implementation of efficient b2g and v2g in practical cases

Digital Object Identifier:10.11989/JEST.1674-862X.80715105

Implementation of Efficient B2G and V2G inPractical CasesMD Shahrukh Adnan Khan* | Kazi Mahtab Kadir | Md. Ibrahim Ibne Alam | Md.Khairul Alam | Jianhui Wong | Aseef Iqbal

Abstract—In this paper, building to grid (B2G) and vehicle to grid (V2G) have been defined with clear and practical

understanding. Both of them are new generation technologies which are the essential part of smart city living and

crowd energy clustering. Firstly, an in-detailed overview has been provided with an introduction to B2G and V2G

followed by a historical overview and theoretical analysis in respect to smart city planning. Next, a review is

conducted on current and previous smart living research, which deals with B2G and V2G. Efficient B2G and V2G

implementations in practical cases then have been discussed. Lastly, both of these technical prospects have been

analyzed in crowd energy diagram.

Index Terms—Building to grid (B2G), crowd energy, practical implementation, smart city living, vehicle to grid (V2G).

1. IntroductionA next generation evolvement is knocking at the world door and the people today are eagerly welcoming it. The

smart city living is not a dream anymore rather it is rapidly rushing to its success story. The next generation smartcity planning is one of the most eagerly awaited technological advances that young generation wants currently. Theworld is running towards smart enhancements. Smart building, smart vehicle, smart technology, intelligent city, andgreen city all are part of the next generation smart city planning[1]-[4]. Building to grid (B2G) and vehicle to grid(V2G) are the most advance level technologies of smart city planning. Both of them are newly innovativeconcepts, which are yet to be implemented practically in different cities. Even though, experimentalimplementations are taking place in different parts of the world, there is still an ample space to conductelementary research and define both the B2G and V2G clearly and their influence in smart city living. Thecurrent generation technology includes G2B (grid to building) and G2V (grid to vehicle). For a bidirectional *Corresponding authorManuscript received 2018-07-15; revised 2018-10-02.M. S. A. Khan, K. M. Kadir, Md. I. I. Alam, and Md. K. Alam are with University of Asia Pacific, Dhaka 1205, Bangladesh (e-mail:[email protected]).J. Wong is with the Department of Electrical and Electronic Engineering, Universiti Tunku Abdul Rahman, Kampar 31900,Malaysia.A. Iqbal is with Chittagong Independent University, Chittagong, Bangladesh.Color versions of one or more of the figures in this paper are available online at http://www.journal.uestc.edu.cn.Publishing editor: Yu-Lian He

JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY, VOL. 16, NO. 4, DECEMBER 2018 325

energy flow, B2G and V2G are being considered for the next generation technology. For a unity bidirectionalenergy flow, all the four elements B2G, V2G, G2V, and G2B are clustered together which is called “crowdenergy”. In [5] the authors defined crowd energy to be the endeavor of profit/non-profit corporations or individualscombining assets through the help of information and communications technology (ICT) to achieve a societal,economic, and political change for the move from centralized non-renewable energy production to decentralizedrenewable energy production. This next generation technology has not yet been practically implementedcommercially although new research is being conducted in this area[2]-[6]. This paper tries to fill up the research gapin this respect and tries to give a clear concept on these two advanced parts of smart city planning, strategical workflow analysis of B2G-V2G, and case examples with current and future scope including limitations.

2. Building to Grid (B2G)

2.1. Introduction

Smart grid (SG) is the next generation of electric grid and this smarter grid needs B2G as its principal part tooperate properly and serve the future of power network. The existing unidirectional electricity grid is facingnumerous challenges, which cannot be dealt with the current grid topology. The future grid needs to be bidirectionalwhere the grid is a digital network with lots of sensors and will be a self-monitoring and self-healing entity[1].Therefore, a need for integrating B2G emerged and this integration is expected to keep the power system stable byallowing the buildings to contribute in the changes of electricity supply and/or demand. Therefore, B2G can bedefined as the coherent part of the future grid system having reliable interactive environment, where benefits andmotivations of the customers lead them to offering services to the power distribution system.

The consistency of the B2G integration is hoped to ensure a secure, dependable, and robust energy distributionsystem which is able to support high penetrations of a demand and green environment[2]-[4]. The B2G concept is notonly about providing energy to the grid; but more so about giving maximum achievable support to the grid from thebuildings. There are mainly two segments of B2G, in one segment the research is oriented on the demand or loadrelated optimization, whereas the other segment is concentrating on using buildings to supply energy to the grid[3].With ongoing research on smart grid and B2G, some new terms have materialized like intelligent building,prosumers, demand management system (DMS), and demand response (DR)[4]-[9]. The collaboration and efficientcommunications of all these with building and grid are hoped to make the whole B2G concept successful. In Fig. 1,B2G communications network has been depicted where it can be seen that, single or multiple buildings need tomaintain the communications with the grid through a building energy management system (BEMS). The BEMSprovides information mainly to the market operators who are responsible for the balancing of bidirectional energytransmission between the building and grid. The grid with its different demands (e.g. peak shaving, voltage andfrequency regulation, etc.) asks the market operators to provide support and the market operators try to comply.Also in the diagram the renewable energy sources (RESs) and loads are shown; these energy sources andintelligent loads are optimized by different optimization methods. One such optimization method is shown in Fig. 2where the methodology is similar to the found in [6] to [10].

2.2. Case Study

The idea of B2G emerged at the start of this millennium and became highly popular in the research and powersector along with the smart grid. After that, a boon can be observed in the last decade in the field of B2G research.There are a lot of journals and papers in the field of B2G; in the following paragraphs, some of them aresummarized chronologically.

326 JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY, VOL. 16, NO. 4, DECEMBER 2018

Buildings in the city consume a large amount of energy from the grid (around 33%); therefore, BEMSs areimportant for the power system. The authors in [11] discussed about the smart grid BEMS (SG-BEMS) in the JejuIsland that was developed by Korean Telecomm (KT), whose architecture is shown as Fig. 3. BEMS can providesignificant support to grid by reducing energy consumption and by load management to perform peak shaving andvoltage and frequency regulation. As ancillary services, BEMS helps to reduce the emission of carbon and provide

Distribution system operator (DSO)

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(BEMS)

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(MO)

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station

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Fig. 1. Generalized B2G network.

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for grid?

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flow

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Fig. 2. Sample flow chart of optimization technique.

KHAN et al.: Implementation of Efficient B2G and V2G in Practical Cases 327

support to RESs to be used widely. BEMS is also very important for the customers who want to join the energymarket[10]-[12].

To help the buildings connect with the grid properly building agent scheme was proposed in [12]. The proposedbuilding agent is supposed to work as a load management gateway by proper utilization of load models andcommunications. The main idea behind the building agent was that SG could not function properly if the control ofloads is handed over to the grid even on a small scale. Therefore, the agent hides the complexity of the buildingfrom the grid and provides only the information needed by the SG to perform flawlessly.

Commercial and private buildings can accommodate photovoltaic (PV) cells and with optimal powermanagement mechanism, these PVs can provide a range of support to the grid. Intensive PV penetration of thegrid to offer peak shaving services at minimal cost was the main objective in [13]. They proposed an optimalpredictive power-scheduling algorithm and after extensive real time simulation, it was found that 13% electricity billcould be minimized. Also the algorithm needed a high level of forecasting accuracy to provide expected results. In[14], the objective was to increase the monetary value of the PV solar production by providing ancillary services(i.e. active power supply, demand side management (DSM)).

Integration of buildings to the grid cannot be done with the current status of the buildings; these buildings needto be automated through building automation system (BAS), which is an essential element of building managementsystem (BMS)[6]. With BAS, the users have the ability to control energy and media consumption that is important tooptimize the energy management system (EMS). In [6], they delineated the importance and applications of BAS insmart grid and showed that DR can be attained easily with the help of BAS. Also in that paper they hoped toperform further research on the implementation of BAS in AutBudNet laboratories and other AGH-University ofScience and Technology (AGH-UST) buildings and afterwards they came up with further findings in [15]. Theimportance of BAS was also emphasized by [7], the authors indicated that typical BASs do not consider the useractivities and behavior, which are responsible for the wastage of almost one third of the consumed energy.Therefore, intelligent buildings must have the technology to recognize user activities and behavior and with thesetaken into account, minimizing energy consumption should be tried.

A study on B2G was done by Lawrence Berkeley National Laboratory[4], where the feasibility of implementingB2G in India was checked. Motivating the local power markets towards the implementation of smart grid was the

Opweator

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Fig. 3. SG-BEMS architecture[11].


main objective of the study. The study also promoted DMS to be incrementally used in existing and yet to be builtcommercial buildings. DR is one of the key features of DMS, the authors in [8] extensively examined all models ofDR up to that time and concluded that the demand and resource both are highly diversified; therefore a singlemodel for DR is impractical. In another work with DR[9], the authors studied a DR model which was based uponbidirectional energy trading facility and hybrid energy system. They argued that conventional DR with itsunidirectional topology could not support the users efficiently. The users can store energy for future use or can sellenergy to the power system with the presented model. Another control approach, model predictive control (MPC)was proposed in [16]; the approach was to regulate frequency by exploiting the flexibility of heating ventilation andair conditioning (HVAC) on the demand side. The MPC method used load forecasts and ramping rates of differentproviders to perform the frequency regulation service.

Bidirectional optimization model to achieve higher load factors with reduced power consumption was proposedin [17]. The case study on an office building in Michigan Technological University (MTU) showed that the presentedmodel works properly and can reduce electricity cost by 25% with a better load factor. The same authors in theirsubsequent work proposed a novel B2G indexing technique based on the building energy cost and load factors ofthe nodes[18]. The objective of the paper was to facilitate the users by minimizing the energy cost and the gridby ensuring the maximum load penetration with a greater load factor. The model parameters were collectedfrom MTU and the performance was evaluated for 33-node test feeder supporting B2G based commercialbuildings; a promising performance was observed. Nonetheless, the model performance was observed togreatly depend on the price of energy, load flexibility, customers’ choice, grid controlling ability of theequipment, and weather forecasting accuracy.

Thermal electric storage (TES) devices have the capability of shifting the demand in time and form energyarbitrage[19]. Hence, TES merged with B2G can provide DSM to perform better. The study in [19] was done forresidential buildings in Ireland and showed that if TES works on 01:00 to 04:00 in the night, then it not only shiftsthe demand but also creates energy arbitrage. Moreover, the B2G model with its rolling optimization approachallows preservation of some energy in the TES, which can be used in the next day. It was also observed that byusing the TES, the total demand could be reduced substantially. Further studies on the all island power system(AIPS) of Ireland were conducted by the same authors in [20] to achieve further optimization.

With the increment of unpredictability in demand, the grid cannot rely on traditional DR methods, which aremostly manual and rule based. DR-advisor is a data driven approach proposed in [21], which is quite reliable(93%), cost effective, and fast to cope with the demand fluctuations. With real time scenario and tariff structure DR-advisor was simulated in University of Pennsylvania; it was observed that it could save around 38% of the summerenergy bill. The main challenge was to evaluate data rapidly, take quick decisions about the refrainment of energyusage, and provide a monetary benefit.

In recent years, Brazil is harnessing a large amount of energy through its installed PV cells which are currentlysupporting the national grid by providing a maximum of 51% coverage of the overall demand[22]. To increase theefficiency of the PVs and find the feasibility of investment, the authors in [22] performed a simulation for net plusenergy buildings with energy plus software. After performing the simulation for four metropolitan areas, theysuggested that rooftop systems are more efficient and viable. Also they found that the energy compensationsystem of Brazilian PV could be quite beneficial for the prosumers. Installing PV in buildings may seem quitelucrative after the example of Brazil, however in [23] the authors discussed some facts, which might limit thewillingness to invest in grid connected PV cells mounted in buildings. They carried out a case study in Beijing andfound that many people residing there were not interested in investing in PV, as the static payback period (SPBP)is low. The case study showed that even with political incentives and available investment area for building


integrated grid connected PV (BIGCP) installations (AIABI), support from locality may not be favorable. They alsopointed out that single family residing in Beijing should not be encouraged to carry out BIGCP installations.

In some recent studies of B2G, DR with ancillary services and predictive power flow control has been discussedin [10] and [24]. They suggested that for a faster DR, there is no better way but to improve communicationslatency. Also the consumers need to be aware of their preferences, as a single change in the control inputs ofHVAC can create drastic effects in performance. In the MPC framework presented in [10], the method can solvethe duck-curve problem by reducing the maximum load ramp rate. The authors used a Monte-Carlo basedsimulation to simulate the control of power flow between grid, solar panels, and energy storage systems. Theauthors in [25] discussed techno economic methods to get a clear idea of investment feasibility. They argued thattraditional engineering economic methods should be reformed to comprehend market based PV investment. Fig. 4shows the techno economic structure from prosumer’s perspective.

3. Vehicle to Grid (V2G)

3.1. Introduction

V2G is one of the avenues through which smart grid is implemented. V2G involves the exchange of power andinformation between electric vehicles (EVs) and power grid. This allows the EVs to provide several DR services tothe grid while allowing EV users to enjoy different monetary and other form of incentives[26].

Control and maintenance of EVs through aggregators/utility bodies can be connected in three configurations.These are vehicle-to-house (V2H), vehicle-to-vehicle (V2V), and vehicle-to-grid (V2G). V2H involves energyexchange between EV and local home micro-grid, V2V involves energy exchange between grid-enabled group ofEVs, and V2G employs energy interchange between grid and EV clusters through the control of aggregators. Thispaper mainly focuses on the V2G concept.

Power flow between EV and grid can happen in two different methods, namely unidirectional V2G (or G2V) andbidirectional V2G (true V2G).

Unidirectional V2G is simply the flow of power from grid to EV for charging the EV battery. It does not requireany special apparatus except a charging outlet and it does not add to EV battery degradation due to cycling. DR

Climate/

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Electricitymarket

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Fig. 4. Techno economic configuration from prosumer’s perspective[25].


services can be included if a simple and cheap controller is added to handle the charging rate. EV owners need tobe incentivized to make them take part in these services thus ensuring G2V during off-peak hours and restrictedduring peak hours. However, for other important DR services, true V2G is necessary.

Bidirectional V2G, as the name implies, relates to the bidirectional power flow between grid and EV. The EVsrequire bidirectional chargers which have a bidirectional ac/dc converter (allowing power factor correction) and abidirectional dc/dc converter (which manages battery charge/discharge current)[27].

3.2. V2G Advantages

V2G advantages include active power and reactive power management, valley filling, harmonics filtering, peakshaving, reduction of utility expenses, enhancement of load parameters, reduced of carbon footprint, tracing ofRESs[28], frequency management, power failure recovery, etc.[26].

Unidirectional V2G (G2V) enables auxiliary services for the grid by changing EV charging rate according topower generation companies. This is handled by entities called aggregators, which combine and manage thecharging process for a large group of EVs. These auxiliary services can be power grid management, which allowsgrid frequency balancing between the production and load, and allocation of spinning reserve that providesadditional quick response generation capacity to meet sudden losses in generation. Thus the EVs becomedistributed energy assets[28]. Reactive power support can also be achieved through voltage and frequencymanagement. It also enables power factor correction, which limits power line losses and overloading of powerdevices[26]. Frequency management can be done by turning large generators on/off but these can be expensive sofast charging/discharging EVs can provide a cheaper alternative[28]. Grid connected EVs (GEVs) can providereactive power support because of capacitors in their chargers[26].

Traditional power generation produces many emissions. RESs can limit these emissions but their generation issporadic and dependent on environmental factors. But V2G can help mitigate the sporadic nature of RES[26].Studies have also shown that V2G could reduce greenhouse gases more than if only plug-in EVs (PEVs) wereimplemented[27].

3.3. V2G Challenges

V2G challenges include the following. EVs taking part in V2G (bidirectional V2G) will have morecharging/discharging cycles compared to EVs not taking part or only employing G2V. So these EVs will have fasterthan normal battery degradation. The degradation will be worse with deeper battery depth of discharge andfrequency of battery cycles. V2G control mechanisms can be formulated to reduce the impact of these processesbut a balance should be sought between the financial gain and longevity reduction[26].

V2G implementation requires high investment costs regarding the smart grid and bidirectional-charginginfrastructure. Also repeated battery charging/discharging cycles will increase conversion losses. Increase in PEVdemand will require extra generation capacity[27].

EV owners might get anxious whether the amount of charge left will be sufficient for the trip back to home, ifthey take part in V2G activities. This can be mitigated using properly planned and appropriately distributed chargingstations[26].

Also the smart grid, which will be the backbone for V2G, will require sufficient monitoring to detect anomalies,capability to resist hacking of communications and power networks necessary for V2G, improvement in powerquality, enhanced reliability and efficiency, etc.[28].

3.4. Case Study

In [29] the authors evaluated the capability of adjustable PEV control for bidirectional charging using


intermittent wind power to enhance the grid energy distribution and stabilization of power generation anddemand without compromising PEV user demands. For this purpose, the following three energy distributionmethods have been proposed. The first one is called ‘valley searching dispatching method’, where chargingor discharging cannot be suspended and where it looks for valley of wind generation in a 24 h period andsubsequently raises the valley by bidirectional charging of PEVs. The second one is ‘interruptible dispatchingmethod’ where charging/discharging rates are same as before but can be suspended if necessary. Here thecut level, i.e. power generation/utilization magnitude that needs to be selected to get time periods forbalancing of load/generation, should be less than load profile. In each iteration cut level is increased tilliteration limit is reached or all PEVs reach the minimum battery state of charge (BSOC). The third method is‘variable-rate dispatching method’, which is similar to the second method but the rate can be changedaccording to charging/discharging generation and utilization rather than the number of PEVs that can takepart in the process. The numerical simulation was carried out based on MATLAB using deterministic plusstochastic model and assessed by checking coordination between power production & demand and usercontentment. The results showed that the coordination of power production/utilization got better for all threemethods but the latter two had better results regarding the decrease of wind energy wastage at night,minimizing daytime flow of energy from grid and overall increase of user contentment. Furthermore, theproposed methods can be implemented with nominal technical obstacles. Although the models wereproposed for dispersed wind power and PEVs, it could be adapted for other generation/load scenarios. Forfuture work, further research is necessary to evaluate whether the harmful effect on battery longevity due tothat the charging/discharging outweighs the above mentioned benefits[27],[28].

The authors of [30] explained peak-shaving and valley-filling in the context of a V2G control scheme. Peakshaving and valley filling involve the distribution of loads between peak and off-peak hours using bidirectionalcharge/discharge of V2G enabled EVs. In order for the target power curve and V2G plan curve to closely followeach other, an objective function was proposed with the aforementioned peak-shaving and valley filling systems. Asimulation was devised with parameters including the numbers of EVs, EV batteries, user settings, etc. and datafrom representative cities. The results of the simulation pointed to the fact that if the EV number is increased or ifthe mean target is decreased, the effectiveness of peak shaving and valley filling is increased. Quantitatively if thestandard deviation and root mean square of the difference of the two curves are below 10 then the curves followeach other well.

In [31] the authors discussed several technologies related to gridable electric vehicles (GEVs) i.e. V2H, V2V,and V2G. V2H comprises of a singular GEV and residence where it makes the daily residence load curve uniformthrough active power exchange and utilizes a charger capacitor for reactive power support. V2V entails severalGEVs and smart homes where an aggregator is used for synchronized control of V2V and power grid. V2H is asub-set of V2V. V2G involves a greater number of GEVs compared to the previous cases and employs smarthomes, parking lots, rapid charging stations, etc. for energy exchange. GEV aggregators handle the distribution ofreactive power, active power, and other grid optimization activities. For modelling purposes, household deviceswere assumed to be daily load profiles, GEVs were modelled as mathematical equations and V2H, V2V, and V2Gsystems were modelled according to their aims and limitations which include peak load reduction, reactive powercompensation, etc. The authors put forward a mathematical model to improve the quality of GEV power distributionsystems using the target function and limitations. The simulation showed that V2G causes load shift by increasingload demand. It was seen that V2G could reduce peak load and valley in the load curve could be increased.Furthermore, night charging of GEVs coincides with the excess power production. Also GEVs can reduce linelosses, voltage variation, etc.


The self-governing V2G control scheme suggested by the authors in [32] is a spread out spinning reservesystem useful for sporadic RESs based on droop control. In the control scheme, balance control is used forhandling BSOC; i.e. smart charging for vehicle user’s planned charging. The purpose of these schemes wasto enable a lower carbon footprint energy system by aggregating sporadically available RESs. The simulationinvolved two interconnected grids, two V2G groups, and a simple lithium ion battery model. One of the V2Ggroups had two types, medium (Mitsubishi i-MiEV/EV1) and large (Nissan Leaf/EV2) sized batteries and theother group had only small (Toyota Prius/PHV). The results showed that frequency deviations caused by theRES variation were counteracted by the V2G in a reasonable amount of time. Smart charging of EV1 did notchange thermal power generation and EV1 did not provide any spinning reserve for the grid, but EV2 andPHV supported good quality frequency control. The simulation quantitatively established that V2G control hada quicker response than governor-less control of thermal power generation. Also the capacity of the PHVbattery was found to be adequate for the spinning reserve. Limitations of the study included further necessaryresearch regarding the efficiency of V2G control, effect on battery longevity, secure connection to grid, etc.

The authors of [33] enquired into the likelihood of diminishing load inconsistency in residence micro-grid bycontrolling charging schemes of PHEVs. A mathematical model was formulated with two PHEVs where Case1 had a typical initial condition and Case 2 had optimized parameters. In both cases, the two PHEVs hadbidirectional power flow between micro-grid and them. In Case 1, initial BSOC was assumed whereas Case 2had optimal battery charge. For Case 2, optimization resulted in the reduction of load power curve’s meanand standard deviations. Furthermore, the routing of power towards the micro-grid when required resulted inthe further reduction of these parameters. It was found that energy losses arose due to that the chargers andsubsidies could be provided because they can encourage proper usage and controlled charging of PHEVs,which can benefit the grid. For simplification, some parameters were assumed to be known in advance e.g.the load curve of house appliances. The authors hope to use dynamic programming and other details forbetter accuracy in the future.

In [34] the authors were focused on V2G control involving primary frequency control (PFC) of energy grids. Adecentralized V2G control (DVC) scheme was put forth where a charging/discharging apparatus managesbidirectional power between grid and EV to limit frequency variation, sustaining of BSOC, and fulfillment of chargingdemand. For this purpose, BSOC holder (BSH) was formulated, which was based on starting state of charge(SOC), to maintain EV BSOC using adaptive droop to sustain remaining battery energy levels. The control schemeis adaptive enough to maintain different staring SOC levels with frequency control. For charging demand, anotherV2G scheme was used called charging with frequency regulation (CFR) which consists of frequency droop control(to enhance frequency quality) and scheduled charging power (to reach charging demand). CFR is based on EVplugged-in time and typical SOC. It was shown in the simulation that the proposed DVC can limit frequencydeviation and meet the charging demand in a two region connected power grid. Furthermore, the DVC is shown tobe better than autonomous distributed V2G control discussed in Section 1.

The authors of [35] discussed a framework for lithium ion battery degradation dependent on different EV loadoutlines using the simulation of varying conditions. The battery degradation model was based on calendar agingand cycle aging. Calendar aging is the loss of usable lithium ions due to unwanted chemical reactions betweenelectrode and electrolyte, which is influenced by electrochemical voltage and temperature. Cycle aging involvesbattery charging/discharging cycle depth and the total number of said cycles where degradation increases for bothcases. The simulation parameters had different driving cycles (i.e. type of battery usage based on synthetic loadsand real life car driving patterns), charging strategies and grid services (i.e. charging/discharging patterns based onthe typical duration of travel and grid demand), daily duty cycle (i.e. a combination of the above mentioned


parameters), and battery environment temperature. The result of the simulation showed that battery longevity couldbe extended by time-managed charging and demand-controlled charging i.e. delaying charging until remainingBSOC is not sufficient for the next scheduled trip. The simulated grid services for peak shaving decreased longevityand increased costs due to higher cycling.

The authors in [36] presented a control scheme for V2G with EV aggregation to allow supplementary frequencyregulation (SFR). The proposed model consists of an EV aggregator, EVs, and their charging locations. The EVcharging locations evaluate frequency regulation capacity (FRC) and issue management assignments to the EVs.

Table 1: Current world scenario of V2G and B2G with limitations and further scope

B2G V2G

Pilot projects orexperimentalimplementation

Korea, Brazil, India, USA, China, Poland, Netherlands,Ireland.

Israel, China, Netherlands, Japan (Tokyo), Italy (Pisa,Rome, Milan), Spain (Malaga City), Ireland, Denmark(Parker Project), Solihull, UK (The Net-Form Project),Georgia, USA (electric school bus), Spain, Finland,Greece, France[37]-[40].

Limitations

·Majority of the current commercial and residentialbuildings are not suitable to support B2G[6].·BAS which is a basic part of smart grid and B2G, doesnot consider user activities and behavior, which results inone third wastage of the consumed energy[7].·Forecasting of load is not accurate, resulting in erroneousoutput from the optimization algorithms[13],[21].·Load modelling of flexible loads like HVAC is notadequate[16],[19].·RESs are lightly integrated in B2G due to userunawareness; also in some cases people are unwilling toinvest in RES[23].

·Electric vehicles taking part in bidirectional V2G will havemore charging/discharging cycles thus having faster thannormal battery degradation[26].·V2G implementation requires high investment costs. Alsorepeated battery charging/discharging cycles will increaseconversion losses[27].·EVs owners may remain constantly worried about theamount of charge left in the vehicles, if they take part inV2G activities[26].·Smart grid being the backbone for V2G, will requiresufficient monitoring to detect anomalies, capability toresist hacking of communications and power networksnecessary for V2G, improvement in power quality,enhanced reliability and efficiency etc.[28].

Further scope

·Implementing BAS in all the buildings is the first steptowards B2G[6].·Intelligent buildings of the future need to consider useractivities and behavior to mitigate energy wastage[7].·Developing new DR model for highly diversified demandsand resources is necessary[9].·Improving algorithms for BEMS, building agent, and PVpenetration in grid are required to achieve optimalperformances[11]-[13].·Precise forecasting of load is essential for almost allsmart grid optimization algorithms. Systems need to bedeveloped to forecast load accurately[13],[21].·Motivating the local power markets to incorporate B2Gand using DMS in existing and yet to be built buildings areneeded[4].·TES or HVAC devices merged with B2G can provideDSM; developing proper models of these devices isessential[19].·Communications latency between building and gridneeds to be improved[10],[24].·Engineering economic methods should be reformed tocomprehend market based RES investment[25].

·Properly planned and appropriately distributed chargingstations are needed for EVs, so that EVs can be used inV2G without facing any problems by the user[26].·The depth and frequency of charging/discharging of thebattery need to be calculated with V2G controlmechanisms to reduce the impact of these processes. Abalance between financial gain and longevity reductionneeds to be maintained[26].·Research on V2G systems to perform active power andreactive power management, valley filling, harmonicsfiltering, peak shaving, reduction of utility expenses,enhancement of load parameters, reduction of carbonfootprint, and tracing of RESs can be done[26],[28].·EVs role as distributed energy assets like grid frequencybalancing between production & load, and spinningreserve to provide additional quick response generationcapacity can be explored[28].·RESs are green energy however their generation issporadic and dependent on environmental factors. ButV2G can help mitigate the sporadic nature of RES.Integrating RES with V2G system optimally is ofparamount importance[26],[29].·Connecting vehicles securely with grid needs furtherresearch[32].

Crowd energy and smartcity living status

From the case studies it can be seen that G2B and B2G for the local building’s grid is already in use in differentplaces. The case for V2G is similar to that of B2G. In Fig. 5 we can see that V2G is just a sub-set of a crowd energycluster. At this stage only a few pilot projects are available as described above.But for proper smart city implementation of crowd energy, the grid needs a wide variety of DR services both frombuildings and vehicles working together. For this purpose holistic V2G-B2G research and pilot projects are needed ona bigger scale.

World map scenario As shown in Fig. 6.


In the case of frequency control, EVs perform the management assignments as mobile storage equipment. TheFRC evaluation is based on available V2G power and expected V2G power is implemented, taking into accountboth frequency management and charging demand. The simulation is done in simulink environment and MonteCarlo sampling is performed to calculate BSOC level. The results for a model, based on real life two regionconnected grids in China, indicate that variations of area control error and grid frequency can be efficiently inhibited.Also required BSOC levels can be obtained by stabilizing regulation-up and regulation down functions.

4. B2G and V2G: Current World ScenarioB2G and V2G are the inseparable part of smart grid, which is the key to our better living in the future.

Therefore, to ensure a better and greener place to live for our next generation, more research should be done and

Renewable energy sources

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High voltage line

Low voltage line

Smart meter

Smart home

V2H, V2V, & V2G structures

V2H H2V

PEV

PEV Charging station

with smart metering

V2G/G2V

V2VAggregator

Uni-directional power flow

Bi-directional power flow

Communication line

Next generation smart city planning

Crowd energy clustering for bi-directional energy flow

00000

0000000

Fig. 5. Generalized V2G network.


for that the current world scenario needs to be known. After the thorough literature review done by the authors,Table 1 is presented here with some of the pilot projects being operated at present throughout the world[37]-[40]. Alsothe limitations and scope of research are given in a concise and clear way. The authors hope that researchers willfind the table helpful for their future endeavors.

5. Conclusion: Summary, Limitations, and Future ScopeThe conclusion of this research comes up with few important findings and proposals. First, knowledge

sharing and distribution are the next key-step to make the B2G and V2G like advanced technology anessential part of smart city planning. Renewable energy such as wind turbines, solar panels, and hybridstorage devices like supercapacitor can play a major part in next generation energy sector. Most of theresearchers have not yet been properly motivated and informed about this new field of research—crowdenergy and smart city living. It is believed that upon getting the concept of these technologies beingimplemented into next generation smart living, many researchers and scientists will take active part in this.

The primary limitation faced in this field of study is to find adequate information to perform research. As, bothB2G and V2G are the next generation technologies for smart living, not enough verified cases and informationcould be found to conduct further study currently. Therefore, conducting the research is challenging. Apart from it,few projects are currently being implemented in Asian countries. However, information again could not be gathered,as not all of them were in operational. Governments as well as research institutes, and other related organizationsshould come forward to sponsor more projects in this area. In Bangladesh, the present government has taken thistechnology into key-consideration and already the Ministry of Power Energy and Mineral Resources has askedresearch proposals from individuals as well as academic institutes and other organizations for funding the researchgrant in smart vehicle charging, energy management, and power and renewable sector. Furthermore, The Ministryof Power Energy and Mineral Resources in Bangladesh formed a separate council namely EPRC (BangladeshEnergy and Power Research Council) to focus in energy and power sector research[37]-[40]. Proper initiatives fromcountries like Bangladesh and others, hopefully within the next decade, world will be emerging to the nextgeneration smart city where B2G and V2G based crowd energy will be implemented.

Legends

B2G

V2G

Fig. 6. World map scenario of B2G and V2G (Project places are indicated as per our findings, more projects may be

active currently).


AcknowledgmentThe authors would like to acknowledge the Institute of Energy, Environment, Research and Development

(IEERD), Department of Electrical and Electronic Engineering, and overall University of Asia Pacific, Bangladesh tomake the platform for this research.

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MD Shahrukh Adnan Khan has an all through first class academic career in his life. He obtainedhis Ph.D. degree from University of Nottingham, Semenyih, Malaysia in 2011 with an outstandingrecord-breaking result. He achieved the Member of the Institution of Engineering and Technology(MIET) certificate from Institution of Engineering and Technology (IET), Stevenage, UK in 2018.Currently, he is an assistant professor at University of Asia Pacific, Dhaka, Bangladesh. His currentinterest lies in energy storage, renewable energy, electrical machines, smart living, real time controlsystem, optical fiber, advance modulation techniques, and environmental science. He has over 50publications in high quality peer reviewed journals and conferences. Furthermore, He is an IEEE

Young Professional and Life-Fellow in Notre Dame Alumni Association & Nottingham Alumni Association.

Kazi Mahtab Kadir received his B.S. degree (Honors) in electrical and electronic engineering fromIslamic University of Technology, Dhaka, Bangladesh in 2010. He obtained his M.S. degree inelectrical engineering (EE) from University of Houston (UH), Houston, USA in 2013. Aftergraduation, he worked in the US oil & gas industry in the field of maintenance and projectengineering for approximately 2 years and currently he is working as a lecturer with University ofAsia Pacific. His research interests include electronics, micro-controllers–—FPGAs, smart grid, andsignal processing.



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http://www.northsearegion.eu/media/4308/v2g-projects-in-europe.pdf

Md. Ibrahim Ibne Alam was born in Dhaka, Bangladesh in 1991. He received both B.Sc. and M.Sc.degrees in electrical engineering from Bangladesh University of Engineering and Technology,Dhaka, Bangladesh in 2014 and 2017, respectively. He is currently an assistant professor withUniversity of Asia Pacific. His areas of interest are underwater MAC protocol, wireless,communications, smart grid, digital communications, and signal and image processing.

Md. Khairul Alam obtained his B.Eng. degree from University of Asia Pacific (First Class Honors) in2010. His undergraduate thesis title was “Microcontroller Based Intelligent Traffic Control System”.He is pursuing his M.Sc. degree with Islamic University of Technology. And he has joined as a full-time faculty in the Department of Electrical and Electronic Engineering, University of Asia Pacificsince October, 2010. His current research interests are embedded systems, wirelesscommunications, and computer programming.

Jianhui Wong received her B.Eng. degree (Honors) in electrical and electronic engineering, M.S.degree in engineering science (electrical), and Ph.D. degree in engineering (electrical) fromUniversiti Tunku Abdul Rahman, Kampar, Perak in 2009, 2012, and 2015, respectively. She was anelectrical consultant in M&E Consulting Firm prior joining in the Department of Electrical andElectronic Engineering, Universiti Tunku Abdul Rahman, as an assistant professor. Her researchinterest includes power system fault analysis, energy management system, and smart grid.

Aseef Iqbal received his Ph.D. degree in mechatronics, robotics, and automation engineering fromUniversiti Islam Antarabangsa Malaysia, Selangor, Malaysia in 2015. He is currently serving as anassistant professor at Chittagong Independent University, Chittagong, Bangladesh with ademonstrated history of working in the higher education industry. He is skilled in human-robotinteraction, mechatronics, computer vision, artificial intelligence, and mobile robotics.


implementation of efficient b2g and v2g in practical cases

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