Effects of uncertainty characterization of energy demand of a neighborhood on stochastic day-ahead scheduling

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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.

TaalEngels
TitelProceedings - 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 productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Aantal pagina's6
ISBN van elektronische versie9781728106526
DOI's
StatusGepubliceerd - 1 jun 2019
Evenement19th 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 201914 jun 2019

Congres

Congres19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2019
LandItalië
StadGenoa
Periode11/06/1914/06/19

Vingerafdruk

Scheduling
Uncertainty
Energy
Output
Uncertain Parameters
Probability Distribution Function
Gaussian Mixture Model
Normal distribution
Probability distributions
Distribution functions
Gaussian distribution
Schedule
Quantify
Deviation
Model
Grid
Distinct
Minimise
Demand
Simulation

Trefwoorden

    Citeer dit

    Shafiullah, D. S., Haque, A. N. M. M., & Nguyen, P. H. (2019). Effects of uncertainty characterization of energy demand of a neighborhood on stochastic day-ahead scheduling. In 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
    Shafiullah, D.S. ; Haque, A.N.M.M. ; Nguyen, P.H./ Effects of uncertainty characterization of energy demand of a neighborhood on stochastic day-ahead scheduling. 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.
    @inproceedings{76d765ba71ce426c872bfccaa2d33cc9,
    title = "Effects of uncertainty characterization of energy demand of a neighborhood on stochastic day-ahead scheduling",
    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.",
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    Shafiullah, DS, Haque, ANMM & Nguyen, PH 2019, Effects of uncertainty characterization of energy demand of a neighborhood on stochastic day-ahead scheduling. in 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.

    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.

    Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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    AU - Shafiullah,D.S.

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    AU - Nguyen,P.H.

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    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.

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    Shafiullah DS, Haque ANMM, Nguyen PH. Effects of uncertainty characterization of energy demand of a neighborhood on stochastic day-ahead scheduling. In 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. Beschikbaar vanaf, DOI: 10.1109/EEEIC.2019.8783801