How digitalization provides the foundation to secure grid resilience and business success
Preparing and training for drastic scenarios like blackouts during natural disasters is nothing new to utilities and grid operators. Yet the current COVID-19 pandemic did present them with an entirely new and unexpected reality. It challenged operators to manage an increasingly complex grid without their regular workforce. On the one hand, experience showed that contingency planning worked, with the result that the grid remained stable. On the other hand, it revealed that embedded intelligence and automation can help to balance supply and demand by increasing transparency and accelerating decision-making. As simple as this may sound, it is actually quite challenging in practice.
Ensuring grid resilience in tough and demanding times
Utilities and grid operators face a number of constraints in their mission to operate the grid efficiently and make it more resilient. Cost and time pressures significantly limit the available options. Furthermore, qualified employees are hard to find and unexpected crises like the COVID-19 pandemic suddenly limit the head count available to manage day-to-day business. (Thankfully personnel restrictions thus far were due to infection prevention and not widespread infection among key staff members.) Finally, demands from society in general are rising as consumers expect options to connect private generation to the grid, anytime & anywhere EV charging and an “unobtrusive” infrastructure (e.g. invisible transmission lines, wind turbines). Essentially, a system that is getting closer and closer to its operational limits is nonetheless expected to accommodate a growing number of new requirements – although for many years now, infrastructure upgrading and expansion have not been able to keep pace with current developments in many regions. In short, utilities and grid operators face a catch-22 as they struggle to balance a dynamic and expanding threat landscape with the growing constraints of their daily operational reality.
Building intelligence into the grid through digitalization
Digitalization is the key to harmonizing these conflicting demands and unlocking a whole new opportunity space in the process. It has the potential to build energy intelligence into the grid – across all voltage levels and right up to the grid edge. A key challenge facing market players is the huge number of devices that needs to be connected and controlled. In this context, a digital twin of the distribution grid can help to analyze and determine the most probable future usage scenarios and assess the potential value of sensors, actuators and other automation equipment at different points in the grid.
But digitalization is an enabler rather than an end in itself. Powered by artificial intelligence, automation technologies can support and optimize all key grid management tasks:
- Building knowledge: Collected data (e.g. hundreds of TB from smart meters every year for a single utility) are turned into actionable insights.
- Making decisions: Acquired insights substantiate and inform all grid management decisions, and AI-enabled algorithms, based on experience and expert knowledge, can manage parts of the grid independently.
- Taking action: The combination of energy automation software and actuators at the hardware-level autonomously controls the flow of electricity by managing decentralized devices like intelligent local substations, batteries, EVs and heating systems.
Master rising pressures and boost performance with automation and AI
Intelligent solutions like digital substations, virtual power plants and platform-based demand response programs are a cost-efficient alternative to hardware expansions. A digital twin of an energy grid is a practical, multi-purpose tool. Fusing actual data from the grid with external data sources, it can save costs by avoiding a trial-and-error approach. By preempting potential incidents, it allows faster decision-making and rapid deployment of the right corrective action. AI-powered solutions like digital power quality monitoring and analytical services are a cost-efficient way to gain greater insights into the system (e.g. through as-a-service models). They can help to detect anomalies and avoid power-quality faults and damage to sensitive power electronics.
Especially in times of crisis, fast decision-making and the right course of action are essential. Automation could be described as the “golden ticket”. The DynaGridCenter Research Project is creating an assistance system for automated grid control which constantly analyzes the grid status and makes suggestions for validated preventive and reactive measures. A more hands-on solution is digitally enabled fault localization in the cloud, where faults are quickly reported, evaluated and transmitted to the maintenance personnel’s mobile devices. This reduces the time needed to resolve the issue and helps to better utilize the available personnel. The combination of autonomous decision-making and assistance systems frees up resources for more business-critical tasks without endangering the safety and security of power supply.
In addition, improved utilization of existing grid resources reduces the need for physical extensions and avoids the “not in my backyard” phenomenon. Faster responses to outages, better service quality and possibly even new customer-centric services further boost acceptance of necessary changes to the energy landscape and, ultimately, the image of utilities.
Automation and AI – Just do it
There are several challenges when it comes to the implementation of more automation and AI across grids. The further development of a highly digitalized and connected infrastructure and the limited willingness of regulators to reimburse these investments are just two prominent examples. In this context, courage and openness to utilize new solutions is needed to fully advance digitization in the energy industry. Utilities and grid operators are urged to develop a clear digitalization roadmap, integrate more automation across all voltage levels and build AI into their daily business. Learning from successes and failures, they are challenged to move forward with bold action plans.
