Plug-in Electric Vehicles Charging Research Articles

Charging a renewable future: The impact of electric vehicle charging intelligence on energy storage requirements to meet renewable portfolio standards

Abstract Increased usage of renewable energy resources is key for energy system evolution to address environmental concerns. Capturing variable renewable power requires the use of energy storage to shift generation and load demand. The integration of plug-in electric vehicles, however, impacts the load demand profile and therefore the capacity of energy storage required to meet renewable utilization targets. This study examines how the intelligence of plug-in electric vehicle (PEV) integration impacts the required capacity of energy storage systems to meet renewable utilization targets for a large-scale energy system, using California as an example for meeting a 50% and 80% renewable portfolio standard (RPS) in 2030 and 2050. For an 80% RPS in 2050, immediate charging of PEVs requires the installation of an aggregate energy storage system with a power capacity of 60% of the installed renewable capacity and an energy capacity of 2.3% of annual renewable generation. With smart charging of PEVs, required power capacity drops to 16% and required energy capacity drops to 0.6%, and with vehicle-to-grid (V2G) charging, non-vehicle energy storage systems are no longer required. Overall, this study highlights the importance of intelligent PEV charging for minimizing the scale of infrastructure required to meet renewable utilization targets.

Metaheuristic optimization algorithms for the optimal coordination of plug-in electric vehicle charging in distribution systems with distributed generation

This paper proposes three metaheuristic optimization techniques to solve the plug-in electric vehicle (PEV) charging coordination problem in electrical distribution systems (EDSs). Optimization algorithms based on tabu search, greedy randomized adaptive search procedure, and a novel hybrid optimization algorithm are developed with the objective of minimizing the total operational costs of the EDS, considering the impact of charging the electric vehicle batteries during a specific time period. The proposed methodologies determine a charging schedule for the electric vehicle batteries considering priorities according to the PEV owners charging preferences. A 449-node system with two distributed generation units was used to demonstrate the efficiency of the proposed methodologies, taking into account different PEV penetration levels. The results show that the charging schedule found makes the economic operation of the EDS possible, while satisfying operational and priority constraints.

Smart Grid Cost-Emission Unit Commitment via Co-Evolutionary Agents

In this paper, the uncertainty of wind, solar and load; smart charging and discharging of plug-in hybrid electric vehicles (PHEVs) to and from various energy sources; and the coordination of wind, solar power, PHEVs and cost-emission are considered in the smart grid unit commitment (UC). First, a multi-scenario simulation is used in which a set of valid scenarios is considered for the uncertainties of wind and solar energy sources and load. Then the UC problem for the set of scenarios is decomposed into the optimization of interactive agents by multi-agent technology. Agents’ action is represented by a genetic algorithm with adaptive crossover and mutation operators. The adaptive co-evolution of agents is reached by adaptive cooperative multipliers. Finally, simulation is implemented on an example of a power system containing thermal units, a wind farm, solar power plants and PHEVs. The results show the effectiveness of the proposed method. Thermal units, wind, solar power and PHEVs are mutually complementarily by the adaptive cooperative mechanism. The adaptive multipliers’ updating strategy can save more computational time and further improve the efficiency.

Combined Online and Delayed Coordinated Charging of Plug-In Electric Vehicles Considering Wind and Rooftop PV Generations

Emerging smart grid technology is creating unique opportunity for wide applications of renewable distributed generation resources, smart appliances and plug-in electric vehicles (PEVs). This paper presents a combined online (real-time) and delayed (overnight and next day) PEV coordination scheme based on fuzzy reasoning and maximum sensitivity selections (MSS) considering wind and rooftop PV generations. The proposed combined online fuzzy-based and delayed MSS-based (OL-F/DL-MSS) algorithm aims at reducing energy generation cost by charging the electric vehicles during high renewable energy generation periods. To include vehicle owners’ satisfaction, they are classified into three groups; high priority consumers requiring expensive PEV charging as soon as arriving home during early evening hours, low priority consumers requesting cheap overnight PEV charging and medium priority consumers requiring inexpensive PEV charging within 24 hours. PEV coordination is performed to minimize the cost of generating energy and regulate node voltages while utilizing the available renewable energy resources for vehicle battery charging. Detailed simulations for uncoordinated and combined online/overnight coordinated PEV charging are presented for a 449 node smart grid test system consisting of the IEEE 31 node medium voltage distribution network, 3 wind turbines and 22 low voltage 19-node residential feeders with 88 rooftop PVs and 264 PEVs.

Stochastic control of smart home energy management with plug-in electric vehicle battery energy storage and photovoltaic array

Abstract Energy management strategies are instrumental in the performance and economy of smart homes integrating renewable energy and energy storage. This article focuses on stochastic energy management of a smart home with PEV (plug-in electric vehicle) energy storage and photovoltaic (PV) array. It is motivated by the challenges associated with sustainable energy supplies and the local energy storage opportunity provided by vehicle electrification. This paper seeks to minimize a consumer's energy charges under a time-of-use tariff, while satisfying home power demand and PEV charging requirements, and accommodating the variability of solar power. First, the random-variable models are developed, including Markov Chain model of PEV mobility, as well as predictive models of home power demand and PV power supply. Second, a stochastic optimal control problem is mathematically formulated for managing the power flow among energy sources in the smart home. Finally, based on time-varying electricity price, we systematically examine the performance of the proposed control strategy. As a result, the electric cost is 493.6% less for a Tesla Model S with optimal stochastic dynamic programming (SDP) control relative to the no optimal control case, and it is by 175.89% for a Nissan Leaf.

Optimizing plug-in electric vehicle and vehicle-to-grid charge scheduling to minimize carbon emissions

Electric vehicles are an emerging technology with significant potential for reducing carbon dioxide emissions. Yet strategies to minimize carbon dioxide emissions by strategically charging during different times of day have not been rigorously explored. To identify possibilities for minimizing emissions from plug-in electric vehicle use, daily optimized charging strategies over each electricity reliability region of the United States are explored. Optimized schedules of plug-in electric vehicle charging for standard and vehicle-to-grid use were compared with pre-timed charging schedules to characterize the potential for carbon dioxide emission reductions across charging characteristics, regional driving, and marginal energy generation trends. It was found that optimized charging can reduce carbon dioxide emissions over pre-timed charging by as much as 31% for standard use and 59% for vehicle-to-grid use. However, some scenarios of vehicle-to-grid participation were found to increase carbon dioxide emissions by up to 396 g carbon dioxide per mile by displacing stored energy from more carbon-intense energy generation periods. Results also indicate that plug-in electric vehicle charging emissions can vary widely for a given energy efficiency rating. Current energy efficiency ratings may lead to incorrect assumptions of plug-in electric vehicles emissions compared to conventional gasoline vehicles due to varying regional and temporal emissions. To coincide with the push for lower greenhouse gas emissions from transportation, charging times for plug-in electric vehicles should target periods where charging promotes carbon dioxide reductions, and electric vehicle energy efficiency ratings should be reconsidered in order to promote sustainable plug-in electric vehicle use moving forward.

Online optimal variable charge-rate coordination of plug-in electric vehicles to maximize customer satisfaction and improve grid performance

Participation of plug-in electric vehicles (PEVs) is expected to grow in emerging smart grids. A strategy to overcome potential grid overloading caused by large penetrations of PEVs is to optimize their battery charge-rates to fully explore grid capacity and maximize the customer satisfaction for all PEV owners. This paper proposes an online dynamically optimized algorithm for optimal variable charge-rate scheduling of PEVs based on coordinated aggregated particle swarm optimization (CAPSO). The online algorithm is updated at regular intervals of Δt=5min to maximize the customers’ satisfactions for all PEV owners based on their requested plug-out times, requested battery state of charges (SOCReq) and willingness to pay the higher charging energy prices. The algorithm also ensures that the distribution transformer is not overloaded while grid losses and node voltage deviations are minimized. Simulation results for uncoordinated PEV charging as well as CAPSO with fixed charge-rate coordination (FCC) and variable charge-rate coordination (VCC) strategies are compared for a 449-node network with different levels of PEV penetrations. The key contributions are optimal VCC of PEVs considering battery modeling, chargers’ efficiencies and customer satisfaction based on requested plug-out times, driving pattern, desired final SOCs and their interest to pay for energy at a higher rate.

An Integrated Planning Framework for Different Types of PEV Charging Facilities in Urban Area

To build a properly planned infrastructure for plug-in electric vehicle (PEV), charging will bolster their market acceptance. Different types of PEV charging facilities for private PEVs, including public charging spots deployed in public parking lots (PLCSs) and roadside fast-charging stations (FCSs), are substitutes for each other. This paper proposes an integrated planning framework for them in an urban area from the perspective of a social planner. The planning objective is to minimize the social costs of the whole PEV charging system. The proposed framework decouples the planning for different types of charging facilities. The spatial and temporal charging demands for FCSs are generated by a charging demand forecasting method, when the quantities of different types of PLCSs are given. The optimal siting and sizing problem of FCSs is solved by Voronoi diagram together with particle swarm optimization algorithm. By traversing the quantities of different types of PLCSs, the optimal planning results are obtained. The effectiveness of the proposed framework is verified via a case study of a real-urban area in China. The substitution effect between different types of charging facilities is studied. The impacts of the ambient temperature, the private charging spot possession rate, and the service level of PLCSs on the planning results are also assessed.

Electric vehicle charging algorithms for coordination of the grid and distribution transformer levels

The need to reduce greenhouse gas emissions and fossil fuel consumption has increased the popularity of plug-in electric vehicles. However, a large penetration of plug-in electric vehicles can pose challenges at the grid and local distribution levels. Various charging strategies have been proposed to address such challenges, often separately. In this paper, it is shown that, with uncoordinated charging, distribution transformers and the grid can operate under highly undesirable conditions. Next, several strategies that require modest communication efforts are proposed to mitigate the burden created by high concentrations of plug-in electric vehicles, at the grid and local levels. Existing transformer and battery electric vehicle characteristics are used along with the National Household Travel Survey to simulate various charging strategies. It is shown through the analysis of hot spot temperature and equivalent aging factor that the coordinated strategies proposed here reduce the chances of transformer failure with the addition of plug-in electric vehicle loads, even for an under-designed transformer while uncontrolled and uncoordinated plug-in electric vehicle charging results in increased risk of transformer failure.

Optimal Coordinated Management of a Plug-In Electric Vehicle Charging Station under a Flexible Penalty Contract for Voltage Security

The increasing penetration of plug-in electric vehicles (PEVs) may cause a low-voltage problem in the distribution network. In particular, the introduction of charging stations where multiple PEVs are simultaneously charged at the same bus can aggravate the low-voltage problem. Unlike a distribution network operator (DNO) who has the overall responsibility for stable and reliable network operation, a charging station operator (CSO) may schedule PEV charging without consideration for the resulting severe voltage drop. Therefore, there is a need for the DNO to impose a coordination measure to induce the CSO to adjust its charging schedule to help mitigate the voltage problem. Although the current time-of-use (TOU) tariff is an indirect coordination measure that can motivate the CSO to shift its charging demand to off-peak time by imposing a high rate at the peak time, it is limited by its rigidity in that the network voltage condition cannot be flexibly reflected in the tariff. Therefore, a flexible penalty contract (FPC) for voltage security to be used as a direct coordination measure is proposed. In addition, the optimal coordinated management is formulated. Using the Pacific Gas and Electric Company (PG&E) 69-bus test distribution network, the effectiveness of the coordination was verified by comparison with the current TOU tariff.

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

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

Simultaneous planning of plug-in hybrid electric vehicle charging stations and wind power generation in distribution networks considering uncertainties

Recently, plug-in hybrid electric vehicles (PHEV) are becoming more attractive than internal combustion engine vehicles (ICEV). Hence, design and modeling of charging stations (CSs) has vital importance in distribution system level. In this paper, a new formulation for PHEV charging stations is presented with the strategic presence of wind power generation (WPG). This study considers constraints of the system losses, the regulatory voltage limits, and the charge/discharge schedule of PHEV based on the social behavior of drivers for appropriate placement of PHEV charging stations in electricity grid. The role of CSs and WPG units must be correctly assessed to optimize the investment and operation cost for the whole system. However, the wind generation owners (WGOs) have different objective functions which might be contrary to the objectives of distribution system manager (DSM). It is assumed that aggregating and management of charge/discharge program of PHEVs are smartly carried out by DSM. This paper presents a long-term bi-objective model for optimal planning of PHEV charging stations and WPG units in distribution systems which simultaneously optimize two objectives, namely the benefits of DSM and WGO. It also considers the uncertainty of load growth, electricity price and PHEV access to the charging station using Mont-Carlo simulation (MCS) method. Initial state of charge uncertainty is also modeled based on scenario approach in PHEV batteries and wind turbine power generation using weibull distribution. Non dominated sorting genetic algorithm (NSGA-II) is used to solve the optimization problem. The simulation has been conducted on the nine-bus system.

Parzen Window Density Estimator-Based Probabilistic Power Flow With Correlated Uncertainties

This paper presents a numerical-based algorithm to solve the probabilistic power flow problem. Parzen window density estimator is used to efficiently estimate probabilistic characteristics of power flow outputs. Correlations between wind generation, load, and plug-in hybrid electric vehicle charging stations are taken into account. The proposed algorithm works properly for random variables with various probability distribution functions and is very useful when limited information is available for each random variable. The algorithm is tested on the IEEE 14-bus and IEEE 118-bus systems considering correlated and uncorrelated conditions. Comparison between the proposed algorithm with 2n, $\text{2n} + 1$ point estimation methods as well as Monte Carlo simulation and linear diffusion method are provided. In addition, probability density and cumulative distribution functions are determined using the proposed algorithm, diffusion method, and the combined Cumulants and Gram-Charlier for $\text{2n} + 1$ point estimation method. Error indices are introduced to evaluate all random variables in a single benchmark. Simulation results show the effectiveness of the proposed algorithm to provide complete statistical information for probabilistic power flow outputs.

Vehicle to grid system to design a centre node virtual unified power flow controller

Centre node unified power flow controller can fulfil various power flow control objectives, such as the needs of reactive shunt and series compensation, phase shifting and ensure higher degree of control freedom. However, as they are expensive, they are not widely used. The potential of a low-cost solution that utilises the capabilities of plug in electric vehicle (PEV) in vehicle-to-grid mode of operation for the design of a centre node virtual unified power flow controller (CVUPFC) using PEV charging stations is explained. Simulations are performed to establish that the proposed CVUPFC improves the power quality utilising PEV charging stations as DC bus for the converters with higher degree of freedom to control.

Extended Second Price Auctions With Elastic Supply for PEV Charging in the Smart Grid

In this paper, we explore the question of efficient allocation of energy, while buying the same from generation companies, to plug-in electric vehicles (PEVs) by aggregator (electricity utility or load serving entities) through auction mechanisms. Recognizing the practical limitations of the Vickrey–Clarke–Groves mechanism, which would be natural to apply in this context, we investigate two practical mechanisms that can be viewed as extensions of second price auction mechanisms and have limited message (bid) complexity. In the first mechanism, the elastic-supply multi-level second price, each PEV agent submits a number of price bids, one for each of a given set of energy levels (energy quantities). In the second mechanism, the elastic-supply progressive second price, the PEV agents submit a 2-D bid indicating the price as well as the desired energy quantity. Taking into account differences across PEV-owners in terms of their willingness-to-pay values and charging time constraints, we analyze the social optimality and incentive compatibility properties of the two auction mechanisms. We also complement our theoretical findings with numerical simulations.

Real-time plug-in electric vehicle charging strategies for current and voltage unbalance minimization

Plug-in electric vehicles (PEVs) have been proposed as one of the solutions to reduce transportation dependency on oil. Nevertheless, if PEVs are introduced as a new load on the grid without any charging strategy, the grid's power quality will be deteriorated. Among the power quality parameters, current and voltage unbalances would be affected. This paper proposes a real-time strategy in order to minimize the current unbalance factor (CUF) caused by PEVs and housing on a common connection point (CCP) in a residential network. As a consequence of the CUF minimization, the voltage unbalance factor (VUF) is significantly reduced. The CUF at a CCP is formulated as a function of the loads in-phase and quadrature currents (PEVs and householders included). This objective function is minimized under maximum rating and charging priority constraints using algorithm Active-set. The minimization process is a part of the strategy which consists in adapting the optimization problem in real-time to changing conditions such as PEV's arrival and departure times, PEV's battery capacity variety, a random initial state-of-charge (SOC) and the householder's consumption. The strategy was tested on a low voltage network simulated on Matlab /Simulink. Results show that the current balance index is increased more than 400%, the voltage unbalance index is reduced 17% and some of the CCPs in the electric network simulated, are re-balanced (based on the EN50160 standard's definition) thanks to the CUF minimization.

Online Intelligent Demand Management of Plug-In Electric Vehicles in Future Smart Parking Lots

This paper proposes an online intelligent demand coordination of plug-in electric vehicles (PEVs) in distribution systems. The proposed method is based on the assignment of scores to PEVs through a fuzzy expert system. As well, without violation of grid operational constraints, the PEVs are optimally charged in order to maximize the owners' satisfaction in terms of the energy delivered. The optimization problem of online PEV charging is defined as mixed-integer nonlinear programming. Simulation on a typical distribution network proves the effectiveness of the proposed methodology. Results of the analysis indicate that for more critical PEVs, which have shorter parking duration and higher required charging time, the proposed solution outperforms in more robust energy delivery to the PEV and, accordingly, more satisfaction for the owner.

Online scheduling of plug-in vehicles in dynamic pricing schemes

In recent years there has been an increase in the use of plug-in electric vehicles (PEVs). Though it has many advantages, PEVs could stress the grid if their charging schedule is not managed properly. There are several issues to be addressed such as (i) Scheduling of PEV under different types of Tariffs (ii) Prediction of load profile and charging requirement. This paper presents three algorithms for scheduling PEV charging by an aggregator under different conditions. This paper has an aim to present the approaches for different possible cases to schedule PEVs charging. In order to achieve this ambition, first, a simple placement algorithm to schedule the PEVs is developed provided prices are predetermined. Then the scheduling problem is formulated as a single stage decision making problem in case prices are not known in advance and PEVS are independent atomic loads. However, it has been researched that PEVs are independent non-atomic loads. Finally, this paper modifies other cases and then the problem is formulated as a multistage decision making problem to determine the optimum schedule in more realistic scenario. In all cases, proposed algorithms are simulated for a building with an objective to schedule PEVs charging during working hours such that the total cost of energy is minimized and the requirements of user are also satisfied.

A general corridor model for designing plug-in electric vehicle charging infrastructure to support intercity travel

This paper proposes to optimally configure plug-in electric vehicle (PEV) charging infrastructure for supporting long-distance intercity travel using a general corridor model that aims to minimize a total system cost inclusive of infrastructure investment, battery cost and user cost. Compared to the previous work, the proposed model not only allows realistic patterns of origin–destination demands, but also considers flow-dependent charging delay induced by congestion at charging stations. With these extensions, the model is better suited to performing a sketchy design of charging infrastructure along highway corridors. The proposed model is formulated as a mixed integer program with nonlinear constraints and solved by a specialized metaheuristic algorithm based on Simulated Annealing. Our numerical experiments show that the metaheuristic produces satisfactory solutions in comparison with benchmark solutions obtained by a mainstream commercial solver, but is more computationally tractable for larger problems. Noteworthy findings from numerical results are: (1) ignoring queuing delay inducted by charging congestion could lead to suboptimal configuration of charging infrastructure, and its effect is expected to be more significant when the market share of PEVs rises; (2) in the absence of the battery cost, it is important to consider the trade-off between the costs of charging delay and the infrastructure; and (3) building long-range PEVs with the current generation of battery technology may not be cost effective from the societal point of view.

Stochastic Coordination of Plug-In Electric Vehicles and Wind Turbines in Microgrid: A Model Predictive Control Approach

To realize the synergy between plug-in electric vehicles (PEVs) and wind power, this paper presents a hierarchical stochastic control scheme for the coordination of PEV charging and wind power in a microgrid. This scheme consists of two layers. Based on the non-Gaussian wind power predictive distributions, an upper layer stochastic predictive controller coordinates the operation of PEV aggregator and wind turbine. The computed power references are sent to the lower layer PEV and wind controllers for execution. The PEV controller optimally allots the aggregated charging power to individual PEVs. The wind controller regulates the power output of wind turbine. In this way, a power balance between supply and demand in a microgrid is achieved. The main feature of this scheme is that it incorporates the non-Gaussian uncertainty and partially dispatchability of wind power, as well as the PEV uncertainty. Numerical results show the effectiveness of the proposed scheme.