Microgrids represent a smart solution to increase power system reliability through the development of self-supplied islands that integrate distributed power generation and other smart technologies. In this paper, we formulate and solve an optimal generation rescheduling and load shedding problem in microgrids to determine a stable equilibrium state following outages. To address this problem, focused on MV distribution systems, we use a new methodology based on the use of fuzzy numbers is proposed. The approach allows representing the sources of uncertainty in the data or approximations made during the computation and considering many possible scenarios in case of outages. In order to demonstrate the performance and the effectiveness of the proposed method, several simulations have been carried out on a 69-bus radial distribution system, and results have been compared with those obtained by using a stochastic optimization approach. The encouraging results are presented and discussed.

Generation Rescheduling and Load Shedding in Distribution Systems Under Imprecise Information

CALDERARO, Vito
;
GALDI, Vincenzo;GRABER, GIUSEPPE;PICCOLO, Antonio
2018-01-01

Abstract

Microgrids represent a smart solution to increase power system reliability through the development of self-supplied islands that integrate distributed power generation and other smart technologies. In this paper, we formulate and solve an optimal generation rescheduling and load shedding problem in microgrids to determine a stable equilibrium state following outages. To address this problem, focused on MV distribution systems, we use a new methodology based on the use of fuzzy numbers is proposed. The approach allows representing the sources of uncertainty in the data or approximations made during the computation and considering many possible scenarios in case of outages. In order to demonstrate the performance and the effectiveness of the proposed method, several simulations have been carried out on a 69-bus radial distribution system, and results have been compared with those obtained by using a stochastic optimization approach. The encouraging results are presented and discussed.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4670396
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