SoftCPS REC Simulator: an energy-community RL simulator fork focused on EV/BESS/PV, electrical-service constraints, and community-market experimentation.
Project description
SoftCPS REC Simulator
softcpsrecsimulator is a simulation framework maintained by the SoftCPS Research Group for
energy-community studies with reinforcement learning.
It is a fork-based project used for research and experimentation on:
- electric vehicles (EVs) and chargers,
- stationary batteries (BESS),
- photovoltaic generation (PV),
- electrical-service constraints (single/three-phase),
- local community market settlement and KPI analysis.
- entity-mode RL observation contracts with derived forecasts, physical deadlines, feasible action capacity and requested/limited/applied action feedback.
Project Positioning
This package is a fork project built from the CityLearn codebase and extended for SoftCPS REC simulation needs.
Installation
pip install softcpsrecsimulator
Python Usage
For compatibility with existing ecosystems, the Python module path currently remains:
from citylearn.citylearn import CityLearnEnv
Source and Documentation
- Source: https://github.com/Soft-CPS-Research-Group/Simulator
- Original upstream project: https://github.com/intelligent-environments-lab/CityLearn
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