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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: 1.9.0. This release adds passive energy provenance through stationary batteries, EVs and community sharing, useful solar-energy indicators and carbon accounting at the external grid boundary. EV departure feasibility uses the concrete battery physics. KPI exports include reproducibility manifests. Existing instantaneous PV and gross-import emissions remain available with their historical meanings. See the release notes for validation and limits.

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

Metadata

Release files for softcpsrecsimulator 1.9.0

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Source distribution for softcpsrecsimulator 1.9.0
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Built distribution (wheel)

Table of built distributions (wheels) for softcpsrecsimulator 1.9.0
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softcpsrecsimulator-1.9.0-py3-none-any.whl Python 3 none any Details for softcpsrecsimulator-1.9.0-py3-none-any.whl

Total release size: 1.2 MB

Release files / softcpsrecsimulator-1.9.0.tar.gz

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