Smart Plugs: History Edit
Subjects: Physics, Applied
  • Smart Plug
  • Internet of Things
  • Smart City

1. Introduction

Electricity forms the backbone of the modern world but the rises in energy demand with the increasing urbanization over the past decade have overloaded the current energy ecosystem and electricity grids all over the world. Hence, there is an urgent need to shift towards a smarter energy infrastructure which is more efficient and interconnected. The use of smaller localized and distributed energy generation, incorporating renewable sources of energy, help in creating a dynamic energy ecosystem wherein the consumers of electricity can also play a vital role in a distributed generation of power.
The consumers are now not only limited to the end user of energy but also contribute to energy generation through local grids as well and thus can be recognized as prosumers. The electrical nodes in the existence of prosumers are recognized as smart homes/society. Therefore, it is necessary to have smart distribution systems that enable the bi-directional flow of power and interaction between the components of the microgrid. At this stage, the use of smart plug (a device that can be used for efficient energy management and load monitoring in the home environment with the help of the Internet of Things (IoT) theme) is a possible solution to make smart-meters more intelligent and establish the compatibility between smart-meters and Phasor Measurement Units (PMUs).
The utility can use the IoT theme as a viable solution to acquire the data from both smart-meters and PMUs that will not only help the utility for load forecasting but also offer optimal pricing to prosumers/consumers. However, a major limitation with IoT for smart city applications is the lack of interoperability, especially at the device level operation. To realize a secure Cyber-Physical System (CPS), it is very essential to develop for all the solutions of a smart city using a common standard. The smart plug can be used as a Plug-and-Play device, which is a very important aspect to popularize the concept of a smart home/smart society. This will not only reduce the latency and the cost of operation while using the smart plug for control and the monitoring of electrical loads but also encourages the development of low-bandwidth solutions for internet independent data communication networks [1]. At this stage, the available/developed IoT solutions can encourage the use of load monitoring at the smart city level. It requires the bulk of data handling to be coordinated by centralized supervisory control and monitoring system, namely, the Supervisory Control and Data Acquisition (SCADA) or control centre. Highly dense city populations increase strains on power distribution networks. Thus, solutions need to be ‘Smart,’ that is, highly efficient, economically viable and sustainable as well. Information and Communication Technology (ICT) is the key factor for such cities to address the challenges in a smart manner. Here, the smart plug technology has recognized its presence to deal with such issues.
One of the objectives related to the design of a smart grid system is to combine the traditional grid with the latest ICT, resulting in an optimized energy management system [2]. The management of generation and distribution resources can only be performed by the concerned authorities and personnel in the on/off-grid called the Utility. Optimization of the energy consumption in the off-grid environment is something where every individual (prosumer) in the grid ecosystem can contribute while the same is managed by the Utility in a grid-connected integrated ecosystem [1,2,3,4,5,6,7].
Optimized energy consumption not only means efficient utilization but also a reduced loss of energy. For this to take place, we must first be able to make accurate and precise data measurements to analyze the cause of energy wastage. The energy consumption data from the present generation of Electronic Meters gives us only data of the energy consumed by any building or space in (kilo-Watt-hour) kWh but not the energy loss during the stipulated time durations. In the present-day technology, there are gaps in the existing device architectures (electronic meters), as well as in human behaviors, which cause energy wastage and are left undetected by the energy data supplied by the electronic meters. Hence, there is a strong need to measure, analyze and detect the exact sources of wastage in any energy consuming space at an appliance level. Hybrid Appliance Load Monitoring System (HALMS) is one such research area that deals the same. The need for HALMS arises from the need for a more connected and efficient electrical grid and eventually a smarter energy infrastructure that can meet future energy demands. The recent developments in DC microgrids, off-grid energy generation and Distributed Energy Resources (DERs) have indicated the requirement of HALMS that will be inevitable for efficient energy management.
Appliance Load Monitoring (ALM) has been an area of research for a long time; however, the recent appearances of HALMS add a new frontier to the research directions. It was formally introduced for the first time by GW Hart [8], who proposed a Non-Intrusive Load Monitoring (NILM) based approach for the identification of connected loads. Since then there has been a considerable advancement in the research and implementation on the NILM side. While the NILM based devices and techniques have come a long way since the time of their formal introduction, they have not been able to reach a mature implementation stage due to various reasons. NILM based systems insist on single point sensing from where all the aggregated load data is collected and then again disaggregated to analyze the power consumption of each load [9]. Despite the advancements, this method has not been able to provide the level of accuracy required for a successful commercial and wide-scale implementation. Apart from that, these systems are passive in the sense that very few of them provide any real-time insights regarding the appliance level consumption.
The utilities can provide suggestions regarding the optimum usage only after the analysis of the aggregated data. The algorithms used are usually not applicable to all scenarios and usually computation intensive. Despite all these limitations, NILM based ALM has given us some valuable insights as to how to approach the load monitoring problem in a better way. Further, we discuss the different works in NILM in recent years, which have been decisive in driving the research towards an improved load monitoring system. These works include hardware approaches, software and middleware design as well as algorithm analysis for load identification [10,11,12].
The second approach for ALM is gaining popularity in terms of Intrusive Load Monitoring (ILM). This approach makes use of individual device sensing using smart devices and sensors (smart outlets). Researchers and Industry, after a long time, have turned to this approach after NILM accuracy hit a plateau [11]. Although, ILM has been largely ignored since a long time due to the installation complexity and cost the recent developments in IoT and smart/intelligent sensing systems have led to the development of cheaper and convenient solutions, which can match NILM based devices in terms of cost and ease of use [13,14,15,16,17,18,19]. ILM also has the obvious advantage of accurate load monitoring, remote control of devices, more accurate load identification and real-time insights of energy consumption due to the distributed sensing approach.
There are a lot of commercial products when it comes to NILM and ILM. However, the bitter part of the fact is that both techniques lack the efficacy as standalone techniques for large scale and long-term implementation [11]. The major difference between both techniques is that while the NILM techniques are more suitable for the utility side but not the useful insights to consumers/prosumers; the ILM products cater more to the individual. A certain level of synchronization is still missing between both the approaches which are needed to bridge the gap leading to an efficient hybrid load monitoring technique. On the ILM side, smart plugs present a viable solution to complement NILM devices appropriately for developing a better approach to load monitoring. In the present work, we discuss how smart plugs have become a major subject of research under ILM techniques. A detailed discussion on the various features of the available smart plugs in the market and the scope of research are done in the further sections. The possibility of new features, advantages, disadvantages, ease of implementation and various use cases have also been elaborated in detail. This paper presents a comprehensive review of the smart plug technology as a potential contributor to Green Energy and the smart grid/micro-grids and smart cities.

2. Latest Technologies

Of all the NILM and ILM solutions being used for load monitoring, smart meters and smart plugs have seen a great rate of acceptance among users. Although Smart meters have been getting more attention compared to smart plugs, the present smart meter technology fails to address all aspects for effective load monitoring, such as prosumers being unable to make energy consumption/production changes, unable to ensure the security and privacy of metering data, unable to manage and store vast quantities of the metering data collected. In this regard, we see smart plugs as a potential complimentary solution that can improve the process of efficient and low-cost load monitoring. Until now, smart plugs have been a part of the smart homes but this review tries to present a smart plug as an integral building block of the smart grid environment as well.
Smart plugs can be an indispensable technology when it comes to seamlessly connect the end user to the utility with accurate information of energy consumption, leading to better services, energy saving and cost reductions on both ends.
In this section, the paper highlights various possible features to be included in a smart plug that facilitate not only the individuals but also make the process of load and energy monitoring easier for the utility as well. Some of these features have already been introduced in commercial smart plugs in the market. Commercial smart plugs offer single/limited features in their products. As shown in Figure 1, the use of separate plugs to access different features is inconvenient and expensive for the end user. It is noticed that any such available plugs with multiple features also tend to be cost intensive.
 
Figure 1. Features and Technologies used in the smart plug.
 
A large focus of the smart plug market remains on the aesthetic value of the smart plug instead of the technical capabilities. There is a substantial technical gap in the development of an all feature equipped smart plug which can be called actually smart in every respect. The next part details the range of possibilities when it comes to the functionalities of smart plugs and how each feature has been attempted using various research techniques. Here, this study presents the following features that are essential in a smart plug:

2.1. Energy Management Using Smart Plugs

Energy monitoring and its management are one of the most basic features and are the motivation for the development of smart plugs. All smart plugs in the market provide a feature where the energy consumption of the plugged-in device can be accessed on an hourly, daily, weekly, monthly and even yearly basis. This feature not only makes the consumers more energy aware but also helps them in understanding their consumption patterns. It can also help in identifying devices that are consuming high energy and can be replaced by more energy efficient devices. Utilities and appliance companies can use this data to recommend an upgrade and replacement for the older power-hungry devices. Usually, energy monitoring requires a current sensor and a voltage sensor or an Integrated circuit which can identify the current, voltage, power factor, real power and apparent power. For voltage sensing, the simplest technique involves stepping down the voltage to a low power level usually 5V. Current sensing can use conventional sensors such as ACS712 [20] or MEMS sensors which are gaining great popularity due to their compact size and high accuracy and sensitivity [18]. A very latest Hall Effect sensor can also be added in this line to measure electrical parameters.

2.2. Device Identification

Device Identification is a very interesting area of research currently being extensively explored in Load Monitoring and Energy Management systems. By identifying the devices being used in space, energy consumption can be efficiently monitored, scheduled and controlled. In recent years, device identification using Smart meter data have gained a lot of traction as a research problem [21,22,23,24,25,26]. Smart meter data uses disaggregation for device identification which has been successful to some extent but a unified approach to identifying all kinds of loads using these algorithms still needs to be figured out. It has also been pointed out that using smart plugs along with smart meter data can provide more accurate results and achieve a more efficient approach to device identification [22,27]. It should also be noted that the smart meter data disaggregation algorithms have not achieved the required accuracy and are still in the research phase. Apart from that, these algorithms require extensive training before implementation in real-time. Exploration of Semi-supervised and Unsupervised machine learning algorithms still needs to be systematically approached. A large amount of research still needs to be done for real-time identification of devices and smart plug data will prove to be a breakthrough.
 
A great advantage of the Device Identification feature is that it can enhance the accuracy of other sensors and features that can be fitted in the smart plugs. It will also provide users a way to control the consumption of their devices in real-time. Paradiso et al. [23] have pointed out that disaggregation algorithms require high-frequency data sampling for accurate device identification which might increase the implementation cost of the Smart metering hardware. An Artificial Neural Network (ANN) based approach for device identification was proposed using low-frequency data sampling smart plug data [25]. Further, Zoha et al. highlight the issue of identification of low power appliances in disaggregation algorithms [21]. Most of the disaggregation algorithms are unable to identify and differentiate low power appliances. In this article, Hidden Markov Models (HMM) and Factorial Hidden Markov Models (FHMM) for identification of low power appliances in the presence of high-power loads have been explored. It is to be pointed out that such a continuous effort in Device Identification needs to be explored in conjunction with smart plug data so that the efficacy of a hybrid approach to device identification can be evaluated.

3. Applications of Smart Plugs

Smart plugs fall under an ILM (Intrusive Load Monitoring) type of application as pointed out by Ridiet al. [11], which exists at the plug level and hence ismore localized in comparison to the ILM1 type sub-meters or NILM devices, such as smart meters, which are located at a few or single locations in space. They also have a better ability to interact with the environment in comparison to ILM3 type devices, which are usually embedded in the circuit. Basically, ILM 1 relies on sub-meters that typically measure the consumption, ILM 2 uses metering devices placed at the plug level, ILM 3 uses metering devices placed at the appliance level. On one side, where the NILM based devices, such as smart meters, can mainlybe used only for energy monitoring and data collection along with some basic communication.Smart plugs, on the other hand, can be used for a wide spectrum of applications apart from energy monitoring and even compliment NILM based devices.
Figure 2 shows various applications of future generation smart plugs. These smart plugs are too vital for mankind, particularly considering the future needs and living styles of people. Some of the possible application scenarios for smart plugs are discussed further. Some of these applications are currently in use and some others prove to be viable for further research and implementation.
Figure 2. Smart Plug Applications: Present and Future.

3.1. Energy Monitoring and Control

The most basic application of the smart plug is Energy monitoring and Control. The fundamental difference between Smart Plugs and the smart meters is that Smart meters are another type of device being used for energy monitoring but they do not provide the feature to control individual devices. Apart from that, smart plugs offer real-time insights with greater accuracy at device level whereas most of the smart meters use disaggregation algorithms to analyze the smart meter reading for measuring device level consumption. While smart plugs may have additional features to manage energy consumption on a daily basis, the insights provided by smart meters are mostly passive and require a longer cycle of observation and analysis.

3.2. Electricity Theft Detection

Electricity Theft incurs huge losses for utilities all over the world. The annual monetary losses in the top 50 emerging economies alone were estimated to be around USD 58.7 Billion [67]. With the traditional systems, it was comparatively easier to spot electricity theft manually but was highly inefficient owing to the vulnerabilities and malpractices in the system. With the increasing installation of smart meters, these incidents are becoming more and more discrete and difficult to detect. Smart meters are being hacked by tech-savvy malicious users to gain access and manipulate their electricity readings illegally. Although there has been a lot of research on electricity theft detection using smart meter data (fine-tuned predictive models for calculating various losses), it has been difficult to implement the same in real-time [42,45,67,68,69,70,71,72,73,74,75]. With the implementation of smart plugs, the individual energy reading from any device can easily be compared with the smart meter data to infer whether electricity theft is taking place or not. Thus, smart plugs can provide valuable insights and a solution to the problem of electricity theft.

3.3. Energy Saving and Awareness

A certain feature of smart plugs such as standby power saving and occupancy detection will help to save energy without annual intervention by the user. The analysis of the energy savings made over a month or a year can then be included in the electricity bill to enhance energy awareness in the public domain. Also, the detection of faulty devices consuming excessive energy proves to be a benefit to both the user as well as utility. The user, on the one hand, gets to save on the electricity bill and on the other hand, the utilities can advertise up gradation and sale of new energy-saving devices. The utilities can easily provide the users with suggestions to improve their savings on energy bills by giving personalized recommendations and energy statistics on demand.

3.4. Efficient Smart Grid, Microgrid and/or Off-Grid Operations

One of the major goals of Smart grid technology is to achieve bi-directional communication between the generation end and the user end seamlessly. The present smart grid infrastructure having generation and distribution facilities in the grid must be established a communication infrastructure where the information exchange is frequent and two-way [2]. There is a clearly visible gap that needs to be bridged between the users and the distribution facilities. Smart meters are one of the devices, which are an attempt to bridge this gap but they pose considerable limitations concerning quality, frequency and accuracy of data. Wider participation of consumers in the real-time pricing of electricity based on direct communication between consumer and utility/service providers can help in managing load rejection better. Here, it is to be noticed that smart plugs can serve as the point of communication between the consumer and the utility for effective participation in the real time pricing process.
Microgrid and Off-grids will form a major part of the Smart Grid infrastructure in the near future [39,40,76,77,78]. These technologies are being evolved for minimal energy loss and continuous monitoring to ensure maximum efficiency. Many remote locations are being electrified and powered by off-grid systems where a conservative approach to energy usage is mandatory to cater to maximum users for maximum time.
There are various facilities bound to off-grid and microgrid systems such as motors, pumps, lighting, charging stations and so forth, which form the basic infrastructure in any location whether remote or accessible. The proper monitoring, scheduling and control of these facilities according to time of use, power available and other environmental conditions can go a long way in establishing efficient power supply systems.
Reliability is the main impact index to measure the effectiveness of any off-grid/grid integrated system. Smart plugs can play a vital role in the effective implementation of Microgrids/Smart grid. According to Reference [4], Demand Response and DER scheduling are important aspects of a smart grid that needs to be intelligently addressed. The algorithm suggested by the authors requires the device-level information to assess the time for which a certain device is unutilized. In such a scenario, smart plugs can prove to be a useful accessory. Moslehi and Kumar suggested that integrating the renewable sources of energy from consumer premises can be useful from the reliability perspective of the smart grid. At the office/home level, smart plugs have the potential to report the required data and control devices at lowered costs and increased benefit to the consumer as well. They also stressed the importance of addressing the volatility of renewable resources by harnessing data using ICT [4]. The volatile aspect of the renewable resources, which include Demand Response, availability, forecasting and scheduling can be brought down to an acceptable level. Smart plugs, if properly customized to suit the type of renewable source being handled, can log important data for analysis, forecasting, scheduling and time-based, event-based or availability-based control of the connected devices.
In off-grid based systems, Smart plugs can prove to be a great energy management tool. They can be used at various points in the off-grid system to monitor and control the energy usage, collect energy data and set schedules for the operation of connected devices for better demand response and efficient utilization of resources. At present, the smart plugs are mostly being used at the home or office level. Their implementation at the higher end of the hierarchy of the Energy ecosystem that is, at the substation, utility, microgrid and grid level is still to be explored. Core research to customize the smart plug to the needs of the utility, substations and microgrids can be instrumental in achieving the goal of a smarter grid. The smart plug-based system is modular, easy to replicate, scalable and customize so that they can be beneficial in the long run for a utility leading to a substantial reduction in cost and time of implementation [5]. Thus, the complete chain of the Smart grid components involving the generation, distribution and user can be seamlessly connected resulting in optimal operation of each component by benefitting from each other’s data and insights. Consequently, smart plugs can prove to be an important building block towards the fruitful implementation of a Smarter Grid.

3.5. Building Big-Data Framework

NILM has been a widely researched topic in recent years. There are very few elaborate datasets available to establish the ground truth for carrying out uniform research [68,69]. It is very difficult to compare the research findings of any group of researchers unless they are using a common dataset for evaluating their algorithms. The huge variations in research data used for developing, validating and testing algorithms is one of the major issues in the real-world implementation of research in the area. The data used by disaggregation algorithms for NILM is either artificially fabricated data or the dataset is very narrow to include the complete research scenario. Other issues are the unavailability of finer accurate device level data to establish ground truth and excessive noise in the smart meter data. There is also a problem of obtaining high-frequency sampling data from designed/upcoming smart plugs which is more immune from noise interference. Smart plugs can be used for establishing research data which can then provide a reliable way to evaluate and simulate NILM environments [11].

3.6. Other Auxiliary Applications

Apart from the general energy management-based application of the smart plug, the sensor data in smart plugs may be used for realizing many other applications. The occupancy detection feature is very useful for security purposes as well as for Elderly care [79,80]. The thermal and overload protection also provides an added security feature against fire hazards. Indoor positioning helps the smart plug to react instinctively to the presence of the user. The smart plug features can also add to the comfort of the user in any space by providing an automatic environment and device control. Henceforth, a smart plug with multiple sensors can be effectively used to increase the efficiency of any space in terms of energy, safety and comfort. Further research needs to be focused on integrating more and more features in the smart plug to enhance adaptability.

4. Conclusions

The review presents the various paradigms in smart plug technology. Smart plug technology will be widely used for monitoring and control energy consumptions in any spaces that incorporate electrical devices. A lot of work is continuously being done to test and integrate newer communication protocols and features into the smart plug for use in a variety of scenarios in the Energy Ecosystem. It is also observed that the applications of smart plugs are not limited to energy management and will only increase with the integration of more intelligence. With the introduction of new efficient and cost-effective IoT technologies in the future, the smart plug is expected to be in high demand in the coming years and this work intends to be a positively aggressive initiation of the future work of the group. The future work of the group is focused on developing cost-effective IoT based Energy management systems for off-grid systems with a special focus on smart plugs. We also intend to make contributions toDevice identification in smart plugs and Electricity Theft detection in the future. We firmly believe that the smart plug can prove to be a vital component in smart energy management systems leading to an even smarter grid and Smart Cities.