Feasibility study: investigation of car park-based V2G services in the UK central hub

The increasing uptake of electric vehicles, and the established practice of long-term parking at stations and airports, offers an opportunity to develop a flexible approach to help with the energy storage dilemma. This paper investigates the feasibility of using a number of EV batteries as an energy storage and grid balancing solution within the UK Central Hub area. Here, the capital cost of the vehicle is a sunk cost to the EV owner. The potential income generated, or discount on long-term parking, is an additional benefit of ownership. This paper considers the income available to a small and large size car park from the different market mechanisms to offer grid support in the UK and contrasts this with the complexity and costs of the EV charging infrastructure required within these types of scheme

The structure of the resistant nucleus from hydrolyzed cholla gum

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Optimized time step for electric vehicle charging optimization considering cost and temperature

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Charging Architectures Integrated with Distributed Energy Resources for Sustainable Mobility

Abstract This paper introduces a study on the charging infrastructures, integrated with distributed energy sources, showing their ability to support the electric and hybrid mobility in a smart grid scenario. This analysis starts from a description of the main AC and DC architecture and then goes through the advantages derived by the integration of renewable energy sources within the existing electric power network. A section of this paper is then dedicated to the main technologies of energy storage systems, which allow and support the integration of unpredictable energy sources into the grid. Finally, the power on-board and off-board vehicle charging devices are analyzed with specific focus on PWM control schemes, for the regulation of AC/DC and DC/DC power converters, and on grid operations (V2G) related to different aggregation schemes

Coordinated bidding of ancillary services for vehicle-to-grid using fuzzy optimization

Electric vehicles (EVs) can be effectively integrated with the power grid through vehicle-to-grid (V2G). V2G has been proven to reduce the EV owner cost, support the power grid, and generate revenues for the EV owner. Due to regulatory and physical considerations, aggregators are necessary for EVs to participate in electricity markets. The aggregator combines the capacities of many EVs and bids their aggregated capacity into electricity markets. In this paper, an optimal bidding of ancillary services coordinated across different markets, namely regulation and spinning reserves, is proposed. This coordinated bidding considers electricity market uncertainties using fuzzy optimization. The electricity market parameters are forecasted using autoregressive integrated moving average (ARIMA) models. The fuzzy set theory is used to model the uncertainties in the forecasted data of the electricity market, such as ancillary service prices and their deployment signals. Simulations are performed on a hypothetical group of 10 000 EVs in the electric reliability council of Texas electricity markets. The results show the benefit of the proposed fuzzy algorithm compared with previously proposed deterministic algorithms that do not consider market uncertainties

Optimal charging strategies for unidirectional vehicle-to-grid using fuzzy uncertainties

Vehicle-to-grid (V2G) is a way to effectively integrate electric vehicles (EVs) with the power system network. Unidirectional V2G is especially attractive because it is easily implementable and requires little additional hardware. An EV can be effectively charged during its parking time, and can potentially generate revenues for the EV owner. It can also provide services to the electrical grid through an aggregated scheme. The aggregator combines the capacity of many EVs and bids them into electricity markets. In this paper, a novel charging scheme is proposed that optimizes the charging of EVs and the bidding of ancillary service in the electricity market, considering the different market uncertainties using fuzzy set theory. The electricity market parameters are forecasted using ARIMA models. Simulations performed on a hypothetical group of EVs in the ERCOT area show the benefits of the proposed scheme