HERNANDEZ-CEDILLO, MONICA (2022) Towards Smarter Electric Vehicle Charging with Low Carbon Smart Grids: Pricing and Control. Doctoral thesis, Durham University.
|PDF - Accepted Version|
Environmental and political directions indicate transition to a decarbonized transportation system is necessary as it is one of the most pollutant sectors regarding greenhouse gas emissions. Research in Demand Side Management suggests that its tools are the most cost-effective option for improving the performance of the grid without incurring into high infrastructure investments, hence reducing the payback for start-ups in the sector. This Thesis proposes solutions to tackle 5 objectives around this area of research: 1-2 are related to developing a demand response pricing and EV smart charging strategies, 3-4 are related to developing a multi-objective charging scheme in order to ensure fairness and reduction of CO2eq emissions, and 5 is related to testing parameters of EV charging to understand future improvements and limitations in the proposed models. Chapter 3, that tackles objectives 1-2, proposes a data-driven optimisation algorithm with pricing and control modules that communicate with each other to achieve a successful integration with the grid by charging at the right price and expected time. The results show customers can be positively engaged with pricing signals while providing support to the grid. Chapter 4, which tackles objectives 3-4, proposes a multi-objective EV charging formulation that include perspectives of EV users, a carbon regulator and a charging station operator. The multi-objective formulation is solved with a genetic algorithm in order to find the fairest and the greenest solution. Results which are evaluated using different scenarios show different weights to each objective function can differ based on the charging location and EV charging availability. Finally, Chapter 5 which tackles objective 5, shows a sensitivity analysis where improvements in revenues, reduction of carbon emissions and bidding capacity depend on the evaluation of EV users’ parameters, and the charging station control and sizing.
|Item Type:||Thesis (Doctoral)|
|Award:||Doctor of Philosophy|
|Keywords:||Smart grid; Demand response; EV charging; Renewable energy; Multi-objective Optimisation; Reduction of Carbon Emissions.|
|Faculty and Department:||Faculty of Science > Engineering, Department of|
|Copyright:||Copyright of this thesis is held by the author|
|Deposited On:||13 Oct 2022 09:28|