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Review

Protection Schemes of Meshed Distribution Networks for Smart Grids and Electric Vehicles

by
Stavros Lazarou
,
Vasiliki Vita
and
Lambros Ekonomou
*
Department of Electrical and Electronic Engineering Educators, School of Pedagogical & Technological Education (ASPETE), Heraklion Attikis, 141 21 Athens, Greece
*
Author to whom correspondence should be addressed.
Energies 2018, 11(11), 3106; https://doi.org/10.3390/en11113106
Submission received: 20 October 2018 / Revised: 5 November 2018 / Accepted: 6 November 2018 / Published: 10 November 2018
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Abstract

:
This paper reviews protection schemes for meshed distribution networks. It gives emphasis to the increasing penetration of electric vehicles, their charging patterns, and to the increasing value of distributed generators, especially from renewables. It includes a preliminary analysis on system planning with electric vehicles that is studied probabilistically and a more detailed analysis of the expected changes introduced by these new loads. Finally, a real time hardware-in-the-loop review analysis for protection systems and the open source networks available for protection studies from several sources are also provided. This work could be useful as a collective review of the recent bibliography on protection for meshed networks, giving emphasis to electric vehicles and their real time simulation.

1. Introduction

Electric vehicles, storage devices, and distributed generation are changing the operational characteristics of distribution grids across the world [1]. These new components could create under specific operational conditions bidirectional energy flows. This affects grid planning, since they are optimized for one direction flows and their protection. Based on these circumstances, the established procedures of organizing distribution networks on radial configurations cannot be holistically applied [2], even if they are comparatively simple and well tested. Therefore, it is expected that meshed network systems will be increasingly adopted [3], offering the capability of bidirectional flows, higher penetration of renewables, and lower expansion requirements for accommodating new demanding loads such as electric vehicles.
Apart from the challenges created from the connection of these new components, the advancements achieved through the improved technological capabilities of today, are able to further provide protection and control enhancements. These include an extended online communication system that is able to safely support distribution grid expansion and higher computational capabilities applied to all system components. As such, distribution network planning has been naturally evolving from radial to the more complex meshed configuration, taking advantage of these communication and computational enhancements. The optimization methods for network planning are evolving accordingly [4]. The optimization patterns are increasingly calculated, on generalized level, through wider use of supercomputers. This knowledge is transferred to the system operation level, and new, more complicated patterns are being implemented [5]. Application of the aforementioned patterns is increasingly leading to a meshed network.
As expected, given that relevant challenges have been overcome, there are operational benefits. Several studies demonstrate the improvements of meshing the network [6,7] in terms of performance and efficiency [8]. Distributed generation, which is mostly applied to networks compared to electric vehicles and storage [9], has already received much attention [10]. It is nowadays a main component in the procedure of network planning [11] and being investigated on a variety of cases [12]. Nowadays, the distribution network is increasingly simulated as a whole system of lines, loads, and production, intermittent or not, that is connected to the transmission network to receive the services is not able to domestically create. It has to be mentioned that several optimization methods still consider network radiality as a major characteristic [13], however these approaches are decreasing in time.
The main new operational challenges are connected to the electric vehicles’ load and production from renewable sources. These components demonstrate substantial intermittency [14] caused by the inherited performance of primary renewable energy sources such as solar and wind, as well as by the behavioral patterns of electric vehicles’ owners. Especially for the charging patterns of electric vehicles, it is important to investigate how it will affect the operation of the distribution system. As a matter of fact, networks are already under strain due to the existing loads, and electric vehicles will increasingly require more energy. Behavioral technologies that aggregate the consumption, moving it to high renewable production or low overall consumption times, could delay expansion needs, improving quality of life using the possible limited resources. The research community is able to capture this phenomenon using probabilistic simulation methods. The most random probabilistic method is Monte Carlo [15]. For steady state probabilistic load flows it is the method of choice [16], but also for other power system probabilistic phenomena during planning [17] and/or expansion [18,19]. Except from distribution networks planning, of course, this method is used to a wide variety of applications [20], relevant to power systems.
Continuing this analysis, one of the reasons meshed networks were not widely used in the past is that they demonstrate technical challenges [21] as far as their protection is concerned. Protection patterns need to take into consideration the special characteristics of the meshed network combined with its new components as they operate in a given moment. System topology, and hence protection procedure, change all the time based on how production is connected, which electric vehicles are charging, and how the other loads are behaving. Given this, the operational characteristics of electric vehicles, distributed generations, and storage need to be considered. Specifically, microgenerator dynamic performance and topology altering are concerns due to renewables intermittency fault current variations [22]. The above could lead to protection nonselectivity, resynchronization issues and spurious separations or false trips [23]. The phenomenon of nonselectivity happens when larger portions of the network than necessary are disconnected to clear a given fault. In this case, except from the inconvenience of the unserved loads, it more difficult to locate the fault and consequently repair it. Resynchronization issues could happen in the case of islanding operation of the system, even in a short time. Distribution generators continue instantaneously to serve the load without respecting synchronization principles of voltage and frequency at the disconnected system node. Spurious separations and false trips could happen under several operational conditions of high load or production or to system branches that under specific conditions are overloaded. Several studies were conducted to counter these challenges and protection operational limitations for the distribution grid have been specifically defined and quantified [24]. However, all these need to be tested and safe proved under all operational conditions. This is an exercise that requires substantial computational resources, even for a comparatively small network.
Due to the above, a concept of adaptive protection in the general framework of increasing intelligence for the distribution grid [25] has emerged. Researchers differentiate protection systems to active, passive, and hybrid [26], based on their capability to take decisions using measured data at other nodes than the ones they are connected to or communicated with them. A central decision-making center connected to each piece of protection equipment could act as the brain of the system. This configuration requires robust decision-making procedures, availability of calculation power, and the capability to communicate its decisions and receive measurements and commands. Adaptive protection is a prerequisite for the dynamic update of protection settings using a network configuration [27] that takes into consideration problem specificities such as imbalances at the distribution level and communication constraints [28], however it can possibly lead to suboptimal operation. An alternative could be to apply, on a distribution level, the traditional protection schemes used at the transmission level. This solution could also demonstrate some challenges [29]: Differential protection schemes need redundancy for communication failures. Distance protection relays may have inaccurate reads for short distances. Voltage protection systems may trip under normal voltage drops and they strongly depend on grid topology. Standardization of these procedures remains an issue of paramount importance [30].
Having mentioned the above, this paper reviews recent work on the protection of distribution networks, giving emphasis to electromobility. It is organized as follows. Section 2 is dedicated to electric grid expected changes due to the connection of electric vehicles. Section 3 provides information on new protection techniques as well as on the research related to relays coordination that takes into consideration distributed generators, transportation loads, and storage. Section 4 investigates hardware-in-the-loop real time simulation protection research, giving emphasis to the implementations that meet the emerging smart grids needs. Finally, Section 5 provides technical literature networks that can be used by researchers to attain knowledge in this specific field.

2. Electric Vehicles Connected to Smart Grids

Electric vehicles are expected to substantially affect power system planning and its operation (Figure 1) due to their extended requirements of energy, however they are able to offer enhanced demand response capabilities in combination to the connected distributed generators [1] as well as storage and other ancillary services. Ancillary services from electric vehicles could be offered through scheduled charging and storage. Being parked most of the time, they may charge when electricity prices are lower. This may occur when renewables intermittent production is momentarily higher. Another option is when other loads suddenly decrease and inflexible power stations such as lignite, coal, and nuclear are not able to immediately adjust their production. Storage could also be an alternative. Electric vehicles are equipped with large batteries that can retransfer the energy stored to the grid, under the procedure widely known as vehicle to grid (V2G). However, this additional flow of energy increases electricity losses, there also exists the possibility that charging cannot be accommodated from the existing electricity network. Henceforth, potential reinforcements have to be planned wisely. A generic approach is proposed in this review (Figure 2), facilitating the researchers to understand the important emerging factors for enhancing electric vehicles’ penetration to the grid.
Several studies, of increasing complexity, strive to meet this challenge. Morais et al. [31] evaluated the impact of electric vehicles on the smart grid. In this case, three distinctive operational patterns are considered where electric vehicles charge in an uncoordinated manner, they charge in a smart manner and/or they charge and discharge in a smart manner. Being able to charge and discharge, it is possible to store energy and act in an aggregated manner as a provider of ancillary services, easing bottlenecks in energy flows and improving grid’s performance and its efficiency. Rahbari et al. [32] proposed a solution for controlled charging of plug-in electric vehicles taking into consideration the presence of renewables intermittent generation; it is being tested on the IEEE-26 buses case. In this case, the capacity factor for the electricity grid is increasing, providing the opportunity to provide energy when consumption is lower or energy production from the renewables is higher. Farid et al. [33] point out the importance of a holistic approach to control all smart grid elements. Their approach describes the grid with a 3-d structure that includes on the x-axis production, transmission & distribution, and load; on the y-axis, the fact that system is physical as well as cyber; and on the z-axis, the dispatchability and the system’s stochastic character. Such an approach points out the importance of interoperability between stakeholders as well as equipment. Zakariazadeh et al. [34] proposed a method for electric vehicles to be aggregated to a virtual power plant. In this way, the offering of ancillary services to the grid becomes better organized to the current market operation and the capacity factor could be increased. Bhattarai et al. [35] developed an optimized aggregation method for organizing the distribution network in areas that take into consideration distribution of loads and costs for optimal participation to the market. Knezović et al. [36] explained the barriers of electric vehicles’ wider integration to the system and how these can be lifted. The barriers are not only infrastructure- and technology-related but also regulatory- and market-oriented. As a matter of fact, as long as technology improves and research advances, technical challenges can be overcome and only cultural- and market-oriented barriers remain. The authors propose a roadmap of four stages to overcome these barriers. Abdelsamad et al. [37] proposed an innovative method for designing the secondary distribution network in a manner to be better able to accommodate plug-in electric vehicles. They take into consideration system overall costs and transformers loss of life. The connection of electric vehicles, due to their ability to behave, are storage devices for energy provide the opportunity to the distribution system operators to delay lines’ expansion costs improving system performance in total. Sadeghi and Kalantar [38] propose a planning method taking into consideration uncertainties due to distributed generators and economic conditions. As mentioned before, intermittency is inhered to renewables and electric vehicles, but it can be also observed in the behavior of other stakeholders such as market participants. These uncertainties are needed also to be studied in order to achieve adequate understanding of the whole energetic systems. Sun et al. [39] characterized plug-in electric vehicles demand based on battery state of charge and temporal travel purposes. They use a stochastic approach and they are able to demonstrate benefits to network planning. Arias et al. [40] predicted, in a stochastic optimal manner electric vehicle connected to the distribution network, charging behavior. Wu and Sioshansi [41] provided an optimization model, which is able to schedule electric vehicle charging in a manner that relieves network constraints. Hu et al. [42] proposed an electric vehicle coordinated charging procedure for alleviating smart grid operational congestions. On a similar pattern, Beaude et al. [43] sought to reduce charging effects of the distribution network. Xiang et al. [44] develop optimal management procedures for aggregated plug-in electric vehicles charging to active distribution networks. Sundström and Binding [45] analyzed electric vehicle charging constraints to the distribution network on voltage and power level and how they can be avoided.
Wang et al. [46] also provided decentralized solutions for electric vehicles and distributed generators from renewable sources. Shuai et al. [47] approached electric vehicle charging from the economic point of view. Peng et al. [48] reviewed issues of active power management of power grid using electric vehicles. Pavic et al. [49] increased system flexibility using electric vehicles. This is an important trait that is transferred to the transmission system. Lazarou et al. [50] studied the connection of electric vehicles giving priority to the grid. Aggregated cohorts of electric vehicles are able to offer expensive flexibility services to system that otherwise shall be covered by gas or hydro, where available, power plants. This being said, electric vehicles as such is possible to contribute more actively in the mitigation of climate change and the decarbonization of power supply in total. Mojdehi and Ghosh [51] demonstrate a procedure for compensating electric vehicles as ancillary service providers. Daina et al. [52] explains the potential implications of electric vehicle charging per se. Weiller et al. [53] approached this subject from an industrial point of view. Schmidt et al. [54] researched electric vehicles from the transportation demand angle. Sato et al. [55] analyzed issues of standardization in electric vehicle charging, because all procedures have to be accepted by the complete group of market participants, which is possible to be achieved efficiently through global standardization procedures. All the above are complex exercises that require substantial computational power.
The probabilistic approaches for solving electric vehicles and distributed generation challenges are computationally intensive. These is due to the complexity of the problem that needs to involve the behavioral characteristics of vehicle users, the constraints of the operation, and capacity of the grid, as well as, the intermittency of the connected production from renewable sources. Adding to the above, the increasing use of storage needs to be considered. High performance computer use has proliferated to the degree that they are able to be widely used, even for studies to smaller networks. Green et al. [56] approached the problem of smart grids with electric vehicles’ computational requirements using industrial solutions such as high-performance computers (HPC). They take into consideration the complexities imposed by the combination of several layers of probabilistic phenomena happening at the same time to a multilayered smart grid. Procopiou et al. [57] used HPC for simulating electric vehicles charging behavior at the secondary distribution network level. Their findings, based on a real system, demonstrate the future requirements of computational resources at smart grids, even on secondary level. This is an observation of paramount importance, since millions of kilometers of secondary network are currently operating, and the extensive computational resources required to optimize it could potential become an important factor of its operational cost. A generic description is provided to Table 1.

3. Protection of Smart Grids

The protection schemes currently used at the distribution level are distinguished in active, passive, and hybrid systems [23]. Several approaches have been developed with the most widely used to be current limiters, centralized protection, distance protection, protection based on variables, differential protection, multi-agent protection schemes, and others, all based on relays (Figure 3). Current limiters are usually semiconducting devices that decrease the maximum current during faults. This is paramount importance, since the equipment, especially at distribution level, is scaled to sustain maximum currents. Circuit breakers are also not able to interrupt too large current fault. On the other hand, distributed generators, especially the rotating ones (with synchronous or asynchronous generators connected), as well as rotating machines could increase fault currents during to their inherited inertia. Distant protection is based on the fact that electrical characteristics change if a fault occurs on a line. Its resistance, impedance, and capacitance change, and, instantaneously, the distance protection measures the difference and activates the circuit breakers required to clear the fault. Differential protection can be applied to twin lines with the same characteristic and loading or/and at the start and the end of a given line. When a fault occurs to one of the twin lines and measurements change, it is clear that protection shall be activated. Similarly, when a measurement at the beginning is different to the end, after considering line’s electrical characteristics, then the protection system shall be activated. Other types of protection are more sophisticated and increasingly used to distribution networks.
At this point it is of paramount importance to coordinate relays’ operation. To achieve this goal except from the location of the fault, the flow of power has to be known. As an example, at Figure 4 the same relay RBC is able to interrupt fault F1 in the first case and F2 in the second. The vice versa faults are to be interrupted only by different relays not depicted at this figure. For meshed distribution networks this procedure is becoming more complex, however researchers were able to develop methods with adequate results [58]. Singh et al. [59] propose an adaptive method for relay coordination based on fuzzy logic. Advanced control techniques could potentially widely used for protection purposes. This is due to their capability to provide results without the complete description of the system under investigation but only based to previous measurements. However, this approach bears the risks of spurious operation or protection malfunctions.
Ates et al. [60] modify relays’ parametrization to achieve selectivity. Protection selectivity could be achieved through adjusting the parametrization of the existing equipment. This has the benefit of lower initial resources deployment, but its application may be limited. Similarly, Chen et al. [61] coordinate relays’ operation to a meshed network with distributed generation. Chabanloo et al. [62] use fault current limiters to achieve protection coverage, having also connected distributed generators at an urban distribution grid. Jalilian et al. [63] propose a method based on post-fault current. Costa et al. [64] use a metaheuristic method for achieving coordination. Saleh et al. [65] and Hussain et al. [66] provide the relevant optimization procedures. Corrêa et al. [67] propose an online method for coordinating the protection relays. Ojaghi and Ghahremani [68] developed new characteristics for overcurrent relays to cover selectivity issues.
Scientists conduct research on several additional issues related to protection of smart grids. Khorramdel et al. [69] and Khakimzyanov et al. [70] contribute in locating the fault. Locating the exact location of the fault could the basis for the design of new protection systems, or simply activating the nearest circuit breakers to clear it. Also, being aware of the exact location of the fault facilitates potential system repairs if needed. Esmaeili et al. [71] provide a procedure for limiting fault current. Jamali and Borhani-Bahabadi [72] propose a new protection method for radial and meshed networks, which have distributed generators connecting, without using communication capabilities. Bukhari et al. [73] use fuzzy logic for protection purposes. He et al. [74] propose a new method for smart grid protection purposes. Rahman et al. [75] use multi-agent systems for fault diagnosis at the network distribution feeder and its reconfiguration. Pathirana et al. [76] developed a new methodology for active protection systems. Todorović et al. [77] propose a method for converter operation that respects short circuit levels. Zhang et al. [78] propose a method for protection of closed loop, meshed networks. Finally, Li et al. [79] and Fazio et al. [80] investigate anti-islanding techniques and procedures.
Anti-islanding requires that the distribution grid is always connected to the main network. This increases operational safety, since all distributed generators require to disconnect if main grid is not available due to any reasoning. On the other, this request decreases the potential for continuously serving the load, even if the main grid is not available but the connected distributed generators and storage are able to cover it. Future efforts of this nature will keep production online as much as possible and additionally take care of resynchronization and addressing issues of quality. A generic description is provided to Table 2.

4. Real-Time Simulation Capabilities for Smart Grids Protection

The validation approaches for understanding the exact behavior of smart grids systems, including when it comes to protection operations, are software simulation, laboratory testing, hardware in the loop, and measurements in the field [81]. Testing the exact behavior of the protection system, taking into consideration the specificities of protection equipment operation, could be a demanding procedure. Traditional digital simulation software could offer good insight into the real operation of the system; however, it does not have the capability to calculate hardware limitations that need to be considered for the safe operation of the system. On the other hand, connecting the real protection equipment to test it directly to the real system could be very demanding in terms of resources and it could be impossible under certain circumstances. Real time, hardware on the loop simulators are able to combine the benefits of software simulation and hardware testing. The simulator performs the necessary calculations and then feeds them to its analog inputs and outputs. When the protection relays are connected hardware can be tested without the need of the real electricity network.
From the above it appears that hardware in the loop is the prominent and most effective solution for developing modern procedures. Real time simulation combines effectively the capabilities of simulation software and is able to approach the complexity and specificity of new equipment having hardware interfaces at signal and network level. Hardware in the loop finds applications on different aspects, except from protection of power systems but for the specific needs of smart grids it can effectively perform electromagnetic transients and phasor and voltage control simulations [82]. Apart from the above, real time simulators are especially useful for simulating microgrids, they are composed of a computing part which has been uploaded with the simulation model of the equipment not available in the laboratory and the proper interfaces to the physical system [83], as depicted to Figure 5. The physical problem is simulated at the processing unit in real time. As digital technology necessitates, the system splits the time in short intervals and calculates problem’s solutions for the next time step using the information imported from the previous. Based on this, processing power should be enough to be able to perform all necessary calculations in time less than it needs the simulated real system. After this, the values are converted to analog signals and if necessary, they are amplified. The output is connected directly to the equipment to be tested. The behavior of the equipment is measured, and data are reconverted to digital signals and fed to the computational module for the next simulation step. In this manner, the equipment can be tested without having it connected to the real system, facilitating the testing procedure in a safe manner. There are several types of real time simulators able to cover all the needs of smart grids simulations. However, they have their own limitations due to the models and their capabilities [84]. On the smart grid level, the work of several researchers has shown very good performance of the system [85].
As far as hardware is concerned, target computers are categorized in two major types [83]. Type A [86] real time simulators have custom built processors, whereas type B [87] work with general purpose CPUs. As far as software is concerned, it is developed by the vendor, or it is based on proprietary software such as MATLAB/Simulink [88]. The target computers’ operating system could be Windows or Linux or an alternative operating system for special purposes. The solution methodology is based to the principle that the simulation is done into steps, of which the results are provided to the Input/Output Interface in such a way that represents the behavior of a real system. There are limitations are based on the complexity of the model, the processing power, and the time step. A time step combined with inadequate computational power and a complex model could lead to simulation instability and inaccurate results, which is the main challenge of the system as a whole. Having mentioned the above the main vendors are RTDS [86], OPAL-rt [87], MATLAB xPC target [89], dspace [90], Applied Dynamics International [91], and Typhoon RTDS [92].
Real time simulators of all types able to perform hardware-in-the-loop have similar general setting. The simulation model is developed on a host personal computer, which serves as the interface for extracting the simulation results. The compiled model is uploaded on the real-time simulator target computer. The results are channeled to analog and digital inputs and outputs, being there to serve as the interface of the simulator to the equipment under study. Complex simulation systems could also include fast communication networks to operate in parallel with more than one target computers being remotely located.
The main open source solution to real time simulation is the virtual test bed [93,94]. However, real time simulation may cover only a part of the requirements for a cyber-physical energy system, which in fact shall include additional intelligence.
As previously mentioned above, real time, hardware-in-loop capable simulators have been a useful tool used for protection studies. Using these systems, researchers and product development engineers are able to predict the operational behavior of relays and protection systems in general. Several laboratories have developed advanced systems and they open them to researchers across the world for academic purposes [95]. Smaller portable devices are available for restricted simulations [96]. All these hardware-in-the-loop simulators are optimized for protection purposes [97], for phasor measurement system [98] testing, to replicate microgrids’ dynamic performance [99] and for other purposes. Specific examples for protection systems configuration are provided in past works [100,101].
Another important issue is the islanding behavior of distributed generators and storage devices at the distribution level [102,103,104]. To avoid incorrect operation of the grid and maintain safety precautions, it is not allowed under current standardization for distribution generations to operate if central connection is not available. According to current procedures, this part of the system requires to be de-energized for safety purposes. The devices and procedures for anti-islanding protection are also tested in real time simulators [105,106] and in the future could be tested on microgrids, it is expected that the islanding operation of the distribution network could be a part of de facto reality. Of course, in islanding operation the distributed generators and the available storage devices have to able to cover the load and operate safely. A generic description is provided to Table 3.

5. Open Access Distribution Networks Available for Protection Studies

Open access distribution networks are of paramount importance as case studies for applying electricity system research. They are used to test case new procedures and methodologies. For a newcomer on the field, without access to new networks, bibliography offers openly solutions on a variety of voltage levels, complexities, and for several applications. Distribution networks, which are the main focus of this review, are mostly provided by the major engineering such as IEEE [107,108] or other organizations [109]. Renowned institutions in Europe such as the Joint Research Centre [110] and in the United States the Pacific Northwest National Laboratory [111] have also published their own models for research purposes. Individual laboratories also propose their own configurations. For example, a distribution line is provided at [112] and a model for real time is available at [113].
Studies for smart grids protection systems are usually at the distribution voltage level, which is at the level of kV up to 35 kV. However, the bibliography has smart grids examples on higher voltage level [114]. In this case, the authors use a selection of IEEE high voltage test cases, for example IEEE 14 Bus [115] and IEEE 30 Bus [114] that are part of the American Electric Power System, IEEE 17 Generator dynamic test case [116], and IEEE 30 Bus dynamic test case [117], which is believed that is part of the New England power system. Except from the above, individual researchers use lines of their adjacent network to conduct their research. An example of protection studies distribution network in this category is available at [118].
There are journals able to publish only datasets such as Data in Brief from Elsevier [119] and Data from MDPI [120], where the researchers are able to find new datasets on every subject including simulation networks. Using the same simulation networks in different cases by different researchers enhances the capability of the research community to compare new methodologies. On the other hand, applications of some methodologies to specific local networks are important to enhance the applicability of the research and its engineering importance. A generic description is provided to Table 4.

6. Conclusions and Future Developments

This paper reviewed protection systems as they evolve due to the expected connection of electric vehicles. The first part of this paper includes a description of network planning in the presence of electric vehicles and distributed generators, as well as it incorporates recent relevant research. The second part of this review analyses the availability of real time hardware in the loop capabilities to test protection systems and the open access distribution networks available for studies in the technical literature. With the knowledge collected in this manuscript, a researcher is able to understand the recent efforts of the connection of electric vehicles, state-of-art tools, and networks to be used for protection studies.
Based on the above, the future electricity grid is expected to become the main carrier for transferring energy to the final users. It will cover, except from the traditional loads, new requirements of transportation in a wider scale with higher penetration of electric vehicles. However, this emerging situation will create technical challenges to the existing infrastructure that needs to effectively accommodate these new loads. According to the authors’ perspective, except from reinforcing the existing grid, it is of paramount importance to enhance grid’s performance, applying in a wider scale the meshed distribution network. Protection issues will eventually arise that could be solved using real time simulators. Research community could contribute in this direction using the available open source real networks.

Author Contributions

L.E. provided the supervision and reviewed the paper. V.V. and S.L. conducted the research and wrote the paper.

Funding

The authors acknowledge financial support for this work from the Special Account for Research of ASPETE, under the project “DECA”, through the funding program “Strengthening research of ASPETE faculty members”.

Acknowledgments

The authors appreciate the productive comments they received from the reviewers of the Energies journal and those who read this manuscript as a preprint on researchgate.com and provided their feedback. Without the support from Evangelos Kotsakis and Heinz Wilkening, this work would have been impossible to be produced.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Power system enhancements due to the connection of electric vehicles.
Figure 1. Power system enhancements due to the connection of electric vehicles.
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Figure 2. The expected evolvement towards solving the technical challenges for increasing electric vehicle penetration to the electricity network.
Figure 2. The expected evolvement towards solving the technical challenges for increasing electric vehicle penetration to the electricity network.
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Figure 3. Protection schemes (rewritten from [23]).
Figure 3. Protection schemes (rewritten from [23]).
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Figure 4. Relay direction vs. power flow direction [63].
Figure 4. Relay direction vs. power flow direction [63].
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Figure 5. Basic Hardware in the loop simulation concept [83].
Figure 5. Basic Hardware in the loop simulation concept [83].
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Table
Table 1. Distribution networks protection studies.
Table 1. Distribution networks protection studies.
ReferenceDescriptionResearch MethodContribution
[1,14,31,36]Explanation of the barriers and the benefits for increasing the penetration of electric vehicles, evaluation of the impactAnalysis, scenarios’ developmentQualifying and quantifying the importance of electric vehicle barriers for their connection to the grid
[15,16,17,18,19,20,33,37,38,39,40,41,42,43,44,45,46,47,48,49,50]Network planning for increasing penetration of electric vehiclesPower flow analysis, probabilistic and optimization methods (Monte Carlo), novel approaches for depicting the electricity networkProposals for advanced network planning to accommodate increasing connection of electric vehicles
[32,34,35,51,52,53,54,55]Electric vehicles charging through aggregators and standardizationAnalysis, market principles, optimization methods, Virtual Power Plants, scenarios’ analysisPrinciples for development of aggregators for EVs charging, standardization
[56,57]High-performance computing for distribution grid and electric vehicles simulationProbabilistic methodsSimulation of extended operational scenarios
Table
Table 2. Distribution networks protection studies.
Table 2. Distribution networks protection studies.
ReferenceDescriptionResearch MethodContribution
[2,3,4,5,6,7,8,9,10,11,12,13]Meshed networks in the bibliography Analysis of the state-of-the-art situationReview of the state-of-the-art on the subject
[21,22,23,24,25,26,27,28,29,30]Protection types and their characteristicsAnalysis of the state-of-the-art situationDescription of active, passive and hybrid systems, differential, distance, overcurrent and voltage protection
[58,59,60,61,73,74,75]Protection methods for covering meshed networks, intermittent renewables and electric vehiclesFuzzy logic, multi-agent, traditional protection proceduresApplying alternative methods for protection
[62,63,71,77]Protection procedures for covering meshed networks, intermittent renewables and electric vehiclesFault current limiters, converter operationApplying alternative methods for protection
[64,65,66,67,68,76,78,79]Protection procedures for covering meshed networks, intermittent renewables and electric vehiclesMetaheuristic methods and relevant optimization, overcurrent relaysApplying alternative methods for protection
[69,70]Protection procedures for covering meshed networks, intermittent renewables and electric vehiclesFault locationEnhancing the understanding of the operation of the system
Table
Table 3. Distribution networks protection studies.
Table 3. Distribution networks protection studies.
ReferenceDescriptionResearch MethodContribution
[81,82,83,84,85]Real-time simulators types, applications and evolvementBibliographical reviewTechnology and its advancements review
[79,80,95,96,97,98,99,100,101]Real-time simulator applications for network protectionHardware-in-the-loop simulationsEquipment testing and development for networks’ protection
[102,103,104]Real-time simulator applications for islanding protectionHardware-in-the-loop simulationsEquipment testing and development for islanding protection
[86,87,88,89,90,91,92,93,94]Real Time Digital Simulators manufacturers and other developersN/AAvailability of specialized solutions for each application, including smart grids’ protection
Table
Table 4. A generic description of distribution networks availability for protection studies.
Table 4. A generic description of distribution networks availability for protection studies.
ReferenceDescriptionContribution
[113,114,115,116]IEEE networksDistribution grids for research purposes
[108,109,110,111]Laboratories’ networksDistribution grids for research purposes
[118,119]Journals for publishing datasetsAvailability of datasets, including networks for research purposes

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MDPI and ACS Style

Lazarou, S.; Vita, V.; Ekonomou, L. Protection Schemes of Meshed Distribution Networks for Smart Grids and Electric Vehicles. Energies 2018, 11, 3106. https://doi.org/10.3390/en11113106

AMA Style

Lazarou S, Vita V, Ekonomou L. Protection Schemes of Meshed Distribution Networks for Smart Grids and Electric Vehicles. Energies. 2018; 11(11):3106. https://doi.org/10.3390/en11113106

Chicago/Turabian Style

Lazarou, Stavros, Vasiliki Vita, and Lambros Ekonomou. 2018. "Protection Schemes of Meshed Distribution Networks for Smart Grids and Electric Vehicles" Energies 11, no. 11: 3106. https://doi.org/10.3390/en11113106

APA Style

Lazarou, S., Vita, V., & Ekonomou, L. (2018). Protection Schemes of Meshed Distribution Networks for Smart Grids and Electric Vehicles. Energies, 11(11), 3106. https://doi.org/10.3390/en11113106

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