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.
  • dynamic member and asset topology with repeatable multi-episode reset semantics.

Current source release: 1.5.6. Dynamic environments restore their schema-loaded topology before replaying episode events, preventing structural state from leaking between training episodes.

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

Project links

Key dates

PyPI data
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  • Released:
Newer release available (1.8.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for calof from gravatar.com calof

Credits

Author: Soft-CPS Research Group, Jose Ramon Vazquez-Canteli, Kingsley Nweye, Zoltan Nagy

License

MIT License (MIT)

Requires

Python >=3.9

Provides Extra

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1.8.0

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1.7.0 This release

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1.6.1

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0.1.0

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