Code and data files for the article "Peer-to-Peer (P2P) Electricity Markets for Low Voltage Networks"

Web application for distribution network management. Developed with Angular framework on the client-side and C# for the backend logic.

Jorge Fernandez's Code to compute the power flow of an electrical network with the fixed point method

Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials. Blockchain for Smart Grid Energy Management ensures secure, transparent, and efficient energy distribution, enabling real-time monitoring and trusted peer-to-peer energy transactions using distributed ledger technology.

Dockerized environment for GridLAB-D, enabling the simulation and logging of power grid models

This repository is related to my another repository (https://github.com/jddeguia/energy-output-profiling)

Contains code for Adaptive protection platform in Smart grids

A real-time data pipeline to optimize the integration of renewable energy sources (solar, wind) with traditional energy sources in a smart grid. The system ingests simulated energy production and demand data, balances supply-demand, and recommends battery/storage usage for efficient load distribution.

Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials. Energy Trading Platform using Blockchain enables secure peer-to-peer energy exchange, ensures transparent transactions, and supports fair pricing through decentralized and tamper-proof ledger technology.

Smart grid simulator frontend

DDS8558 smart meter Python module with examples.

This is a repository for synchro-waveforms in power systems

Codesys code running in the switch container PLC

A list of my scientific publications and some selected artifacts

AC Power Monitoring System

Smart grid simulator API

Python toolkit for automating smartgrid data model project creation. Features code generation for model definitions, enums, XML data, converter methods, and server classes. Includes a CMS with CRUD operations for managing project specifications.

This project uses a Deep Neural Network (DNN) to forecast household appliance energy consumptionn based on the UCI Energy Dataset. The goal is to achieve a low Mean Squared Error (MSE) and capture realistic daily load patterns such as the evening energy peak between 17:00-20:00 when most families return home from work or other engagements.