Beast code in Giters

Maggiexmq's starred repositories

Forecasting-EV-Charging-Station-Load

400

ev_controller

Markov Decision Process and Model Predictive control for EV charging station

Language:Jupyter NotebookMIT2600

Short-term-load-forecasting-using-ensemble-modelling-and-optimization-for-demand-response

forecasting and optimization - Coded in Python

Language:Jupyter NotebookMIT2500

This code was written within the dissertation of Ola Pronobis "Charge management concepts with integrated requirements management in case of unpredictable behavior of electric vehicle fleets" at TU Braunschweig. More information can be found in the published dissertation. The code includes a fleet simulation and an integrated optimization algorithm to dynamically charge a fleet of electric vehicles. Ecological and/or economic goals can be set. The algorithm takes into account the CO2 emissions in the electricity mix, the loads at the site, the driving forecasts, the current electricity prices and the renewable energies generated.

Language:Python1000

How-Many-EVs-are-Needed-to-Reach-CO2-Emissions-Goals-A-Case-Study-from-Montreal-Canada

Files and scripts used for the research project entitled "How Many EVs are Needed to Reach CO2 Emissions Goals? A Case Study from Montreal, Canada"

Language:Python300

Comparing-Electric-vs-Gasoline-Vehicle-Emissions

A comparison of the CO2 emissions generated by traditional cars, or Internal Combustion Engines (ICE) and Electric Vehicles (EV), which use electricity generated from various sources.

Language:Python500

carbon_aware_ev_charging

Minimizing Carbon Emission during EV Charging

Language:Jupyter Notebook1300

Bidding-EV-Aggregators

Input data used in Soares et al "Optimal Coordinated Bidding of EV Aggregators in Electricity Markets using Decentralized Optimization with Network Validation" IEEE ACCESS 2020

100

DR-of-Residential-Houses-Equipped-with-PV-BB-Systems-An-Application-Study-Using-EAs_Energies2020

This repository contains the experimental set up used for the paper: "Demand Response of Residential Houses Equipped with PV-Battery Systems: An Application Study Using Evolutionary Algorithms" Published in Energies journal MDPI 2020

Language:MATLAB1000

anciML

Spring 2020 CE2095 Final Project for predicting / optimizing ancillary services by grid-scale batteries

Language:Jupyter NotebookMIT400

ASAM

Ancillary Services Acquisition Model (ASAM) - Agent-based model to simulate processes of ancillary services acquisition and electricity markets

Language:PythonGPL-3.01200

EMPC

Model predictive control(mpc), demand response and energyhub modeling in R

Language:R1400

Electric_Vehicle_Charging_Simulation

This project implements Q-Learning to find the optimal policy for charging and discharging electric vehicles in a V2G scheme under conditions of uncertain commitment of EV owners. The problem is modelled as a multi-objective multi-agent cooperative game. Project is part of fulfillment criteria for ECE 730 course at the University of Alberta.

Language:Python3600