Abstract
The paper presents the effects of different statistical representation of energy demand of a neighborhood on day-ahead scheduling. A stochastic energy hub model is developed to schedule the energy supply and storage components in day-ahead basis. The PV supply, electrical and thermal demand are considered as the uncertain parameters. In order to model them statistically, three different types of Probability Distribution Functions (PDFs) have been applied including uniform, normal distributions and Gaussian Mixture Model. The main objective is to minimize the amount of electrical energy purchased from the grid, where the stochastic outputs are compared with deterministic output. Two distinct parameters have been used to quantify the differences. Relative Mean Absolute Error (RMAE) represents the accuracy of the approach and bound deviation represents the reliability of the stochastic approach. Simulation analyses on the neighbourhood surrounding VU Medical Center and University campus in Amsterdam reflect that the GMM model representation is the most accurate and reliable.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019 |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Number of pages | 6 |
| ISBN (Electronic) | 9781728106526 |
| DOIs | |
| Publication status | Published - 1 Jun 2019 |
| Event | 19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019 - Genoa, Italy Duration: 11 Jun 2019 → 14 Jun 2019 |
Conference
| Conference | 19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019 |
|---|---|
| Country | Italy |
| City | Genoa |
| Period | 11/06/19 → 14/06/19 |
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Keywords
- distribution
- energy hub
- probability
- scheduling
- stochastic
Cite this
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Effects of uncertainty characterization of energy demand of a neighborhood on stochastic day-ahead scheduling. / Shafiullah, D.S.; Haque, A.N.M.M.; Nguyen, P.H.
Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019. Piscataway : Institute of Electrical and Electronics Engineers, 2019. 8783801.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Effects of uncertainty characterization of energy demand of a neighborhood on stochastic day-ahead scheduling
AU - Shafiullah, D.S.
AU - Haque, A.N.M.M.
AU - Nguyen, P.H.
PY - 2019/6/1
Y1 - 2019/6/1
N2 - The paper presents the effects of different statistical representation of energy demand of a neighborhood on day-ahead scheduling. A stochastic energy hub model is developed to schedule the energy supply and storage components in day-ahead basis. The PV supply, electrical and thermal demand are considered as the uncertain parameters. In order to model them statistically, three different types of Probability Distribution Functions (PDFs) have been applied including uniform, normal distributions and Gaussian Mixture Model. The main objective is to minimize the amount of electrical energy purchased from the grid, where the stochastic outputs are compared with deterministic output. Two distinct parameters have been used to quantify the differences. Relative Mean Absolute Error (RMAE) represents the accuracy of the approach and bound deviation represents the reliability of the stochastic approach. Simulation analyses on the neighbourhood surrounding VU Medical Center and University campus in Amsterdam reflect that the GMM model representation is the most accurate and reliable.
AB - The paper presents the effects of different statistical representation of energy demand of a neighborhood on day-ahead scheduling. A stochastic energy hub model is developed to schedule the energy supply and storage components in day-ahead basis. The PV supply, electrical and thermal demand are considered as the uncertain parameters. In order to model them statistically, three different types of Probability Distribution Functions (PDFs) have been applied including uniform, normal distributions and Gaussian Mixture Model. The main objective is to minimize the amount of electrical energy purchased from the grid, where the stochastic outputs are compared with deterministic output. Two distinct parameters have been used to quantify the differences. Relative Mean Absolute Error (RMAE) represents the accuracy of the approach and bound deviation represents the reliability of the stochastic approach. Simulation analyses on the neighbourhood surrounding VU Medical Center and University campus in Amsterdam reflect that the GMM model representation is the most accurate and reliable.
KW - distribution
KW - energy hub
KW - probability
KW - scheduling
KW - stochastic
UR - http://www.scopus.com/inward/record.url?scp=85070807016&partnerID=8YFLogxK
U2 - 10.1109/EEEIC.2019.8783801
DO - 10.1109/EEEIC.2019.8783801
M3 - Conference contribution
AN - SCOPUS:85070807016
BT - Proceedings - 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019
PB - Institute of Electrical and Electronics Engineers
CY - Piscataway
ER -