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Citation: GOUGH, R. ...et al., 2017. Vehicle-to-grid feasibility: A technoeconomic analysis of EV-based energy storage. Applied Energy, 192, Additional Information:• This paper was accepted for publication in the journal Ap- 1 Vehicle-to-Grid Feasibility: A Techno-economic Analysis of EV-based Energy StorageRebecca Gough*⁺ Abstract:The potential for electric vehicles to obtain income from energy supplied to a commercial building together with revenue accruing from specific ancillary service markets in the UK is evaluated in this work. A hybrid time-series/probabilistic simulation environment using real-world data is described, which is applied in the analysis of electricity trading with vehicle-to-grid to vehicles, buildings and markets. Key parameters are found to be the electric vehicle electricity sale price, battery degradation cost and infrastructure costs. Three vehicle-to-grid scenarios are evaluated using pool vehicle trip data, market pricing index data and half-hourly electricity demand for a commercial building. Results show that provision of energy to the wholesale electricity market with additional income from the capacity market results in the greatest projected return on investment, producing an individual vehicle net present value of ~£8,400. This is over 10 years for a vehicle supplying energy three times per week to the half-hour day-ahead market and includes the cost of installing the vehicle-to-grid infrastructure. The analysis also shows that net income generation is strongly dependent upon battery degradation costs associated with vehicle-to-grid cycling.

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The electric vehicles (EV) market is projected to continue its rapid growth, which will profoundly impact the demand on the electricity network requiring costly network reinforcements unless EV charging is properly managed. However, as well as importing electricity from the grid, EVs also have the potential to export electricity through vehicle-togrid (V2G) technology, which can help balance supply and demand and stabilise the grid through participation in flexibility markets. Such a scenario requires a population of EVs to be pooled to provide a larger storage resource. Key to doing so effectively however is knowledge of the users, as they ultimately determine the availability of a vehicle. In this paper we introduce a machine learning model that aims to learn both a) the criteria influencing users when they decided whether to make their vehicle available and b) their reliability in following through on those decisions, with a view to more accurately predicting total available capacity from the pool of vehicles at a given time. Using a series of simplified simulations, we demonstrate that the learning model is able to adapt to both these factors, which allows the required capacity of a market event to be satisfied more reliably and using a smaller number of vehicles than would otherwise be the case. This in turn has the potential to support participation in larger and more numerous market events for the same user base and use of the technology for smaller groups of users such as individual communities.

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Grapevine leafroll-associated virus 3 (GLRaV-3), an economically significant pathogen of grapevines, is transmitted by Pseudococcus calceolariae, a mealybug commonly found in New Zealand vineyards. To help inform alternative GLRaV-3 control strategies, this study evaluated the three-way interaction between the mealybug, its plant host and the virus. The retention and transmission of GLRaV-3 by P. calceolariae after access to non-Vitis host plants (and a non-GLRaV-3 host) White clover (Trifolium repens L. cv. “Grasslands Huia white clover”), Crimson clover (T. incarnatum), and Nicotiana benthamiana (an alternative GLRaV-3 host) was investigated. For all experiments, P. calceolariae first instars with a 4 or 6 days acquisition access period on GLRaV-3-positive grapevine leaves were used. GLRaV-3 was detected in mealybugs up to 16 days on non-Vitis plant hosts but not after 20 days. GLRaV-3 was retained by second instars (n = 8/45) and exuviae (molted skin, n = 6/6) following a 4 days acquisition period on infected grapevines leaves and an 11 days feeding on non-Vitis plant hosts. Furthermore, GLRaV-3 was transmitted to grapevine (40−60%) by P. calceolariae second instars after access to white clover for up to 11 days; 90% transmission to grapevine was achieved when no alternative host feeding was provided. The 16 days retention period is the longest observed in mealybug vectoring of GLRaV-3. The results suggest that an alternative strategy of using ground-cover plants as a disrupter of virus transmission may be effective if mealybugs settle and continue to feed on them for 20 or more days.

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