Stochastic Predictive Control of Multi-Microgrid Systems

Najmeh Bazmohammadi; Ahmadreza Tahsiri; Amjad Anvari-Moghaddam; Josep M. Guerrero

doi:10.1109/EEEIC.2018.8494353

Stochastic Predictive Control of Multi-Microgrid Systems

Najmeh Bazmohammadi, Ahmadreza Tahsiri, Amjad Anvari-Moghaddam, Josep M. Guerrero

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

4 Citationer (Scopus)

95 Downloads (Pure)

Abstract

This paper presents a stochastic predictive control algorithm for a number of microgrids connected to the same distribution system. Each microgrid includes a variety of distributed resources such as wind turbine, photo voltaic units, energy storage devices and loads. Considering the uncertainty of loads and renewable-based distributed resources, the power management problem is formulated in the framework of stochastic control. The microgrids operation are coupled through a joint probabilistic constraint which requires the power flow from utility to each microgrid limits to a pre-specified range. Utilizing probabilistic distribution function of uncertain variables, the deterministic counterpart of the problem is derived and a close-optimal solution of the problem is achieved. The Monte-Carlo simulation analysis is used to justify the robustness characteristics of the solution.

Originalsprog	Engelsk
Titel	Proceedings of 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) : EEEIC 2018
Antal sider	5
Forlag	IEEE Press
Publikationsdato	16 okt. 2018
Sider	1-5
Artikelnummer	8494353
ISBN (Trykt)	978-1-5386-5187-2
ISBN (Elektronisk)	978-1-5386-5186-5
DOI	https://doi.org/10.1109/EEEIC.2018.8494353
Status	Udgivet - 16 okt. 2018
Begivenhed	IEEE 18th International Conference on Environment and Electrical Engineering and 2nd Industrial and Commercial Power Systems Europe - Palermo, Italien Varighed: 12 jun. 2018 → 15 jun. 2018 https://www.eeeic.net/eeeic/

Konference

Konference	IEEE 18th International Conference on Environment and Electrical Engineering and 2nd Industrial and Commercial Power Systems Europe
Land/Område	Italien
By	Palermo
Periode	12/06/2018 → 15/06/2018
Internetadresse	https://www.eeeic.net/eeeic/

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10.1109/EEEIC.2018.8494353

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Citationsformater

Bazmohammadi, N., Tahsiri, A., Anvari-Moghaddam, A., & Guerrero, J. M. (2018). Stochastic Predictive Control of Multi-Microgrid Systems. I Proceedings of 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe): EEEIC 2018 (s. 1-5). Artikel 8494353 IEEE Press. https://doi.org/10.1109/EEEIC.2018.8494353

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title = "Stochastic Predictive Control of Multi-Microgrid Systems",

abstract = "This paper presents a stochastic predictive control algorithm for a number of microgrids connected to the same distribution system. Each microgrid includes a variety of distributed resources such as wind turbine, photo voltaic units, energy storage devices and loads. Considering the uncertainty of loads and renewable-based distributed resources, the power management problem is formulated in the framework of stochastic control. The microgrids operation are coupled through a joint probabilistic constraint which requires the power flow from utility to each microgrid limits to a pre-specified range. Utilizing probabilistic distribution function of uncertain variables, the deterministic counterpart of the problem is derived and a close-optimal solution of the problem is achieved. The Monte-Carlo simulation analysis is used to justify the robustness characteristics of the solution.",

keywords = "Multi-microgrid system, renewable energy resources, stochastic control, uncertainty management",

author = "Najmeh Bazmohammadi and Ahmadreza Tahsiri and Amjad Anvari-Moghaddam and Guerrero, {Josep M.}",

year = "2018",

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doi = "10.1109/EEEIC.2018.8494353",

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booktitle = "Proceedings of 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)",

publisher = "IEEE Press",

note = "IEEE 18th International Conference on Environment and Electrical Engineering and 2nd Industrial and Commercial Power Systems Europe, EEEIC 2018 ; Conference date: 12-06-2018 Through 15-06-2018",

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Bazmohammadi, N, Tahsiri, A, Anvari-Moghaddam, A & Guerrero, JM 2018, Stochastic Predictive Control of Multi-Microgrid Systems. i Proceedings of 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe): EEEIC 2018., 8494353, IEEE Press, s. 1-5, IEEE 18th International Conference on Environment and Electrical Engineering and 2nd Industrial and Commercial Power Systems Europe, Palermo, Italien, 12/06/2018. https://doi.org/10.1109/EEEIC.2018.8494353

Stochastic Predictive Control of Multi-Microgrid Systems. / Bazmohammadi, Najmeh; Tahsiri, Ahmadreza; Anvari-Moghaddam, Amjad et al.
Proceedings of 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe): EEEIC 2018. IEEE Press, 2018. s. 1-5 8494353.

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

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T1 - Stochastic Predictive Control of Multi-Microgrid Systems

AU - Bazmohammadi, Najmeh

AU - Tahsiri, Ahmadreza

AU - Anvari-Moghaddam, Amjad

AU - Guerrero, Josep M.

PY - 2018/10/16

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N2 - This paper presents a stochastic predictive control algorithm for a number of microgrids connected to the same distribution system. Each microgrid includes a variety of distributed resources such as wind turbine, photo voltaic units, energy storage devices and loads. Considering the uncertainty of loads and renewable-based distributed resources, the power management problem is formulated in the framework of stochastic control. The microgrids operation are coupled through a joint probabilistic constraint which requires the power flow from utility to each microgrid limits to a pre-specified range. Utilizing probabilistic distribution function of uncertain variables, the deterministic counterpart of the problem is derived and a close-optimal solution of the problem is achieved. The Monte-Carlo simulation analysis is used to justify the robustness characteristics of the solution.

AB - This paper presents a stochastic predictive control algorithm for a number of microgrids connected to the same distribution system. Each microgrid includes a variety of distributed resources such as wind turbine, photo voltaic units, energy storage devices and loads. Considering the uncertainty of loads and renewable-based distributed resources, the power management problem is formulated in the framework of stochastic control. The microgrids operation are coupled through a joint probabilistic constraint which requires the power flow from utility to each microgrid limits to a pre-specified range. Utilizing probabilistic distribution function of uncertain variables, the deterministic counterpart of the problem is derived and a close-optimal solution of the problem is achieved. The Monte-Carlo simulation analysis is used to justify the robustness characteristics of the solution.

KW - Multi-microgrid system

KW - renewable energy resources

KW - stochastic control

KW - uncertainty management

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BT - Proceedings of 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)

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Bazmohammadi N, Tahsiri A, Anvari-Moghaddam A , Guerrero JM. Stochastic Predictive Control of Multi-Microgrid Systems. I Proceedings of 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe): EEEIC 2018. IEEE Press. 2018. s. 1-5. 8494353 doi: 10.1109/EEEIC.2018.8494353

Stochastic Predictive Control of Multi-Microgrid Systems

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