Electric vehicles can become integral parts of a smart grid, since they are capable of providing valuable services to power systems other than just consuming power. On the transmission system level, electric vehicles are regarded as an important means of balancing the intermittent renewable energy resources such as wind power. This is because electric vehicles can be used to absorb the energy during the period of high electricity penetration and feed the electricity back into the grid when the demand is high or in situations of insufficient electricity generation. However, on the distribution system level, the extra loads created by the increasing number of electric vehicles may have adverse impacts on grid. These factors bring new challenges to the power system operators. To coordinate the interests and solve the conflicts, electric vehicle fleet operators are proposed both by academics and industries. This paper presents a review and classification of methods for smart charging (including power to vehicle and vehicle-to-grid) of electric vehicles for fleet operators. The study firstly presents service relationships between fleet operators and other four actors in smart grids; then, modeling of battery dynamics and driving patterns of electric vehicles, charging and communications standards are introduced; after that, three control strategies and their commonly used algorithms are described; finally, conclusion and recommendations are made.

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Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects

The paper aims to determine the day-ahead market bidding strategies for retailers with flexible demands to maximize the short-term profit. It proposes a short-term planning framework to forecast the load under dynamic tariffs and construct biding curves. Stochastic programming is applied to manage the uncertainties of spot price, regulating price, consumption behaviors, and responsiveness to dynamic tariffs. A case study based on data from Sweden is carried out. It demonstrates that a real-time selling price can affect the aggregate load of a residential consumer group and lead to load shift toward low-price periods. The optimal bidding curves for specific trading periods are illustrated. Through comparing the bidding strategies under different risk factors, the case study shows that a risk-averse retailer tends to adopt the strategies with larger imbalances. The benefit lies in the reduction of low-profit risk. However, the aversion to risk can only be kept in a certain level. A larger imbalance may lead to a quick reduction of profit in all scenarios.

Purchase Bidding Strategy for a Retailer With Flexible Demands in Day-Ahead Electricity Market

In this article, a methodology for implementation of an automated transition of a solar PV array and battery integrated unified power quality conditioner (PV-B-UPQC) between standalone and grid connected modes of operation is presented and analyzed. This system consists of a shunt and series active filters connected back to back with a common dc-link. The system addresses the issue of the integrating power quality improvement along with the generation of clean energy. Moreover, due to the automated transition, the critical loads have continuous power supply irrespective of grid availability. The key challenges addressed are implementation of automated transition in a PV-B-UPQC system with minimal disturbance to the local loads. The system operation is validated through experimentation under a number of dynamic conditions such as automated transition, supply voltage variations, unavailability of the grid, variation in solar power generation, load variation, etc., which are typically encountered in a modern distribution network.

Performance Analysis of Solar PV Array and Battery Integrated Unified Power Quality Conditioner for Microgrid Systems

Road transport today is dominated by oil-delivered fuels and internal combustion engines and such a high level of dependence on one single source of primary energy carries strategic, climatic and economic risks [1]. Electric mobility offers an opportunity for diversification of the primary energy sources used in transport, but also brings new risks, technological challenges and commercial imperatives. Large penetration of Electric Vehicles (EV) will increase the electricity demand and load forecasting plays a central role in the operation and planning of electric power. This paper proposes a short-term load forecast model using Support Vector Machines, an artificial intelligence technique. A realistic scenario is studied to test the performance of the suggested model. The accuracy of the method is evaluated through a comparison with a Monte Carlo forecasting technique.

Forecasting Electric Vehicle charging demand using Support Vector Machines

A state-of-charge (SOC) balancing method which accounts for state-of-health (SOH) status of battery cells is presented in this article. The data collected from aging experiments conducted in the laboratory indicates that there is correlation between the minimum impedance of the battery and capacity fading which can be used to predict capacity capability of the battery. However, this relationship is complex and nonlinear. In this article, artificial neural network (ANN) is utilized to learn this relationship and an ANN-based capacity estimator is developed to predict available capacity of battery cells. An online impedance measurement method with improved measurement resolution is presented to obtain a more accurate minimum impedance for the ANN-based capacity estimator. The estimated capacity from the ANN capacity estimator is fed to an SOC balancing controller to calculate SOC values for the battery cells. A battery cell with worse health has lower available capacity than a battery cell with better health, and, therefore, its SOC value is adjusted to a smaller value when SOH or capacity estimation functionality is activated. Because of this mechanism and the control principle of the presented SOC balancing controller, the system draws energy at a slower rate from the battery cell(s) with lower SOH and draws energy at a faster rate from the battery cell(s) with higher SOH such that all battery cells reach their end of discharge at the same time. This results in extending operation time of the system, makes best use of energy from every battery cell, and avoids over-discharging battery cells with lower SOH. The presented method is evaluated using results obtained from a laboratory experimental setup.

State-of-Charge Balancing of Lithium-Ion Batteries With State-of-Health Awareness Capability

This paper analyzes the configuration, design, and operation of multi-MW grid connected solar photovoltaic (PV) systems with practical test cases provided by a 10-MW field development. In order to improve the capacity factor, the PV system operates at its maximum power point during periods of lower irradiance, and the power output is limited to a rated value at high irradiance. The proposed configuration also incorporates a utility scale battery energy storage system (BESS) connected to the grid through an independent inverter and benefits of the experience gained with a 1-MW 2-MWh BESS large demonstrator. The BESS power smoothing and frequency regulation capabilities are illustrated though combined theoretical and experimental studies. The behavior of the grid connected PV and BESS combined system is studied using a modified IEEE 14 bus test system implemented in PSCAD/EMTDC. This paper also includes a sizing exercise for energy storage in order to provide dispatchable PV power.

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Incorporating Battery Energy Storage Systems Into Multi-MW Grid Connected PV Systems

Typically, solar inverters curtail or “clip” the available power from the photovoltaic (PV) system when it exceeds the maximum ac capacity. This article discusses a battery system connected to the dc link of an inverter to recuperate this PV energy. Contrary to conventional approaches, which employ two dc–dc converters, one each for the battery and solar PV system, the proposed configuration utilizes a single dc–dc converter capable of simultaneously operating as a charge controller and a maximum power point tracking (MPPT) device. In addition to improving the overall system capacity factor, increasing the conversion efficiencies, and ensuring MPPT stability, the proposed configuration offers a simple solution for adding energy storage to existing PV installations. With this configuration, the excess power that will otherwise be curtailed due to inverter rating limitations is stored in the battery and supplied to the grid during periods of reduced irradiance. Moreover, a systematic methodology for sizing a dc-bus connected battery to minimize total PV energy curtailed was developed using an annual PV generation profile at the Louisville Gas and Electric and Kentucky Utilities E. W. Brown solar facility at Kentucky. The detailed behavior of the proposed system and its power electronics controls and operations were validated with case studies developed in PSCAD/EMTDC software for variable power generation and PV output power smoothing.

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The Design and Analysis of Large Solar PV Farm Configurations With DC-Connected Battery Systems

Due to the recent inflow of electric vehicles (EVs) to the automobile market, new concerns have risen with respect to the additional electrical load and the resultant effects on an overloaded electric grid. It takes approximately three to four hours to fully charge the battery of some common EVs depending on battery capacities. Either for convenience purposes or possibly necessity due to limited electric range on EVs, some EV owners may desire to charge their EV while at work in addition to charging at home. These forward-thinking daytime charging providers are typically Commercial and Industrial (C&I) electric ratepayers, or other large electric consumers which constitute the majority of businesses, shopping centers, academic campuses and manufacturing facilities. C&I consumers generally pay for electricity consumption based not only on total energy consumed, but also on peak electric demand measurements because certain portions of the working day are traditionally associated with C&I peak load periods. Electricity consumption during these periods results in higher electricity costs due to differences in the billing structures. Therefore, uncontrolled EV charging in large numbers potentially creates significant peak demand increases resulting in higher electric costs for the C&I ratepayer and exacerbating grid issues relating to electric generation and grid stability. Proper scheduling of EV charging is a necessity to avoid such increases in peak electric demand. Scheduling can be implemented in a number of ways including traditional process control strategies such as model predictive control or machine scheduling algorithms. When viewed as a parallel machine scheduling problem, charging stations can be considered as identical machines and the EV charging transactions required can be seen as jobs with varying processing times and due dates. The goal of such an algorithm is to schedule EV charging so that total electricity cost is minimized but off-peak charging activity is maximized. Due to the nature of electricity billing structures and cost calculations for C&I ratepayers, the machine scheduling algorithm must use time index based operations. This paper examines the EV charge scheduling problem and casts it as a parallel machine scheduling problem with availability constraints. Particularly, this study develops several heuristic scheduling algorithms such as Greedy Local Search and Simulated Annealing. These algorithms are evaluated using MATLAB (Mathworks MATLAB R2012b, 2012) to compare performance. In addition, the general-purpose optimization solver CPLEX (Ibm, ILOG CPLEX, 2012) is used to determine exact solutions for small size problems, which is then used to benchmark the performance of the other algorithms studied.

Analysis of electric vehicle charge scheduling and effects on electricity demand costs

The large scale deployment of Electric Vehicle (EV) charging infrastructure can result in high added utility costs due to the peak demand cost structure in utility bills for commercial and industrial users. A method to minimize this disincentive to EV adoption is proposed that relies on forecasting demand so that EV charging activity can be intelligently controlled. This study examines multiple forecasting models and techniques to determine the optimal algorithm for use in the proposed control system. Simulation results are presented for each of the forecasting algorithms with the best mean absolute percent error of 1.26% using a neural network with averaging. This results in a reduction in the peak demand electric costs of approximately 95%.

Analysis of forecasting algorithms for minimization of electric demand costs for electric vehicle charging in commercial and industrial environments

The impact of Battery Electric Vehicle (BEV) charging on the electrical demand of a commercial / industrial energy consumer is examined. A Power Monitoring and Control System (PMCS) is introduced as a method to limit the disincentive to large scale BEV adoption resulting from increased demand costs associated with uncontrolled BEV charging. Reductions in added costs of up to 90% are possible with the incorporation of an intelligent charge control system. Additionally, the PMCS enables transformative charge sharing topologies, including Vehicle to Grid (V2G) and Vehicle to Vehicle (V2V), as solutions for peak shifting and demand response techniques during high demand periods.

Nicholas Jewell

Papers

Incorporating Battery Energy Storage Systems Into Multi-MW Grid Connected PV Systems

The Design and Analysis of Large Solar PV Farm Configurations With DC-Connected Battery Systems

Analysis of electric vehicle charge scheduling and effects on electricity demand costs

Analysis of forecasting algorithms for minimization of electric demand costs for electric vehicle charging in commercial and industrial environments

A Power Monitoring and Control System to minimize electricity demand costs associated with Electric Vehicle charging stations