Stars
MESMO - Multi-Energy System Modeling and Optimization
Reinforcement learning control of a Battery Energy Storage System (BESS) in real-time electricity markets, using value and policy iteration to derive optimal bidding and operation strategies.
Reinforcement Learning for Real time Pricing and Scheduling Control in EV Charging Stations
Agent-Based Modeling in Electricity Market Using Deep Deterministic Policy Gradient Algorithm
Deep Reinforcement Learning based Real-time Renewable Energy Bidding with Battery Control
A gymnasium-compatible framework to create reinforcement learning (RL) environment for solving the optimal power flow (OPF) problem. Contains five OPF benchmark environments for comparable research.
This project is the source code of paper "Optimizing bidding strategy in electricity market based on graph convolutional neural network and deep reinforcement learning" in Applied Energy 2025.
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
An elegant PyTorch deep reinforcement learning library.
Grid2Op a testbed platform to model sequential decision making in power systems.
Python library for computing local energy market (LEM) prices for a Renewable Energy Community (REC) that can promote the minimization of the members’ operation cost with energy.
HAMLET: Hierarchical Agent-based Markets for Local Energy Trading
PIDE – Simulation tool for DER integration and autonomous reactive-power control in distribution grids (Photovoltaic Integration Dynamics and Efficiency).
GridAttackSim: Smart Grid Attack Simulation Framework
The Distributed Generation Market Demand (dGen) model simulates customer adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the United States an…
Open Platform for Local Energy Markets
AI-driven energy distribution optimization system using Reinforcement Learning
Open-source Distributed Energy Resources (DER) Model that represents IEEE Standard 1547-2018 requirements for steady-state and dynamic analyses
A Framework for developing Deep Reinforcement Learning environments using OpenDSS and Gymnasium for electric power distribution systems research optimization and control. We include common grid ser…
A Reinforcement Learning-based Volt-VAR Control Dataset
A Gym-like environment for Volt-Var control in power distribution systems.
My Exploration on Deep Reinforcement Learning Survey
In this repository, we explore the application of Transformer-based Reinforcement Learning approaches to solve complex real-world energy management problems using the CityLearn environment (Challen…
Official reinforcement learning environment for demand response and load shaping