Abstract

Application of micro-grids (MGs) as a solution for future energy systems are significantly increasing in recent years. On the other hand, more stringent requirements are added to grid codes (GCs) of low-voltage distribution networks. Accordingly, this paper proposes a low-voltage ride-through (LVRT) control scheme for four-wire multi-source MGs. The novel strategy is composed of two layers for controlling each phase of the grid-connected sources independently. The primary layer contains a reverse-droop function for each phase and fractional-order sliding-mode-control (FOSMC). The secondary layer determines the total requested reactive power for each phase during symmetrical and asymmetrical voltage drops. Then, the mentioned power is shared among the phases of each inverter based on the reactive power-sharing strategy and GC requirements. Based on the proposed grid-following controller, the voltage of faulty phases is compensated by reactive power injection. In addition, power quality indexes are kept in acceptable ranges during abnormalities, and the FOSMC performs better than the conventional methods. The effectiveness of the proposed scheme is verified through offline simulations in MATLAB/Simulink as well as validation by real-time results.

Introduction

1.1. Background
The utilization of small-scale distributed energy resources (DERs) in low and medium-voltage networks is increasing, and as a result, their sudden and collective disconnection from the power system might cause voltage collapse on the grid during severe voltage perturbations. Therefore, the severe impact of the DERs cannot be ignored since their contribution to the power supply is considerable. Thus, the incremental usage of DER systems has become leverage for setting new requirements for power systems. These regulations are called grid codes (GCs), which vary from country to country and cover some grid-connected power sources such as wind turbines and photovoltaic (PV) systems [1]. The mentioned regulations force the sources to stay connected to the main grid during temporary voltage sags and support it by injecting reactive current. An essential requirement of the GC, which has gained much attraction in recent studies, is the low-voltage ride-through (LVRT) capability of the distributed generators (DGs). LVRT curves of the German and Danish GCs are highlighted in Fig. 1a [2]. Besides, in Fig. 1b amount of the reactive power injection per voltage drop is depicted for different scenarios [3]. Most studies cover symmetrical voltage disturbances that only include a minor percentage of the faults such as [4,5], etc. However, asymmetrical faults such as single-phase short-circuits are more probable with an 85 % occurrence rate [6]. In most cases, the conventional three-phase control methods for compensating voltage drops are not applicable for asymmetrical faults since they might affect the operation of safe phases by deteriorating voltage imbalance. Also, reactive power-sharing and reference power tracking in multi-source MGs are important operation factors, which has not been addressed sufficiently in previous studies.

Conclusion

MGs integration into power systems has boosted the system’s reliability and caused operational challenges for the network operators. Hence, some countries have considered a set of requirements to improve the functionality of their power systems. For this purpose, in this paper, an LVRT scheme is proposed for four-wire MGs, which can control each phase of the DGs independently through a grid-following method. The local controller of the DGs, known as the primary layer, utilizes reverse-droop control for voltage and frequency regulation and a nonlinear FOSMC. On the other hand, the secondary layer fulfills the modern GC requirements by calculating the required reactive power for the faulty phases. Next, this amount is shared between the corresponding phases of the DGs based on their free capacity. The effectiveness of the presented controller is proved by analyzing the results obtained from implementing 30 % single-phase, two-phase, and three-phase voltage sags. Accordingly, each DG phase injects reactive power during voltage sags according to the determined amount. Also, comparing the injected reactive power with the amounts of German GC demonstrates that the reactive power injection is done with acceptable accuracy. The results point out that during the external sags, the voltage of PCC, inverter terminals, and load terminals are restored to their nominal values, 30 % compensation, as if there is no perturbation. Consequently, the presented controller can realize the GC requirements and ride through during severe voltage conditions. Moreover, this control method keeps the power quality indices like THD and voltage imbalance factors in acceptable ranges during the sags. Also, the superiority of designed FOSMC over conventional current controllers such as PI and SMC are demonstrated. This outperformance is highlighted with several aspects such as MPAE index, THD values, voltage compensation, and reactive power tracking during the imposed voltage sags. It is shown that the best value for the alpha is 0.76, by which the response of the controller is fast and accurate. Finally, the simulations are validated through real-time results. Our future work is focused on the LVRT capability of the MGs including both grid-forming and grid-following inverters. In addition, the effect of grid strength and R/X ratio of the power lines can be discussed in the future investigations, which has not been addressed sufficiently in this literature.

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

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.

Abstract

1. Introduction

2. PUC inverters

3. Microgrid

4. proportional–integral (PI) control of PUC inverters

5. MPC of PUC inverters

6. Simulation results

7. Hardware-in-loop (HIL) validations

8. Conclusions

Declaration of competing interest

References

Abstract

     In this paper, an outline of microgrid arrangements and control methods at various hierarchical levels of Packed U-Cell (PUC) converters are provided. The paper discusses the control of these topologies using various techniques. The goal of these control strategies is to maintain a small THD, superior steady-state, fast-dynamic response, and high-power factor while balancing capacitor voltages under different operating conditions. The PI current controller is modelled on five and seven-level PUC inverters. The PI voltage and current controllers have also been modelled on a seven-level PUC inverter. Thereafter, model predictive control of five, seven and fifteen level inverters are also formulated and then simulated using MATLAB/Simulink. Finally, HIL validation of different PUC inverters is performed.

Introduction

     The electrical power generated from renewable energy resources (solar, wind and hydro, etc.) are either used for domestic purposes directly or they are supplied to the grid [1]. For example, the power generated by a solar plant is of dc nature which is first conditioned and then inverted to get an alternating output. This ac power is then synchronized with the grid according to the grid’s characteristics (frequency and phase angles) [2]. In each of the steps, there are many parameters to be controlled in an accurate manner so that power quality is maintained. For this purpose, the most suitable consideration is the multi-level family converters.

     Due to improved power quality compared to their two-level counterparts in recent times, multilevel inverter (MLI) topology has become increasingly accepted in commercial applications. The reduced harmonic deformation, improved waveform like a sinusoidal waveform and enhanced use of switches due to diminished voltage stress. MLI have their uses in various fields of renewable energy sources including, HVDC, distributed generation (DG) system, application of industrial drive systems, uninterruptible power supplies, etc. [3], [4], [5], [6]. Based on a few semiconductor devices coupled with different dc links, MLIs generate sinusoidal waveforms at their outputs through staircase waveforms. MLI has traditionally been used in three major topologies in commercial applications within the last few decades: Cascade H-Bridge (CHB) inverters, Flying Capacitor (FC) inverters and Neutral Point Clamped (NPC) inverters. Each MLI topology, however, have some drawbacks, such as the increasing count of source necessities, the balancing of capacitor voltage, and the high necessity for switches counting in the CHB, FC, and NPC network topologies, respectively [3].

Conclusions

     The main purpose of this work is to present that MPC can be a better alternative to the classical PI control when applying such control techniques on different PUC topologies. Although MPC is known for its limitations in terms of the steady-state error, MPC can be one of the greatest alternatives to the classical PI due to the simplicity of designing this control, the ability to achieve multiple objectives, and the high accuracy in transient conditions.

     In this paper, MPC was successfully applied on five, seven and fifteen level PUC inverters and compared with PI control when applied on the same topologies. Although the converters’ voltages usually have higher THD when MPC is applied, the easy implementation of MPC and the capability to control more than one control variables are the some of the advantages which increase the chances of using MPC in such applications.

     In this work, using MPC, the capacitor voltages of the different PUC inverters were well regulated to follow their reference values using a single controller. However, in order to achieve similar performance using the classical PI control, two control loops, voltage and current control loops, were required to be designed which significantly increase the complexity of this control.

Abstract

     Distributed model predictive control (DMPC) schemes have become a popular choice for networked control problems. Under this approach, local controllers use a model to predict its subsystem behavior during a certain horizon so as to find the sequence of inputs that optimizes its evolution according to a given criterion. Some convenient features of this method are the explicit handling of constraints and the exchange of information between controllers to coordinate their actuation and minimize undesired mutual interactions. However, we find that schemes have been developed naively, presenting flaws and vulnerabilities that malicious entities can exploit to gain leverage in cyber-attacks. The goal of this work is to raise awareness about this issue by reviewing the vulnerabilities of DMPC methods and surveying defense mechanisms. Finally, several examples are given to indicate how these defense mechanisms can be implemented in DMPC controllers.

Introduction

     The last years have witnessed a growing interest in distributed control methods due to their superior scalability in large-scale applications such as smart grids (Qi et al., 2011, Yazdanian and Mehrizi-Sani, 2014), water systems (Negenborn, van Overloop, Keviczky, & De Schutter, 2009), and traffic control (De Oliveira & Camponogara, 2010). This approach considers the overall system as an aggregation of smaller pieces, i.e., subsystems, which are locally managed by control units referred to as agents (Kordestani et al., 2021, Scattolini, 2009), whose combined decisions determine the overall performance due to the subsystems’ coupling, e.g., in the control objectives and the system constraints. Moreover, this decomposition may be the only choice regarding the control architecture in applications where the implementation of a centralized controller becomes unfeasible due to the problem size or the existence of multiple independent decision-making entities.

Conclusions

     The evolution of technology is leading towards a world of pervasive connectivity where control systems are expected to play a major role. In this context, DMPC algorithms are likely to become essential because they enhance their capabilities with the progress of technology and they offer a means to coordinate control actions in order to attain optimal performance in a scalable manner. However, an interconnected world will also offer significant opportunities for cyber-attacks, which may have devastating consequences if they affect critical infrastructures. Even nowadays we can find every now and then headlines in major newspapers that show how severe cyber-threats can become, with some notorious attacks disrupting applications such as nuclear plants, power grids, smart buildings and autonomous cars, to make a few examples.

     In this article we have reviewed the most vulnerable spots in the control infrastructure that can be exploited to attack DMPC methods. In addition, we have seen that the algorithms have inherent vulnerabilities because their design is usually based on the assumption that every controller in the network will be compliant with the algorithm employed. To deal with these issues, we have presented detection and mitigation mechanisms that can be used to make these schemes resilient. In particular, we have seen that learning methods can make a difference in defensive tasks because of their superior flexibility to adapt to the nominal operation conditions.

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

1. Introduction

2. Microgrid Control

3. Overview of AI framework for microgrid control

4. AI in Hierarchical control – review and possible improvement

5. Networked microgrid

6. Discussion and future scopes

7. Conclusion

Acknowledgement

References

Abstract

     Microgrids are gaining popularity by facilitating distributed energy resources (DERs) and forming essential consumer/prosumer centric integrated energy systems. Integration, coordination and control of multiple DERs and managing the energy transition in this environment is a strenuous task. Classical control techniques are not enough to support dynamic microgrid environments. Implementation of Artificial Intelligence (AI) techniques seems to be a promising solution to enhance the control and operation of microgrids in future smart grid networks. Therefore, this paper briefly reviews the control architectures, existing conventional controlling techniques, their drawbacks, the need for intelligent controllers and then extensively reviews the possibility of AI implementation in different control structures with a special focus on the hierarchical control layers. This paper also investigates the AI-based control strategies in networked/interconnected/multi-microgrids environments. It concludes with the summary and future scopes of AI implementation in hierarchical control layers and structures including single and networked microgrids environments.

Introduction

     Microgrids can be distinguished from any distribution network containing DERs by two distinct features. First, their capabilities to operate in an islanded mode confirms the resiliency and reliability of the network. Second, to appear as controlled and coordinated units viewing from the upstream network [1]. Microgrids provide noteworthy benefits to consumers as well as utilities, the majority of which include; higher reliability by incorporating flexibility at the community layer distribution network, improved power quality by managing flexible loads, reduced carbon emission by the diverse DERs, lowering transmission & distribution losses, cheap energy supply utilising more renewable resources, and the possibility of active participation in the energy markets [2], and must fully satisfy the network and load requirements at islanded mode [3].

     Taking into consideration the large share of stochastic natured DERs comes with lots of uncertainty and a range of instances where effective controlling of the microgrid network becomes very critical. Interconnecting multiple microgrids as a network of microgrids can be an effective solution to accommodate and improve the operation quality of the large number of DERs. It has also been recognised that when multiple microgrids, geographically close to each other, are tied together through the distribution line to form a networked microgrid, the reliability and resilience of the interconnected microgrid can be significantly increased [4, 5].

Conclusion

     This paper provides an overall review of AI-based control in microgrid environments. An overview of existing traditional control methods, their drawbacks, the need for AI techniques and their implementation at the different levels have been reviewed and future scopes have been presented. Despite system model complexities and challenges, it is found that AI can certainly be an important tool to enable the seamless integration and control of DERs at the local and networked levels. NN-based models are getting more focus on all levels. Most of the implemented AI techniques are physical model-based, whereas data-driven techniques should also gain more interest. Since a data-driven model doesn’t require extensive physical system information, the design becomes less complex. However, in some specific applications like inertia estimation in the primary control, there is a lack of data available at the low voltage distribution network level. As ESS is becoming a core part of decarbonising the smart and microgrid networks, the datasets for ESS SoC levels which are accessible and open to the public require extensive preprocessing to improve the data quality and better predictability.

Abstract

Keywords

1. Introduction

2. Methodology

2.1. LIBs and system boundaries

2.2. The battery system of electric passenger cars

2.3. The phase of production

2.4. The phase of usage

2.5. The phase of recycling

3. Life cycle environmental impact assessment

4. Conclusions

CRediT authorship contribution statement

Declaration of Competing Interest

Acknowledgments

Appendix.

References

Abstract

The use of electric vehicles is for reducing carbon emissions, thereby reducing environmental pollution caused by transportation. However, the large-scale production and application of electric vehicle batteries have brought another notable issue, i.e., the production and application of these batteries also cause environmental pollution. Particularly, the precious metal materials used in the batteries are harmful to human health and the surrounding ecological system. Nowadays, many types of batteries are available. It is essential to understand which of them is most suitable for electric vehicles from the perspective of environmental protection. To answer this question, the life cycle environmental impact assessment of LiFePO 4 battery and Li(NiCoMn)O2 battery, which are being popularly used in pure electric passenger vehicles, are conducted in this paper. The research has shown that the two types of batteries show different environmental impact features in different phases. For example, LiFePO 4 batteries are more environmentally friendly in the phase of production, while Li(NiCoMn)O2 batteries are more eco-friendly in the application and transportation phases. Despite this, LiFePO 4 batteries are generally more environmentally friendly than Li(NiCoMn)O2 batteries from the perspective of the entire life cycle. In addition, the research results also suggest that due to the heavier mass, LiFePO 4 batteries can probably gain more benefit when used for energy storage.

1. Introduction

Electric vehicles (EVs) are considered to be carbon-free, which helps to slow climate change, improve public health, and reduce ecological damage. Coupled with the increasing pressure to achieve the net-zero target, the global EV market has taken a huge leap forward in the past decade (Du et al., 2017). In 2019, the number of light EVs globally reached 2,264,400 units, which is 9% higher than in 2018. Take the world’s largest EV market, China, as an example, only 5,000 EVs were sold in the Chinese market in 2011. In 2018, China sold 984,000 pure EVs, which was an increase of 50.8% over the previous year (China Association of Automobile Manufacture, 2019). In 2019, more than 1 million EVs were sold in China, and the stock of EVs reached 3.8 million (Sun et al., 2020). These data imply that there will be more and more EVs running on the road in the following years.

Since batteries are the only source of power for EVs, the globally booming EV market means that a huge number of Lithium-ion power batteries (LIBs) will be produced, used, and disposed of in the future (IEA, 2020). For example, the cumulative installed capacity of LIBs reached about 206 GWh in China by the end of 2019 (MIIT, 2019). Among these LIBs, LiFePO4 batteries and Li(NiCoMn)O2 batteries dominate the market, but their market shares changed over time. The market shares of LiFePO4 batteries, Li(NiCoMn)O2 batteries, and lithium titanate (LTO) batteries were respectively 28%, 18%, and 21% before 2016 (MIIT, 2019). Since 2016, the market share of Li(NiCoMn)O2 batteries increases rapidly. During the period from 2016 to 2018, more than 80% of electric passenger cars in China used Li(NiCoMn)O2 batteries. This situation changed again in 2019 when manufacturers favored using LiFePO4 batteries to power electric passenger cars.