Modeling a Hybrid Microgrid Using Probabilistic Reconfiguration under System Uncertainties

Hadis Moradi, Mahdi Esfahanian, Amir Abtahi and Ali Zilouchian
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Hadis Moradi: Computer and Electrical Engineering Department, Florida Atlantic University, Boca Raton, FL 33431, USA
Mahdi Esfahanian: Computer and Electrical Engineering Department, Florida Atlantic University, Boca Raton, FL 33431, USA
Amir Abtahi: Ocean and Mechanical Engineering Department, Florida Atlantic University, Boca Raton, FL 33431, USA
Ali Zilouchian: Computer and Electrical Engineering Department, Florida Atlantic University, Boca Raton, FL 33431, USA

Energies, 2017, vol. 10, issue 9, 1-17

Abstract: A novel method for a day-ahead optimal operation of a hybrid microgrid system including fuel cells, photovoltaic arrays, a microturbine, and battery energy storage in order to fulfill the required load demand is presented in this paper. In the proposed system, the microgrid has access to the main utility grid in order to exchange power when required. Available municipal waste is utilized to produce the hydrogen required for running the fuel cells, and natural gas will be used as the backup source. In the proposed method, an energy scheduling is introduced to optimize the generating unit power outputs for the next day, as well as the power flow with the main grid, in order to minimize the operational costs and produced greenhouse gases emissions. The nature of renewable energies and electric power consumption is both intermittent and unpredictable, and the uncertainty related to the PV array power generation and power consumption has been considered in the next-day energy scheduling. In order to model uncertainties, some scenarios are produced according to Monte Carlo (MC) simulations, and microgrid optimal energy scheduling is analyzed under the generated scenarios. In addition, various scenarios created by MC simulations are applied in order to solve unit commitment (UC) problems. The microgrid’s day-ahead operation and emission costs are considered as the objective functions, and the particle swarm optimization algorithm is employed to solve the optimization problem. Overall, the proposed model is capable of minimizing the system costs, as well as the unfavorable influence of uncertainties on the microgrid’s profit, by generating different scenarios.

Keywords: microgrid; distributed energy resources; optimal operation; energy management; uncertainty (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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