Planning Of Fast Charging Stations Research Articles

A joint planning strategy for EV charging system towards net-zero transportation electrification

Electric vehicles (EVs) are pivotal in the progression toward sustainable transportation. However, a growing body of recent studies has challenged the often-assumed notion that electric vehicles are often considered to be net-zero emissions. These studies suggest that electric vehicles could potentially generate substantial emissions due to their reliance on fossil fuels to generate electricity, especially when receiving fast charging services. This phenomenon has been mitigated with the use of renewable energy sources in the power system. However, due to the intermittent nature of renewable energy sources, additional requirements have arisen. To address this situation, this paper presents an interdisciplinary study of collaborative fast-charging stations (FCS) and distributed renewable energy planning based on the interactions between traffic and power networks in order to increase the adoption rate of electric vehicles and reduce emissions caused by traffic and power systems. The locations and sizing of fast-charging stations, distributed photovoltaic generation, and renewable energy power supply for electric vehicles are determined in the proposed strategy through multi-objective integer programming. The proposed planning strategy considers heterogeneous electric vehicle driving range constraints, the operation security of the power distribution system, the reliability and expansion of the current power distribution, as well as different types of air pollutant classifications resulting from the charging behaviors. To guarantee the feasibility and accuracy of the planning results, an interactive algorithm is applied to solve the multi-objective integer model. Several cases are conducted to validate the proposed approach. The results demonstrate that the proposed planning strategy has good performance in the planning of fast-charging stations and distributed renewable energy integration.

Bilevel optimization approach to fast charging station planning in electrified transportation networks

Unplanned en-route charging of electric vehicles (EVs) can unexpectedly cripple traffic conditions. As less-effective location of fast charging station (FCS) may exacerbate this issue, the effects of FCS on EVs' charging behaviors should be incorporated at the system planning stage. This paper proposes a strategic-charging-behavior awared model in the context of electrified transportation network environment. This model is formulated into a bilevel mixed-integer programming problem. A newly designed network equilibrium model targets on the low-level problem, which is to model the drivers' charging reaction to a certain FCS layout. In considering the EV self-serving routing and charging behaviors as well as power network constraints, the upper-level problem is formulated for location and sizing decision-makings, of which the objective is to minimize the overall traffic time and investment cost. To handle the proposed bilevel problem, a descent algorithm is further developed. To examine the effectiveness of the proposed approach, extensive numerical experiments are conducted, through which the obtained results demonstrate that the EV charging demand can perfectly be met while traffic congestions can be alleviated to a great extent.

Fast-charging station location problem: A two-phase approach with mathematical program with equilibrium constraints considering charging choice behaviour

Electric vehicles (EVs) produce zero emissions, which makes them an essential solution for reducing air pollution in urban areas. Fast-charging station (FCS) planning is crucial for ensuring a successful transition to EVs. Therefore, to address the FCS location problem (FCSLP) for EVs in urban areas, a two-phase approach consisting of data processing and model optimization is proposed. During data processing, the spatiotemporal charging demand distribution, existing FCSs, and potential locations for new FCSs are sequentially identified. The results are then used in the model optimization phase, where a spatiotemporal mathematical program with equilibrium constraints (MPEC) model is developed to minimize the total social cost (TSC). The MPEC model integrates the maximal coverage location model and charging equilibrium model, which is formulated as a variational inequality to capture the charging choice behaviour of the EVs within the FCS service radius. To solve the MPEC model, a modified genetic algorithm is developed, in which the charging equilibrium model is iteratively solved. Finally, a case study is conducted in a practical area in Shenzhen, China. The results indicate that a trade-off exists between the TSC and the system service level concerning the optimal FCS locations under a specific FCS service radius. Moreover, the FCS service radius significantly influences EV drivers’ charging choice behaviour and FCS planning.

Planning of EV Charging Station With Distribution Network Expansion Considering Traffic Congestion and Uncertainties

Proper planning for electric vehicle (EV) charging stations along with the required expansion of the distribution network is essential for the continuous growth of EV and conventional loads. This paper proposes a realistic and sustainable solution for deciding the location and capacity of the EV fast-charging stations along with the optimal multistage expansion of the distribution network to cope with future load growth. An overlaid traffic network with a distribution network is taken for the realistic approach. EV user convenience factor and user happiness factor are proposed along with the distribution network energy loss for better planning of fast-charging stations. To get charged, the appropriate fast-charging station and its best route are decided through minimum energy consumption by the EVs, considering traffic congestion and distances. Up-gradation of lines, up-gradation of the substation, addition of new fast-charging station, allocation of DSTATCOM and allocation of renewable-based distributed generation are performed for distribution network expansion planning. The whole work is formulated in three layers with a sub-layer. Uncertainties of EV driving patterns, energy requirement, traffic congestion, load and renewable generation are undertaken by the 2m point estimation method. The solution is carried out along with validation using different metaheuristic methods and integer linear programming.

Multi-stage planning of fast charging stations for PEVs using traffic-based approach

The rising environmental concerns and easily accessible power system infrastructure have elevated the use of plug-in electric vehicles (PEVs) in transportation systems. However, PEVs growth is moderate owing to range anxiety of drivers. In this context, fast charging stations (FCSs) deployment is an emerging development to lower the range anxiety of drivers. Though, heavy deployment of PEVs has up-surged the need of load demand on power systems. This requires a coordinated operation between power systems and future transportation systems to meet their demands. The FCS investors have keen interest in the net present value (NPV) of the project. On the other hand, the utility wants to keep its peak demand within the limits to ensure the safe-operation. This creates a contradiction between the FCS investor and utility’ objectives. Thus, this paper proposes an analytical approach for traffic-based multi-stage planning of FCS that aids FCS investor by considering PEV traffic, staggered investments, convenience of PEV drivers, profit on investments, and capacity limit of FCS imposed by the utility. The formulation for NPV is modified to account the revenue incurred owing to staggered investments. Proposed methodology determines a set of few sub-solutions constrained by mean service time of PEVs. This facilitates FCS investor to select the planning solution on the basis of his/her satisfaction. Detailed investigation of the proposed methodology highlights the applications and importance on assessing the FCS investors’ concerns while considering the affecting factors of transportation system, power system, drivers’ concerns, and profit margins altogether.

Could Petrol Stations Play a Key Role in Transportation Electrification? A GIS-Based Coverage Maximization of Fast EV Chargers in Urban Environment

To achieve net-zero in the transportation sector, there is a need to deploy public electric vehicle (EV) fast chargers to boost customer confidence. Currently, charge point operators and EV manufacturers focus on deploying chargers to cover highway networks and the progress in urban environments is inadequate. This is becoming an obstacle for potential EV buyers who do not have access to dedicated chargers or cannot afford expensive EVs with larger battery packs. Since mainstream combustion engine vehicle drivers are accustomed to using petrol stations, this paper examines the suitability of such sites as candidate locations to deploy fast chargers. Spatial analysis is carried out by comparing their coverage performance with existing locations of the fast chargers. More specifically, the location problem is modelled as a maximum coverage location problem (MCLP) and solved using a geographic information system (GIS) based platform. The spatial optimization problem is solved using a linear-programming relaxation based MCLP algorithm developed in Python. Five cities with growing populations, namely San Clara, CA, Salt Lake City, UT, Raleigh, NC, Denver, CO, and Los Angeles, CA, are chosen as case studies. The location analysis is carried out with two demand metrics (population and road traffic) using actual GIS data collected from public authorities. The results show that deploying fast chargers at existing fuel stations significantly increases the coverage needed for EVs. This study will provide useful insights into EV fast charging station planning in urban cities, as the related research is still in its infancy.

Planning strategy of fast-charging stations in coupled transportation and distribution systems considering human health impact

The proliferation of the electric vehicles field brings attention to fast-charging station planning. Recently, some studies found electric vehicles that receive fast-charging service may also bring much air pollution due to fossil-fuel fired power generation. Hence, depending on the power generation mix, electric vehicles may have adverse environmental impacts. The current studies of fast-charging station planning only focus on power system planning and often ignore other parts of the transportation system such as internal combustion engine vehicles and gasoline stations. Moreover, most the current studies lack a comprehensive planning strategy for the fast-charging station that involves elements above simultaneously. In this context, an interdisciplinary study is implemented on the coordinated planning strategy of the fast-charging station in coupled transportation and power distribution systems. The proposed strategy with a multiple objective integer model can measure the impact of air pollutions from electric vehicles on air quality and human health in different areas. Considering the traffic flow assignment, the driving range logic constraints are used. The branch flow model is applied to power distribution systems. To measure the impacts of air pollutions on air quality and human health, the air pollution from the Road traffic model, the Gaussian Plum Model and the C-R function are adopted. Finally, numerical experiments are conducted to verify the proposed strategy and the mathematical model. The fast-charging station location and size can be determined by the mathematical model. The transition of gasoline station to fast-charging station is suggested according to the model as well.

Multi-objective optimal planning of fast charging stations by considering various load models in distribution system

Abstract Electric vehicles (EVs) load and its charging methodologies play a significant role in distribution system planning. The inaccurate modelling of EV load may overload the distribution system components, increase in Network Power Loss (NPL) and Maximum Voltage Deviation (MVD). The Constant Power (CP) load model is more popularly used to model both the conventional and EV loads in the distribution system. But the CP load modelling cannot provide accurate information of EV charging process. In this paper, the EV load is modelled as constant Impedance-constant Current-constant Power (ZIP), Exponential, Constant Current and Constant Power load models and the conventional loads are modelled as Residential–Industrial–Commercial (RIC) and Constant Power load models. With these EV and conventional load models, the optimal site and size of Fast Charging Stations (FCSs) in the distribution system have been determined. Further, to analyse the impact of load of FCSs in the distribution system, the distribution indices are calculated. The multi-objective hybrid SFL-TLBO algorithm has been used to determine the optimal location and size FCSs with the minimization of NPL, MVD and EV User Cost (EVUC) in the distribution system. To consider the uncertainty of the initial SOC of EVs, the Monte-Carlo simulation technique has been used. These studies have been carried out on 38-bus distribution system and substantiate results are presented.

Integrating Transit Route Network Design and Fast Charging Station Planning for Battery Electric Buses

There are currently rising applications of battery electric buses in public transportation system, given their energy efficiency and societal benefits. However, limited range and time-consuming charging processes significantly reduce the operational efficiency of battery electric buses system. Thanks to fast charging technology, battery electric buses can run continuously through rational planning of fast charging system. Therefore, to achieve an effective and economic electric transit route network, electric transit route network and fast charging stations should be simultaneously and systematically deployed. This study develops a bi-level programming framework to formulate the electric transit route network design problem. Where, the upper-level model is to determine the route structure and charging station location to optimize the total costs of both user and operator, the lower-level passenger assignment model is to calculate user cost for upper-level model. Then, the proposed model is solved by a modified genetic algorithm (GA), which can search all possible route structures through specifically designed genetic operators. A numerical example from Swiss road network is employed to demonstrate the applicability of bi-level model and performance of modified GA.

Planning PEV Fast-Charging Stations Using Data-Driven Distributionally Robust Optimization Approach Based on ϕ-Divergence

Plug-in electric vehicles are widely acknowledged as an effective tool for numerous environmental and economic concerns. In this article, a novel model for the planning of fast-charging stations is established based on a data-driven distributionally robust optimization approach, which aims to minimize the expected planning cost for both transportation network and distribution network. φ-divergence, a statistical measure, is utilized to establish the serviceability constraints. On the other hand, a modified capacitated flow refueling location model is employed to develop the location constraints. In addition, ac power flow constraints are developed to model the operation of DN with the penetrations of PEVs. Finally, a case study is illustrated to validate the proposed planning model.

Robust chance-constrained programming approach for the planning of fast-charging stations in electrified transportation networks

In this paper, a bi-level programming model is established to address the planning issues of fast-charging stations in electrified transportation networks with the consideration of uncertain charging demands. The capacitated flow refueling location model is considered in the upper level to minimize the planning cost of fast-charging stations while the traffic assignment model is utilized in the lower level to determine the spatial and temporal distribution of plug-in electric vehicle flows over entire transportation networks. Such bi-level model unveils the inherent relationship among charging demands, electrical demands and the spatial and temporal distribution of plug-in electric vehicle flows. Robust chance constraints are formulated to characterize the service abilities of fast-charging stations under distribution-free uncertain charging demands, where the ambiguity set is constructed to estimate the potential values of the uncertainties based on their moment-based information, such that the robust chance constraints can exactly be reduced to mixed integer linear constraints. By introducing new variables, the bi-level model is then reformulated into a single-level mixed integer second-order cone programming model so as to be solved via off-the-shelf solvers, which guarantee the optimality of the solution. A case study is conducted to illustrate the effectiveness of the proposed planning model, which reveals three critical factors that significantly impact the planning outcomes.

Multi‐objective optimal planning of FCSs and DGs in distribution system with future EV load enhancement

Current trends suggest that electrical vehicle (EV) is a promising technology for road transportation. There is a substantial increase in the number of EVs due to improved energy efficiency and reduction in environmental impact as compared with internal combustion engine vehicles. The improper planning of fast charging stations (FCSs) and distributed generations (DGs) hurts the distribution system. So the distribution system operator has a significant challenge to identify the optimal location and sizing of FCSs in the distribution power network. This study presents optimal planning of FCSs and DGs with the account of the present and future increase in EV population. A multi-objective optimisation problem is formulated for optimal planning of FCSs and DGs with the objective of minimising the voltage deviation, distribution network power loss, DGs cost and the energy consumption of EV users. This problem is solved for different levels of increase in EV population for different cases. A novel hybrid shuffled frog leap-teaching and learning based optimisation algorithm is proposed and implemented to solve the considered multi-objective problem. The performance of the proposed algorithm is compared with prior-art algorithms in the literature.

Scenario-based planning of fast charging stations considering network reconfiguration using cooperative coevolutionary approach

Abstract In recent years, plug-in electric vehicles (PEVs) have attracted a great deal of attention because of environmental issues. For large-scale integration of PEVs, appropriate siting and sizing of PEV charging stations are essential. In this paper, a framework for simultaneous fast charging stations (FCSs) planning and distribution network reconfiguration is proposed. The distribution network reconfiguration is effectively used to optimize objective functions. The objective function considered is defined as the sum of the initial investment and energy loss costs subject to different distribution system constraints. In the model, a scenario-based approach is used for taking into account uncertainties of traditional electric load, FCS load, and electricity price. To reduce the computational burden of the problem, a scenario reduction technique is utilized. A cooperative coevolutionary genetic algorithm (CCGA) is employed to solve the problem. The FCSs locations and the network switches are coded separately, and they evolve cooperatively as two different species, i.e., the location species and the switch species. Furthermore, an efficient technique called Dijkstra's algorithm is used to calculate FCSs sizes in which the nearest FCS to PEVs is determined based on their driving distance from FCSs on their path to their destination. Finally, the performance and the effectiveness of the proposed method are demonstrated by numerical results. More specifically, use of the proposed scenario-based approach yields to a more accurate solution than deterministic method. In addition, usage of network reconfiguration in FCSs planning both reduces the total costs and improves the voltage profile.

Optimal location and sizing of fast charging stations for electric vehicles by incorporating traffic and power networks

Widespread adoption of electric vehicles (EVs) relies on a dependable public charging station (CS) network. CS locations should assure that vehicle users can reach the CS within the EV driving area. This study introduces a technique for optimal location and sizing of fast CSs (FCSs) that considers transportation loss, grid power loss and build-up costs. Google Maps API, battery state of charge, road traffic density and grid power losses are considered in the suggested method. A recently introduced binary lightning search algorithm is also implemented as an optimisation technique for FCS planning. The capability of the suggested method was tested in an urban area. Results reveal that the suggested technique can obtain the optimal location and sizing of FCS that can aid EV drivers, FCS builders and the utility grid. Furthermore, the suggested method obtained more realistic results compared with the traditional methods.

PEV Fast-Charging Station Siting and Sizing on Coupled Transportation and Power Networks

Plug-in electric vehicle (PEV) charging stations couple future transportation systems and power systems. That is, PEV driving and charging behavior will influence the two networks simultaneously. This paper studies optimal planning of PEV fast-charging stations considering the interactions between the transportation and electrical networks. The geographical targeted planning area is a highway transportation network powered by a high voltage distribution network. First, we propose the capacitated-flow refueling location model (CFRLM) to explicitly capture PEV charging demands on the transportation network under driving range constraints. Then, a mixed-integer linear programming model is formulated for PEV fast-charging station planning considering both transportation and electrical constraints based on CFRLM, which can be solved by deterministic branch-and-bound methods. Numerical experiments are conducted to illustrate the proposed planning method. The influences of PEV population, power system security operation constraints, and PEV range are analyzed.

A Second-Order Cone Programming Model for Planning PEV Fast-Charging Stations

This paper studies siting and sizing of plug-in electric vehicle (PEV) fast-charging stations on coupled transportation and power networks. We develop a closed-form service rate model of highway PEV charging stations' service abilities, which considers heterogeneous PEV driving ranges and charging demands.We utilize a modified capacitated flow refueling location model (CFRLM) to explicitly capture time-varying PEV charging demands on the transportation network under driving range constraints. We explore extra constraints of the CFRLM to enhance model accuracy and computational efficiency.We then propose a stochastic mixed-integer second order cone programming (SOCP) model for PEV fast-charging station planning. The model considers the transportation network constraints of CFRLM and the power network constraints with AC power flow. Numerical experiments are conducted to illustrate the effectiveness of the proposed method.