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The Industrial Internet offers the possibility of remote monitoring and control of the infrastructure. It provides the potential to understand and respond to events in real-time. We can re-imagine problems like load shedding to be able to pick off specific appliances during supply shortages. If we want to make use of renewable resources like wind and solar, the time of use is very important. So, we can install a new class of energy aware machines and appliances that suspend discretionary power use until the time is right. We can build an intelligent infrastructure that can respond to events in real time and avert problems and the infrastructure itself can be made robust, self-healing.
The first challenge is to add the enabling technology into the infrastructure. These are telemetry and control systems; a typical network requires thousands of sensors, which provide information about the status. Then there are the control devices, which are the smart meters and other network control equipment. There is a cost associated for the necessary equipment and engineering roll-out. Following this deployment, we can receive huge amounts of data from across the grid, more or less on a continuous basis, and we have the basic mechanism to enable remote control. The problem is how to process this information, what are the algorithms? Moreover, how do we process it in a timely manner?
There are also new problems for example around security. With the power to control every aspect of the infrastructure remotely, there is also the possibility to hack and interfere or wreck the asset. Digital security is a complex area, and so the exposure is real and with the advent of any new technology, there is uncertainty. Around the world, the roll out of smart meters has sometimes met stiff resistance from consumer groups citing privacy issues and fears about demand based pricing and its effect on vulnerable members of the community?
The Asian and Pacific regions are no less diverse than the world as a whole in their adoption of the digital grid. There are however a number of dimensions. We can look at roll-out of smart meters, which has been led in Australia-New Zealand and North Asia: China, Korea and Japan. However, there is a variance on how the devices are used. In some cases it is just automation of standard practices, for example, a remote disconnect. Though there is a possibility to implement, for example, real time pricing, or discrete load shedding, there does not appear to be too many examples.
Leaving the consumer aside, there is a steady growth in the amount of intelligence being added to grids and networks. Again it is a question of how well is the technology exploited? Certainly Utilities will want to monitor data collectively for alert conditions.
There are two areas ripe for improvement. One is the use of visualization, both in the field, where we have the possibility of Google Glass type enablement of field workers, and also back at the central office where we can “fly through” the infrastructure and “see” problems like failing equipment. The second area is the wider use of predictive analytics and data mining techniques, and deploying these in real time for infrastructure that can adapt and prevent problems occurring.
In terms of valuing digital adoption, it is disadvantageous that most grid and network companies in the Asian region are regulated government businesses and are measured on traditional performance frameworks. So, for example, a discrete load shedding program may provide enormous benefits and alleviate supply shortages considerably, without actually alleviating supply shortages, but the problem becomes one of incentive and reward for the grid company. Unless government and regulators can frame the promise for their constituents, there are not the normal commercial incentives for innovation.
The best examples are government funded or original R&D by the Utilities themselves to pilot new ideas successfully and then to lobby for the regulatory or legislative changes required to put adopt new practices and standards. Governments and utilities can undertake projects to adopt new technologies. We also see entrepreneurial companies developing new products and services, for example, in the areas of high performance computing that tackles the big data problem, and which every industry faces in one form or another. At the infrastructure level, we see tools for data mining and predictive analytics and new are tools for visualization.
There is also a large and growing number of operational technology equipment to manage infrastructure. These are not mere sensors, but advanced technology assemblies, for example, distribution management systems, SCADA systems, and so on. These have grown up as purpose built proprietary systems and while robust, often they are not easily incorporated into the wider IT landscape. The IT-OT integration challenge is now being addressed both on a technology level and organizational level to provide a holistic technology landscape.
Automating the infrastructure will see downward pressure on field engineering labor forces, especially on the maintenance side, as less on site work is required. Routine inspections and preventative maintenance will give way to event based maintenance as systems will alert for ailing components.
Site work too will see more generalized technical engineering, because maintenance tasks will be better supported by technologies such as visualization, on-site instruction, and virtual skilled specialists aiding workers in the field. Thus, we can see outsourcing in this area too, as more broadly based specialist field engineering can tap into wider range of engineering infrastructure.
On the other hand, the old mechanical grid infrastructure is giving way to a far more sophisticated machine and as such, needs more tech savvy workers to support and maintain it. Also on the increase for electricity infrastructure is a more complex system that caters for varying renewable supply, micro and home generation. The uptake of electric vehicles to harness clean energy will also contribute to a more sophisticated infrastructure. Thus, more labor support is required to manage such a system.
