Abstract
Improving the survivability of critical loads after extreme events is essential to enhance the resilience of power systems, especially for distribution networks. A distribution network with various operational resources can be separated into several sub-distribution networks without electrical connections. Maintaining the power supply with acceptable power quality to critical loads in such separated distribution networks is a challenging task for the operators of power systems. In this paper, an optimization model is proposed to maximize the ability to supply power to critical loads in distribution networks. Moreover, a GPU was employed to accelerate the proposed model using genetic algorithm. With the acceleration of the GPU platform, the solving time was reduced and the population size can be enlarged to enhance the convergence rate and convergence quality of the algorithm. Finally, case studies were carried out in IEEE 33-bus and 118-bus systems, and the effectiveness of the method was validated by comparing the solution results on GPU and CPU platforms.
Highlights
In recent years, a number of extreme natural events have brought huge economic losses to the society (Panteli and Mancarella, 2015a; Wang et al, 2016; Mohamed et al, 2019)
The results showed that the graphics processing unit (GPU) can accelerate the quadratic assignment problem with Genetic algorithms (GAs) effectively
The results show that the effect of speedup in solving the optimization model of a larger scale distribution network will be more powerful
Summary
A number of extreme natural events have brought huge economic losses to the society (Panteli and Mancarella, 2015a; Wang et al, 2016; Mohamed et al, 2019). The cost of setting up powerful control and communication systems limits the application of distributed and parallel GAs. In this study, the proposed optimization model was solved by GA and accelerated on a graphics processing unit (GPU) platform by implementing a parallel computing structure. To ensure a stable power supply of critical loads after extreme natural events, it is necessary to dispatch all types of controllable operational resources in the distribution networks to regulate. The objectives of the proposed optimization model network are to minimize the active power loss, voltage deviations of buses, and operational costs by dispatching controllable voltage regulating devices. It includes three main objectives as follows, 1. Due to the fluctuating output power of renewable resources, controllable devices need to respond to fluctuating output power in a short time to reduce voltage deviations
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