A Healer Reinforcement Approach to Self-Healing in Smart Grid by PHEVs Parking Lot Allocation | IEEE Journals & Magazine | IEEE Xplore

A Healer Reinforcement Approach to Self-Healing in Smart Grid by PHEVs Parking Lot Allocation


Abstract:

Self-healing is one of the essential properties of smart grid. Improving the self-healing capability of the smart grid is referred to as healer reinforcement. In this pap...Show More

Abstract:

Self-healing is one of the essential properties of smart grid. Improving the self-healing capability of the smart grid is referred to as healer reinforcement. In this paper, a new healer reinforcement approach is introduced, contributing PHEVs, through optimal parking lot (PL) placement and sizing, under contingencies, by considering the available control and protective devices. Moreover, the PL placement and sizing problem formulation is extended by considering PHEVs participation as both backup and storage units in the self-healing process. PHEVs contribution as backup units could provide electricity for faulted zone customers. Furthermore, demand variations during the restoration might lead to congestion occurrence in the backup feeder. Hence, the PHEVs contribution as storage units could prevent congestion occurrence and enable execution of the best restoration strategy, through charging in light load and injecting power to the backup feeding path in the peak load of repair time. In addition, the stochastic nature of PHEV owners' behavior is modeled in service restoration and the possibility of reactive power injection by PLs is considered in the service restoration process. The proposed formulation is applied to a standard test system (RBTS-4) to minimize a combined effect of the customer-based (SAIDI) and cost-based (TCR) reliability indices. The simulation is conducted to investigate the effects of physical limitations on candidate locations as well as PHEVs participation as both backup and storage units. Two sensitivity analyses are conducted on the objective function weighting coefficient and the total number of required charging stations to evaluate the effect of optimization parameters on final PL locations and sizes.
Published in: IEEE Transactions on Industrial Informatics ( Volume: 12, Issue: 6, December 2016)
Page(s): 2020 - 2030
Date of Publication: 13 July 2016

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