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1 Recent Advances In Energy Management For Green-IoT: An Up-To-Date And Comprehensive Survey Sana Benhamaid, Abdelmadjid Bouabdallah, Hicham Lakhlef To cite this version: Sana Benhamaid, Abdelmadjid Bouabdallah, Hicham Lakhlef. Recent Advances In Energy Management For Green-IoT: An Up-To-Date And Comprehensive Survey. Journal of Network and Computer Applications (JNCA), 2022, 198, pp /j.jnca hal HAL Id: hal Submitted on 12 Jan 2022 HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

2 Recent Advances In Energy Management For Green-IoT: An Up-To-Date And Comprehensive Survey Sana Benhamaid a, Abdelmadjid Bouabdallah a, Hicham Lakhef a a Sorbonne Universités, Université de Technologie de Compiègne, CNRS, CS 60319, Compiègne, France Abstract Internet-of-Things (IoT) refers to the massive network interconnection of objects often equipped with ubiquitous intelligence employed to provide smart services to end users. However, one of the substantial issues of IoT is the limited energy of IoT devices that are expected to run consistently for a long period of time without battery replacement. Moreover, in the wake of pervasive IoT, the number of IoT devices has exploded and lead to a tremendous rise in IoT networks carbon footprint. In this regard, Green-IoT and energy management of IoT emerged as challenging and attractive research topics for both academia and industry. In this paper, we conduct a comprehensive and an up-to-date survey on recent energy management techniques in IoT networks. We start by presenting the challenges of energy consumption in IoT networks. Then, we will present novel and well-known energy management approaches for IoT but focus on the most recent solutions proposed in each approach. Next, we will provide a comprehensive survey of the most recent energy management solutions for IoT ecosystem. We will also present recent trends and new research perspectives that can be exploited for energy conservation in IoT networks. Finally, we will give recommendations on how to exploit the techniques presented in our survey to achieve the IoT applications QoS requirements. Keywords: Internet-of-Things (IoT), Green-IoT, Energy Management, Energy Harvesting, Energy Saving. 1. Introduction The Internet of Things (IoT) is a paradigm that envisions to connect everyday objects and integrate them to the internet using microcontrollers, transceivers, and protocol stacks. This paradigm attracts the attention of both academia and industries and aims at the digitization of a plethora of applications with high societal relevance. The IoT applications in our daily life activities are manifold, from smart transportation that offers ground breaking progress in the transportation and logistic industry to smart home that facilitates its in-habitants activities. IoT covers several fields such as smart traffic, healthcare, agriculture, industry 4.0 and can even be found in our everyday objects such as pens, clothes, etc. As a result, the world has known an explosive growth in the number of these devices. According to IHS Markit, 125 billions of IoT devices are expected to be connected with each other by 2030 [1]. This number is highly influenced by the emergence of 5G networks and the exploitation of new spectrum frequencies [2]. Moreover, IoT devices are usually small and battery limited and the exchange of massive information between these devices gives rise to enormous energy requirements. These requirements are often not supported by IoT devices and can quickly lead to battery depletion and the network s death. So, how can we develop energy-efficient solutions that will not only reduce the energy consumption in IoT networks and their carbon footprints but also increase the networks lifetime? Accordingly, we witness the emergence of the Green Internet of Things which focuses on saving and managing energy in IoT networks in order to optimize and reduce the energy consumption and prolong the IoT networks lifetime. In the next few years, Green IoT will make significant changes in our daily life and will help realize Preprint submitted to Elsevier January 12, 2022

3 Table 1: The Table Shows a Comparison of the Main Features of This Survey With Those found in Literature [3] [4] [5] [6] This survey Green IoT enabling technologies No Yes Yes No Yes Energy harvesting Yes No No Yes Yes Energy saving Yes Yes Yes Yes Yes Taxonomy of the energy management solutions Yes Yes No Yes Yes Intelligent energy management solutions No No No No Yes IoT Network Softwarization energy saving solutions No No No No Yes 5G IoT energy saving solutions No No No No Yes Social IoT energy management solutions No No No No Yes Backscatter Communication technology No No No No Yes Wake-up Radio technology No No No No Yes 2 the vision of green ambient intelligence [7]. In this survey, we are going to present a wide range of energy harvesting and energy saving techniques and set out the recent research trends for Green IoT such as green 5G IoT, green social IoT, green networks softwarization, backscatter communications, etc. This paper is organized as follows : In Section 2, we present the background information on the energy management works presented in the literature. In Section 3, we describe the problem and challenges of energy consumption in IoT networks. In Section 4, we discuss and select some of the energy harvesting and energy saving techniques presented in the literature focusing on the most recent researches. In Section 5, we present the energy management technologies for IoT ecosystem. In Section 6, we discuss the recent trends and research perspectives that can be exploited in energy management for IoT. Finally, we shall conclude the paper by providing an insight into open research problems and the future of Green IoT. 2. Background The subject of energy management for IoT networks has been widely studied and various reviews and surveys were presented throughout the years. Researchers are interested in energy efficient WSNs since they are widely deployed and omnipresent in IoT. In [3], the authors presented battery-driven energy conservation and energy harvesting schemes used to design algorithms in Wireless Sensor Networks (WSNs). The authors focused on reviewing the protocols used for the energy management of wireless sensor networks. Compared to this survey, our survey does not only focus on sensors but the different devices IoT networks can include. In [6], the authors surveyed energy saving and energy management schemes and energy efficient protocols for WSNs focusing on transmission power management and miscellaneous techniques. Compared to our survey, this survey does not include the energy harvesting and wireless charging solutions of IoT networks. In [8], the authors surveyed the energy harvesting approaches used in WSNs which help to improve WSN lifetime. Compared to our survey, this survey focus only on energy harvesting of wireless sensor networks and not the different types of devices we can have in IoT networks. In our work, we present solutions not only energy saving and harvesting solutions for sensors but also rich resources devices. In the recent years, more and more authors started to use the

4 Figure 1: Taxonomy of Energy Management Techniques 3 Green IoT appellation. Green IoT refers to the energy efficient hardware or software procedures used to reduce the greenhouse effect of existing IoT applications and services as well as the impact of greenhouse effect of IoT itself [5]. It represents all the enabling technologies for Green IoT such as Green RFIDs (radio frequency identification), Green Datacenters, Green Sensor Networks, Green Cloud Computing, Green Edge Computing and Green Communications. In [9], a Green IoT life cycle is presented which includes: green design, green production, green utilization, green recycling in addition to green IoT technologies such as green tags, green sensing networks and green Internet technologies. In [4], the authors discussed IoT techniques to achieve Green IoT and proposed a taxonomy of energy efficient based on the technologies used in IoT while in [10], the authors discussed energy efficiency techniques of different components and layers of IoT networks. Compared to our survey, the previous works do not include the energy harvesting techniques for IoT networks. In Table 1, we present a comparison between our survey and the other energy management and Green IoT surveys. It is clear through Table I that, in our survey, we chose to present novel schemes that were not discussed in the surveys presented previously. The main purpose of our paper is to provide an up-to-date state-of-art about energy management and Green IoT. Our comprehensive survey discusses the different and most recent techniques used in order to reduce energy consumption and increase the networks lifetime in IoT networks. Therefore, this article aims to serve as a guideline for researchers interested in the trends of energy management IoT networks. 3. Problem statement and challenges Since IoT applications and services are reaching almost every corner our life, the carbon footprint of these applications is increasing proportionately with the number of IoT devices. IoT networks are composed of devices that can be rich or limited in energy resources. The aim of Green IoT is to reduce the energy consumption of both types of devices while still being able to effectively perform their tasks and services. Key and widespread IoT technologies such as wireless sensor networks are composed of small and battery limited sensor devices that gather information and build efficient and reliable information and communication systems. However, these devices are typically used to perform monitoring and control tasks in IoT applications over long periods of time and without interruption. Sensors can also be placed in areas which are difficult to access (e.g. under water systems, military systems, unsafe environments, etc.) and can quickly run out of energy. Consequently, replacing their batteries frequently becomes an arduous and costly task. Energy storage and power management should also ensure the constant availability and operability of IoT de-

5 Table 2: The Table Shows The State-Of-The-Art of Energy Harvesting In The literature Energy Harvesting Technologies References RF Source energy harvesting Backscatter Communications [11], [12], [13], [14], [15], [16], [17], [18] SWIPT [19], [20], [21], [22], [23],[24], [25], [26], [27] MET [28],[29], [30], [31] Solar Energy Harvesting [32], [33], [34], [35], [36] Thermal Energy Harvesting [37], [38], [39], [40] Mechanical Energy Harvesting [41], [42], [43] vices given that, neither cable-power nor battery replacement are feasible options in harsh conditions. Many energy saving software and hardware solutions, which we will present in our survey, have been developed in order to reduce IoT networks energy consumption and maintenance cost. Currently, the world experiences a scarcity in energy sources in addition to the important emission of CO2 resulting of human activity. Greening information and communication technologies becomes an imperious need considering the damages caused on our environment. Consequently, energy harvesting solutions has been studied in order to power IoT devices with green and renewable energies such as solar energy, wind energy, mechanical energy, etc. Since Green IoT is a newly studied research field, one essential domain that is not widely discussed is energy measurement. In fact, with the battery limited nature of IoT devices and their high number and energy consumption, measuring and analyzing the energy consumption of IoT devices is key to achieve applications QoS requirements. Due to the high cost and large size of the existing commercial energy measurement solutions, researchers proposed various alternatives. However, these alternatives are often limited with their complexity, measurement range and accuracy [44]. IoT energy measurement tools should also be standardized to be able to support all types of devices with their different complexities at a low cost. As we can see, although the revolution of IoT changing our 4 lives has started, many challenges still need to be overcome in terms of energy consumption and environmental issues. Accordingly, numerous potential solutions in different disciplines are being explored to achieve this goal that requires a lot of efforts. 4. Energy management Techniques In order to tackle the energy issues in IoT networks, two approaches are usually employed: energy harvesting and energy saving. Energy harvesting can be described as the mechanisms employed to produce energy from a network ambient environment to provide an uninterrupted power supply for the network devices. On the other hand, energy saving schemes focus on developing algorithms, protocols and even hardware solutions to minimize the network energy consumption and maximize its lifetime. Based on these two categories, we propose a technique-based taxonomy (Fig. 1) which represents the recent energy management solutions studied in the literature in order to extend IoT networks lifetime and reduce the carbon footprint of those networks. Our taxonomy includes recent energy management techniques that can be found in the literature and not discussed in previous surveys. As shown in Fig. 1, we divide the energy harvesting category in the taxonomy depending on the energy sources into four sections: RF energy harvesting in which we include Backscatter

6 Figure 2: Backscatter Communication paradigms Communications (BCC) [11], a novel energy harvesting technology that have not been mentioned in previous energy management and Green IoT surveys, Mobile Energy Transfer (MET) and Simultaneous Wireless Information and Power Transfer (SWIPT) technology. We also discuss the most recent researches in energy harvesting techniques with green sources such as : solar, thermal and mechanical energy. We will present these techniques more deeply later in this section. The second category of energy management solutions we represent in the taxonomy includes energy saving solutions. As shown in Fig. 1, we classified energy saving solutions depending on the technology they employ. Unlike existing energy management surveys, in our survey, we introduce recent intelligent energy saving techniques that use Machine Learning (ML) and Deep Learning (DL) to reduce the energy consumption in IoT networks. We present energy efficient data collection techniques used in IoT networks as well as, sleep/wake-up schemes such as Duty Cycling and the recent low power Wake-Up Radio technology which have not been presented in the existing energy management and Green IoT surveys. In what follows, we will discuss the energy management techniques for IoT networks. First, we start by presenting the energy harvesting techniques followed by the energy saving techniques discussed in the literature. It is worth mentioning that we will present and focus more deeply on the emerging energy management techniques which have not been discussed in previous surveys Energy Harvesting Techniques We will discuss the energy harvesting techniques existing in the literature. We focus our review on the recent solutions proposed for each one of these techniques. In Table 3, we present the concept and challenges of the energy harvesting techniques presented in our survey and Table 2 summarizes the energy harvesting works organized by the nature of the harvested energy: RF, solar, thermal and mechanical energy RF energy harvesting (Wireless Power Transfer Techniques) In what follows, we will present RF energy harvesting techniques focusing deeply on backscatter technology since it is an emerging technique in energy harvesting that has not been presented in previous surveys. 1. Backscatter Communications One of the major issues that IoT networks face is the limited network lifetime due to the battery dependency of IoT devices. Having these devices completing task over a long period of time could easily lead to battery depletion and induce a replacement cost. In some cases, sensors do not need to be kept active consistently and will wear out their batteries unnecessarily. Backscatter communications allows battery-less devices to transform wireless signals and use them as a source of power and a communication medium. Having battery-less devices is a major advantage of backscatter communications as it helps reducing energy consumption, removes the constraint of

7 Table 3: The Table Shows The Energy Harvesting Techniques and their challenges Energy Harvesting Techniques Concept Challenges Backscatter communication SWIPT MET Solar Energy Harvsting Thermal Energy Harvesting Wind/Water Energy Humain Motion -Batteryless devices -Use RF signal to code information and send it to the receiver -Battery less or battery powered devices -The information and power for the device are sent through the same signal -Use a mobile node to power IoT devices -Use solar energy to power devices -Generate electric power from the difference in temperature -Generate electric power from water and wind movement -Generate electric power from human movement -Short range -Efficient information coding -Security -High deployment cost -Efficient information decoding -Interference -Short range -Security -The energy consumption of the mobile node -Interference -Light availability -Energy transport -Efficient power generation -Difficult to deploy -Unpredictable -Low energy -Differs from one person to another 6

8 frequent battery replacement and averts from undesired performances due to energy shortage. According to [11], there are three different paradigms of Backscatter Communications (Fig. 2): Monostatic Backscatter Communication (MBC), Bistatic Backscatter Communication (BBC) and Ambient Backscatter Communication (ABC). Monostatic Backscatter Communication: as shown in Fig. 2(a) MBC system is composed of two components: a reader and a backscatter transmitter (e.g. a RFID tag). The specificity of MBC is that the receiver and the RF source are located in the reader. The RF source sends a signal that activates the tag which modulates the signal and reflects it back to the backscatter receiver. Since the RF source and the receiver are located in the same device, the modulated signals may suffer from a round trip path loss which makes it more suited for short-range RFID application. Bistatic Backscatter Communication: the main difference between BBC and MBC systems lies in the separation of the RF source (renamed the carrier emitter in BBC) and the backscatter receiver in different components which significantly reduce the round-trip path-loss problem (as represented in Fig. 2(b)). Moreover, by optimaly placing carrier emitters, the overall communication range can be extended and the system performance is greatly improved. Although the deployment and maintenance of the carrier emitters in BBCs are costly, their design remains simple which makes their manifacturing cost lower than MBCs. Ambient Backscatter Communication: in Ambient backscatter communication systems (Fig. 2(c)) and similarly to BBC systems, ABC systems carrier emit- 7 ter is separated from the backscatter receiver. However, the carrier emitter, in this case, can be an already available ambient RF source in the environment such as a Wi-Fi access point or a cellular base station. In [12], an ABS system is presented as a batteryless device (tag or sensor) which can harvest wireless energy from a RF source and then transmit 0 or 1 bit to a sink node through reflecting or non-reflecting states. In addition to having virtually no cost of deployment, the manufacturing costs and power consumption of ABCs can be reduced by the use of low-cost and low-power components. Various works studied the backscatter communications and its impact on the energy consumption of IoT networks and proposed solutions to further improve the energy efficiency of these systems. In [13], the authors proposed a cooperative ambient backscatter communication system which allows a device to recover information from both the ambient backscatter devices and the RF source. The proposed solution enhances the maximumlikelihood detection performance if the backscatter device symbol period is longer than the RF source symbol period. This solution can be upgraded to be exploited in smart home and wearable applications. In order to further enhance the energy efficiency of backscatter communications, various works studied the optimization of the communication parameters. The authors in [14] proposed a joint optimization based on the source antenna number and power splitting factor under the condition that the minimum energy required by the destination node is met while in [15], the authors proposed a solution to optimize the needed power to transmit for a base station and the radio of backscattered signal of the IoT devices in the context of massive IoT. For complex IoT systems, other parameters (such as the number of base stations, the number of backscatter devices and their positions) need to be optimized in order to be energy efficient and

9 to adapt to the IoT application needs. The authors in [16], focused on the link layer of backscatter communication systems using centralized MAC protocols, while other solutions used distributed MAC protocol [17],[18]. Distributed MAC protocols are more suitable than the centralized protocols for large-scale IoT networks with unstable network topologies and high topology maintenance cost. Although backscatter communications seem ideal to achieve green IoT, it still has to overcome some challenges such as the risk of interference when there is a high number of nodes as well as, the bit rate limitations as it is not simple to implement very complex modulations on a backscatter tag due to its limited capability in manipulating the RF signal and performing baseband processing. In addition to that, backscatter communications networks need to overcome the short range limitation and the high deployment cost they may generate. 2. Simultaneous Wireless Information and Power Transfer (SWIPT) SWIPT is one of the most attractive and challenging technologies in energy management. It is a technique that enables the simultaneous transfer of information and power wirelessly [19]. Theoretically, SWIPT allows an IoT device to harvest energy and receive RF information from the same RF signal that carries both energy and information. Therefore, an IoT device can be self-powered by receiving data. However, in most of the cases, it is not possible to perform energy harvesting and information decoding simultaneously from the same signal. The information could be altered or lost by the energy harvesting operation. Thus, different receiver architectures were designed to achieve SWIPT: Separate Receiver, Time Switching (TS), Power Splitting(PS) and Antenna Switching [19]. In SWIPT, the power and information needed for an IoT node are sent at the same time which keeps the node alive and extend the lifetime of IoT networks. Various solutions have been proposed to adapt and achieve SWIPT. For example, in [20], the authors provide a comprehensive study related to SWIPT in different types of wireless communication setups. In [21], the authors proposed a dual-battery green energy harvesting architecture that achieves SWIPT. It consists in using one battery to power the device while the other one is harvesting energy in order to reduce energy shortage probability. However, this solution is limited to small IoT networks with devices that can support a dual battery. The proposed solution also adds complexity to sensor devices and is expensive for massive IoT networks with a high number of devices. In [22], the authors proposed a collaborative energy and information transfer protocol for WSN that does not require the power and information splitter component which is convenient for IoT networks and reduces the sensors complexity and cost. In [45], the authors studied the optimization of the energy efficiency in a SWIPT-based distributed antenna system by coordinating the energy harvesting and information decoding for IoT devices. The proposed solutions take into consideration the number of IoT devices in the network. SWIPT was also associated with deep learning to further enhance the energy saving potential of this technology. In [24], the authors proposed a solution based on deep learning to minimize the total transmit power of a multi-carrier NOMA SWIPT system while satisfying the Quality-of- Service (QoS) requirements of each user. Deep learning was also used with SWIPT in [25]. The authors proposed a dual mode SWIPT system that uses an adaptive mode switching based on deep learning to exploit both single tone and multi-tone SWIPT. Although deep learning proves to be efficient for SWIPT-based system, it also requires an important amount of resources that should be considered to build green and sustainable IoT networks. In [26], the authors proposed a hybrid resource and task 8

10 does not support free charging. Mobile energy transfer consists in giving to a mobile node (such as a car, a bus, etc), the ability to transfer energy to the devices within its transmission range. Figure 3: Mobile Energy Transfer RBC scenario algorithm in a SWIPT system that enables the devices to do their requirement tasks in a distributed manner. However, this solution should be improved to take into consideration delay-sensitive tasks for it to satisfy the realtime requirement of IoT applications. Finally in [27], the authors proposed a cooperative SWIPT scheme that consists of a conflict-free schedule initialization algorithm, a cooperative resource allocation algorithm and a heuristic algorithm to obtain a maximum energy efficiency in the transmission schedule with the appropriate resource allocation policy. SWIPT is theoretically a very efficient energy harvesting technique. However, this technique offers several challenges. In SWIPT, the information and energy are sent through the same signal. This signal needs to be efficiently decoded in order to recover the correct information. The signal can also be altered by the energy harvesting mechanisms or by interference. SWIPT is limited in range since the signals can weaken when crossing long distances. Finally, signals that are sent to the devices can be hijacked by hackers. Therefore, a level of security should be added in order to protect potential private and personal data. 3. Mobile Energy Transfer Mobile energy transfer is an ambitious energy efficient technique for IoT networks. It extends wireless power transfer technologies since the use of classical wireless power transfer technologies is not suitable if the node is isolated, placed in areas which are difficult to access and Various works have studied the use of mobile energy transfer technology for IoT networks energy management. The energy consumption of the mobile node is a key challenge of mobile energy transfer enabled IoT networks. In [28], the authors proposed a solution to minimize the total energy consumption of a mobile base station while supporting the data and energy demands of IoT devices to guarantee the QoS. This study shows that various parameters can impact the energy consumption of MET based IoT networks such as the mobile base station locations and its movement speed, the number of devices and their tolerable time for delivering data and energy as well as the transmission powers (the power needed for energy and data transmission). In [29], the authors proposed a hierarchical and wireless energy transfer using mobile chargers in Wireless Rechargeable Sensor Networks while in [30], the authors presented one of the mobile energy transfer technologies called Adaptive Resonant Beam Charging (ARBC). As shown in Fig. 3, it consists of two specially separated components: a transmitter and a receiver (car, TV, etc.) and in some cases, a relay (UAV in Fig. 3) which is used to relay the energy between the transmitter and the receiver. In [31], the authors proposed a mobile power network based on long range and short range wireless power transfer technologies and discussed the challenges of MET such as interference, efficiency, range and topology Solar energy harvesting Given its abundance in the environment, solar energy is considered as an affordable and clean energy source that could eliminate the energy shortage problem in IoT networks. This 9

11 technique directly converts sunlight into usable electricity which is convenient for location with high light availability. However, the solar energy harvesting technique flaws arise in locations where light availability cannot always be guaranteed. Additionally, transporting solar energy from a location to another when needed may cause considerable energy waste which shows that there is still room for research and improvement in this area. In the literature, various authors studied solar energy harvesting for IoT networks. In [32], the authors presented a review on solar energy harvesting for IoT applications and discussed the use of artificial intelligence and more specifically artificial neural networks to enhance and rapidly design energy harvesting systems for IoT applications. Numerous authors proposed solutions on solar energy harvesting for WSNs in [33], [46] and [34]. In [33], the authors proposed an intelligent solar energy harvesting system based on maximum power point tracking for wireless sensor nodes used in IoT, which prefers to use the solar power and takes the lithium battery as a supplementary under the condition of inadequate illumination. This solution offers IoT systems more reliability in case of scarcity in solar energy in the area. In [46], the authors proposed an energy prediction algorithm that uses the light intensity of fluorescent lamps in an indoor environment for wireless sensor nodes for IoT. This solution can be particularly helpful to achieve green and sustainable smart home since lamps can be used to power the sensors used in the smart home. In [34], the authors proposed a new routing algorithm that aims to improve the lifetime of sensor nodes as well as the QoS under variable traffic load and solar and RF energy availability. The proposed algorithm significantly improves the energy efficiency of the IoT network even if the number of nodes increases drastically which constitutes a suitable solution for massive, complex and scalable IoT networks. In [35] and [36], the authors proposed a solution to improve solar energy harvesting in wearable IoT applications. In [35], the authors proposed an MPPT algorithm based on the output current to extract the maximum power from a flexible solar panel to power wearable pulse sensors. Finally, in [36], the authors presented a self-powered wearable IoT sensor network that uses solar energy and is designed for safety environmental monitoring Thermal Energy Harvesting Thermal energy is the energy that comes from heat and variation in temperature. The human body and the environment can be a source of thermal energy. For example, a difference in temperature can be created by the human body and the environment depending on the health condition, the location of a person, or body motion. There are two techniques that are used in thermal energy harvesting: thermometric, a technique that directly converts temperature difference into usable energy and pyroelectric, a technique that generates electrical energy when there is a change in temperature. It is important to efficiently generate electric energy from thermal generator in order to extract maximum electric energy to power devices and exploit the maximum potential of this approach. In the literature, various authors tackled the subject of thermal energy harvesting for IoT. In [37], the authors discussed the use of thermal energy harvesting approach to power up a wireless sensor node for IoT applications. This study shows the efficiency of using thermal energy to power IoT nodes and the importance of using thermal energy harvesting in long-term IoT applications. Thermal energy harvesting proves to be efficient for various IoT applications. For example, in [38], the authors studied the use of energy thermal harvesting for self-powered smart home sensors. For healthcare, in [39], the authors studied a human body heat-based solution for wearable biomedical IoT nodes. Finally, in [40], the authors proposed a solution to optimize the energy consumption of thermal energy in IoT nodes by proposing an intelligent control of the individual parts of an IoT node. Unlike in [37], This solution uses a battery to store the harvested energy and then uses the nodes battery to power the application. 10

12 Table 4: The Table Shows The Utility Of Energy Harvesting Techniques In Major IoT Applications Energy Harvesting Technologies Smart Building Healthcare Agriculture Industrial IoT RF Source Energy Harvesting Backscatter Communications SWIPT MET Solar Energy Harvesting Thermal Energy Harvesting Mechanical Energy Harvesting Human motion Wind/Water Flow Mechanical Energy Harvesting Mechanical energy harvesting consists in harvesting the energy that results from movement, pressure, human activity or any type of motion from an object. It is considered as a very efficient energy harvesting solution since the more and faster the object moves the more energy is produced to do more tasks. Mechanical energy comes from various sources such as wind and water flow or human motion and can be converted into electrical energy and exploited to supply IoT devices. For example, it can be interesting to use the energy produced from human motion to power a human temperature monitoring device that the user wears or powering neighbouring houses using energy produced by wind and water flow. Mechanical energy harvesting offers a wide range of possibilities which makes it an ideal solution for creating green and sustainable applications. In the literature, mechanical energy harvesting have been thoroughly discussed. Several authors focused on wind flow and hydro in [41] and [42] respectively. In [41], the authors discussed the use of wind energy in autonomous WSNs and weather forecast to improve the prediction of wind condition in the near future. In this solution, the authors used a one and two hours weather forecast window in case of zero wind energy which shows that the unpredictable nature of wind energy makes it unsuitable for IoT applications that require constant availability and real-time performances. In [43], the authors studied kinetic energy harvesting for wearables resulting from human motion. This study shows that human motion can produce an impressive amount of energy per day however, since the energy produced can vary from one person to another it can be difficult to develop a solution that is destined to a large population. In [42], the authors described the conception, modelling, calculation, design, manufacturing and validation of the performances of energy harvesting devices deployed in river streams to be employed as a power source for pollution monitoring wireless sensor clusters Discussion The energy harvesting techniques presented in the previous sections are employed to produce energy from their surrounding environment and to power IoT networks. In Table 3, we summarize the energy harvesting techniques presented previously and the ability to use them in, what we consider, the major IoT applications (smart home, healthcare, agriculture and industrial IoT). For RF source harvested energy, SWIPT and MET can be applied to the use cases listed previously. In case of backscatter communications, the technology is suitable for smart homes, agriculture applications and indoor medical appli- 11

13 Table 5: The Table Shows The State-Of-The-Art of Energy Saving In The literature Energy Saving Technologies References Sleep Scheduling Wake-Up Radio [47], [48], [49], [50], [51],[52], [53],[54] Duty Cycling [55], [56],[57] Energy Efficient Data Collection Data Aggregation [58], [58],[59], [60], [61] Probability Approach [62] Compressive Sensing Approach [63], [64], [65], [66] Mobility based approaches [67], [68], [69], [70],[71] cations. Backscatter communications is still at a development stage, therefore, it faces limited-range, security and interference problems which makes it not suitable for applications that handle critical data and require a high level of reliability such as industrial and outdoor medical applications. Solar energy harvesting is largely used in smart homes and buildings, healthcare and agriculture. However, while solar energy can be used in industrial IoT, the network designers should take into consideration the uncontrollable nature of solar energy and how it can affect the performance of the high reliability requirements of industrial IoT applications. As shown in Table 4, thermal energy can be employed in different IoT applications as it can be frequently produced and stored to supply IoT devices. In case of mechanic energy harvesting, human motion is considered suitable for healthcare application. However, it does not produce enough energy to be helpful in the other applications. Wind and water flow can produce enough energy for various application, however, the uncontrollable nature of those energies is unsuitable for industrial IoT applications which require high availability and no energy shortage Energy Saving techniques In this section, we discuss the existing energy saving techniques in the literature by presenting and focusing on the most recent solutions proposed in each one of these techniques. In Table 5, we summarize the energy saving works studied in our survey Sleep/Wake-Up Techniques 1. Wake-Up Radios: Wake-Up Radio is a novel hardware solution that has been developed to reduce the energy consumption of devices and extend the network lifetime. It consists in having a low power radio attached to the device main radio in charge of waking it up when an incoming signal is sensed. In IoT systems, a Wake-up Radio enabled IoT device has been defined in [47]. It is a microcontroller unit associated with a main radio and a wake-up receiver. The microcontroller and the main radio are in deep sleep most of the time. As shown in Fig. 4, when the receiver receives a request by means of a wake-up signal transmitted by an external node, it generates an interrupt signal to wake up the microcontroller to perform certain tasks and transmit data back via its main radio. Based on power usage, three types of wake-up radios have been identified [48]: active, passive and semi-active wake-up radios. Active wake-up radios require continuous external power supply such as batteries. Passive wake-up radios do not need energy from power supplies. They harvest energy from the transmitted signal instead. In semi-active wake-up radios, some components of the receiver require continuous power from an external source 12

14 while the RF front-end remains passive. Wake-up radios can be particularly useful in a smart home/office scenario, or in an industrial context like warehouses and can be enhanced further to be used in smart cities [49]. However, due to the sleep and wake-up nature of wake-up radio, we can expect an increase in data reception latency. According to [48], designing wake-up radio in a larger environment requires considering different design aspects such as: Power consumption: the node s power consumption should not exceed the main radio s sleep power to keep the balance between the energy saved and used by the device. Time to wake-up: the time needed to wake-up the device radio should be minimum in order to reduce the latency and thus enlarge the spectrum of applications Wake-up Radios can be used for. False wake-ups and interference: due to interference and wrongly receiving signals intended to other devices, a node or several nodes could be unnecessarly activated causing huge energy waste. Thus, designers should find the right compromise and find solutions to those challenges without increasing the energy consumption. Sensitivity: in Wake-up Radio environment, the sensitivity of the receiving node has a considerable impact on the power demand as having high sensitivity requires high power demand at the receiving side and having low sensitivity means needing high radiated power at the transmitter side. Communication range: the range of a Wake-up Radio has an impact on the type of application it is used in, as well as on the system s power consumption. For example, having short range communication would require a multi-hop communication which will increase the nodes density and the power consumption. 13 Data rate: having a higher data rate in a Wake-up Radio context is a way to improve energy efficiency and wake-up latency. Similarly, having a longer bit duration also increases the communication range and the reliability of the wake-up signal. Cost and size: the Wake-up Radio should be cost effective in order to be integrated into the IoT context and existing sensor nodes. The cost should not exceed 5-10 per cent of the complete sensor node cost. Frequency regulation: the design of Wake-up Radios should adhere to frequency regulations in industrial, scientific and medical (ISM) bands. It should also be adapted to communication standards such as the maximum allowed effective radiated power (ERP) used to transmit the Wake-up signal (WuS). In the literature, various studies proved the efficiency of low power wake-up radios energy saving. In [54], the authors studied the design (from physical to media access control) and utilization of passive wake-up radios in IoT applications. This review also discusses the limitation of wake-up radios such as the short communication range, the high latency, lack of standardization, mobility as well as contention and channel access. In [50], the authors proposed GREENROUTES, an energy-aware cross-layer routing protocol for wake-up radio-based green networks. This protocol selects the relay nodes based on their distance in hops from the sink ans uses semantic addressing to enhance capabilities. Since wake-up radios are sensible to the distance between nodes, it would be interesting to see if this protocol is efficient in case the nodes are distantly distributed. The authors in [51] analyzed Bluetooth Low Energy (BLE) beacons scenarios in IoT applications and discussed the benefits of integrating wake-up radios in the BLE protocol stack which can help both reduce the time to contact all the beacons and also reduce the current consumption. Low power wake-up radios networks still suffer from the

15 Figure 4: Concept of Wake-Up Radio limited range of their components. This is why, in [52], the authors tackled the wake-up radios limited range issue and proposed a design and implementation of long range low-power wake-up radio for IoT devices that increases the lifetime of the devices and reduces the power consumption and overall cost in a large scale deployment. Another approach was proposed in [53] to improve the energy efficiency and communication range of wake-up radios in IoT systems. The proposed approach consists in applying error correction codes on wake-up signals. Since wake-up radio suffer from short range capability and low signal sensibility, wake-up radio commonly misinterpret the wake-up signal which leads to performance degradation of the system. Code correction on wake-up signals constitutes an efficient solution to improve the robustness and sensitivity of wake-up devices. 2. Duty-cycling: Duty-cycling, also known as sleep schedule, is considered one of the most effective ways of saving energy and extending networks lifetime. Duty-cycling algorithms are used to reduce the node s battery consumption by alternating between sleep and wake-up modes. The node is usually set in a low power mode and only wakes up when needed (e.g. data reception, radio transmission). Those algorithms usually fall under 3 categories: topology control protocols, sleep/wake-up protocols and MAC protocols with low duty-cycles. Duty-cycling have been studied broadly and due to its constant relevance, several recent works on duty cycling and its impact on the energy consumption of IoT networks have been proposed. In [55], the authors provided a qualitative and quantitative comparison between the families of asynchronous duty-cycling methods, their characteristics and the most suited Green IoT applications for each of them. Asynchronous duty-cycling methods undergo an excessive transmission energy consumption while waiting for the receiver wakes up in addition to the multiple unicast transmissions that not only consume energy but can also lead to a network congestion. In [56], the authors proposed an algorithm to optimize the dutycycle duration of low-power IoT devices equipped with energy harvesting circuits in Wi-Fi based IoT networks. This solution use an optimal weighting moving-average filter to predict the harvested energy and find the optimal weighting factor to estimate the duration of the off period for IoT devices. The devices will exploit the off periods to harvest enough energy for the data acquisition. Finally, in [57], the authors proposed a modified duty cycling efficient path planning algorithm for an IoT-based precision agriculture system. The proposed duty-cycling algorithm is performed on the data transmission phase and the path planning algorithm studies all the path and chose the shorted path. This solution proves to be effective even with the a high number of devices. However, 14

16 since the solution s performance depends on the clusters, it would be interesting to optimize the cluster organization in order to reduce the overall energy consumption Energy Efficient Data Collection Millions of IoT devices are deployed in intelligent applications like smart homes, smart transportation, and so forth. These devices generate huge amounts of data. In order to communicate its data to the base station or to his neighbours, an IoT device will consume large amounts of energy. Moreover, IoT networks are usually constituted of a large number of devices that have limited batteries. Once emptied or depleted, those batteries are often difficult to replace especially if the devices are placed in an isolated or a difficult to access position. One promising solution is to collect the generated data and communicate it in an efficient way to reduce the transmission energy cost. Several data collection approaches have emerged in order to reduce the energy consumption, delay and maximize the network lifetime [63]. 1. Data Aggregation approach: This approach consists in reducing the number of transmission in a network and remove redundant and duplicated transmission of data. Data aggregation is a the procedure of one or a number of nodes that gather data from other nodes for a period of time, aggregate them and transmit the aggregated result. In [63], the authors divided the data aggregation techniques into three classes : tree based, cluster based and centralized based. In [58], the authors presented a systematic review on the data aggregation mechanisms for IoT and presented a detailed comparison of the techniques used in each class. The authors found out that tree-based techniques offer low energy consumption, high network lifetime and great scalability compared to the other techniques while cluster-based techniques offer low traffic and high fault tolerance. On the other hand, centralized techniques focus mainly on security aspects. The latency, network lifetime as well as device heterogeneity should also be considered in data aggregation techniques. In [59], the authors did a review on tree-based data aggregation techniques. The tree architecture is composed of nodes structured in the form of a tree. A tree contains leaf nodes and forwarding nodes. The authors found out that tree-based aggregation consumes less energy than clustering and grid-based aggregation. However, in a tree-based approach, the failure of a intermediate node affects the whole topology and disturbs the network activity. In [60], the authors proposed a cluster-based data collection technique that uses a sensor based cluster formation algorithm to elect the controllers and handle the different types of sensors. This algorithm is used alongside a multi-tier aggregation function. This solution allows us to extend the network lifetime, maximize the coverage and eliminate redundant data at the controller. However, using these structures can have major drawbacks. For example, tree and cluster structures increase the vulnerability of the network and make it less secure. They also do not take into consideration sleep and transmission latency. In [61], the authors proposed a centralized-based data aggregation for IoT which gathers data and sends it to a centralized IoT gateway. This method also proposes an efficient sleep scheduling method along with data aggregation in order to ensure data consistency in the network. Compared to the tree and cluster structures, the centralized structure is more secure. However, it has low fault tolerance. 2. Probability based approach: The probability based approach allows us to save energy by using approximation models to find correlation between data. The goal of probability based solutions is to predict the value of the sensed data. In [62], the authors proposed a Bayesian inference approach that allows us to avoid the transmission of highly correlated sensor data and reduce IoT networks energy consumption. This approach was applied to a hierarchical architecture with smart devices and data centers and proved to reduce the number of transmitted 15

17 data with good information accuracy in three different scenarios. In general, it is difficult to implement since the devices that help achieve the probability based approach can be increasingly complex and the models are not optimal considering the high correlation between the devices data. 3. Compressive sensing based approach: Compressive sensing consists in compressing the collected data and reduce the quantity of data sent throughout the network. Compressive sensing help reduce the size of the sensory information matrix as possible to reduce information and communication redundancy. According to [63], compressive sensing approaches can be divided into three categories : Conventional compression, Distributed source coding and Distributed compressive sensing. In [64], the authors presented a review on data compression techniques from different perspectives such as data quality, coding schemes, data type and applications. Data compressing techniques are described by their compatibility, representation efficiency, computational complexity and memory management. Data compressing solutions should optimize these characteristics in order to enhance the efficiency of IoT networks and reduce the devices complexity. In [65], the authors proposed a clustering routing protocol based on energy efficient compressive sensing that finds the optimal size of a cluster, the optimal distribution of cluster heads and also alleviate the hot spot problem. This solution reduces efficiently the overall energy consumption of the network and extend its lifetime. In this case, there is a temporal correlation between data which means it would be interesting to use a predictive approach in order to reduce the energy consumption resulting from the spacial-temporal correlation. In [66], the authors employed distributed source coding between two node in a NOMA pair to optimize the performance and minimize the energy consumption in resource-constrained cellular IoT networks. This solution focuses only on mobile broadband applications, where transmissions typically occur in the downlink with full-buffered traffic. 4. Mobility based Data Collection: Recently, mobile data collection approaches have been widely used as a mean to develop energy efficient IoT networks and extend their lifetime. Mobile data collection allows battery-limited devices to save their transmission energy and thus extend their lifetime. Mobile data collection can be achieved by three methods. The first method consists in using static sensor nodes and a mobile sink that will collect data from these nodes when they are sufficiently close to it. The second method consists in having a static sink and mobile nodes. Since the nodes can move, they improve the network coverage and decrease the number of nodes needed (e.g. we can have less sensors in the network since one sensor can move and cover other areas of the network). This strategy has been studied widely in the recent years the number of mobile is increasing. The third method consists in having mobile sensor nodes and a mobile sink. There have been various studies that focused on energy efficient mobility-based data collection in the literature. In [67], [68] and [69], the authors studied the use a mobile sink (UAV) with fixed nodes and proposed a solution that optimizes the UAV trajectory in order to ensure an energy efficient data collection. In [70], the authors proposed an algorithm for a distributed bus-based data collection to maximize the lifetime and throughput of WSNs. On the other hand. Researchers also studied the use of a mobile nodes with a static sink. In [71], the authors proposed a solution based on the Internet of Vehicles that allows traffic data gathering architecture with an efficient sink node selection. This solution shows only certain vehicles and paths should be selected to reduce the network energy consumption. As shown in the latter works, mobility-based data collection is a very promising energy saving technology if the mobile sink has a 16

18 low carbon footprint and is provided with an optimized the IoT infrastructures from data centers to the IoT nodes. These trajectory which constitutes a challenging research area. developments emerged with the exploitation of new communication paradigms like 5G and the rapid growth of technologies Discussion such as artificial intelligence, cloud, etc. In this section, we will The energy saving techniques presented in the previous section are used to reduce the energy consumption of IoT devices works ecosystem. In Table 7, we summarize the energy man- present the recent advances in energy management for IoT net- as much as possible. In Table 6, we summarize the energy saving techniques presented previously and highlight the pros and networks. agement solutions studied for cloud, fog and edge enabled IoT cons of using these techniques in IoT networks Energy management in IoT-Cloud Computing In case of sleep/wake-up schemes, both duty-cycling and wakeup radios considerably reduce the nodes energy consumption. With the start of the IoT revolution, the number of IoT devices has exploded and with it, the amount of data collected While wake-up radios is an on-demand sleep and wake-up scheme, duty cycling schemes can be divided into three categories: on by those devices. However, since IoT devices do not possess demand, asynchronous and schedule rendez-vous. Accordingly, the computational power to process all of the collected data, wake-up radio schemes are able to reduce the idle listening and cloud computing is considered as a very promising paradigm overhearing that duty-cycling may suffer from. However, it has that provides high-performance computing capability and highcapacity storage to support the ubiquitous need for data collec- a limited communication range, a high deployment cost and can suffer from high interference. Duty-cycling, on the other hand, tion and processing in IoT networks. Cloud computing allows in addition to idle listening, overhearing and over-emitting can the battery-dependant IoT devices to offload various tasks to the also struggle with high sleep latency (e.g. sending data to a cloud (e.g. data cleaning) and conserve their energy. Although node who is not ready to receive data) and data transmission it can be convenient to use this technology, it is well-known that latency (e.g. waiting for the next wake-up phase to send data). the cloud servers that host cloud applications are highly energy Choosing either wake-up radios or duty-cycling depends essentially on the network needs and requirements. energy consumption by using its resources efficiently. Reducing consuming. Therefore, it is also necessary to reduce the cloud energy consumption in Cloud computing can also be achieved Energy efficient data collection approaches are efficient schemes by the use of low-power devices and the virtualization of services. to reduce the energy consumption of IoT. However, they still have challenges and issues to overcome such as security and latency issues for cluster-based and tree-based data aggregation In the literature, a large scope of energy saving solutions in or the hardly achievable sensory data sparsity in data compression. Mobility based approaches can also be subject to data [72], the authors proposed an energy-efficient architecture for cloud computing enabled IoT networks have been studied. In loss and energy starvation if the system policy used is not well IoT that allows the system to predict the sleep interval of sensorsbased IoT networks upon their remaining battery level and re- defined. duce the energy consumption of sensor nodes and cloud resources. However, this solution is not recommended for ap- 5. Recent Advances in IoT ecosystem Energy Management plication with strict real-time requirements since it is specific to In order to achieve Green-IoT, several new approaches for systems with a sleep/wake-up strategy. Various solutions proposed energy saving in cloud computing by using service com- energy management have been explored by the researchers to reduce the energy consumption of the different components of 17

19 Table 6: The Table Shows The Pros And Cons Of The Energy Saving Techniques Discussed In The Survey Energy Saving Technologies Pros Cons Reduce the power consumption of battery powered networks Short communication range Sleep/Wake-Up Wake-Up Radio On demand sleep /wake-up High deployment cost Reduce idle listening and overhear- High interference ing Reduce the power consumption of High sleep latency Duty-Cycling IoT devices On demand / asynchronous / sched- Data transmission latency Idle listening, over-emitting and ule rendez-vous overhearing Mobile Data Collection Data aggregation Reduces energy consumption Reduces traffic load Risks of high latency Less secure for the non-centralized approaches Probability based Predict sensing data Save transmission energy Not accurate Add communication costs Compressive Sensing Reduce information redundancy Save communication energy Restricted to non-complex sensor data Not always feasible Reduce routing distance and trans- High energy consumption to recover Mobility based mission costs High network flexibility and cover- dysfunctional mobile nodes Optimal Path for the mobile node is age difficult to find Table 7: The Table Shows The State-Of-The-Art of Recent Energy Management Approaches For IoT In The literature Energy Management Approach References Cloud Computing [72], [73], [74], [75], [76], [77], [78] Fog Computing [79], [80], [81], [82], [83], [84], [85], [86], [87] Edge Computing [88], [89] [90], [91], 18

20 position optimization. In [73], the authors proposed a multicloud IoT service composition algorithm (E2C2). The composition of services is energy-aware and integrates the minimum number of IoT services to meet a user s requirements. This solution outperforms similar algorithms have been previously proposed: All Cloud, Base Cloud, Smart Cloud [74] and COM2 [75]. To further improve service composition solutions, we should take into account not only the cloud providers aggregate energy, but also the energy needed for the service execution. In [76], the authors presented a partitioning method of an application into offloadable and non-offloadable components to reduce the energy consumption and the execution time. This solution offloads components to the cloud however, it requires high network reliability in order to ensure the application well functioning and availability. Other solutions focused on energy saving using power-aware virtual machine placement in cloud providers. In [77], the authors also proposed a virtual machine placement algorithm in order to reduce the number of active physical machines and reduce their energy consumption while also load balancing between the active physical machines. However, in this solution a static virtual machine placement is done which means once a virtual machine is placement it can not be migrated to another physical machine. In [78], the authors proposed an ant system for dynamic virtual machine placement that reduces the number of active physical machines and reduces the overall energy consumption of the data center. In dynamic virtual machine placement, virtual machines can be migrated from one physical machine to another. Therefore, security strategies should be applied during migration in order to protect private data from hackers attacks Energy management in Fog Computing Cloud computing offers a platform and frees businesses and end users from the burden of many specification details. However, it does not constitute an effective solution for latency sensitive applications and often fails to meet their delay requirements. Consequently, Fog computing emerged as a new paradigm that constitutes an extension of cloud computing at the edge of the network rather than a replacement to it. Fog Computing is a highly virtualized platform that provides computing, storage, and networking services between end devices and traditional Cloud Computing Data Centers[79]. Fog Computing platforms are designed to support the mobility, geo-distribution in addition to low latency required in various IoT application (e.g. industrial-iot). These characteristics make the Fog platforms suitable to support the energy constraints of Green-IoT applications. Various Fog computing solutions have been proposed in the literature to reduce the energy consumption in IoT systems. In [80], the authors studied the sustainability of Fog Computing and its performances compared to traditional Cloud in terms of power consumption, CO 2 emissions and cost in IoT networks. Fog computing enhances the performance of latency sensitive applications in terms of QoS and eco-friendliness. In [81], the authors identified major features (such as dynamic discovery, configuration, management of devices, multi-protocol support at different layers of the network architecture, mobility etc.) that fog computing platforms need to support toward building sustainable sensing infrastructure for smart city. Fog computing platforms should built in a manner that different applications can use. Such platforms should also offer build-in support for different types of communication and data analytics frameworks in order to implement the different features of fog-iot platforms. In [82], the authors proposed a solution that allows a number of IoT devices to select a fog instance that will receive their data considering the energy consumption (e.g energy consumed by the transport network, energy to process and store the data, energy to forward the data to the cloud if needed) and the latency (transmission latency, processing latency, forwarding to cloud latency). In [83], the authors reviewed the AI based application placement solutions that address latency, resource utilization and energy consumption issues in fog enabled IoT networks. Fog application placement solution focus mostly 19

21 on achieving efficient load balancing methods. Machine learning and more specifically, reinforcement learning algorithms, are widely used and combined with evolutionary algorithms in order to solve the fog application placement problem. Algorithms can be combined to cover each others weaknesses and achieve challenges such as security, privacy, availability, etc. In [84], the authors proposed an energy efficient fog-based IoT scheme that reduces end-devices energy consumption for industrial IoT by reducing their transmission. This scheme uses an MQ Telemetry Transport broker to predict future data measurements through prediction techniques, operate as gateway and offload expensive data processing from the Cloud to the Fog. In order to reduce the energy consumption in fog-based IoT networks, various fog computing models were proposed. In [85], the authors proposed a linear fog computing model to reduce the total energy consumption. Although linear models are simple and useful they are only suitable for IoT networks with few number of sensor nodes. Consequently, in [86], the authors proposed a tree-based fog computing model that supports a large number of IoT devices and reduces the total electric consumption compared to the Cloud Computing model. Finally in [87], the authors proposed a distributed (online) learning model on the sensor device. The model is created by mining the collected data that approximate the data stream behavior and then transmitting only the updated model parameters to a fog computing system. This solution saves energy in IoT devices by preventing unnecessary uplink transmission Energy management in Edge Computing With the increasing number of IoT devices, the data produced by those devices is also escalating explosively. Processing efficiently those data then becomes a necessity in Green- IoT. Therefore, edge computing technologies emerge as a very attractive solution for energy management in IoT as they bridge the gap between the limited capability of low-powered devices and their computational demands. Edge computing allows the workload to be offloaded from the cloud to a location closer to the source of data that need to be processed. It offers the possibility to downstream data on behalf of cloud services and upstream data on behalf of IoT services. It consequently has the potential to extend the lifetime of battery constrained IoT devices, reduce the network traffic, increase the bandwidth, improve the privacy and achieve a considerable energy and communication delay saving. Different approaches have been proposed to achieve energy saving in Edge computing based IoT networks. Energy efficiency using edge computing can be achieved through different schemes : from offloading strategies to data reduction schemes as well as the efficient management of IoT devices and applications. In [88], the authors tackled the scalability and energy consumption challenges that IoT faces by proposing a novel architecture including the cloud, mobile edge computing and IoT. It includes a framework which develops a selective offloading scheme to reduce the energy consumption of IoT devices and the overhead of edge devices. However, this architecture needs to overcome many challenges related to nodes identification by the virtual machines (especially if they change location) as well as the nodes and virtual machines migration. In fact, if a mobile node changes its location, the system should find an efficient strategy that can either migrates the virtual machine or not based on the QoS requirements of the application. In [89], the authors proposes a reinforcement learning based offloading scheme for IoT devices with energy harvesting. IoT devices select an edge device and their offloading rate according to their current battery level, the previous radio transmission rate to each edge device, and the predicted amount of harvested energy. This solution also includes a deep reinforcement-based offloading scheme to further accelerate the learning speed. In [90], the authors also combined Edge Computing with deep reinforcement learning in an IoT-based energy management system to reduce the energy consumption in a smart home context. The proposed solution consists in an offline and an online phase. During the offline phase, a deep neural network is trained and a deep reinforcement learning model based on Q-learning is built and exploited 20

22 data rate increase up to 10 Gb/s and uses multiple technologies (e.g. massive multi-input multi-output (MIMO), small cells, beam forming, etc.) in order to support the tremendous number of users. Consequently, 5G IoT suffers not only from spectrum efficiency but also energy efficiency problems that are causing negative effects on the environment and human health. This exponential growth has raised the energy consumption and carbon footprint of those networks to alarming rates which led both the industry and academia to turn towards green 5G communications. Figure 5: Research Perspectives in energy management for IoT during the online phase. The proposed solution proves to reduce the energy cost. Such solutions can be proposed to reduce the energy consumption of smart homes in a smart city context. In [91], the authors focused on compressing the data collected by the edge device before their transmission. The proposed solution is based on an error-bounded lossy compressor designed for high performance applications with high data production. The proposed compression algorithm proved that the extracted data produced similar classification accuracy as the compressed data. 6. Research Perspectives in IoT Energy Management In this section, we will discuss the use of approaches like social IoT 5G-IoT and network softwarization as well as artificial intelligence and Cognitive Radio IoT in order to reduce the global energy consumption of IoT networks. We will also present the most recent solutions proposed for this purpose. In Table 8, we summarize the papers studied for each approach. In the literature, various studies discussed the importance of energy efficiency in 5G networks. In [92], the authors presented an overview of energy-efficient techniques for 5G networks covering resource allocation, network planning, network deployment, hardware solutions, etc. In [93], the authors presented a detailed survey on energy efficient cellular networks and proposed a spectrum sharing solution for prolonged battery life in IoT networks. In [94], the authors provided an overview of several recent solutions for the wireless powering of devices exploiting either near-field or far-field techniques. These techniques are expected to have a leading role in the realization of 5G-IoT networks. In [95], the authors proposed a probabilistic decay feature-based solution which focused on the energy consumption of spectrum sensing for IoT devices in 5G networks. In [96], the authors proposed an integrating and energy efficient system model for 5G-IoT that uses a massive MIMO array to replace the single remote antenna and cellular partition zooming (CPZ) mechanism as a select-and-sleep mechanism, shortening consequently the distance between the components and reducing the number of routers Energy management techniques in 5G IoT With the emergence of 5G IoT, up to 120 billion devices will be connected all over the world. In order to meet the intense user demands, the devices will not be limited to smart phones but also home appliances, cars and other devices and will contribute to build a smart society. 5G IoT also claims a 6.2. Energy Efficient Cognitive Radio IoT With the high number of IoT devices connected to the internet, the frequency spectrum used by those devices to communicate are getting crowded. Cognitive radio is an approach that aims to improve the usage efficiency of the radio electromagnetic spectrum by using all available frequencies. The 21

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Recent Advances In Energy Management For Green-IoT: An Up-To-Date And Comprehensive Survey - PDF Free Download

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Recent Advances In Energy Management For Green-IoT: An Up-To-Date And Comprehensive Survey Sana Benhamaid, Abdelmadjid Bouabdallah, Hicham Lakhlef To cite this version: Sana Benhamaid, Abdelmadjid Bouabdallah,

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mechanical energy, sum of the kinetic energy, or energy of motion, and the potential energy, or energy stored in a system by reason of the position of its parts. Mechanical energy is constant in a system that has only gravitational forces or in an otherwise idealized system-that is, one lacking dissipative forces, such as friction and air resistance, or one in which such forces can be reasonably neglected. Thus, a swinging pendulum has its greatest kinetic energy and least potential energy in the vertical position, in which its speed is greatest and its height least; it has its least kinetic energy and greatest potential energy at the extremities of its swing, in which its speed is zero and its height is greatest. As the pendulum moves, energy is continuously passing back and forth between the two forms. Neglecting friction at the pivot and air resistance, the sum of the kinetic and potential energies of the pendulum, or its mechanical energy, is constant. Actually the mechanical energy of the system is diminished at the end of each swing by the tiny amount of energy transferred out of the system by the work done by the pendulum in opposition to the forces of friction and air resistance. The mechanical energy of the Earth- Moon system is nearly constant as it is rhythmically interchanged between its kinetic and potential forms. When the Moon is farthest from Earth in its nearly elliptical orbit, its speed is least. Its kinetic energy has become least, and its potential energy is greatest. When the Moon is closest to Earth, it travels fastest; some potential energy has been converted to kinetic energy.

The Editors of Encyclopaedia Britannica Adam Augustyn.

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Mechanical energy | Definition, Examples, & Facts

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mechanical energy, sum of the kinetic energy, or energy of motion, and the potential energy, or energy stored in a system by reason of the position of its parts. Mechanical energy is constant in a system that has only gravitational forces or in an otherwise idealized system-that is, one lacking dissipative forces, such as friction and air resistance, or one in which such forces can be reasonably neglected.

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Open access peer-reviewed chapter

Written By

Chukwuemeka Emmanuel Okafor and Komla Agbenyo Folly

DOI: 10.5772/intechopen.111399

From the Edited Volume

Edited by Zongjie Wang and Abdollah Younesi

Abstract

The negative environmental impacts of conventional power generation have resulted in increased interest in the use of renewable energy sources to produce electricity. However, the main problem associated with these non-conventional sources of energy generation (wind and solar photovoltaic) is that they are highly intermittent and thereby result in very high fluctuations in power generated. Hence, mechanical energy storage systems can be deployed as a solution to this problem by ensuring that electrical energy is stored during times of high generation and supplied in time of high demand. This work presents a thorough study of mechanical energy storage systems. It examines the classification, development of output power equations, performance metrics, advantages and drawbacks of each of the mechanical energy storage types and their various applications in the grid networks. The key findings in this work are the strategies for the management of the high costs of these mechanical storage devices. These include deployment of hybrid energy storage technologies, multi-functional applications of mechanical energy storage systems through appropriate control methodologies and proper sizing strategies for cost effectiveness and increased penetrations of renewable energy sources in the power grid.

Keywords

mechanical energy storage

renewable energy

intermittent

performance measures

power systems

Chukwuemeka Emmanuel Okafor*

University of Cape Town, Cape Town, South Africa

Komla Agbenyo Folly

University of Cape Town, Cape Town, South Africa

*Address all correspondence to: okfchu002@myuct.ac.za

1. Introduction

Until now, the entire energy sector depends on fossil fuels for the generation of electricity, but more environmentally friendly options are advancing in the form of renewable energy sources. The transition from conventional (traditional) power plants to more environmentally friendly options will necessitate a need for more flexibility in the generation, transmission, and consumption of electricity. Energy storage systems (ESSs) can provide the flexibility that is needed for a robust high quality stable electrical system when technically integrated into the grid network. The following are some of the features of energy storage:

Being able to store energy at the time of excess electricity production and making it available for use within a very short time window when the need arises.

With the help of energy storage technologies, energy can be stored and made available at the very point where it is needed whether at the transmission, distribution, or consumption levels. This flexibility ensures the postponement of infrastructural upgrades in generation, transmission, and distribution networks.

The consumption of energy varies with time. Sometimes the demand is high while at other times the demand is low. Energy storage devices can be deployed to meet the varying energy demands per time.

Energy storage technologies such as pumped-hydroelectric storage (PHS), battery energy storage system (BESS), supercapacitors, etc. are flexible in providing multiple services to the grid. They can serve as loads during their charging process and therefore offer a service to the grid like voltage rise mitigation, while in their discharging mode, they can be controlled to provide peak-shaving service, frequency support or inertia support.

Finally, energy storage technologies have the flexibility of participating in some special services to the grid for example the black start service. The system offering the black start service must possess the capacity to move from shutdown into operation without the aid from the grid. This type of service is required during periods of persistent blackouts. Energy storage technologies with high energy capacity like PHS, compressed air energy storage (CAES), and gravity energy storage (GES) can provide excellently the black start service to the grid.

There are six different categories of ESS, and these are: mechanical, thermal, chemical, electrochemical, electrical and hybrid system. Each category has unique characteristics in terms of life cycle, discharge time, discharge loss, energy density and power rating. All these characteristics account for their suitability for specific applications in the power system.

In mechanical energy storage system (MESS), there is a conversion of energy from mechanical to electrical form [ 1]. In times of low energy demands, electrical energy is taken from the grid and stored until the time of high demand when it is then converted back to electrical energy and transmitted back to the grid [ 2]. The flexibility in the conversion processes of MESSs accounts for their global applications [ 3]. MESSs are classified as pumped hydro storage (PHS), flywheel energy storage (FES), compressed air energy storage (CAES) and gravity energy storage systems (GES) according to [ 1, 4]. Some of the works already done on the applications of energy storage technologies on the grid power networks are summarized on Table 1.

References

Contributions

Years of publication

[ 1, 3, 5, 6, 7]

The works present an in-depth review of energy storage technology types and their applications in the grid power networks.

2019, 2014, 2011, 2009, 2013

[ 8, 9]

The papers present the economic and reliability impacts of energy storage systems in power system networks.

2018, 2021

[ 10, 11]

The works discuss the application of energy storage systems in different levels of grid voltage. Besides, the conditions for integration of energy storage into the grid for proper compatibility with the operational codes and standards were emphasized.

2016, 2017

[ 12, 13, 14]

The authors explore the possible approaches of combining applications of energy storage systems. The technical requirements for the combination of applications were also discussed.

2004, 2003, 2019

[ 15, 16]

The works evaluate the challenges militating against the massive deployment of large-scale energy storage technologies for grid applications considering the various economic, legislative, and technical aspects.

2016, 2020

[ 17]

The work provides an in-depth review of the methodologies of storage sizing and placement on the grid networks. It covers several areas such as analytical approach, mathematical programming, exhaustive search, and heuristic methods.

2016

[ 18, 19, 20]

The papers perform a detailed analysis of power electronics converters used in interfacing energy storage systems with the grid network.

2016, 2017, 2020

[ 21]

The work discusses some of the software used in the simulation and analysis of energy storage systems and specific energy storage applications they are designed to implement.

2017

[ 22]

The work evaluates the impact of energy storage systems on the economic operation of distribution systems

2020

Considering the works summarized in Table 1, the authors have done extensive research on energy storage integration to the grid network taking into accounts several aspects such as energy storage technology types, applications (both single and combined), limitations and challenges of energy storage systems, power electronic converters for energy storage interface. Simulation tools (software) for energy storage systems and storage system placement and sizing. However, to the best of our knowledge, nothing has been done on how to manage the high cost of energy storage system which according to [ 22] is the main reason hindering the massive deployment of energy storage system in the grid.

Therefore, the principal contribution of this work is bringing to light certain key approaches that should be addressed to manage the high costs of energy storage systems, thereby promoting their massive deployment into the grid networks.

The block diagram showing a simple classification of mechanical energy storage systems according to [ 23, 24] is given in Figure 1.

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2. Pumped hydro-electric storage (PHS)

The PHS is a utility-scale energy storage technology that has been in implementation since 1890s [ 25]. It has a high commercial acceptance, and it is well-established. In PHS, large amount of water is delivered to an upper reservoir, during period of excess generation of electricity and it is converted back to electricity through the use generator and turbine during period of shortage of electricity. An illustration of pumped hydroelectric storage is shown in Figure 2 while Table 2 shows the performance measures.

Efficiency (%)

70-85

Number of cycles (in cycles)

10,000-35,000

Expected life(years)

30-60

Specific energy (Wh/kg)

0.5-1.5

Specific power (W/kg)

N/A

Power capacity cost ($/kW)

600-2000

Energy capacity cost ($/kWh)

0-23

* BOP ($/kWh)

270-580

Power conversion system (PCS) ($/kW)

0-4.8

** O&M ($/kW-yr)

3-4.4

Maturity of the technology

Mature/ Implemented.

It was reported in [ 26] that one of the earliest technologies for the storage of energy is PHS. The operating cost for energy units for PHS as compared to other energy storage systems has been reported to be the cheapest according to [ 18]. In 2017, there were about 270 PHS stations globally producing 127 GW of electricity [ 27]. The United States and the European Union (EU) have 40 and 160 PHS stations respectively while the rest of the world has 70 PHS units [ 27].

2.1 Output energy equation of a PHS

Pumped-hydro-electric storage is generally used for energy-based applications because of its ability to deliver power for very long period in several hours [ 28]. It functions by utilizing the potential energy of water due to the force of gravity. When using the power from the grid during a period of low demand, water is pumped from lower reservoir to the upper reservoir. In the time of high demand of power, the water stored in the upper reservoir is released into the lower reservoir for the operation of the turbine and generator in order to inject power into the grid [ 24].

The energy stored, according to [ 29], depends on the volume of the water and the height of the waterfalls. This is described in Eq. (1).

where E phs is the stored energy in joules, ℓ is the density of water equivalent to 1000 kg/m ³, g is the acceleration due to gravity equivalent to 9.8 m/s ², h is the height of the waterfalls (in meters) and V the volume of water stored in the upper reservoir in m ³.

2.2 Advantages and drawbacks of PHS

PHS has a high-power capacity ranging from some MW to about 3GW with a cycle efficiency of approximately 70-85 cycles and over 40 years lifetime [ 4, 6]. However, the drawbacks of a PHS lie in getting available sites to accommodate two large reservoirs and dams, the long-time involve in the preparation of the site, a high capital cost (in hundreds to thousands of millions of dollars) for construction, and environmental issues (removing trees and vegetation from a large amount of land) prior to the reservoir being flooded [ 30, 31]. The United States has an existing 23GW of PHS capacity installed [ 32].

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3. Compressed air energy storage (CAES)

In this storage system, power from the grid (during period of low demand) is used to pump air into underground geological formation until the air is at high pressure [ 33]. During discharge, air at high pressure is drawn from the storage cavern and undergoes a heating and expansion process inside high- and low-pressure turbines, for conversion into kinetic energy and thereafter, transformed into electrical energy in a generator [ 29, 34]. One innovative system in the application of CAES is by combining it with a wave energy system according to [ 6]. The well turbine being an integral component of the wave energy system [ 35] uses the excess power produced by the renewable energy sources during the period of peak production. Through these methods, most of the renewable energy generated during the period of peak production would be used if not immediately. The excess power generated during peak production is used in storing fresh air in salt caverns [ 36]. The CAES turbine, using this fresh air can generate 3 times the output power for the same natural gas input [ 37]. With this innovation, a higher efficiency of the system is achieved.

An illustrative topology of a CAES is shown in Figure 3 while the performance metrics are given in Table 3.

Efficiency (%)

57-85

Cycle lifetime (cycles)

N/A

Expected lifetime (years)

20-40

Specific energy (Wh/kg)

6-30

Specific power (W/kg)

N/A

Power capacity cost ($/kW)

400-800

Energy capacity cost ($/kWh)

2-140

BOP ($/kWh)

270-580

* PCS ($/kW)

46-190

O&M ($/kW-yr)

1.6-29

Maturity

Commercial

The current technological advancement on the improvement of the efficiency of CAES is focused on developing an advanced adiabatic CAES (AA-CAES) through which air is adiabatically compressed and pumped into an underground cavern. The success of this depends on the compressor and the expanded trains of the CAES [ 33].

In natural gas power plants with attached CAES, the air at a high pressure is mixed with natural gas for combustion [ 38]. The integration of CAES systems to an existing grid is relatively easy due to its similarity to a conventional gas combustion system [ 39].

CAES can be effectively utilized through the implementation of a hybrid energy storage system. A hybrid energy storage system involves the integration of different energy storage technologies for the implementation of several functions in the grid network [ 40]. To achieve this, the technical characteristics of the different storage technologies such as the power and energy capacities, response time and discharge time should be known. Besides, the application technical features should be analyzed for proper hybridization of different technologies. Examples of such hybridization include, CAES with flywheel examined in [ 40], CAES and supercapacitor energy storage and pumped hydro energy storage with CAES in [ 7]. CAES creates a potent energy reserve [ 41] and has three main components namely compressors, air storage reservoir and expanders [ 42].

3.1 Advantages and drawbacks of CAES

CAES has a large energy capacity, little geographical dependency, long lifespan, and low cost per kW [ 43]. The CAES system has a lifetime of about 40 years with an energy efficiency of 71% [ 29]. CAES owing to its installed capacity which is from 35 to 300 MW is deployed in the grid to support load leveling, voltage, and frequency control, etc., [ 44].

However, CAES suffers the same limitations as PHS, that is difficulty in locating a specific geographic place for installations. To overcome this limitation, high-pressure carbon fiber tank air storage is proposed for the implementation of distributed CAES system [ 12, 45, 46].

Also, CAES installations have relatively low efficiency [ 33]. The recent developments of hybrid CAES plants with offshore and onshore wind plants shows increased overall efficiency with reduced fluctuations in the power output.

3.2 Output energy equation of a CAES

CAES is an energy-based storage system that utilizes the principle of the gas turbine to produce electricity. Excess electricity (during periods of peak production) is used to compress and store air at very high pressure. When electricity is needed, the stored air at high pressure is deployed to drive a turbine to generate electricity [ 47, 48].

The power taken by each compressor (P C) is given in Eq. (2) according to [ 49].

where K is the adiabatic exponent, Q, the mass flow rate of air, R is the gas constant, T in is the temperature inlet of the compressor, η c is the efficiency of the compressor and β is the compressor ratio. The air temperature of the outlet (T out) of each compressor stage could be expressed as in Eq. (3) according to [ 49].

Considering a compressor with many stages, n and charging time (t c), the total energy taken by the compressor (W c) can be evaluated using Eq. (4).

where η c is the efficiency of the air compressor. Applying the adiabatic efficiency to compute the actual shaft power of the turbine, the power of each turbine (P e) is given in Eq. (5) according Ref. [ 49].

P e=K K−1 QRT η e [1−EXR K−1 /K E5

where, Q is the mass flow rate of the turbine, η e is the turbine efficiency, EXR is the expansion ratio of the turbine and T is the temperature of the turbine.

For a multi-stage turbine of m-number of stages, having a discharging time of t e, the total electrical power output (W e) is calculated using Eq. (6).

where, η g is the efficiency of the generator.

3.3 Improving the efficiency of a CAES system

Energy storage efficiency is the principal factor militating against the development of CAES [ 49]. The energy efficiency of CAES depends on the energy efficiencies of all the units making up the CAES. These include the compression unit, air storage unit, heat regeneration unit and turbine generation unit. Thus, improving the performance of each of these units enhances the efficiency of CAES.

3.3.1 Compression subsystem

In order to have a compression subsystem with a high efficiency the exhaust temperature of the compression should be increased. This will result in an improvement in the heat storage temperature of the system and its storage efficiency.

3.3.2 Air storage subsystem

Underground salt cavern can be used to store large amount of air, and this will reduce the pressure fluctuations and consequently improves efficiency [ 49].

3.3.3 Regeneration subsystem

The efficiency of CAES is directly proportional to the temperature of the heat regeneration subsystem. As the temperature of the regeneration unit increases, the system efficiency also increases. In places where there is abundant supply of wind and solar energy, they can be adequately harnessed to provide heat for the regeneration subsystem, thereby increasing its temperature.

3.3.4 Turbine generator subsystem

The turbine generator unit is one of the major component parts of a CAES. It participates in the thermoelectric conversion in the energy-discharging process. The development of new and efficient air turbine will help in increasing the efficiency of CAES in general.

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4. Flywheel energy storage (FES)

A flywheel energy storage (FES) is a rotating disk that can store or dissipate mechanical kinetic energy utilizing rotatory inertia [ 16]. An illustrative topology of an FES is shown in Figure 4 and its performance metrics is given in Table 4.

Flywheel (low speed)

Flywheel (high speed)

In FES energy is stored in the angular momentum in a rotating mass [ 46]. Unlike PHS and CAES, FES is a power-based energy storage system [ 50]. It is deployed in applications that require short duration with short discharge time in the range of 1-100 s [ 37]. FES is used for voltage support [ 28], frequency support [ 37], fluctuation suppression, and provision of short-duration power quality. Globally, several FES systems are in use with an installed capacity of more than 940 MW [ 36].

4.1 Energy output and shaft factor equations of a flywheel

The rotating mass(disk) is mechanically connected to the shafts of the machines [ 29]. The energy stored by flywheels is given in Eq. (7) according to [ 48].

where E fes, (joules) is the energy stored by flywheel, J (in kgm ²) is the inertia of the rotating parts, W is the rotational speed in rad/s. The moment of inertia J is defined according to Eq. (8)

where X is the distance from the axis of rotation, differential mass = dM x. Considering a flywheel with radius r, having a mass M concentrated in the rim, the solution of the integral equation becomes Eq. (9).

The energy stored by the flywheel becomes E fes = 1 2 JW ², where W is the angular velocity. Thus Eq. (9) is transformed into Eq. (10).

The energy density (E mass) is given in Eq. (11).

To obtain the volume energy density, (E volume), we divide Eq. (10) by the volume V, therefore we have the volume density given as in Eq. (12)

E volume=E fes V=1 2 M V r 2 W 2E12

But density ρ =mass volumeE13

The density is given by Eq. (13)

Therefore, the volume energy density is now expressed as shown in Eq. (14).

For the flywheel's rim, the tensile stress is given by σ, which is defined in Eq. (15) according to Ref. [ 36].

Thus, from Eq. (15),

where σ max is the maximum tensile strength of the material (rim). For maximum kinetic energy per volume of the flywheel will depends on high tensile strength. Therefore, the choice of the maximum value of E volume or E mass will depend on the area of application. The maximum energy density (E mass(max)) with respect to mass is given by Eq. (17).

The general expression for the maximum energy density (E mass(maxg)) of a flywheel with respect to mass is given in Eq. (18).

where K is the shape factor. The values of K for several flywheel shapes are provided in Table 5.

Flywheel shape

Shape factor (K)

Constant stress disc

0.931

Flat unpierced

0.606

Thin rim

0.500

Rod or circular brush

0.333

Flat pierced disc

0.305

4.2 Components of a flywheel energy storage

FES is made up of several parts namely motor-generator system, a motor control system, bearings, a flywheel, and a flywheel housing [ 35]. The type of bearings used in a flywheel is very important because of mechanical friction which is responsible for loss of energy in flywheel energy storage. Mechanical bearings are not ideal for FES because of the constant need for lubrication and maintenance. In modern FES, mechanical bearings are replaced with magnetic bearings. These can levitate the shaft thereby reducing the impacts of friction and the need to lubricate [ 35]. The motor-generator unit of a FES has a dual function. It can operate as a motor during the time of excess production of electricity in order to gain kinetic energy. When there is a need for electricity, it acts as a generator and converts the kinetic energy stored in the flywheel to electrical energy [ 51, 53]. FES housing provides a vacuum for the placement of FES. The housing guarantees that there is no loss of energy through air friction.

4.3 Advantages and drawbacks of FES

The merits of FES over other mechanical energy storage technologies include low costs of maintenance, very high efficiency, high power density, and long lifetime [ 38]. Its disadvantages are low energy densities and very high losses due to friction [ 54].

4.4 New advances in FES technology

There are several advances in FES technology geared towards reducing energy losses, improving efficiency, and widening the scope of applications.

Among these innovations is the use of high-temperature superconducting (HTS) bearings. This has the capacity of improving the overall round-trip efficiency of FES technology to over 90% according to [ 39].

In the area of applications, FES could be deployed in hybrid systems comprising of fuel cells, flow cells, ultra-capacitors, lithium-ion batteries, and small co-generation systems with low temperatures according to [ 43]. Besides, other new areas of applications of FES systems are in Kinetic Energy Recovery System [ 45], and in electric vehicle propulsion system [ 46], KERS/ERS.

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5. Gravity energy storage (GES)

In GES electric pumps are used to pump water under a movable rock piston and through that, the rock mass is lifted. In the time of low production of electricity, the water which is already under high pressure from the rock mass is released to a turbine for the generation of electricity via a generator. Through this method, large quantities of water can be stored for several hours between 6 and 14 hours and can be made available to produce electricity when the needs arise. The size of the storage could be chosen between 1 to 10GWh and the rock piston diameter should be at least 100 meters.

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6. Applications of mechanical energy storage systems in power system grid

With the increasing penetration of renewable energy sources in the grid network and the variability of these energy sources, it becomes necessary to bring a balance between power generation and demand. Therefore, energy storage systems integration into the grid becomes absolutely necessary [ 29]. The applications of mechanical energy storage systems in smart grid could be divided into energy-based and power-based applications.

6.1 Energy-based applications

Sufficient storage capacity is a requirement for energy-based applications to participate in very long discharges in a time window of one or more hours. PHS, CAES, and GES are used for energy-based applications discussed in the subsequent sub-sections.

6.1.1 Load following

Load following is a service in which the energy storage technology e.g., CAES, provides power for a long-time range (one or more hours) [ 16]. The intermittency of renewable energy sources accounts for the imbalance between generation and loads leading to variations in voltage and frequency. Mechanical energy storage systems such as PHS, CAES, and FES can provide the needed power to compensate for imbalance and stabilize the system frequency and voltage.

6.1.2 Peak-shaving

In this service, mechanical energy storage technologies, such as PHS, CAES, and GES are used to store energy during the time of excess production of power and to inject back energy into the grid during limited generation of power. In this service, power is delivered by the storage technology for several hours.

6.1.3 Transmission line curtailment

The capacity of a transmission line determines the optimal flow of power through it. When this exceeds the line capacity, the generation must be curtailed. In transmission line curtailment, CAES/PHS technology is used to inject power into power into the networks in a time window of 5-12 hours in line with the transmission line capacity. The applications of CAES for transmission curtailment are examined in [ 55, 56, 57].

6.1.4 Unit commitment

Owing to the uncertainties concerning variations in wind and of solar irradiation, managing the commitment of wind turbines and solar panels to meet the estimated demand always is difficult. Therefore, the use of energy-based storage system such as PHS in the networks may be useful to combat the effects of uncertainties in wind forecasting and to reduce the energy reserves if the system during its normal operation. In [ 58], the unit commitment problem was formulated in a power system with wind generation and CAES.

6.1.5 Spinning reserve

Mechanical energy storage systems such as PHS, CAES and GES can be used to compensate for unexpected contingencies for example the failure of a generating unit.

6.2 Power-based applications

In this application premium is placed on mechanical energy storage being able to charge or discharge within a very short interval of time (in milliseconds of time). FES is the best type of mechanical energy storage system for power-based applications because of its very short response time. Other energy storage systems that can be used for power-based applications include battery energy storage systems, [BESS], super-capacitors, and superconducting magnetic energy storage system (SMESS) [ 50]. The following subsections discusses some of the power-based applications where FES and other non-mechanical energy storage systems (such as BESS, super-capacitors and SMESS) could effectively be deployed.

6.2.1 Voltage control support

For the maintenance of proper voltage levels in the power system grid, the regulation of the flow of reactive power into power system network is very important. FES can effectively be used for both active and reactive power control thereby providing an excellent voltage control in the grid network.

6.2.2 Provision of inertia support

With the replacement of the fossil-fuel based power plants with the non-synchronized renewal energy power plants in the modern power grid, the total synchronized inertia of the system is getting diminished. This, if not checked, could result in frequency instability of the power system during contingency events. Mechanical energy storage systems especially FES (due to their short response time) can be used to emulate the provision of inertia of synchronous -based generators.

6.2.3 Fluctuation suppression

Certain loads in power systems (like electronic devices) are highly sensitive to non-sinusoidal voltage and current characteristics. FES may be used to inject power of high quality by quickly charging and discharging thereby smoothing out very short-term fluctuations.

6.2.4 Provision of a short duration power quality

Mechanical energy storage system especially FES can be deployed for the provision of short-duration power quality by supplying active power for very short duration in the range of 1-10 seconds.

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7. Managing the high cost of mechanical energy storage systems

Energy storage systems especially PHS, CAES, and FES have been identified as a key device for realizing the goal of having high renewable penetration (wind and solar photovoltaic) in the modern grid. However, the extremely high cost of energy storage systems can constitute a barrier to achieving the above-mentioned goal. One way towards overcoming the challenge of high cost of energy storage systems is by the implementation of hybrid energy storage system. This involves the integration of different energy storage technologies for the implementation of several functions in the network. To achieve this, the technical characteristics of the different technologies such as the power and energy capacities, response time, and discharge time should be known. Besides, the application technical features should be analyzed for the proper hybridization of different technologies. Table 6 shows the summary of application technical characteristics. Considering the various possible combine applications listed in section four of this work and referring to Table 5 of applications technical characteristics, PHS and FES can be integrated together to provide for both power and energy-based applications. PHS can manage the energy-based applications such as peak shaving, and capacity firming while the FES can provide frequency excursion suppression, grid angular stability and grid voltage stability. Also, a black start application, frequency stability, regulation control and fluctuation suppression can be implemented by combining PHS and FES.

Applications

Required time response

Reference duty cycle

ESS power (MW)

ESS AC voltage (kV)

Full power discharge duration

A second approach towards managing the extreme high cost of energy storage systems is by implementing the multi-functional utilization of the storage systems. In this approach, a single energy storage system is controlled in such a way to execute several functions. This will improve the cost effectiveness of energy storage system and will reduce the significant slack period of the storage system. However, the implementation of energy storage for multi-functional utilization will require the development of appropriate control methodologies. Without these, it will be impossible to utilize energy storage for multi-purpose applications.

Besides, the choice of suitable storage technology is very crucial for the multi-functional operation of an energy storage system. Some storage technologies are energy based, capable of delivering power over a prolonged period (e.g., PHS, CAES, etc) while others are power based (i.e., FES, SMESS), only being able of delivering high impulse power for few seconds. Therefore, it is necessary to evaluate the technical features of the various storage technologies to make appropriate choice of the required storage system.

Finally, good sizing methodology must be developed, bearing in mind that when energy storage systems are undersized, the reliability of the system becomes impaired while over sizing of the energy storage systems may results in less cost effectiveness.

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8. Conclusion

In this work, a study on mechanical energy storage technologies and their various applications in the grid networks are presented. Their operating principles, topologies, various subsystems, performance measures, advantages and drawbacks were discussed.

In addition, the work also presents the development of output power equations for each mechanical energy storage type based on the fundamental principles of potential energy due to gravity, air compression atvery high pressure, and kinetic energy of rotating masses.

Moreover, the capacity of energy storage systems in providing flexibility that is essential for robust, high quality stable electrical systems, in a grid network with a high penetrations of renewable energy sources were examined in detail.

Further highlights in the work include key approaches to improving the efficiency of mechanical energy storage systems especially compressed air energy storage system and flywheel energy storage system. Such strategies take in cognisance the need of improving the performance of every subsystem and the deployment of new advances in technology such as the use of high temperature superconducting bearings for FES.

Lastly, the management of the high cost of energy storage systems through the implementation of hybrid energy storage systems, multi-functional applications of energy storage technologies and appropriate sizing methodologies were discussed. The diligent pursuit of these strategies will surely reduce the costs factor in energy storage systems and consequently result in more of their deployment into the grid networks.

References

1. 23. Jung J. 21 December 2023. [Online]. Available: http://www.jume.vfrgs.bu/bitstream/handle [Accessed: December 21, 2023]

2. 25. Fortune Business insights, Energy and Power. 2022. [Online]. Available: http://www.fortunebusinessinsights.com/industry-reports [Accessed: December 21, 2022]

3. 32. Why energy storage technologies (Pumped Hydropower) [online]. Available from: https://energystorage.org/why-energy-storage/technologies/pumped-hydropower. Accessed: January12, 2021]

Written By

Chukwuemeka Emmanuel Okafor and Komla Agbenyo Folly

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The history of robots combines the best of science fiction and real life technology. From Isaac Asimov to modern industrial robots, keep reading to explore the fascinating history of robotics

When many Americans think of the word "robot," years of science fiction portrayals and action movies immediately come to mind. And while science fiction often misses the mark, the history of robots actually owes quite a debt to science fiction masters like Isaac Asimov.

However, to truly understand the history and evolution of robotics, we have to define the term. That's surprisingly difficult to do. For our purposes, we're going to define a robot as a machine that's capable of carrying out routine or complex actions that are programmed by engineers. Today, robots can be used for surgery, massage therapy, space exploration, manufacturing, and code analysis, but the earliest robots were far more primitive - they were tools that could tell time or automatons that could perform for entertainment.

Broadly defined, humans have been developing robotics and automata for hundreds of years. With this in mind, let's get into the curious history of robots and how far we've come in the branch of robotics engineering.

Invent like an Egyptian: early robotics

The Egyptian water clock is one of the very first cases of "robotics" in human history. The oldest example of the water clock, found in the tomb of Amenhotep I, dates as far back as 1500 BCE.

An outflow water clock was marked along the inner container with measurement lines. The container was filled with water, which would drip over time. To tell the time, the owner would simply check the water measurement. Imagine only having to fill your watch with water when the battery died.

However, what truly made this invention remarkable wasn't the use of water to tell time. Rather, it was that the force of the water in the clock would bang gongs or strike bells on the hour with human figurines.

Greece began using water clocks by 325 BCE. And, just 25 years later, the second known advancement in robotics was invented by Greek mathematician Archytas. Archytas designed and built what's now called The Pigeon, a mechanical bird that could be propelled into the air by using steam.

Leonardo Da Vinci was another remarkable mind in the engineering field. In 1495, Da Vinci designed and built what's now called the Robot Knight. According to Mark Elling Rosheim's Leonardo's Lost Robots, the robot could sit, stand, and move its arms using pulleys and cables.

Ducks And Trumpets: The Evolution Of Automation

In the western world, we only truly began to see the evolution of modern automation in the 17th century. Jacques de Vaucanson, a French inventor, developed three automata. The first automaton was capable of playing up to 12 songs on a flute. We can only thank De Vaucanson that it wasn't a recorder.

The second automaton could play a tambourine, drum, and flute. And the third, and most renowned, was a duck.

The duck was capable of flapping its wings, moving, quacking, and even "eating." The real-life movements and sounds could be compared to today's baby doll. However, the first modern automaton would be invented in 1810 by Friedrich Kauffman of Germany. This robot was designed to look like a soldier. By using automatic bellows, the soldier would blow a trumpet.

Developments In Mechanical Programming

The development of mechanical programming was advanced by Ada Lovelace. Ada Byron, the Countess of Lovelace, was an English mathematician known for writing the first algorithm for the Analytical Engine.

The Analytical Engine was a general-purpose computer proposed by Lovelace's husband Charles Babbage, another mathematician. It was Lovelace who was able to recognize the machine's applications and explain the machine's function to the British establishment between 1842 and 1843.

Lovelace died at the age of 36 and Babbage was never able to complete the Analytical Engine. However, the engine served as the precursor for today's digital computer.

Further Advancements: Started From The 1800s And Now We're Here

In 1898, famed inventory Nikola Tesla constructed a wireless torpedo that could be controlled with a remote. It was a process he called "tele-automation," and the robotic torpedo was demonstrated at Madison Square Garden.

However, the term "robot" wasn't used until 1921 when Karel Capek, a Czech writer, coined the word to describe automata in fiction. The complementary term "robotics" would later be made famous by writer Isaac Asimov in 1942. After the World Wars, Isaac Asimov's robots didn't just capture the popular imagination of Post-War America; they kickstarted a new era in robotics history.

As soon as 1946, the Electronic Numerical Integrator and Computer, or ENIAC, was officially constructed. The ENIAC was one of the first electronic general-purpose computers and was programmed by Betty Jennings, Frances Spence, Marlyn Wescoff, Kay McNulty, Betty Snyder, Ruth Lichterman, and various others. Notably, Adele Goldstine authored the ENIAC's program manual.

Then in 1950, Ida Rhodes co-designed the C-10 programming language for UNIVAC I. UNIVAC I was the computer system that would later be used to determine the U.S. census. Also in 1950, George Devol would invent Unimate, the first industrial robot. Unimate could transport die castings and weld them into automobiles. Similar to modern automation in manufacturing and other industrial fields, these industrial robots would be programmed for a specific function as a means of replacing unskilled labor. Unimate was one of the most important milestones in the history of robots.

The 1960s and 1970s were the decades of arm-like automatons. Shakey (1966), the Stanford Arm (1969), and the Silver Arm (1974) gave rise to Puma350 (1985) and CyberKnife (1992), which both served as innovative robotic technology in the medical field.
In fact, these arm-like automatons resemble much of modern robotics. One such robot, developed by Albert Zhang, is the Expert Manipulative Massage Automation or Emma. A product of AiTreat, a Singaporean startup, Emma is a one-armed robot engineered to provide massage therapy to human patients.

Modern robotics in everyday life

The automated side of robotics are well-known by many Americans even if they haven't been given a name. How often have you noticed automated machines during the three hours you've been sucked into watching How It's Made?

These automated machines replace repetitive manual labor to give humans the ability to learn new skills in the same field. For instance, in the shipping industry it takes 15 seconds for the average employee to assemble a complete box (including bubble wrap, tape, and barcode) for shipping.

To assemble a box in that time requires familiarity, technique, and speed. Yet such a job doesn't pay a high salary.

Robotics that replace manual labor such as box assembly create higher-level job openings in the industry. These positions require greater skill and pay higher wages.

Further advancements in technologies since the 2000s have led to more advanced automation and artificial intelligence. Automated machines are programmed to perform one action over and over and are used today in manufacturing, maritime exploration, space exploration, military, and commercialized agriculture.

Artificial Intelligence, or AI, is programmed to assess an environment and take action to succeed at a programmed goal. Recent advancements in this field have led to software capable of preventing identity theft, producing relevant search queries for search engines, and cracking ciphers for the FBI. As we look to the future history of robotics, AI will likely play a major role.

Video-on-demand websites such as Netflix and Hulu already use predictive analytics to recommend genres and shows to viewers. Algorithms that cluster recommendations based on show similarities improve customer satisfaction.

Businesses are also prone to use sentiment analysis software to get an in-depth look into public opinions on products and services. This helps businesses market better to consumers. It also keeps them in the know regarding negative feedback so they can respond swiftly to minimize damage.

The future of robotics: where do we go from here?

The future of robotics is difficult to gauge because of the rate of innovation. However, it's predicted that robots will most likely play a greater role in the home and in the business world.

Products such as Google Home, Amazon Echo, and Apple's Siri have grown in popularity in recent years. Smart Homes have also been gaining traction because of their convenience and ability to save on utility bills, increase comfort, and improve security.

Microsoft, Google, and Amazon have also been developing greater technologies for the business world. For instance, users of Microsoft Office 365 can now receive and make business calls within Microsoft Teams without using another app. What's more, language recognition has seen new innovation with Google's Pixel Buds, which translate up to 40 languages in real time.

Automated robots are also expected to become more commonplace outside of manufacturing and shipping industries. Up to 35% organizations in health, logistics, and utilities are expected to begin exploring the use of automated robots as soon as 2019.
Innovations in technologies such as self-driven cars may be less likely to take as big a stake in the future. Car accidents involving self-driven vehicles show the coexistence between impulsive human drivers and careful self-driving cars may be difficult.

Space exploration is another field where robotics are expected to improve human advancement. Since the Soviet Union's Mars 2 became Earth's first interplanetary robot when it landed on Mars in 1971, engineers have been developing newer and greater technologies.

For instance, one of NASA's newest developments, the ISS Robotic External Ammonia Leak Locator, detects ammonia leaks on space stations. By using a robot to detect leaks and to repair them in the future, the risk for NASA's crew members is reduced.

That being said, the innovations of new technology will continue to rapidly develop, but not necessarily in the way science fiction predicts. Rather, breakthroughs in automation and programming will continue to improve what humans have been seeking to advance for years: communication, education, and life itself.
CEO/Publisher
Cutting Tool Engineering
847-714-0176
dspaeth@ctemedia.com

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From single-task machines to backflipping robots: the history of robots

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The history of robots combines science fiction and real life technology. From Isaac Asimov to modern industrial robots, keep reading to explore the history