Electrified Transportation Network

EVs are recognized as distributed loads and power sources with random motion characteristics in the power distribution networks (PDN); therefore, they serve as connecting points for the distribution network and transportation network (PDN-TN), and have significant impact on the stability and reliability of the routine operation of the coupled networks. In order to guarantee the safe operation of PDN-TN, it is necessary to evaluate the reliability of this coupled network. In this paper, the trip chaining theory is applied to describe the travel plan of EVs, and semi-dynamic flow allocation (SDTA) method is adopted to propose a simulation calculation method of energy flow between two networks based on user equalization (UE) criterion. The new reliability evaluation index for energy flow between the coupled networks is further proposed to better evaluate the reliability. Taking into account the daily trip characteristics of EVs, vehicles types, and charging modes, a coupled network reliability evaluation method based on sequential Monte Carlo simulation is put forward. Finally, the practicability of the proposed method is verified by an IEEE-RBTS BUS-6 test system and a 12-node transportation network.

Metropolitansworldwide are increasingly adopting electric taxis (ET) to address concerns about transportation-related emissions. However, the widespread deployment of electric taxis presents challenges in terms of increased electricity demand and changing demand profiles. This transition impacts both the urban transportation network (TN) and the electricity power distribution network (PDN), highlighting the interdependence between these two systems. In this paper, we propose a two-stage stochastic programming planning model that aims to optimize both the TN and PDN, enabling efficient deployment of charging stations and grid upgrades. Our model seeks to strike a balance between meeting ET drivers' charging preferences, minimizing the costs associated with infrastructure deployment and grid expansion, and harmonizing the coordination between the TN and PDN. Additionally, we explore the potential benefits of utilizing an autonomous ET fleet to enhance overall system performance.

The proliferation of electric vehicles (EVs) calls for an effective planning of charging stations by coupling the transportation network (TN) with the power distribution network (PDN). This paper conducts an interdisciplinary study on the coordinated planning strategy of EV charging stations in a coupled traffic-electric network. A comprehensive planning model is proposed, which determines the optimal expansion strategies for both TN and PDN, including sites and sizes of new charging stations, charging spots, TN lanes, and PDN lines. In TN, we use an unconstrained traffic assignment model (UTAM) to explicitly capture the steady-state distribution of traffic flows. In PDN, operating conditions are described by a linearized DistFlow. Moreover, EV charging demands and interdependency of TN and PDN are characterized by EV charging station location model. To retrieve a global optimal solution, an equivalent mixed-integer linear programming model with UTAM constraints is formulated for the coordinated planning model. Numerical experiments on coupled traffic-electric networks are conducted to validate the effectiveness of the proposed model and the solution method.

The emerging photovoltaic (PV) integration technology called solar road (SR) gives road infrastructure the possibility of not only carrying traffic loads but also generating electricity. The versatility of SR leads to the nexus between transportation network (TN) and power distribution network (PDN). Along with the increasing penetration of electric vehicles (EVs), the installation of SR can promote the reliance of transportation and electrical networks to a higher degree. In order to cope with the severe challenges brought by SR and EV to the TN-PDN coordinated operation, this paper suggests a traffic-power coordinated operation model incorporating road solar generation (TPOSR), where the impact of the traffic flow and SR on the coupled network is studied. Specifically, the user equilibrium (UE) principle is adopted to describe the traffic flow distribution in TN, while the Distflow model is employed to model the power flow in PDN. In TN, the SRs serve as links that let the vehicles through. Moreover, from the perspective of PDN, the SR output model considering traffic shading effect is proposed, based on traffic assignment problem (TAP) and traffic flow theory. In the proposed TPOSR model, we assume the independent system operator, a public organization, can imposes road tolls and dispatch generators to promote coordinated operation of the coupled network. Numerical results demonstrate the effectiveness of the proposed TPOSR for reducing social costs and improving SR performance.

The rapid adoption of electric vehicles (EVs) is driving increased charging demand, intensifying the interdependence between power distribution and transportation networks (PTNs). The main challenge in coordinating the EV routing and charging management within the PTN lies in the intractable nonlinear relationship between electricity price signals in the power distribution network (PN) and EV charging and routing behaviors in the transportation network (TN). Therefore, this paper proposes a novel constraint learning approach using XGBoost to solve the nontrivial bilevel optimization problem of vehicle charging within the PTN. XGBoost is first employed to capture the complex relationship between charging prices and optimal charging demand, leveraging its strong approximation capability to construct a tree-based surrogate model that approximates the TN decision model. Then the trained surrogate model is equivalently reformulated as mixed−integer linear programming (MILP) constraints and integrated into the vehicle charging optimization framework. Shapley values are further used to analyze the explainability of the tree-based surrogate model. Based on the constraint learning, a data-driven hybrid optimization model is developed for vehicle charging. Case studies validate the approximation accuracy and explainability of the tree-based surrogate model, as well as the effectiveness of the optimal strategy within the PTN.

With an increasing deployment of plug-in electric vehicles, evaluating and mitigating the impacts of additional electrical loads created by these vehicles on power distribution grids become more important. This paper explores the use of prices of electricity at public charging stations as an instrument, in couple of road pricing, to better manage both power distribution and urban transportation networks. More specifically, a multi-class combined distribution and assignment model is formulated to capture the spatial distribution of plug-in electric vehicles across the transportation network and estimate the electrical loads they impose on the power distribution network. Power flow equations are subsequently solved to estimate real power losses. Prices of electricity at public charging stations and road tolls are then optimized to minimize both real power losses in the distribution grid and total travel time in the urban transportation network. The pricing model is formulated as a mathematical program with complementarity constraints and solved by a manifold suboptimization algorithm and a pattern search method. Numerical examples are presented to demonstrate the proposed model and solution algorithms.

Switching operation of 10kV line's ring network is one of the effective technical measures to reduce power failure and improve supply reliability of urban power distribution network. The switching operation of ring network in urban power distribution network is not only affected by operation mode of power system and structure of distribution network, but also restrained by factors such as load and voltage difference prior to closing loop operation, and the overlarge circulating power load during closing loop operation can endanger the safety of power distribution network and equipments. Based on the complicated structure of power distribution network with multiple segments and connections in Nanyang city, the mathematic model of switching operation of ring network in power distribution network is established, which conducts load flow calculation and analysis of closing loop operation with the integrated analysis system of power system for 10kV circuits of different 220kV systems and under different operation modes. This is to protect the setting value and dynamic stability limitation checking of the equipment. Through practicing, method of reducing power flow of closing loop can be proposed and the management system of urban distribution ring network operation standard can be established.

In terms of characteristics in urban active power distribution networks and trend of increasing penetration in distributed power, loss reduction problems in urban power distribution networks are studied in this paper. Considering not only random fluctuations of distributed power sources and loads, but also energy efficiency load management technology, air conditioning load, lighting load, and water heater load in controllable energy efficiency load are focused as research objects in this paper where its sensitivity to various influencing factors is analyzed. Moreover, mathematical models of controllable energy efficiency loads for air conditioning, lighting, and water heaters are established to implement mathematical modules for comprehensive damage reduction measures and improved harmony search algorithm is adopted. In addition, optimized load management is performed on the basis of it. Besides it, according to analysis and calculation of actual distribution network in certain city, correctness and superiority of research method are verified, and impact of optimized energy management technology on network loss of distribution network is analyzed as well.

As the most promising alternative to internal combustion engines (ICEs), electric vehicles (EVs) have an excellent development outlook. The charging route scheduling of EVs can simultaneously affect traffic congestion in the transportation network (TN) and power flow distribution in the power distribution network (PDN). The research on TN and PDN coupling networks based on the static traffic flow model is relatively mature; however, it ignores that the traffic flow will spread across periods in a short scheduling period. In this paper, a semi‐dynamic traffic flow model is proposed to represent the dynamic propagation characteristics of EVs and ICEs flow. Furthermore, the cost of carbon emission and system operation are combined as the overall goal of system optimisation. Since the model has become a more complex non‐linear model, this paper proposes to combine the heuristic sequential boundary tightening and binary expansion method to linearise the model. The study compared four cases and found that a 20% penetration rate of EVs can reduce carbon emissions by 4.2% while reducing the system's total cost by 10%. Moreover, the impact of network congestion on the spatiotemporal distribution of traffic flow and power flow in the coupled network is alleviated.

O-D Demand Adjustment Problem with Congestion: Part I. Model Analysis and Optimality Conditions.- Generating Highway Travel Times with a Large-Scale, Asymmetric User Equilibrium Assignment Model.- An Efficient Algorithm for a Bicriterion Traffic Assignment Problem.- A Stochastic User Equilibrium (SUE) Path Flow Estimator for the Dedale Database in Lyon.- Modelling and Performance Analysis of Urban Transportation Networks.- Zone Planning in Public Transportation Systems.- Multicriteria Evaluation Model of Public Transport Networks.- Multi-Objective Approach for Designing Transit Routes with Frequencies.- Relationship between Parking Location and Traffic Flows in Urban Areas.- A DSS Prototype for Urban Intermodal Path Planning with Parking Management.- Structure of a Dynamic Network Loading Model for the Evaluation of Control Strategies.- Dynamic Traffic Assignment in Congested Networks.- A System Optimal Traffic Assignment Model with Distributed Parameters.- Stochastic Assignment Models for Transit Low Frequency Services: Some Theoretical and Operative Aspects.- A Parallel Approach to Large-Scale Nonlinear Network Optimization.- Data Management of Large-Scale Transportation Networks.- Why Regulate Prices in Freight Transportation Markets?.- Optimal Freight Transport Pricing and the Freight Network Equilibrium Problem.- The Impact of Predictive Information on Guidance Efficiency: An Analytical Approach.- Dynamic Traffic Prediction for Motorway Networks.- A Parking Simulation Model for Evaluating Availability Information Service.- Flexible Dispatching Control Tools in Public Transport.- Queuing Optimization of Signalized Intersections.- A Model for Real-Time Traffic Coordination Using Simulation Based Optimization.- An Approximate Labelling Algorithm for the Dynamic Assignment Problem.- A Heuristic Algorithm for the Vehicle Routing Problem with Backhauls.- Hybrid Genetic Algorithms for Bus Driver Scheduling.

An approach to transportation network analysis via transferable utility games

The large-scale deployment of distributed renewable energy sources enables traditional consumers to become prosumers in power distribution systems. Moreover, the increasing prevalence of electric vehicles (EVs) strengthens the coupling between the electricity and transportation sectors. It is highly desirable to propose a framework to effectively manage the emerging proactive prosumers and flexibility introduced by EVs in the coupled power distribution and transportation networks. In this context, this paper proposes a game-theoretic model for coupled transportation systems and transactive energy communities with prosumers to analyze the multi-period interactions of stakeholders and determine their optimal dispatch, aiming to minimize operational costs. A distribution-level transactive market design is proposed to enable the transactions of profit-maximizing prosumers in the energy community. In the transportation network, the routes and departure times of utility-maximizing drivers constitute a user equilibrium, where no drivers can reduce travel cost by unilaterally changing route or departure time. The existence and uniqueness of the Nash equilibrium of the proposed multi-period game models are mathematically proven. The equilibrium solution can be obtained by solving its equivalent optimization problem. The numerical results demonstrate and analyze the time-dependent interactions among drivers and prosumers under the Nash equilibrium in the coupled transportation-power network.

Electric vehicles (EVs) and fast charging infrastructure (FCSs) have developed at a rapid pace, strengthening the coupling between power distribution networks (PDNs) and transportation networks (TNs). This has created new challenges for cross-domain coordination and sustainable energy management. However, the majority of previous research relies on deterministic assumptions, ignoring the stochastic nature of traffic demand and load, and lacks effective general-purpose algorithms for large-scale optimization problems, which results in poor solution quality and slow convergence in practical applications. This paper suggests an uncertainty-aware Stackelberg game model to address these problems, where the multi-class vehicle users and PDN operator are followers and the FCS aggregator is the leader. To more accurately depict decision-making procedures and system interactions, a multi-scenario stochastic representation is explicitly included. A hybrid surrogate-assisted metaheuristic algorithmic framework is presented to address the intricate bi-level programming problem created by the Stackelberg game. Early on, the radial basis function-based surrogate model is used for quick global exploration, and later on, metaheuristic algorithms like adaptive particle swarm optimization and advanced differential evolution are used for more focused local search. Lastly, case studies validate the efficacy and superiority of the model and algorithm by showing that the suggested approach performs better than pure surrogate or metaheuristic approaches in terms of solution quality, and achieves 0.86%–1.05% higher average profit compared to deterministic approaches while maintaining robust worst-case performance with up to 2.66% improvement in minimum profit.

The stability of a user equilibrium determines whether it could be realized or sustained in a transportation network, when subject to perturbations in traffic conditions and drivers' route choice behaviors. In this paper, with a simple deterministic dynamical system model of the traffic assignment problem, we first develop a systematic approach to analyzing asymptotic stability and instability of user equilibria in static transportation networks with fixed demand. We then discuss two examples of non-monotone traffic assignment problem. Compared with existing approaches, the new approach is simpler to use and leads to results consistent with the former. This study could be helpful for analysis and design of real transportation networks.

The interdependency of transportation and electric power networks is becoming tighter due to the proliferation of electric vehicles (EVs), which introduces additional difficulties in the planning of the two networks. This paper presents the enhanced solution for the coordinated planning of multiple facilities in the two networks, including electric power lines, transportation roads, energy storage systems and fast charging stations. In order to calculate the optimal solution for the proposed coordinated planning problem, we introduce the applications of linear optimization theory including Karush-Kuhn-Tucker conditions, the big M method, and a linear expression of power loss to transform the nonlinear planning problem into a mixed-integer quadratically constrained programming (MIQCP) formulation, which is solved by commercial solvers. The proposed MIQCP formulation is decomposed into two corresponding subproblems by Lagrangian relaxation to represent transportation and electric power networks. The case studies validate the proposed planning model and demonstrate that the proposed solution can enhance the coordinated network planning with the proliferation of EVs.