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Download fileStochastic unit commitment in microgrids based on model predictive control
conference contribution
posted on 2018-08-15, 08:38 authored by Lazaro Alvarado Barrios, Juan Boza Valerino, Alvaro Rodriguez del Nozal, Juan Manuel Escano, Jose L. Martinez-Ramos, Francisco Gonzalez-LongattFrancisco Gonzalez-LongattThis article deals with the problem of Stochastic Unit Commitment (SUC), considering the stochastic nature of demand and meteorological phenomena. This paper shows the optimal operation of a hybrid microgrid composed of the following generation units: wind turbine (WT), photovoltaic solar panel (PV), diesel engine generator (DE), micro-turbine (MT), as
well as storage devices such as Battery Energy Storage (BES), considering its constraints and the requirements of the reserve generation. For this purpose, a Model-based Predictive Control (MPC), which uses dynamic models of prediction of renewable power and demand in real time, is developed, allowing feedback at each step of time, which corrects the uncertainty of the models. A comparison with a classic UC formulation has been made. The results reach a lower cost solution.
Funding
The authors would like to acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness under Grants PCIN-2015-043 and ENE2015-69597-R and AEI/FEDER by grant TEC2016-80242-P.
History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
SEST 2018. Smart Energy Systems and TechnologiesCitation
ALVARADO BARRIOS, L. ... et al, 2018. Stochastic unit commitment in microgrids based on model predictive control. Presented at SEST 2018, International Conference on Smart Energy Systems and Technologies, Seville, Spain, 10-12 September 2018.Publisher
© IEEEVersion
- AM (Accepted Manuscript)
Acceptance date
2018-08-01Publication date
2018Notes
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.ISBN
9781538653265Publisher version
Language
- en