Public Electric Vehicle Charging Infrastructures Planning and Operation: Comparison
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Planning public electric vehicle (EV) charging infrastructure has gradually become a key factor in the electrification of mobility and decarbonization of the transport sector. In order to achieve a high level of electrification in mobility, in recent years, different studies have been presented, proposing novel practices and methodologies for the planning and operation of electric vehicles charging infrastructure. 

  • public charging infrastructure planning
  • electric vehicles
  • charging station operator
  • mobility service operator
  • power system network
  • distribution system operator
  • transmission system operator

1. Introduction

Recently, electric vehicles (EVs) are becoming a potential substitution for internal combustion engine vehicles (ICEVs) in transportation and mobility [1][2][3]. EVs require access to charging points and the type and location of the chargers are not entirely the choices of the vehicle owners [1]. Government and electric utility companies’ policies, as well as techno-economical advances and barriers, have a decisive impact in the pace of EV charging infrastructure building [1][2][3]. Depending on EV inventory, travel patterns, transportation modes, and urbanization trends, the location, distribution, and type of electric vehicle recharging facilities can differ by region and time [1]. The installation of public chargers has increased seven-fold in the years 2015–2020 [1]. While most EV charging is conducted at home and at workplaces, the deployment of public charging points will be critical as countries leading EV deployment are entering a stage wherein EV owners will demand higher autonomy and simplicity [1][2][3]. European countries, for the most part, have not met the electrical vehicle supply equipment (EVSE) targets recommended for public charging points set by the Alternative Fuels Infrastructure Directive (AFID) [1]. However, the success of electric vehicles is due to multiple factors, such as the ongoing decrease in battery costs, the increased availability of electric car models as well as public charging stations, and the acceptance of EVs by fleet operators, also complemented by local policy measures [1][2][3]. Sustained political support is one of the main pillars of electromobility [2]. Therefore, European Union (EU) policies, programs, and initiatives that support synergies between the transport and energy sectors are needed to encourage the end-user to move toward more sustainable mobility, such as EVs.
Regarding the policies for mobility, the EU has set targets to become carbon neutral by 2050 and achieve a 55% reduction on the 1990 emissions by 2030 [1][2][3]. To achieve these goals, passenger cars and vans (light commercial vehicles), which today account for about 12% and 2.5% of total EU CO2
emissions, must be decarbonized urgently [1][2][3]. Actions should be taken to boost the deployment of zero-emission vehicles, as vehicles launched from the mid-2030s are expected to remain on the road until 2050 [1][2][4]. By 2022, global sales of electric cars will have increased at a significant rate. With this increase in sales of zero-emission vehicles, nearly 10% of global car sales were electric in 2021, four times the market share in 2019 [2].
Thereby, the charging infrastructure of EVs is typically analyzed with different approaches considering different parameters: type of vehicles, localization, cost of the grid infrastructure, etc. The authors of [5][6][7][8][9][10][11][12][13][14] mainly theorize that the electrification of heavy traffic of the road transport sector could be achieved through the use of static charging, an electric road system (ERS), and electricity to produce a fuel (such as hydrogen or synthetic hydrocarbons) for on-board use in internal combustion engines [15].

2. Public EV Charging Infrastructure Value Chain

Concerning the charging infrastructure ecosystem, the value chain is differentiated into three blocks of the different agents and operators, as it is described in Figure 21: Electric vehicle ecosystem, EV charging infrastructure, and service and power system infrastructure.On the one hand, is considered an agent any individual who uses a vehicle, such as the EV car end-user, i.e., the driver, regarding the electric vehicle ecosystem. On the other hand, an operator is described as any system manager that interferes in the value chain by providing services to the EV. On the second block is presented the EV charging infrastructure and service; it is included the CPO, which takes care of operating the charging stations, or the MSO, which provides information about the location of the charging stations, managing the energy consumption and the cost of the charge, etc., to increase the user comfort of the EV agent. Finally, in the power system infrastructure block, the distribution system operator (DSO) and the transmission system operator (TSO) manage the electricity supply from the grid to the charging point at the local and national levels, respectively.
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Figure 21.
Charging infrastructure value chain.
  • Electric vehicle: This block encompasses both the electric vehicle and its environment, including the vehicle’s technical features and the user of the vehicle. Thus, the user may not necessarily be the owner of the electric vehicle, including new forms of mobility, such as car-sharing and company fleets that have emerged. The role of the vehicle owner could be evolving into that of the end-user, who utilizes the technology but does not necessarily own it. The driver of a vehicle is adapting to these new mobility models.
    –
    EV ecosystem: the actors involved with the EV considering not only the technical characteristics of the vehicle but also the possible concerns of the vehicle’s user.
  • EV charging infrastructure and service: this group encompasses the electric vehicle charging infrastructure, from the installation of the charging equipment by the CPO to the management of the services provided by the MSO. These two differentiated actors, CPO and MSO, who may be part of the same company, are responsible for distinct tasks. The CPO is responsible for the implementation of the charging infrastructure, taking into consideration technical and financial aspects. The MSO, on the other hand, manages the operation of the service and facilitates its usage for the end-user.
    –
    Charging Point Operator (CPO): electric utilities and specialized service companies, primarily those fast charger installers, which set up and maintain public charging stations. This category also includes the design and manufacture of charging hardware, such as pedestals, sockets, and charging cables.
    –
    Mobility Service Operator (MSO): provides charging and different mobility services to end-users for public charging points. These services can consist of maps of charging points, billing mechanisms, and roaming services that allow the end-user to recharge using different charging networks.
  • Power system infrastructure: this block covers electricity system operators, including those responsible for managing the electricity grid at the local level (DSO) and at the regional or national level (TSO). Both DSOs and TSOs have similar objectives of ensuring grid stability at a lower cost. The primary difference between them lies in their power transfer capability and distances. The transmission system is typically a highly interconnected system designed for transferring large amounts of electricity from generators to consumption points.
    –
    Distribution System Operator (DSO): distributes electricity from the transmission grid substations to the final-end consumers, which in this case are the charging points.
    –
    Transmission System Operator (TSO): transmits electrical power from bulk generation plants over the electrical transmission grid to a transmission substation, where the distribution grid is supplied.
These defined roles in the charging infrastructure ecosystem define categories that have specific interests: user-centered (EV), infrastructure- and service-centered (CPO and MSO), and power system-centered (DSO and TSO).

3. Agents and Operators of the Charging Infrastructure Ecosystems

3.1. Electric Vehicle

The electric vehicle ecosystem has undergone recent transformations due to the emergence of new technologies and vehicle designs, requiring users to adjust to these changes. Thus, the automotive fleet is one of the most relevant element to consider when planning charging stations for EVs. Different types of vehicles use one or more electric motors for propulsion. Regarding that, vehicles can be divided according to weight into different categories: light duty vehicles (LDVs) and heavy duty vehicles (HDVs) [5]. Moreover, according to their electrification level, EVs can be divided between battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs).
Once the different EV configurations have been analyzed, the existing charging technologies are also examined through the literature available, which could affect the end-user. The electrification of the vehicle fleet typically considers multiple scenarios and technologies [4][15]. Moreover, user behavior should also be considered when analyzing the impact of mobility electrification [7][16][17][18][19]. The following is a description of the technologies involved and the various electric vehicle charging solutions that the user has had to deal with.

4.1. Operating Principle of Electric Vehicle Ecosystem

This section will analyze the objectives sought concerning the EV, for the two points of view described in Figure 53, the objectives related to the components or characteristics of the EV itself, and the benefits or user-related concerns for using an EV versus a conventional vehicle.

4.3.1. User Comfort

Some of the papers analyzed for the different categories are described as examples.
  • Energy supply: optimizing the energy required to supply the grid to cover the energy demand for charging electric vehicles at a local or regional level.
    (a)
    Integration of renewable energies.
  • Network connection: focused on the issues that could influence the network due to the implementation of an electric vehicle charging infrastructure at a local or regional level.
  • One-Way Charging describes the straightforward operation where a charging point is directly connected to the grid and receives power when plugged in. It has the disadvantage of not controlling the charging of the vehicle, which can lead to an undesirable charging pattern (i.e., when electricity prices are high due to high demand and could significantly increase the utility bill of the operator managing this solution) [20][21].
  • Smart charging enables users to schedule charging. It can actively vary the charge rate (the amount of energy consumed from the grid at any given time) and is typically used to charge when tariffs are low. This charging option can help save on electricity costs and reduce the EV’s load on the grid when plugged in for charging. Charging station integration with energy management software is required for this charging option [20][21][22].
  • Bidirectional Charging (V2X) includes Smart charging characteristics and enables EV batteries to store and discharge energy back and forth to and from the building/home/grid.
    This technology allows EVs to become portable energy storage units for renewable sources such as solar and wind, which are variable. Bidirectional charging can also include V2G, vehicle-to-building (V2B), or vehicle-to-home (V2H) [22][23][24] which allows for excess stored energy from an EV battery to be injected back into a building or home. In addition to reducing the cost of charging, it also helps to save on a building’s utility bills because the EV is providing a portion of the facility’s energy needs [24]. This configuration is most cost-effective when the vehicle charging infrastructure is connected to the same utility meters as the facility [20][21]. Furthermore, it can support critical loads of buildings such as data servers, computers, emergency lights, water pumps, elevators, etc. [22][23][24]. In power supply emergencies such as power failures, the resilience of the building and the entire grid system can be increased [24]. Thus, generating flexibility without affecting the standard functionality of any building or house could be possible by applying V2B strategy [20][21][22][23][24]. Bidirectional charging (V2X) solutions, therefore, emerge as valuable off-grid investment alternatives, confirming their role as an effective means of hedging against long-term uncertainties [23].
Regarding the different bidirectional charging technologies (V2G/V2B/V2H/V2X), V2G deserves special attention due to the fact that public charging points are typically connected to public grids. V2G describes the case when an EV is used as a storage system that can give power back to the grid. Thus, it can also supply energy in return to the grid in peak hours and could contribute, for example, in fulfilling the peak energy demands as the peaking plants [20][21][22][23][24][25][26][27][28][29][30][31][32][33].
Generally, V2G operation requires charging stations with some control and protection functions (mode three for alternating current (AC) or mode four for direct current (DC) charging, according to IEC 61851-1 standard) [22]. Thus, this operation is carried out by using the pre-installed inverters in the EVs when parked at the charging booths or through purposely installed mega chargers equipped with high-rated inverters [22][27]. Moreover, it can also be considered a distributed energy resource, as a fleet of a few advanced EVs can provide backup power to the grid in megawatts (MW) [27]. In the literature, several algorithms are proposed with the motivation to implement the V2G technology practically. There are certain scenarios that demonstrate that it is possible to use modern EVs to help stabilize the grid through the deployment of the V2G system [27]. However, still, the practical implementation of V2G technology is absent [25][27]. Battery degradation may be a significant barrier to V2G-based services, as the additional battery cycling caused by V2G could reduce the lifetime of the battery [22]. Moreover, coupling the deployment of V2G, V2H, and V2B could be one of the policies to increase the number of EVs, provide a more efficient interconnection between energy generation and consumption, decrease peak demand, and increase global energy efficiency [24]. From an economic point of view, Luo et al. [30] proved that the prospects of bi-directional use of EVs are challenged by the high investment and Operations and Maintenance (O&M) costs of bi-directional charging stations and the strength of future distribution systems, although allocation schemes in a bi-directional V2G environment are, in most scenarios, more cost-effective than those in a uni-directional V2G environment.

3.2. Charging Infrastructure and Service

ERS technologies can be categorized according to the type of energy transfer and placement relative to the road [5][34][35][36]. Therefore, six different groups can be identified: overhead conductive, road-bound conductive, road-bound inductive, road-bound capacitive, and road-side conductive [5][34][35][36]. Figure 42 presents a summary of those technologies followed by a description of them.
Charging tech
Figure 42.
Diagram of existing charging technologies for EV.
  • (a)
    Minimize grid losses;
    (b)
    Minimize voltage drop;
    (c)
    Maximize grid reliability;
    (d)
    Flexibility.
  • Costs: focused on the costs associated with the operation of the charging points in the grid from the DSO point of view.
    (a)
    Minimize grid connection costs;
    (b)
    Minimize grid expansion costs;
    (c)
    Minimize grid operation costs;
    (d)
    Minimize electricity generation costs;
    (e)
  • Plug-in chargers: it is widely recognized that slow chargers, mainly intended for private use, and fast chargers are the most prevalent types of EV chargers today [2][4][21][37]. Fast chargers are designed to reduce the charging time for EVs and have a rated power range from 150 to 400 kW. There is also specialized fast charging technology for high-power EVs on highways that can connect directly to the medium-voltage (MV) grid [5][36][38].
  • Overhead conductive or catenary charging: consists of two supply lines that are installed at a height of approximately 5 meter (m) above the pavement for electric roads, where the vehicle must be supplied with a connector similar to a current collector or a pantograph to connect it to the power supply lines [34][35][36][38].
  • Road-bound conductive: defined by having power conductors located close to the vehicle from the road surface. The electrical connection between the electric vehicle and the charging solution is established underneath the vehicle [34][35][36].
  • Road-side conductive: composed by the power conductors placed on the side of the road, keeping the road surface unchanged. Thus, this infrastructure could have lower maintenance and installation cost [36].
  • Road-bound inductive: the energy of the charging solution is transmitted from the road to the vehicle without the need for a conductive connection by using two sets of coils, primary (installed on the road) and secondary (installed under the vehicle) [5][35][36][38].
  • Road-bound capacitive: is characterized by the transfer of high-frequency power from the road to the vehicle through capacitive coupling between metal plates on the road and the vehicle [36].

3.2.1. Charging Station Operator (CPO)

The CPO can be described as a medium to large-scale electrical service provider and specialized service company for charging station installations, typically for fast chargers [37]. The CPO can manage the installation, operation, and maintenance of public charge points [37][39]. These CPOs typically manage different charging technologies; as presented in Figure 42, the leading technologies are plug-in charging, catenary charging, inductive charging, and Battery Swapping Station (BSS) [5][34][35][36][40]. Plug-in chargers are typically characterized by the power output and its type. Depending on the output power, there are various commercially available modes. Mode 1 (up to 3.7 kW) and Mode 2 (up to 22 kW) operate on AC, while Mode 3 can either use AC or DC [5]. Mode 3 chargers are considered fast chargers, and the power levels can range from up to 43.5 kW for AC 3-phase technology to up to 400 kW for DC [5][36][38]. Standards are being developed for high-power chargers up to 600 kW, Mode 4, with an increasing interest in what are referred to as mega-chargers up to 1 MW HDVs [5][41]. The International Electrotechnical Commission (IEC) and International Organization for Standardization (ISO) are harmonizing international standards [5][41]. ISO regulates electric shock safety, battery systems, fuel efficiency measurement, and communication compatibility between the vehicle and the power grid, whereas IEC is responsible for standardizing electrical components such as batteries and charging connectors [41].  Whereas the major IEC standards related to the EV charging system mainly consist of regulations for the charging interface that connects the vehicle to an external power supply, standards related to the charging devices, and the communication between the vehicle and the external device, are also relevant to the EV charging system [5][41]. These could be summarized in IEC 61851-21-1 and IEC 61851-24, which define the requirements for the conductive connection of an EV to an AC or DC supply, and the digital communication between a DC charging station and an EV, respectively [5][41].  Catenary charging is a method of charging electric vehicles on electric roads by using two supply lines that are placed above the road at a height of about 5 m, requiring the vehicle to use a connecting device that resembles a pantograph to establish contact with the supply lines [5][34][35][36][38]. Different studies that have analyzed this technology [34][35][36][38][42][43] generally acknowledge that catenary charging is mainly feasible for powering HDVs such as buses and trucks since it is infeasible to install the connection device in smaller vehicles. In addition, long stretches of more than 1 kilometer (km) can be electrified simultaneously, given that the catenary is difficult for humans or animals to unintentionally reach [36]. This technology is being evaluated on public roads in Sweden, where a 2 km stretch of highway is equipped with 650–750 V DC overhead power lines to which plug-in hybrid trucks equipped with pantographs are connected to run in full electric mode [36].  Inductive charging is a technology that transfers charging solution energy from the road to the vehicle without requiring a conductive connection. It uses two sets of coils, the primary (installed on the road) and the secondary (installed under the vehicle) [5][35][36][38]. The electromagnetic field is generated by a transmitter coil, which is typically integrated into the road, and a coupled receiver is installed on the vehicle, followed by a rectifier [5]. Moreover, inductive power transfer can also be designed for contactless or wireless power transfer, although very few authors consider wireless solutions [42][44][45][46][47].

3.2.2. Mobility Service Operator (MSO)

It an independent agent that interacts with these two operators by providing information about the energy management of the different charging stations, either given by the user when detecting an incident or by the service provider itself. For example, in [37][39][48][49][50][51][52][53], the authors analyze the influence that software regarding energy management could have on the power system and EV charging infrastructure. Therefore, MSO is responsible for offering the charging services, among other services and products related to end-users, prioritizing end-user comfort. Consequently, different studies have been carried out to analyze not only the benefits or its influence on the comfort-user side but also the influence that this software related to energy management could have on the electric system and the EV charging infrastructure. Furthermore, other studies focus on solving congestion at charging stations, i.e., reducing waiting time. Chowdhury et al. [48] introduces the challenges of modeling and sizing the capacity needed for EV charging infrastructure for systems with a slow dynamic component, including the potential for demand bursts during a peak hour interval. To address these concerns, a simulation tool focusing on a normal distribution representing events within a daily (24 h) or peak hour (rush hour) interval has been designed and integrated with a novel capacity planning methodology.

3.3. Power System Infrastructure

The power system infrastructure is composed of the distribution system and the transmission system. Distribution grids supply electricity to end-customers and are the connection points for industry and small and medium-scale distributed generation facilities, such as wind and solar farms. DSOs distribute electricity from the transmission grid to end-users, typically through MV networks (2 to 36 kV) and low-voltage (LV) (e.g., 50 Hz 230 V phase-to-ground in Europe or 60 Hz 110 V in the USA), [22]. The main concerns at the national or regional level are the efficient and secure operation of its power system. Calvillo and Turner [54] demonstrate the importance of the “smartness” and locating of EV charging for the cost of network reinforcement, which has an influence on the network investment and energy costs of different EV charging options. Several technical and economic factors must be considered to fully exploit the flexibility of electric vehicles [39]. In particular, a framework for purchasing flexibility by DSOs should be developed. Such a framework exists for the procurement of flexibility services through TSOs in the form of a balancing market [22][39]. Then, the TSOs present a plan of the balancing services (which includes the frequency regulation), the optimization of the network-wide costs of generation, and the support of the renewable energy sources [22]. EV integration into distribution systems is demanding, by reason of the further restrictions that EV charging infrastructure can introduce, and rewarding, as smart charging or V2G can provide more flexibility for improving operations and planning [46]. Thus, EVs are able to support DSOs with a variety of services, including investment planning, congestion management, voltage regulation, and backup power supply in operational time frames. 

3.3.1. Distribution System Operator (DSO)

Several studies analyzed the impact that electromobility, i.e., electric vehicles and EV charging infrastructure, could have on the distribution grid [9][10][13][22][30][32][46][55][56][57][58][59][60][61][62][63][64]. The widespread deployment of EV charging infrastructure has the potential to have an impact on power systems, particularly distribution networks, due to the increase in EV penetration rates. These impacts can be categorized as load and voltage issues, which can result in active power losses, congestion of distribution system assets, degradation and failure of equipment, and affect the quality of the service provided to end-users [22]. However, positive effects such as mitigating the variability in renewable energy production and reducing the cost of renewable energy externalities can be achieved by integrating EVs into the power system. The DSO has to address various challenges regarding EVs and EV charging infrastructure integration in the distribution grid. In the work of Gonzalez Venegas et al. [22], it is established that V2G-able EVs faces considerable regulatory and technical challenges to provide flexibility and recommends simplifying and standardizing connection procedures and adapted metering options. EVs can support DSOs with various services, including investment planning, congestion management, voltage regulation, and backup power in operational time frames. It also describes the main barriers to the implementation of EVs flexibility services in distribution networks, which are both economic and institutional. In the work of ur Rehman [27] is analyzed the V2G system as an enabler for electricity supply companies to further improve power quality, with enhanced efficiency on both transmission and distribution sides, eliminating the chances of generator overloading.

3.3.2. Transmission System

System-wide services such as frequency control reserves and energy arbitrage for TSOs can be provided by the flexibility of the EV [22], where energy arbitrage consists of capturing the value of price differences in the electricity market by adjusting the pricing process according to the strategies of TSOs in the electricity markets [5][22][39]. Moreover, Rauma et al. [65] studies EVs as a flexibility supplier with extensive commercial charging data, and introduces a method for identifying a load curtailment schedule with the least negative impact on charging service quality. It can serve the capacity market as a demand response [22]. Suggested methodologies may be beneficial to grid operations, reducing generation costs and facilitating renewable energy integration [22][66].

4. Analysis of Objectives for Agents and Operators in the Value Chain

Making the best or most effective use of a situation or resource is part of every optimization process. This is also true for part of the value chain, as different actors and agents will have particular objectives in their optimization process. The objective functions to be achieved by these different actors involved in the EV charging infrastructure value chain vary according to the particular requirements in operation or planning. The objectives are classified into three main blocks: electric vehicle, EV charging infrastructure and service, and finally, power system infrastructure. Figure 53 presents a scheme of the different operation objectives considering the actors described in the previous section.
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Figure 53.
Value chain actors’ main objectives.

Regarding the electric vehicle, the category of electric vehicle ecosystem has been divided between the objectives related to the vehicle itself, its components and specifications, and the customer. The targets considered for the EV user emphasize the main concerns that the final user could have, such as range anxiety and the waiting and charging time of the vehicle.

Secondly, EV charging infrastructure and service have two subsections relating to infrastructure, the CPO and service and the MSO. Both are involved in managing EV charging points; however, they focus on different objectives. On the CPO side, the objectives analyzed may vary depending on the overall objective, which is optimizing energy supply or costs. Regarding the MSO, the objectives are related to the management of the information coming not only from the CPO or DSO to operate the recharging points but also from the end-user itself through some software, such as an app.

The third block, concerning the power system infrastructure, defines the objectives of the DSO and the TSO. Both operators have similar objectives since both operate the grid but on different scales.

An Table 1 shoverview of the literature reviewed in this paper and s the major operating objectives analyzed therein is presented in Table 1, sorted by year of publication, where the “x” symbol indicates items that take account of the objectives listed, while the “-” symbol indicates those that do not. In order to explain some of the interesting methodologies used to achieve these operational objectives, the following sections describe some of the papers listed in the classification tables.

Table 1.

Overview of the objectives of existing literature works addressing the planning of electric vehicle charging infrastructure.

  • Minimize charging costs;
  • (f)
    Minimize carbon emission costs.
  • EV parameters: optimized the EV components and characteristics:
    (a)
    Battery size;
    (b)
    Battery lifetime;
    (c)
    Charging power.
  • EV user: focused on the EV user concerns and benefits:
    (a)
    Battery range;
    (b)
    Charging time;
    (c)
    Benefits.
  • ]
  • investigates an integrated framework for urban fast charging infrastructure. Ref
  • [81] notes that a wise choice of powertrain design is necessary to maximize the energetic performance achievable by a hybrid electric vehicle (HEV) as far as the on-road category is concerned. Thus, the model presented in that paper could be considered as an effective tool to support the HEV design optimization phases.
  • Energy demand:

4.4. Operating Principle of Distribution System Operator (DSO)

4.4.1. Energy Supply

  • EIntegration of renewable energy supplyies: optimizing tThe energy required to supply the grid to cover the energy demand for chargingwork in electric vehicles at a local or regional level.[17][22] examines the integration of electric vehicles into distribution systems and identifies the various technical, economic, regulatory, and user-related challenges and constraints.
    (a)
    Integration of renewable energies.
  • Battery lifetime: Ref. [22] indicates how battery degradation can represent a significant impediment to V2G-based services, having a major impact on the viability of business models for service flexibility. They propose algorithms and different practices to improve battery life with smart charging and V2G.
  • Ref. [
  • Network connection: focused on the issues that could influence the network due to the implementation of an electric vehicle charging infrastructure at a local or regional level.
    (a)
  • Charging power:
  • Ref. [19] develops a travel pattern model based on the MCS, and then builds a bi-level charging station planning model with that EV traffic data. Also, the study conducted in [85] considers different conditions concerning EV charging infrastructure. First, it considers the capacity planning of public charging stations as an essential factor in facilitating the broad market penetration of EVs. Therefore, an optimization model for charging station capacity planning is presented to maximize the FQoS.
 
  Electric Vehicle Ecosystem Charging Point Operator Mobility Service Operator

4.1.2. EV User

Some of the studies considered are analyzed for the different categories.
  • ] proposes to set out the requirements for the performance of a national electricity infrastructure suitable for the implementation of charging on the move. A simulation tool is presented that investigates the implementation of dynamic charging and the impact of system design variables from the estimation of the energy requirements of EVs also considering road traffic data for the predicted energy demand of Great Britain.
  • (a)
  • Minimize grid connection costs;
    (b)
    Minimize grid expansion costs;
    (c)
    Minimize grid operation costs;
    (d)
    Minimize electricity generation costs;
    (e)
    Minimize charging costs;
    (f)
    Minimize carbon emission costs.
Distribution System Operator
Transmission System Operator
Year Ref. (1) EV parameters (2) EV user (1) Energy supply optimization (2) Costs

4.4.12. Energy SupplyNetwork Connection

  • I
  • Mintegration of renewable energieimize grid losses: ThRef. work in [1770][22] examines the integration of electric vehicles into distribution systems and identifies the various technical, economic, regulatory, and user-related challenges and constraints. presents an automated network planning tool that finds the optimal expansion planning measures for future network states aiming to reach a valid state without overloaded electrical equipment and without voltage limit violations at minimum cost. Therefore, the investment has a greater effect on preserving the network structure, since the tool prioritizes replacing equipment with shorter lifetimes, which present a higher risk of failure, and considers the cost of investing in new equipment.
  • Minimize voltage drop: In [82] a new approach has been proposed for determining the suitability of a distribution grid for accommodating EVs, which is not dependent on specific charging patterns and can be applied at any time. This method utilizes an MCS to evaluate the grid’s capacity for EV charging. The results of the study have revealed that the grid’s ability to accommodate EV charging is influenced by factors such as the minimum background voltage, maximum power consumption, and potential planning risks. This was demonstrated through its application to two existing LV networks.
  • Maximize grid reliability: Ref. [24] presents a new approach for the combined use of V2H and V2B technology in various scenarios, such as when employees own EVs, when a company operates a shared fleet of EVs, or in a leasing setup, among others. This approach leverages the energy storage capabilities of EVs to introduce a significant amount of electrical storage into the grid system without the need for additional investments, thus offering greater flexibility and mobility. The proposed technology has the potential to reduce peak demand on the grid and increase the use of renewable energy sources. The results indicate that the methodology is viable and can be applied to other cases, significantly contributing to improved energy efficiency, reduced peak demand in buildings, and increased EV adoption in transportation to workplaces.
  • Flexibility: Refs. [17][22] highlights that EVs can help postpone or prevent the need for costly upgrades in uncertain situations, thus decreasing the risk of unproductive investments. Therefore, EVs can help optimize the use of existing infrastructure by providing peak shaving and voltage support services, and by providing fault restoration or isolation services to minimize non-served power. Furthermore, flexibility platforms have the potential to create new business models.
(1) User comfort
(2) Energy management
(1) Energy supply
(2) Network connection (3) Costs (1) Energy generation (2) Network impact (3) Costs
2014 [67] - x x x x - - - - - - -
2015 [18] - x - - x x - - - - - -
2016 [68] x x x x x - - - - - - -
2016 [69] - x x x x x - - x - - -
2017 [70] - - - - - - x x x - - -
2017 [71] - - - - - - x x - x x -
2017 [35] x x x x - - - - x - - -
2017 [34] x - x x - - - - x - - -
2017 [72] x - x - - x - x - - x -
2017 [73] x x - - x - - - - - - -
2018 [7] x x x x x x - x x - - -
2018 [14] - - x - x x x x - - - -
2018 [19] - x x x x x - x x - x -
2018 [74] x x x - x - - - - - - -
2018 [75] x x x x x x - x x - x x
2019 [47] - - x x x - - - x - - -
2019 [30] x x x x x x x x x - - -
2019 [32] x x x x x x - x x - - -
2019 [76] x x x x - - x x x x x -
2019 [11] - x x x x x - x x - x -
2019 [77] x x x x - x x x x - x x
2020 [78] x - x x x x - x - - - -
2020 [79] x x x - x x - x x - - -
2020 [31] x x x x x x - x x - x -
2020 [80] x x x x - x - - x - - -
2020 [4] x x - - x - - - - - - -
2020 [33] x x x - - x x x x - - -
2020 [15] x - - x - - - - x - x x
2020 [6] - - x - - x - x - - x -
2020
  • Battery range:
  • Ref.
  • [
  • 7] presents an overview of different studies examining infrastructure needs in relation to consumer preferences for this charging infrastructure, and the ways in which users interact with and exploit this infrastructure.
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2021 [24] x x x x x x - x x - x -
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  • Charging time:
  • Papers
  • [
  • 7][68][67][96] analyze the problem of charging time that the EV user may have and how to address it. In [67], a mixed-integer programming model is developed to solve the problem of placing vehicle charging stations and maximizing the number of people who can accomplish round-trip itineraries.
  • Benefits: In [17][22], from the end-user point of view, the benefits that can be obtained, such as bill optimization, self-consumption, and standby power, among others, are analyzed. The study also concludes that, while economic incentives can help increase user acceptance, other approaches, such as awareness raising, load data sharing, and even gamification, can increase end-user engagement.

4.1.3. Overview of EV Ecosystem Operating Principles and Opportunities for Improvements

Consumer preferences for charging locations, specifically convenient and cost-effective options like home and workplace charging, are important considerations in developing effective EV charging infrastructure [7][8][16][17][22][49]. Policy mechanisms such as cost-reduction initiatives and consumer awareness campaigns have proven effective in promoting EV adoption [74]. Battery analysis plays a critical role in optimizing charging infrastructure and addressing challenges like congestion and charging delays [68][75]. Limited availability of charging infrastructure, particularly in certain regions or apartment complexes, can hinder the convenience and accessibility of EV charging [7]. Range anxiety, the fear of running out of battery power, is another concern that affects EV user confidence and their willingness to adopt electric vehicles. The fundamental questions of EV cost and range can only be answered with the support of a robust EV charging station infrastructure [9]. Thus, challenges remain, including limited charging infrastructure availability and range anxiety, impacting user convenience and confidence [7][9]. Despite these challenges, the EV industry offers significant benefits, such as reduced pollution, noise emissions, and oil dependence, contributing to transportation decarbonization [73][74]. Strategies have been proposed to enable EVs to plan their charging/discharging based on driving routes and cost considerations, while EV charging stations select the optimal electricity supplier [31].

In summary, understanding consumer preferences for charging locations, implementing effective policies, and addressing challenges in battery technology and user concerns are crucial for the successful adoption of EVs and the development of sustainable charging infrastructure.

4.2. Operating Principle of Charging Station Operator (CPO)

In this part, the objectives pursued concerning the CPO are analyzed, so the main objectives functions for the hardware part of the EV charging infrastructure will be studied, as described in Figure 53.
  • Minimum power
  • : Ref. [78] presents a technique for modeling the electrical load of an electric road equipped with a dynamic wireless power transfer (DWPT) system and of the households in the nearby area.
  • Minimize grid losses;
  • (b)
    Minimize voltage drop;
    (c)
    Maximize grid reliability;
    (d)
    Flexibility.
  • Monitoring charger status
  • : Ref. [31] designs a scheduling scheme that centered mainly on guaranteeing the rewards of all agents (e.g., EVs, charging stations, and ESs) participating in V2G and G2V operation. Thus, EVs individually plan their charging/discharging based on the shortest driving route and cost/benefit offered by charging stations. Moreover, with this approach, each charging station could find the optimal electricity supply to purchase electricity from the wholesale market.
  • Costs: focused on the costs associated with the operation of the charging points in the grid from the DSO point of view.
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2022 [5] x x x x x x x x x - - -
2022 [95] x x x x - - x x x x x -
2022 [23] x x x x - x - x x - x x
2022 [12] - x x x x x - - - - - -
2023 [49] - x x x - x - - - - - -

 

4.1. Operating Principle of Electric Vehicle Ecosystem

Typically, EV targets result from compiling several primary indicators, including battery power, range, charging lead time, etc. In the following, the different subcategories are used to refer to the objectives or characteristics of each subcategory of objectives.

4.1.1. EV Parameters

Some of the papers analyzed for the different categories are described below.
  • Battery size: To focus on the issue of range anxiety, Ref. [8

4.4.23. Network ConnectionCosts

  • Minimize grid lconnection cossests: Ref. [7032] prsuggesents an automated network planning tool that finds the optimal expansion planning measures for future network states aiming to reach a valid state without overloaded electrical equipment and without voltage limit violations at minimum cost. Therefore, the investment has a greater effect on preserving the network structure, since the tool prioritizes replacing equipment with shorter lifetimests a multi-year high penetration management (HPM) plan using Cost Benefit Analysis (CBA) to minimize the financial effects of using PEV charging strategies in a distribution system. The plan is based on the optimal allocation of two different smart charging strategies at different points in the distribution system over the planning period. To evaluate the financial impact, the study takes into account a cost called “Cost of infrastructure upgrade”, which present a higher risk of failure, and considers the cost of investing in new equipmentrefers to the cost of upgrading distribution lines due to increased congestion caused by increasing non-PEV and PEV loads over the planning period.
  • Minimize voltage droprid expansion costs: InRef. [8233] a new approach has been proposed for determining the suitability of a esents a plan for expanding a distribution grid for accommodating EVs, which is not dependent on specific cnetwork to include EV charging patterstations and can be applied at any time. This methodsolar panels, which utilizes an MCS to evaluate the grid’s capacity for EVV2G technology in the operation of the charging. The results of the study have revealed that the grid’s ability t stations to minimize expansion costs through smart V2G operation, taking into accommodate EV charging is influenced by factors such as the minimum background voltage, maximum power consumption, and potential planning risks. This was demonstrated through its application to two existing LV networksunt uncertainties in solar energy availability. The model is formulated as an optimization problem and aims to minimize expansion costs by optimizing EV operation patterns while satisfying all technical constraints. The results obtained from the model indicate that the V2G operation in the charging stations reduces expansion costs by 450%.
  • Maxinimize grid reliabilityoperation costs: Refs. [2417
  • (b)
  • Min. power;
  • (c)
  • Monitoring charger status.
  • Costs: focused on the costs associated with the implementation or operation of the charging points:
    (a)
    Electricity costs;
    (b)
    Infrastructure costs.
Ideally, an optimal EV charging infrastructure results from a combination of factors, including the energy required to meet the demand, monitoring charger status to optimize their use, the associated costs, etc. The different sub-categories of objectives are discussed in more detail below.

4.2.1. Energy Supply Optimization

Some of the papers considered are analyzed for the different categories as examples.

4.2.2. Costs

From the objectives described in Table 1 with respect to the costs that the CPO may consider, some of the papers analyzed for the different categories are described as examples.
  • Electricity costs: A scheduling algorithm for the charging and discharging of EVs in public parking lots is proposed in [88]. The approach includes a demand response-based method to sell or purchase energy from electric vehicles during high and low price periods, respectively, based on maximizing the benefits of parking. The optimization procedure aims to maximize parking, vehicle, and distribution network benefits to reduce distribution network costs and increase financial benefits. In this mechanism, the strategy of load scheduling plays an important role.
  • Infrastructure costs: Ref. [68] proposes an optimal configuration for PEV charging infrastructure along a corridor, aiming to minimize the overall cost of the system. The study emphasizes the importance of considering the balance between the cost of charging delays, infrastructure expenses, and battery costs. It highlights that, while charging delays and infrastructure costs are comparable, the cost of batteries is significantly higher, underscoring the need for cost-effective decision-making in planning EV charging infrastructure.
  • Comfortable access: In [14] using a system dynamics model, two cause–effect pathways have been identified in the driving mechanism, which is mostly controlled by the key parameters of “Distribution of charging station” and “Planning of energy storage system”. It is determined that, in constructing the hybrid model, the load satisfaction level and the integration of distributed renewable energy were mainly considered to make the distribution plan of charging stations more optimal.
  • Mapping of accessible charging points: Ref. [47] describes the demands on the performance of a national electricity infrastructure for the implementation of on-road charging. Thus, a potential charging scheme is presented that includes infrastructure such as 30 kW chargers, each 1.5 m long, installed every 2.1 and 4.3 m on highways and rural roads, respectively.
  • Convenient billing: In [5], a decision support tool is proposed for the planning of high-power charging infrastructure for EVs, considering the interests of all stakeholders, including DSOs, end-users, and service providers. The authors present guidelines and recommendations for a cost-effective and comprehensive charging infrastructure planning process by reviewing different methodological approaches proposed in recent research.

4.3.2. Energy Management

Some of the studies considered are analyzed for the different categories.
  • Management energy consumption: Refs. [37][39][50][51][52][53] analyze the influence that software regarding energy management could have on the power system and EV charging infrastructure. Ref. [53] introduces an oemof as a novel method for modeling, representing, and analyzing energy systems, offering a collection of tools for creating detailed energy system models through collaborative development using open processes.
  • Minimum waiting time to access charger: Ref. [27] suggests an optimal method for integrating electric vehicles into the smart grid using V2G technology and a network of charging stations, involving an hierarchical bi-directional aggregation algorithm.
  • Energy supply optimization: optimizing the EV charging demand:
    (a)
    Energy demand;

4.2.3. Overview of CPO Operating Principles and Opportunities for Improvements

CPOs face several challenges for EV adoption. Researchers have developed optimization models and heuristic approaches to maximize the number of users served and determine suitable locations for charging stations, taking into account budget constraints and round-trip itineraries. These approaches address the challenges of underdeveloped charging infrastructure and the planning of charging stations to support large-scale EV deployment [11][67][75]. One major challenge is the selection of optimal charging station locations, considering budget constraints and the impact on the number of potential EV users served [67][75]. Limited existing infrastructure poses limitations in realizing the optimal placement of charging stations, as the road and grid infrastructure were not developed with EVs in mind [75]. 

Therefore, the development of a well-established charging infrastructure is crucial to address concerns such as underdeveloped charging infrastructure and charge scheduling in charging stations, which requires exploring different approaches, objective functions, and optimization algorithms [75][9].

Thus, while the number of EVs continues to rise, the planning of the charging infrastructure must consider the power demand and load patterns. DWPT systems, such as charge-on-the-move technology, offer a solution for EV charging. Modelling the load from electric roads equipped with DWPT systems and existing household load patterns is crucial for long-term grid planning. Understanding the peak loads from different sources and their temporal variations enables efficient grid operation and planning [78].

To overcome these shortcomings, researchers have proposed optimization models and simulation tools to address the challenges of charging infrastructure planning and optimal placement of charging stations [30][78][49]. The integration of information-sharing systems to provide waiting time predictions to users has also been identified as a way to enhance the performance and utilization of charging systems [49].

In conclusion, from the perspective of CPOs, the shortcomings in the implementation of EVs revolve around the selection of optimal charging station locations, underdeveloped infrastructure, charge scheduling, integration with the power grid, and the need for information sharing. Addressing these challenges through optimization models, smart charging concepts, and information-sharing systems will be crucial in building a robust charging infrastructure to support the widespread adoption of EVs.

 

4.3. Operating Principle of Mobility Service Operator (MSO)

4.3. Operating Principle of Mobility Service Operator (MSO)

  • Maximize the use of charging points:
  • Ref.
  • [88] addresses the problem of charging and discharging electric vehicles in public parking lots with the goal of maximizing the benefits for the parking lot. The proposed solution is a scheduling algorithm that uses an optimization process to coordinate the charging and discharging of vehicles to take advantage of high and low energy prices. The algorithm’s main objective is to create a system that benefits the parking lot, vehicles, and distribution network as much as possible.

4.3.3. Overview of MSO Operating Principles and Opportunities for Improvements

From an MSO perspective, the widespread adoption of EVs is hindered by the limited travel range and inadequate charging infrastructure [19]. Zang et al. [19] propose a bi-level planning model for charging stations that maximizes the travel success ratio and considers the placement of charging stations on users’ travel routes utilizing a queuing theory and a greedy algorithm to determine station capacity. Thus, comfortable access and sizing of charging stations, taking into account factors such as underdeveloped infrastructure, are key considerations for the large-scale deployment of EVs [75].

Comprehensive evaluation models and methods have been proposed to assess the network planning of charging stations, considering various factors such as spatial and temporal characteristics, user feedback, and operational impacts [16].

In addition, comfortable access and convenient billing are crucial perspectives in charging infrastructure [19][75][30][11][8][37][27][88][16]. Users require easy access to charging points, and efficient billing systems contribute to user satisfaction. Zang et al. [19] address this challenge, where the placement of charging stations on travel routes maximizes the travel success ratio, ensuring convenient access for users.

Energy consumption management and minimizing waiting time are important aspects of charging infrastructure [88]. Moreover, maximizing the use of charging points is crucial to ensure the efficient utilization of the infrastructure [19].

To summarize, even though there has been significant progress in the EV charging infrastructure planning, the studies conducted in this area still have addressed crucial aspects such as the placement, sizing, and joint deployment of charging stations. The consideration of factors such as user travel patterns, queuing theory, and distributed generation resources have been critical in this regard. However, certain limitations remain in terms of outdated infrastructure and a lack of focus on managing energy consumption, minimizing waiting time, and maximizing charging station utilization. It is recommended that future research should aim to overcome these limitations and provide comprehensive solutions for establishing an efficient and user-friendly EV charging infrastructure.

4.4. Operating Principle of Distribution System Operator (DSO)

 
  • ]
  • presents a new approach for the combined use of V2H and V2B technology in various scenarios, such as when employees own EVs, when a company operates a shared fleet of EVs, or in a leasing setup, among others. This approach leverages the energy storage capabilities of EVs to introduce a significant amount of electrical storage into the grid system without the need for additional investments, thus offering greater flexibility and mobility. The proposed technology has the potential to reduce peak demand on the grid and increase the use of renewable energy sources. The results indicate that the methodology is viable and can be applied to other cases, significantly contributing to improved energy efficiency, reduced peak demand in buildings, and increased EV adoption in transportation to workplaces.
  • [22] examines various network tariffs that are used to cover the costs of operating and planning distribution and transmission grids. It notes that tariffs can vary based on time and location, with different rates for different time periods such as peak and off-peak hours or for different regions of the grid. Ref. [79] investigates EV scheduling strategies using CBA to obtain an optimal scheduling scheme. Thus, in order to maximize the benefits of integrating EVs into the distribution system, by applying Active Power Dispatch (APD) and Reactive Power Dispatch (RPD) to minimize system losses using V2G, the charging of EVs must be coordinated with the available V2G technology.
  • FMinimize elexibilityctricity generation costs: RThefs. authors of [1725][22] highlights that EVs can help postpone or prevent the need for costly upgrades in uncertain situations, thus decreasing the risk of unproductive investments. Therefore, EVs can help optimize the use of existing infrastructure by providing peak shaving and voltage support services, and by providing fault restoration or isolation services to minimize non-served power. Furthermore, flexibility platforms have the potential to create new business models. evaluate the economic benefits of incorporating EVs into the grid using an electricity cost model. They examine cost reduction under three different charging modes: random charging, controlled charging, and V2G charging. The results indicate that the adoption of EVs can significantly enhance the power grid’s load factor and decrease the cost of power supply. Additionally, the study found that the impact of the different charging modes on the power grid varies.
  • Minimize charging costs: The authors of [83] develop a method for analyzing the infrastructure of EV FCSs using an agent-based modeling approach. The algorithm takes into account EV user behavior and the variable energy pricing of high-power charging at different FCSs to assess the advantages of a dynamic pricing strategy for utilizing EV charging as a means of spatial flexibility. The findings reveal that dynamic pricing can be an effective means of influencing EV charging behavior and enhancing the flexibility of the active management of distribution systems.
  • Minimize carbon emission costs: The authors of [81] introduce a novel method for forecasting carbon dioxide emissions from HEVs. The model, which is based on deep neural networks, uses a pipeline of neural networks and a Dynamic Programming (DP) algorithm to identify the relationship between the design features of the HEV and the main outputs of the DP, including the powertrain feasibility and CO2

4.4.3. Costs

  • emissions from the tailpipe.
  • Minimize grid connection costs: Ref. [32] suggests a multi-year high penetration management (HPM) plan using Cost Benefit Analysis (CBA) to minimize the financial effects of using PEV charging strategies in a distribution system. The plan is based on the optimal allocation of two different smart charging strategies at different points in the distribution system over the planning period. To evaluate the financial impact, the study takes into account a cost called “Cost of infrastructure upgrade”, which refers to the cost of upgrading distribution lines due to increased congestion caused by increasing non-PEV and PEV loads over the planning period.
  • Minimize grid expansion costs: Ref. [33] presents a plan for expanding a distribution network to include EV charging stations and solar panels, which utilizes V2G technology in the operation of the charging stations to minimize expansion costs through smart V2G operation, taking into account uncertainties in solar energy availability. The model is formulated as an optimization problem and aims to minimize expansion costs by optimizing EV operation patterns while satisfying all technical constraints. The results obtained from the model indicate that the V2G operation in the charging stations reduces expansion costs by 450%.
  • Minimize grid operation costs: Refs. [17][22] examines various network tariffs that are used to cover the costs of operating and planning distribution and transmission grids. It notes that tariffs can vary based on time and location, with different rates for different time periods such as peak and off-peak hours or for different regions of the grid. Ref. [79] investigates EV scheduling strategies using CBA to obtain an optimal scheduling scheme. Thus, in order to maximize the benefits of integrating EVs into the distribution system, by applying Active Power Dispatch (APD) and Reactive Power Dispatch (RPD) to minimize system losses using V2G, the charging of EVs must be coordinated with the available V2G technology.
  • Minimize electricity generation costs: The authors of [25] evaluate the economic benefits of incorporating EVs into the grid using an electricity cost model. They examine cost reduction under three different charging modes: random charging, controlled charging, and V2G charging. The results indicate that the adoption of EVs can significantly enhance the power grid’s load factor and decrease the cost of power supply. Additionally, the study found that the impact of the different charging modes on the power grid varies.
  • Minimize charging costs: The authors of [83] develop a method for analyzing the infrastructure of EV FCSs using an agent-based modeling approach. The algorithm takes into account EV user behavior and the variable energy pricing of high-power charging at different FCSs to assess the advantages of a dynamic pricing strategy for utilizing EV charging as a means of spatial flexibility. The findings reveal that dynamic pricing can be an effective means of influencing EV charging behavior and enhancing the flexibility of the active management of distribution systems.
  • Minimize carbon emission costs: The authors of [81] introduce a novel method for forecasting carbon dioxide emissions from HEVs. The model, which is based on deep neural networks, uses a pipeline of neural networks and a Dynamic Programming (DP) algorithm to identify the relationship between the design features of the HEV and the main outputs of the DP, including the powertrain feasibility and CO2
  • emissions from the tailpipe.

4.4.4. Overview of DSO Operating Principles and Opportunities for Improvements

The integration of EVs into distribution systems presents challenges and opportunities for DSOs. Various studies have explored the state of the art and methodologies used in this domain. Some studies emphasize the importance of network expansion planning, considering scenarios of additional distributed generation installation and the provision of balancing power [70][71]. Others propose multi-objective functions for EV charging station allocation, optimization models that jointly deploy EV charging stations and distributed generation resources, or investigate the impact of integrating EVs and charging infrastructure on distribution networks [14][30]. There have also been discussions on the potential of EVs to offer flexibility services and optimal siting of EV charging infrastructure [17][22]. 

The authors of [76][33] investigate the impact of integrating EVs and charging infrastructure on distribution networks, addressing load flow patterns, grid operation, and network expansion cost reduction through V2G technology. Mulenga et al. [82] develop a stochastic approach to estimate EV charging hosting capacity in distribution networks, considering uncertainties and charging sizes. Non-linear optimization models for coordinated EV charging and optimization strategies for distributed generation and EV programs have been proposed as well [86]. Some studies analyze the impact of EV integration on power quality, grid stability, and the interaction between EVs and renewable energy sources [90][79]. Galadima et al. [77] discuss optimal siting of EV charging infrastructure, considering its impacts on the distribution network and the integration of renewable energy resources. Lastly, the literature on the optimal planning of charging stations emphasizes the need for coordinated planning between transportation and distribution networks [10].

Moreover, optimizing grid connectivity, expansion, operation, electricity generation, charging, and carbon emissions costs is crucial. Minimizing grid connection costs is essential to maintain affordable power quality, and automated network expansion planning tools aid in long-term planning [70][77][10][9]. Optimizing the allocation of charging stations and distributed generation resources, considering vehicle-to-grid capabilities, minimizes electricity generation costs [30][17][22][79][10][9]. Minimizing charging costs requires considering factors such as charging satisfaction and stable power system operation [14][76][77][90][9][10][95].

While significant progress has been made in understanding and addressing the challenges of integrating EVs into distribution systems, several shortcomings remain. One common limitation is the lack of widespread implementation of bidirectional chargers and communication protocols for V2G technology, as noted by [17][22] This limits the potential for EVs to provide flexibility services to the electricity system. Additionally, economic and regulatory barriers, such as a lack of regulatory frameworks to value flexibility at the distribution level, create uncertainty regarding the value of flexibility services offered by EVs [17][22][9][5][95][31]. Furthermore, the optimal siting and sizing of EV charging infrastructure is a complex task that requires coordination between transportation and distribution networks, but the literature lacks a comprehensive and unified approach to address this challenge [10].

4.5. Operating Principle of Transmission System Operator (TSO)

The last category will analyze the objectives sought in relation to the TSO; thus, the main objectives of an EV charging infrastructure’s impact on the grid at a national level will be studied, as described in Figure 53.
In this case, among the objectives related to the TSO can be found the integration of renewable energies to support the grid (at the national level), minimizing the impact that the charging infrastructure can have on the grid, how flexibility can affect the grid, as well as the costs associated to a connection, operation, expansion, etc.

4.5.1. Energy Generation

  • Integration of renewable energies: Ref. [95] examines a way to efficiently select the most suitable distribution route for mobile power supply from various options, by maximizing customer demand and minimizing costs, in order to increase the usage of PV power. It incorporates an energy blockchain to enhance the security of electricity transactions and designs the supply chain for PV-energy storage–charging. Thus, the study verifies the effectiveness and applicability of the proposed method by conducting parameter variation of NSGA-II and comparing it with two other algorithms, namely Genetic Algorithm (GA) and Multi-Objective Particle Swarm Optimization (MOPSO).
  • Energy generation: optimizing the energy required to supply the grid to cover the energy demand for charging electric vehicles at a national level.
    (a)
    Integration of renewable energies.
  • Network impact: focused on the impact that the implementation of electric vehicle charging infrastructure could have on the grid at a national level.
    (a)
    Minimize EV charging infrastructure impact;
    (b)
    Flexibility.
  • Costs: focused on the costs associated with the operation of the charging points in the grid from the TSO point of view.
    (a)
    Minimize grid connection costs;
    (b)
    Minimize grid expansion costs;
    (c)
    Minimize grid operation costs.

4.5.2. Network Impact

  • Minimize EV charging infrastructure impact: Ref. [72] studies how energy demand fluctuates over time and location on a specific road and evaluates the effect of an electrified road on the stationary electricity system, considering different options for electrification and drive trains. The modeling results show that if static or electric road systems were implemented, the peak power demand for the hour of the regional power system sizing could potentially increase by 1–2%, assuming full electrification of the current traffic flow, which is comparable to that of a large industry. In addition, Ref. [16] proposed a comprehensive system and evaluation method for EV charging networks under the coupling of the road network and electric power system, by developing an EV displacement model based on the displacement probability matrix to analyze the spatial and temporal characteristics of EVs analyzing the coupling relationship among users, charging network, road network, and electric power system.
  • Flexibility: In [27], for the integration of EVs in the Smart Grid (SG) using V2G technology through a charging station network, an optimal hierarchical bidirectional aggregation algorithm is proposed. The developed model predicts the power demand and performs day-ahead (DA) load scheduling in the SG by optimizing the charging/discharging tasks of EVs as a voltage and frequency stabilization tool in the SG. Ref. [28] proposes a multi-stage system framework to account for an integrated EV dynamic wireless charging system in a smart city considering both optimal placement strategy for installing charging points in a city road network and dynamic V2G scheduling.

4.5.3. Costs

  • Minimize grid connection costs: Ref. [23] presents a multi-stage probabilistic planning framework, which can determine optimal investment strategies to minimize expected system costs and reduce the risk of failed investments. It shows that G2V, V2G, and V2B are effective off-grid alternatives to conventional reinforcement, providing significant economic savings and hedging against uncertainty.
  • Minimize grid expansion costs: Ref. [15] is a study on the feasibility of an electric road system in Norway and Sweden. The analysis takes into account factors such as traffic volume, the potential for reducing CO2
  • emissions, and infrastructure costs to identify the most beneficial roads and vehicle types for electrification. The results show that 70% of the traffic and 35% of the total vehicle kilometers could be electrified by electrifying 25% of the selected roads. However, the investment in the infrastructure would be substantial, with an estimated cost in the range from EUR 2700 to 7500 million, assuming an ERS investment cost of EUR 0.4 to 1.1 million per km.
  • Minimize grid operation costs: Ref. [23] suggests G2V, V2G, and V2B investment and operation schemes for the problem of planning large-scale, long-term network expansion under multi-dimensional uncertainties. Ref. [16] presents a multilevel stochastic planning framework that can determine an optimal investment strategy that minimizes expected system costs and reduces stranded investment costs.

4.5.4. Overview of TSO Operating Principles and Opportunities for Improvements

The integration of EVs into power distribution systems presents challenges for TSOs [76]. Existing research has focused on user equilibrium models to analyze the flow of vehicles and electricity in these systems, aiding in long-term planning and short-term operation [76]. The implementation of an ERS in Norway and Sweden has demonstrated potential CO2 emissions mitigation through partial or complete electrification [15]. V2Xs strategies have been suggested to increase EV penetration, reduce peak demand, and enhance energy efficiency [24]. However, there are some shortcomings. Integrated modeling approaches considering both transportation and power distribution networks are lacking, limiting the understanding of their combined effects [10]. Deregulation of the electricity market and supportive government measures are needed for the active participation of EVs as a power source [25]. Consideration should be given to the increase in regional peak loads and the trade-off between national and county-level optimization [92]. Accurate representations of mobility and charging patterns specific to each country are crucial, along with mechanisms for smart V2G interactions [91].

While the studies provide valuable insights, further research is necessary to address the identified shortcomings and enhance the understanding of the interplay between transportation systems, power distribution systems, and EV integration [76][31][15][25][24]. Transmission system operators can benefit from a more comprehensive understanding and integrated modeling approaches to optimize power grid operation and planning in the context of increasing EV adoption.

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