Potential of increasing PV self-consumption with a combination of smart grid technology and storage in electric vehicles in the residential sector; A case study in Utrecht, the Netherlands
Kam, M.J. van der
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In this Master thesis a model is presented which was developed to simulate the increase of self-consumption of photovoltaic (PV)-power by storing energy in electric vehicles (EVs) using smart grid technology for a case study in the residential sector. The case study consists of a micro-grid including three PV-installations, an office, internet servers, three to five households and two to five EVs in the area of Lombok in Utrecht, the Netherlands. Four scenarios that differ in the amount of kWp for the PV-installations, the number of households and the number of EVs have been constructed. Three different possible smart grid control algorithms are presented that manage the (dis)charging profile of multiple EVs, either in real-time or using linear optimisation with predictions for PV-power and electricity demand. The control algorithms are simulated for a year for all scenarios using data for PV-power and electricity demand from the Netherlands and are evaluated for PV-power self-consumption and relative demand peak reduction. Furthermore a sensitivity analysis is performed in order to test the results for changes in input data and model structure. Results show that smart storage of electricity in EVs can increase self-consumption with 15% to 35%, reduce energy send to the main grid with 5 to 8 MWh per year and increase relative peak reduction with 55% to 75%, depending on which control algorithm and scenario is chosen. Furthermore, in a comparison of scenarios it is shown that installing additional solar power is not advisable when evaluating for self-consumption, because the simulations show that additional solar power will not be used within the micro-grid. Based on the results it can be concluded that designing an EV-charging control algorithm based on linear programming is the best way to increase self-consumption in this case study, because it scores best on self-consumption and relative peak reduction and is least sensitive for the aspects researched in the sensitivity analysis.