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Saving Power with the Smart Grid and the IoT

By Maurizio di Paolo Emilio for Mouser Electronics

The smart grid is defined as a set of new technologies, sensors, and equipment to manage considerable energy resources, to improve the reliability, efficiency, and safety of the entire industrial, residential and commercial chains. The main advantage of smart grids is the ability to integrate renewable energy sources into the system and supervise consumption and power production thanks to a bidirectional flow of data (Figure 1).

Figure 1: Block diagram of a general smart grid (Source: Texas Instruments)

Homeowners have begun installing smart appliances and renewable energy resources on their premises to optimize their costs and increase efficiency. As smart grid concepts have emerged rapidly in recent years, many research institutes have laid the foundations for a better understanding of the technology. Due to the low cost and ease of implementation, there is a growing demand for intelligent sensor networks to be used on a large scale for power grids. The result is easily verifiable as a massive volume of different varieties of data to manage and analyze. Processing and analyzing these data reveal details that can help experts improve the electricity grid to optimize performance. The technology to collect vast amounts of data is available today but managing data efficiently and extracting the most useful information remains a challenge.

Big Data

Thanks to advanced data analysis almost in real time, anomalies, trends, possible security breaches, and other costly business interruptions can be detected early and changed. Manufacturers use data analysis to forecast demand accurately, estimate peak energy consumption for residential and commercial consumers, and establish demand management programs.

Transmission and distribution entities use analysis to identify power supply anomalies, detect and avoid interruptions before they occur, and then restore service as quickly as possible after an event. Consumers can reduce costs by setting various working methods for their appliances. Other smart devices use data analysis to ensure regulatory compliance from the point of power production to the point of consumption, as well as to analyze costs and benefits for consumers and utilities. With increasing data flow, deep learning is becoming a key role in providing big data predictive solutions. Deep learning is challenging enough for computational resources; it requires more powerful GPUs, high-performance graphics processing units, large amounts of storage to train models.

Cloud and Fog Computing

The challenge for IoT devices in a smart grid is the continuous generation of data, the analysis of which must be very rapid so that the concept of real-time is valid. The corrective action in response to a temperature sensor must occur as fast as possible to optimize the reaction and make the correct decision. In various application contexts, the cloud offers companies a scalable way to manage all aspects of an IoT implementation, including device localization and management, billing, security protocols, data analysis, and more. Many technology operators have introduced cloud-as-a-service offerings for the IoT, including Microsoft, Amazon, and IBM with its platforms.

The 5G networks are supposed to connect vehicles equipped with radar and LiDAR technologies, image processing cameras, and a whole range of cloud services to support entertainment and predictive maintenance in real time.

The Fog Computing solution minimizes latency, offering a local approach for data analysis and management. It extends the cloud to be closer to the "things" that produce and send data. The transition between cloud computing and fog computing requires a high-speed network with the ability to perform data transmission in real time and program different devices.

Fog Computing processing pushes “intelligence” to the local network level, processing the data in an IoT gateway node. Edge computing, on the other hand, drives the intelligence, processing power, and communication capabilities of an edge or appliance gateway directly into devices such as programmable logic controllers (Figure 2).

Fog Computing processing has the advantage of allowing a single, powerful device to process data received from multiple endpoints and send information exactly where it is needed. Compared to the Edge, Fog Computing is more scalable as it allows a centralized system to have a more comprehensive view of the network since it has more data points that feed the information.

Figure 2: Edge, Cloud and Fog Computing (Source: Author)

A Smart Meter in an Intelligent Network

The IoT platform aims to extend its functionality to all those residential security and energy management systems where wireless connectivity and the use of sensors are essential requirements for intelligent measurement systems.

The scarce water resources of the earth imply the design of measurement and detection tools able to keep track of the actual use. As the population of our planet increases, the distribution of this precious resource becomes more and more critical and requires some precautions to avoid waste. The meters need updates to be more precise, smarter, and more sensitive to small leaks. Mechanical water meters have poor performance at low flow rates and cannot be networked.

Flow meters are gaining widespread use in commercial, industrial and medical applications. The main advantages of using this type of flow meter are greater accuracy, low maintenance of moving parts, non-invasive flow measurement, and the ability to diagnose the health of the meter regularly. The MAX35101 can measure the time interval between two events up to a resolution of 4ps, all through a pair of pulse drivers and receivers that are connected to ultrasonic transducers to send and detect pulses in a conductive medium.

In addition to sensors, nodes and gateways must be designed to minimize energy consumption, provide reliable and robust network connections, and extend the range of wireless connection as far as possible. The heart of IoT systems is a processor or microcontroller (MCU) that processes data and manages software stacks connected to a wireless device for connectivity. This type of technology also allows utilities to migrate from analog systems to a digital and wireless two-way street.

With smart meters, utilities can learn information about consumption, pressure, or temperature from remote substations or production facilities in isolated areas to ensure that pumps and motors work correctly and identify problems before equipment failures. Smart meters allow utilities to support their customers better, as they can monitor and wirelessly manage their energy consumption daily, which can help reduce utility bills.

One of the protocols for smart meters is the Wireless M-Bus: a wireless version of the traditional M-Bus for meter-reading applications (gas, water, and electricity) to satisfy the need for a smart grid meter, initially defined for the 868 MHz frequency band and then extended to other bands. Wireless M-Bus or Wireless Meter-Bus is the European standard (EN 13.757-4), intended as a system for networking and remote reading of domestic utility meters. It defines the bidirectional communications between the user’s smart meters and the central data loggers for data collection and analysis (Figure 3).

Figure 3: General layout of a smart meter (Source: Texas Instruments)

Wireless M-Bus requires a software stack to support various modes and options. The stack works on an MCU that also controls the radio transceiver. Depending on the capabilities, the stack can invoke hardware for specific functions or implement those on the stack.

Silicon Labs presented the industry's first M-Bus wireless platform solution designed to simplify the development of smart meters. It encompasses the Wireless M-Bus software stack and a starter kit to accelerate time to market. The Silicon Labs EFM32 Gecko microcontrollers are 32-bit devices based on the ARM Cortex-M4 architecture.

Renesas offers MCU solutions for the smart meter market with 32-bit RX600, while the RL78 16-bit family of microcontrollers is optimized for cost and low power consumption and is suitable for battery-powered modules and devices. The "Connect it!" platform is based on the RL78 MCU with the addition of Analog Devices transceivers ADF7023 and ADF7021-N. Texas Instruments offers support for its MSP430 microcontrollers (such as MSP430FG4618, MSP430G2955, MSP430F5438A), and RF transceivers such as the CC1200 and CC1120/1125. For unidirectional Wireless M-Bus products, CC115L or high-performance CC1175 transmitting devices are also supported.

Texas Instruments also offers SimpleLink™ CC26xx Ultra Low Power Wireless MCUs for Bluetooth Smart, ZigBee and 6LoWPAN and ZigBee RF4CE remote control applications. They provide suitable battery life and allow operation on small button batteries and in energy harvesting applications (Figure 4).

Figure 4: Block diagram of the CC26xx MCU (Source: Texas Instruments)

SoC and FPGA for Smart Grid Implementations

In order to confer a certain degree of "intelligence" to a smart grid, the equipment of an electrical network integrates a combination of blocks for signal processing and for communication management, as well as dedicated hardware blocks. Thanks to the continuous increase in resources and levels of integration of the FPGAs, several smart grid applications are now using an FPGA or an SoC for the implementation of all these blocks, with distinct advantages in terms of flexibility, reliability, maintainability, and costs. The Cyclone V series SoC includes an Arm Cortex-A9 dual-core processor running at 800 MHz, embedded flash memory, RAM, cache, GPIO and communication ports usually used in smart grid systems. The FPGA structure provides smart grid developers with numerous advantages and offers various opportunities in terms of integration, performance acceleration, and upgrade possibilities. The high levels of integration allow, for example, to reduce the number of components required, with a consequent improvement in the MTBF / Fit rate (failure rate). The presence of memories with error correction codes together with the use of multiple processors helps to ensure better operating reliability.

Conclusion

The smart grid is drastically changing the way companies operate, effectively representing a new industrial era with Industry 4.0 techniques. With this technology, utilities are equipped to provide power more efficiently, improve operations, reduce emissions and management costs, and get back to work after failures as quickly as possible.

Maurizio Di Paolo Emilio holds a Ph.D. in Physics and is a telecommunication engineer and journalist. He has worked on various international projects in the field of gravitational wave research. Working as a software/hardware developer in the data acquisition system, he participated as the designer of the thermal compensation system (TCS) for the optical system used in the Virgo/Ligo Experiment (an experiment for detection of the gravitational wave that achieved the 2017 Nobel Prize in Physics). Actually, he collaborates with University of L'Aquila and INFN to design devices for radiobiological and microscopy applications and new data acquisition and control systems for space applications. Moreover he works in the software/hardware engineering field as editor and technical writer. He is the author of several books published by Springer, as well as numerous scientific and technical publications on electronics design.