The world has entered into the fourth industrial revolution. Beginning with power generation through steam and hydraulic turbines in the late eighteenth century to increased industrialisation through assembly lines in the twentieth century and electronic automation through computers/internet in the period 1970s-2000s, we have moved to digital supply networks today. In this digital age, appliances, consumers and supply networks are digitally connected and can be controlled remotely through the integration of information technology (IT) and operational technology (OT). Technology advances have brought in disruptive changes across sectors including energy, automobile, transportation, retail, aerospace and hospitality. The foundational technologies (that is, processing power, storage and bandwidth) have been growing exponentially for almost five decades.
Furthermore, the cost of technology such as transistors, computing, storage and the internet has undergone a drastic reduction over the years. The merger of advanced OT with IT, data and analytics is driving another industrial revolution – Industry 4.0.
Over the years, there has been a trend of increasing convergence in IT and OT. While in the 1960s-70s the use of data was limited to the corporate domain with the use of mainframe systems, digital networks such as primitive enterprise resource planning (ERP) without integration emerged in the 1980s-90s. During the 2000-10 period, some level of interface between IT and OT networks was witnessed with new corporate systems demanding data from operations. Beyond 2012, the integration of IT and OT systems has grown gradually with industrial IoT and smarter devices/systems coming to the fore. Now we are witnessing the impact of IT and OT integration in our daily lives in the form of smart homes (with digital assistants, smart appliances, etc.), connected transport systems (for instance, online cab booking portals) and connected logistics (for instance, courier or food delivery).
The benefits of technology convergence are aplenty. One of them is a reduction in utility costs due to optimisation of technology spend, elimination of duplication in technology, project scaling, knowledge sharing, and consistent approach to supply and vendor management.
In addition, convergence leads to increased asset efficiency and reliability through improved predictive and unplanned maintenance, better decision-making, reduction in truck rolls and unnecessary field crew dispatches, and improved network forecasting. Further, convergence enhances compliance with health and safety standards, mitigates the risk of future integration project failures, and increases regulatory and environmental compliance.
Lastly, this convergence improves customer service through real-time response to customer enquiries, and greatly improves crisis response and planning.
Key lessons – technology convergence
Technology convergence is not just about implementation – people and effective governance are the key determinants of success. Utilities should use a common model so that both IT and OT talk the same language. The use of standard technology operating systems, databases and middleware in OT systems creates the need for new processes like upgrade cycles, release management and practices that were traditionally reserved for IT only.
It must be noted that during convergence projects, defining the tangible benefits is not easy as they may/may not be sizeable. Also, there are multiple ways to stage the roll-out of technology convergence and many benefits can be obtained without combining the organisational structures. The utility’s leadership/chief information officer (CIO) should be clear on what to do with the data from technology convergence and clearly articulate the integrated application landscape.
The issues of security, operations integrity and risk management have to be at the heart of any technology convergence project’s design. As IT becomes prevalent in OT, the vulnerability of OT systems is increases. The convergence project should, therefore, outline robust methods for protecting OT to ward off any possible cyberthreats or cyberattacks.
Analytics in power utilities
Industry 4.0 is transforming power utilities by increasing their reliability and efficiency. Globally, power utilities are keeping pace with technologies such as OT/IT, advanced robotics, big data, autonomous machine learning, internet of things (IoT) and artificial intelligence (AI), among others. Analytics forms a key part of these technologies and requires a strong underlying information gathering system for transmission and distribution (T&D) and generation utilities. While network management and customer management systems are crucial for the T&D segment, distributed control systems are being deployed in generation.
Many state-run T&D utilities in the country are gradually deploying network management systems such as meter data acquisition system, GIS, supervisory control and data acquisition, etc. To that extent, the sector is still a step away from gaining the advantages of analytics and related systems. In the near future, analytics is expected to play a greater role in applications such as condition-based monitoring and predictive asset maintenance. Network operators can combine analytical tools and remote sensors for maintenance as well as the replacement of poor assets.
Further, the use of analytics in customer management can redefine customer experience in terms of redressal of complaints and providing quality power supply. Utilities are also deploying energy management systems, which can help discoms detect unusual energy consumption/ electricity thefts. Meanwhile, demand-side management and demand response systems can help discoms effectively meet electricity demand through energy efficiency measures and scheduling of certain curtailable loads.
In the generation segment, most newly built power plants have access to 5,000-6,000 real-time sensor information per unit in the distributed control system (DCS), which is capable of capturing up to 15,000 data points. These data points are based on parameters such as temperature, pressure, vibration and noise levels. which help in assessing the performance of key equipment in real time. Traditionally, however, inputs from the DCS are used by the control room engineers only to monitor the performance of various equipment, and not just for analytics. In the recent past, there have been innovations on predictive analytical tools that gather DCS data (OT) and also ERP data (such as maintenance logs, asset logs, operating histories, etc.) to pick-up patterns of machine performance under various operating conditions, which in turn, is used to predict potential failures and improve efficiency and reliability.
In today’s digital age, the pace of technological disruption is undoubtedly set to increase. With new technologies such as IoT, AI, robotics and virtual reality, it is imperative for utilities to rethink not just their technology strategy but also their entire operational strategy. IT and OT had long been separated into different groups but followed similar trajectories of growth and are now being integrated with one another. As physical assets such as substations, transformers, transmission lines, etc. are increasingly being equipped with sensors and communication systems, streaming of data through intelligent IT systems is possible that provides real-time insight and control. Now as OT and IT blend into each other, their capabilities, processes and best practices need to blend as well.
(With inputs from a presentation by Gaurav Angira, Director, Energy and Resources, Deloitte Consulting)