Flow Battery Energy Scheduling Optimization Code Dataset

#code generation #energy scheduling optimization #constraint-based programming #flow battery energy management #energy storage scheduling #power system optimization

5000
0.5GB
JSON
CATL
MOBIUSI INC

Updated:2026-10-19

AI Analysis & Value Prop

This dataset pairs flow battery energy scheduling problems with corresponding optimization code in text-to-code examples. The problem statements cover key operational details such as scheduling objectives, state of charge, charging and discharging power, efficiency, and operating constraints. These examples turn storage requirements into optimization programs for code SFT, scheduling logic learning, and algorithm prototyping for flow battery energy storage systems.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
optimization_code	string	Optimization program for flow battery energy scheduling generated from the scheduling problem description.
scheduling_problem	string	Source text specifying requirements such as scheduling objectives, state of charge, charging and discharging power, efficiency, and operating constraints.

Compliance Statement

Authorization Type	Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What does the dataset contain?: Each example pairs a scheduling problem description with corresponding flow battery energy scheduling optimization code. The problem statements may cover scheduling objectives, state of charge, charging and discharging power, efficiency, and operating limits.

Which model training tasks is it designed for?: The dataset is intended for text-to-code code SFT. It can help train models to interpret flow battery scheduling requirements and generate related optimization programs, and can also support scheduling logic analysis and algorithm prototyping.

Does the dataset specify a programming language or optimization solver?: The available information does not specify a programming language, solver, or standardized runtime environment. Before use, review the code conventions in the examples and adapt or validate them for the target training framework or deployment environment.

How can the data be obtained and used?: Contact the dataset provider for the files and delivery details. Preserve the pairing between each problem description and its code, and apply any required format conversion, quality checks, and dataset splitting for your task.

What are the licensing terms and usage boundaries?: The licensing scope is governed by the terms published by the dataset provider. The examples are suited to model training, research, and scheduling algorithm prototyping; review them against device parameters, safety requirements, and engineering validation results before deploying a real storage system.

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Cite this Work

@dataset{Mobiusi2026,
  title={Flow Battery Energy Scheduling Optimization Code Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/fb0f06f45cf3ab874878392de6592185?cate=3},
  urldate={2026-10-09},
  keywords={flow battery, energy scheduling, storage optimization, optimization code, code generation, code SFT, charge discharge scheduling, state of charge},
  version={1.0}
}

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