Improving the timeliness and precision of demand planning

As we mentioned in the prior article, certain areas of the country such as Northern Virginia and Silicon Valley have become the homes of large clusters of data centers. These regions present unique challenges for traditional demand forecasting methodologies, with the surge in data center development often outpacing the readiness of electric transmission infrastructure. It has become increasingly common for data center developers to purchase their own multi-million-dollar, transmission-scale transformers to accelerate the electrical provisioning process.

Modern data science and AI capabilities can help here too. It is possible to develop a better understanding of the demand arising from these regions by using a higher resolution analysis – for example, at a block-by-block or substation by substation level – and combining this more granular data with modern predictive analytics.

These analytical tools can offer more accurate predictions of future demand curves based on historical data and a broader portfolio of inputs. Thereafter, these granular predictions can be accumulated to give very accurate predictions of load in a hierarchical way, offering planners greater understanding of what is causing stress today and what will cause greater stress tomorrow.

Better leverage new analytics

Synchrophasor technologies, a recent addition to grid monitoring arsenals, monitor resonances on the network that are a symptom of newer sources of energy supply, and provide holistic insights akin to infrared scans in medical diagnostics. Unlike traditional Supervisory Control and Data Acquisition (SCADA) data, which offers detailed component-level information, synchrophasor data offers a broader view, locating systemic and potentially destructive frequency disturbances in regions of the network. As we discussed in the prior article, new greener sources of energy can add disturbances to the transmission network in very unpredictable ways, and synchrophasor technologies happen to be well tuned to find and describe these disturbances.

The challenge with this new technology is the scale of data to be acquired and assessed. Synchrophasor data is high-velocity, highly granular data, with each piece of equipment generating data at least 60 samples per second and often multiples of that rate. Proper use of this new technology requires very advanced data selection, storage, and analytical techniques, and this is where AI and Machine Learning algorithms enter the picture.

This is a new technology, and analytical techniques are still being discovered, refined, and occasionally thrown away in favor of better ones. But the combination of this new type of network health measurement, empowered by rapidly developing AI tools and techniques, makes it possible to understand fully how greener sources of energy are affecting the network, modify the network so it is more accepting of these newer sources of energy, and even increase the effectiveness and productivity of new energy sources.

Future prospects

In addition to the highlighted advancements, data science applications continue to evolve, aiding the energy sector in detecting climate risks, optimizing power distribution, and enhancing grid stability. For example, innovations such as Battery Energy Storage Systems (BESS) leverage AI algorithms to optimize energy storage and manage fluctuations in renewable energy generation.

In addition, real-time, AI-enabled monitoring through fixed and drone-based video systems promises further enhancements in network health assessment, bolstering resilience against evolving operational challenges. The many applications and uses of AI to help our transmission networks adapt to future energy supplies and demands defy enumeration.

However, new sources of energy supply – primarily solar and wind – behave in ways that are very different from traditional sources built on spinning turbines, and the network needs to evolve to better accommodate them. Across the U.S., there are 2,600 Gigawatts of new renewable sources being proposed; the entire installed base in the U.S. is now roughly 1,300 Gigawatts (up only slightly from about 1,000 Gigawatts in 2010). These new sources aren’t affecting the network, they are completely redefining what a power network is. The ongoing challenges at the intersection of AI, data science, and energy infrastructure heralds a transformative era. By harnessing these technologies, utilities can fortify their transmission networks, mitigate operational risks, and foster sustainable growth in a dynamic energy landscape.

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