Technologies that Drive Digital Transformations for Electric Utilities

We often hear in various industry forums and discussion circles that the digital transformation that unlocks and enables modern grid orchestration must rely on more than technology alone, and include fundamental shifts in organizational structure, business models, and culture.
That is true, but technology will nonetheless still play a critical role. Over the next two to five years, five key technologies will have a profound impact on the way electric utilities build, deploy, and maintain grid orchestration systems. They are as follows:

Breaking down applications into smaller, manageable microservices helps navigate the increasing complexity of systems as the number of functions and their interactions grow. Microservices – and the container technologies currently used to implement them – bring modularity, scalability, and mechanisms to manage the complexity of the expanding interplay of functions of large software systems. When combined with efficient messaging, container orchestration, and modular cybersecurity, microservices form the basis for delivering extensible, event-driven, Zero Trust grid security-compliant architectures.

EaC is an approach and mindset that advocates representing all aspects of an application’s lifecycle, infrastructure, and configuration as code – human-readable and version-controlled files that can be executed by machines. Embracing a code-centric approach to infrastructure and configuration management simplifies change and auditing processes, enhances collaboration, and accelerates development and deployment cycles.

Infrastructure as code (IaC), application composition as code, workflows and configuration as code, and more, all allow modern version control systems and deployment mechanisms to dramatically simplify deployment automation, rollout, rollback, and change control in a modern software solution. This enables a secure and fast evolution of software functions, with significant time-to-value benefits.

Strictly speaking, the term ‘cloud’ simply implies a particular computing style based on the use of scalable and elastic capabilities to deliver defined services using internet technologies. Leveraging the power of cloud (whether public, private, or hybrid) computing liberates utilities from physical infrastructure constraints and opens the door to resource elasticity, capacity on demand, and time of use-based billing. Moreover, adoption of cloud-native technologies can unlock some of the advantages of cloud architectures, like flexible resource utilization, even in air-gapped and OT environments. Grid orchestration software built with cloud-native architectures allows the cloud to be readily adopted when the cost, resource, regulatory, and security dimensions make sense.

Digital transformation requires connecting to data across many different systems via many different technologies. Traditional software applications, regulatory and security segregations, and the business organization itself, create data silos, which are among the biggest impediments to digital transformations. DataOps provides the processes, practices, and machinery to securely free data from these traditionally isolated data repositories. It is at the intersection of Agile, DevOps, and Lean practices, using automation to accelerate time-to-value, time-to-insight, promote collaboration, enable and expediate new data applications and analytics, and improve the reliability of operational data.
Streamlining data operations and implementing a DataOps approach ensures that data flows seamlessly across the entire organization. This in turn fosters the data-driven decision-making that empowers utilities to harvest valuable insights and optimize their strategies. A data fabric affords the technologies that allow on-demand access to data across the organization. It provides the ability to easily find, integrate with, and combine disparate data sources to transform data into actionable intelligence. It provides the infrastructure, governance, and life-cycle management for data-centric analytics, simplifying the process of extracting value from data and supporting increasingly diverse data landscapes.

The integration of AI/ML capabilities revolutionizes utilities’ ability to process and analyze vast amounts of data, uncover patterns, and predict future trends. This intelligence, alongside physics-based models, enables predictive actions and guidance, improved risk management, forecasting (e.g. for load and generation) and automation, all of which maximize efficiency and reliability. The ease of access to well-managed data enables AI/ML analytics to be rapidly developed and robustly deployed alongside physics-based analytics. Securely delivering AI/ML in operational environments will enable leveraging progressively intelligent decision support and policy-guided smart automation.

For more information on the above technologies and how they can help drive your digital transformation, I invite you to read my recent whitepaper on the topic.