Cite as: Shariati, Omid; Smith, Stefan (2024): Heavy Goods Electric Vehicle (HGEV) Price-Managed Depot Charging Demand Modelling Software. 
University of Reading. Software. https://doi.org/10.17864/1947.001306

Copyright 2024 University of Reading. This code is made available under the terms of the GNU General Public License 3.0: https://www.gnu.org/licenses/gpl-3.0.html. 
The accompanying documentation is issued under a Creative Commons Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0/.

%-------------------------------------------------------------------------------------------------------------------------------
%------------------------Introduction to Heavy Goods Electric Vehicle (HGEV) Price-Managed Depot Charging------------------------
%--------------------------------------------------Demand Modelling Software-----------------------------------------------------
%-------------------------------------------------------------------------------------------------------------------------------
% Introducing the "Heavy Goods Electric Vehicle (HGEV) Price-Managed Depot Charging Modelling":
% This software package is designed to model the charging demand profile of a fleet of Heavy Goods Electric Vehicles (HGEV) operating
% in a depot style while managing the charging slots to minimise the total price considering the electricity price variation in the region
% of operation (UK for the current setting). To effectively utilise the software, the following factors must be available or defined
% for the model:
%---Case Factors:
%--- 1) Size of the Fleet: Number of vehicles in the fleet.
%--- 2) Specific Energy Consumption: Energy consumption per unit distance for the fleet vehicles. Averaging may be required depending on the
% data available.
%--- 3) Battery Capacity: Capacity of the batteries installed in the vehicles.
%--- 4) Charge Point Power: Power rating of the charging points available at the depot.
%---Country/Operating Zone Based Factors:
%--- 1) Daily Price Profile: which reflects Market Index Price (MIP), considering the network extra charge, variation across a typical day.
%--- 2) Daily Travelled Distance: The average distance travelled by vehicles in the country/operational zone (Currently set for the UK).
%-------------------------------------------------------------------------------------------------------------------------------
% In a depot operational style, the fleet returns to the depot at the end of its duty, and the vehicle batteries are fully recharged
% overnight to be ready for the next daily operation. To quantify the demand of the fleet, the model has been developed with the
% consideration of a price-based managed charging strategy.
% The electrification of the transport sector - on a large scale - will bring challenges to the grid in terms of the demand level and use of grid
% infrastructure. Generally, at the present state, the grid has enough spare generation to accommodate the demand from the transport sector at
% the current scale. However, this generation is available at off-peak times, when the demand arising from other sectors is low. Additionally,
% the price of electricity and the charge for the use of the grid is low in the off-peak times. Therefore, the fleet operator would need to
% have optimising measures in place to minimise their total cost of electricity use. There are various methods ranging from linear programming,
% dynamic programming to the application of machine learning techniques that have been discussed in the literature for vehicle charging optimisations.
% Here, it has been sufficient to use linear optimisation techniques, at this stage, for the cost minimisation of fleet charging at depots.
% The algorithm starts by initialising the required parameters and variables such as charging power of the charging points, the depot capacity,
% the start and stop time of the charging event, the vehicle battery capacity, the price vector, and the state of charge (SoC) of the vehicles.
% The required energy of the vehicles is obtained based on the battery capacity of the vehicles and the SoC of the vehicles when they arrive at
% the depot. Then the algorithm follows the charging cost minimisation principle, as given above, to find the minimum charging cost of each vehicle
% and the fleet. The process is built on price-minimised charging of an individual vehicle, where the algorithm minimises the cost of the total energy
% required by an individual vehicle. This process is repeated according to the number of vehicles in the fleet to complete the recharging of
% the whole fleet to be ready for the next daily operation according to the time and depot capacity constraints.
%------------------------------------------------------------------------------------------------------------------------------
%---Note: Before proceeding, please unzip the folder "PriceManaged_HGEV_Depot_Charging_Model.zip" and carefully follow the instructions
% provided in the "UserGuide" file located within.
%-------------------------------------------------------------------------------------------------------------------------------