# Benchmark-Test-Set

**Repository Path**: vickylast/Benchmark-Test-Set

## Basic Information

- **Project Name**: Benchmark-Test-Set
- **Description**: Benchmark Test Set for dynamic economic emission dispatcher
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-12-07
- **Last Updated**: 2021-05-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Benchmark-Test-Set

5_Unit_Generator_Coefficients.xlsx and 5_Unit_B_Matrix.xlsx are the configurations for 5 unit system  

10_Unit_Generator_Coefficients.xlsx and 10_Unit_B_Matrix.xlsx are the configurations for 5 unit system  

BTS_5_unit_system.xlsx and BTS_10_unit_system.xlsx contain the Benchmark Test Set for 5 unit system and 10 unit system respectively  

BTS_MOPPO_Result_5_unit_system.xlsx and BTS_MOPPO_Result_10_unit_system.xlsx contain the dispatching results of the dispatcher trained with MOPPO for every task in BTS  

# Cite

@article{SHAO2021107047,
title = {An agile and intelligent dynamic economic emission dispatcher based on multi-objective proximal policy optimization},
journal = {Applied Soft Computing},
volume = {102},
pages = {107047},
year = {2021},
issn = {1568-4946},
doi = {https://doi.org/10.1016/j.asoc.2020.107047},
url = {https://www.sciencedirect.com/science/article/pii/S1568494620309856},
author = {Zhuang Shao and Fengqi Si and Huaijiang Wu and Xiaozhong Tong},
keywords = {DEED, Multi-objective policy optimization, Reinforcement learning, Artificial neural network},
}