The Distribution of Energy and Role of Automation
Originally published in Automation &Distribution Journal, June/July 2021
Energy distribution systems, an inter-networked and intricately balanced collection of sources and load, are experiencing a time of unprecedented change. Following decades of underinvestment, energy systems have become a priority area to address local issues of critical infrastructure rebuilding and
resiliency as well as customer empowerment while also supporting the wider goals of climate change, energy sustainability and economic development.
Automation, which is an inherently data driven effort, has become an integral part of the energy industry. Evolving from pneumatically driven components designed to mimic manual human actions, initially automation was implemented through relay logic or a series of standalone controllers, depending on the industry sector. Today, programmable controllers and protection devices are almost ubiquitous through the industry – used for data collection, breaker reclosing, metering points and everything up to full plant and system control. This has a number of results – the first is that it challenges staff to develop new skills, the second is that it allows for remote visibility and operations of facilities and the third is that it enables data collection which sets the stage for welcoming the next industrial revolution of cyber-physical systems and AI. However, these essential systems also open new
cybersecurity risks, create dependence on communications infrastructure and challenge traditional architectures.
Power plants made their debut in the eighteenth century and were initially controlled either manually or through the use of mechanical automation. As technology advanced, the means by which to maintain stability of the system and to protect the equipment also advanced. Automation has evolved through
manual, pneumatic, electric and now programmable means of control. This advancement has been spurned by technology and the industrial revolutions and has generated positive changes within the industry. However now we are embarking upon a different set of changes - the thought process underpinning the planning and distribution of energy is changing. Although we will likely still have
large centralized harvesting centres that are close to the energy sources which capitalize on networks of transmission and distribution infrastructure to deliver the product to customers, there is a shift taking place towards the acceptance, use and deployment of Distributed Energy Resources (DER). This shift moves energy harvesting closer to the user and reduces the dependence on costly and vulnerable transmission systems and transforms distribution systems into bidirectional networks.
Programmable automation systems started out with limited I/O, only collecting data from sensors and transmitters connected via voltage and current signals to costly centralized processors for interpretation, calculation and storage. Modern automation systems also have the ability to be distributed, placing I/O, processing and in many cases intelligence in the field using both distributed I/O systems, networked processors and edge devices. IIOT enabled devices and standardized
communications protocols have streamlined the manner in which data is organized, formatted and collected, but have also enabled collection of greater amounts of data. Currently, systems collect all of this data, serve it up for realtime and short term trending functions and then send it off to longer term storage. Having all of this data stored in a data historian, data warehouse or data lake allows visualization and analysis. It empowers the organization to identify patterns and correlations in the data which triggers action. This provides the foundation for data hungry advances such as machine learning, predictive control, mixed reality and applications of AI. Enterprises now have the unprecedented ability to automate both industrial and business processes.
Prosumers now have the ability to both produce and consume energy on the same network, all monitored, controlled and regulated by IIOT enabled edge devices feeding optimized system models to determine needs, constraints and pricing. In addition to this, the increased level of data inflow also provides support to
additional functions within the energy ecosystem. To use an example to illustrate, most energy companies are asset intensive organizations. These assets require time and effort to ensure that they are in optimal working condition to deliver energy to consumers. Starting at the equipment level, machine learning algorithms, such as decision trees, fed from real operating data have the ability to optimize the operation of the equipment and hence the operation of the system overall. Additionally, similar algorithms can be used to extend preventative maintenance program into predictive maintenance programs which have the potential to cut costs, which is immediately reflected in the bottom line. If we
collect all of the decision trees within the maintenance programs for the equipment, then we have a random forest to deploy the asset management program. Ensuring that all of the assets are kept in the best operating condition possible ensures that availability is maximized. This sets the stage to develop
and implement economic and control mechanisms that allows dynamic balancing of supply and demand – a transactive market. A transactive energy approach provides greater efficiency of usage of grid assets, including generation and storage. Additionally it also provides greater resilience and reliability while engaging the consumer by providing them with choice. Traditionally the realm of
large utilities and enterprises, now individuals would have the ability to transact on the market with the resources available to them. Automation provides the marginal pricing, time shifting and the ability to maximize the use of assets, not to mention the optimization. The convergence of IT and OT systems
allows transactions to be automated in the background, allowing energy to be generated and delivered in exchange for value. This continues until a sufficient supply is established to stabilize pricing and satisfy demand. Although prosumer ownership of renewables works well on the electrical side, it is not as common in other areas of energy. Consequently, we see Energy as a Service models being
established by manufacturers, consultants and utilities. This concept is already being used with Virtual Power Plants (VPP) whereby the utility owns the generation and interconnection infrastructure placed in a distributed fashion across residential rooftops and simply rents the rooftop space from consumers.
The utility uses the energy generated to offset purchased power and the homeowner receives a steady income source from the utility.
Where does this leave us? What is the path forward? The energy industry will continue to evolve. It has always been dynamic, fearless and willing to be at the edge of technology. There are many advancements that have already started such as VPPs, that will continue to be innovations within the industry. We have been seeing the convergence of IT and OT for some time now with the introduction
of client/server technology, thin clients, virtualization and “as a Service” models into the automation world. These technologies both supply greater levels of data and also provide access to a wider range of data sources to the automation system which, in turn, allows for more accurate predictions and better decision making. This is complemented with OT advancements such as two wire ethernet and modern standardized IP based communications protocols. Data lakes and Data warehouses are being implemented to capture and store the new levels of data being generated and cloud based infrastructure will enable and simplify analysis and processing of the data. Companies look to machine learning and
machine learning algorithms to identify patterns within their data and manage and understand the information in addition to making predictions regarding courses of action. Digital twins are being used to plan, design and test systems prior to implementation to reduce the time from concept to production. When combined with technology such as augmented reality, they can form a powerful diagnostic and troubleshooting tool that is safe and allows technical staff to diagnose and understand issues before arriving on site thus ensuring that they carry appropriate tooling and safety equipment with them so that outage time is reduced and safety is increased.
Artificial intelligence, machine learning and augmented reality will continue to be applied to new situations and other areas of the energy value chain. While I forsee an industry that will evolve with and embrace the technology of today and of tomorrow, the industry has to adapt quickly to new technology and innovations while tempering the implementations with common sense, so as to observe ethical data practices, as we enter the age of intelligent machines.
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