Plug-in Electric Vehicle Parking Research Articles

A multi-objective two-stage stochastic programming approach for wind farms and demand side provider decisions using the weighted metric method

The rapid expansion of wind-based renewable energy sources highlights the critical need to enhance reserve capacity within power systems. This research aims to develop an effective model for harnessing the combined potential of flexible demand-side resources, acting as demand-side providers (DSPs), to address the inherent unpredictability of wind power. The DSP framework comprises three demand response aggregators: the capacity market program (CAP), energy storage systems (ESSs), and plug-in electric vehicle parking lots (PEVs PLs). To achieve this, a multi-objective two-stage stochastic programming model is introduced. It not only minimizes operational costs for system operators but also maximizes returns for demand-side resource owners supervised by DSPs in the energy and reserve market environment. The problem is tackled using a mixed-integer linear programming (MILP) approach employing a weighted metric methodology. Within this framework, the Weibull probability distribution function (WPDF) is utilized to model wind power uncertainty, while the truncated Gaussian distribution function (GDF) encapsulates the uncertainty regarding electric vehicle owner behavior. The results demonstrate that employing this method for simultaneous programming of energy and reserve not only reduces system operator costs but also enhances profits for resource owners.

A hybrid Robust-Stochastic optimization model for planned outage based Day-Ahead scheduling of a Plug-in electric vehicles parking lot

The growth of plug-in electric vehicles (PEVs) in recent decades and their impact on the operation of distribution networks have necessitated the requirement for PEV parking lots (PEPLs). With respect to the development of technology advances, PEPLs can benefit from charging the PEVs and selling the discharge power of PEVs (with V2G ability) to the grid. Besides, the load growth, the aging of the components and their thermal limitations enforce the distribution network operator to de-energize some of the zones to lighten the peak load. This de-energization with day-ahead notification is accounted for as a planned outage (PO). Such outages can have an adverse effect on the PEPL’s daily revenue. This paper introduces a hybrid robust-stochastic model for optimal day-ahead scheduling of the PEPL with renewable generation and considering the inherent uncertainties. The proposed model of the day-ahead scheduling is subject to the PO notified by the distribution network operator and concerns profit maximization in a way that mitigates the power shortage problem in the PO period. This PO-oriented scheduling helps the owner of the PEPL to successfully provide service for the PEVs at a minimum cost.

Optimal Allocation of Plug-in Electric Vehicle Parking Lots for Maximum Serviceability and Profit in the coupled distribution and transportation networks

Optimal planning and management of electric vehicle parking lots (EVPLs) can be an effective approach for improving the operation of both the distribution system and traffic networks. However, the limited land areas of cities can be an obstacle for constructing a large number of parking lots (PLs). This paper proposed a model for optimal siting and sizing of EVPLs as well as their charging schedule to maximize the total profit of their owners, while maximum parking demand of plug-in electric vehicles (PEVs) can be satisfied. In the proposed model, the purpose of trips, number of PEVs, plus their arrival and departure time in different urban areas are considered. Distribution network constraints are also taken into account using linearized load flow equations. The proposed model is implemented in a 37-bus distribution system coupled with a 25-node transportation network which includes four different areas in terms of PEV travel type. The simulation results show the effectiveness of the proposed model to cover the parking demand of PEVs with a limited number of PLs.

New model of PEV parking lots in the presence of demand response uncertainties

In the presence of today’s environmental concerns, electric transportation services provide significant role of presenting secure and sustainable energy schemes. For this issue, plug-in electric vehicles (PEVs) are an unavoidable part of future smart grids which can provide clear and secure power for different types of electrical energy consumers. In this case, PEVs-based demand response schemes present positive effects of supplying electric powers through smart grid consumers. In the case of evaluating PEV uncertainties, this paper presents a new demand response scheme considering the optimal performance of PEV parking lots through two different reserve markets and electrical energy systems. For this issue, based on the concepts of motivation-based and cost-based demand response programs (DRPs), an adaptive mathematical-based model is developed which considering different time steps, the optimal performance of PEV parking lots are evaluated. Also, based on the performance of several DRPs, the operational performance of PEVs are investigated accurately in which proper participation values with respect to each DRP scheme is estimated. In the case of electricity market uncertainties within different time durations, by using a stochastic-based programming structure, the stochastic performance of PEV parking lots are developed through optimization mathematical model. Numerical results indicate the effectiveness of the proposed approach for optimizing PEV parking lots through different DRPs with considerable effect through supplying sustainable energy.

A new operation strategy of PEV parking and hydro storage in smart grid environment

PurposeThe purpose of this study is to solve the optimal operation strategy problem of plug-in electric vehicles (PEV) parking as a demand response (DR) program and hydro storage as an energy storage system in a smart grid environment using a heuristic algorithm.Design/methodology/approachStudying the smart grid with DR, renewable energy resources and energy storage systems is necessary. To do this, the heuristic optimization algorithm is developed to solve the scheduling problem. This deterministic algorithm benefits from the definition of appropriate fitness functions.FindingsFor validation, it is shown that reduction of 1.1%–12.5% in pollution and 8.8%–34.8% in total cost are achieved, as compared with literature. Also, the suggested operation strategy of PEVs parking and hydro storage results in reducing the total cost by 6.21%.Originality/valueDR programs such as PEV parking play a major role in smart grid developments. Also, energy storage systems such as hydro storage lead to better performance of distributed generations and lower costs and pollution by thermal units. However, based on the literature, the effects of PEV parking and hydro storage on smart grid operation strategy are not considered. Therefore, contributions of this study are: effects of hydro storage on the smart grid are considered, effects of PEV parking on the smart grid are considered, a heuristic algorithm is developed to solve operation strategy problem for PEV parking and hydro storage in a smart grid environment.

Optimal design of electric vehicle charging stations for commercial premises

Influx of plug-in electric vehicles (PEVs) creates a pressing need for careful charging infrastructure planning. In this paper, the primary goal is to devise a closed-form expression for the PEV charging station capacity problem. Two types of commercial charging stations are considered. The first problem is related to the calculation of the optimal service capacity for charging lots located at workplaces where PEV parking statistics are given as a priori. The second problem, on the other hand, is related to the optimisation of arrival rates for a given station capacity. In the second part, the mathematical models are expanded for the case where multiple charger technologies serve customer demand. This time the goal is to calculate the optimal customer load for each charger type according to its rate. Calculations are carried out for both social and individual optimality cases. Markovian queues are used to model the charging station system to capture the complex interactions between customer load, service waiting times, and electricity cost. The related optimisation problems are solved using convex optimisation methods. Closed-form expressions of station capacity and optimal arrival rates are explicitly derived. Both analytical calculations and discrete-event simulations are carried out and the results show that 60% of the waiting times and 42% of the queue length can be reduced by optimal capacity planning.

Co-Optimization of Energy Losses and Transformer Operating Costs Based on Smart Charging Algorithm for Plug-In Electric Vehicle Parking Lots

The global transport sector has a significant share of greenhouse gas emissions. Thus, plug-in electric vehicles (PEVs) can play a vital role in the reduction of pollution. However, high penetration of PEVs can pose severe challenges to power systems, such as an increase in energy losses and a decrease in the transformers' expected life. In this article, a new day-ahead co-optimization algorithm is proposed to reduce the unwanted effects of PEVs on the power system. The aim of the proposed algorithm is to minimize the cost of energy losses as well as transformer operating cost by the management of active and reactive powers simultaneously. Moreover, the effect of harmonics, which are produced by the charger of PEVs, are considered in the proposed algorithm. Also, the transformer operating cost is obtained from a method that contains the purchase price, loading, and losses cost of the transformer. Another advantage of the proposed algorithm is that it can improve power quality parameters, e.g., voltage and power factor of the distribution network by managing the reactive power. Afterward, the proposed algorithm is applied to a real distribution network. The results show that the proposed algorithm optimizes the daily operating cost of the distribution network efficiently. Finally, the robustness of the proposed algorithm to the number and distribution of PEVs is verified by simulation results.

A techno-economic analysis: Urban reconfigurable microgrids participating in short-term regulating power markets

Power markets are undergoing structural alterations in accordance with emerging technologies. Traditional market rules and participants' behaviour are currently dominated by conventional power plants and synchronous generators. Renewable energy sources have gradually found their market share in the generation party specifically in developed countries. Apart from new energy resources, Demand Response Programs (DRP) have garnered attention due to substantial capabilities and potentials. This paper particularly sheds light on two flexible loads of Plugin Electric Vehicles (PEV) and cooling/heating responsive loads. PEV parking lots can be modelled as stochastic battery storages partially available for power exchange over short intervals. Besides, cooling/heating systems are naturally shiftable loads in short time horizons without deteriorating the resident's convenience. This study provides a comparative view of short-term power regulating markets in terms of bidding constraints and payment mechanisms. Consequently, a typical park & ride is considered as a reconfigurable urban microgrid to participate in short term regulating markets under different constraints. The results confirm that the internal coordination between the sub-units of microgrid brings more flexibility to enhance the total profitability. In addition, it is shown that the bidding constraints, as well as time slots, can be influential particularly in intermittent situations.

Electric vehicle parking lots as a capacity expansion option in distribution systems: a mixed‐integer linear programing‐based model

Plug-in electric vehicles (PEVs) are considered as innovative solutions to achieve sustainable development. The installation of PEV parking lots is the main requirement for using PEVs. Another requirement is the expansion of the distribution system to meet a portfolio of demands with different attributes including parking lots. In this study, an innovative framework for integration of parking lots in distribution system planning (DSP) problem is presented. The storage capability of gridable parking lots (GPLs) is used to postpone investments in distribution system expansion. To this end, first, a novel stochastic GPL allocation model is proposed in which GPLs can act as charging stations and storage elements. Then, an integrated model for DSP problem is presented considering different objectives of distribution system company and GPL owners. Finally, an efficient linearisation lemma is applied to solve the proposed DSP model. The numerical studies demonstrate the proficiency of the proposed DSP model.

A Resilience-Based Architecture for Joint Distributed Energy Resources Allocation and Hourly Network Reconfiguration

As a result of the recent innovations in the deployment of plug-in electric vehicles (PEVs), this technology can play an important role as a distributed energy resource in supplying the system demand of the power systems of the future. This paper introduces a methodology for optimal coordinated allocation of wind farms (WFs), energy storage systems, and PEV's parking lots considering demand response programs and hourly distribution network reconfiguration in normal and severe contingency conditions. In the proposed methodology, the participation of different types of loads is also examined. The objective function is to minimize the total costs of purchased power from the upstream network and WFs, along with the costs of commercial/industrial loads flexibility and residential loads curtailment. To validate the performance of the proposed methodology, it is implemented on the well-known IEEE 33-bus distribution test system. The simulation results validate the feasibility and effectiveness of the proposed approach.

Design and Planning of a Multiple-Charger Multiple-Port Charging System for PEV Charging Station

Investment of charging facilities is facing deficit problems in many countries at the initial development stage of plug-in electric vehicles (PEVs). In this paper, we study the charging facility planning problem faced by a PEV charging station investor who aims to serve PEV customers with random behaviors and demands (but follow a series of predicted distributions) with lower economic costs of both charging facilities and practical operation. First, we design a multiple-charger multiple-port charging (MCMP) system which provides the capability of scheduling a limited quantity of chargers to serve more PEVs. Second, we further develop a general two-stage stochastic programming model to plan the MCMP system inside a PEV parking lot with consideration of coordinated charging. Third, we classify MCMP stations according to three different connecting patterns between chargers and parking spaces. Fourth, the general planning model is simplified (as a reference) based on the classification. Finally, we complement our analysis through case studies.

Simultaneous determination of optimal capacity and charging profile of plug-in electric vehicle parking lots in distribution systems

Although future widespread use of plug-in electric vehicles (PEVs) will considerably reduce the greenhouse gas emissions, their charging demand will pose significant challenges to the grid. Aggregated charge scheduling of PEVs in parking lots (PLs) can mitigate the challenges, if the size and place of the PLs for PEVs are optimally determined. This paper proposes an optimization-based problem to not only determine the optimal size and place of PLs for PEVs, but also optimally schedule the charge and discharge of PEVs in the located PLs. The operation costs of distribution system, including the cost of purchasing energy from upstream grid and the cost of PEV charging in PLs, are considered as the objective of the proposed optimization problem, subject to the distribution system constraints. The proposed method is examined in a test system, considering different penetration levels for PEVs. In addition, the impact of results is investigated on the reliability of distribution system. The results show that decrements in the total cost of operation may lead to a lower reliability for the distribution system. The sensitivity of the results to the maximum charging rate of the parked PEVs in the located PLs is also reported in this paper.

Optimal Operation of Emerging Flexible Resources Considering Sub-Hourly Flexible Ramp Product

To deal with high variability of stochastic market operations and with the aim of assuring a feasible and economic operation under high renewable energy sources penetration, in this paper two applicable solutions are proposed: 1) the incorporation into the electricity market of emerging flexible resources, including demand response, bulk energy storages, and plug-in electric vehicle parking Lots considering their own specific characteristics; and 2) a new flexible ramp market in order to cope with sudden variations and guarantee the rampability of reserve capacity provided by a generation portfolio. On this basis, an integrated stochastic day-ahead market clearing model has been developed to solve the energy, reserve, and flexible ramp scheduling considering a real-time power balance problem, reflecting the 5 min.-by-5 min. renewable energy source and demand changes. Numerical tests are conducted on a modified IEEE RTS 24-bus and the obtained results validate the applicability of proposed model.

Effects of PEV Traffic Flows on the Operation of Parking Lots and Charging Stations

The introduction of plug-in electric vehicles (PEVs) in the electrical system is bringing various challenges. The main issue is incorporating the PEV owner’s preferences in the models. One of the main attributes representing the preference of the owners is their travel purposes, impacting on the traffic flow pattern. The PEVs’ traffic pattern defines the required charging schedule of the PEVs, and consequently, characterizes the operation of the charging facilities such as PEV parking lots (PLs). The deployment of resources such as PEV PL requires a detailed modeling of the factors affecting their operation. In this regard, this paper aims to model the power flow of the PEVs based on their traffic flow. Different travel types and purposes are considered for the PEVs traffic modeling. Two types of charging infrastructure (i.e., PLs and individual charging stations) are considered. The study is performed on a distribution network categorized based on the consumption patterns of the zones.

Evaluating the benefits of coordinated emerging flexible resources in electricity markets

Increasing share of variable renewable energy sources (VRESs) with the aim of tackling climate changes impose several techno-economic challenges to power system operation. VRESs reduce the available flexibility by displacing existing flexible units due to their priority in dispatch and simultaneously enhance the need for additional flexibility due to their uncertain nature. In this light, the system is faced with a flexibility gap. One way to cover the created flexibility gap is the incorporation of emerging flexible resources into power systems operation. On this basis, this paper proposes a comprehensive flexible generation portfolio including bulk energy storages (BESs), plug-in electric vehicle parking lots (PEV PLs), and demand response (DR) programs. A stochastic market-based model is proposed to coordinate the interactions among these flexibility providers considering different sets of uncertainty, such as wind power generation and PEV owner’s behavior. Finally, various generation mixtures are prioritized based on the system operator’s economic, technical, and environmental desires to provide a guideline to opt the most effective generation mixture in the context of flexibility promotion.

Modelling probabilistic transmission expansion planning in the presence of plug‐in electric vehicles uncertainty by multi‐state Markov model

Increasing penetration of plug-in electric vehicles (PEVs) in the power system makes a need to consider the impact of PEVs on the transmission expansion planning (TEP) studies especially for large-scale PEV parking lots. Modelling of PEVs (as vehicle to grid (V2G)) is highly dependent on the owner's behaviour. In this study, a systematic method based on multi-state Markov model is utilised to represent the uncertainty of V2G's presence. To investigate the impact of PEVs, a probabilistic TEP in the presence of V2Gs (P_TEPV2G) is proposed. In the proposed TEP model, the objective function consists of the total line and risk costs (RCs). Moreover, the optimal place and capacity of PEV parking lots are considered as the decision variables. It is assumed that there is an electric vehicle fleet operator for the management of electric vehicles. Because P_TEPV2G is a complex and non-linear optimisation problem, an improved cuckoo search algorithm (ICS) is utilised to effectively solve the problem. On the IEEE 24-bus system, simulation results show that the availability of the PEVs at the proper bus leads to decreasing RC and deferring construction of new transmission lines.

Distributed cooperative control for economic operation of multiple plug-in electric vehicle parking decks

This paper addresses the economic operation of multiple plug-in electric vehicle parking decks using novel distributed cooperative control methods. In this paper, we proposes a consensus-based distributed cooperative control algorithm to determine the best operation strategy for the operator/owner of multiple parking decks. The proposed distributed control approach completely eliminates the reliance on a central controller to make an optimal decision while still satisfying a variety of global and local constraints. Case studies demonstrate the accuracy, robustness, effectiveness, and scalability of the proposed distributed cooperative control method for solving the economic operation problem of multiple plug-in electric vehicle parking decks under various operating conditions. The distributed control ideas contained here can apply to many other smart grid applications.

Optimal Behavior of Electric Vehicle Parking Lots as Demand Response Aggregation Agents

With increasing environmental concerns, the electrification of transportation plays an outstanding role in the sustainable development. In this context, plug-in electric vehicle (PEV) and demand response have indispensable impacts on the future smart grid. Since integration of PEVs into the grid is a key element to achieve sustainable energy systems, this paper presents the optimal behavior of PEV parking lots in the energy and reserve markets. To this end, a model is developed to derive optimal strategies of parking lots, as responsive demands, in both price-based and incentive-based demand response programs (DRPs). The proposed model reflects the impacts of different DRPs on the operational behavior of parking lots and optimizes the participation level of parking lots in each DRP. Uncertainties of PEVs and electricity market are also considered by using a stochastic programming approach. Numerical studies indicate that the PEV parking lots can benefit from the selective participation in DRPs.

Flexible interaction of plug-in electric vehicle parking lots for efficient wind integration

The increasing share of uncertain wind generation has changed traditional operation scheduling of power systems. The challenges of this additional variability raise the need for an operational flexibility in providing both energy and reserve. One key solution is an effective incorporation of plug-in electric vehicles (PEVs) into the power system operation process. To this end, this paper proposes a two-stage stochastic programming market-clearing model considering the network constraints to achieve the optimal scheduling of conventional units as well as PEV parking lots (PLs) in providing both energy and reserve services. Different from existing works, the paper pays more attention to the uncertain characterization of PLs takes into account the arrival/departure time of PEVs to/from the PL, the initial state of charge (SOC) of PEVs, and their battery capacity through a set of scenarios in addition to wind generation scenarios. The results reveal that although the cost saving as a consequence of incorporating PL to the grid is below 1% of total system cost, however, flexible interactions of PL in the energy and reserve markets can promote the integration of wind power more than 13.5%.

Allocation of Plug-In Vehicles' Parking Lots in Distribution Systems Considering Network-Constrained Objectives

A recent solution to tackle environmental issues is the electrification of transportation. Effective integration of plug-in electric vehicles (PEVs) into the grid is important in the process of achieving sustainable development. One of the key solutions regarding the need for charging stations is the installation of PEV parking lots (PLs). However, contrary to common parkings, PLs are constrained by various organizations such as municipalities, urban traffic regulators, and electrical distribution systems. Therefore, this paper aims to allocate PLs in distribution systems with the objective of minimizing system costs including power loss, network reliability, and voltage deviation as possible objectives. A two-stage model has been designed for this purpose. PLs' behavior considering market interactions is optimized at the first stage to provide profit to the PL owner. At the second stage, the PL allocation problem is solved considering various network constraints. Conclusions are duly drawn with a realistic example.