Generation Scheduling With Renewable Energy Sources an Improved Firefly Optimization Algorithm

R. Saravanan; S Sooriya Prabha

doi:https://doi.org/10.51976/ijari.711917

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Generation Scheduling With Renewable Energy Sources an Improved Firefly Optimization Algorithm

R. Saravanan, S Sooriya Prabha

Vol 7 , Issue 1 , January - March 2019 | Pages: 111-117 | Research Paper

https://doi.org/10.51976/ijari.711917

Received: December 18, 2018 | Revised: February 20, 2019 | Accepted: February 28, 2019 | Published Online: March 25, 2019

Author Details ( * ) denotes Corresponding author

1. * R. Saravanan, Department of Mechanical Engineering,, Chaitanya Bharathi Institute of Technology, Proddatur, Andhra Pradesh, India (saravan_tanj@yahoo.co.in)

2. S Sooriya Prabha, Department of Mechanical Engineering,, Chaitanya Bharathi Institute of Technology, Proddatur, Andhra Pradesh, India

Many optimization methods are employed in power system scheduling of generating units. Here in this paper firefly algorithm is proposed for solving the generation scheduling (GS) problem to obtain optimal solution in power systems by considering the reserve requirement, wind power availability constraints, load balance, equality and inequality constraints in wind thermal coordination. The firefly algorithm is a new meta-heuristic and swarm intelligence based on the swarming behavior of fish and bird in nature. The proposed firefly algorithm method is applied to a different test system holds 30 conventional units and 4 wind farms. The performance of proposed FFO is found for the test system by comparing the results of it with different trails and various iterations among five different populations say 10, 20, 30, 40 and 50.Computation of the solution for different populations in the system reveal that the best schedules attained by applying the firefly algorithm method. It also shows that as population size decreases the total cost value is also decreasing. The performance of FFO algorithm is efficiently proved by comparing the result obtained by FFO with the particle swarm optimization method (PSO).

Keywords

Particle Swarm Optimization; Generation Scheduling; Renewable Energy

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