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Real-Time Smart Charging Based on Precomputed Schedules
Abstract
Employees are increasingly using electric vehicles (EVs) as their choice of company car. Charging infrastructure is limited by undersized connection lines and a lack of charging stations on company premises. Upgrades require significant financial investment, time and effort. Smart charging represents an approach to making the most of existing infrastructure while satisfying charging needs. The objective of smart charging depends on the business context. Objectives of interest include fair share maximization, electricity cost minimization, peak demand minimization and load imbalance minimization. During business hours, EV arrivals and departures are predictable while still containing uncertainty. To utilize this knowledge ahead of time, this work presents a novel approach for combining day-ahead and real-time planning for smart charging. First, we model the problem using mixed integer programming for day-ahead planning to precompute schedules. Next, we propose a schedule guided heuristic which takes as input precomputed schedules and adapts them in real-time as new information arrives. Both methods use a parameterized weighting mechanism to flexibly combine and emphasize individual objectives of smart charging. Experimental results from simulations show significant benefits of combining day-ahead and real-time planning over using a single planning approach in isolation. Improvements include increased fair share and decreased energy costs.
... Researchers have proposed various models to optimize smart EV scheduling in commercial charging stations. Studies such as [7], [8], [9], [10], [11], [12], [13], [14], [15], [16] formulate EV scheduling as an online problem. ...
... Fallah-Mehrjardi et al. [12], in their paper, formulated a multistage stochastic programming model for EV scheduling in a smart parking lot with uncertain conditions. While Oliver et al. [13] used precomputed offline schedules for real-time scheduling; neither their research nor the studies in [9], [10], [11], [12], [13], [14], [15] considered the problem of on-site renewable energy at the charging station. Moreover, the charging stations in [13], [14], [15] only allowed the unidirectional power flow, and the scheduling problem was not formulated as a convex optimization problem. ...
... Fallah-Mehrjardi et al. [12], in their paper, formulated a multistage stochastic programming model for EV scheduling in a smart parking lot with uncertain conditions. While Oliver et al. [13] used precomputed offline schedules for real-time scheduling; neither their research nor the studies in [9], [10], [11], [12], [13], [14], [15] considered the problem of on-site renewable energy at the charging station. Moreover, the charging stations in [13], [14], [15] only allowed the unidirectional power flow, and the scheduling problem was not formulated as a convex optimization problem. ...
This article presents real-time Pareto-optimal scheduling for bidirectional electric vehicle (EV) charging in a commercial charging station with on-site renewable energy and battery energy storage to optimize several objectives. To incorporate the inherent uncertainty in the model, mixture density neural networks are presented to estimate the parameters of the probability distribution of demands and deadlines using a negative-log-likelihood loss function. From the joint distribution of demands and deadlines, future EV charging requests are estimated. Furthermore, we formulate the control problem as a multiobjective stochastic convex optimization problem from the perspective of the charging station operator, which simultaneously aims to minimize the total cost of charging, frequent change in charging rates, maximum demand of the charging station and battery degradation costs subject to various system constraints. We empirically evaluate the proposed scheduling policy for optimality gap, competitive ratio, and robustness, and show that the proposed scheduling policy reduces cost by about
over the benchmark scheduling policies.
... This requires deciding which EVs may charge at which charging station and at which power during which time periods. The issue finally leads to the approach of a coordinated charging scheme, which can be classified into vehicle-to-charging station assignment, offline or day-ahead planning and realtime planning [8]. ...
... Optimizing power allocation for different stations [9], infrastructure usage [10] and minimizing distance driven for EV users [11] have been the primary considerations in vehicle-tocharging station assignment before vehicles access the power distribution network. This problem is independent of the time of planning and related works usually addressed the assignment problem together with either day-ahead and real-time planning or both [8]. Some previous studies considered the process after the vehicle-to-charging station assignment, involving the optimizing coordinated charging schemes for different chargers in the station based on objectives such as minimizing charging costs [12], maximizing user charging demand [13] and maximizing grid power stability [14] [15]. ...
... Establishing the priority of EV users has been a typical approach to deciding which chargers may load in addition to the most direct average allocation in charging power [18]. For example, maximizing the desired battery SOC [19], reducing load imbalance [20], order by the time [8][21] [22] (e.g., first come, first served -FcFs, first departure, first served -FdFs) and order by fairness [23] [24]. These studies solved the problem with a reasonable approach to keeping the optimization objectives feasible. ...
The widespread adoption of electric vehicles (EVs) with fast-charging capability has presented significant challenges to the safe operation of power distribution networks. Issues such as node overvoltage and frequency fluctuations should be addressed to ensure power quality while maximizing charging Quality-of-Service (QoS) for EV users. This challenge is particularly crucial for fast-charging events that have more flexible demands. Charging priority has traditionally been used to determine an EV charging scheme, including waiting time, arrival order and other factors. However, the scheduling potential predicted from the vehicle side has not been fully considered in the priority establishment when determining real-time operation. To address this gap, a bi-level framework is proposed for a coordinated charging scheme at the fast-charging station that factors in demand-based priority. The framework considers available charging power allocation at different priority levels and optimizes the charging scheme under the same priority level based on three real-time QoS objectives. This approach can improve the average charging QoS for EV users compared to traditional algorithms without considering the demand priority. In robustness evaluation, the QoS of scheduled charging events can reach 86.9% (median) of the optimal solution, and the range of fluctuations in different trials is relatively more stable.
... In this work we present an open source package containing a smart charging algorithm proposed in previous work [1] together with experimental results from applying the heuristic in a one-year field test. The algorithm is flexible with regard to aspects such as EV fleet composition, charging station setup or hardware used. ...
... The algorithm is flexible with regard to aspects such as EV fleet composition, charging station setup or hardware used. We refer to [1] for a detailed discussion of the smart charging algorithm and in this work focus on interoperability and the validation of the algorithm. ...
... There is a large body of related work on smart charging for EV fleets. Typical objectives in smart charging include minimizing energy costs [1], [2] and maximizing driver satisfaction [3]- [5]. Approaches to smart charging include methods such as mixed integer programming [6], queuing theory [7], game theory [8] and evolutionary algorithms [9]. ...
Smart charging assigns charging capacities between vehicles in limited charging infrastructures. Smart charging solutions are becoming widespread with increasing numbers of commercial offerings. However, commercial solutions are intransparent regarding algorithm implementation. In contrast, open source solutions are transparent and open for collaborative development and scientific research. This paper presents an open source package with a smart charging algorithm. The algorithm schedules heterogeneous fleets of vehicles for charging while ensuring a fair share for each vehicle. We present implementation aspects of the smart charging algorithm including data structures and REST application programming interfaces. Additionally, the smart charging algorithm was validated in a one-year field test. Experimental results of the field test show EVs at six charging stations can be scheduled for charging when the grid connection only allows two EVs to charge concurrently. Runtime measurements demonstrate the smart charging algorithm is applicable in real time and scales to large fleet sizes.
... Apart from relying on human knowledge, optimization methods can be applied to guide rule-based control. Ref. [74] pre-computed the charging scheduling via MIP and then applied a rule-based algorithm based on the pre-computed schedule. Further, rule-based algorithms can be calibrated to improve performance. ...
... For example, Ref. [132] has integrated both earliest deadline first (EDF) and least laxity first (LLF) to maximize energy delivery. Ref. [74], with the same objective, showed a different sorting-based algorithm. These problems where sorting-based algorithms are directly applicable have the same feature: the overall EV charging load from different time slots is decoupled and readily or easily available. ...
... The current version of the SCS implements a scheduling procedure illustrated by the flowchart in Figure 2. The initial concept is presented in [13,22], and the corresponding implementation is available online on GitHub [23]. The main goal of the overall process (see also the pseudo-code in Algorithm 1) is to share the basically limited charging power at a given location among the connected EVs in a fair manner. ...
... Flowchart diagram of the scheduling procedure based on[13] with the main calculation steps and optionally involved data sources (Fleet Management, Vehicle Backend, Mobile App, EMS, Site Management). ...
Smart charging is a means of monitoring and actively controlling EV chargers to optimize the distribution and consumption of energy with a focus on peak-load avoidance. This paper describes the most important requirements that have influenced the design and implementation of the “Smart Charging System” (SCS). It presents the architecture and main functional building blocks of the SCS, which have been realized in an iterative development process as an extension component of the already existing open-source solution “Open e-Mobility”. We also provide details on the functionality of the core smart charging algorithm within SCS and show how various data sources can be utilized by the system to increase the safety and efficiency of EV charging processes. Furthermore, we describe our iterative approach to developing the system, introduce the real-world testbed at SAP Labs France in Mougins/France, and share evaluation results and experiences gathered over a three-year period.
... Most of the controlled charging of electric vehicles (EVs) shares one or more of the four prevailing primary targets [13][14][15][16]: (i) grid impact mitigation, (ii) profit maximisation, (iii) enhancing the service to EV users and (iv) increasing the utilisation of renewable energy resources. To achieve multiple targets with one charging method, EV charging power is usually not the only parameter that must be tuned, especially in complex systems where other relevant parties are involved [13,[17][18][19]. ...
... The above-mentioned primary targets become competitive towards each other under some operational conditions. For example, it is suggested in [14] that prioritizing peak shaving can approximately half the maximum demand but leads to a 5-10% increase in average energy costs as a trade-off. Further, while the method proposed in [15] successfully satisfies the load congestion and voltage droop constraints relative to 70% of exceeded constraints in the uncontrolled method, it is suggested that location-specific customer demands may lead to a fairness challenge that needs further investigation. ...
This paper benchmarks the performance of three practical electric vehicle (EV) charging scheduling methods relative to uncontrolled charging (UNC) in low-voltage (LV) distribution grids. The charging methods compared are the voltage droop method (VDM), price-signal-based method (PSM) and average rate method (ARM). Trade-offs associated with the grid performance, charging demand fulfilment and economic benefits are explored for three different grid types and four increasing levels of EV penetration for summer and winter. This study was carried out using grid simulations of six existing Dutch distribution grids, and the EV charging demand was generated based on 1.5 M EV charging sessions; therefore, the findings of this research are relevant for actual case studies. The results suggest that the PSM can be a preferred strategy for achieving a charging cost reduction of 6–11% when the grid performance is not a bottleneck for the given EV penetration. However, it can lead to an increased peak loading of the grid under certain operational conditions, resulting in a charging energy deficiency ratio of 4–8%. The VDM should be preferred if user information on the parking time and energy demand is not consistently available, and if the mitigation of grid congestion is critical. However, both unfinished charging events and charging costs increase with the VDM. The ARM provides the best balance in the trade-offs associated with the mitigation of grid congestion and price reduction, as well as charging completion. This research provides a perception of how to select the most appropriate practical charging strategy based on the given system requirements. The outcome of this study can also serve as a benchmark for advanced smart charging algorithm evaluation in the future.
... The problem is formulated as a finite-horizon dynamic programming and solved via a model predictive control-based algorithm. The charging coordination problem is formulated as a mixedinteger program in [11] for day-ahead scheduling. Then, the precomputed schedules are updated online with the newly received information. ...
... -(11),(15),(18),(21) and(22)D v,t ∈ [0, 1], ∀v ∈ V, ∀t ∈ T . ...
Charging coordination is employed to efficiently serve electric vehicle (EV) charging requests without overloading the distribution network. Parameters such as parking duration, battery state-of-charge (SoC), and charging amount are provided by EVs to the charging coordination center to schedule their charging requests efficiently. The existing literature assumes that the customers always provide correct information. Unfortunately, customers may provide false information to gain higher charging priority. Assessing the impact of cheating behavior represents a significant and open problem. Herein paper, the impact of providing false information (e.g., parking duration) on the efficiency of the charging coordination mechanism is investigated. The charging coordination strategy is formulated as a linear optimization problem. Two different objectives are used to assess the impact of the objective function on the amount of performance degradation. Our investigations reveal that the degradation of the efficiency of the charging coordination mechanism depends on the percentage of cheating customers and cheating duration versus the typical parking duration. In addition, the impact of cheating behavior increases with the number of deployed chargers. Thus, the severity of the cheating impact will increase in the future as more fast chargers are allocated in charging networks.
... A flexible method named grid picking mainly aimed at avoiding transformer overload and maximizing charging income was proposed in [10] to solve such problems as limited capacity of transformer in the residential area and lack of operating motivation on the part of the residential property. [11] modelled a smart charging method combining day-ahead and real-time, with the goal of minimizing charging costs, peak demand and load imbalance, considering the fairness of the charging plan. [12] proposed a fair distribution method of charging services based on fluid model in the situation of limited charging facilities. ...
... in addition to (11)(12)(13), ensuring the consistency of the scheduling plan for the next execution interval. At the same time, modify the optimization objective 2 to: ...
While providing charging service for electric vehicles, charging stations are also aggregation centers which supply adjustment capabilities to the power grid. It is necessary for charging stations to take care of all parties’ interests with respect to users’ independent choice. This paper focuses on multi-element charging stations which contain conventional load, energy storage and distributed renewable energy generation besides charging piles. Firstly, charging price mechanism that improves participation motivation of users in providing charging flexibility were designed. Next, rational model based on expected utility and bounded rational model based on prospect theory were established respectively. Model predictive control(MPC) has been selected to handle uncertainties caused by users’ behavior and output of distributed renewable energy generation. Both operating economy and adjustment flexibility of charging stations have been taken into account in proposed comprehensive scheduling strategy. Based on the setting of dummy variables, double-layer optimization has been converted into single-layer optimization which can reduce the calculation time significantly, making the strategy suitable for online application. Advantage of the bounded rational model in predicting users’ behavior was verified by experiments towards real people. The selection of time scale for MPC was discussed through simulation experiments in different scenarios. In the simulation experiment, the proposed scheduling model integrating social-physical methods has shown good performance in power grid requirement following and economic optimization.
... Table 1 summarizes the comparison between the relevant studies and this work. Note that Frendo et al. (2019) does not need the overall EV charging load approximation validation as they applied an optimal individual model to compute the day-ahead scheduling, which provides the optimal overall load with the given parameters. ...
... One drawback is the potential increase in the complexity of implementation, particularly in large-scale deployments [7]. Additionally, the algorithm relies on accurate information about the EV fleet, real-time pricing data, and grid conditions, which may pose challenges in practice [8]. Moreover, the frequent on-off charging events associated with smart charging algorithms can lead to increased battery degradation and reduced battery life, impacting the overall longevity and costeffectiveness of EVs [9]. ...
This paper introduces a novel scheduling framework designed to manage the charging of electric vehicles (EVs) in a way that considers its effects on the power grid. Leveraging the Alternating Direction Method of Multipliers (ADMM), the methodology offers a significant advantage by enabling decentralized sub-problems, allowing for efficient and rapid solutions. The methodology developed as an algorithmic framework incorporates various scheduling approaches for EV charging, including demand management techniques like valley filling and peak shaving, along with real-time pricing (RTP) considerations. These strategies aim to modify individual electricity consumption patterns to reduce peak demand, ultimately enhancing energy efficiency and ensuring the stability of the power system. The results of the study highlight the crucial role of distributed optimization in improving both demand management strategies and cost objectives. The results indicated that the proposed method shows significant improvement in overall energy efficiency when compared to the state-of-the-art centralized convex optimization framework.
... Furthermore, numerous studies currently explore the joint dispatch of electricity-traffic coupling systems (ETCSs). The scheduling process incorporates multi-objectives, encompassing minimizing user travel costs, charging costs [7,8], maximizing user satisfaction and EVCS profit [9][10]. Reasonable approaches encompass establishing rational bus electricity prices [11][12][13], implementing road congestion charges and so on. ...
... In [41] the DC communication standard of ISO15118 and its detailed structure is described and analyzed. In [42] a real-time smart charging, based on precomputed schedules, is assessed as a solution to mitigate the problem of uncoordinated and uncontrolled charging, minimizing electricity cost, peak demand, and load imbalance. ...
The increasing concern over the environmental impact of fossil fuels and associated CO2 emissions created a growing interest on the use of electric vehicles (EVs) and green energy utilization. In this context, the widespread adoption of EVs should be accompanied by the introduction of generation from renewable energy sources (RES). That insertion, at the distribution level, presents challenges that result from their intermittent nature, requiring demand-response measures that can be addressed by adjusting the charging processes to match the available power. In the framework of EVs renting companies, it is essential to have an efficient charging management that allows achieving high levels of self-consumption and self-sufficiency, lower operational costs and lower payback periods for the investments made. The utilization of digital twins (DTs) can be key to achieve those goals, providing accurate simulations and predictions. Their use in the context of EV charging can offer valuable insights into optimizing charging scheduling and predicting energy demands, taking into consideration distinct scenarios. This paper presents the work done to implement DTs of a set of charging stations (CSs) and EVs, which allow the modeling and improved management of the charging processes of EV fleets, for a set of CSs, integrating RES. In this charging context, experimental results using the DT were applied considering a predicted mobility. The applied scenarios supported an effective and optimized managing performance, reaching low paybacks and high self-sufficiency values. The obtained results show that this method is a viable and cost-effective solution for companies renting EVs.
... Table 1 summarizes the comparison between the relevant studies and this work. Note that Frendo et al. (2019) does not need the overall EV charging load approximation validation as they applied an optimal individual model to compute the day-ahead scheduling, which provides the optimal overall load with the given parameters. ...
Existing studies have applied a two-stage approach for large-scale smart EV charging to reduce computation time. The first stage approximates the optimal overall load, and the second prioritizes charging. However, validation and analysis are missing to address whether and why the two-stage approach is suitable. Besides, the existing studies lack exploring different methods to prioritize charging. This work summarizes the two-stage approach and identifies potential concerns. Further, this work validates the approach regarding maximizing the PV-EV temporal load matching at a workplace. Simulation results show that the two-stage approach achieves only 1.7% lower performance than the optimal solution if the optimal overall load is unavailable. Otherwise, lower approximation errors lead to higher final scheduling performance. This work also applies an aggregated model to approximate the overall load. The final performance is only 0.7% lower than when the optimal load is available. Furthermore, this work explores different methods to prioritize charging. Simulation results show that the Least Laxity First performs slightly better, while the Earliest Deadline First can significantly reduce the switching frequency (29.6%). Based on those simulation results, this paper concludes that the two-stage approach can be suitable for aiming to match large-scale EV charging with PV generation.
... As surveyed in [4,29,39], a considerable body of literature has been published on controlled EV charging, proffering a rich arsenal of techniques to cater for various operational objectives, such as relieving power system congestion [1], maximizing EV owners' convenience [38], minimizing charging expenses [11], enhancing voltage profile [6], valley filling [12,38], to name a few. From a methodological standpoint, the existing approaches can be broadly categorized into three groups: approximation algorithms, heuristics/metaheuristics, and Deep Learning (DL) based methods. ...
... As surveyed in [4,29,39], a considerable body of literature has been published on controlled EV charging, proffering a rich arsenal of techniques to cater for various operational objectives, such as relieving power system congestion [1], maximizing EV owners' convenience [38], minimizing charging expenses [11], enhancing voltage profile [6], valley filling [12,38], to name a few. From a methodological standpoint, the existing approaches can be broadly categorized into three groups: approximation algorithms, heuristics/metaheuristics, and Deep Learning (DL) based methods. ...
With the electrification of transportation, the rising uptake of electric vehicles (EVs) might stress distribution networks significantly, leaving their performance degraded and stability jeopardized. To accommodate these new loads cost-effectively, modern power grids require coordinated or ``smart'' charging strategies capable of optimizing EV charging scheduling in a scalable and efficient fashion. With this in view, the present work focuses on reservation management programs for large-scale, networked EV charging stations. We formulate a time-coupled binary optimization problem that maximizes EV users' total welfare gain while accounting for the network's available power capacity and stations' occupancy limits. To tackle the problem at scale while retaining high solution quality, a data-driven optimization framework combining techniques from the fields of Deep Learning and Approximation Algorithms is introduced. The framework's key ingredient is a novel input-output processing scheme for neural networks that allows direct extrapolation to problem sizes substantially larger than those included in the training set. Extensive numerical simulations based on synthetic and real-world data traces verify the effectiveness and superiority of the presented approach over two representative scheduling algorithms. Lastly, we round up the contributions by listing several immediate extensions to the proposed framework and outlining the prospects for further exploration.
... For the fixed decision time slot duration, this paper chooses the most often used value in the literature: 15 min [7,47,48], corresponding to the typical communication time between the energy management units and the grid [49]. The experimental setting chooses the least number of EVs with a proper final average SoC to limit the computational complexity. ...
Intelligent charging solutions facilitate mobility electrification. Mathematically, electric vehicle (EV) charging scheduling formulations are constrained optimization problems. Therefore, accurate constraint modeling is theoretically and practically relevant for scheduling. However, the current scheduling literature lacks an accurate problem formulation, including the joint modeling of the nonlinear battery charging profile and minimum charging power constraints. The minimum charging power constraint prevents allocating inexecutable charging profiles. Furthermore, if the problem formulation does not consider the battery charging profile, the scheduling execution may deviate from the allocated charging profile. An insignificant deviation indicates that simplified modeling is acceptable. After providing the problem formulation targeting the maximum possible vehicle battery state of charge (SoC) on departure, the numerical assessment shows how the constraint consideration impacts the scheduling performance in typical charging scenarios (weekday workplace and weekend public charging where the grid supplies up to forty vehicles). The simulation results show that the nonlinear battery charging constraint is practically negligible: For many connected EVs, the grid limit frequently overrules that constraint. The resulting difference between the final mean SoCs using and not using accurate modeling does not exceed 0.2%. Consequently, the results justify simplified modeling (excluding the nonlinear charging profile) for similar scenarios in future contributions.
... As EV capacity surges, future V2G projects will most likely arbitrage real-time electricity prices that are highly volatile and uncertain, and EVCS must consider price uncertainties. Frendo et al. and Ahmad et al. [31], [32] use day-ahead prices to forecast the next day's LMPs, incorporating EVCS controller using MILP formulation. Zhang et al. [33] develop the charging control deep deterministic policy gradient, which models EV charging as a Markov decision process (MDP) and optimizes user satisfaction and charging costs with the output of a long short-term memory (LSTM) network that approximates sequential energy price dynamics. ...
The surging adoption of electric vehicles (EV) calls for accurate and efficient approaches to coordinate with the power grid operation. By being responsive to distribution grid limits and time-varying electricity prices, EV charging stations can minimize their charging costs while aiding grid operation simultaneously. In this study, we investigate the economic benefit of vehicle-to-grid (V2G) using real-time price data from New York State and a real-world charging network dataset. We incorporate nonlinear battery models and price uncertainty into the V2G management design to provide a realistic estimation of cost savings from different V2G options. The proposed control method is computationally tractable when scaling up to real-world applications. We show that our proposed algorithm leads to an average of 35% charging cost savings compared to uncontrolled charging when considering unidirectional charging, and bi-directional V2G enables additional 18% cost savings compared to unidirectional smart charging. Our result also shows the importance of using more accurate nonlinear battery models in V2G controllers and evaluating the cost of price uncertainties over V2G.
... The aforementioned papers mainly use more conventional mathematical programming methods to show the benefits of optimally scheduling the charging process of BEBs. However, these methods can have long computation times which make them appropriate for day-ahead scheduling, but less suitable for solving real-time charging scheduling problems [19,20]. This is important to enable smart charging in real-world applications such as bus depots and is currently missing in the scientific literature. ...
To improve the air quality in urban areas, diesel buses are getting replaced by battery electric buses (BEBs). This conversion introduces several challenges, such as the proper control of the charging process and a reduction in the operational costs, which can be addressed by introducing smart charging concepts for BEB fleets. Therefore, this paper proposes a real-time scheduling and optimization (RTSO) algorithm for the charging of multiple BEBs in a depot. The algorithm assigns a variable charging current to the different time slots the charging process of each BEB is divided to provide an optimal charging schedule that minimizes the charging cost, while satisfying the power limitations of the distribution network and maintaining the operation schedule of the BEBs. A genetic algorithm is used to solve the formulated cost function in real time. Several charging scenarios are tested in simulation, which show that a reduction in the charging cost up to 10% can be obtained under a dynamic electricity price scheme. Furthermore, the RTSO is implemented in a high-level charging management system, a new feature required to enable smart charging in practice, to test the developed algorithm with existing charging infrastructure. The experimental validation of the RTSO algorithm has proven the proper operation of the entire system.
... Because of their longer dwell times and predictable mobility patterns, EVs enable smart charging practices in the workplace. The smart charging algorithm involves decision-making about EV scheduling in order for both the charging station operator and the EV user to fully benefit from smart charging [49]. The proposed framework is illustrated in Fig. 3. ...
This study proposes a new multi-criteria decision-making model to determine the best smart charging scheduling that meets electric vehicle (EV) user considerations at work-places. An optimal charging station model is incorporated into the decision-making for a quantitative evaluation. The proposed model is based on a hybrid Power Heronian functions in which the linear normalization method is improved by applying the inverse sorting algorithm for rational and objective decision-making. This enables EV users to specify and evaluate multi-criteria for considering their aspects at workplaces. Five different charging scheduling algorithms with AC dual port L2 and DC fast charging electric vehicle supply equipment (EVSE) are investigated. Based on EV users from the field, the required charging time, EVSE occupancy, the number of EVSE units, and user flexibility are found to have the highest importance degree, while charging cost has the lowest importance degree. The experimental results show that, in terms of meeting EV users’ considerations at workplaces, scheduling EVs based on their charging energy needs performs better as compared to scheduling them by their arrival and departure times. While the scheduling alternatives display similar ranking behavior for both EVSE types, the best alternative may differ for the EVSE type. To validate the proposed model, a comparison against three traditional models is performed. It is demonstrated that the proposed model yields the same ranking order as the alternative approaches. Sensitivity analysis validates the best and worst scheduling alternatives.
... If a charging session spans over 2 scheduling horizons, we cut it to the scheduling horizon where the EV had a longer connection time. The often-used decision time slot τ in the literature is 0.25 h (15 min) [15], [61], [62], which corresponds to the typical communication time between the energy management units and the grid [63]. However, to reduce the computation complexity and time, we set τ to 0.5 h. ...
Integrating electric vehicle (EV) charging infrastructures into the utility grids requires solutions for coordinated charging of EVs. Load management systems aim at computing coordinated charging schedules for electric vehicles based on predetermined charging objectives. Contributions on coordinated charging for EVs predominantly assume that the EVs or the charging stations are controllable entities in the load management systems. However, in practice, charging infrastructures may consist of controllable and uncontrollable entities. This paper proposes architecture and control strategies for EV charging infrastructures consisting of controllable and uncontrollable entities. Simulations based on real-world charging sessions show how the share of uncontrollable entities in a charging infrastructure affects the performance of different control strategies in the system architecture. We show that a certain number of uncontrollable entities in a charging infrastructure does not affect the scheduling objectives significantly. EV fleet and charging infrastructure operators can develop pragmatic investment and operation strategies based on the proposed control strategy and architecture.
... Many control algorithms and associated systems have been proposed in the literature [8,9]. For example, authors propose to provide primary reserve [10,11], i.e., to change the charging power of the EV according to the frequency deviation of the network, or to limit EV load to the power network capacity available [12][13][14]. Other authors propose to provide reactive power [15] or to minimize the effects on the grid of the rapid photovoltaics (PV) production output fluctuations due to cloud transients [16] or even to balance wind energy [17]. Other control algorithms aim to minimize the charging cost for the user [18], for a fleet manager [19] or for a parking operator [20]. ...
Sales of electric vehicles, for commercial use and personal use, keep rising. In parallel of the development of the associated Electric Vehicle Charging Infrastructure (EVCI), systems for controlling the charging of EVs will have to be developed in order to reduce the impact of such a development on the power grid. In this paper, we present a supervision system that controls the electric vehicle charging of employees of CEA Cadarache research center. The EVCI of Cadarache, set up in 2016, is constituted of more than 80 22-kW AC charging points spread over 30 zones. This EVCI currently supplies more than 376 vehicles including taxis, service vehicles as well as employees’ vehicles. This infrastructure is one of the largest private EVCIs in the region. The supervision system controls Electric Vehicle (EV) charging in real-time according to two objectives: respecting user preferences, by fully charging the EV battery, and synchronizing the power consumption of a fraction of the EVCI, i.e., 24 charging points, with the power production of a solar photovoltaic plant. This paper details the supervision system that is used to carry out these experiments and presents experimental results. These results show that it is technically feasible to increase (up to 60 percentage points) the self-production ratio while satisfying EV users.
... Depending on EVSE types, the infrastructure cost differs greatly for fleet size. Therefore, most studies have focused on smart charging strategies in order not only to reduce operational charging costs, but also to use the charging infrastructure efficiently [6], [7]. It is shown that the smart charging algorithm can provide effective use of EVSEs in real-time with reduced charging costs. ...
As the transition to electric mobility is expanding at a rapid pace, operationally feasible and
economically viable charging infrastructure is needed to support electrified fleets. This paper presents a
co-simulation of optimal electric vehicle supply equipment (EVSE) and techno-economic system design
models to investigate the behaviors of various EVSE configurations from cost and technical aspects. While
the system design optimization is performed for a grid-tied PV system, the optimal EVSE model considers
all EVSE options that are currently installed at workplaces. To investigate the impact of the EV utilization
rate, three fleet sizes are considered, which are generated based on real EV fleet data. Furthermore, the
impact of electricity rates is also explored through an innovative business EV-specific (BEV) rate and a
conventional time-of-use (ToU) tariff. It is shown that investing in grid-tied renewable energy technologies
for workplace charging infrastructure supply can lower charging costs. Cost savings differ from EVSE types
and fleet size under the BEV rate, while EVSEs display similar cost-saving behavior under the ToU tariff
irrespective of fleet size. DC Fast Charging (DCFC) EVSE is found to be highly sensitive to fleet size as
compared to AC EVSEs. Moreover, DCFCs make better use of the BEV rate, which makes their economics
competitive as much as AC EVSEs. Finally, it is found that the fleet size and AC EVSE types have a minor
effect on the use of renewable energy in contrast to the DCFC case.
... Reference [57] presents an optimal day-ahead and real-time planning for smart charging. It combines both day-ahead planning with real-time coordination. ...
The electrical sector has been evolving. This situation is because new methodologies emerge to deal with the high penetration of distributed energy resources (DER), mainly electric vehicles (EVs). In this case, energy resource management has become increasingly prominent due to the technological advances that are taking place, mainly in the context of smart grids. This factor becomes essential due to the uncertainty of this type of resource. To solve problems involving variability, methods based on computational intelligence (CI) are becoming the most suitable because of their easy implementation and low computational effort, more precisely for the case treated in this thesis, evolutionary computation (EC) algorithms. This type of algorithm tries to mimic behavior observed in nature. Unlike deterministic methods, the EC is tolerant of uncertainty, and thus it is suitable for solving problems related to energy systems. These systems are usually of high dimensions, with an increased number of variables and restrictions. Here the CI allows obtaining a near-optimal solution in good computational time with low memory requirements. This work's main objective is to propose a model for the energy resource scheduling of the dedicated resources for the intraday context, for the our-ahead, starting initially from the scheduling done for the day ahead, that is, 24 hours for the next day. This scheduling is done by each aggregator (in total five) through metaheuristics to minimize the costs or maximize the profits. These aggregators are inserted in a smart city with a distribution network of 13 buses with a high penetration of DER, mainly renewable energy and EVs (2000 EVs are considered in the simulations). Several scenarios are generated through Monte Carlo Simulation using the forecast errors' probability distribution functions, the normal distribution function for the day-ahead to model the uncertainty associated with DER and market prices. Multiple scenarios are developed through the highest probability scenario from the day-ahead when it comes to intraday uncertainty. In this work, local electricity markets are used as a mechanism to satisfy the energy balance equation where each aggregator can sell the excess of energy or buy more to meet the demand. Several recent and modern metaheuristics are used to solve the proposed problems in the thesis, namely Differential Evolution (DE), Hybrid-Adaptive DE with Decay function (HyDE-DF), DE with Estimation of Distribution Algorithm (DEEDA), Cellular Univariate Marginal Distribution Algorithm with NormalCauchy Distribution (CUMDANCauchy++), Hill Climbing to Ring Cellular Encode Decode UMDA (HC2RCEDUMDA). Results show that the proposed model is effective for the multiple aggregators. The metaheuristics present satisfactory results and mostly less than 5% variation in costs from the day-ahead except for the EV aggregator. A Wilcoxon test is also applied to compare the performance of the CUMDANCauchy++ algorithm with the remaining metaheuristics. CUMDANCauchy++ shows competitive results beating all algorithms in all aggregators except for DEEDA, which presents similar results. A risk aversion strategy is implemented for an aggregator in the day-ahead context to get a safer and more robust solution. Results show an increase of nearly 4% in day-ahead cost but a reduction of up to 14% of worst scenario cost.
... The objectives for charging scheduling are typically flattening the grid load [3], reducing the charging cost [4], and maximizing the delivered energy to EVs [5]. Traditional computational scheduling methods include genetic programming [3], quadratic programming [6], and mixedinteger programming [7]. We refer to [8], [9] for a review of smart charging objectives and algorithms. ...
In recent years, several optimization techniques have
been proposed for electric vehicle (EV) charging scheduling. A common approach to intelligent scheduling is day-ahead
planning, assuming full arrival time, departure time and energy
demand knowledge or having them forecasted. However, the
result from the day-ahead scheduling is limitedly applicable
due to the uncertainties from the charging behaviors. With the
deployment of the EV charging communication protocol defined
in ISO 15118, it is realistic to assume that the EV will publish
the departure time and the energy demand upon arrival. Thus,
real-time scheduling, making decisions at each decision timeslot,
can adapt to the new information and increase scheduling performance.
Traditional model-based approaches like model predictive
control (MPC) still require models, for example, for the future
arrival times to solve the scheduling problem. Reinforcement
learning (RL), a model-free approach, has also been successfully
applied to real-time scheduling. RL can learn how to make
decisions without relying on any system knowledge. This paper
proposes a new action space construction method for an RL as
proposed in a preceding work. The resulting action space size is
significantly reduced compared to the original approach. Further,
we compare the performance of a novel prioritized RL method to
the original method. A publicly available charging session dataset
is used for performance comparison in contrast to the original
method. It is shown, that the prioritized RL performs better.
... Such algorithms can have difficulties to scale with the number of vehicles. These problems can be partially solved by using pre-computed schedules together with their real-time adaptation [2], or by de-centralization of the control, in the sense that at least some of the computation is performed by the controllers themselves instead of centrally. The decentralized approaches are often based on iterative protocols, where the grid provides some charging signal. ...
The problem of coordinating the charging of electric vehicles gains more importance as the number of such vehicles grows. In this paper, we develop a method for the training of controllers for the coordination of EV charging. In contrast to most existing works on this topic, we require the controllers to preserve the privacy of the users, therefore we do not allow any communication from the controller to any third party. In order to train the controllers, we use the idea of imitation learning -- we first find an optimum solution for a relaxed version of the problem using quadratic optimization and then train the controllers to imitate this solution. We also investigate the effects of regularization of the optimum solution on the performance of the controllers. The method is evaluated on realistic data and shows improved performance and training speed compared to similar controllers trained using evolutionary algorithms.
... QoS aspects are mainly discussed in combination with charging station sizing (Bayram et al. 2011;Islam et al. 2018;Ul-Haq et al. 2013). However, a few papers investigate QoS and fairness based on other charging parameters in their allocation mechanisms (Frendo et al. 2019;Rezaei et al. 2014;Al Zishan et al. 2020;Zhou et al. 2013;Zhou et al. 2014), but they either ignore the impact on the low voltage grid or do not consider controllability limitations of existing EV communication protocols. ...
Due to the increasing battery capacity of electric vehicles, European standard electricity socket-outlets at households are not enough for a full charge cycle overnight. Hence, people tend to install (semi-) fast charging wall-boxes (up to 22 kW) which can cause critical peak loads and voltage issues whenever many electric vehicles charge simultaneously in the same area.This paper proposes a centralized charging capacity allocation mechanism based on queuing systems that takes care of grid limitations and charging requirements of electric vehicles, including legacy charging control protocol restrictions. The proposed allocation mechanism dynamically updates the weights of the charging services in discrete time steps, such that electric vehicles with shorter remaining charging time and higher energy requirement are preferred against others. Furthermore, a set of metrics that determine the service quality for charging as a service is introduced. Among others, these metrics cover the ratio of charged energy to the required energy, the charging power variation during the charging process, as well as whether the upcoming trip is feasible or not. The proposed algorithm outperforms simpler scheduling policies in terms of achieved mean quality of service metric and fairness index in a co-simulation of the IEEE European low voltage grid configured with charging service requirements extracted from a mobility survey.
... Frendo et al. [11] model the BEVs of employees charging at a company site. Combining day-ahead and real-time planning, the charging process can be optimized taking into account individual objectives such as fair share maximization, electricity cost minimization, peak demand minimization and load imbalance minimization. ...
... Frendo et al. [10] model the BEV charging processes of employees at a company site. Combining day-ahead and real-time planning, the charging process can be optimized by taking into account individual objectives such as fair-share maximization, electricity-cost minimization, peak-demand minimization and load-imbalance minimization. ...
... The authors in [2], [5]- [7] have employed the well-known probability distribution functions such as Normal and Gaussian to generate samples for each travel parameter. The authors in [8] have employed a joint probability distribution function to generate the departure time and arrival time of the PEVs. In [9], to investigate the dynamic effects of the PEVs demand and wind energy in the power system stability, the Quasi-Monte Carlo (QMC) method has been employed. ...
Growing penetration of Plug-in Electric Vehicles (PEVs) in the transportation fleet and their subsequent charging demands introduce substantial intermittency to the electric load profile which imposes techno-economic challenges on power distribution networks. To address the uncertainty in demand, a novel deep learning-based approach equipped with a hybrid classification task is developed which can take into account the travel characteristics of the PEV owners. The classification structure helps us scrutinize the PEVs demand by allocating a specific forecasting network to each cluster of travel behavior patterns. In our hybrid classification task, first, an unsupervised classifier discerns hidden travel-behavior patterns between the historical PEVs data by clustering them; then, a supervised classifier directs each new PEV data to its appropriate clusterspecific forecasting network. The deep learning-based forecasting and classification networks are constructed based on the Long Short-Term Memory networks to investigate long-and short term features in PEV behaviors. The data-driven structure of our proposed method enables us to observe and preserve the correlation between PEV travel data parameters (departure time, arrival time and traveled distance) and avoid the generation of unrealistic travel samples found in scenario-based approaches. To verify the effectiveness of the proposed method in practical environments, we have studied the impact of the precise forecasting of the PEVs demand in an aggregator’s financial profit in the energy market of the California Independent System Operator market. The numerical results confirm the outstanding performance of our proposed deep learning-based method in forecasting PEVs demand against benchmark approaches in this field such as Monte Carlo, Quasi-Monte Carlo, and Copula with only a 6.77% error in comparison with real data.
The rising demand for electric vehicles (EVs) in the face of limited grid capacity encourages the development and implementation of smart charging (SC) algorithms. Experimental validation plays a pivotal role in advancing this field. This paper formulates a hierarchical mixed integer programming (MIP) EV SC algorithm designed for low voltage (LV) distribution grid applications. A flexible receding horizon scheme is introduced in response to system uncertainties. It also considers the practical constraints in protocols such as IEC/ISO 15118 and IEC 61851-1. The proposed algorithm is verified and assessed in a Power Hardware-in-the-Loop (PHIL) testbed that incorporates models of real LV distribution grids. Furthermore, the algorithm's capabilities are examined through eight scenarios, out of which four focus on the uncertainties of the input data and two address the engagement of extra grid capacity restrictions. The results demonstrate that the SC algorithm adequately lowers the EV charging cost while fulfilling the charging demand, and substantially reduces the peak power as well as the overloading duration, even when faced with input data uncertainty. The additional grid restrictions in place are proven to improve peak demand reduction and overloading mitigation further. Finally, the limitations and potentials of the developed algorithm are scrutinised.
The surging adoption of electric vehicles (EV) calls for accurate and efficient approaches to coordinate with the power grid operation. By being responsive to distribution grid limits and time-varying electricity prices, EV charging stations can minimize their charging costs while aiding grid operation simultaneously. In this study, we investigate the economic benefit of vehicle-to-grid (V2G) using real-time price data from New York State and a real-world charging network dataset. Our V2G approach incorporates nonlinear battery models and price uncertainty to provide a realistic estimation of cost savings from different V2G options. The proposed control method is computationally tractable when scaling up to real-world applications. We show that our proposed algorithm leads to an average of 21% charging cost savings compared to uncontrolled charging when considering unidirectional charging, and bi-directional V2G enables additional 17% cost savings compared to unidirectional smart charging. Our result also shows the importance of using more accurate nonlinear battery models in V2G controllers and evaluating the cost of price uncertainties over V2G.
In recent years, electric vehicles (EV) have gained tremendous popularity, and a lot of research is being carried out in this area, leading to a lot of available development opportunities. This has necessitated electric vehicle charging stations (EVCS) that support fast charging. According to the new policies that are being announced, the number of electric vehicles is going to increase, highlighting the demand for efficient and smart electric vehicles. With this increase in demand, EVCS will require the improvement in the relevant technology to obtain better and efficient coordination among the increasing amount of EV and EVCS. It also engenders the need for a charging station design that requires minimal maintenance, minimal human intervention, and judicious use of the available power supplied by the power grid without disturbing steady-state stability. Technologies that meet the design requirements described above can be incorporated into an EVCS in this paper. To maximize the utilization of power at existing EV charging infrastructure, and to avoid overloading of system limits, smart charging algorithm has been designed. By analyzing the power demand of each EV connected to charging station, suitable power allocation is done based on priority flags marked to each EV continuously. This process happens independently without any external assistance. Finally results can be observed for improved power utilization at an existing charging infrastructure.KeywordsSmart chargingCharging stationElectric vehicleEV charging optimization
Today, the problems of power consumption and environmental pollution caused by road traffic are very serious. Automobiles produce large amounts of carbon monoxide and nitrogen, dust, and particles, which are the primary user of the fuel. Unconventional design methods will prevent the international process of replacing fuel-filled vehicles with electric vehicles. The promotion and adoption of electric vehicles can effectively alleviate these problems, but the planning of electric vehicle charging facilities needs to be resolved as soon as possible. The state-of-the-art mode and power model of public charging equipment proposed in this paper can solve the problem of positioning charging equipment in large-scale transmission networks and research on related charging materials and location planning and energy model design methods that are beneficial to the future development.
Encrypting data before sending it to the cloud ensures data confidentiality but requires the cloud to compute on encrypted data. Trusted execution environments, such as Intel SGX enclaves, promise to provide a secure environment in which data can be decrypted and then processed. However, vulnerabilities in the executed program give attackers ample opportunities to execute arbitrary code inside the enclave. This code can modify the dataflow of the program and leak secrets via SGX side channels. Fully homomorphic encryption would be an alternative to compute on encrypted data without data leaks. However, due to its high computational complexity, its applicability to general-purpose computing remains limited. Researchers have made several proposals for transforming programs to perform encrypted computations on less powerful encryption schemes. Yet current approaches do not support programs making control-flow decisions based on encrypted data.
We introduce the concept of dataflow authentication (DFAuth) to enable such programs. DFAuth prevents an adversary from arbitrarily deviating from the dataflow of a program. Our technique hence offers protections against the side-channel attacks described previously. We implemented two flavors of DFAuth, a Java bytecode-to-bytecode compiler, and an SGX enclave running a small and program-independent trusted code base. We applied DFAuth to a neural network performing machine learning on sensitive medical data and a smart charging scheduler for electric vehicles. Our transformation yields a neural network with encrypted weights, which can be evaluated on encrypted inputs in . Our protected scheduler is capable of updating the encrypted charging plan in approximately 1.06 seconds.
The increasing use of artificial intelligence (AI) by public actors has led to a push for more transparency. Previous research has conceptualized AI transparency as knowledge that empowers citizens and experts to make informed choices about the use and governance of AI. Conversely, in this paper, we critically examine if transparency-as-knowledge is an appropriate concept for a public realm where private interests intersect with democratic concerns. We conduct a practice-based design research study in which we prototype and evaluate a transparent smart electric vehicle charge point, and investigate experts’ and citizens’ understanding of AI transparency. We find that citizens experience transparency as burdensome; experts hope transparency ensures acceptance, while citizens are mostly indifferent to AI; and with absent means of control, citizens question transparency’s relevance. The tensions we identify suggest transparency cannot be reduced to a product feature, but should be seen as a mediator of debate between experts and citizens.
The push for transport electrification has increased worldwide due to growing concerns about carbon emissions by conventional fossil fuel based vehicles. With the push of transport electrification, the exiting power systems utility grid is also evolving. Electric vehicles (EVs) are becoming popular and gaining the market share in due course of time. The increase in EVs demands more power to charge which results in a significant impact on the utility grid. Dependency on renewable energy sources and the use of local energy storage has increased. Inculcating the incremental addition of EVs and the integration of renewables and local energy storage requires overhauling the planning, monitoring, operation, and maintenance of the power system and its components. Smart charging is an EV charging technique that focusses on reducing the impact of increased power demand and helps in the integration of renewables and local energy storage. Smart charging adds flexibility in the operation of power system components with added functionalities that give augmented monitoring and control to EV users and the power system operator. The goals of smart charging are set to unleash coherency between transport electrification, low‐carbon emission generation, and utilization of electricity. This chapter will define the context of “smart” with respect to “smart charging”, present an outlook towards its role and impacts on the utility grid and connected entities, and describe the enablers of smart charging, markets, and the operation of the global energy system.
We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States. The architecture enables real-time monitoring and control and supports electric vehicle (EV) charging at scale. The ACN adopts a flexible Adaptive Scheduling Algorithm based on convex optimization and model predictive control and allows for significant over-subscription of electrical infrastructure. We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals. We demonstrate how the Adaptive Scheduling Algorithm handles these challenges, and compare its performance against baseline algorithms from the deadline scheduling literature using real workloads recorded from the Caltech ACN and accurate system models. We find that in these realistic settings, our scheduling algorithm can improve operator profit by 3.4 times over uncontrolled charging and consistently outperforms baseline algorithms when delivering energy in highly congested systems.
We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States. The architecture enables real-time monitoring and control and supports electric vehicle (EV) charging at scale. The ACN adopts a flexible Adaptive Scheduling Algorithm based on convex optimization and model predictive control and allows for significant over-subscription of electrical infrastructure. We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals. We demonstrate how the Adaptive Scheduling Algorithm handles these challenges, and compare its performance against baseline algorithms from the deadline scheduling literature using real workloads recorded from the Caltech ACN and accurate system models. We find that in these realistic settings, our scheduling algorithm can improve operator profit by 3.4 times over uncontrolled charging and consistently outperforms baseline algorithms when delivering energy in highly congested systems.
In the first volume of Energy and AI, Frendo et al. reported a data-driven smart charging approach for heterogeneous electrical vehicle fleets. A data-driven regression model of charging profiles is proposed for the development of this smart charging algorithm to increase the efficiency of charging infrastructures significantly.
The computing devices in cloud or fog data centers remain in the continuous running cycle to provide services. The long execution state of large number of computing devices consumes significant amount of power which emit equivalent amount of heat. The high powered cooling systems are installed for data centers to avoid reduced performance of the devices due to heat. In cloud based infrastructure, the longer response time between service provider and end-users is challenging for efficient power utilization in smart grid. In this paper, fog based model is proposed for a community to ensure real time energy management service provision with minimal network latency and efficient resource allocation technique. Moreover, a mechanism is proposed to calculate the energy demand for computing devices and cooling system for fog data center. In this paper, two scenarios are proposed to analyze the energy cost for a community and fog data center by integrating green power supply. The simulations show that green community with green fog is 15.09% more cost efficient as compared to the community with utility’s power supply only.
The problem of optimally scheduling the charging demand of electric vehicles within the constraints of the electricity infrastructure is called the charge scheduling problem. The models of the charging speed, horizon, and charging demand determine the computational complexity of the charge scheduling problem. We show that for about 20 variants the problem is either in P or weakly NP-hard and dynamic programs exist to compute optimal solutions. About 10 other variants of the problem are strongly NP-hard, presenting a potentially significant obstacle to their use in practical situations of scale. An experimental study establishes up to what parameter values the dynamic programs can determine optimal solutions in a couple of minutes.
In this paper, the aggregation of electric vehicles (EVs) and fast charging station (FCS) is modeled as a leader-followers game to provide regulation reserves for power systems. The leader of the game is FCS operator, who manages local sources and sets energy/reserve prices for EVs to increase its revenue, with the consideration of uncertain renewable sources and reserves called by the independent system operator. On the other hand, EVs act as the followers to obtain a tradeoff between the benefits from energy consumption and reserves provision, by deciding their charging and reserve strategies. The proposed game is reformulated as a bi-level optimization problem, which is solved by a mathematical programming with equilibrium constraints method. Furthermore, the existence of Stackelberg equilibriums has been proved. Effectiveness of the proposed game is verified by both single-period and multiple-periods simulation study. Simulation results demonstrate that the proposed game can increase the benefits of FCS operator and EVs simultaneously, compared with the centralized management method.
Autonomous vehicles (AVs) will revolutionarize ground transport and take a substantial role in the future transportation system. Most AVs are likely to be electric vehicles (EVs) and they can participate in the vehicle-to-grid (V2G) system to support various V2G services. Although it is generally infeasible for EVs to dictate their routes, we can design AV travel plans to fulfill certain system-wide objectives. In this paper, we focus on the AVs looking for parking and study how they can be led to appropriate parking facilities to support V2G services. We formulate the Coordinated Parking Problem (CPP), which can be solved by a standard integer linear program solver but requires long computational time. To make it more practical, we develop a distributed algorithm to address CPP based on dual decomposition. We carry out a series of simulations to evaluate the proposed solution methods. Our results show that the distributed algorithm can produce nearly optimal solutions with substantially less computational time. A coarser time scale can improve computational time but degrade the solution quality resulting in possible infeasible solution. Even with communication loss, the distributed algorithm can still perform well and converge with only little degradation in speed.
Plug-in hybrid electric vehicles (PHEVs) have emerged as an important tool in reducing greenhouse gas emissions, due to their lower dependency on fossil fuel. Since, for cost efficiency, PHEVs have a limited battery capacity, they must be recharged often and especially after trips. Thus, efficient battery charging plays an important role on the success of PHEVs commercial adoption. This paper surveys the state-ofthe- art of existing PHEV battery charging schemes. We classify these schemes into four classes, namely uncontrolled, indirectlycontrolled, smart and bidirectional charging, and review various existing techniques within each class. For uncontrolled charging, existing studies focus on evaluating the impact of adding variable charging load on the smart grid. Various indirectly-controlled charging schemes have been proposed to control energy prices, in order to indirectly influence the charging operations. Smart charging schemes can directly control a rich set of charging parameters to achieve various performance objectives, such as minimizing power loss, maximizing operator’s profit, ensuring fairness, etc. Finally, bidirectional charging allows a PHEV to discharge energy into smart grid, such that the vehicle can act as a mobile energy source to further stabilize the grid, which is partially supplied by intermittent renewable energy sources. This survey provides a comprehensive one-stop introductory reference to quickly learn about the key features and technical challenges, addressed by existing PHEV battery charging schemes in smart grid.
The ability to control commercial buildings and electric vehicles (EVs) is a promising source of demand flexibility. In some cases, buildings and EVs share common infrastructure (e.g. a transformer) or interact with each other to accomplish a goal (e.g. reduce local peak demand). In such cases, the building and EV demand scheduling problems are effectively a single demand scheduling problem. Ideally, it would be solved as a single optimization problem. However, doing so might not be possible due to a number of concerns (e.g. data privacy). This paper proposes the use of a mixed-integer adaptation of the Dantzig-Wolfe decomposition to solve the building-EV demand scheduling problem in a decentralized fashion. The effectiveness of the proposed methodology is demonstrated in three case studies where the building and EV problems are coupled by either: i) demand limits, ii) a peak demand charge, or an iii) itemized billing tariff. Results show that the optimal solution can be reached while sharing a minimal amount of information. Furthermore, we show that the proposed methodology is scalable.
To accommodate the increasing electric vehicle (EV)
penetration in distribution grid, coordinated EV charging has
been extensively studied in the literature. However, most of the
existing works optimistically consider the EV charging rate as a
continuous variable and implicitly ignore the capacity limitation
in distribution transformers, which both have great impact on
the efficiency and stability of practical grid operation. Towards a
more realistic setting, this paper formulates the EV coordinated
discrete charging problem as two successive binary programs.
The first one is designed to achieve a desired aggregate load
profile (e.g., valley-filling profile) at the distribution grid level
while taking into account the capacity constraints of distribution
transformers. Leveraging the properties of separable convex
function and total unimodularity, the problem is transformed
into an equivalent linear program, which can be solved efficiently
and optimally. The second problem aims to minimize the total
number of on-off switchings of all the EVs’ charging profiles
while preserving the optimality of the former problem. We prove
the second problem is NP-hard and propose a heuristic algorithm
to approximately achieve our target in an iterative manner.
Case studies confirm the validity of our proposed scheduling
methods and indicate our algorithm’s potential for real-time
implementations.
In this paper, smart charging strategies, incorporating a unified grid-to-vehicle (G2V) and vehicle-to-grid (V2G) charging framework, are proposed for optimal integration of plug-in electric vehicles (PEVs) within the existing distribution system infrastructure. Two smart strategies with objective functions considering minimization of total daily cost and peak-toaverage ratio (PAR), respectively, are developed to study the impact on PEV charging from an economic and a technical perspective. The proposed strategies are implemented for PEV charging at workplace car parks located in a 37-bus distribution system and an analytical study is presented to evaluate the maximum possible PEV penetration that the existing distribution infrastructure can accommodate corresponding to the two strategies. A comparative analysis of the two strategies is performed in terms of various economic and technical benefits that are derived. Moreover, a performance comparison of the two strategies in presence of slow and fast charging of PEVs is also presented. Finally, an investigative study is conducted for both the strategies to evaluate the maximum PEV penetration that can be integrated in the upcoming years without infrastructure reinforcement. The simulation results present a comprehensive evaluation of the two proposed strategies.
The problem of large-scale charging of electric vehicles (EVs) with consumer-imposed charging deadlines is considered. An architecture for the intelligent energy management system (iEMS) is introduced. The iEMS consists of an admission control and pricing module, a scheduling module that determines the charging sequence, and a power dispatch module that draws power from a mix of storage, local renewable energy sources, and purchased power from the grid. A threshold admission and greedy scheduling (TAGS) policy is proposed to maximize operation profit. The performance of TAGS is analyzed and evaluated based on average and worst-case performance measures and the optimality of TAGS is established for some instances. Numerical simulations demonstrate that TAGS achieves noticeable performance gains over benchmark techniques.
Smart interactions among the smart grid, aggregators, and EVs can bring various benefits to all parties involved, e.g., improved reliability and safety for the smart gird, increased profits for the aggregators, as well as enhanced self benefit for EV customers. This survey focuses on viewing this smart interactions from an algorithmic perspective. In particular, important dominating factors for coordinated charging from three different perspectives are studied, in terms of smart grid oriented, aggregator-oriented, and customer-oriented smart charging. Firstly, for smart grid oriented EV charging, we summarize various formulations proposed for load flattening, frequency regulation, and voltage regulation, then explore the nature and substantial similarity among them. Secondly, for aggregator-oriented EV charging, we categorize the algorithmic approaches proposed by research works sharing this perspective as direct and indirect coordinated control, and investigate these approaches in detail. Thirdly, for customer-oriented EV charging, based on a commonly shared objective of reducing charging cost, we generalize different formulations proposed by studied research works. Moreover, various uncertainty issues, e.g., EV fleet uncertainty, electricity price uncertainty, regulation demand uncertainty, etc., have been discussed according to the three perspectives classified. At last, we discuss challenging issues that are commonly confronted during modeling the smart interactions, and outline some future research topics in this exciting area.
The market for battery powered and plug-in hybrid electric vehicles is currently limited, but this is expected to grow rapidly with the increased concern about the environment and advances in technology. Due to their high energy capacity, mass deployment of electrical vehicles will have significant impact on power networks. This impact will dictate the design of the electric vehicle interface devices and the way future power networks will be designed and controlled. This paper presents the results of an analysis of the impact of electric vehicles on existing power distribution networks. Evaluation of supply/demand matching and potential violations of statutory voltage limits, power quality and imbalance are presented.
In this article we explore how a single standard dealing with vehicle-to-grid (V2G) mobility, ISO 15118, is coproduced in divergent ways across Asia, Europe, and North America. Specifically, ISO 15118 enables V2G as it oversees the communication between electric vehicles (EVs) and electric vehicle service equipment. It allows for bidirectional electricity flows and thereby offers electricity grids the use of EV batteries for grid services like frequency control and demand side management. We observe that highly technical and invisible standards like these are understudied in the energy literature and commonly misinterpreted as purely technical in scope. Hence in our contribution we offer such a study and use ISO 15118 to show how even in the most technical and invisible of cases, politics is still at work. We argue that standards, through a process of coproduction, are of vital importance for the governance of energy systems and play a major role in energy transitions through the various nontechnical assumptions scripted into them. Drawing from a synthesis of the literature on standardization, innovation studies, and science and technology studies, we thus analyse the implementation of ISO 15118 in the United States, China, Denmark and the Netherlands. We find a detailed technical standard that is implemented with differing degrees and in different sociotechnical and institutional contexts. We conclude by suggesting what this selectivity, compliance and fragmentation means for electric mobility and energy policymaking more generally.
We propose and evaluate controllers for the coordination of the charging of electric vehicles. The controllers are based on neural networks and are completely de-centralized, in the sense that the charging current is completely decided by the controller itself. One of the versions of the controllers does not require any outside communication at all. We test controllers based on two different architectures of neural networks - the feed-forward networks and the echo state networks. The networks are optimized by either an evolutionary algorithm (CMA-ES) or by a gradient-based method. The results of the different architectures and the different optimization algorithms are compared in a realistic scenario. We show that the controllers are able to charge the cars while keeping the peak consumptions almost the same as when no charging is performed. Moreover, the controllers fill the valleys of the consumption thus reducing the difference between the maximum and minimum consumption in the grid.
Security of the smart grid is at risk when the vulnerabilities of the Electric Vehicle (EV) infrastructure is not addressed properly. As vehicles are becoming smarter and connected, their risk of being compromised is increasing. On various occasions and venues, hacking into smart or autonomous vehicles have been shown to be possible. For most of the time, a compromised vehicle poses a threat to the driver and other vehicles. On the other hand, when the vehicle is electric, the attack may spread to the power grid infrastructure starting from the electric vehicle supply equipment (EVSE) all the way up to the utility systems. Traditional isolation-based protection schemes do not work well in smart grid since electricity services have availability constraints and few of the components have physical backups. In this paper, we propose a Mixed Integer Linear Programming (MILP) model that jointly optimizes security risk and equipment availability in the interdependent power and electric vehicle infrastructure. We adopt an epidemic attack model to mimic malware propagation. We assume malware spreads during EV charging when an EV is charged from an infected EVSE and then travels and recharges at another EVSE. In addition, it spreads through the communication network of EVSEs. The proposed response model aims to isolate a subset of compromised and likely compromised EVSEs. The response model minimizes the risk of attack propagation while providing a satisfactory level of equipment availability to supply demand. Our analysis shows the theoretical and practical bounds for the proposed response model in smart grid in the face of attacks to the electric vehicle infrastructure.
Wind generators and plug-in hybrid electric vehicles (PHEVs) are increasing rapidly in modern power grids. Despite all their merits, these two classes of sources are limited by some practical constraints which disqualify each of them from effectively contributing separately to the primary frequency regulation in power grids with reduced inertia, such as microgrids. However, when combined with proper control and coordination, wind generators and PHEVs can compensate for the individual drawbacks of each source and effectively participate in the frequency regulation. A cooperative control strategy that considers the practical limits of both sources is not available in the literature. To fill this gap, in this study, smallsignal analysis is employed to investigate which frequency regulation method, droop or virtual inertia, is more suitable for such cooperation. The centralized and distributed control structures are examined as two possible coordination methods to ensure that the wind generator and PHEVs constraints are not violated and also that the communication system delay is considered. Based on a detailed analysis, the advantages, disadvantages and appropriate applications of the centralized and distributed structures are discussed. Time-domain simulation results, obtained by using a typical microgrid system, validate the analytical results.
The major debates on environmental pollutions, abundant availability of renewable resources, scarcity of fossil fuel resources in certain parts of the world, and geopolitical conflicts have underscored the need for promoting the use of renewable and distributed power generation. Since the availability of renewable energy resources such as wind energy depends on variable atmospheric conditions, a massive integration of renewable energy would require additional reserves to compensate the power deficiency for supplying loads. However, the increasing use of electric vehicles (EVs) along with their charging demands can apply a significant amount of load to the power grid, which can cause stability concerns if not control properly. In this paper, EVs charges are applied to the secondary frequency control and an optimized fuzzy controller is considered to compensate the variable unbalances between demand and generation. To verify the performance of the proposed control method for reducing frequency deviations, several case studies are considered in this paper and simulations are carried out in MATLAB/SIMULINK environment. The simulation results illustrate a good performance of proposed method.
The electric vehicle (EV) market has been growing rapidly around the world. With large scale deployment of EVs in power systems, both the grid and EV owners will benefit if the flexible demand of EV charging is properly managed through the electricity market. When EV charging demand is considerable in a grid, it will impact spot prices in the electricity market and consequently influence the charging scheduling itself. The interaction between the spot prices and the EV demand needs to be considered in the EV charging scheduling, otherwise it will lead to a higher charging cost. A day-ahead EV charging scheduling based on an aggregative game model is proposed in this paper. The impacts of the EV demand on the electricity prices are formulated with the game model in the scheduling considering possible actions of other EVs. The existence and uniqueness of the pure strategy Nash equilibrium are proved for the game. An optimization method is developed to calculate the equilibrium of the game model through quadratic programming. The optimal scheduling of the individual EV controller considering the actions of other EVs in the game is developed with the EV driving pattern distribution. Case studies with the proposed game model were carried out using real world driving data from the Danish National Travel Surveys. The impacts of the EV driving patterns and price forecasts on the EV demand with the proposed game model were also analysed.
This paper presents a mixed integer linear programming (MILP) model to optimize the costs of maintenance and extra hours for scheduling a fleet of battery electric vehicles (BEVs) so that the products are delivered to prespecified delivery points along a route. On this route, each BEV must have an efficient charging strategy at the prespecified charging points. The proposed model considers the average speed of the BEVs, the battery states of charge (SOC), and a set of deliveries allocated to each BEV. The charging points are located on urban roads and differ according to their charging rate (fast or ultra-fast). Constraints that guarantee the performance of the fleet’s batteries are also taken into consideration. Uncertainties in the navigation of urban roads are modeled using the probability of delay due to the presence of traffic signals (PTS), schools (PS), and public works (PPW). The routes and the intersections of these routes are modeled as a predefined graph. The results and the evaluation of the model, with and without considering the extra hours, show the effectiveness of this type of transport technology. The models were implemented in AMPL and solved using the commercial solver CPLEX.
Electric vehicle (EV) charging stations are increasingly set up to meet the recharge demand of EVs, and the stations equipped with local renewable energy generation need to optimize their charging. A basic challenge for the optimization stems from inherent uncertainties such as intermittent renewable generation that is hard to predict accurately. In this paper, we consider a charging station for EVs that have deadline constraints for their requests and aim to minimize its supply cost. We use Lyapunov optimization to minimize the time-average cost under unknown renewable supply, EV mobility, and grid electricity prices. We model the unfulfilled energy requests as a novel system of queues, based on whose evolution we define the Lyapunov drift and minimize it asymptotically. We prove that our algorithm achieves at most
more than the optimal cost, where the parameter
trades off cost against unfulfilled requests by their deadlines, and its time complexity is linear in the number of EVs. Simulation results driven by real-world traces of wind power, EV mobility, and electricity prices show that, compared with a state-of-the-art scheduling algorithm, our algorithm reduces the respective charging costs by 12.48% and 51.98% for two scenarios.
This paper presents a recharging scheme for plugin (hybrid) electric vehicles. Despite their many advantages such as reducing carbon footprint, lower fuel cots, and high performance, uncoordinated recharging of electric vehicles in a high-penetration system can increase system peak load and create new peaks in the demand profile, hence reducing system reliability and operational integrity. To optimize electric vehicle recharging costs and prevent such reliability problems, a dynamic stochastic optimization method is proposed that formulates a stochastic linear programming approach taking into account load, electricity pricing, and renewable energy generation uncertainties, and solves the day-ahead problem in an offline fashion. A second online stage is also proposed that uses offline solutions, collects real-time system data, and adjusts recharging schedules to obtain a better recharging scheme once system uncertainties are revealed. The proposed method is robust to variations in different stochastic parameters, has a low communication requirement, and benefits both users and the power utility. Recharging system structure, data models, and mathematical formulation of the proposed method are presented. Results demonstrate that unlike other recharging schemes, the proposed method does not increase system peak, does not create new peaks, and fills the valleys of demand profile to optimize power system operations.
Smart Electric Vehicle (EV) charging deals with
increasing demand charges caused by EV load on Electric Vehicle
Supply Equipment (EVSE) hosts. This paper proposes a realtime
smart charging algorithm that can be integrated with
Commercial & Industrial (C&I) EVSE hosts through Building
Energy Management System (BEMS) or with utility back office
through the Advanced Metering Infrastructure (AMI). The
proposed charging scheme implements a real-time water-filling
algorithm (RTWF-n1) able to reduce the peak demand and to
prioritize EV charging based on the data of plugged-in EVs. The
algorithm also accommodates utility and local Demand Response
and Load Control (DRLC) signals for extensive peak shaving.
Real-world EV charging data from different types of venues are
used to develop and evaluate the smart charging scheme for
demand charge reduction at Medium & Large General Service
locations. The results show that even at constrained venues such
as large retails, monthly demand charges caused by EVs can
be reduced by 20-35% for 30% EV penetration level without
depreciating EVs’ charging demand.
Energy storage and reactive power supplied by electric vehicles (EV) through vehicle-to-grid (V2G) operation can be coordinated to provide voltage support, thus reducing the need of grid reinforcement and active power curtailment. Optimization and control approaches for V2G-enabled reactive power control should be robust to variations and offer a certain level of optimality by combining real-time control with an hours-ahead scheduling scheme. This paper introduces an optimization and control framework that can be used for charging batteries and managing available storage while using the remaining capacity of the chargers to generate reactive power and cooperatively perform voltage control. Stochastic distributed optimization of reactive power is realized by integrating a robust distributed sub-gradient method with conditional ensemble predictions of V2G capacity. Hence, the proposed solutions can meet system operational requirements for the upcoming hours by enabling instantaneous cooperation among distributed EVs.
In this work, we propose a novel charging algorithm for Electric Vehicles (EVs) in smart grids. Unlike traditional charging methods, this algorithm is designed to exploit the flexibility of the EVs’ load to absorb the unforeseen fluctuations in the net-load caused mainly by the intermittency of the renewable energy sources (wind energy). In this paper, we first formulate the problem with traditional charging algorithms in the presence of renewable energy sources (RES). Second, we show that the overall energy consumed by the overall load in the system can be estimated ahead of time despite the stochastic behavior of the net-load. Third, a detailed description of our online algorithm shows how EVs’ charging decision is taken by the utility server in real time—not ahead of time—according to the current situation of the net-load. Also, unlike most of the charging algorithms in literature, our proposed algorithm considers keeping the charging current constant, thus, imposing less technical and engineering requirements and complexity on the EVs’ charging infrastructure. Finally, to test the performance of our online charging algorithm, a tool has been developed in Java to simulate our algorithm. A comprehensive performance evaluation of our algorithm against a traditional Ahead-of-Time charging algorithm shows clear improvements achieved by our algorithm in absorbing the unexpected fluctuations in the netload caused mainly by the stochastic behavior of the produced wind power.
A vehicle-to-vehicle (V2V) energy swapping strategy can provide an alternative fast charging way for gridable electric vehicles (GEVs) to relieve the charging overload problem in the power system during peak-demand hours. The main challenges in designing an efficient V2V energy swapping strategy are i) to stimulate mobile GEVs to participate in an energy swapping transaction that balances supply with demand at aggregators, and ii) to achieve optimal energy utilization and individual GEVs’ profits. In this paper, we present a novel smart grid architecture with enhanced communication capabilities for mobile GEVs, via a heterogeneous wireless network-enhanced smart grid. We propose an online V2V energy swapping strategy based on price control. Specifically, mobile GEVs with surplus energy are motivated by getting paid to contribute to a V2V energy swapping transaction at aggregators with energy-hungry GEVs. To evaluate the performance of the proposed V2V energy swapping strategy, a realistic suburban scenario is developed in VISSIM to track the GEVs’ mobility using the generated simulation traces. Extensive simulation results are given to demonstrate the efficacy of the proposed V2V energy swapping strategy.
This paper presents a distribution locational marginal pricing (DLMP) method through chance constrained mixed-integer programming designed to alleviate the possible congestion in the future distribution network with high penetration of electric vehicles (EVs). In order to represent the stochastic characteristics of the EV driving patterns, a chance constrained optimization of the EV charging is proposed and formulated through mixed-integer programming (MIP). With the chance constraints in the optimization formulations, it guarantees that the failure probability of the EV charging plan fulfilling the driving requirement is below the predetermined confidence parameter. The efficacy of the proposed approach was demonstrated by case studies using a 33-bus distribution system of the Bornholm power system and the Danish driving data. The case study results show that the DLMP method through chance constrained MIP can successfully alleviate the congestion in the distribution network due to the EV charging while keeping the failure probability of EV charging not meeting driving needs below the predefined confidence.
The widespread diffusion of Electric Vehicles (EVs) gives a concrete answer to the growing environmental problems linked to the mobility in urban areas. This paper deals with a particular management problem related to the EVs charging operations: the integration of the EVs with the power distribution system. Possible electrical grid disruptions due to uncoordinated charging operations and the need of guaranteeing to drivers a certain level of confidence while travelling with an EV explain the efforts in the identification of a smart approach for the EVs charging management problem. In this work, a hierarchical mathematical programming approach is considered and a system made up of two interdependent optimization models is introduced in order to identify the optimum spatial and temporal scheduling of EVs charging operations in an urban area served by several charging stations. Moreover, a Mixed Integer Linear Programming (MILP) formulation for the Vehicle-to-Charging Station Assignment Problem is proposed and a preliminary example of application is presented.
Energy crisis and environmental issues have encouraged the adoption of electric vehicle as an alternative transportation option to the conventional internal combustion engine vehicle. Recently, the development of smart grid concept in power grid has advanced the role of electric vehicles in the form of vehicle to grid technology. Vehicle to grid technology allows bidirectional energy exchange between electric vehicles and the power grid, which offers numerous services to the power grid, such as power grid regulation, spinning reserve, peak load shaving, load leveling and reactive power compensation. As the implementation of vehicle to grid technology is a complicated unit commitment problem with different conflicting objectives and constraints, optimization techniques are usually utilized. This paper reviews the framework, benefits and challenges of vehicle to grid technology. This paper also summarizes the main optimization techniques to achieve different vehicle to grid objectives while satisfying multiple constraints.
Coordinated charging is an effective charging plan for PEVs to improve the overall system energy utilization and prevent the overload of an electric power grid. On the other hand, PEVs, which have energy storage and controllable loads, can be discharged to help the grid to smooth the fluctuations, for example, introduced by distributed generators (DGs). Either to prevent overloading or to regulate the power grid, most existing charging/discharging plans focus on temporal charging/discharging coordination for parked vehicles. However, for moving vehicles, spatial coordination can also bring benefits to the grid. For spatial coordination, the range anxiety problem should be carefully handled, since PEVs cannot reach some charging stations due to the limited battery levels. In this article, by exploiting both spatial and temporal coordinations, we introduce an online PEV charging/discharging strategy considering range anxieties. To collect real-time information for the proposed online strategy, a heterogeneous wireless infrastructure is proposed by integrating cellular networks with vehicular ad-hoc networks (VANETs). Challenging issues are discussed in terms of modeling PEV mobility, network selection for real-time information delivery, balancing the trade-off between the grid power utilization and drivers??? preferences, and modeling business revenues for charging/discharging. Case studies demonstrate that joint spatial and temporal charging coordination can effectively improve power utilization and avoid overloading the power grid.
Driven by new laws and regulations concerning the emission of greenhouse gases, carriers are starting to use electric vehicles for last-mile deliveries. The limited battery capacities of these vehicles necessitate visits to recharging stations during delivery tours of industry-typical length, which have to be considered in the route planning to avoid inefficient vehicle routes with long detours. We introduce the electric vehicle-routing problem with time windows and recharging stations (E-VRPTW), which incorporates the possibility of recharging at any of the available stations using an appropriate recharging scheme. Furthermore, we consider limited vehicle freight capacities as well as customer time windows, which are the most important constraints in real-world logistics applications. As a solution method, we present a hybrid heuristic that combines a variable neighborhood search algorithm with a tabu search heuristic. Tests performed on newly designed instances for the E-VRPTW as well as on benchmark
A real time method to manage charge scheduling of plug-in electric vehicles (PEVs) is proposed in this paper. The proposed method is based on assigning scores and prioritized PEVs using a fuzzy expert system. Further, the PEVs are optimally charged to maximize the owners' satisfaction, in terms of the delivered energy, without violating system operational constraints. The simulation on a typical distribution network is carried out, which proves the effectiveness of the proposed methodology.
This paper proposes a novel online coordination method for the charging of plug-in electric vehicles (PEVs) in smart distribution networks. The goal of the proposed method is to optimally charge the PEVs in order to maximize the PEV owners' satisfaction and to minimize system operating costs without violating power system constraints. Unlike the solutions reported in the literature, the proposed charging architecture guarantees the feasibility of the charging decisions by means of a novel prediction unit that can forecast future PEVs power demand and through an innovative two-stage optimization unit that ensures effective charging coordination. Coordinated PEV discharging also enables improved utilization of power system resources. Simulation results for a typical distribution network are provided as a demonstration of the effectiveness of the proposed architecture.
This paper proposes a novel load management solution for coordinating the charging of multiple plug-in electric vehicles (PEVs) in a smart grid system. Utilities are becoming concerned about the potential stresses, performance degradations and overloads that may occur in distribution systems with multiple domestic PEV charging activities. Uncontrolled and random PEV charging can cause increased power losses, overloads and voltage fluctuations, which are all detrimental to the reliability and security of newly developing smart grids. Therefore, a real-time smart load management (RT-SLM) control strategy is proposed and developed for the coordination of PEV charging based on real-time (e.g., every 5 min) minimization of total cost of generating the energy plus the associated grid energy losses. The approach reduces generation cost by incorporating time-varying market energy prices and PEV owner preferred charging time zones based on priority selection. The RT-SLM algorithm appropriately considers random plug-in of PEVs and utilizes the maximum sensitivities selection (MSS) optimization. This approach enables PEVs to begin charging as soon as possible considering priority-charging time zones while complying with network operation criteria (such as losses, generation limits, and voltage profile). Simulation results are presented to demonstrate the performance of SLM for the modified IEEE 23 kV distribution system connected to several low voltage residential networks populated with PEVs.
Spatio-temporal coordinated V2V energy swapping strategy for mobile PEVs
- M Wang
Lademanagement für Elektrofahrzeuge
- S Detzler
S. Detzler, "Lademanagement für Elektrofahrzeuge," Ph.D. dissertation,
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Spatio-Temporal Coordinated V2V Energy Swapping Strategy for Mobile PEVs
- M Wang
- M Ismail
- R Zhang
- X Shen
- E Serpedin
- K Qaraqe
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1566-1579, May 2018.
- S J Maher
- T Fischer
- T Gally
- G Gamrath
- A Gleixner
- R L Gottwald
- G Hendel
- T Koch
- M E Lübbecke
- M Miltenberger
- B Müller
- M E Pfetsch
- C Puchert
- D Rehfeldt
- S Schenker
- R Schwarz
- F Serrano
- Y Shinano
- D Weninger
- J T Witt
- J Witzig
S. J. Maher, T. Fischer, T. Gally, G. Gamrath, A. Gleixner, R. L.
Gottwald, G. Hendel, T. Koch, M. E. Lübbecke, M. Miltenberger,
B. Müller, M. E. Pfetsch, C. Puchert, D. Rehfeldt, S. Schenker,
R. Schwarz, F. Serrano, Y. Shinano, D. Weninger, J. T. Witt, and
J. Witzig, "The SCIP Optimization Suite 4.0," Aug. 2017.