The Intelligent Grid: Smart, Autonomous, Predictive
By Nishant Jain
The Intelligent grid transformation will be the backbone of our new energy infrastructure. CIC defines an Intelligent Grid as having 3Ds – Digitalized, Decarbonized and Democratized – which is actualized by some key elements of ‘smart’, ‘autonomous’ and ‘predictive’.
Smart – The key element for a system to get “smart” is its ability to exchange signals between its components. The underlying technology has evolved from the Internet of Things (IoT) toa combination of low power sensors, connected device, computing, and software to enable digital monitoring and control of systems.
For example, a smart building orchestrates various systems within its ecosystem, such as HVAC, lighting, and security, within a single platform, allowing disparate systems to communicate and enable optimum performance. A smart mobility solution rests within this system and enables our vehicles to communicate key parameters like fuel status, engine parameters, etc. along with intelligent fleet management to know when devices can be used for storage, or balancing the grid.
From the utilities’ perspective, these data-points from buildings, e-vehicles, and public lights provide real-time, accurate status visibility and enable customized solutions, which greatly enhance customer engagement. For instance, an energy supplier can offer a customized electricity rate plan to its consumers, which would encourage a consumption shift towards non-peak hours, ultimately creating a win-win scenario. Such an ecosystem brings a shift from “autocracy” of few large utilities to “democratization” in which utilities and consumers both participate to make optimum decisions.
Prosumer engagement and impact matrix
|Customer Engagement Initiatives||Description||Impact on Energy Consumption Reduction|
|Customized Rate Plan||An optimum plan based on a user’s consumption data, the respective sub-station region, and utilities’ generation pattern||High|
|Value-added Services based on Energy Consumption Analytics||Actionable insights for reducing consumption; rewards for behaviour shift||Medium|
|Customized Third-Party Offers||Customized offers for appliances, software from third party providers with clear ROI based on data shared by utilities||Medium-Low|
In many developed countries that have a deregulated retail electricity market, which allows for a choice of suppliers and price negotiation, customers can choose from a portfolio of customized rate plans that best suit their needs. In turn, suppliers provide the best customer service to retain their market share.
Beyond the utility, customers can also interact with each other in their communities, leading to opportunities for peer-to-peer trading of surplus self-generated power sources like rooftop solar PV, diesel gen-sets, fuel cells, battery storage etc.
Autonomous – As the grid becomes smarter with millions of connected devices and sensors, decision making shift towards decentralization. Grid control mechanisms also mirror this shift of decentralization and point towards a system made of scalable cellular blocks. which can independently function off the grid when isolated.
An autonomous grid with a central control hub and with autonomous parts brings immense resiliency to a system by eliminating single points of failure. Apart from an AI-based distribution grid system, real-time speed is the key in its success, and underlying technologies which enable the transition to autonomous grids are edge computing and mission-critical communications. The set-up of real-time communication and resiliency sets up the perfect stage for the integration of renewables in the form of DERs and microgrids. Thus, autonomous grids enable “decarbonization” as the intermittent nature of renewable generation requires high levels of resiliency.
Predictive – A flexible and resilient grid has a price to pay as it carries a higher “standard deviation” of control parameters. Meaning, real-time reactive response to contingencies is not sufficient to meet the required Service Level Agreement. With the availability of significant amounts of grid system data and associated environmental data like weather and traffic information, emergency situations can be predicted and pre-emptive corrections can be made. The underlying technology is the cutting-edge of self-learning algorithms at the core of today’s AI engines, which will improve over time. The systems which can predict contingencies like weather anomalies, physical attacks, and cyberattacks while providing reactive solutions lead to the complete “digitalization” of the grid.
In the coming years, it will be imperative and interesting to see the complex confluence of cutting-edge technologies, innovative devices, and new standards and protocols disrupting the power value chain. These changes to the industry will be key in mitigating the effects of climate change via decarbonization, risk reduction by decentralization, and cost optimization by digitalization. The changing climate of the power industry presents new opportunities as well as risks for the players in the ecosystem, in which CIC is well positioned as an enabler in this transformation.