IoT in Power System

IoT technology used in overhead transmission lines not only carry out line state monitoring but also improve the perception of power transmission line in operation condition, including meteorological conditions, ice cover, ground wire breeze vibration, conductor temperature and sag, transmission line windage yaw, tower inclination and others…

 Internet of Things (IoT) is one of the gravitate developments and reforms in today’s world. Since 1999, when the idea was first presented by Massachusetts Institute of Technology, IoT has been adopted by many governments such as the US, Japan, China, and the European Union for various purposes. IoT can effectively integrate the infrastructure resources in communications and electrical power system and improve the utilization efficiency of the system.


  The implementation of IoT in power system must rely on the line monitoring and real-time control in all aspects of the grid operating parameters, and the basic characteristics are grid information, communication, and automation. Meanwhile, IoT technology is used to implement 
1. Comprehensive perception 
2. Reliable transmission
3. Intelligent processing.

  Fusing both – IoT and machine learning, technologies will definitely help the mankind to overcome real-time difficulties. It will be able to effectively integrate the infrastructure resources in communications and electrical power system, increase the level of power system information, and improve the utilization efficiency of infrastructure in the existing power system. If IoT technology is used in the smart grid, important technical support – real time monitoring, maintenance assist, fault location detection for the generation, transmission, substation, distribution, electricity and other aspects of power grid can be effectively provided.


  Two most important challenges in the power system are blackouts and load forecasting. In India, more than 30% of electrical energy is lost in the process of transmission. Fault conditions in the transmission system will lead to power system blackouts – this fault is for the most part sudden – and it is difficult to locate the failure. One of the most appealing applications of IoT is also extended in load forecasting. Hence, now-a-days, all utilities in the electronic units (EU) should compulsorily have smart meters to support this IoT based smart load forecasting.

  Since 2007, the US started transitioning to IoT technology in grids. Research firm – International Data Corporation (IDC) estimates that global expenditure on IoT devices and services will multiply from $656 billion in 2014 to $1.7 trillion in 2020. IoT technology used in overhead transmission lines not only carry out line state monitoring but also improve the perception of power transmission line in operation condition, including meteorological conditions, ice cover, ground wire breeze vibration, conductor temperature and sag, transmission line windage yaw, tower inclination and others. To study the reliability of this concept, National Institute of Standards and Technology (NIST) developed a conceptual model for the smart grid to set the stage for a better understanding of the smart grid technology. The NIST conceptual model consists of seven domains, namely: bulk generations, transmissions, distributions, consumers, markets, operations and service providers. This conceptual model will enable the researchers to have a detailed idea of Smart grid practically and paves a way to develop this model into real ones.

*XML – Extensible Markup Language *HTTP – Hypertext Transfer Protocol *TLS – Transport Layer Security
*TCP – Transport Layer Protocol *IPv6 – Internet Protocol Version 6 *CoAP – Constrained Application
Protocol * DTLS – Datagram Transport Layer Security *UDP – User Datagram Protocol *6LoWPAN – IPv6
over Low power Wireless Personal Area Networks

Communication Layer


  IoT deployed grid users communicate in two-way directions by utilizing several wireless and wired communication protocols such as Zigbee, Wi-Fi, home plug, power line carrier, General Packet Radio Service (GPRS), WiMax, LTE, Lease line, and Fibers. Several software packages were updated and many are being developed to accommodate the new grid operation, maintenance, and management such as,

1. Distribution management system (DMS)
2. Geographic information systems (GIS)
3. Outage management systems (OMS)
4. Customer information systems (CIS)
5. Supervisory control and data acquisition system (SCADA).

Network Topologies

  IoT communications are based on wireless networks technologies. Regardless of the technology, these networks can be classified based on their functionality within the power system. These classifications, as reported in the literature, are:

• Home area network
• Neighborhood area network
• Access network
• Backhaul network
• Core and external networks.

  These networks connect many smart grid objects such as home appliances, smart meters, switches, recloses, capacitors bank, integrated electronic devices (IEDs), transformer, relays, actuators, access points, concentrators, routers, computers, printers, scanners, cameras, field testing devices, and other devices. All these appliances and devices are geographically distributed throughout the grid, starting from residential units to substations and upto utility data and command centers.

  A practical power system controlled by IoT requires a network which spans many square kilometers. There aren’t a lot of technologies which can achieve the requirements. Hence, considering the current state-of- the-art technology and from a real-time application point of view, web service approach is considered as one of the earliest possible implementations of IoT in power systems in the immediate future. The main components of a web service based approach are summarized:

Web Service Approach for IoT Service Architecture

  There are many different standards for communication between IoT devices. Some royalty-free standards are touched here since they are easier to adopt by various consumers using IoT services.

• Application and Transport Layers:

  The complexity of native Hypertext Transfer Protocol (HTTP) makes it unsuitable for a straight deployment on constrained IoT devices. The main limiting factor is the large amount of heavily correlated (i.e.redundant) data.

  The Constrained Application Protocol (CoAP) overcomes these difficulties by proposing a binary format transported over User Datagram Protocol (UDP), handling only the retransmissions strictly required to provide a reliable service.

• Network Layer: IPv4 and IPv6

  IPv4 is the leading addressing technology supported by Internet hosts. However, Internet Assigned Numbers Authority (IANA), the international organization that assigns IP addresses at a global level, has announced exhaustion of IPv4 address blocks. IoT networks, in turn, are expected to include billions of nodes, each of which shall be (in principle) uniquely addressable. A solution to this problem is offered by the IPv6 standard, which provides a 128-bit address field, thus, making it possible to assign a unique IPv6 address to any possible node in the IoT network.

• URI mapping:

  The Universal Resource Identifier (URI) mapping technique involves a particular type of HTTP-CoAP cross proxy, the reverse cross proxy.

Link Layer Technologies

  Consequently, link layer innovations empowering the acknowledgment of an urban IoT framework are grouped into unconstrained and compelled advances. The first group includes all the traditional LAN, MAN, and WAN communication technologies, such as Ethernet, Wi-Fi, fiber optic, broadband Power Line Communication (PLC), and cellular technologies such as UMTS and LTE.

  The constrained physical and link layer technologies are generally characterised by low energy consumption and relatively low transfer rates, typically, smaller than 1 Mbit/s. The more prominent solutions in this category are IEEE 802.15.4, Bluetooth and Bluetooth Low Energy, 8 IEEE 802.11 Low Power, PLC, NFC and RFID

*HTTP – Hypertext Transfer Protocol *TCP – Transport Layer Protocol *IPv6 – Internet Protocol Version 6
*UDP – User Datagram Protocol *OLSR – Optimized Link State Routing Protocol *OSPF – Optical low-pass
filter *BGP – Border Gateway Protocol *ICMP – Internet Control Message Protocol *CoAP – Constrained
Application Protocol * DTLS – Datagram Transport Layer Security *6LoWPAN – IPv6 over Low power
Wireless Personal Area Networks



  Servers are needed as the backend to control the overall system. Redundant servers are needed to back up the system in case of a server failure. An important backend component is the Enterprise resource planning systems (ERP). ERP components support a variety of business functions and are precious tools to manage the flow of information across a complex organization, such as a city administration. Interfacing ERP components with database management systems that collect the data generated by the IoT allows for a simpler management of the potentially massive amount of data gathered by the IoT.


  The typical architecture of IoT solutions is usually far more complex than the architecture of most enterprise systems. One of the main factors that increase the complexity of IoT systems is that backend services residing in the data center, which is the heart of most enterprise systems, are actually just a piece of the bigger IoT picture. With IoT solutions, we have to deal with a myriad of devices working in the field. Because the nature of these devices is very different from the web, desktop, or even mobile clients, we need an intermediate architectural element that will act as a proxy between the world of field devices and the enterprise data center. What we need is an IoT gateway.


  In a communications network, a network node is a connection point that can receive, create, store or send data along distributed network routes. Each network node — whether it’s an endpoint for data transmissions or a redistribution point — has either a programmed or engineered capability to recognize, process and forward transmissions to other network nodes.

Challenges in IoT

• Stringent latency requirements
• Network bandwidth constraints
• Resource-constrained devices
• Uninterrupted services with intermittent connectivity to the Cloud
• New security challenges

Security Challenges

• Protecting resource-constrained devices
• Assessing the security status of large distributed systems in a trustworthy manner
• Responding to security compromises without causing intolerable disruptions.

  Another key future technology which has a lot of potentials is Fog Computing. Fog computing basically overcomes various disadvantages in a conventional architecture of IoT systems.

Features of Fog Computing

• Carry out a substantial amount of data storage at or near the end user (rather than storing data only in remote data centers).
• Carry out a substantial amount of computing and control functions at or near the end user 
• Carry out a substantial amount of communication and networking at or near the end user (rather than routing all network traffic through the backbone networks).

  The most significant advantage of incorporating Fog Computing Architecture would be in reducing the data bottleneck. Computations which are smaller can be performed in a distributed manner which reduces the load on the server as well as the communication system.

Applications in Real-Time Systems

  Energy consumption and production is a very difficult task – though can be performed using standard load profiles. Doing the same with Microgrids is way more difficult using the conventional methods due to the non-linearity of load curves of the devices. The ability of IoT to generate meaningful data can vastly improve prediction accuracy in Microgrids. For example, by equipping street lights with sensors and connecting them to the network, cities can dim lights to save energy, only bringing them to full capacity when the sensors detect motion. This can reduce energy costs by 70% to 80%.

Load Forecasting

  Load Forecasting requires extracting usage patterns for electricity out of load curves. It can be done using classifiers that can identify devices in the load curve. To create such classifiers, the overall load of a microgrid is measured along with load curves by single devices. Classification can be more effective if smart devices which can record their consumption are used. Hence, classification in the initial learning phase of the smart microgrids is not needed. Adding forecast information to local energy production planning can lead to significant cost reduction, even if the rest of the grid contains no other smart devices. It is also very important in the integration of renewables into the grids. For instance, it is possible to increase the solar power penetration if suitable measures are taken concerning solar radiation forecasting.

Dynamic pricing

  Dynamic pricing is assumed to be the next pricing policy, as utility companies can give incentives to consumers to balance the overall load. It is assumed that smart devices are the building blocks of future smart microgrids. A smart device should have the following abilities: 

1. Control its consumption while fulfilling its local goals
2. Communicate with other devices within the microgrid
3. Behaves collaborative to achieve a global goal

  An important tool required for dynamic pricing is a smart meter. The metering device is the interface between the local microgrid and the grid of the utility company.

Smart meters

  The first key step towards a smart grid that makes the IoT real is the mass deployment of smart meters.

  The smart meter is a device which is fixed to the consumer side of a smart grid network. Smart meters must allow integrating devices that can act in both roles, e.g. an electric car that can be used to store energy to compensate energy needs at peak or when the price is high. A digital two-way smart meter enables two-way communication between utilities and customers. It records electricity usage and reports it to the central utility. The load functions map a load to each point in time. Energy production is modeled as negative load. In addition, it provides real-time power consumption data to users through a web browser or mobile app, helping them make informed load usage choices, achieving demand response management for the utility. In the US the IEEE 802.15.4 2.4 GHz ZigBee® standard is being used in combination with Smart Energy application profile. Other countries such as the UK or Japan are evaluating Sub-1 GHz RF or PLC solutions for greater reach or a combination implementation with both hybrid RF and PLC. To optimize the global objective, i.e. minimizing the energy costs, the devices have to coordinate their plans.

  Some of the other IoT devices which sophisticate the working of grids are smart charge devices, smart plugs, agent switcher, and home controllers. Therefore, to achieve both Forecasting and Dynamic pricing, it is required that smart devices can collaborate with each other.

Device Collaboration

  A device can be considered to be collaborative if the information that it sends to others is truthful, and it helps to solve the global optimization problem, i.e. minimizing energy costs, while ensuring that its local goals are satisfied. The load information of all smart devices is sent to a coordinator agent that aggregates these curves. Afterward, it sends the aggregated curve to the devices with the highest priority, asking him to optimize its consumption role. Smart devices should hence be able to collaborate with each other so each smart device will help to achieve the overall goal, i.e. minimizing energy costs.

  Thus optimization and healthy competition will be fostered if the data generated from the previous phases allows all stakeholders to make informed decisions about power usage, generation, and future investments.

The Future

  Although the mass adoption of connected technology is likely in the long term, the majority of consumers (87%) haven’t heard of the term “The Internet of Things”. The Internet of Things (IoT) is expected to grow to 50 billion connected devices by 2020 (Cisco, 2011) providing valuable information to consumers, manufacturers and utility providers. In the simplest terms, building a smart grid means securing the future of energy supply for everyone in a rapidly growing population with a limited power production capacity. The grid topology needs to adapt and shift from a centralized source to a distributed topology that can absorb different energy sources in a dynamic way. There is a need to track real-time energy consumption and demand to the energy supply: this goes with the deployment of more remote sensing equipment capable of measuring, monitoring and communicating energy data that can be used to implement a self-healing grid, increase the overall efficiency, and increase the level of self-monitoring and decision making. The connected smart grid provides a communication network that will connect all the different energy-related equipment of the future.

  IoT can lead to large-scale improvements, like most emerging concepts some of the technical, legal, and economic aspects of the IoT have to be dealt carefully before it becomes a mature and ready-to-use technology. The extant situation requires new standards supporting automation for widespread adoption of the IoT. From a technical aspect, new software is required to efficiently analyze the myriad amount of data that will be generated by thousands of IoT sensors. In addition, internet connectivity must be economically viable, stable, and pervasive, and should comprise innovative routing algorithms for error-free data transfer.

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