### Uittreksel

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.

Taal | Engels |
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Titel | 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 |

Plaats van productie | Piscataway |

Uitgeverij | Institute of Electrical and Electronics Engineers |

Aantal pagina's | 6 |

ISBN van elektronische versie | 9781728106526 |

DOI's | |

Status | Gepubliceerd - 1 jun 2019 |

Evenement | 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, Italië Duur: 11 jun 2019 → 14 jun 2019 |

### Congres

Congres | 19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019 |
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Land | Italië |

Stad | Genoa |

Periode | 11/06/19 → 14/06/19 |

### Vingerafdruk

### Trefwoorden

### Citeer dit

*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*[8783801] Piscataway: Institute of Electrical and Electronics Engineers. DOI: 10.1109/EEEIC.2019.8783801

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*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.*, 8783801, Institute of Electrical and Electronics Engineers, Piscataway, Genoa, Italië, 11/06/19. DOI: 10.1109/EEEIC.2019.8783801

**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.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › 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

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 -