4.6 Article

Optimal Chance-Constrained Scheduling of Reconfigurable Microgrids Considering Islanding Operation Constraints

Journal

IEEE SYSTEMS JOURNAL
Volume 14, Issue 4, Pages 5340-5349

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2020.2964637

Keywords

Microgrids; Batteries; Uncertainty; Optimal scheduling; Switches; Reliability; Wind forecasting; Chance-constrained goal programming (CCGP); microgrid; probability of islanding operation (PIO); reconfiguration; spinning reserve

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Microgrid concept is one of the suitable strategies for increasing resilience and preventing load curtailment, especially in emergency conditions. Operation in islanded mode is one of the unique features of microgrids that can provide numerous benefits for both consumers and energy producers. Unlike the conventional distribution networks, reconfigurable microgrids enable the reconfiguration process to achieve optimal structure. In this article, a new optimal strategy for scheduling of reconfigurable microgrids considering islanding capability constraints is presented. To demonstrate the successful islanding operation, the islanding capability is considered as a probability of islanding operation (PIO) index which shows the probability, that the microgrid has adequate level of spinning reserve to meet the local load. Taking into account the forecast errors of generated power by renewable energy resources (PV and wind) as well as load demand, the 13-interval approximation is used for the simplification of nonlinearity of PIO. The scheduling of reconfigurable microgrid with islanding operation constraints is formulated as a chance-constrained goal optimization problem, where the objective is defined as minimizing the total operation cost of microgrid in terms of fuel cost, reliability cost, cost of purchasing power from the mains, and switching cost. The proposed method is implemented on a 10-bus radial reconfigurable microgrid test system with photovoltaic (PV) panels, wind turbines, battery, and microturbines, with different levels of PIO. The numerical results show the effectiveness of the proposed scheduling method.

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