Abstract
The paper details a stochastic programming model to optimize decisions on battery charging and Grid ancillary services at Electric Vehicle (EV) Charging Points. First and second decision stages deal with stochasticity on EV staying pattern. Day-ahead and Intraday electric markets are included respectively as first and second stages for energy and reserves prices. Day-Ahead decisions - first stage - are hourly energy purchases and sales and, also upward-downward reserve sales. Intraday decisions - second stage - deals with different scenarios of vehicle staying and supplying reserves. The global objective function prioritizes supplying energy to EV batteries and at the same time minimizes the net expected energy cost at the EV Charging Point taking into account energy and reserve markets. A 50 plug-in vehicle parking is analyzed with household, commercial and mixed staying patterns and several stochastic arrival-departure scenarios. Output comparison is shown between Day-Ahead and Intraday decisions and resulting average cost per kWh.
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