with existing grid infrastructures, or ideal positioning for later development.
Pre-construction planning and design. Before even breaking ground, AI-driven iterative and 4D design can provide general contractors with detailed construction plans, schedules, and “digital twin” site models and equipment designs tailored to specific solar equipment, site conditions, and restrictions.
During the pre-construction phase, these virtual solar system and equipment models can be used to test potential scenarios, optimize equipment and site layout, and design for increased efficiency, helping stakeholders maximize system output while identifying and avoiding future issues. AI-driven planning can reduce the need for on-site customization and adjustments during (or post) construction, resulting in a dramatic reduction in costs. It also eliminates potential delays and costly changes to the project plan and scope.
Construction cost reduction. Recent advances in AI technology have introduced solutions proven to reduce the cost of major infrastructure construction by as much as 30 percent. Large and complex projects (such as solar power system construction) benefit especially from the support of AI-driven construction optimization, which maximizes the use of resources onsite. From reallocation of labor and equipment to dynamic scheduling, optimization tools offered by a company like ALICE Technologies can improve resource use and efficiency on even the most complex solar project.
Overcome construction delays. When it comes to the build and installation of major utility infrastructures, time is money. The potential for costly delays in solar energy construction or interconnection is real, but when solar construction goes sideways, AI-driven tools can identify options for redeploying resources and maintaining project progression by suggesting options for task, equipment, or labor resequencing to keep projects moving forward.
When supply chain issues, specialized labor shortages, or interconnection delays occur, on-the-go scheduling adjustments and rapid recovery (courtesy of AI) provide a tremendous advantage in managing complex construction.
Streamlining interconnection. The integration of solar systems into existing energy grids requires the optimization of their production. Because electricity generated by solar power is intermittent, careful consideration and planning of supply and storage needs is critical to avoid disruptions in service or overwhelm of current grid systems. To ensure a successful large-scale expansion of solar power generation, carefully conducted analysis and forecasting of solar power production and supply is pivotal to successful operation and regulation.
As use of renewable energy continues to evolve and expand (both literally, and as a share of the global power supply), accurate solar power generation predictions become increasingly important for forecasting power demand, improving production uptime, and expanding energy systems and storage capacity. AI’s ability to accurately assess and analyze massive quantities of complex data – combined with predictive abilities that allow it to suggest innovative alternative pathways – can make it incredibly valuable to the interconnection process.
Forecasting and analyzing solar system performance. With AI, vast quantities of environmental data can be analyzed continuously and consistently, enabling accurate forecasting and real-time adjustments to current conditions. This leads to improved planning, storage, and operational efficiency, eliminating unnecessary power waste or shutdowns due to weather, environmental hazards, or mismatches in supply/demand, as well as reducing equipment malfunctions and damage.
AI is already being used by some solar energy providers to optimize power system performance and predict maintenance needs. AI can identify patterns that may indicate future performance based on solar conditions, environmental data, and past maintenance records, as well as anticipate future challenges, repair needs, or likely upgrades. As a result of this information, optimized performance and maintenance schedules can be created, which maximize system efficiency over the long term.
Demand scheduling. Related to the above, analysis of solar power use and future forecasting of energy demand is supported by the implementation of AI. Poor demand forecasting can cause power outages, brownouts, and renewable energy curtailment. Yet AI systems possess the potential to identify intricate usage patterns and highlight potential issues… before they occur. By using historical consumption data, AI can provide insight into consumer demand (both on an individual and collective basis), revealing data helpful to system optimization.
Through savvy application of artificial intelligence, solar energy developers can reduce delays, minimize build costs, and improve project time-to-completion. Throughout a solar project lifecycle, AI can also be applied to maximize solar energy output, while minimizing operational expense and risk. And this is just the beginning; the exploration of new applications for AI-driven technologies will undoubtedly provide brighter pathways for solar power innovation as the industry matures and advances.
To achieve global goals of net-zero emissions by 2050, the renewable energy sector will need all the help it can get. When supporters of greener, cleaner energy get on board with singing AI’s praises, we’ll start to see real advancements – and quickly. Here comes the sun. Doo-doo-doo-doo.
René Morkos is an adjunct professor at Stanford University, and CEO of ALICE Technologies. Inventor of the world’s first Generative Construction Simulator and Optimizer, Morkos obtained his Ph.D. in artificial intelligence for construction as a Charles H. Leavell Fellow at Stanford University, where he currently teaches in the Civil and Environmental Engineering department. He is a second-generation civil engineer, with over 23 years of experience in construction.
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