Summary

Electric double-layer capacitors (EDLCs) have the potential to replace aluminum electrolytic capacitors for alternating current line-filtering, aligning with the trend of device miniaturization. However, it is challenging for EDLCs to simultaneously achieve rapid ion migration, electrical response, and high areal and volumetric capacitances. Here, we demonstrate that three-dimensional, structurally integrated multi-layer carbon tube (3D-MLCT) frameworks are used as electrodes for high-performance filtering EDLCs. By simply increasing the number of tube layers, a high specific areal capacitance of 3.08 mF cm−2 at 120 Hz is achieved with a phase angle of −80.1°, exhibiting excellent capacitance and fast frequency response. The outstanding performance of the 3D-MLCT is attributed to the high density, high orientation, and high integrality of carbon tube arrays that facilitate and accelerate ion distribution onto the electrode surface. The findings of this work provide a solution for developing next-generation miniaturized filter capacitors with high capacitance and fast frequency response.

Introduction

Converting alternating current (AC) into direct current (DC) is essential for powering electronics.1,2,3 In the process, filter capacitors play a crucial role in smoothing the ripples in the rectified DC signal.4,5,6,7 Aluminum electrolytic capacitors (AECs) dominate this field but occupy the most significant volume in circuits.8,9,10 Electric double-layer capacitors (EDLCs), storing energy physically through reversible ion adsorption at the electrode-electrolyte interface, have been anticipated for fast frequency response applications.11,12,13,14,15 EDLCs are considered promising alternative devices for AC line-filtering to meet the demands of miniaturized electronic devices owing to their several-orders-of-magnitude-higher specific capacitance than commercial AECs.16,17,18,19,20,21,22,23 However, conventional EDLCs behave more like resistors than capacitors and possess poor frequency response performance at an indicator frequency of 120 Hz because of their tortuous and complicated pore structure as well as the high resistances of the electrodes.24,25,26,27

The first filtering EDLC was reported by Miller et al.,1 with discrete vertically oriented graphene (VOG) nanosheets grown on nickel substrates as electrodes, demonstrating an outstanding AC line-filtering performance. Subsequently, various carbon-nanomaterial-based electrodes with high orientation, thin thickness, and macro-porous structures have been explored for filtering EDLCs.28,29,30,31,32,33,34,35 However, the specific areal (CA) and volumetric capacitances (CV) are still at a low level—the bottleneck of miniaturized filter devices.36,37,38,39

Results and discussion

The fabricating process of 3D-MLCT is schematically illustrated in Figure 1. A 3D-AAO template with interconnected vertical and lateral channels was synthesized by anodizing impure aluminum foil in phosphoric acid electrolyte at 0°C under a voltage of 195 V (Figure S1).40,41 The template was placed into a horizontal tube furnace and heated to 1,000°C at 10°C/min under an Ar atmosphere. The first CT layer was deposited on the pore walls of the 3D-AAO template with a flow of acetylene gas under vacuum conditions for 50 min. Subsequently, an AlOx layer was deposited via an ALD process at 250°C on the inner wall of the first CT layer using trimethyl aluminum (TMA) and ozone (O3) as the precursors.42 The second CT layer was deposited on the AlOx surface via a second-time CVD process. Similarly, repeating the ALD and subsequent CVD processes can prepare a third CT layer. Finally, the 3D-MLCT was obtained after removing the AlOx layer(s) and 3D-AAO template in hydrofluoric acid solution, and the spacing of adjacent CT layers can be precisely controlled by the thickness of the AlOx layers. Furthermore, the size of the 3D-CT films can be adjusted by varying the area of the 3D-AAO as needed, and we prepared a 3D-CT film with an area of approximately 24 cm2 (see Figure S2).

Abstract

The use of smart inverter capabilities of distributed energy resources (DERs) enhances the grid reliability but in the meanwhile exhibits more vulnerabilities to cyber-attacks. This paper proposes a deep reinforcement learning (DRL)-based defense approach. The defense problem is reformulated as a Markov decision making process to control DERs and minimizing load shedding to address the voltage violations caused by cyber-attacks. The original soft actor-critic (SAC) method for continuous actions has been extended to handle discrete and continuous actions for controlling DERs’ setpoints and loadshedding scenarios. Numerical comparison results with other control approaches, such as Volt-VAR and Volt-Watt on the modified IEEE 33-node, show that the proposed method can achieve better voltage regulation and have less power losses in the presence of cyber-attacks.

Introduction

S Mart control of distributed energy resources (DER) in distribution systems is bringing a fundamental shift in how these networks are maintained within the security limits. Historically, control methods were designed based on conventional approaches, where cyber threats have not been paid attention [1]. The idea of introducing internet protocols in the electrical network to use more advanced protection and control components has created the need to defend the cyberattacks. Furthermore, a study on [2], [3] has showed that only 62% of cyber-attacks can be recognized after they cause massive damage to the system, which makes it a critical issue for system designers. Nowadays, the digital transformation of the electrical distribution systems has forced lots of restrictions and regulations that must be applied for achieving a secure and resilient system [4]. In the context of cyberphysical security [5], [6], smart attackers can initiate false data injection attacks (FDI) [7], where a slight change in any of the controllable devices (i.e., smart inverters, smart ring main units and digital relays), can result in disturbing the networking security without being detected by existing defense approaches. In this paper, we propose a learning-based approach for the mitigation of cyber-attacks on connected loads and DERs. Deep reinforcement learning (DRL) was opted for its superior capability of learning the power system constraints and achieving optimal control strategy

Numerical Results

TS A modified IEEE 33-node system with three-phase loads and 4 DERs that are utility-owned (i.e., each DER consists of 1 ES unit and 1 PV with installed capacities of 500 kW each unit), is used for testing. The system is modeled using OpenDSS and is configured to be in the grid connected mode. At the first solution evaluated using OpenDSS, no voltage violation has been observed during the normal operation. In addition, 4 tie switches have been added to the network an sectionalizers are considered for operation. The learning environment is designed according to OpenAI Gym [14], which is a common interfacing library to define DRL environment for the agent. The SAC algorithm is implemented using PyTorch. Specifically, in the SAC, both actor and critic networks are designed as feed-forward neural networks with three hidden layers of 50, 100 and 50 neurons and a ReLU activation function for each layer. Other SAC hyper-parameters are as follows: Adam optimizer is used with a learning rate of 0.0001 and discount factor γ is set to 0.9. The target network is updated by tau = 0.001 and random process is applied for better exploration with α = 0.1, β = 0.1 ρ = 10 and µ = 0.1; the replay buffer size is 100000 with batch size 256. The offline DRL training spends around 3 hours and 30 minutes on a laptop computer with 3.6GHz Intel i7 processor and 32.0 GB RAM. The proposed DRL defense algorithm is compared with other control algorithms, such as the Volt-VAR and Volt-Watt using the default control curves in OpenDSS [15]). Also, the MPC algorithm is implemented following [16] based on the problem formulation in Section II using the same control and state variables for the proposed DRL algorithm. Attacks are initiated for few timesteps by changing the load and DER setpoints (% power change of loads and/or DERs) in OpenDSS using python interface.

Abstract

I. Introduction

II. Three-Level Neutral Point Clamped Inverters

III. Proposed Neutral Point Clamped Inverters

IV. Operation of the Proposed Three-Level NPC Inverter

V. Comparison of the Proposed and Conventional Inverters

VI. Experimental Results

VII. Conclusion

References

Abstract

     The traditional neutral point clamped (NPC) inverter has short-circuit problem. The risk of short-circuit can be decreased by using dead-time in the switching signals. However, the dead-time decreases the achievable output voltage and causes distortion in the waveforms. To overcome the short-circuit problem, dual-buck NPC (DB-NPC) and split-inductor NPC (SI-NPC) inverters have been researched. However, the voltage stress of the two external diodes in the DB-NPC inverter is higher. On the other hand, the SI-NPC inverter has a problem of generating huge voltage spikes in the dead-time, which can destroy the semiconductor devices. In addition, the SI-NPC inverter cannot provide reactive power. This article presents a family of NPC inverters consisting of single-phase, three-phase, and cascaded inverters. The proposed inverters have no short-circuit and dead-time issues, therefore no high voltage and current spikes are caused. Also, the dead-time in the switching signals can be minimized. As a result, the magnitude of the output waveforms can be increased, and quality can be improved. Unlike the DB-NPC inverter, the voltage stress of all the semiconductor in the proposed inverter is lower, and unlike the SI-NPC inverter the proposed inverter provides reactive power. In this article, the proposed three-level NPC inverter is analyzed, designed, and tested. The voltage stress of the semiconductor devices in the proposed inverter is half of the source voltage, whereas in the conventional DB-NPC inverter the voltage stress of the two external diodes is the source voltage. In addition to the aforementioned benefits, the proposed cascaded inverter reduces the total number of inductors. To verify the analysis, detailed simulation, and experimental results of the proposed three-level inverter with input voltage 640 V, output power 1.2 kW, and output voltage 220 Vrms are provided.

Introduction

     T HE traditional H-bridge inverter is shown in Fig. 1. The voltage stress on its switches is the input voltage Vdc. Therefore, the H-bridge inverter is well suitable for low-voltage applications. In high-voltage applications [1] such as HVdc transmission [2], large motor drive [3], locomotive [4], solid-state transformers [5], and reactive power compensation [6], the multilevel inverters are preferred. Flying capacitor [7], neutral point clamped (NPC) [8], and cascaded inverters [9], [10] are the famous multilevel inverter topologies.

     The flying capacitor inverters synthesize output voltage by adding voltages of the flying and dc-link capacitors. The NPC inverters synthesize output voltage by adding voltages of the dc-link capacitors. The cascaded inverters synthesize output voltage by adding voltages of the series-connected H-bridge cells. The cascaded inverters are highly modular and can reach a higher output voltage [11]. Also, they can bypass faulty cells [12]. The multilevel inverters have the advantages of lower switch voltage stress, lower switching losses, smaller output filter, better output waveforms, and lower electromagnetic interference (EMI) noise issues.

     The multilevel inverters are also getting popular in lowvoltage and low-power applications due to the possibility of obtaining lower common-mode voltage and higher efficiency over the H-bridge inverter. In [13], multilevel inverters have been discussed for the photovoltaic (PV) systems. In [14], a half-bridge three-level NPC inverter is compared with a halfbridge two-level inverter for PV applications. It is found in [14] that a lower leakage current and higher efficiency can be realized with the NPC inverter.

Conclusion

     This article presented new types of single-phase, three-phase, and cascaded NPC inverters. The proposed inverters have no short-circuit risk. The dead-time in the switching signals can be reduced. As a result, the distortion in the output waveforms can be decreased, higher switching frequencies can be used for the passive components size reduction and maximum available output voltage gain can be reached. Unlike the conventional SI-NPC inverters, the proposed inverters do not generate high-voltage spikes in the dead-time and provide reactive power. Unlike the DB-NPC inverters, the voltage stress of all the external diodes in the proposed inverter is lower. The proposed cascaded inverter inherits all the features of the proposed three-level inverter. In addition, it reduces the number of inductors by sharing the inductors between the cascaded units.

     A 1.2-kW hardware prototype was fabricated and tested with various loads at the input voltage 640 V, output voltage 110/220 Vrms, output power 1.2 kW, line-frequency 60 Hz, and switching frequency 30 kHz. The experimental results verified that the proposed inverter could generate good output waveforms, provide reactive power, works with dead and overlap-times, and obtains higher efficiency.

Abstract

1. INTRODUCTION

2. FIVE-LEVEL SWITCHED CAPACITOR INVERTER

3. MODULATION TECHNIQUE

4. PHOTOVOLTAIC APPLICATIONS OF THE SUGGESTED INVERTER

5. SIMULATION RESULTS

6. CONCLUSION

DISCLOSURE STATEMENT

REFERENCES

Abstract

     This paper proposes a switched-capacitor based single-phase five-level inverter configuration that operates under boost operation and generates a voltage that is more than the DC source voltage. The proposed five-level inverter uses a capacitor and boots the output voltage. In this proposed inverter, capacitor gets charged in parallel while it discharges in series connections so that output voltage may attain higher magnitude than the DC source voltage. Sinusoidal Pulse Width Modulation-based techniques are considered to produce the required gate pulses for operating the switching devices of the inverter. The five-level switched-capacitor inverter is combined with the PV system via DC–DC boost converters to extract the maximum power using MPPT algorithm. To verify its capability, the PV-based system is further integrated to the utility grid. The operation and performance of the suggested switched-capacitor inverter coupled with the grid-connected PV system are also analyzed by developing its model in MATLAB/Simulink environment.

Introduction

     Power electronic converters are getting evolved due to technological advancement in the design of power electronic devices as well as increase in its demand for more applications. The growth in demand for inverters has been observed because due to its application in the renewable energy source. The lesser costs of modern power electronics components make them useful for manufacturing and helping them to compete in the market. However, multi-level inverters use more switches; it is still economical to replace two-level inverters with MLIs because of several benefits such as lesser harmonic content, switching stress, and power losses [1–3]. Many scholars have paid attention to improve inverter topologies and their control methods. The lesser number of power electronics components used in multilevel inverters can further reduce the manufacturing cost [4–6]. The Cascaded H-bridge (CHB) inverter [7] and Neutral Point Clamped (NPC) inverter [8,9] are two standard multilevel inverters and having several industrial applications such as EV/HEV, HVDC, renewable energy sources, high-power motor drives, FACTs, STATCOM [10,11]. Several three-phase and single-phase MLI topologies of high voltage and medium power have been implemented for different applications [12,13]. A five-level inverter using two diodes, six switches, and two capacitors is proposed in [14].

CONCLUSION

     The proposed five-level switched-capacitor inverter topo logy for the PV application has been investigated in this paper. The developed single-phase switched-capacitor based inverter configuration consists of eight switching devices, only one DC source and one switched-capacitor. The inverter can produce five levels output voltage and the maximum amplitude can be twice the magnitude of DC voltage source. Moreover, it is also noticed that the charging as well as discharging of the switched-capacitor in the proposed multilevel inverter topology is symmetrical for both polarities of the output. Hence, the issue of capacitor voltage balancing is solved by using the switched capacitor of the suggested rating. The model of the recommended inverter-based grid-tied PV system is established in MATLAB/ Simulink environment and the performance of inverter under different modulation indexes is found satisfactory. The effectiveness of a grid-tied system was also investigated under different irradiance. Hence it can be resolved from the analysis that the proposed inverter is proficient for the application of a grid-tide PV system.

Abstract

Keywords

1. Introduction

2. Related work

3. Application research of Internet of Things in solar photovoltaic power station monitoring and management system

4. Development of integrated computer-aided design system for solar photovoltaic buildings

5. Conclusion

Declaration of Competing Interest

References

Abstract

At the same time of economic development, the increasing scarcity of energy has had a great impact on people’s development. People’s production and life demand for electricity is also increasing rapidly, and solar power technology has received more and more attention. As a new energy source, solar energy has the advantages of environmental protection and sustainability, and it has no regional restrictions, can be used on-site, and designed to scale. Solar power generation is an important way to use solar energy. As the main component of the grid-connected power generation system, solar grid-connected inverters complete the tracking problem of the maximum power point in the photovoltaic array and transmit electrical energy to the grid through a set of control algorithms. The electrical energy is transmitted to the grid through the inverter, consistent with solar energy. In order to solve the problems of low integration, low energy efficiency, low reliability, high power consumption and lack of effective monitoring measures for solar energy devices. This article starts with the design of the solar cell integrated system, and through detailed analysis of the solar production system and building integrated planning, establishes the shadow radiant energy model of the solar cell system building electrical and solar cell system based on the Internet of Things, and designs an object-based Networked comprehensive auxiliary platform.

1. Introduction

Today’s society is an information society centered on the Internet. Digitization, informationization and world economics are the hallmarks of the current era. With the continuous rapid development of information technology, the process of world economics has been continuously strengthened. The use of computer technology, communication data and the Internet to complete e-logistics and Internet commerce has been widely used in society. The continuous improvement of information technology and information storage is just the prelude to the transformation of the IT industry. The cost of information dissemination is getting less and less, and it is bound to usher in the era of the Internet of Things dominating. As a post-IP product, the Internet of Things is developing rapidly and will become popular in the future. This is a major plan to realize the intelligentization of the world.

Abstract

1 Introduction

2 Proposed topology

3 Modulation and capacitance design

4 Comparison analysis and losses calculation

5 Simulation and experiment analysis

6 Conclusion

References

Abstract

     Multilevel inverters (MLIs) play an important role in research on renewable energy conversion. However, in traditional designs, the high-voltage stress of switching devices and the large number of switches limit the wide application of the inverter. To ameliorate these problems, this paper proposes a switched-capacitor multilevel inverter (SCMLI). When compared with traditional MLIs, the proposed SCMLI utilizes a switched-capacitor structure, where the capacitors can achieve voltage self-balancing without auxiliary methods. Thus, it permits changes of the positive and negative polarity of the output level without the need for an H-bridge. In addition, with the augment of the level in the expanded SCMLI structure, the maximum blocking voltage can be kept constant. To show the advantages of the proposed structure, an extensible single dc source five-level SCMLI prototype has been built. Through a comparative analysis with different topologies, this paper also presents the advantages of the proposed topology in terms of the output voltage gain, number of output levels, and voltage stress. Finally, the correctness and feasibility of the proposed inverter are validated by extensive experiments.

Introduction

     In recent years, multilevel inverters (MLIs) have become attractive converters in numerous applications such as renewable energy systems, motor drives, distributed generation systems, UPS systems, and fexible alternating current transmission system (FACTS) [1–3]. When compared with the traditional two-level inverters, MLIs have many advantages such as lower total harmonic distortion, reduced dv/ dt stresses, lower switching frequency, etc. [4]. In general, classic MLIs include neutral-point-clamped (NPC), fying capacitor (FC), and cascaded H-bridge (CHB) inverters. However, NPC and FC inverters employ a large number of clamping diodes and capacitors along with the increase of output levels, which inevitably raises the cost of topologies and the complexity of control strategies [5, 6]. Cascaded H-bridge MLIs have problems in terms of the lack of boost capacity and the need for multiple isolated dc sources [7, 8].

     To alleviate the problems of traditional MLIs, switchedcapacitor multilevel inverters (SCMLIs) have been proposed in [9–14]. SCMLIs can achieve multilevel output with a single dc source and fewer power devices. The fve-level inverter proposed in [9] uses only eight switches, but the topology contains two extra diodes and the working states for most of the switch pairs are inconsistent. In [10], a sevenlevel inverter with a single dc voltage source and 12 switches was proposed. Although the topology in [11] employs ten switches to output seven levels, there are still four extra diodes and ten gate drivers are required. The same issue can be found in [13], where the nine-level inverter can achieve twice voltage gain using many diodes and drivers. In addition, two capacitors are discharged for most of a cycle, which may bring about a large voltage ripple and the imbalance of capacitor voltage.

Conclusion

     This study presents an extensible single dc source fve-level MLI based on a switched-capacitor structure. The proposed MLI eliminates the need for H-bridge due to the inherent polarity reversal capability. In addition, the voltage of two capacitors can be self-balanced without any auxiliary circuits. Although ten switches are employed, only two switches have a voltage stress of 2Vdc, and only fve gate drivers are required, which signifcantly reduces the total voltage stress and cost of proposed inverter. In addition, the output levels and boosting factor can be raised by employing multiple switched-capacitor cells. Moreover, the maximum blocking voltage for all of the switches is kept within 2Vdc in the extended structure. A comparative study with other topologies showed that the proposed inverter has the advantages of reducing the TSV, the MBV, and the number of the power devices. Finally, the correctness and feasibility of the proposed multilevel inverter are validated through the simulation and experiment results obtained from a laboratory prototype.

Abstract

1 Introduction

2 Theoretical Background

3 Related Works

4 Methodology

5 Experiments and Results

6 Conclusions

Declarations

References

Abstract

     RoboCup 3D Soccer Simulation is a robot soccer competition based on a high-fidelity simulator with autonomous humanoid agents, making it an interesting testbed for robotics and artificial intelligence. Due to the recent success of Deep Reinforcement Learning (DRL) in continuous control tasks, many teams have been using this technique to develop motions in Soccer 3D. This article focuses on learning humanoid robot behaviors: completing a racing track as fast as possible and dribbling against a single opponent. Our approach uses a hierarchical controller where a model-free policy learns to interact model-based walking algorithm. Then, we use DRL algorithms for an agent to learn how to perform these behaviors. Finally, the learned dribble policy was evaluated in the Soccer 3D environment. Simulated experiments show that the DRL agent wins against the hand-coded behavior used by the ITAndroids robotics team in 68.2% of dribble attempts.

Introduction

     RoboCup is an international academic competition created to foster robotics and artificial intelligence research [27]. It has an ambitious long-term goal of having a team of humanoid robots beating the human soccer World Cup champions by 2050. There are many leagues with different game rules and constraints on robot designs to accelerate progress towards this objective.

RoboCup 3D Soccer Simulation (Soccer 3D) is a league of RoboCup based on a robot soccer simulator with a high-fidelity simulation model of the Nao humanoid robot. The particular contributions to RoboCup reside in being a research environment for high-level multi-agent cooperative decision-making, and humanoid robot control [44]. A simulation environment is convenient for machine learning algorithms due to their need for large amounts of data [36]. Dealing with real robots is time-consuming due to the need to recharge batteries or reallocate robots manually to set up experiments. Moreover, experience collection may be largely accelerated by running many simulations in parallel and executing in faster than real-time. Unfortunately, transferring behaviors learned in simulation to real robots is challenging due to the so-called reality gap. Still, some works have succeeded in doing so, usually by executing a final fine-tuning process on the real robot [36].

Conclusions

     Our main objective was to learn high-level soccer behaviors using reinforcement learning in this work. We addressed the problem with state-of-the-art model-free deep reinforcement learning algorithms, namely DDPG, TRPO, and PPO. Therefore, we learned behaviors while dealing with the complex dynamics of a humanoid robot.

     To facilitate, we used a hierarchical approach where the agent learns to command a model-based walking engine based on the Zero Moment Point (ZMP) concept. The walking engine receives the desired velocities in forward, lateral and rotational directions and outputs the joint angles.

     We developed a DRL framework for integrating DRL algorithms with the RoboCup 3D Soccer Simulation environment to accomplish our objective. In our results, PPO achieved the best performance, which was expected, and effectively learned humanoid robot behaviors.

Highlights

Abstract

Keywords

1. Introduction

2. Cyber–physical DC microgrids

3. Attack detection design

4. Performance validation

5. Conclusion

CRediT authorship contribution statement

Declaration of Competing Interest

Acknowledgments

Appendix.

References

Abstract

DC microgrids are considered as the next generation of power systems because of the possibility of connecting various renewable energy sources to different types of loads based on distributed networks. However, due to the strong reliance on communication networks, DC microgrids are vulnerable to intentional cyber-attacks. Therefore, in this paper, a robust cyber-attack detection scheme is proposed for DC microgrid systems. Utilizing the parity-based method, a multi-objective optimization problem is formulated to achieve robust detection against electrical parameter perturbations and unknown disturbances. An analytical solution is then provided using the singular value decomposition approach. With the disturbance decoupling scheme, the presented detection strategy can monitor the system with only local knowledge of the DC microgrid. The proposed method is easy to design and with less computation complexity. The performances of the provided scheme are validated by simulation tests and experimental results.

1. Introduction

DC microgrids (MGs), known as next-generation power systems, have received significant attention in recent years because of their ability to transmit power from renewable energy sources and energy storage devices to various loads with greater efficiency and reliability than the AC grid. As a distributed power supply, the DC MG can be operated independently or connected to the utility grid. Such applications can be found in power generations [1], smart houses [2], transportation systems [3], etc.

Due to the rapid developments of the Industry 4.0 paradigm, information technology-based solutions have been widely used in industrial processes. The revolutionary changes have seen the emergence of cyber–physical systems where large amounts of data are exchanged between multiple devices in real time [4]. Accordingly, the framework of DC MG tends to be more distributed, intelligent and tightly integrated with the network. However, due to the strong reliance on the communication technologies, DC MGs are more vulnerable to security threats [5] and have a higher risk of being compromised by malicious attackers.

In general, the functionality of a potential microgrid controller relies heavily on the reliability of the data received by the measurement devices or sensors. For example, if the sensors or communication links are compromised by an attacker, the controllers may receive faulty data and therefore make inappropriate control decisions [6], leading to the undesirable power-sharing [7], frequency oscillating [8] and stability issues [9]. As a result, renewable energy generating units may not be able to produce the maximum amount of available power from nature or meet the appropriate power sharing between microgrids, and energy storage devices may not be able to provide the required amount of power or operate with optimal economic dispatch [10]. More seriously, attackers may be able to disrupt the system without adequate security protection in terms of hardware or software policies, leading to significant social losses. Examples include the nuclear facility struck by Stuxnet malware [11], power outage event [12], [13] and nuclear plant blackout accident [14]. Considering the huge impact of attacks on microgrid systems, it is vital to provide an effective detection scheme to counter cyber attacks.

Abstract

     With the emergence of battery-based electric vehicles, transportation systems gradually leave using fossil fuel-based combustion engines. Due to their reasonable performance, Lithium-ion batteries have become one of the major batteries used for electric vehicles. Although these batteries are being used in most companies, their high production cost, rare raw material, and short life cycle have raised important incentives for their recovery process. However, locating a recovery center for end-of-life Lithium-ion batteries is a multi-aspect decision making problem influenced by many criteria. For this purpose, a novel integrated decision-making model is developed based on Measuring Attractiveness by a Categorical Based Evaluation TecHnique (MACBETH) for calculating the criteria weights and Weight Aggregated Sum Product ASsessment (WASPAS) methods under the fuzzy environment with Dombi norms for evaluating the alternatives to address recovery center location selection problem considering technical as well as environmental, economic, and social aspects. To show the reliability and applicability of the developed method, a real-world case study in Istanbul is investigated. The developed method is used to evaluate six potential locations for the possible establishment of a recovery center. Results showed that Tuzla district is the most suitable location for opening a recovery center for end-of-life Lithium-ion batteries. Tuzla is in a very good position in terms of proximity to suppliers, transportation and location. To illustrate the robustness of the obtained results, extensive sensitivity analysis tests are performed.

Introduction

     Electric vehicles (EVs) are an appealing solution for the decarburization of the transportation sector (Romero-Ocaño et al., 2022, Zhang et al., 2020). It is estimated that more than 125 million EVs will be on the road worldwide by 2030 (Hua et al., 2020). Lithium-ion batteries (LiBs) have exponential growth and a key portion of industry investments (Chen et al., 2019, Cui et al., 2022). An automobile Lithium-ion battery (ALiB) is a major component of an EV (Pelletier et al., 2017, Ramoni and Zhang, 2013). ALiBs provide the required energy storage for EVs due to the superiority of high energy density, high output voltage, low self-discharge rate, and long cycling life (Tang et al., 2019, Wang et al., 2022). They are composed of a cathode, an anode, an electrolyte, and a separator (Olivetti, Ceder, Gaustad, & Fu, 2017). The useful lifetime of ALiBs is 120,000–240,000 km (Onat, Kucukvar, Tatari, & Zheng, 2016).

     Approximately 11 million ALiBs are expected to be sold worldwide by 2020 (Li et al., 2018; Alamerew & Brissaud, 2020). Due to the degradation in capacity and quality, the service life of these complex multiple material products, which belong to class 9 of dangerous goods, is 5–10 years (Li et al., 2018; Chen et al., 2019, Tang et al., 2019, Alamerew and Brissaud, 2020, Li et al., 2020). ALiB is replaced when the capacity has reached 70–80 % of its initial capacity (Alamerew and Brissaud, 2020, Hua et al., 2020, Ramoni and Zhang, 2013).

Conclusions

     The emergence of EVs has been a noticeable point for transportation systems to transform from fossil fuel-based vehicles to cleaner vehicles which require lower energy costs and also produce lower negative environmental, economic, and social impacts. The utilization of ALiBs is of great importance for EVs, but more and more valuable resources are being depleted without appropriate recovery. Therefore, countries should consider establishing recovery centers for ALiBs as soon as possible. However, locating a recovery center is a complex and multi-aspect decision-making problem. For this purpose, we developed a novel decision-making approach based on the MACBETH-D-WASPAS model under the fuzzy environment. The proposed integrated fuzzy decision-making approach empowers experts in the field of LiB management to enhance their decision-making capabilities and select the most suitable location for an EoL ALiB recovery center. Besides, the real-life case study of Istanbul is provided to show the feasibility and applicability of the developed methodology for solving the recovery center location selection problem. Results showed that Sariyer and Büyükçekmece are top first and second locations that a recovery center for an EoL ALiB recovery center. On the other hand, results pointed out that Ümraniye is the least preferred location for establishment of an EoL ALiB recovery center.

Highlights

Abstract

Keywords

Abbreviations

1. Introduction

2. Microgrid model

3. Description of the proposed multi-functional algorithm

4. Simulation results

5. Comparison with other existing techniques

6. Conclusion

CRediT authorship contribution statement

Declaration of Competing Interest

References

Abstract

A large part of the current microgrids (MGs) operate in grid-connected mode and act as slaves following the voltage and frequency dictated by the main grid. Therefore, these MGs are not expected to be operated in parallel as islands. Hence, according to the IEEE 1457-2018 standard, the power generation units in these grid-connected MGs (GCMGs) have to be disconnected in less than 2 s if islanding occurs. On the contrary, if the islanding operation (IO) does not occur, these MGs exchange power with the electrical grid. This paper explores the feasibility of a multi-functional algorithm (MA) for these GCMGs in an attempt to address three tasks simultaneously; (i) power sharing, (ii) voltage support and, (iii) islanding detection (ID). The MG object of study comprises an electronically interfaced photovoltaic (PV) unit supplemented by a battery energy storage system (BESS) equipped in combination with a bidirectional charger. The model of the GCMG has been implemented in MATLAB, where an extensive set of simulations has been performed. The results demonstrate the effectiveness and benefits of the proposed MA, which fulfils several functions all at once.

1. Introduction

Conceptually, MGs are self-sufficient entities formed by an aggregation of generation units, energy storage systems and a set of controllable loads [1]. These MGs can be either connected to the grid (i.e., the abovementioned GCMGs) or operating in stand-alone mode [2]. Even though these GCMGs can be powered by different technologies (Wind, PV, mini-hydro, etc.), rooftop PV units are becoming very popular in residential and industrial applications due to their multiple benefits. Even though these GCMGs require the main grid, if both generation and storage devices are properly coordinated and sized, the amount of energy exchanged with the grid can be significantly reduced. Therefore, these GCMGs are becoming less grid-dependents in terms of exchanged energy.

In this regard, multi-functional algorithms are gaining momentum in MGs. For example, a fully distributed MA control has been proposed in [3], while references [4], [5], [6] used MAs for optimal MG management. If the peak power is shaved and the energy supplied by the grid is curtailed, this will imply a reduction in the electricity cost [7], [8]. Undoubtedly, this aspect is crucial for these MGs; for instance, see the techno-economic analysis carried out in [9].

Albeit these MGs offer several advantages for the users and the DNs (e.g., losses reduction and voltage support) [10], [11], some issues may occur due to their inherent intermittency. In particular, voltage regulation is among the most concerning issues for the distributor operators (DSOs). Although these PV-based distributed generations (DGs) are commonly operated at unity power factor, some grid codes require reactive power support by the voltage source converters (VSCs). Therefore, optimal Volt/VAR strategies have been investigated for such purposes, see [12]. Presently, the electronically-interfaced grid-connected generation units which provide grid support are also known as grid-following VSCs, see [13].

Abstract

     Advanced models of Artificial Intelligence enable systems of IoT to work with great flexibility to the needs of users. In this article we present our developed IoT system for driving support by the use of type-2 fuzzy logic control module. We have developed the IoT system to collect the data about driving conditions and evaluate them adjusting to the needs of user. Applied module of fuzzy logic of the second type was used in analysis of accelerometers signals to flexibly adjust to uncertainty of evaluation of driving expectations of each driver. Our developed system was tested in different cars by driving on various roads and results show excellent efficiency.

Introduction

     Development of real-time Internet of Things (IoT) applications improves technological advances in intelligent transportation and car diagnostic systems. We can read about recent ideas which make Cyber–Physical Systems (CPS) not only supportive to humans but also predictive in potential malfunctions of appliances that we all use in our daily routine. In smart car IoT systems are developed by using sensors and devoted smart technologies to compose innovative control processing.

     IoT systems for cars can work with many different features used in control and diagnostics to autonomous driving aspects. In Cao et al. (2020) was presented a model for smart front detection by using Singular Spectrum Decomposition (SSD) model. Model explained in Luque-Vega, Michel-Torres, Lopez-Neri, Carlos-Mancilla, and González-Jiménez (2020) was using SPIN-V sensors to improve procedure of safe parking. We can also implement models which will prevent collisions and help to maintain elements of car engine and suspension in good conditions. Idea presented in Krishnan (2018) was based on readings from ultrasonic sensor which measured distances to objects and helped in managing of safe drive. Very important for smart cars is also design of software. In practice we can use operating system in smartphones and just implement apps to connect to car sensors, i.e. Minnetti et al. (2020) proposed a smartphone based flush measurement to improve car body assembly procedure. System presented in Saeliw, Hualkasin, Puttinaovarat, and Khaimook (2019) was developed as mobile app to support parking procedure by using rfid infrastructure. In Xu et al. (2020) was presented an interesting discussion on networking, communication and applicable wireless technologies which serve as data transfers for smart cars. The model presented in Olivares-Rojas, Reyes-Archundia, Gutiérrez-Gnecchi, González-Murueta, and Cerda-Jacobo (2020) used 5G internet infrastructure to improve smart metering of multi-tire objects.

Conclusions

     In this article we present a type-2 fuzzy system for smart driving support. Proposed system is working in IoT infrastructure of accelerometer sensors placed in a car. Data collected in the car is stored in data base from which can be shared with other users of the system, so that results of each driving can be compared to other drivers and also all users can benefit from shared knowledge of road conditions in different localizations. Our proposed model is using knowledge base developed for expert system evaluating road conditions. Proposed model composition enabled better adjustment to driving style which may differ for different people. Thus, all the features of driving can be better evaluated with tolerance to the style of driving of different people.

     Results of our experiments showed that developed system is very efficient. We have done experiments by using different cars in various locations. We have also asked various drivers to collect data for us. Proposed type-2 fuzzy logic system enabled flexible adjustment to various driving style and various expectations of drivers, however showing correct result of road condition evaluation in each case.