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Review

Electric Vehicle Integration in Coupled Power Distribution and Transportation Networks: A Review

by
Jingzhe Hu
1,
Xu Wang
2,* and
Shengmin Tan
3
1
Engineering Training and Innovation Education Center, Shanghai Polytechnic University, Shanghai 201209, China
2
Key Laboratory of Control of Power Transmission and Conversion, Shanghai Jiao Tong University, Shanghai 200240, China
3
State Grid Yangzhou Power Supply Company, Yangzhou 225100, China
*
Author to whom correspondence should be addressed.
Energies 2024, 17(19), 4775; https://doi.org/10.3390/en17194775
Submission received: 9 August 2024 / Revised: 17 September 2024 / Accepted: 20 September 2024 / Published: 24 September 2024
(This article belongs to the Special Issue Advanced Optimization and Control Strategies of Electric Vehicles and Green Energy Systems)
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Abstract

:
Integrating electric vehicles (EVs) into the coupled power distribution network (PDN) and transportation network (TN) presents substantial challenges. This paper explores three key areas in EV integration: charging/discharging scheduling, charging navigation, and charging station planning. First, the paper discusses the features and importance of EV integrated traffic–power networks. Then, it examines key factors influencing EV strategy, such as user behavior, charging preferences, and battery performance. Next, the study establishes an EV charging and discharging model, with particular emphasis on the complexities introduced by factors such as pricing mechanisms and integration approaches. Furthermore, the charging navigation model and the role of real-time traffic information are discussed. Additionally, the paper highlights the importance of multi-type charging stations and the impact of uncertainty on charging station planning. The paper concludes by identifying significant challenges and potential opportunities for EV integration. Future research should focus on enhancing coupled network modeling, refining user behavior models, developing incentive pricing mechanisms, and advancing autonomous driving and automated charging technologies. Such efforts will be essential for achieving a sustainable and efficient EV ecosystem.

1. Introduction

EVs offer significant advantages over traditional internal combustion engine vehicles in terms of operating costs, energy efficiency, and reducing greenhouse gas (GHG) emissions. It is estimated that 54% of vehicle-owning U.S. households would experience savings with annual reductions of more than 2.3 metric tons of CO2 per household and a decrease in energy burden of over 0.6% [1]. These benefits make EVs an important tool in addressing urban air pollution and climate change. The International Energy Agency (IEA) forecasts that the number of EVs on the road could increase from approximately 2 million in 2018 to around 280 million by 2040. More specifically, the number of plug-in electric vehicles (PEVs) is expected to reach about 130 million by 2030 [2,3]. The global EV market is growing rapidly.
The interaction of the PDN and the TN is crucial due to the rising adoption of EVs and the push towards decarbonization. This interaction offers significant environmental benefits by reducing GHG emissions and leveraging renewable energy for vehicle charging [4]. Economically, it optimizes energy use and allows shared investments in infrastructure, enhancing system efficiency [5]. Moreover, it improves grid resilience, as EVs can act as mobile energy storage during outages, and fosters technological advancements in smart charging and vehicle-to-grid (V2G) technologies [6].
Research in the field of EV integration has made significant progress, the state of the art in this field includes:
  • Charging and Discharging Scheduling: Studies have explored different charging models, pricing mechanisms, and integration approaches to optimize EV charging and discharging. These include bi-level models considering the PDN and EV objectives, various pricing strategies, and integration approaches [7,8];
  • Charging Navigation: Researchers have developed navigation models that incorporate real-time traffic information and user preferences to optimize charging routes and minimize charging costs and waiting times. These models consider multiple entities such as the TN, the PDN, EVs, and EV stations, and employ advanced optimization techniques to address the complexities of EV charging navigation;
  • Charging Station Planning: Studies have focused on optimizing the placement and sizing of charging stations to maximize captured traffic flow and minimize power loss in PDNs. This includes planning for different types of charging stations and integrated charging station types.
This paper aims to contribute to the field of EV integration by providing a comprehensive review of the state of the art and identifying key research gaps and future directions. The paper focuses on three key areas:
  • Charging and Discharging Scheduling: We discuss the complexities introduced by factors such as pricing mechanisms and integration approaches, and explore potential future research directions such as EV cluster charging strategies and incorporating weather conditions and road types into the models;
  • Charging Navigation: We highlight the importance of real-time traffic information and the integration of power, transportation, and information networks for accurate and efficient charging navigation. We also discuss the challenges posed by uncertainties and propose potential solutions such as integrating more real-time information and considering network latency in path planning;
  • Charging Station Planning: We explore the importance of multi-type charging stations and the impact of uncertainty on charging station planning. We propose future research directions such as integrating uncertain factors such as charging methods and energy consumption patterns into the models.
This paper will offer a thorough examination of the key challenges that are receiving focused research attention, concentrating on the optimization of charging and discharging scheduling, charging navigation and optimal planning of charging station. The remainder of this paper is organized as follows. Section 2 describes the coupled traffic–power system. Section 3 provides a classification of the existing works in EV charging and discharging scheduling. Section 4 provides a classification of the existing works in charging navigation. Section 5 reviews the planning of charge stations. Section 6 identifies some potential research areas. Section 7 concludes this paper.

2. Coupled Traffic–Power Networks

The increasing electrification of transportation systems and the growing interdependence of traffic and power networks necessitate a closer examination of their integrated behavior. This section explores the characteristics of the coupled traffic–power network, and the importance of EV integration and its influence factors.

2.1. Characteristics of Coupled Traffic–Power Networks

As the electrification of transportation systems accelerates, the connection between the PDN and the TN is becoming increasingly integrated. The coupling of the two systems occurs at multiple levels: physical, informational, and control.
At the physical level, EVs and charging stations serve as critical nodes. The charging behavior of EVs introduces a significant load that cannot be ignored by the PDN, while their movement patterns constitute a form of traffic flow within the TN. Charging stations not only represent the entry points for EV charging loads into the PDN, but also function as essential nodes, which influence the traffic flow of EVs. At the control level, it is feasible to achieve integrated management of both the PDN and the TN. For instance, in optimizing PDNs, the charging status of EVs, including the geographic distribution of charging stations, must be considered. Similarly, when planning EV charging schedules and travel routes, it is crucial to account for both the state of the TN and the constraints of the PDN, minimizing the impact on the distribution network [9]. At the informational level, integrating electrical data such as power, voltage, and current with traffic data such as traffic flow and road conditions is necessary to develop more effective control strategies.

2.2. Importance of EV Integration in Traffic–Power Networks

The coupling of traffic and power networks is not only inevitable but also essential for developing more accurate models and maximizing societal benefits.
  • More Accurate Models
Models based on a single network are often inaccurate. When studying EV charging and discharging strategies, from the perspective of the PDN, EVs are considered a type of electrical load. However, if EV charging management does not account for traffic factors, many EV users may flock to the same low-cost charging stations, leading to longer queues. Conversely, if only traffic congestion and charging station queues are considered without factoring in electricity prices, EV users may end up at charging stations with higher costs, contradicting their expectations.
To address the incompleteness of single-network models, early studies attempted to simulate the effects of another network with simplified models. For instance, reference [10] transforms the issue of travel distance into a time-based problem, introducing traffic information into PDN modeling. Reference [11] employed incentive pricing to simulate price fluctuations caused by grid congestion. However, these early models were relatively simple and could not accurately describe the coupling between power and traffic networks. Recent studies have proposed more sophisticated frameworks. For example, reference [12] presents an integrated framework that captures the interaction between traffic, EV users, and the PDN. Reference [13] models EV driving and charging behaviors as a traffic allocation problem to address the Traffic User Equilibrium (TUE) model.
  • More Societal Benefits
Optimizing a single network may only benefit either the PDN or the TN, whereas coupling these networks can maximize overall societal benefits.
Studies have shown that a well-designed EV dispatching strategy can help achieve equilibrium within the coupled traffic–power network [14,15]. Reference [14] uses Locational Marginal Pricing (LMP) and travel time estimates to reflect congestion levels in both the PDN and the TN. Reference [16] examines the impact of traffic flow on power demand and electricity prices, as well as the feedback effect of electricity price fluctuations on traffic flow distribution. This study proposes a decentralized optimization model to address both the economic dispatch of the grid and traffic allocation problems. Reference [17] introduces a safety index that converts travel distances into electrical distances, thereby improving the safety of the PDN during EV route planning. Reference [18] coordinates the coupling of EVs with the traffic–power network, reducing both traffic congestion and charging costs. These studies all consider the coupling between traffic and power networks during EV dispatching, allowing EV routes to be guided by traffic flow while enhancing the safety and economic operation of the PDN.
In terms of planning, once the PDN and roads are constructed, grid congestion and road traffic issues become irreversible. Therefore, it is essential to jointly plan grid expansions, road development, and the siting and sizing of charging stations, as highlighted in references [19,20]. Reference [21] employs an unconstrained traffic assignment model to address the non-convexity associated with the Bureau of Public Road (BPR) function, transforming the complex problem of charging station planning and coupled traffic–power network expansion into a solvable Mixed-Integer Linear Programming (MILP) problem. Building upon this, reference [20] uses an enhanced traffic assignment model to more accurately depict the dynamic interactions between charging stations and road investments, incorporating user equilibrium conditions to reflect EV users’ route selection behavior and proposing a unified strategy for planning charging stations and traffic–power network expansion. Reference [22] introduces a microscopic modeling approach to simulate the impact of charging stations on traffic flow, proposing a multi-objective planning model that takes into account costs and imbalanced traffic flows. These studies demonstrate that considering the coupling between traffic and power networks during the planning phase can maximize societal benefits.

2.3. Factors Influencing EV Integration into Traffic–Power Networks

The factors influencing EV integration can be broadly categorized into subjective and objective factors. As shown in Figure 1, these two categories interact with one another, making the analysis of EV dispatch factors highly complex.

2.3.1. Charging Mode

EVs can be recharged through various charging modes, including slow charging, fast charging, dynamic wireless charging (DWC), and battery swapping. The choice of parking location is closely related to the selected charging mode. Table 1 summarizes the different charging modes available for EVs. Each mode caters to distinct needs and operational scenarios, providing flexibility and convenience for EV users. Consequently, when designing EV dispatch strategies, it is essential to account for user preferences for different charging methods in various contexts. However, considering multiple charging modes simultaneously adds significant complexity to the model, as it must accommodate a range of user preferences and operational requirements.

2.3.2. User Behavior and User Preference

When scheduling EVs, it is crucial to fully consider user behavior and preferences in order to enhance user participation in an orderly charging strategy. Some users are highly price-sensitive, while others prioritize charging convenience. Reference [30] categorizes EVs into four types based on user willingness, which allows for the creation of a more flexible charging model for AC/DC hybrid PDNs. Reference [31] optimizes the scheduling of EV charging time and energy flows, reducing electricity costs without causing additional battery degradation. Reference [32] advances multi-lane speed planning and lane-changing strategies, taking into account drivers’ comfort, safety, and lane-changing behaviors for hybrid electric vehicles (HEVs). Reference [33] considers the trade-offs between time-aware fairness and overall waiting time, using charging cost and total travel time as indicators of user preferences. Reference [34] introduces a piecewise linear charging demand function, which reflects the relationship between charging prices and optimal charging power. Reference [35] employs a machine learning algorithm to predict charging budgets and expected State of Charge (SOC) as user preferences, utilizing a bi-objective optimization model for EV scheduling in workplace parking lots. Finally, reference [36] introduces a data-driven framework that integrates system dynamics and agent-based modeling to predict levels of EV penetration.

2.3.3. Battery Performance

In recent years, EV users have become increasingly concerned with battery performance. Reference [37] established a charging model for electric buses, considering battery life cycle costs within a regional electric–thermal integrated energy system. Reference [38] established a charging and swapping model for electrochemical lithium batteries, which is capable of adapting to battery temperature and degradation levels. Reference [39] used an augmented epsilon-constraint-based technique to optimize battery life, charging costs, and grid load, significantly reducing customer charging costs and battery wear. Reference [40] established a detailed fuel-cell hybrid electric vehicles (FCHEVs) model, coordinated the charging and hydrogen refueling behavior of FCHEVs. Reference [41] enhances the optimization of EV charging navigation which considers battery degradation as an operational expense, accommodating both urgent and economical charging requirements and enabling flexible scheduling to optimize costs and preserve battery health. Given the nonlinear dynamics of battery charge levels and charging durations, reference [42] presents a nonlinear charging function for EVs to encapsulate the limitations on driving range imposed by charging. Reference [18] incorporates road types into EV energy consumption as a cost factor in the battery discharge process. Reference [43] proposed an EV energy management system composed of a supercapacitor and two types of batteries, which reduces battery stress and extends battery lifespan. Reference [44] proposes a fuzzy logic control algorithm to control EV dynamic behavior and allows energy efficiency improvement and driving range increase.
These studies focus on modeling various aspects of battery degradation, life cycle costs, fuel cell characteristics, and charging functions, emphasizing the role of battery performance in EV user decision-making.

3. Charging and Discharging Scheduling

3.1. Charging Model

EV charging models are typically structured as bi-level models, as shown in Figure 2. On the network side, the focus is on PDN modeling, with optimization goals evolving from initially considering only power generation costs to now accounting for objectives such as peak-valley differences, voltage deviations, grid losses, and carbon emissions. In addition to grid constraints, traffic network constraints are also incorporated. For EV charging models, optimization objectives often include minimizing charging costs and battery degradation, while constraints typically focus on maintaining SOC levels. More refined EV models include additional constraints such as proximity to charging stations and congestion levels at these stations. For example, reference [45] developed a bi-level optimization model, where the upper level oversees the scheduling of the integrated energy system, and the lower level determines EV charging and discharging strategies. This model employs a bi-level reinforcement learning approach to address high levels of uncertainty.
Some studies focus on optimizing from a single entity’s perspective or combine the goals of two entities into a single-layer model. References [33,46] both adopt a single-layer approach from the EV perspective, aiming to minimize total user costs or total travel time. Reference [47] integrates both PDN and EV objectives, proposing strategies to reduce grid costs and EV charging losses.
Current charging strategies mainly focus on short-term, region-specific EV dispatching. Future research could explore EV cluster charging strategies over a week or coordinate EV charging across multiple distribution network areas to prevent transformer overload in specific zones. These strategies could guide EV charging over broader timeframes and geographical regions. Additionally, future studies could incorporate other factors such as weather conditions, seasonal variations, and road types, such as highways and urban roads.

3.2. Pricing Mechanism

The pricing mechanism for EV charging and discharging is a crucial strategy for managing energy exchange and optimizing traffic flow. By employing well-considered pricing strategies, it is possible to incentivize off-peak charging for EVs. This approach not only enhances economic efficiency but also promotes environmental sustainability, which is essential for the broad adoption and ongoing development of EVs.
The current strategies for EV pricing primarily encompass Time-of-Use (TOU) pricing, LMP, spatiotemporal pricing, and market-based pricing. Table 2 summarizes the EV charging and discharging pricing mechanisms.
  • TOU
Reference [48] explores the impact of various TOU pricing strategies on EV charging behaviors. By guiding EV users to charge at different times, it aims to lower peak demand, mitigate voltage fluctuations, and reduce line losses. Reference [49] improves upon TOU pricing by categorizing load periods and employing a demand–elasticity pricing matrix to gauge the correlation between EV charging demand and pricing.
2.
LMP
Reference [50] introduces a distribution LMP approach to alleviate congestion, where EV clusters act as price recipients, utilizing demand elasticity curves for modeling. Reference [51] introduces a charging service fee based on LMP to steer EV charging behavior, aiming to reduce traffic congestion and improve the integration of renewable energy. Reference [13] takes into account GHG price, employing optimal power flow (OPF) and carbon tracing methodologies to determine the carbon-inclusive charging price, substantially decreasing GHG emissions in the coupled traffic–power network.
3.
Spatiotemporal Pricing
Acknowledging that TOU pricing may not alleviate charging pressure on the residential PDN, reference [52] recommends an area-based pricing strategy, fully accounting for local capacity and utilization rates to optimize EV charging behaviors. Reference [53] integrates location-based data into the pricing model, employing a high-resolution, dynamic spatiotemporal distribution simulation framework for accurate estimation of the dispatchable capacity of EVs.
4.
Market-Based Pricing
In reference [54], the EV cluster acts as a price-maker in the electricity market, and the impact of different EV charging periods on the PDN was studied. Reference [55] examines the non-cooperative game theory-based charging pricing strategy among multiple charging network operators, proposing an iterative approach leveraging the best response strategy to identify the Nash equilibrium. Reference [56] introduces a dynamic charging pricing strategy, taking into account the utilization rates of charging infrastructure, traffic dynamics, and renewable energy generation. Recognizing that EVs, as distinct economic entities, may not achieve the optimal collective outcome through non-cooperative interactions, reference [57] employs inverse optimization techniques to formulate a comprehensive pricing framework aimed at realizing a cost-minimizing social optimum operational status.
A reasonable pricing mechanism is an effective measure to encourage EV participation in orderly charging. Currently, research on pricing mechanisms has reached a certain level of depth. The next research direction will focus on the market pricing of V2G participation.

3.3. Integration Approach

DR strategies utilizing EVs rely heavily on the effective aggregation of EV resources. Various integration approaches have been identified, including EV clusters, V2G, vehicle-to-everything (V2X), and the synergy with additional energy resources. The characteristics and application scenarios of these approaches are summarized in Table 3.
  • EV Cluster
EV cluster or unidirectional vehicle-to-grid (V1G) denotes a congregation of EVs within a defined geographical area or charging station, pivotal for the collective study and management of multi-vehicle charging dynamics [58,59]. Reference [30] leverages EV aggregators to orchestrate EVs within AC/DC hybrid networks, curtailing renewable energy wastage and mitigating the electricity system’s economic burden.
2.
Synergy with Additional Energy Resources
EVs can be harmonized with a constellation of energy resources, including energy storage devices, wind energy, photovoltaics, and microgrids, to escalate the operational efficiency of renewable energy generators. Reference [39] examines the joint dispatch of EVs and wind generators to heighten the harnessing of wind power potential. Reference [60] integrates EV clusters with active battery supercapacitors, embedding constraints on parking status and the chronology of charging and discharging within the EV model, refining the model’s comprehensiveness.
3.
V2G
The V2G paradigm is an intelligent grid mechanism, permitting bi-directional energy transactions between EVs and the grid [61]. Reference [62] integrates power equilibrium, charging expenses, ancillary service remuneration, battery attrition, and range concern into a V2G framework. Reference [63] constructs a Stackelberg game-theoretic model between V2G operators and EVs, aimed at honing the flexibility and efficacy of EV charging schedules with data privacy. Reference [64] introduces a multifaceted V2G charge and discharge strategy, guided by user credibility, battery degradation, and user engagement, employing an accelerated genetic algorithm to ascertain indicator weights, obviating disputes stemming from subjective judgments. Reference [65] proposes an Adaptive Interaction Artificial Neural Network-based V2G and grid-to-vehicle (G2V) Power Management Controller (PMC) for scheduling EV charging and discharging. In reference [46], an improved hybrid algorithm is presented to solve the optimal scheduling problem under the objectives of minimizing grid load variance and minimizing user charging costs, with scheduling results provided for different EV access methods. Its simulation results are shown in Table 4. The total cost of charging and the peak–valley difference in V2G scenarios is reduced by 68.1% and 80.8%, respectively, compared to disordered charging. This demonstrates that through proper control, V2G significantly lowers operational costs and peak–valley differences compared to both disordered charging and V1G scenarios, highlighting its importance in improving future PDN operations.
4.
V2X
The V2X spectrum, encompassing V2G, vehicle-to-building (V2B), vehicle-to-vehicle (V2V), and vehicle-to-home (V2H), facilitates expansive energy management through dynamic interactions between EVs and diverse infrastructures [66]. References [67,68] explore V2V technologies to diminish charging expenditures and enhance the utilization of renewable energy sources. Reference [69] introduces a distributed non-cooperative model tailored for energy communities endowed with V1G and V2B functionalities, designed to optimize the energy distribution of EVs within such communities.
Effective integration of EVs presents numerous challenges. It requires substantial investment in charging infrastructure and grid upgrades. Additionally, managing the charging and discharging schedules of a large number of EVs necessitates advanced control systems and algorithms, moreover, enabling EVs to participate in electricity markets and provide ancillary services calls for regulatory and market reforms.

4. Charging Navigation

The burgeoning adoption of EVs has far surpassed the establishment of charging stations, complicating the spatial dynamics between these two elements. This mismatch between the distribution of EV charging demands and the availability of charging stations has led to an uneven distribution of utilization rates, with some stations being underutilized while others experience excessive queuing [70]. It is thus imperative to investigate the navigation of EV charging to guide motorists towards charging stations with minimized charging costs and waiting times, thereby enhancing the overall efficiency of charging station usage [71,72].

4.1. Navigation Model

The task of navigation involves selecting the optimal route and charging station, as well as scheduling charging operations. In typical models, multiple entities are considered: the TN, the PDN, EVs, and EV stations, as is shown in Figure 3. From the perspective of the TN, the goal is to improve traffic efficiency and provide charging routes and station information to other entities. The PDN’s objective is to manage charging schedules based on navigation outcomes and relay the status of the grid, such as congestion, back to the TN. For the EV model, the goal is to evaluate charging routes based on user preferences, considering factors such as charging cost, battery wear, travel time, and waiting time, with constraints including user station selection preferences and range anxiety. The optimization goal for EV stations is to maximize the station’s charging load, with station capacity serving as the primary constraint.
Many studies integrate these entities into a bi-level model. Reference [18] treats the EVs and the TN as one model and the PDN as another, introducing a two-stage framework aimed at EV navigation and power flow optimization. In reference [73], the TN, the PDN, and EVs are combined in one stage to select charging routes, effectively addressing range anxiety and improving overall efficiency and user satisfaction. The EV station is handled in a separate stage, where the charging schedule within the station is developed. Reference [74] addresses challenges posed by non-cooperative EVs, proposing a two-tiered operational algorithm. The upper tier focuses on demand response (DR) through power flow analysis, while the lower tier alleviates traffic congestion by calculating EV navigation. Some studies focus on a single entity. For instance, reference [75] considers all EVs awaiting charging as the main subject, establishing a single-level optimization model with the goals of maximizing energy requested by EVs, minimizing total response time, reducing charging costs, and minimizing battery degradation. Reference [76] takes into account the difference in elevation between the locations of EVs and charging stations.

4.2. Real-Time Traffic Information

Early studies on charging navigation typically used static traffic models based on historical data. Reference [77] introduces a predictive model for queue times at charging stations based on the charging intentions of other EVs, facilitating strategic routing. Reference [78] introduces a stochastic decision-making framework. Addressing EV en-route charging, reference [79] proposes a method for allocating EVs to charging stations that promise the shortest waiting times. However, with the swift evolution of the EV sector and the accelerated rhythm of modern life and travel, relying on static charging data to forecast vehicle charging demands and to refine charging paths may introduce considerable delays [80,81].
With the progression of mobile communication technologies, EV users and charging stations are now equipped to acquire real-time insights into traffic conditions and the status of surrounding charging facilities, enabling the optimization of charging navigation, driving speeds, and lane-changing maneuvers [27,82]. Reference [82] presents an optimization framework for fast-charging navigation that takes into account real-time TN and PDN status, with a focus on spatial load distribution. Reference [83] introduces a charging navigation model with the objective of minimizing charging duration, proposing a practical swarm-optimization algorithm to achieve this goal. While previous research has concentrated on the modeling of EVs, electrical systems, and transportation, reference [71] shifts the focus to the spatiotemporal implications of EV charging decisions. It utilizes an event-driven dynamic queue model to simulate charging station queues, employing a price-incentivized charging navigation strategy to refine EV charging rout. Reference [84] articulates a dynamic navigation and charging strategy, which involves the categorization of private EVs with similar travel and charging requirements and the assignment of differentiated charging priorities based on real-time operational data. It offers a combination of fixed travel routes and flexible charging options to minimize detours due to charging needs. These studies incorporate various dynamic factors, such as traffic flow, PDN load, and user behavior, into charging navigation models, resulting in high complexity. Moreover, real-time scheduling demands fast computation. How to improve model accuracy while ensuring computational efficiency is a key issue worth exploring in future research.
Various factors involved in EV charging navigation are shown in Table 5. Optimizing EV charging routes is a complex problem involving user preferences, the PDN, and the TN [85]. The existing literature has explored various aspects of EV navigation and charging optimization. However, there is a notable gap in the comprehensive incorporation of real-time data into these models. Most studies do not fully integrate real-time information. To address these gaps, future research should focus on integrating power, transportation, and information networks. Advanced communication technology and high computational power are needed to incorporate more real-time information into the study. Additionally, the transportation network exhibits latency. Considering this latency in future path planning will make scheduling strategies more aligned with real-world conditions.

5. Planning of Charging Stations

Planning EV charging stations is critical to the coupled traffic–power network. Charging stations serve as a bridge between the PDN and the TN. Properly planned charging stations can maximize captured traffic flow and minimize power loss in the PDN [86].

5.1. Type of Charging Stations

Charging station planning encompasses strategic planning for slow charging stations (SCSs), FCSs, and DWC infrastructure, as well as integrated charging station types.

5.1.1. Single-Type Charging Stations

A preponderance of scholarly work has concentrated on a single type of station. Reference [87] introduces an optimization planning of charging stations under conditions of EV charging congestion, employing a robust optimization model that linearizes queuing constraints. Reference [88] constructs a dual-layer model for charging station planning, predicated on EV range anxiety, with the upper layer aimed at maximizing service to the EV population and the lower layer accounting for user equilibrium in route selection. Reference [89] integrates EV charging demand and route choice into the planning, proposing a bi-level model where the upper level, solved by the PSO-DS algorithm, identifies optimal charging station locations, and the lower level addresses EV charging scheduling and traffic flow distribution as a combined convex optimization problem. References [90,91] delve into the optimal siting of DWC infrastructure. Reference [90] evaluates DWC facilities with considerations for revenue, travel time, and cabling investment, utilizing traffic wave analysis to dissect the spatiotemporal traffic flow and Nesterov’s model to tackle traffic assignment challenges. Reference [91] takes into account EV travel routes, PDN losses, and traffic flow, and applies a long-term stochastic scenario-based mathematical model to simulate road conditions. Reference [92] seeks to meet service demand, a challenge exacerbated by the intermittent charging needs of electric buses, and presents a novel mathematical formulation to address the simultaneous optimization of charging infrastructure and vehicle schedules in electric bus systems. Reference [93] employed a combination of DG and a distribution static compensator in charging station planning to reduce the impact of EV charging on the PDN.

5.1.2. Multi-Type Charging Stations

Planning for various charging station types can capitalize on the unique attributes of each charging technology, providing a more authentic representation of real-world usage scenarios. Reference [94] presents an integrated layout plan for traditional stations and DWC infrastructure, applying a dynamic traffic assignment model to gauge real-time traffic patterns, ensuring that the charging infrastructure is strategically placed to satisfy user demands while achieving optimal utilization and profitability. Reference [19] examines the charging demands of EVs and gas vehicles (GVs), formulates a joint planning model for the multi-type EV charging stations and GVs, and the expansion of the PDN and natural gas network (GDN), proposing an MILP approach to solve it. Reference [95] proposes an optimal layout method for EV charging stations and renewable energy generation, aimed at supporting EV charging and renewable energy generation by optimizing the location of Energy Hubs (EHs).
The research on charging station planning is evolving from a technology-specific focus to more integrated, multi-modal frameworks. Future studies should continue to explore how different types of charging infrastructures can coexist and interact with each other and broader energy networks, while addressing both technical and behavioral factors.

5.2. Uncertainty

When planning EV charging infrastructure, there are distinct uncertainties that differ from those in EV charging scheduling and navigation. Figure 4 illustrates the uncertainties that need to be considered in EV charging scheduling, navigation, and station planning. In all three areas, real-time load distribution and congestion in the PDN and the TN must be taken into account. For EV charging scheduling, uncertainties focus on charging demand, including charging time, frequency, duration, and battery state [96]. In EV charging navigation, the focus is on user preferences such as charging speed, cost, station brand preference, and wait times.
Charging station planning, as a long-term optimization process, requires consideration of a broader range of uncertainties. Predicting future demand for EV charging is challenging due to the evolving nature of EV adoption rates and usage patterns [97]. Ensuring that the local grid can support the additional load from EV charging stations involves uncertainties in future energy consumption patterns and potential upgrades needed for the grid infrastructure [98]. Rapid advancements in EV technology and charging methods may render current infrastructure obsolete or insufficient, necessitating flexible and scalable planning [99]. In reference [87], the element of uncertainty in charging demand is seamlessly integrated. Reference [91] leverages stochastic programming methodologies, applying Monte Carlo sampling and scenario reduction techniques to address the initial battery SOC forecast error of EVs. Reference [100] incorporates the uncertainties associated with PDN failures and charging station equipment malfunctions, strategizing charging station site selection under the most adverse failure scenarios.
Table 6 presents the factors that need to be considered when planning EV charging stations. Existing research typically models the PDN and the TN independently, with uncertainties primarily centered on user demand and SOC. Looking ahead, uncertain factors such as charging methods and energy consumption patterns should be integrated into future studies for a more comprehensive approach.

6. Potential Research Areas

The analysis of current research reveals several areas requiring further exploration to fully integrate EVs in coupled traffic and power systems. It is crucial to address the complexities of traffic–power network modeling, user behavior modeling, and incentive pricing mechanism. Additionally, advancements in autonomous driving and automated charging technologies present new challenges and opportunities. This section outlines potential research directions in detail.

6.1. Traffic–Power Network Modeling

The large-scale integration of EVs and distributed generators introduces significant uncertainty into TNs, with numerous parameters becoming variable. Additionally, the bidirectional power flow in V2G systems complicates power flow characterization, making it challenging to accurately represent grid dynamics. A data-driven approach can be employed to establish operational characteristics, such as reference [36], allowing the system to rely less on physical models and improve robustness. However, artificial intelligence (AI) and data-driven methods are highly dependent on the quality of raw data, and the accuracy of the model fitting is directly influenced by data quality. A key research direction for the future will be determining how to accurately represent network topology using AI and data-driven approaches, even when data are limited or of poor quality.
Currently, the flexibility of the coupled traffic–power network is primarily explored through physical connections between the two systems. In the future, as power, transportation, and information networks become more tightly integrated, the flexibility within coupled traffic–power networks must be further explored. The interaction between traffic flow and power flow will require more in-depth research. Analyzing EV behavior based on multi-source information from power, traffic, and information systems, while enhancing the precision and real-time modeling of coupled traffic–power networks, will be a major focus in future studies.

6.2. User Behavior Modeling

User behavior modeling is crucial for designing charging incentives and planning charging routes. EV load characteristic modeling and user driving and charging behavior have become new research hot spots. While most current studies focus on the driving and parking characteristics of EV groups, future research could delve deeper into individual factors and more refined modeling for individual differences such as user psychology, route selection habits, and the frequency of route changes. Moreover, combining these user characteristics with existing traffic–power coupled network models is an emerging area of interest, but there are no mature methodologies to reference. Integrating big data, cloud computing, and AI technologies presents a viable path forward.
On the other hand, user behavior modeling requires diverse data sources. Effectively accounting for the varying needs of multiple stakeholders while protecting user privacy is critical. Reliable and practical data privacy protection strategies are urgently needed. Future EV scheduling algorithms could be designed with data minimization principles in mind. Additionally, data encryption techniques, blockchain technology, and secure sharing mechanisms can be employed to handle multi-stakeholder information, minimizing privacy risks during data exchanges between different entities.

6.3. Incentive Pricing Mechanisms

Existing electricity pricing incentive mechanisms often fail to effectively guide the orderly charging of EVs. Many EV owners prioritize minimizing battery wear over marginally lower charging costs, which limits the effectiveness of current DR programs and pricing schemes. When financial incentives are outweighed by considerations such as convenience, time, and battery health, EV users are less likely to participate in these programs. Future incentive mechanisms and charging infrastructure will need to address these factors simultaneously.
Charging price mechanisms must capture user demand by focusing on user satisfaction, which includes charging convenience and battery performance. Future research could explore real-time pricing strategies based on traffic, grid load, user behavior, and weather conditions. For underutilized charging stations, dynamic pricing strategies based on real-time utilization rates could be developed. To address long queuing times at high-demand charging stations, user behavior could be modeled based on the urgency of charging needs, with real-time incentive pricing strategies and charging resource allocation algorithms that prioritize urgency.

6.4. Autonomous Driving and Fully Automated Charging Technology

Autonomous driving and fully automated charging technologies are among the latest and most promising research directions in EV technology development. However, limited research has focused on the interaction between autonomous driving technologies and coupled traffic and power networks. Future research could investigate the real-time exchange of information and energy between autonomous vehicles, the PDN, and the TN to respond to issues such as charging station failures, grid congestion, road incidents, or natural disasters. Additionally, studies could focus on real-time adaptive strategies for autonomous driving and fully automated charging in scenarios where power and traffic loads fluctuate in minutes.

7. Conclusions

At present, a significant body of research focuses on the interactive response of EVs within coupled traffic–power networks. This paper provides a comprehensive review of the current research status following three primary themes: EV charging and discharging strategies, optimization of EV charging navigation, and charging station planning, summarizing the latest research outcomes and proposing future research directions. In terms of EV charging and discharging strategies, existing studies utilize short-term, region-specific, bi-level dispatching models involving the PDN and EVs to analyze the effects of various pricing mechanisms and integration approaches on dispatch strategies. In the area of optimizing EV charging navigation, multi-entity models and real-time traffic information are emerging as mainstream trends. With respect to charging station planning, coordinating the placement of charging stations with the planning of the PDN, the TN, gas stations, energy hubs, and other resources has been shown to yield positive environmental and economic impacts. Additionally, several important areas warrant further investigation, including coupled network modeling, user behavior models, incentive pricing mechanisms, and autonomous driving and automated charging technologies. Addressing these challenges will be critical to the future development of a sustainable and efficient EV ecosystem.

Author Contributions

Conceptualization, J.H. and X.W.; methodology, J.H. and S.T.; formal analysis, J.H. and S.T.; writing—original draft, J.H., X.W. and S.T.; writing—review and editing, J.H. and X.W.; supervision, X.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (No. 52277110) and the Inner Mongolia Science and Technology Plan under Grant 2022JBGS0043.

Data Availability Statement

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

J.H. is deeply grateful to her lovely daughter Yoyo Wang, whose support has been invaluable throughout this research journey.

Conflicts of Interest

Author Shengmin Tan was employed by the company State Grid Yangzhou Power Supply Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Factors considered in EV control strategy.
Figure 1. Factors considered in EV control strategy.
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Figure 2. EV charging model.
Figure 2. EV charging model.
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Figure 3. EV navigation model.
Figure 3. EV navigation model.
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Figure 4. Uncertainties considered in EV charging scheduling, navigation, and station planning.
Figure 4. Uncertainties considered in EV charging scheduling, navigation, and station planning.
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Table 1. Charging modes for EV.
Table 1. Charging modes for EV.
Charging ModeCharging DurationCharging Time, SOCResearch FocusFeatures
Slow charging6 to 8 hMainly used in residential and workplace settingsCharging schedule optimizationSuitable for overnight charging; long-duration charging; mostly for vehicles parked for extended periods [23,24]
Fast charging15 min to 2 hMainly used for en-route charging; located at transportation hubs and marketplacesCharging navigation and station locationSignificant impact on traffic flow; suitable for quick charging needs [25,26]
DWCReal-time charging without parkingCharging on the move; mainly applied to highways and specific routesDeployment and optimization of DWC infrastructureMitigates range anxiety; addresses issues of high battery costs and large battery sizes
Battery swappingBattery replaced in minutesDedicated battery swapping stationsCharging optimization and station planningEliminates waiting time by replacing the battery instead of charging it [27,28,29]
Table 2. Charging pricing mechanisms.
Table 2. Charging pricing mechanisms.
Pricing MechanismFeaturesAdvantagesLimitations
TOUElectricity prices vary by time of dayReduces peak loadLimited impact on localized grid pressure; may not alleviate charging stress in specific areas
LMPPricing varies by power location and time, considering grid congestionRelieves grid congestionComplex implementation; requires accurate grid data and market management
Spatiotemporal PricingCombines temporal and spatial factors, adjusting pricing based on local grid load and utilizationPrecisely optimizes regional charging behavior; prevents localized grid overloadComplex implementation; requires high-resolution data and computational power
Market-Based PricingMarket and game theory-based pricingConsiders market dynamics, user behavior, and grid status; flexible pricing; improves efficiencyUsers may not achieve optimal collective outcomes; requires complex market management and multi-stakeholder participation
Table 3. Comparison of EV Integration Approaches.
Table 3. Comparison of EV Integration Approaches.
Integration ApproachFeaturesApplication Scenarios
EV clusterCentralized management of multiple EVs to optimize the charging process and mitigate the negative impact of EVs on the PDN, which is one-direction charging schedule.Charging scheduling optimization; DR
Synergy with additional energy resourcesIntegration with energy storage, microgrids, and renewable resources.Smoothing renewable fluctuation; improving energy utilization
V2GEnables EVs to send electricity back to the grid, facilitating bi-directional energy exchange.Frequency regulation; peak shaving and valley filling; backup power supply
V2XExtends beyond the grid to interact with homes, buildings, and other entities.V2H: Provides electricity to homes during peak usage times; reducing household electricity costs.
V2B: Offers energy storage and emergency power supply for commercial buildings; optimizing energy consumption and management.
Table 4. Simulation comparison for different strategies.
Table 4. Simulation comparison for different strategies.
StrategyTotal Cost of Charging/CNYLoad Peak-Valley Difference/kW
Foundation load-2074.21
Disordered charging6848.833357.58
Ordered charging3208.841513.31
Ordered charging and discharging2184.35642.69
Table 5. Factors involved in EV charging navigation.
Table 5. Factors involved in EV charging navigation.
ReferenceCoupled PDN and TNReal-Time Traffic ConditionUncertaintySingle EVObjectives
[71]√√Charging station queue×The lowest charging cost of charging stations
[77]××Travel and waiting time×Waiting time
[42]×××√Total route time
[32]×√××Cost of fuel and electricity; acceleration reflecting comfort; different lane traffic efficiency
[14]√√Waiting Time×Total area electricity cost
[17]√××√Electrical distance revising based on security index
[74]√×EV uncertainty×Voltage deviation;
power loss
[18]√×First departure time; parking duration×Loss cost; battery degradation cost; charging cost
[41]√√××Total electricity cost of the PDN; battery degradation cost; charging cost
[78]××Total energy cost on the selected path√Energy cost
[79]×××√Total route time
[82]√√××Station charging capacity
[83]×√××Waiting time
[84]√√Day-ahead scheduling of the PDN and the TN×GHG emission; travel cost
[73]√√Traffic flow×Charging cost; time consumption; energy consumption cost
[75]××EV uncertainty×EV energy requested; total response time; charging cost; battery degradation
[76]××××Minimize the total energy consumption
Table 6. Factors involved in EV charging station planning.
Table 6. Factors involved in EV charging station planning.
ReferenceStation TypePDN ConstraintsPlanning with Other NetworksUncertaintyObjective
SystemEV User
[87]FCS××Recharging demandInvestment costTravel time
[88]FCS×××Serving the maximum number of EVsDriving range; charging time
[89]FCS×××Power loss; voltage deviationTravel time; travel costs
[90]DWC infrastructure√××Investment costPower purchase cost; extra travel time
[91]DWC infrastructure√×SOCInvestment cost; power lossesBattery cost; routing cost; maintenance cost
[94]SCS, FCS, DWC infrastructure×××Number of chargers required at a charging facility; captured traffic flows×
[19]refueling station for EVs and GVs√PDN, GDN×Investment cost; electricity generation and purchase costs; gas purchase cost; penalty charges for renewable energy spillage×
[22]FCS√PDN×Investment cost; average unbalance of all traffic roads in a day around; power loss×
[20]FCS√PDN, TN×Investment cost; the electricity generation and purchase cost; penalty cost for renewable energy spillageTravel cost
[21]FCS√PDN, TNSOC×Travel time with no delays.
[100]FCS, SCS√×Equipment or network failure; EV travel behaviorInvestment and construction cost; voltage fluctuation and branch lossDriving and charging expenses; the duration of driving and charging
[92]FCS×××Investment costOperational cost
[95]FCS, energy hub√×Charging demand; distributed generationMinimizing EV charging radius coverage and distance from power substations; EH generation; minimizing the number of EH units×
[93]FCS√××Power loss; voltage stability; reliability.×
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Hu, J.; Wang, X.; Tan, S. Electric Vehicle Integration in Coupled Power Distribution and Transportation Networks: A Review. Energies 2024, 17, 4775. https://doi.org/10.3390/en17194775

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Hu J, Wang X, Tan S. Electric Vehicle Integration in Coupled Power Distribution and Transportation Networks: A Review. Energies. 2024; 17(19):4775. https://doi.org/10.3390/en17194775

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Hu, Jingzhe, Xu Wang, and Shengmin Tan. 2024. "Electric Vehicle Integration in Coupled Power Distribution and Transportation Networks: A Review" Energies 17, no. 19: 4775. https://doi.org/10.3390/en17194775

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