6 Key Benefits of Cloud-Ready Grid Software

As utilities continue to deploy grid software solutions, it is becoming clear that on-premises deployments are no longer enough on their own. Increasingly, utilities must have a variety of deployment options to choose from – on-premise, cloud, hybrid, and edge.

Utilities can best capitalize on deployment flexibility through cloud-ready grid software, like the solutions of our GridOS® grid orchestration software portfolio. Cloud-ready grid software can be deployed in any of the above environments – whichever is the most efficient, effective, and most practical deployment for a given use case.

Let’s take a look at some major benefits of cloud-deployed grid software:

Utilities are facing major increases in the volume of their grids’ assets (especially in terms of DERs), smart sensors, and phasor measurement units (PMUs) – and the data generated by all three. The compute power and storage needed to make grid automation possible needs to scale proportionally with this immense and rapid growth.

In addition, utilities need to increase their resiliency to ever-common severe weather events. Running the types of detailed multi-interval simulations and scenario analyses required to understand near-term reliability threats and developing effective plans to correct or prevent them requires significant compute power and storage. However, this scaling in compute and storage is needed only for small durations of time (during the event or while the simulations or analyses are run).

Finally, as utilities’ workforces become more decentralized and distributed and grow beyond the control room, the software used to manage the grid also needs to scale up to accommodate large numbers of remote users located beyond the control room.

In any of the above situations, high compute power and/or storage are needed fast – much faster than on-premises data center hardware can offer. Cloud-deployed solutions can quickly and easily scale up or down as needed, ensuring higher compute power or storage availability when needed. This improves resiliency and reliability, which are especially important during events such as outage situations, or while running large-scale simulations or scenario analysis, when it is not possible to add on-premises data center hardware quickly.

Additionally, it is not cost-effective for utilities to spend hundreds of thousands of dollars on on-premises hardware to provide this seldom-needed compute power for small durations of time. The most practical and economical option is to invest in cloud-based compute elasticity that can be tapped whenever needed. Also, non-production environments – such as training, development, or quality assurance – are often underutilized compared to production environments, leading to idle hardware capacity and unnecessary support costs.

By transitioning these environments to the cloud and paying only for what they use, utilities can reduce their costs while achieving flexibility.

The electric grid is a national security concern. Digital threats such as cyberattacks, malware, and ransomware events are increasing in scope and frequency every year. Utilities, especially smaller ones and those limited to on-premises environments, may lack sufficiently advanced security measures such as continuous and consistent network monitoring, encryption features, latest updates, and security patches to stay on top of the latest cybersecurity advancements.

Investing in cloud technologies opens a world of new cybersecurity possibilities for utilities. Cloud deployments provide utilities with a cloud provider’s own, unique cybersecurity measures, which are typically more sophisticated and robust than most utilities’ internal capabilities. Specific offerings can vary between providers, but some of the most important include:

Traditionally, OT systems focused on data collection from PMUs to enable monitoring and control of the physical grid network. IT systems, on the other hand, focused on data-centric use cases that required significant computational and data storage resources.

Today, grid IT-OT convergence is becoming increasingly relevant for utilities because of the extension of IoT sensing to grid and grid-adjacent assets, resulting in increasingly large volumes of data. In many cases IoT sensors can also analyze data for better decision making. As more and more grid assets like DERs contain built-in IoT sensors, the OT side of the grid is becoming more distributed and connected to the larger sensor network. It is also becoming more data-centric, just like IT systems.

In addition, solving many emerging grid use cases requires access to large amounts of data from both IT and OT systems. For example, utilities with high DER integration need to optimize DERs to ensure grid reliability from an operational perspective, while also looking at economic considerations including unlocking DER flexibility to sell excess energy in local markets.

To achieve this techno-economic optimization, utilities need to bridge DER asset and program data, forecasting data (including external weather data), grid OT data, economic and market data, contractual constraints, customer billing data, and settlement data. All these data sources are spread across IT and OT systems and need to be brought together to drive data-driven decision making and optimization. To address the need for coordinated optimization in a distributed data-centric architecture, AI/ML will play an increasing role and will in turn require access to vast amounts of data from both IT and OT system domains.

As grid IT systems tend to be deployed in cloud, efficient convergence of IT and OT systems will require that the deployment path forward for OT systems embrace cloud deployment as well. Additionally, AI/ML-based software applications tend to be modern, data- centric and microservices-based in nature and as such are purpose-built to be deployed in the cloud. The capability to tap into cloud-based compute and storage elasticity as needed and pay only for those resources consumed will be key to cost-effective solutions, given their resource-intensive requirements.

The sheer size of the grid and the speed at which its operational decisions must be made are resulting in a trend toward distributed optimization, and the execution of applications at the grid edge due to latency/timing requirements. The traditional approach, involving (1) sending grid data to a centralized location to be processed and (2) determining and transmitting the required control actions, becomes extremely challenging from the perspective of grid scale, its distributed nature, and the need to meet required response times due to latency.

Edge computing is critical to address such use cases effectively and efficiently. For edge computing to work successfully, cloud deployments are critical as the distributed nature of the network means that the decisions are taken at the edge and are communicated back to the control room, often over the internet. Such scenarios call for edge deployments, and grid software built with cloud technologies is suited for such use cases.

Utilities can use cloud technologies to help accelerate solution implementation and speed up upgrades by automating testing and deployment of software to secure, customer-specific cloud environments. This approach accelerates solution deployment while ensuring quality and time-to-value. All of these pave the way towards effective grid orchestration and enable a sustainable, reliable, and resilient grid.

Finally, regulators are re-thinking the rules regarding rate base eligibility for cloud-based investments. This enables utilities to recoup costs tied to prudent shifts in operational infrastructure.

For more information on the importance of cloud in unlocking grid software deployment flexibility, check out our whitepaper on the topic.