| dc.description.abstract | Zero emission is a very popular terminology used, and a lot of research is also focused on this. Fossil fuel consumption is one of the big factors that is responsible for carbon emissions. In this scenario, a pilot project is carried out in Norway that focuses on reducing fuel usage at the construction sites where the power grid is not reachable and instead the machines are powered with a battery. A generic scheduling optimization and charging cost optimization with proper scheduling is proposed in this thesis for the ongoing project that involves the battery charging in the area where the grid power is easily available and then these batteries are transported to the remote construction sites to power up the construction machines. The solution for cost optimization is also based on eased charging techniques including the installation of solar panels and charging through electric vehicle chargers. Different methods like Mixed-Integer Linear Programming (MILP) and Large Neighbourhood Search (LNS) algorithms are studied to address the optimization problem. The model as MILP is formed, with the defined objective function, constrains and other parameters, then solved using a large neighbourhood search algorithm with the Microsoft Excel Solver. A study case is formulated to understand the impact of charging scheduling optimization in different scenarios. Power electronics involved in charging stations are simulated through MATLAB/Simulink, using the converter values proposed in another research to demonstrate the charging through low voltage sources like PV and EV chargers. | |
