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LoRaWAN and NbIoT: Drivers of Smart Metering in Electricity Sector in India

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IoT (Internet of Things) has now entered the lifeblood of all the industrial sectors and utilities (electricity/water/gas/district heat). In the utility sector, thanks to advances in IoT technology, data processing and analytics, smart metering is playing a pivotal role in bringing in much-needed efficiency, transparency and superior customer services.

IoT (Internet of Things) has now entered the lifeblood of all the industrial sectors and utilities (electricity/water/gas/district heat). In the utility sector, thanks to advances in IoT technology, data processing and analytics, smart metering is playing a pivotal role in bringing in much-needed efficiency, transparency and superior customer services. The mechanism of smart metering is simple: Consumption Data is collected (by the smart meter) and sent to the cloud via an internet connection for processing.

With other utilities such as water, gas and district heat, consumers are passive recipients of information from the utility. But with electricity, integration of intermittent renewable and EV (Electric vehicle) into the smart grid has transformed consumers into prosumers. That means both consumers and prosumers can participate in flexible demand management. But the flexible demand management needs real-time (15/30min) consumption data from consumers (or prosumers) to gauge micro-generation of electricity and individual consumption habits. This real-time data can be aggregated, analysed and processed to generate meaningful insight (actionable information) enabling the utility to predict demand and design dynamic pricing to shift the load from peak hours to non-peak hours by rewarding consumers. This is a win-win for utility and consumer, as the utility can avoid outages and enhance efficiency, while the consumer can be benefitted from transparent billing and staggered consumption at a cheap price.
The success of smart metering depends on smart devices, connectivity, data management and analytic capability. In that sense, last-mile connectivity is the key to unleash the huge potential of real-time consumption data. Although historical data provides a foundation for establishing trends and patterns, real-time data enables the utility to enhance efficiency, create an alert, improve customer service, detect technical glitches (brownout/blackout) and addresses them in real-time.
Robust connectivity is needed to obtain real-time data at frequent intervals. But due to the complexity of the deployment, lack of interoperability, lack of sufficient coverage, high installation and maintenance costs, cellular networks, RF mesh and satellite connectivity is not preferred for smart metering applications. In a smart metering context, NBIoT(Narrowband IoT) or LoRaWAN (Long Range Wide Area Network)can be deployed to provide backbone support.

NB-IoT is a cellular-grade wireless technology that operates in the licensed spectrum. This asynchronous protocol provides better data throughput, faster response times (latency), and better quality of service, and can be retrofitted to the existing mobile network.But consume more energy in the same data throughout, compare to LoRaWAN. As it solely relies on 4G coverage, it is not preferable in a remote rural residential segment or in a densely populated area (underground structure) where signal penetration is poor.

Compare to NBIoT, LoRaWAN offers many advantages. LoRaWAN is an open-standard connectivity technology. This message-based bidirectional IoT connectivity ecosystem covers all the community best practices and enables the utility to choose several network models (public/private/hybrid). It uses star topology to provide a longer battery lifetime (15-20 years) and long-range connectivity (5 kilometres in urban settings and 15 kilometres in suburban areas). It can comply with government-approved end-to-end future-proof network security standards. As radio waves can pass through elevators, basements, or underground parking garages, they can provide better indoor penetration capabilities in densely populated areas at a low cost per connection comparable to NBIoT.

This asynchronous non-cellular technology supports major metering standards such as M-Bus((Meter-Bus) and DLMS(Device Language Message Specification).

Furthermore,LoRaWan provides low implementation complexity, high scalability, long-term interoperability, the mature ecosystem of certified vendors of devices. As LoRaWAN implement firmware updates over the air (FOTA), it can remotely provide maintenance for electricity infrastructure thus reduces maintenance costs.
In the electricity sector, the smart metering market consists of all residents (smart home), commercial (smart building) and the industrial (smart industry) segment. The residential segment comprises the densely populated urban area, sparsely populated high-end urban areas and rural area. The commercial segment (smart building) is mainly in the central district areas of the cities. Some industrial (smart industry/agriculture) areas are near the urban centre, while others are away from urban centres. As the smart metering market in India is not homogeneous, but fragmented, one fit for all technology cannot be applied in the Indian context. India should explore all the technology for connectivity to address the last-mile connectivity issue. The cellular connectivity (4G) is not uniform across the country’s breadth, and length, particularly in remote areas where the telecom infrastructure is not uniform.

Although cellular activity is robust in the urban area, signal penetration in the basement area or inside the building is poor, making it unsuitable for smart metering. Sometimes, when the data size is higher, LoraWAN’s actual range may be less than the projected one. In that case, smart meters, which can filter out useless data and only send relevant data for processing could be used which reduce data computing cost and network cost (due to reducing the volume of data for processing).

Although LoRaWan is a preferable technology in smart metering, the deployment of the specific technology must be based on the strength of the signal data load, security and total cost of technology (including the cost of frequency, devices, network and data analytics).