Technology can drive efficiency, reliability and innovation in most industries, including the energy sector. No wonder digital transformation is one of the buzzwords of today’s businesses across all sectors.
Digital transformation pertains to incorporating technology into business processes at scale to create or maximise value. In the energy industry, industry players can integrate digital technologies to address specific challenges, from grid management to predictive maintenance, enhancing overall operations.
Many detailed applications and real-world scenarios demonstrate the impact of digital solutions in the energy industry. You can learn about them in detail from providers of data centre management, surveillance cameras, command and control software, specialised security systems, x-ray scanners, cyber protection, and other technology products, services and solutions. You can also read on to learn about a few of these applications.
1.   Smart Grid Management
Advanced metering infrastructure (AMI) and load-balancing algorithms are crucial to smart grids that enhance electricity distribution and management. AMI, which includes Internet of Things (IoT) metres and machine-to-machine (M2M) sensors, continuously logs and reports electricity usage and availability across the network. It can alert a utility company about power outages and their causes.
Artificial intelligence (AI) software can also use real-time power demand data to make instant power generation and distribution decisions. Load-balancing algorithms, meanwhile, can automatically redistribute electricity during peak demand, preventing blackouts.
Data collected through intelligent sensors and devices can also be used in training machine learning (ML) models. ML models can help a power company make accurate projections about future demand, supply and prices.
This can help it gain the upper hand in negotiating contracts with power suppliers, projecting end-user prices and predicting future consumption. It even allows the power company to create a buffer against anticipated pricing surges (or benefit from projected unit price reductions) through options and futures.
2.   Renewable Energy Integration and Decentralised Energy Systems
Power companies can use distributed energy resource management systems (DERMS), actuated via IoT and a combination of hardware and software, to facilitate communication among network assets that generate (including renewable energy plants), store or consume energy. This enables decentralised energy systems.
For instance, it can give rise to virtual power plants (VPPs). VPPs can aggregate power-generating, power-storing and power-consuming assets, linking them together into one decentralised system for trading power according to supply and demand levels.
More importantly, these decentralised systems can easily accommodate renewable energy assets. Power companies can incorporate renewable energy into their electricity networks to meet sustainability objectives. This can enhance their brand reputation and open doors to environmental, social, and governance (ESG) investment opportunities.
3.   Predictive Maintenance and Asset Management
Predictive maintenance relies on IoT devices, M2M sensors, edge computing, cloud computing, data analytics, 5G, eSIM, and other technologies to log and monitor the health of critical equipment. Sensors on turbines in wind farms, for instance, continuously collect data on vibrations and temperature. Meanwhile, sensors in oil pumps can deliver a continuous stream of pressure, flow and temperature information.
Data collected can be sent to automated alert systems. For instance, if equipment and operating conditions approach dangerous levels, an alarm goes off in the control room, flashes across video monitors and is even sent to the handheld devices of in-charge personnel.
The application goes beyond automated alerts, however. For instance, Shell’s Perdido platform in the Gulf of Mexico had a recurring problem it had been battling for 10 years. The pumps responsible for separating oil from gas regularly malfunctioned. Whenever they did, the rig had to scrap an entire batch due to contamination.
The pumps weren’t so easy to maintain, either. Their inaccessible location underwater made regular preventive pump maintenance impractical and expensive.
However, the pumps had sensors. These sensors continued to transmit data to the platform’s computers, providing temperature, pressure and chemical signature information, among others. However, there was too much data and no easy way to make sense of them without data analytics. That said, they left the company’s data analysts stumped – until they used machine learning.
They ran the data through ML models to find recurring patterns that signified impending pump failure. With critical input from an experienced pump operator, the data analytics team refined their algorithm until they experienced a breakthrough. They found that a specific chemical signature preceded 70% of pump malfunction incidents.
With AI automation, the platform set up an early-warning system. Thus, whenever the sensors log the presence of the chemical signature indicating a likely disruption, it alerts the control room. Therefore, the platform can shut off and maintain the concerned pumps, in the process preventing a loss of output.
More importantly, this enabled the shift from preventive to predictive maintenance. Shell didn’t need to stop production for unnecessary upkeep that did not even prevent contamination losses. They only needed to cease operations when the data indicated a disruption was imminent, thereby justifying the steep maintenance costs.
This is the economical option for two reasons. First, there’s less downtime; predictive maintenance is done on an as-needed rather than a regularly scheduled basis. Second, this approach provides much better returns, as businesses can eliminate unnecessary upkeep and associated costs.
4.   Demand Response Programs
Power plants can use demand response programs to adjust electricity consumption during peak periods. Smart thermostats and connected appliances communicate with the grid, allowing utilities to shift or reduce demand without compromising comfort. This prevents grid overloads (which can lead to downtimes) and reduces the need to procure power from costly suppliers to service peak loads.
Ways Technology Helps the Energy Sector
Technology can help energy companies become more efficient and productive. It also drives product and service innovation in the industry.
Various technologies (combined with design and orchestration assistance from a system integrator) have various potential use cases in the energy sector. For instance, power plants can manage their power grids better, and industry players can aggregate renewable energy suppliers and consumers in a decentralised energy distribution system. Oil and gas companies can replace preventive maintenance with predictive maintenance, minimising costs and downtimes and maximising the value of maintenance procedures. Finally, power plants can use technology to prevent grid overloads and reduce the cost of responding to power demand surges.
