Reliability constrained optimization of SAHPS using markov based GA and PSO

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Published Sep 2, 2015
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Volume 11, Issue 3, September 2015

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Manohar Potli
Assistant Professors, EEE, KEC, Kuppam - 517425, Andra Pradesh
B. Chandra Sekhar
Energy Efficiency and Renewable Energy Division, Central Power Research Institute, Bangalore - 560080
J. C. Balachandra
Assistant Professors, EEE, KEC, Kuppam - 517425, Andra Pradesh
Y. Babu Yadav
Assistant Professors, EEE, KEC, Kuppam - 517425, Andra Pradesh
V. Sekhar
Assistant Professors, EEE, KEC, Kuppam - 517425, Andra Pradesh

Abstract

Renewable energy resources like wind, solar, biomass, tidal, hydropower and geothermal constitute a type of power generation and received much attention as alternatives for conventional power generation. Renewable Energy Resources (RER) will help to mitigate the emission of greenhouse gases. In this paper, a study on reliability constrained optimization of Small Autonomous Hybrid Power System (SAHPS) is carried out. It consists of the 10 kW wind unit, 5 kW solar unit, 5 kW pico-hydro unit and 20 kW diesel unit. Hourly speed of wind, solar radiation and water discharge and load profile is obtained using data synthesizer. The objective function with cost and the number of units and reliability constraint is formulated. Cost minimization and optimal sizing of SAHPS is performed using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Later markov models for the wind, solar, pico-hydro and load profile with transitions among all states are developed. Markov models are integrated with GA and PSO techniques to minimize the total cost and get the best combination of generation units. All the above analysis is carried out in the MATLAB>sup/sup< software environment. Results for chronological method and markov method will be presented and analyzed.

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How to Cite
Potli, M., Chandra Sekhar, B., Balachandra, J. C., Babu Yadav, Y., & Sekhar, V. (2015). Reliability constrained optimization of SAHPS using markov based GA and PSO. Power Research - A Journal of CPRI, 547–554. Retrieved from https://node6473.myfcloud.com/~geosocin/CPRI/index.php/pr/article/view/710

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