Natural disasters cause significant damage to the electrical power infrastructure every year. Therefore, there is a crucial need to reduce the vulnerability of the electric power grid against natural disasters. Distributed generation (DG) represents small-scale decentralized power generation that can help reduce the vulnerability of the grid, among many other benefits. Examples of DG include small-scale photo-voltaic (PV) systems. Accordingly, the goal of this paper is to investigate the benefits of DG in reducing the vulnerability of the electric power infrastructure by mitigating against the impact of natural disasters on transmission lines. This was achieved by developing a complex system-of-systems (SoS) framework using agent-based modeling (ABM) and optimal power flow (OPF). N-1 contingency analysis and optimization were performed under two approaches: The first approach determined the minimum DG needed at any single location on the electric grid to avoid blackouts. The second approach used a genetic algorithm (GA) to identify the minimum total allocation of DG distributed over the electric grid to mitigate against the failure of any transmission line. Accordingly, the model integrates ABM, OPF, and GA to optimize the allocation of DG and reduce the vulnerability of electric networks. The model was tested on a modified IEEE 6-bus system as a proof of concept. The outcomes of this research are intended to support the understanding of the benefits of DG in reducing the vulnerability of the electric power grid. The presented framework can guide future research concerning policies and incentives that can strategically influence consumer decision to install DG and reduce the vulnerability of the electric power infrastructure.
G. G. Ali et al., "Reducing the Vulnerability of Electric Power Infrastructure Against Natural Disasters by Promoting Distributed Generation," Natural Hazards Review, vol. 24, no. 2, article no. 04022052, American Society of Civil Engineers (ASCE), May 2023.
The definitive version is available at https://doi.org/10.1061/NHREFO.NHENG-1478
Civil, Architectural and Environmental Engineering
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01 May 2023