V2G Ancillary Services Management Strategy for EVs with Solar Powered Charging Stations based on artificial Intelligence Algorithms

Semaria Ruiz, Jesus Hernandez, David Borge-Diez

Abstract


This research proposes a vehicle to grid strategy based on dynamic optimization for a fleet of public transportation Electric Vehicles (EVs) whose charging station is jointly powered by the conventional electrical network and photovoltaic renewable sources using two neural networks to make the prediction of future outcomes of the energy expenditure of the EVs and the renewable generation. The strategy is
intended to find the optimal decisions for the EVs regarding their charging-discharging schedules, the amount of power they exchange with the electrical network, and their dispatch to perform travels; considering the EVs have the availability to sell energy and provide frequency reserve ancillary services. Allowing with this proposal the estimation of the fleet management plans according to the daily average congestion level in the analysis zone, the required departure schedules of the vehicles in the fleet, and the past measures of solar radiation in the site, which are the inputs variables of the prediction algorithms. The mathematical of the dynamic optimization is set as a convex Mixed-Integer problem and is solved with the iterative branch and cut method; finding that the most profitable options for the EVs owner are sell energy and provide the downward regulation ancillary services, and that the solution is dependent on the accuracy of the
prediction algorithms outputs, hence two high precision neural networks with an error lower than 2% were used.


Keywords


Artificial Intelligence; Electric Vehicles; Neural Networks; Optimization methods; Renewable Energy Sources; Vehicle-to-Grid

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DOI (PDF): https://doi.org/10.20508/ijsmartgrid.v7i4.314.g304

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