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Charging the Utilities Industry with New Technology

Business models change. The utilities industry is no different. Utilities companies are performing business transactions and processes differently today than they did 20 years ago. And 20 years from now, the industry will be very different. But for today, let’s focus on the current trends, including grid modernization, the rise of digital transformation, implementing digital twins, and transitioning to an agile workforce.

Grid modernizations and microgrids provide a better future

As the utilities industry transitions toward more scattered energy resources, companies face a critical choice: embrace this opportunity to improve and adapt or see it as a risk and collapse. Fortunately, the most prominent companies are looking to modernize the power grid, such as by implementing microgrids.

With grid modernization and microgrid implementations, the advanced systems can:

  • Provide revenue to the utility during grid outages
  • Distribute power effectively
  • Save on transmission and fuel costs
  • Provide predictive analytics and maintenance

Microgrids, by definition, are energy generation systems that have the ability to disconnect from the main grid and continue to power isolated facilities even when the larger electrical grid is unavailable. These microgrids provide continuous, reliable energy to community service organizations, critical businesses, first responders, schools, and other essential facilities following a natural disaster.

Grid modernization is already happening. This trend doesn’t follow the norm because these changes are already happening. In fact, it’s not unreasonable to visualize customers having up to five autonomous grid devices at a time — a rooftop solar system, a home battery, a smart thermostat, a smart water heater, and an electronic vehicle (EV) charger. These devices would work off of a microgrid to provide efficient resources during a time of need, or during regular days and nights.

For example, Pacific Gas and Electric (PG&E) has nearly 4 million customers in the San Francisco Bay area. After getting set-up on the modern grid, PG&E would have over 20 million devices to manage on a single grid. Which is a lot, but with autonomous grid enhancements and smart technologies; these advancements can be monitored with ease.

These grid enhancements are happening across the states. Recently, the National Governors Association (NGA) announced it will provide assistance to Maryland, New Jersey, New Mexico, and Rhode Island to modernize its electric power grids as well, addressing topics such as integrating clean energy and electric vehicle charging networks, electrifying the heating sector, and improving system resilience. Due to these changes, governors are seeking solutions to modernize their state’s substructure.

With grid adaptations, automation will go hand-in-hand with it. Artificial intelligence (AI), machine learning (ML) and predictive analytics will play a big role in these modernizations. Yet, a fully autonomous grid probably won’t happen for a while. Why? Simply put, humans still make better judgments than robots. But, AI and predictive analytics are starting to be regarded as “decision support.” In a supportive role, the AI and smart algorithms can lay out multiple scenarios and outcomes, allowing the human controller to make the best decision moving forward.

Digital transformation and digital twins

The digital twin market is exploding. Experts say the market could reach $15 billion by 2023. Utilities companies are among many asset-intensive industries poised to benefit richly from these technologies.

Digital twins are virtual models that are used to obtain both real-time and predictive insights on performances. Leading companies are deploying digital twins into practice to test analytics both real-time and predictive. While running these tests virtually, the twins are optimizing companies’ successful outcomes, while trying to accomplish tasks in the digital world, without the risk of real-world trial and error.

These digital trials save companies tremendous amounts of money because they’re carried out in a digital space. As the digital twin tests and predicts solutions, companies can fine-tune the algorithms to ensure success before deploying it in the real world. This practice eliminates algorithm errors, and analytic mishaps in case a false positive item surfaces by mistake.

Enabling a new workforce

As the utilities industry evolves, companies need to assess how they are meeting new customer and system demands, while continuing to provide safe, reliable, and affordable energy.

In fact, two out of three CIOs agree that there is a talent crisis. Businesses across all industries are finding it difficult to hire the new age of experts. Experts who will enable companies to compete in the digital arena.

New advancements in technology and digital transformations requires employees to learn entirely new skill sets, as everything from operations to planning to field service and customer service is transforming quickly. The rise of digital transformation and technology has created a pronounced skills gap between an aging workforce and the expertise needed to create and manage grids of the future. In order to remain competitive, companies must adapt and provide new infrastructures, business processes, and technologies.

Utilities companies need to take action to acquire new tools and invest in the workforce. These investments facilitate companies to train and educate the current workforce. Thus, sparking interest with new workers coming into the field. This keeps the current workers happy and up-to-date. While also appealing to the younger generation of workers and bridging the workforce skill gap.

With all of the changes happening in the utilities industry, companies seeking to lead these transformations must remain nimble and open to new thoughts, processes, and technologies. Customers expect the best in our service-oriented world. And if they’re not getting what they want with one company, they can switch service providers fairly quickly.

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