Off-Grid Communications For The Masses: Smart Metering

In East Africa, a large percent of the population still does not have access to electrical energy and its benefits. To address this, several companies have developed micro-grids to provide AC power to rural East Africa. In order to sustain these grids, a remote, robust communication system has to be developed for purposes of metering and billing. In this post, I propose several efficient designs of a communication system that could be used to monitor and manage off-grid customers. Specifically, it proposes the technologies that can be used, the hardware and software implementation of such as system and how it can make business sense addressing equipment and operation costs.

 

This post proposes a communications system that tackles the stated situation within the boundaries of the limitations set by a real-world scenario, i.e. budget, energy supply and manpower. A comprehensive approach to systems design is valuable to ensure the sustainability of such a project. Sections in this post will cover the hardware, communications channels and protocols, remote monitoring systems and software that can be used to solve the stated design issue.

The goal is to provide micro-grid providers with a trustworthy system for off-grid power management as well as help the locals with a solution that sufficiently caters to their needs.

 

Hardware Implementation

A reliable remote metering system has to have a few basic characteristics:

1. A smart metering system that connects every household, enabling 2-way data transfer between the customer and utility provider
2. A network technology to enable the 2-way communication (fixed wired or wireless)
3. A software system that actively manages the billing system and analyses usage data

 

With a system defined as in Figure 1, we can start to see how we can bring together the hardware components.

 

Smart Metering

Smart meters are already in the market, such as Hexing Electric HXE 110-KP single phase prepayment smart meter and ZTE ZX E211, Figure 2, single phase prepayment smart meter. These meters meet Standard Transfer Specification (STS) standards and are fit for our application. The ZX E211 is the preferred choice here as its supports a variety of communication protocols (RS485, M-BUS, ZIGBEE, RF-MESH, PLC and GPRS). We will see how these communication protocols will be used in this post.

ZTE ZX E211 LoRa based meter is particularly useful in long distance communication and allows us to adjust several parameters such as the transmission rate and frequency. The main feature is its low power consumption with a transmit current of less than 90mA@ 17dBm, receive current less than 13mA and standby current less than 0.7 uA. Since data communication may occur only few times a day, a majority of the consumption will be the standby current.

Depending on the data that is provided by this meter, or a comparable one in the market, we may choose to consider meters that do not conform to STS standards. This may help us with communication protocols unavailable to us but may limit us in scaling and future upgrades with the national grid.

Fabrication of a communications device alongside the meter may be required to send more usage statistics and deliver the desired data. This can be used for analysis to improve the overall system. This can be covered in a future post with AVR, PIC or FPGA as the processing IC in our DIY smart meter.

 

Communication System

This post will discuss two concepts of a smart meter communications in a rural area based on two assumptions:

1. Location size – Are the residents physically close to each other or spread out?
2. Terrain – Is the area flat or hilly? Dense vegetation cover or dry grassland?

To meet the requirements of the location, I propose two systems that can be established. They are the RF-MESH network and RF-STAR network. Both networks rely on wireless channels to carry data.

 

RF-MESH Network

This type of network allows for data transmission via other wireless devices via a mesh (chain) network using a low power transceiver radio. This network is suitable for close-knit residential areas with few obstacles and is cheap to implement and scale. The architecture consists of low power transceiver radios per every meter box and data concentrators as in Figure 3.

A proposed transceiver is the Silicon Labs Si4463 chip that facilitates the RF communications link. This is a transceiver I’ve worked with before on a previous project. Schematics of the full transceiver system is covered here. It is a low power transceiver with up to 20dBm (100mW) transmitting output power and a receiving sensitivity of -117dBm. Its wireless frequency band is 433.4 – 473.0MHz, and up to 100 channels can be set up with a channel stepping of 400 kHz. A serial port baud rate of 2400bps allows for a baud rate in air of 5000bps and a wireless receiving sensitivity of -117dBm. This gives an operating range of 1000m at clear line of sight between modules under ideal conditions.

A concentrator can then be installed somewhere central in the village to aggregate the data of multiple smart meters and one concentrator may support hundreds of smart meters. This system is immune to sudden channel blocking as communication can flow using alternative paths. The DRF1110N20-C concentrator works well with DRF1110N20-N network nodes on a sub 1GHz channel.

The concentrator can then upload the received data to micro-grid databases at different times of the day depending on the availability of the data network.

 

RF-STAR Network

This network type is of a point to multi-point (PtMP) configuration. This communication system is admittedly more expensive than the RF-MESH network but is suitable in hilly terrains with thick vegetation and obstacles. The architecture consists of high-power radio transceivers with a line-of-sight to an omni-directional antenna radio as in Figure 4.

 

To implement this system, a 2.4GHz ISM channel may be used. A clear line of sight from a transmitter antenna to the receiver should be established, I recently talked about the art of obtaining strong microwave links. The smart meter information is encoded in a TCP/IP stream and sent to the radio communication port. The standard in use is the common TCP/IP system with Cat6 e cables and RJ45 connectors. A static IP is assigned to all the radios in this system and all are pointed towards an omni-directional antenna whose radio is in the same network. Since this is a high-power system, it is only advisable for use in areas where an RF-MESH would not work

In both network architectures, consider the cost of support infrastructure such as towers and masts to house concentrators or antenna radios. In our scenario, based on the assumption that obstacles and houses are relatively low, a 10m wooden or steel mast would suffice.

 

There needs to be a way for connecting the aggregated data to the cloud. A 2G/3G modem may be connected to the concentrator/central radios to push or pull data from the management system at different times of the day. The 2G/3G modem can be topped up periodically. Since the data being sent and received is quite small a 2G GSM network should suffice.

Redundancy in such a system can be with the concentrators or central radios. Such that two concentrators can be installed in one tower.

 

Data Processing

For the architectures stated, the concentrators or tower radios send information intermittently to a MDMS (Meter Data Management System). A MDMS performs real time monitoring to help us understand how the electricity is being used.

Typical parameters that can be sent by the smart meters to the MDMS (upstream) include customer id, meter serial number, current meter reading (kWh units left). The upstream can occur every thirty minutes or one hour to ensure accurate readings. These parameters could be streamed as a JSON (JavaScript Object Notation) format as it uses the least bandwidth resources. Parsing JSON is faster and more efficient than other data formats. JSON can also be decoded into CSV, a format compatible with spreadsheet documents. Example of a JSON script is shown below.

 

{
  "Meter 001": {
    "id": "43001",
    "meter no": "JDNR8456B3",
    "status": "1",
    "phone": "255728512133",
    "rms v": "215",
    "rms i": "10",
    "pf": "0.92",
    "time": "2018-04-23T18:25:43.511Z"
  }
}

 

Since this format can be used for data transmission as well as configuration and database storage, it provides the best method for smart meters to read information from the MDMS (downstream). Depending on the smart meter we chose, a JSON parser API may be required to convert the JSON string to the required format. JSON can be parsed to C/C++ making it highly portable on x86/amd64, ARM and AVR systems. The downstream is much less frequent as the upstream and contains information to specific meters. A good example is ON/OFF remote control to a meter using a JSON script.

Note: For ON/OFF control, the communication system will have to be always ON thus installation should be done in such a way that power is fed directly and after the smart meter.

 

Software Implementation

This section discusses how the software will work with the installed hardware to perform administrative tasks such as remote control, billing and auditing. Software can be developed in-house to reduce costs, to implement mobile software. Monitoring and analysis software could use web-based meter monitoring system. In this case, all the energy and user parameters can be monitored on a web browser in real time online. For micro-grid administrators, this will provide a platform that can be accessed anywhere as long as there is a data connection.

 

Remote Monitoring and Control

Remote monitoring and control is essentially what we have discussed before with uplinking and downlinking of data within our wireless network. The format proposed is the JSON format however this is only in theory and requires real world tests.

Data received can be converted to several formats and analyzed with our web monitoring software. This can give usage statistics that can be used to improve our system and customer support. For control, information can be set to specific smart meters to change the configuration of the meters.

 

Billing

In East Africa, mobile money is widely adopted and that is the interface of choice in our system. The basic energy pricing is based on a tariff that is calculated to give the best ROI. The tariff should also be affordable enough to the target customers.

The API of choice is the MPESA API provided by Safaricom. This is compatible with our proposed communications channels and devices. Since the area is remote, we may have to work with the available mobile money service such as Airtel. The system could also use USSD codes to send and receive billing information.

To integrate a C2B workflow has to be implemented where the user can pay and, based on our tariff, receive a code to input on his/her meter. The meter number will serve as the account number to enable one to pay for power using someone else’s phone. However, administrator communication about the system will be sent to an individual’s phone that was captured during meter installation.

It is at the MDMS level that data from the meters and mobile payment information is reconciled and the system automatically allocates energy resources accordingly. Failure of payment may result in remote shutdown of a meter until payment is provided.

This implementation allows for the system to be billed without any use of middlemen or on-site personnel.

 

Conclusion

The proposed communication system meets the requirements of our scenario. It utilizes cheap products within the stated budget and is easy to install and maintain. However, it remains to be tested in a real-world scenario.

Improvements to this system can be further proposed with new technology in future for even smarter grids. Blockchain technology has been proposed for off-grid metering systems and is worth further research. Upcoming wireless systems à la 5G bring new paradigms and innovation that we can borrow to improve our networks. Data obtained could be analyzed using statistical tools such as linear regression and predictive models such as neural networks.

Off grid systems are important in the information revolution. More people with access to energy and information results in a innovation domino effect that can improve lives in rural Africa significantly. More research is important in this endeavor and your input will be much appreciated. Give a comment if you’ve got anything to add on this (or just observations) and let’s keep up the conversation!