The Future of Smart Grid
The promise of the “Smart Grid” to realize a sustainable, reliable and a modern 21st electricity grid has been underway for over a decade. The utility industry has made significant investments with over $ 3Bn spent annually which is likely to continue and grow. Most companies began their smart grid journey with deployments in smart metering infrastructure, so it in the early days the term smart grid was synonymous with smart metering. Today, smart metering is quite matured with a penetration level of over 50 percent in the US. However, the smart grid realization has moved beyond smart meters to building intelligence in the transmission and distribution network. While the adoption for intelligence and automated control in the transmission system predates the emergence of smart grid in the 2000s, it is the distribution system where the new value pools are realized. A significant number of utilities are already leveraging the benefits of advanced distribution management systems that allow not just monitoring of feeders and the “last mile” - but also can control devices to isolate faults, restore systems automatically, conduct voltage optimization and a variety of energy efficiency functions either through applications like conservation voltage reduction or through a combination of technology and policy driven demand response programs. While the penetration levels are still modest, many utilities have active programs that will become operational over the next two to three years.
As we look into the future, the promise of the smart grid is far from over. Over the next five to ten years, a whole host of new possibilities are emerging. The macro trends centered on decentralization of resources, digitization, and decarbonization are driving the need. The sector’s supply shift towards renewables and the need to incorporate distributed energy resources (DERs) are introducing new operational challenges. Coupled with the changes in demand side with the emergence of smart cities, efficient buildings, smart and connected factories, and electric vehicles, the sector is facing extraordinary levels of complexities that make smart grids a necessity. Increased penetration of intermittent renewable resources at the distribution level will require smart grids to counter new issues created by reverse flow in transformers, dynamic protection and system configuration settings, and voltage and system excursions. As behind the meter resources grow, real-time monitoring will become a requirement for better forecasting, inform revenue planning and real-time system balancing.
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Smart grid will be the platform or the foundation that will enable aggregation of real-time information on electricity consumption and usage with the supply side—which includes generation, transmission and distribution. For instance, the gap that exist today in obtaining real-time information to make intra-day trading decisions creates inefficiencies and forecast errors due limited observability of production and delivery cost (e.g., fuel cost, energy costs, labor costs, outages, local generation, T&D system conditions). With a proliferation of DERs there is already a recognized need for creating a distribution market with the distribution network as a platform for both technical connection as well as for real-time market transactions. To enable such a market that ensures reliable market operations requires a broad range of attributes from the smart grid. The commercial framework has to work in conjunction with system balancing with resources as varied as microgrids, battery storage, solar arrays, electric vehicles, etc., coupled with dynamic load balancing and configuration management of the distribution system
2. Deployment of digital twins:
In the last few years, the term digital twin is used to refer to a digital representation of a physical system. The primary purpose of a digital twin is to conduct real-time optimization by feeding large amounts of data. Digital twins go beyond traditional simulations and models with their ability to replicate the situational context the system, processes and life cycle of the physical system. By applying self learning techniques, artificial intelligence and machine learning, these digital power system models are adaptive and provide a virtual copy of what is happening in the physical world. Digital twin will require to use smart grid data among other data sources to provide one integrated view of the physical system—for asset and system planning, real-time grid operations to operating ancillary support services. With growing complexity in the system, the needs will no doubt keep expanding for more integrated simulation versus siloed analyses to provide insights on asset behavior, making tradeoffs between pushing the operating limits, and guiding capital and resource allocation.
3. Implementing Flexible operations
As generation resources become intermittent and fragmented with decentralization, intraday load profiles become highly uncertain and volatile. As different fit for purpose resources switch on and off, resulting in a rise in the number of operating modes, operational flexibility will become a performance defining attribute. Being able to respond to changing system dynamics will not only require better forecasting techniques based on data, but also fast response controls and in certain cases predictive control of operations. Flexibility as an ancillary service has the potential of becoming a market by itself.
As the industry embark on the next chapter of the smart grid, companies will have to ask themselves how much to bet and invest in their future positioning. For years, their focus, capabilities, and talent structure have been on specific technologies. The future will pose more transformative questions that will have lasting impact on business models, culture, and day-to-day execution. Traditional roles will change including the role of the CIO. There will be a need to acquire and retain data and analytical talent. And adoption and execution of new and emerging technology must be fast and part of on-going business.
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