Data · dataset · 2022
Analyzing Optimal Battery Sizing in Microgrids
Listed in Teesside University Research Data Repository
The dataset is generated using the mixed integer linear programming (MILP).
Description
The MILP is a linear optimization problem extensively used while solving optimal sizing and selection problems of Distributed Energy Resources (DERs) and energy storage in microgrids. The problem is solved by the power balance of different DERs and energy storage as compared to the total load.
Random sizes are entered into respective matrices to form a generation mix and each generation mix is then compared to fully satisfy the load demand. The data set for a residential microgrid with 24255 samples and 40 parameters is extracted using a MILP- based microgrid model. The extracted parameters and their description is enclosed as “Dataset generation techniques factors and external factors.pdf”.
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The dataset is enclosed as "Analyzing_optimal_battery_sizing_in_microgrids_Dataset.xlsx". to use this dataset please cite this article: Hajra Khan, Imran Fareed Nizami, Saeed Mian Qaisar, Asad Waqar, Moez Krichen and Abdulaziz Turki Almaktoom, Analyzing optimal battery sizing in microgrids based on the feature selection and machine learning approaches, Energies, MDPI, 2022,
- This paper is accepted for publication on the 13th of October 2022. It will appear soon online.
Links
Where it is published
- DOI doi.org/10.17632/syt3yrr7f5.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Electrical engineering · Engineering · Humanities · Life Sciences · Machine learning · Social Science
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/syt3yrr7f5.1 | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:group:4008 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Electrical Engineering'] |
| concepts[field].anzsrc:group:4611 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Machine Learning'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |