Electric Vehicles in Energy Systems

One of the major problems that has plagued almost every country in recent decades is the issue of environmental pollution, along with atmospheric pollutants, which most often enters the atmosphere through vehicles and industries. This problem is more prevalent in metropolitan areas and larger population centers. As a result, researchers are looking for ways to reduce the production of atmospheric pollutants, especially carbon dioxide. One solution to this problem is to use electric vehicles (EVs) that do not produce any atmospheric pollutants. Indeed, electrification of the transportation fleet is an inevitable future for modern countries, and it is considered as an essential pillar of the concept of intelligent transportation systems. Therefore, exploring various aspects of the presence of EVs can be a good guide for designers, policy makers and decision makers of transportation systems to get acquainted with the features of this new technology and guide it in the right direction. On the other hand, use of EVs can affect various aspects such as dependence on fossil fuels and the amount of environmental pollution and can be a permanent solution for contaminated metropolises. In this chapter, different aspects of electrification of the transportation fleet are studied and various means to increase the penetration of EVs in near future are considered.

Nowadays, environmental pollution and the limitation of fossil fuels are taken into consideration by many countries. Worries over the pollution from fossil fuels in the transportation sector have prompted the inclination to use electric vehicles (EVs) instead of the conventional internal combustion engines. It is predicted that at least 10% of the US transportation fleet will be changed to EVs by 2020, and they could have 50% of vehicle market share by 2050. Furthermore, by increasing the penetration of the renewable energy resources in the power system, we can use these clean energies to charge EVs; in this way, we can solve air pollution and fossil fuels problems. Implementing this procedure needs many infrastructures to handle the charging demand of the EVs properly. It should be mentioned that by increasing the penetration of the EVs in the power system, we are confronted with large load demand by sharp stochastic behavior which can have significant effects on the power system parameters such as voltage variations and power loss. To handle this problem, we need to charge EVs in smart mode by considering the power system constraints. In this way, some internal units between EV owners and power system operator, which are named as aggregators, are considered. These units buy electricity energy from the power system operator and sell it to the customers (EV owners). The main goal of these units is to find the optimal charging procedure of EVs by considering power system limitations and minimizing the EV owners charging cost. Here, the main challenge for aggregators to find the optimal charging solution is to model the EVs travel behavior and estimate the value and time of their charging demand in a precise manner. If they can estimate these parameters accurately, it is highly profitable for them and has a significant effect on their income. In this regard, we need to find the precise approach to forecast the EVs travel behavior with high accuracy which has a potential to handle large dimension data; because in the near future by increasing the penetration of the EVs in the transportation fleet, we will face big data problem. To handle the large dimension data sets, machine learning tasks, which are developed based on artificial intelligence concept, can be regarded as the best solution, and these approaches have acceptable performance on other data engineering tasks such as time series forecasting, image and voice processing, and pattern recognition. In this chapter, we introduce an artificial intelligence-based approach for modeling the EVs travel behavior and describe it in full details.

Concerns about climate changes and environmental air pollution are leading to the adoption of new technologies for transportation, mainly based on vehicle electrification and the interaction with smart grids, and also with the introduction of renewable energy sources (RES) accompanied by energy storage systems (ESS). For these three fundamental pillars, new power electronics technologies are emerging to transform the electrical power grid, targeting a flexible and collaborative operation. As a distinctive factor, the vehicle electrification has stimulated the presence of new technologies in terms of power management, both for smart homes and smart grids. As the title indicates, this book chapter focuses on the role of off-board EV battery chargers in terms of operation modes and contextualization for smart homes and smart grids in terms of opportunities. Based on a review of on-board and off-board EV battery charging systems (EV-BCS), this chapter focus on the off-board EV-BCS framed with RES and ESS as a dominant system in future smart homes. Contextualizing these aspects, three distinct cases are considered: (1) An ac smart home using separate power converters, according to the considered technologies; (2) A hybrid ac and dc smart home with an off-board EV-BCS interfacing RES and ESS, and with the electrical appliances plugged-in to the ac power grid; (3) A dc smart home using a unified off-board EV-BCS with a single interface for the electrical power grid, and with multiple dc interfaces (RES, ESS, and electrical appliances). The results for each case are obtained in terms of efficiency and power quality, demonstrating that the off-board EV-BCS, as a unified structure for smart homes, presents better results. Besides, the off-board EV-BCS can also be used as an important asset for the smart grid, even when the EV is not plugged-in at the smart home.

Nowadays, vehicle to grid (V2G) capability of the electric vehicle (EV) is used in the smart distribution network (SDN). The main reasons for using the EVs, are improving air quality by reducing greenhouse gas emissions, peak demand shaving and applying ancillary service, and etc. So, in this chapter, a non-linear bi-level model for optimal operation of the SDN is proposed where one or more solar based-electric vehicle parking lots (PLs) with private owners exist. The SDN operator (SDNO) and the PL owners are the decision-makers of the upper-level and lower-level of this model, respectively. The objective functions at two levels are the SDNO’s profit maximization and the PL owners’ cost minimization. For transforming this model into the single-level model that is named mathematical program with equilibrium constraints (MPEC), firstly, Karush–Kuhn–Tucker (KKT) conditions are used. Furthermore, due to the complementary constraints and non-linear term in the upper-level objective function, this model is linearized by the dual theory and Fortuny-Amat and McCarl linearization method. In the following, it is assumed that the SDNO is the owner of the solar-based EV PLs. In this case, the proposed model is a single-level model. The uncertainty of the EVs and the solar system, as well as two programs, are considered for the EVs, i.e., controlled charging (CC) and charging/discharging schedule (CDS). Because of the uncertainties, a risk-based model is defined by introducing a Conditional Value-at-Risk (CVaR) index. Finally, the bi-level model and the single-level model are tested on an IEEE 33-bus distribution system in three modes; i.e., without the EVs and the solar system, with the EVs by controlled charging and with/ without the solar system, and with the EVs by charging/discharging schedule and with/without the solar system. The main results are reported and discussed.

During the past few years, the growth of the greenhouse gas emissions and the global warming from fossil fuels to produce the electrical power, transportation, as well as the finiteness of these resources have become the most critical concern of governments to find the alternative resources for fossil fuels. Solar energy is one of these resources which are clean, unlimited, and completely free resource. However widespread use of that needs to make some changes in the power system thus due to its random production of electrical power, solar energy will be a great uncertainty in power system. Therefore, the power grid will be required to the reliable compensator, which compensate the lack of power by help to the generation sector when the production and consumption is imbalanced. The solution of this problem is energy storage and the flexible loads which have the ability to adjust power consumption and reducing it in the real time. Also transportation is one of the main sources of environmental pollution, to this end, PHEV is presented, but the widespread use of them will be creating a significant load on the grid. For this reason, the managing of these loads is required in order to increase permeability of renewable energy resources (solar energy) in a smart distribution grid and decrease the consumers’ dependence on conventional power grids with fossil fuels. Additionally, the chapter develops a charging management program to increase from renewable resources for penetration. The finding show that, this program is made to increase use of renewable energy resources by consumers and reducing received power of the conventional generation.

Today, internal combustion engines are considered as essential equipment of transportation industry and business. Concerns about oil price fluctuation and climate changes are reasons to find an alternative for fossil fuel-based vehicles. Electric vehicle (EV) with electric drive, good efficiency and no pollution can be a suitable alternative for conventional vehicles. The presence of EVs in the distribution network, especially those with the ability to connect to the grid (V2G), allows the distribution network to meet its required reserve at a lower cost. An individual EV does not have a significant impact on the grid; however, the optimal charge/discharge aggregation of EVs may lead to the better technical and economic performance of the distribution network. In this chapter, an optimal operation model of EVs is presented in the presence of wind turbines and the effects of the coordinate and non-coordinate operation of EVs on the stochastic generation of wind turbines are assessed. The stochastic generation of wind turbine are modeled through Weibull distribution function under various scenarios.

The uncoordinated integration of a great number of Electric Vehicles (EVs) possibly leads to undervoltages and overcurrents in a Distribution Network (DN). It may also increase the power losses in the DN, and in severe cases, it is probable that the DN collapses. Therefore, control and management systems are necessary for avoiding the negative consequences of EV charging. In addition, by the advent of bidirectional chargers and sophisticated EV Supply Equipment (EVSE), objectives beyond saving the DN from becoming upset can be defined for EVs as auxiliary services (also referred to as ancillary services).

This chapter after addressing and comparing different methods for EV charging and discharging management, concludes that the distributed control mechanisms respond to the particular needs in Smart Microgrids (SMGs) as they make the systems largely-scalable and plug-&-playable, while at low computational and communication costs. Then, the specifications of EV batteries and chargers are presented. As modern communications are the backbone of SMGs, the communication-assisted distributed control system can be set up. Therefore, a wide range of auxiliary services are addressed in this chapter and distributed control systems based on the cooperative control are introduced to accomplish them. The control objectives in this chapter (i) facilitate the integration of variable renewable energies (VREs) to SMGs, (ii) respond to the technical challenges toward EV interconnection, and (iii) increase the EV owners’ economic benefits.

Distribution network (DN) operators are facing new challenges such as developing the use of various types of energy sources and increasing the use of plug-in electric vehicles (PHEVs) to reduce the utilization of fossil fuels. High integration of PHEVs and coordination of different energy providers in the electric distribution grid in the paucity of proper planning will impose economic obstacles for DN operators. This chapter will propose an energy and reserve management model for a DN, which contains PHEVs, wind turbines (WTs), photovoltaic system (PV), diesel generators (DGs), upstream grid (UG), and fuel cell (FC). In this study, an operation scheme for the PHEVs aggregator is accomplished with main objective function of decreasing operation costs of DN. The PHEVs aggregator has three various states containing load mode, energy production mode, and idle mode, where the PHEVs aggregator will help the DN as energy storage systems (ESSs). The objective function seeks decreasing the costs of purchasing power from UG, and the production cost of DGs and EVs aggregator. The proposed model has considered both the spinning reserve of DG and EVs aggregator, and the obtained simulation results showed the positive effect of PHEVs aggregator in reducing operation costs

In this chapter, a bidding strategy model is presented for electric vehicles’ (EVs) parking lot to sell the aggregated energy in the distribution network in its vehicle to grid (V2G) mode. The proposed strategy is designed to model the interactive behavior of two market participants: aggregator and EVs parking. The bidding price of the parking is determined through the Stackelberg game theory that is modeled based on a bilevel programming problem. In the upper level of the proposed model, the parking owner offered its optimal bidding price and EVs’ charge and discharge scheduling to maximize its profit. However, in the lower level, the aggregator is trying to maximize its profit by minimizing its payment and provide the expected load in the distribution network, which is under its management. Aggregator endeavors to optimally schedule the portion of energy that is supplied from either main grid or parking lot. In the equilibrium point of the problem, the optimal bidding price of the parking lot and the purchased energy from the parking and main grid is determined. The proposed bilevel programming problem is transformed into a single level mathematical programming problem with equilibrium constraints (MPEC) using the Karush-Kuhn-Tucker optimality conditions. The obtained numerical results are used to assess the proposed bidding strategy of EVs’ parking lot.

The need for effective technologies to deal with environmental issues is one of the basic approaches within the smart grid concept. Electrical vehicles (EV) are promising technology that provides multiple advantages for both utility and consumers. One of the main challenges of EVs is charging management, which effects on efficiency and popularity of EVs operation. For handling this issue, a new concept named battery swapping station (BSS) for more integration of EVs in microgrids is introduced in this chapter. In addition to market participation, BSS as a large energy storage system can provide adequate reserve for microgrid in islanded operation. So, in this chapter, a novel microgrid operation scheduling consisting of BSS is proposed. The problem is formulated as a bi-level problem: the upper-level minimizes microgrid operation cost including generation and purchasing cost, while BSS profit maximization is the target of lower-level. Participation of BSS in the reserve market beside the local generation units, causes the microgrid capability in operating in islanding mode for multiple hours. The proposed model is implemented on the 10-bus microgrid test system where the results show its effectiveness.

Distribution networks are going toward the integration of distributed generators (DGs) to delivering the electrical energy in a cleaner and reliable manner to the customers. Additionally their implementation can yield the improvement in voltage profile and reduction in lost power for distribution companies (DISCO). Along with development of RESs, plug-in electric vehicles (PEVs) with a clean energy have an acceptable growth in both the number and technology. This chapter introduces the planning of PEV charge station and CHP units in distribution networks in the presence of long term demand response (DR) for interested customers. Since these DR customers seek to attain a higher profit by participating in DR and mutually the DISCO seeks to lessen the planning cost, the problem is modelled in a leader-follower Stackelberg framework. To this end, the bi-level planning problem is converted into a single-level problem using the KKT condition and implementing the equilibrium constrained concept for the lower level problem. Furthermore due to the existence uncertainties in the network, the risk management is considered in this chapter by modelling the payoff function of DR customers with conditional value at risk (CvaR).

Development and implementation of energy hubs present a viable means of transitioning towards distributed and renewable energy generation and conversion technologies. Particularly in the residential sector, implementation of an energy hub infrastructure increases the resiliency of the local system against grid failures and provides economic and environment benefits with respect to system operation. However, recent transition towards electric mobility in the transportation sector introduces new challenges to the optimization of dispatch of energy vectors within residential energy hubs. This is partly due to the uncontrolled charging behavior exhibited by large plug-in electric vehicle (EV) fleets. Under uncontrolled charging conditions, residential energy hubs are impacted by significant additional energy consumption loads and must adapt appropriately to ensure optimal operation of the overall system. In this chapter, the impact of two levels of uncontrolled EV fleet charging rates are projected onto a residential energy hub under different distributed energy technology configurations. The effects of these various conditions on the optimal operation of the residential energy hub are evaluated using a mixed-integer linear programming approach. Economic and emission analysis of the results of this study indicate the operating cost-cutting potential of lower EV charging rates and co-generation technologies, as well as the corresponding tradeoff in emission generation. Furthermore, the operating cost and emission impacts of cogeneration of heat and power (CHP) implementation under an Ontario context were examined, indicating up to 34% operating cost reduction and a 49% emission increase resulting from CHP adoption. Finally, the limitations in solar photovoltaic adoption in the residential energy hub were discussed.

These days, due to the increasing of fossil fuel consumption as well as a result of air pollution, electric vehicles (EV) have been given more attention. The number of vehicles have grown significantly in recent years. However, in order to progress in the EV industry, it is essential that we create sufficient infrastructure to supply the EVs’ batteries. The main challenges in the development of EVs are the long charging period of batteries and their high cost. Fast charging technologies, EV’s battery replacements, and charging stations are considered as promising solutions for charging services. EV’s battery replacement stations (EVBRSs) are one of the most suitable solutions to solve this problem. These EVBRSs act as an interface between the consumers and the power grid, which reduces the waiting time for charging the batteries. This method also has some advantages for the power network as well as the consumers.

Nevertheless, not only incorrect charging operations at stations increase unnecessary charging costs, but also it leads to reduce the reliability of the power grid. Accordingly, it is essential to obtain an appropriate policy for the optimal operation of EVBRSs for maximizing profits of EVBRSs and providing appropriate services to consumers. In this work, the appropriate formulation for optimal operation of the EVBRSs is presented, and the information-gap decision theory (IGDT) is used to address the uncertainties. EV’s battery replacement station and the renewable energy systems are considered as the uncertain parameters.

In this chapter, the penetration of electric vehicle (EV) in demand response programs is presented. Firstly, EVs as an essential candidate for demand response program implementation are explained. In this way, both vehicle to grid (V2G) and grid to vehicle (G2V) capability of EVs are described. Actually, by using the pricing mechanism that encourages consumers to shift the EV charging load, the tradeoff between the shift able loads and the incentive cost investigated. Then, the optimization problem in a parking lot (PL) and smart distribution system (SDS) is formulated, and their constraints and uncertainties are displayed. Performance of each demand response programs (DRPs) is analyzed, and the optimum implementation extracted. Next, by considering the fact that all the EVs in parking lots cannot be charged at the same time, charging schedule presented to achieve optimal operation of the parking lot.

For decades, fossil fuels are the main source of energy in the world, but concerns caused by price fluctuations, energy security, and environmental issues such as greenhouse gas emissions from burning these fuels have led that various industries to seek to replace fossil fuels. Transportation is one of the main consumers of fossil fuels, especially oil. The transportation share of the world’s total oil consumed in 2012 was 63.7%. Also, 23% of carbon dioxide produced by fossil fuels in 2012 was related to this sector. Replacing conventional vehicles with hybrid electric vehicles is among the best solutions for environmental and economic issues in the transportation sector. Considering the advantages of electric vehicles, their number is expected to increase rapidly over the next few decades. In 2022, more than 35 million electric cars are expected to be on the road. Electric vehicles must be connected to the power grid to charge their batteries. Therefore, with the widespread presence of these cars, the performance of the power system will change especially in the distribution network. Uncontrolled battery charging can cause undesirable effects such as overload, overvoltage, loss increase, unbalanced load, harmonic, and instability. Demand side management can prevent these problems, and it also flattens the demand curve. In order to solve the problems caused by the use of gasoline cars, it is expected that electric vehicles will gradually replace these cars. Lack of control in charging process will have adverse effects on the network. In this study, after modeling the electric car charging curve, its influence on network demand has been investigated in two uncontrolled charging and controlled charging scenarios. In this study, the controlled charge with the goal of minimizing household electricity consumption costs is investigated. The results show that the lack of control on the car charging time increases the peak demand, while the controllable charge does not increase the peak, and flattens the demand curve. The current chapter will discuss the application of electric vehicles in power grid and its role in demand response in order to improve the demand curve especially in smart homes.