Plug-in Electric Vehicles Charging Stations Research Articles

Demand management and pricing in renewable integrated plug-in electric vehicle charging station using reinforcement learning

ABSTRACT The increasing adaption of Plug-in Electric Vehicles (PEVs) has increased the power demand for charging. Renewable generation can significantly help to meet this demand. The uncertainty of renewable generation and randomness in PEV charging makes the demand management of charging stations challenging. To address these challenges, this study proposes a novel demand management pricing strategy for renewable integrated charging stations. The proposed strategy uses reinforcement learning for charging coordination of PEVs. The renewable generation is estimated using weather data and accordingly PEVs are scheduled for demand management. The strategy decides the PEV power price based on real-time price, renewable tariff and PEV load. The strategy uses the bidirectional power flow with battery degradation cost. The distribution transformer operating cost and loading constraints are included to resemble the real-time environment. The PEV randomness and uncertainty of renewable generations are incorporated in the study. The results of the numerical case study show that the proposed strategy can manage the charging station load efficiently. The proposed strategy has optimized the cost of charging, discharging and charging station profit and increased the service capability of the charging station.

Techno-economic analysis of stand-alone hybrid PV-hydrogen-based plug-in electric vehicle charging station

The increase in the feasibility of hydrogen-based generation makes it a promising addition to the realm of renewable energies that are being employed to address the issue of electric vehicle charging. This paper presents technical and an economical approach to evaluate a newer off-grid hybrid PV-hydrogen energy-based recharging station in the city of Jamshoro, Pakistan to meet the everyday charging needs of plug-in electric vehicles. The concept is designed and simulated by employing HOMER software. Hybrid PV-hydrogen and PV-hydrogen-battery are the two different scenarios that are carried out and compared based on their both technical as well as financial standpoints. The simulation results are evident that the hybrid PV- hydrogen-battery energy system has much more financial and economic benefits as compared with the PV-hydrogen energy system. Moreover, it is also seen that costs of energy from earlier from hybrid PV-hydrogen-battery is more appealing i.e. 0.358 $/kWh, from 0.412 $/kWh cost of energy from hybrid PV-hydrogen. The power produced by the hybrid PV- hydrogen - battery energy for the daily load demand of 1700 kWh /day, consists of two powers produced independently by the PV and fuel cells of 87.4 % and 12.6 %, respectively.

Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience

Rapid placement of electric vehicle charging stations (EVCSs) is essential for the transportation industry in response to the growing electric vehicle (EV) fleet. The widespread usage of EVs is an essential strategy for reducing greenhouse gas emissions from traditional vehicles. The focus of this study is the challenge of smoothly integrating Plug-in EV Charging Stations (PEVCS) into distribution networks, especially when distributed photovoltaic (PV) systems are involved. A hybrid Genetic Algorithm and Simulated Annealing method (GA-SAA) are used in the research to strategically find the optimal locations for PEVCS in order to overcome this integration difficulty. This paper investigates PV system situations, presenting the problem as a multicriteria task with two primary objectives: reducing power losses and maintaining acceptable voltage levels. By optimizing the placement of EVCS and balancing their integration with distributed generation, this approach enhances the sustainability and reliability of distribution networks.

A Stackelberg game-based dynamic pricing and robust optimization strategy for microgrid operations

Energy management algorithms of microgrids have attracted growing attention for the efficient operation and coordination of active end-users. In developing these algorithms, leader–follower game approaches could provide effective strategies when the behavioral and operational characteristics of active end-users are integrated into the methodology. However, since these approaches require the exact implementation of the strategy determined by the leader, it is of great importance to deal with the uncertainty of renewable energy generation, charging/discharging energy of storage systems, and load consumption. Thus, possible negative effects of these uncertainties on the outcome can be minimized with an appropriate billing procedure. In this paper, a Stackelberg game approach is proposed for the energy sharing management of a microgrid including prosumers and plug-in electric vehicle (PEV) charging stations (CSs), and a proper billing scheme is designed to increase the robustness of the approach on real-time applications. In the hereby game-theoretic approach, the energy demands of PEV CSs are determined as variable demands considering the operational characteristics of CSs. The energy planning of CSs is based on determining the number of PEVs that each station can accept over the vehicle charging levels for a specified time interval. Also, by the necessary modifications identified for the presented approach, the microgrid can operate independently from the utility grid and satisfy the requirements of islanded mode for the time slots in which the microgrid exchanges energy with the grid based on the end-game results. The energy management strategy proposed in this study presents a comprehensive solution capable of optimizing energy sharing in microgrids while taking into account end-users’ operational characteristics and network requirements. Furthermore, the designed billing scheme ensures that the accuracy of the proposed game approach remains unaffected by the uncertainties, thus preserving the microgrid’s profit.

Optimal allocation of plug-in electric vehicle charging stations in the distribution network with distributed generation

Optimal allocation of plug-in electric vehicle charging stations in the distribution network with distributed generation

New optimal planning strategy for plug‐in electric vehicles charging stations in a coupled power and transportation network

AbstractThe use of plug‐in electric vehicles (PEV) and their developing technology can create new challenges to the smart power system. The type, method, and time of charging electric vehicles are also other issues. Allocating and determining the optimal capacity of electric vehicle charging stations (EVCS) is related to the technical requirements of the distribution network. This is economically important for the construction of charging stations. This paper proposes a new approach for optimal siting and sizing of PEV charging stations in a coupled electrical and transportation network. This work presents the problem from a techno‐economic point of view of the electrical network as a multi‐objective problem with the objectives of simultaneously reducing the cost of building EVCSs and active power losses. The Pareto method is used to solve the problem and to display optimal points. In order to carry out the simulation, the proposed method is tested on a case study of the standard IEEE 37‐bus network with a 25‐node transport system and the proposed solution in the subject environment. The Floyd–Warshall method is utilized to determine the shortest travel routes for PEVs. The obtained results confirm the effectiveness of the optimal planning of PEV charging stations.

A Stackelberg game approach for energy sharing management of a microgrid providing flexibility to entities

This paper focuses on the energy sharing management of a microgrid including photovoltaic - wind turbine prosumers with energy storage systems, and plug-in electric vehicle charging stations. Motivated by a single-leader multi-follower Stackelberg game approach, the microgrid operator (MGO) is taken as a leader, and prosumers and charging stations act as followers. In similar approaches, buyer and seller roles owned by the prosumers are determined according to their forecasted production and consumption values at the beginning of each time interval, and these roles do not change depending on the obtained final prices for the relevant time interval. In addition, prosumers do not have decision parameters, which they can determine in line with their preferences, in their utility functions. These points significantly limit the prosumers in terms of flexibility, and accordingly, the main aim of this study is to make contributions to these issues. In the proposed Stackelberg game approach, the roles of prosumers can dynamically change as buyers or sellers in response to the pricing policy of the MGO for each time interval. Also, prosumers can take more active roles in the microgrid by shaping their energy consumption strategies with their specified decision parameters. To present the effectiveness of the proposed method, a real-like microgrid simulation is given. The simulation results show that the total profit of the MGO based on the end-game prices has reached approximately twice the total profit based on the utility grid prices. While providing such a significant increase in profit of the MGO, the proposed method reduces the dependency of the microgrid on the utility grid considering the flexibility of the prosumers.

Inducing Human Behavior to Maximize Operation Performance at PEV Charging Station

Plug-in electric vehicle (PEV) charging station service capability is physically limited by the charger availability and local transformer capacity. However, the station operation performance has become an increasingly important factor for enhancing charging service accessibility. In this work, we propose an innovative station-level optimization framework to operate charging station with optimal pricing policy and charge scheduling. This model incorporates human behaviors to explicitly and effectively capture drivers' charging decision process. We propose a menu of price-differentiated charging services, which differ in both the per-unit prices and the energy delivery schedule. Involving human in the loop, the operation model also exploits the capability to alleviate the overstay issue that occurs when a PEV's charging session has completed. We then propose a muli-block convex transformation methodology to reformulate the resulted non-convex problem via Fenchel-Young Inequality; the Block Coordinate Descent algorithm is applied to solve the overall problem and the efficiency enables real-time implementation. As a result, our simulation demonstrates that the proposed control polices can realize benefits in three aspects: (i) net profits gains, (ii) overstay reduction, and (iii) quality-of-service increase.

A Stochastic Optimal Planning Model for Fully Green Stand-Alone PEV Charging Stations

Electric vehicle users need access to recharging services within the all-electrical driving range of their vehicles. However, public charging stations to be located on remote highways are in many cases far from the power grid, where a connection to the power grid may be impossible, costly, or impractical. This article formulates a stochastic mixed integer linear programming (MILP) planning model for energy storage and renewable energy systems to cover the energy demand of stand-alone charging stations for plug-in electric vehicles (PEVs) entirely using green energy generated by renewable energy sources (RESs). The proposed model accounts for the stochastic behavior of PEVs and RESs as well as the technical, economic, and operational characteristics of a variety of energy storage technologies. A customer satisfaction criterion is introduced to allow for a tradeoff between performance and investment costs. The proposed model can also be used for designing fully green PEV charging stations in urban and rural settings to capitalize on PEV environmental benefits by avoiding the use of fossil-fuel-generated electric power in their charging or to reduce the main grid load. A set of numerical studies have been performed in General Algebraic Modeling System (GAMS) environment to demonstrate the feasibility and effectiveness of the proposed model.

Public Plug-in Electric Vehicles + Grid Data: Is a New Cyberattack Vector Viable?

High-wattage demand-side appliances such as Plug-in Electric Vehicles (PEVs) are proliferating. As a result, information on the charging patterns of PEVs is becoming accessible via smartphone applications, which aggregate real-time availability and historical usage of public PEV charging stations. Moreover, information on the power grid infrastructure and operations has become increasingly available in technical documents and real-time dashboards of the utilities, affiliates, and the power grid operators. The research question that this study explores is: Can one combine high-wattage demand-side appliances with public information to launch cyberattacks on the power grid? To answer this question and report a proof of concept demonstration, the study scrapes data from public sources for Manhattan, NY, USA using the electric vehicle charging station smartphone application and the power grid data circulated by the U.S. Energy Information Administration, New York Independent System Operator, and the local utility in New York. It then designs a novel data-driven cyberattack strategy using state-feedback based partial eigenvalue relocation, which targets frequency stability of the power grid. The study establishes that while such an attack is not possible at the current penetration level of PEVs, it will be practical once the number of PEVs increases.

Optimal Allocation of Inverter-Based WTGS Complying With Their DSTATCOM Functionality and PEV Requirements

Recently, the integration of inverter-based wind turbine generation systems (WTGS) and plug-in electric vehicles (PEV) has remarkably been expanded into distribution systems throughout the world. These distributed resources could have various technical benefits to the grid. However, they are also associated with potential operation problems due to their stochastic nature, such as high power losses and voltage deviations. An optimization-based approach is introduced in this paper to properly allocate multiple WTGS in distribution systems in the presence of PEVs. The proposed approach considers 1) uncertainty models of WTGS, PEV, and loads, 2) DSTATCOM functionality of WTGS, and 3) various system constraints. Besides, the realistic operational requirements of PEVs are addressed, including initial and preset conditions of their state of charge (SOC), arriving and departing times, and various controlled/uncontrolled charging schemes. The WTGS planning paradigm is established as a bi-level optimization problem which guarantees the optimal integration of multiple WTGS, besides optimized PEV charging in a simultaneous manner. For this purpose, a bi-level metaheuristic algorithm is developed for solving the planning model. Intensive simulations and comparisons with various approaches on the 69-bus distribution system interconnected with four PEV charging stations are deeply presented considering annual datasets. The results reveal the effectiveness of the proposed approach.

Bilevel Robust Optimization of Electric Vehicle Charging Stations With Distributed Energy Resources

We develop a bilevel model, which captures strategic decision making by plug-in electric vehicle (PEV) owners, to optimize the design of a PEV charging station with distributed energy resources. The upper level of the model determines the optimal configuration of the station and pricing schemes, whereas the lower level captures charging decisions by PEV owners. A robust formulation is employed to capture uncertain wholesale energy prices, renewable resource availability, and PEV flows. The resulting bilevel robust optimization model is transformed into an equivalent single-level optimization problem by replacing the lower level problem with Karush–Kuhn–Tucker optimality conditions. A column-and-constraint-generation algorithm is used to solve the resultant single-level problem. Results from a realistic case study and a parameter analysis demonstrate the effectiveness of the proposed model in capturing the impacts of uncertainty and self-interested behavior by PEV owners.

Steady-state Stability Assessment of AC-busbar Plug-in Electric Vehicle Charging Station with Photovoltaic

Although the deployment of alternating current (AC) -busbar plug-in electric vehicle (PEV) charging station with photovoltaic (PV) is a promising alternative, the interaction among subsystems always causes the instability problem. Meanwhile, the conventional generalized Nyquist criterion (GNC) is complex, and it is not suitable for the design of the AC system. Therefore, this paper proposes a modified infinity-one-norm (MION) stability criterion based on the impedance method to assess the stability of the foresaid charging station. Firstly, the typical structure and operation modes of the charging station are studied. Furthermore, each subsystem impedance matrix is built by small-signal method, and the MION stability criterion based on impedance method is proposed to assess the charging station stability. Compared with the previous simplified stability criteria based on the norm, the proposed criterion has lower conservatism. Furthermore, the design regulation for the controller parameters is provided, and the stability recovery way is provided by connecting the doubly-fed line and energy storage equipment, which are selected based on intermediate variable, i.e., short-circuit ratio (SCR). Finally, the effectiveness and conservatism of the proposed stability criterion are validated through simulation and experimental results.

Hierarchical planning of PEV charging facilities and DGs under transportation-power network couplings

The increasing penetration level of plug-in electric vehicles (PEVs), as well as distributed generators (DGs), imposes significant challenges on power system planning. This work presents a coordinated approach for the planning of PEV charging facilities and DGs, including both the locations and the capacities, with the consideration of the transportation - power network couplings. First, the PEV charging demand is characterized by a temporal-SoC (State of Charge) analysis, and the DG generation uncertainties are modeled by K-means clustering using historical data. The M/M/s/N queuing model is used to formulate the dynamics of charging stations and then obtain the optimal station capacity, including the number of chargers and waiting spaces. Furthermore, the placement of charging stations is optimized by the Floyd Algorithm to minimize the total distance to obtain charging service. Finally, the sitting and sizing of DGs are optimized over multiple objectives, including active power losses, reactive power losses, and voltage deviation. The solution is evaluated through a case study of the IEEE 53-bus test feeder coupled with a 25-node transportation network. It is shown that the proposed solution enables the active and reactive power losses as well as the voltage deviation to be reduced by 37.6%, 44.3%, and 33.6%, respectively, after the optimal integration of PEV charging stations and DGs. The scalability and effectiveness of the solution are further validated in an IEEE 123-bus test feeder coupled with the same transportation network, and the result confirms the effectiveness and scalability of the proposed solution.

Solving Overstay and Stochasticity in PEV Charging Station Planning With Real Data

This article studies optimal plug-in electric vehicle (PEV) charging station planning, with consideration for the “overstay” problem. Today, public PEV charging station utilization is typically around 15%. When un-utilized, the chargers are either idle or occupied by a fully charged PEV that has not departed. We call this “overstay.” This motivates a strategy for increasing utilization by interchanging fully charged PEVs with those waiting for service-an issue which is not well addressed in the existing literature. Thus, this article studies the PEV charging station planning problem taking strategic interchange into account. To our best understanding, this has not been studied in the literature. With interchange, the objective is to enhance the charger utilization rate and, thus, reduce the number of chargers. This potentially reduces the capital investment and operational cost. A novel power/energy aggregation model is proposed, and a chance-constrained stochastic programming planning model with interchange is developed for a public charging station to incorporate customer demand uncertainties. Numerical experiments are conducted to illustrate the performance of the proposed method. Simulation results show that incorporating strategic interchange operation can significantly decrease the number of chargers, enhance utilization and economic efficiency.

전기차 충전 장소를 고려한 그리드 일부하곡선 분석

This paper presents a methodology to calculate the daily load curve per city by taking into consideration a charging station for electric vehicles, which easily determines the effect of the load owing to the charging of electric vehicles in Seoul. In particular, we calculate the charging power of uncontrolled plug-in electric vehicles (PEVs) in terms of the daily running characteristics of vehicle travelling between homes and workplace, with respect to PEV charging stations in the city, according to the market share of PEVs. Furthermore, we calculate the load curve for the simultaneous charging strategy of controlled PEVs in terms of the vehicles’ daily running characteristics between homes and workplaces and a time-of-use (TOU) price plan of the Seoul Metropolitan Government under uncontrolled and controlled charging types. Thus, we are able to analysis the effect of the total existing electricity load and the abovementioned daily load on the grid. Because we can precisely determine the effect of the hourly charging load of PEVs on a power grid in Seoul using these study results, we can establish a PEV charging load management plan to prevent power grid reinforcement and expansion while satisfying the overload constraint of the power grid in Seoul using an appropriate TOU price plan.

Optimal planning of PEV fast charging stations using an auction-based method

In recent years, the plug-in electric vehicles (PEVs) have attracted a lot of attention. For integration of PEVs, proper planning of PEV charging stations is necessary. Location and size of fast charging stations (FCSs) are very important for distribution companies (DISCOs) and FCS investors. In this paper, FCS planning problem is modeled as a mixed-integer nonlinear programming (MINLP) from DISCO point of view. The proposed method is based on the dynamic auction, and a co-evolutionary genetic algorithm is used to solve the problem. FCS investors bid DISCO for energy in different locations. The DISCO, as the auctioneer, determines the location and size of FCSs based on the bids received at each location so that its profit is maximized. In the proposed model, capacitor and feeder installations are considered to satisfy the bus voltage limit and the apparent power capacity constraint of feeders, respectively. The effectiveness of the proposed method is demonstrated by numerical results. Our work shows that the proposed method can be used as a proper tool for fast FCS optimal planning in the presence of private investors of FCSs. Furthermore, in the new environment of power systems, dynamic auctions can be utilized in FCS planning. In addition, the capacitor and feeder installations can be effectively used for getting a better value of DISCO objective function.

Optimal planning of PEV fast charging stations using nash bargaining theory

Abstract In recent years, electric vehicles (EVs), especially plug-in EVs (PEVs), have attracted a great deal of attention due to the environmental issues. For integration of PEVs, appropriate planning of PEV charging stations (CSs) is essential. The allocation of public parking lots and fast charging stations (FCSs) is very important for distribution companies (DISCOs) and private investors. In this paper, the FCS planning problem is modeled as a mixed-integer nonlinear programming (MINLP). The Nash bargaining theory is used to analyze the interaction between the DISCO and FCS owner (FCSO). The location and size of FCS and the price of transacted energy between DISCO and FCSO are obtained, such that the objective functions of both participants are optimized. To solve the problem, the main problem is divided into a number of sub-problems, and each sub-problem is solved using Lagrange multipliers technique. The final solution is the best solution of the optimum answers of sub-problems. The results reveal that the proposed method yield equal profits for the DISCO and FCSO. Moreover, consideration of incentive for DISCO and FCSO can bring additional profits for both sides.

Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches

Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.

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.