In this paper we study and compare the performance of Distributed Firefly Optimized Clustering (DFOC) with Distributed Swarm Optimized Clustering (DSOC) optimization techniques used for the dynamic clustering. Proposed Distributed Firefly Optimized Clustering (DFOC) is an optimization algorithm based on the function of attractiveness of firefly behavior. All the cognitive nodes move towards the brighter firefly with random velocity to form an organized cluster with least computation time. In the existing DSOC method each particle's best position and velocity are evaluated according to the objective function until an optimum global best position is reached. The convergence rate of DSOC is similar to Genetic Algorithm (GA). The proposed DFOC, the SU power is reduced to 7.34% for 100 numbers of SUs.compared to DSOC.
This paper proposes a novel capacity expansion framework for electric vehicle charging stations (EVCSs) through short-term functional decisions and long-term planning under stochastic power demand. Energy resources such as solar, wind, energy storage systems, and microgas turbines supply energy to the microgrid. An EVCS works as a vehicle-to-grid (V-G), and it can send energy to the microgrid. The capacities of solar panels, storage systems, and wind turbines can be expanded by implementing capacity expansion planning in a microgrid. The short-term and long-term expansion problem has been solved by optimizing the hourly operation of the resources and with a five-year planning horizon, respectively. A hybrid algorithm combining the sample average approximation technique and the apriori progressive hedging algorithm (SAAT-APHA) has been proposed in this study. The impact of the availability of different resources, including wind, solar, and V-G power on the system performance has been analyzed. Finally, a comparison has been performed with three other algorithms, and the results show the superiority of the proposed method.
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