Combined Economic and Environmental load dispatch using multi objective Tunicate Swarm Algorithm

  • Soumyadip Roy CAMELLIA INSTITUTE OF TECHNOLOGY & MANAGEMENT
  • Indrajit Dey Camellia Institute of Technology & Management
  • Saikat Singha Roy Camellia Institute of Technology & Management
  • Yousuf Sheikh Camellia Institute of Technology & Management
  • Sukalyan Das Camellia Institute of Technology & Management
Keywords: Tunicate Swarm Algorithm (TSA), Multi-objective economic Environmental scheduling, Meta-heuristic technique

Abstract

Economic utilization and environmental impact are contradictory objectives in the problem of power generation scheduling where sustainable development of a country is partially depended on these two. The objective of this paper is to schedule the output of committed generating units where both cost and emission of electric power generation have been minimized without shedding any load and satisfying all units and system equality and inequality constraints. In this paper, a new swarm behaviour-based meta-heuristic technique named Tunicate Swarm Algorithm (TSA) has been implemented to solve this problem and the efficacy of this algorithm has also been compared. A six generators system connected with four buses has been considered in this paper as a test model.

Downloads

Download data is not yet available.

Author Biographies

Soumyadip Roy, CAMELLIA INSTITUTE OF TECHNOLOGY & MANAGEMENT

Department of Electrical Engineering

Indrajit Dey, Camellia Institute of Technology & Management

Department of Electrical Engineering

Saikat Singha Roy, Camellia Institute of Technology & Management

Department of Electronics and Communication Engineering

Yousuf Sheikh, Camellia Institute of Technology & Management

Department of Electrical Engineering

Sukalyan Das, Camellia Institute of Technology & Management

Department of Electrical Engineering

References

Apostolopoulos T. and Vlachos A., 2010, Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem, International Journal of Combinatorics, Volume 2011.

Cai J., Ma X., Li Q., Li L. and Peng H., 2010, A multi-objective chaotic ant swarm optimization for environmental/economic dispatch, International Journal of Electrical Power & Energy Systems, Volume 32, Issue 5, 337-344.

Damodaran S. K. and Kumar T. K. S., 2017, Economic and emission generation scheduling of thermal power plant incorporating wind energy, IEEE, 1487-1492.

Kadali K. S.,RajajiL.,Moorthy V. and Viswanatharao J., 2017, Economic generation schedule on thermal power system considering emission using grey wolves optimization, Energy Procedia, Vol 117, 509-518.

Kaur S., Awasthi L. K., Sangal A.L. and Dhiman G., 2020, Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization, Engineering Applications of Artificial Intelligence, Vol 90, 103541.

Roy S.,LahaD., DasA., ChatterjeeS. , Biswas M. , Mandal R. K. & Ghosh B. K., 2021, Application of Multi-Objective Particle Swarm Optimization Technique for Analytical Solution of Economic & Environmental Dispatch.

Published
2022-10-11
How to Cite
(1)
Roy, S.; Dey, I.; Roy, S. S.; Sheikh, Y.; Das, S. Combined Economic and Environmental Load Dispatch Using Multi Objective Tunicate Swarm Algorithm. prepare@u_foset 2022.
Section
12th Inter-University Engineering, Science & Technology Academic Meet – 2022