The Impact of Vehicle-to-Grid on Electric Power Systems: A Review

Environmental concerns and fossil fuel shortage put pressure on both power and transportation systems. Electric vehicles (EVs) are thought to be a good solution to these problems. With EV adoption, energy flow is two way: from grid to vehicle and from vehicle to grid, which is known as vehicle-to-grid (V2G) today. This paper considers electric power systems and provides a review of the impact of V2G on power system stability. The concept and basics of V2G technology are introduced at first, followed by a description of EV application in the world. Several technical issues are detailed in V2G modeling and capacity forecasting, steady-state analysis and stability analysis. Research trends of such topics are declared at last.

Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach

This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G user´s profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints

Energy resource scheduling becomes increasingly important, as the use of distributed resources is intensified and massive gridable vehicle use is envisaged. The present paper proposes a methodology for dayahead energy resource scheduling for smart grids considering the intensive use of distributed generation and of gridable vehicles, usually referred as Vehicle- o-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with V2G owners. It takes into account these contracts, the user´s requirements subjected to the VPP, and several discharge price steps. Full AC power flow calculation included in the model allows taking into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33 bus distribution network and V2G is used to illustrate the good performance of the proposed method.

Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

The consumer acceptance of novel vegetable-enriched bread products as a potential vehicle to increase vegetable consumption

Currently UK fruit and vegetable intakes are below recommendations. Bread is a staple food consumed by ~95% of adults in western countries. In addition, bread provides an ideal matrix by which functionality can be delivered to the consumer in an accepted food. Therefore, enriching bread with vegetables may be an effective strategy to increase vegetable consumption. This study evaluated consumer acceptance, purchase intent and intention of product replacement of bread enriched with red beetroot, carrot with coriander, red pepper with tomato or white beetroot (80g vegetable per serving of 200g) compared to white control bread (0g vegetable). Consumers (n=120) rated their liking of the breads overall, as well as their liking of appearance, flavour and texture using nine-point hedonic scales. Product replacement and purchase intent of the breads was rated using five-point scales. The effect of providing consumers with health information about the breads was also evaluated. There were significant differences in overall liking (P<0.0001), as well as liking of appearance (P<0.0001), flavour (P=0.0002) and texture (P=0.04), between the breads. However, the significant differences resulted from the red beetroot bread which was significantly (P<0.05) less liked compared to control bread. There were no significant differences in overall liking between any of the other vegetable-enriched breads compared with the control bread (no vegetable inclusion), apart from the red beetroot bread which was significantly less liked. The provision of health information about the breads did not increase consumer liking of the vegetable-enriched breads. In conclusion, this study demonstrated that vegetable-enriched bread appeared to be an acceptable strategy to increase vegetable intake, however, liking depended on vegetable type.

Challenges to infrastructure delivery during the economic downturn : a qualitative analysis

Infrastructure plays a key role in creating and maintaining sustainable economic growth and a prosperous community. Infrastructure projects generally require a large amount of capital investment, which motivates involvement from the private sector in the delivery process. The Global Financial Crisis placed enormous pressure on both the public and private sectors, as the ability to borrow money for an extended tenor was greatly reduced. This study adopts a qualitative approach to analyse the challenges facing the delivery of infrastructure projects in an Australian context by considering the impacts of the Global Financial Crisis. It is found that the availability of resources and finance are perceived as the biggest challenges, with resources being more of a concern than finance to interviewees. In addition to these findings, ways in which the private sector can be better involved in the infrastructure delivery is discussed.

Nonlinear controller design for vehicle-to-grid systems with output LCL filters

This paper presents a nonlinear controller design for vehicle-to-grid (V2G) systems with LCL output filters. The V2G systems are modeled with LCL output filters in order to eliminate harmonics for improving power qualities and the nonlinear controller is designed based on the feedback linearization. The feasibility of using the appropriate feedback linearization approaches, either partial or exact, is also investigated through the feedback linearizability of V2G systems. In this paper, partial feedback linearization is used to design the controller with a capability of sharing both active and reactive power in V2G systems. The performance of the proposed controller controller is evaluated on a single-phase full-bridge converter-based V2G system with an LCL output filter and compared to that of without any filter. Simulation results clearly demonstrate the harmonic elimination capabilities of the proposed V2G structure with the proposed control scheme.

Mobile application as a tool for urban traffic data collection and generation to advanced traveler information systems using Wi-Fi networks available in urban centers

This paper presents an Advanced Traveler Information System (ATIS) developed on Android platform, which is open source and free. The developed application has as its main objective the free use of a Vehicle-to- Infrastructure (V2I) communication through the wireless network access points available in urban centers. In addition to providing the necessary information for an Intelligent Transportation System (ITS) to a central server, the application also receives the traffic data close to the vehicle. Once obtained this traffic information, the application displays them to the driver in a clear and efficient way, allowing the user to make decisions about his route in real time. The application was tested in a real environment and the results are presented in the article. In conclusion we present the benefits of this application. © 2012 IEEE.