Muhammmad Amjad Mubashir Husain Rehmani and Tariq Umer
COMSATS Institute of Information Technology, Wah Cantt, Pakistan
Urbanization has resulted in grave concerns such as need of good governance and people's quality of living. Therefore, the need of smart solutions to mitigate the bubbling consequences of urbanization are required. For this purpose, smart cities are envisioned as the acceptable and smarter solution to minimize the drastic impacts of urbanization (Celino & Kotoulas 2013). In smart cities, the advances in information and communications technologies (ICT) have been exploited to enhance the people's standard of living. The European Initiative on smart cities by the European Commission has focused on just the four main pillars for the smooth operation of smart cities, namely, infrastructure, heating and cooling, power, and transportation (Saint 2014).
As compared to vehicles that are based on internal combustion engines (ICE), the fuel‐cell electric vehicle (FCEV), the hybrid electric vehicle (HEV), and the plug‐in hybrid electric vehicle (PHEV) have gained much attention for future smart cities. PHEV or electric vehicles (EVs) can now be recharged at home and other available recharging locations such as parking lots (Clement‐Nyns et al. 2009). EVs are recognized as the smart solution for smart transportation in smart cities. With the inclusion of EVs, the benefits of sustainable and green application and communications support in smart cities can be leveraged. The reduction in CO2 and good integration into the potential smart grid has also made EVs as the acceptable solution for the future smart cities (Banister 2011). This smart integration of EVs into smart cities is assumed to improve the reliability of the smart grid and smart transportation systems. From the smart grid perspective, EVs can regulate the voltage and frequency and hence not only the power is managed but also EVs can reduce the power losses. For customers, the minimization of power losses and properly scheduling the recharging duration and time enhances their confidence (Wang et al. 2016).
Smart cities also promise a smart solution for transportation management. The proper traffic control for smart cities to avoid any traffic congestion and to ensure the safety for EVs is also needed (de Andrade et al. 2016). Usually, the automation of the transportation system, traffic‐control through social signals, social traffic, and ITS clouds can be employed to manage the traffic in smart cities (Zhu et al. 2016). In addition to traffic control and management, various attempts have been made from the industry and academia to well integrate EVs into smart cities.
The inclusion of EVs can consume 5% of the existing electric power resources. Therefore, the recharging of EVs should be optimally scheduled to avoid any bottlenecks in the existing power structures. EVs recharging in smart cities can pose various challenges such as:
The existing power structures have been designed to support domestic and commercial needs. To exploit the power resources for the electrification of the transportation can overload the existing power grid. This requires the need of expanding the capacity of the existing power resources to support a large number of EVs into the future smart cities (Majidpour et al. 2015).
The smart recharging of EVs require the smart architecture for optimal and controlled recharging of EVs in smart cities. An extensive effort is required both from the industry and academia to design the wired and wireless‐based communication solutions for the smart recharging architecture of EVs (Chow et al. 2014, Nolte et al. 2009).
A considerable amount of energy can be conserved while optimally recharging EVs in smart cities. The optimal recharging of EVs require a sophisticated EVs recharging architecture. EVs recharging in smart cities requires a smart architecture that can make the smart decisions for efficiently recharging of EVs. EVs can be recharged by employing either the centralized or distributed recharging architecture. Various energy losses due to the voltage, frequency fluctuations, aggregator unit (AU) malfunctioning, interrupted power supply, and bottlenecks in the communications architecture can only be addressed when the recharging architecture (centralized or distributed) is properly monitored or optimally controlled.
The unchecked integration of EVs into the future smart cities can also overload the smart grid and cause serious events such as spinning reserves. This situation can also lead to the destabilization of the smart grid. Therefore, it requires the proper consideration and energy management while considering the potential integration of EVs into the future smart cities (Huang et al. 2014).
With the help of well‐designed scheduling algorithms, various objectives such as frequency regulation, cost minimization, and estimation of the potential usage of electricity can be achieved. EVs recharging in smart cities with proper recharging and scheduling approaches can contribute in stabilizing the smart grid(He et al. 2012).
The large number of EVs integration into the future smart cities is also accompanied by various security threats and data handling issues. However, the potential benefits of EVs can only be realized with a well‐controlled and optimized EVs recharging process. An extensive work has been done on the design of the scheduling approaches to achieve a controlled EVs recharging. As compared to the controlled recharging of EVs with the scheduling algorithms, the uncontrolled EVs recharging can result in:
In this chapter, we have provided the state‐of‐the‐art work on EVs inclusion into smart cities and the various scheduling approaches used for EVs recharging. However, the main contributions of the chapter can be summarized as follows:
The rest of the chapter is organized as follows: Section 18.2 provides the overview of smart cities and EVs. In Section 18.3 the centralized and distributed EVs recharging approaches in smart cities are presented, while in Section 18.4 EVs scheduling approaches with various objectives have been discussed in detail. In Section 18.5, the open issues and challenges, as well as future research directions have been presented. Finally, the conclusion is presented in Section 18.6.
There is no formal definition of smart cities. However, the forces that shape smart cities can be used to define them. Those forces can be the utilization of the advances in ICT for future cities (Angelidou 2015). In addition to these, various communication and business architectural models also shape the smart city dream with an innovation economy (Mulligan & Olsson 2013). The evolution of existing cities to the dynamic infrastructure with the idea of energy efficiency, sustainability, and flexibility needs to consider the above‐mentioned models (Pellicer et al. 2013). Below is the description of the few pillars that shape the future smart cities:
The idea of smart transportation with innovative technology has made the concept of smart cities more attractive and applicable. To make the transportation increasingly intelligent and smart, different smart solutions in smart cities have been incorporated (Wang 2015, Bianchini & Avila 2014). In‐vehicle communication with different quality‐of‐service (QoS) parameters can help in shaping the transportation and making it intelligent and robust (Rahmani et al. 2009). This in‐vehicle communication is utilized in smart cities in shaping the traffic. To properly manage and shape the traffic of EVs in smart cities, the uncertain behavior of EVs can be predicted through in‐vehicle communications or via different scenario generation (Soares et al. 2016).
In addition to shape the traffic for EVs, smart cities also produce efficient and well‐managed parking for EVs. These smart parking lots not only provide a cost‐efficient solution for the parking, but EVs can also be recharged at an economical rate (Barone et al. 2014). Different emergency situations such as need for an ambulance or the immediate need for charging locations have also been addressed in smart cities (Samani & Zhu 2016, Lam et al. 2014).
Conserving energy and supporting the renewable energy resources to lessen the burden on fossil fuels is considered the main contribution of smart cities. The inclusion of EVs into the existing power resources of smart cities demands the efficient management of power resources. For this purpose, EVs are optimally recharged to avoid the extra overloading of the energy reserves (He et al. 2012). In smart cities, the smart grids not only provide the smart control of power production and distribution but also efficiently control and manage the power resources (Kennel et al. 2013, Chen 2010).
A strategic view point regarding the development of various business and economic models is needed to fully benefit from the advances of ICT employed in smart cities. Various multi‐tech and grassroot‐level companies are now deciding to try their luck by initiating various businesses in smart cities (Mulligan & Olsson 2013, Dohler et al. 2013). Therefore, future smart cities need to be further explored for future opportunities regarding various businesses. Now an extensive effort has been made from both industry and academia in developing various business and economic models to facilitate future customers in smart cities (Khatoun & Zeadally 2016).
The traditional TCP/IP protocol has been designed for the conventional networks. To consider the implementation of the TCP/IP for smart cities would be a mistake (Chen 2010). The requirements to design the network for smart cities are totally different, and this is crucial. In smart cities, the network will be more exposed to various severe threats as compared to the conventional networks. While there are many lives at stake in smart cities, securing the network from internal and external parties is the prime goal of network designers. As compared to the conventional networks, smart cities networks have to face the other issues such as self‐healing, handling of enormous data, sender authentication, and per‐hop packet accounting to achieve the trace back for each packet (Vilajosana et al. 2013, Li et al. 2013, Ianuale et al. 2016).
Smart cities provide smart transportation. That has given rise to the concept of EVs. However, the unchecked inclusion of EVs on the smart roads in smart cities needs extra measures to counter the problem of traffic congestion. For this purpose, various predictive models have been proposed to control the traffic in smart cities (Wang et al. 2015). Another solution to avoid traffic congestion in smart cities is to design automated guided vehicles (Herrero‐Perez & Martinez‐Barbera 2010).
Monitoring the traffic in real time, providing a smart solution for health, and protecting the environment are considered the main contributions of smart cities (Solanas et al. 2014). However, to support all these features, advance solutions consisting of wireless and wired networks have been proposed. This has given rise to the issue of interoperability among various networking technologies in smart cities (Avelar et al. 2015). Various innovative technologies have also been proposed to properly integrate EVs with different features such as vehicle‐to‐grid (V2G) (Paffumi et al. 2016). Different test‐beds have also been developed to test the interoperability of different technologies of smart cities (Cardone et al. 2014).
With the concept of smart cities, the wireless sensor networks (WSNs) have gained much attention under the umbrella of the Internet of things (IoT) (Valerio 2016, Zanella et al. 2014). Now, various interoperability issues are tackled while considering the smooth operation of the IoT.
Smart cities not only conserve the energy and protect the environment, but also an extensive effort has been made in developing the green applications for smart cities. Everything in smart cities has been designed to conserve and to efficiently manage energy (Wu et al. 2016). A well‐defined ecosystem for smart cities based on the various architectural models has been chalked out to support the green applications (Schleicher et al. 2016).
As compared to the conventional networks, resources in smart cities are more vulnerable to various threats from the external and internal parties. In smart cities, various life‐supporting phenomena are being smartly controlled. Hence any threat to networks is considered as a threat to many lives of citizens (Saputro & Akkaya 2015). The citizens privacy is also a main concern of smart cities. Therefore, the privacy‐aware smart cities have been envisioned to provide enough security and privacy to smart citizens (Martínez‐Ballesté et al. 2013).
EVs have been introduced with the concept of reduction in the carbon emissions and to support the environment. However, in smart cities EVs have been made so much smarter that they can contribute even to stabilize the power resources and to support the renewable energy resources (Adzic et al. 2013, Rodriguez et al. 2013). The decision of integrating EVs into smart cities can result into various gains such as reduction in fossil fuel consumption and stabilizing the smart grid. The main motivations for using EVs into smart cities can be summarized as follows:
In addition to the above‐mentioned main contributions of EVs in smart cities, EVs are specifically employed in various application scenarios to achieve certain goals (Ferreira et al. 2015). The following are prime objectives that are expected to be achieved from the integration of EVs into smart cities.
Reserved capacity to meet the unexpected peak‐demands of energy is categorized as spinning reserve. To avoid the sudden peak demands and resulting spinning reserves, EVs are optimally recharged to conserve the energy (Darabi & Ferdowsi 2014).
EVs can be recharged from the renewable energy resources even at home or in parking lots. Then EVs can sell the extra energy to the grid and can help in stabilizing the grid. In this way, EVs act as the transporter of the energy and integrate the renewable energy resources into the micro‐grid or smart grids (Yu et al. 2016). Also, the energy from the grid to EVs can be transported to the places where there is energy scarcity (Kavousi‐Fard et al. 2015).
Researchers from the industry and academia are focusing on the various ways to reduce carbon emissions into the environment. To give a healthy environment to the future generations, the future smart cities have been designed from every perspective to support energy‐efficient applications. EVs for this purpose, have been specifically selected as the main transportation mode in smart cities.
The frequency and voltage fluctuations and various other irregularities in the smart grid are taken into consideration while integrating EVs into the smart grid. The optimal recharging of EVs with the help of scheduling and various optimization approaches helps in achieving the objective of reducing the load on the smart grid (Rabiee et al. 2016, Chen & Duan 2014). In addition to optimal scheduling of EVs, various other control methods such as DC/DC and DC/AC converters control for EVs to manage the load in smart cities have been proposed (Tani et al. 2013). The smart scheduling of EVs takes into consideration the optimal recharging of EVs while minimizing the peak load and helps in stabilizing the smart grid in smart cities. However, EVs scheduling in conjunction with the control methods for DC/DC and DC/AC converters not only optimize the load but also minimize the various power losses and regulates the frequency and voltage, as the EVs can operate with of 4 kWh secondary (rechargeable) batteries. Usually, the advance EVs scheduling approaches take into consideration in advance the state of charge (SOC) of the batteries to minimize any threat to the batteries from fluctuating loads. With the knowledge of SOC, the recharging of EVs are then scheduled depending on the peak or off‐peak hours. This helps EVs batteries from demanding any extra requirement.
To optimally recharge EVs in smart cities, the whole recharging process of EVs is categorized into the centralized and distributed approaches for recharging EVs. Depending on the recharging approach, various recharging control methods and charger types have been developed to achieve the specific recharging objectives (Wu, Gilchrist, Sealy & Bronson 2012). Figure 18.1 shows the whole EVs recharging process in smart cities.
The centralized EVs‐recharging approach as discussed in (Nguyen & Le 2014, Han et al. 2010) employs the central entity such as aggregator unit (AU) to collect all the relevant information of EVs for recharging. An AU with the help of charging post manager (CPM) collects and controls the whole recharging process of the large number of EVs.
The AU collects the information about the time of arrival of EVs, state of charge (SoC) of batteries, duration of the recharging, prices of the recharging, and the concerned objective related to the EVs‐recharging process. AUs then transfer the information to the higher unit in recharging structure named distribution system operator (DSO). The DSO then synchronizes with other DSOs and collectivity makes a decision in a centralized fashion. AUs with the help of scheduling and various optimization approaches optimally recharge EVs in a controlled way. Various conflicts related to prices and schedule are also resolved by using the scheduling algorithms for EVs recharging.
The centralized entity AU recharges EVs in a centralized way. Following are the main advantages of the centralized EVs‐recharging approach.
However, the centralized EVs‐recharging approach in smart cities also faces various limitations such as:
The distributed EVs‐recharging approach is discussed in (He et al. 2012, Hu et al. 2014). Various limitations of the centralized EVs‐recharging approach in smart cities are addressed with the introduction of distributed or decentralized EVs approach. In this recharging approach, in addition to the central entity, EVs themselves make the recharging decisions in a distributed manner. As the number of EVs increases, the computational load is also divided according to the situations and number of EVs. The distributed EVs‐ recharging approach in smart cities demands from EVs to be more intelligent and capable of performing the computational decisions. Hence, a simple EVs‐recharging architecture is needed to perform the whole optimal scheduling of EVs recharging in a distributed manner.
The distributed EVs recharging as compared to the centralized approach adds less complexity in the whole recharging process. The main advantages achieved during the distributed EVs‐recharging approach are discussed below:
The distributed EVs‐recharging approach addresses the various limitations of the centralized EVs recharging. However, there are also certain limitations of the distributed EVs recharging approach. Some of the limitations of the distributed EVs recharging are described below:
Integration of the large number of EVs into smart cities will enhance the load on the existing smart grid. It is estimated that the integration of 10% of EVs into smart cities will be a serious blow to the existing power resources. The existing smart grid was actually designed to support the domestic and commercial users. However, electrification of transportation requires extra measures to reduce the impact on the power resources (Su et al. 2012, Hadley & Tsvetkova 2009).
By taking into consideration the various potential effects of EVs integration into smart cities, it is demanded that EVs recharging is made more controlled, optimal, and flexible. For this purpose, EVs in smart cities are recharged with the help of optimal scheduling and optimization approaches (Will & Schuller 2016). With the scheduled EVs‐recharging approach, various objectives such as reduction of power losses, minimization of cost of energy for customers, and V2G capability can be achieved (Singh et al. 2012).
A scheduled EVs‐recharging approach with various control methods, advanced architectures, and chargers seem to conserve the energy in smart cities. (Ni et al. 2015, Rahbari‐Asr & Chow 2014, Pahlevaninezhad et al. 2012). With the help of smart charging approaches (either centralized or distributed), scheduling algorithms, control methods, and smart charging locations, EVs recharging is made optimal, and EVs can contribute in stabilizing the smart grid (Mirzaei et al. 2016). To be more specific, we will discuss various scheduling methods applied to EVs recharging. Below is the detailed discussion of various scheduling approaches with respect to different objectives achieved during EVs recharging approaches in smart cities.
The recharging of EVs in smart cities requires an efficient recharging process to minimize the potential impact on the smart grid. For this purpose, the optimal scheduling approach is usually employed for recharging of EVs in smart cities to achieve one or more objectives. Different objective‐ oriented recharging approaches are used. In this subsection, we will discuss various objectives that will be achieved by utilizing the optimal scheduling approaches in smart cities. Table 18.1 gives a description of various objectives achieved during the scheduling of EVs‐recharging approaches with different conflicting objectives.
It is assumed that recharging of EVs in smart cities is accompanied by various power losses. Therefore, the recharging of EVs is optimized to minimize the power losses of the smart grid. Energy in smart cities can be conserved by reducing the power losses during the EVs‐recharging process. Therefore, EVs recharging in smart cities is optimally scheduled with the help of various algorithms and optimization approaches. To minimize the power losses on the smart grid while recharging EVs in the smart grid, authors in (Clement‐Nyns et al. 2010) have proposed the controlled and coordinated recharging approach. With the help of stochastic programming, not only the power losses are reduced but also the load factor of the main grid is maximized. The relationship between the load factor, load variance, and the power losses has been made in (Sortomme et al. 2011). For this purpose three optimal scheduling algorithms have been proposed. The testing of the algorithms proves that this relationship between the power losses of the distribution system and load factor is independent of the system topology.
The charging of EVs power train is also considered for conserving energy (Pourabdollah et al. 2013). For this purpose, the convex optimization approach is employed, and the power losses such as engine losses and the electric motor losses are also taken into consideration. Various factors such charging time, top speed, and driving patterns are also taken into consideration to optimally recharge EVs. Power train is either recharged at standstill or while driving. Both cases of recharging with an optimal recharging approach have been employed in (Murgovski et al. 2013). The problem of battery dimensioning and power split control is taken into consideration while employing dynamic programming to optimally schedule EVs recharging. The study in (Ko & Jang 2013) discusses the recharging of EVs via wireless power transfer. This require the efficient scheduling approaches to schedule the data and energy packets. For this purpose, the particle swarm optimization approach has been utilized to solve the problem of optimal recharging of EVs.
Table 18.1 Scheduling approaches used for EVs recharging in smart cities with different objectives, main constraints, recharging approach, and solver tools.
Studies | Main Objective Achieved | Main Constraints | Main Solver Tools | Recharging Approach Used |
(Clement‐Nyns et al. 2010, Sortomme et al. 2011, Pourabdollah et al. 2013, Murgovski et al. 2013, Ko & Jang 2013) | Reduces the power losses | Power resource minimization and power cost | Matlab and JADE (Java) | Both centralized and distributed |
(Wu, Aliprantis & Ying 2012, Gan et al. 2013, Bashash & Fathy 2014, Tushar et al. 2014, Yang et al. 2014, Rostami et al. 2015) | Minimizing the total cost of energy for users | Recharging availability and load | Matlab, Simulink and PowerACE | Both centralized and distributed |
(Sortomme & El‐Sharkawi 2012b, a, Jin et al. 2013) | Maximizing the aggregator profits | Electricity balance and rate | Matpower, PSS/E, and GAMS | In most cases the distributed EVs recharging approach is used |
(Han et al. 2010, Rotering & Ilic 2011, Sortomme & El‐Sharkawi 2011, Lin et al. 2014) | Frequency regulation | Minimization of the power resources | PSS/E, and PowerACE | Both centralized and distributed |
(Singh et al. 2012, Richardson et al. 2012, Bai & Qiao 2015) | Voltage regulation | Minimization of the power resources | Matlab, Simulink | Both centralized and distributed |
(Liu et al. 2014, Zhao et al. 2012, Huang et al. 2014, Jin et al. 2014, Zhang et al. 2014) | Support for Renewable energy | Batteries limitations and charging capacitors | Matlab, GAMS, and Matpower | Centralized but in some cases the distributed |
When EVs are optimally recharged, the total cost of energy for the customers tend to decrease. The whole recharging process is optimized in a fashion that not only minimizes the cost of energy for users, but also the profit of the AU tends to increase. A low‐cost scheduling algorithm has been proposed (Wu, Aliprantis & Ying 2012) to minimize the cost for the users. Through this algorithm, the energy demand is forecast in a day‐ahead manner and then applying the scheduling low‐cost algorithm, the rates are negotiated in a bilateral manner. A decentralized scheduling algorithm for recharging of EVs to minimize the load and cost has been proposed in (Gan et al. 2013). The proposed algorithm fills the valley of the electric demands and takes into consideration the elasticity and controllability of EVs load and energy prices. Another scheduling algorithm that addresses the cost‐optimal recharging of EVs has been proposed in (Bashash & Fathy 2014). With the help of convex optimization, not only the energy cost is considered but also the V2G support is analyzed.
Scheduling of EVs recharging in conjunction with home appliances in the smart microgrids has been proposed in (Tushar et al. 2014). The scheduling method has been formulated as mixed integer linear programming (MILP), and then it has been solved optimally to minimize the energy cost and to support the renewable energy resources. This scheduling approach has been designed for the microgrid to schedule the load between EVs recharging and home appliances. Usually, the forecast and the actual load of EVs introduces certain anomalies in the system. This issue has been resolved by introducing the risk‐aware scheduling of EVs to overcome the expected and actual load mismatch and to minimize the energy cost for the users (Yang et al. 2014). This scheduling problem is formulated as a stochastic problem and then is solved with the help of the L‐method. Various uncertainties regarding the price of energy for users during EVs recharging has been addressed in (Rostami et al. 2015). The EVs recharging problem is solved using the krill herd optimization technique. Here, the network topology for the recharging of EVs can be controlled from some remote places.
The scheduling of EVs recharging in a smart city can also result into maximizing the profit of the AU. The AU usually enhances its profit by either supporting the centralized or distributed EVs‐recharging approach.
A scheduling algorithm has been proposed to schedule between the V2G capability and ancillary services while taking into consideration the aggregator and customer profits (Sortomme & El‐Sharkawi 2012b). This scheduling algorithm has been specifically designed for the aggregator to address the issue of peak load shaving and to add flexibility in the system. The proposed algorithm enhances the aggregator profit while supporting the renewable energy resources. A scheduling algorithm to bid for the V2G and ancillary services has also been proposed in (Sortomme & El‐Sharkawi 2012a). This algorithm also takes into consideration the unplanned departure of EVs from the pre‐decided contract and maximizes the profit for the aggregator and customer. The unidirectional V2G support with spinning reserves with this algorithm significant increases the aggregator profit. EVs scheduling for recharging and enhancing the aggregator profit with energy storage capability has been addressed in (Jin et al. 2013). The scheduling problem is formulated as a MILP and then is optimally solved to enhance the aggregator profit. In this study, the aggregator with storage energy minimizes the sudden peak demands. The proposed scheduling approach is also integrated with the communication protocol to provide the efficient interaction between the aggregator, EVs, smart grid, and the energy storage resources.
Frequency fluctuations due to unexpected loads on the smart grid can have serious effects on the smooth operations of electric appliances in smart cities. Therefore, every effort has been made to design the various applications, approaches, procedures, and algorithms to stabilize the frequency in smart cities. The recharging of EVs with various scheduling approaches also helps in regulating the frequency of the smart grid. Authors in (Han et al. 2010) propose various design paradigms for the aggregator to support the V2G capability with frequency regulation. The scheduling problem of EVs recharging in this case is optimally solved, and dynamic programming is applied to compute the charging control for each vehicle. Two other scheduling algorithms that take into consideration the frequency regulation, energy flows, charging time, and V2G have also been proposed in (Rotering & Ilic 2011). To find the optimal solution, dynamic programming has been used, and the two algorithms are designed to take into consideration the energy forecast. Unidirectional V2G capability with frequency regulation has been proposed in (Sortomme & El‐Sharkawi 2011). As the rate for recharging EVs varies during the frequency regulation, this scheduling algorithm has been proposed to set the point for such variations in rate. It also takes into consideration load management and frequency regulation. The optimal scheduling approach for charging and discharging of EVs to regulate the frequency has been proposed in (Lin et al. 2014). In this study, the scheduling problem is formulated as a convex optimization problem, and then the gradient projection method is used to solve the optimization problem. In this forecast‐based scheduling approach, the optimal charging and discharging of EVs with the help of a decentralized scheduling approach helps in regulating the frequency of the smart grid.
The ancillary services provided by a scheduled EVs‐recharging approach also help in regulating the voltage of the smart grid in smart cities. The aggregators with the help of storage energy from EVs not only minimize the peak load but also stabilize the smart grid by regulating the voltages. The authors in (Singh et al. 2012) introduce the scheduling approach for charging and discharging of EVs into the local distribution system. Two fuzzy logic controllers named as the charging station controller and the V2G controller have been proposed to schedule the optimal recharging and discharging of EVs into the smart grid. Controlling the recharging rates can also contribute in stabilizing the smart grid (Richardson et al. 2012). Scheduling of EVs while considering the recharging rates can enhance the efficiency of the power grid. For this purpose, a scheduling approach based on the linear programming is proposed to determine the charging rates for each EVs and to regulate the voltages fluctuations. The bidirectional V2G capability has also been exploited to regulate the voltage of the smart grid during recharging or discharging of EVs (Bai & Qiao 2015). In this proposed approach, the scheduling problem of recharging and discharging of EVs is formulated as a mixed integer quadratic programming model and then solved optimally. This robust optimization model comes out to be efficient for stabilizing the power resources of smart cities.
The integration of renewable energy resources into smart cities not only reduces the carbon emission but also reduces the prices of the energy for domestic and commercial users. EVs in smart cities can also be recharged while exploiting the renewable energy resources. For this purpose, the scheduling approaches for EVs recharging using renewable energy resources have been proposed.
Wind power has been extensively considered for recharging of EVs (Liu et al. 2014, Zhao et al. 2012). For example, the integration of wind power resources for optimal recharging of EVs is practiced in (Huang et al. 2014). The scheduling problem of EVs recharging while employing renewable energy sources is formulated as a Markov decision process (MDP) and then solved using an event‐based optimization approach. The proposed scheme seems to fully support the wind energy for the recharging of EVs. The recharging of EVs from the uncertain renewable energy sources has been discussed in (Jin et al. 2014). The energy from the renewable sources is uncertain and time varying. Therefore, the scheduling of EVs recharging using the renewable energy sources is considered to be a bit complex. Lyapunov optimization is used in solving the recharging problem of EVs while supporting the fluctuating renewable energy resources. The scheduling of EVs recharging while considering the local renewable energy resources, uncertain EVs arrival, and cost minimization has been considered in (Zhang et al. 2014).
Scheduling EVs recharging in smart cities is usually achieved with the help of different optimization approaches (Shen et al. 2014, Okaeme & Zanchetta 2013). To achieve the optimal recharging of EVs in smart cities, the scheduling of EVs recharging problem is solved optimally by using an optimization approach (Azizipanah‐Abarghooee et al. 2016, Majidpour et al. 2015). Major optimization approaches used for EVs recharging are convex optimization, particle swarm optimization, quadratic programming, linear programming, heuristic approaches, and dynamic programming. Figure 18.2 shows the various optimization approaches used for EVs recharging with their respective reference for the work.
The integration of EVs into smart cities requires a thorough re‐assessment of existing energy and other resources. The recharging of EVs in smart cities through smart recharging architectures also has to face different challenges. Regarding EVs recharging in smart cities, below is the description of the some open issues, challenges, and future research directions.
Wireless power transfer has gained much attention in today's research domain. Recharging EVs through wireless power has been envisioned as the most promising solution to enhance the efficiency and flexibility of EVs recharging in smart cities (Ko & Jang 2013). To the best of our knowledge, the wireless recharging of EVs has not been explored in much detail. With the help of wireless rechargers for EVs, various renewable energy resources can be exploited and integrated in smart cities in a more reliable way.
Through the help of V2G capability, EVs can help in stabilizing the smart grid and provide the ancillary services. V2G or G2V capability of EVs can also be enhanced to vehicle‐to‐anything. Through this approach, different domestic and commercial electric appliances can be electrified (Ferreira et al. 2014). However, very limited work has been done on this domain. It requires the thorough understanding of the possibility of recharging other electric devices through EVs.
The unchecked or rapid integration of EVs into the smart grid can lead to the drastic power outage and unexpected loads on the smart grid. Researchers have made a significant contribution in providing the solution for minimizing the load on the smart grid (Chen et al. 2014, Kennel et al. 2013). The impact of large penetration of EVs into the existing power resources and future smart cities has not been analyzed in detail.
The centralized and distributed EVs‐recharging infrastructure requires wired or wireless communication solutions to facilitate the customers. As compared to the distributed EVs‐recharging approach, the centralized EVs recharging requires an advance communication architecture for the recharging of EVs. The main need is the security and privacy of the customer data while providing EVs‐recharging approach. Different solutions regarding the communications of EVs recharging architecture have been proposed (Djahel et al. 2015, Khorov et al. 2015, Alvi et al. 2016, Zhu et al. 2013, Vlacheas et al. 2013, Tal et al. 2015). However, there is still a need for an advanced wired or wireless communication architecture to securely recharge the large number of EVs in the future smart cities.
Making the transportation more intelligent in smart cities requires a thorough control through different online and other applications. Some of the applications have already been proposed to control EVs drive and recharging in smart cities (Liang et al. 2013, Bertoluzzo & Buja 2011, Steenbruggen et al. 2015, Farkas et al. 2015). However, there is a need to develop the various applications to control EVs drives and recharging in smart cities.
Researchers from industry and academia are working on the standardization of communications protocols used for EVs recharging. So far, EVs communication architecture has not witnessed any standard to practically smooth the recharging operation. However, the standard J1772 connector (Pratt et al. 2011) has been proposed for EVs recharging architecture to support the recharging operation in the smart grid.
Electric vehicles (EVs) have been accepted as the smart solution for the transportation in smart cites. The integration of EVs into the smart grid requires a proper scheduling of EVs recharging to conserve the energy and to stabilize the smart grid. The controlled EVs recharging in smart cities has been studied from various perspectives. In this chapter, a detailed discussion of the scheduling of EVs recharging in smart cities have been proposed. EVs in smart cities are usually recharged with a centralized or distributed recharging approach. Different advantages and limitations regarding each recharging approach have been presented. Scheduling approaches for EVs recharging provides different objectives such as reduction in power losses, minimizing the total cost of energy for users, maximizing the aggregator profits, frequency regulation, voltage regulation, and support for renewable energy resources. To achieve the optimal scheduling of EVs recharging, a brief description of optimization techniques to gain various objectives and to allocate the resources has also been made. EVs domain still needs to be further studied from various perspectives such as wireless power recharger and EVs supportive applications. These and other perspectives have been highlighted as future research directions.
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