A Multi-objective PSO (MOPSO) algorithm for optimal active power dispatch with pollution control

A Multi-objective PSO (MOPSO) algorithm for optimal active power dispatch with pollution control

Govind D. Sen Jitendra Sharma  Govind R. Goyal  Alok K. Singh 

Jaipur Institute of Technology, Jaipur 302037, India

Vivekananda Global University, Jaipur 303012, India

Corresponding Author Email: 
govinddeepsen810@gmail.com
Page: 
113-119
|
DOI: 
http://doi.org/10.18280/mmep.040301
Received: 
|
Accepted: 
|
Published: 
30 September 2017
| Citation

OPEN ACCESS

Abstract: 

This article provides the solution of the optimal power flow (OPF) problem of medium electrical systems through an artificial intelligence algorithm. The goal is to minimize the total cost of generated fuel and environmental pollution caused by power generation units based on fossils. System performance is also maintained by limiting generator real and reactive power outputs and power flow of transmission lines in acceptable limits. The power flow equations and load balance equation are considered as equality constraints. The performance analysis of this OPF problem using the Particle Swarm Optimization technique is carried out by checking various combinations of values of the associated parameters. The biobjective problem of generation cost and emission dispatch is solved via weighted sum method for different combinations of weights and a multi-objective problem of minimizing power generation cost and flue gases (NOx, CO2, SO2), is solved by a new algorithm named as Multi-Objective PSO (MOPSO) technique, to find out optimal solution and optimal value of weights. Simulation results for the IEEE 30-bus network with 6 generators system show that by proposed method, an optimal solution can be given quickly.

Keywords: 

Optimal Power Dispatch, Swarm Intelligence, Particle Swarm Optimization (PSO), Multi-objective PSO

(MOPSO), Pareto- front Technique

1. Introduction
2. Problem Formulation
3. Multi-Objective Particle Swarm Optimization
4. Simulation Study & Results
5. Conclusion
  References

[1] Goyal G.R., Mehta H.D. (2015). Optimal dispatch of active and reactive power using cuckoo search method, IJIREEICE. DOI: 10.17148/IJIREEICE.2015.3204

[2] Govind R.G., Mehta H.D. (2015). Multi-objective optimal active power dispatch using swarm optimization techniques, 5th Nirma University International Conference on Engineering (NUiCONE), DOI: 10.1109/NUICONE.2015.7449590

[3] Vaseem K.S., Govind R.G., Mohammad A.K. (2015). Economic generator scheduling using newton Ralph son method, International Journal of Advance Research in Engineering, Science & Technology, Vol. 2, No. 4, pp. 2393-9877.

[4] Ansil S., Govind R., Mohit J., Parmeshwar K. (2017). Performance study of recent swarm optimization techniques with standard test functions, Imperial Journal of Interdisciplinary Research, Vol. 3, No. 4.

[5] Janga R.M., Nagesh K.D. (2007). An efficient multiobjective optimization algorithm based on swarm intelligence for engineering design, Engineering Optimization, Taylor & Francis. DOI:10.1080/03052150600930493.

[6] Pao-La-Or P., et al. (2010). Combined economic andemission dispatch using particle swarm optimization, Wseas Transections on Environment and Development, Vol. 6, No. 4.

[7] Subburaj P., et al. (2007). Optimum reactive power dispatch using genetic algorithm, Academic Open Internet Journal, Vol. 21.

[8] Jumaat S.A., Musirin I., Othman M.M., Mokhlis H. (2011). PSO based technique for loss minimization considering voltage profile and cost function,

International Power Engineering and Optimization Conference, Malaysia.

[9] Abou El Ela A.A., Abido M.A., Spea S.R. (2011). Differential evolution algorithm for optimal active power dispatch, ELSEVIER- Electric Power Systems

Research. DOI: 10.1016/j.epsr.2010.10.005

[10] Bhushan W. (2013). Optimization of reactive power for line loss reduction and voltage profile improvement using differential evolution algorithm,

International Journal of Enhanced Research in Science Technology & Engineering, Vol. 2, No. 12, pp.29-34.

[11] Sakthivel S., Subramanian A., Gajendran S., Selvan P.V. (2013). Reactive power reserve management by using improved particle swarm optimization algorithm, International Journal of Computational Engineering Research.

[12] Mahalakshmi G., Bhavani M. (2014). Power system reactive power optimization using DPSO, IEEE International Conference on Innovations in Engineering and Technology.

[13] Kothari D.P., et al. (2014). Combined active and reactive power dispatch using particle swarm optimization, Proceedings of Informing Science & It Education Conference (InSITE).

[14] Mezura-Montes E., Flores-Mendoza J.I. (2009). Improved particle swarm optimization in constrained numerical search spaces, Springer Berlin Heidelberg, Vol. 193, No. 4, pp. 299–332.

[15] Govindaraj T., Kumar S.U. (2014). Optimal reactive power planning and real power loss minimization using cuckoo search algorithm, International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, Vol. 2.

[16] Sathish K.K., Tamilselvan V. et al. (2018). Economic load dispatch with emission constraints using various PSO algorithms, WSEAS Transactions on Power System, Vol. 3, No. 9.

[17] Shi Y.H. (2014). Particle swarm optimization, electronic data systems, Inc. Kokomo, IEEE Neural Network Society.

[18] Civicioglu P., Besdok E. (2011). A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms, ArtifIntell Rev. DOI:10.1007/s10462-011-9276-0.

[19] Hashmi A., et al. (2013). Comparative study of bioinspired algorithms for unconstrained optimization problems, advances in electronics, electrical and computer engineering. DOI: 10.3850/ 978-981-07-6935-2_28

[20] Krohling R.A., Coelho L.S. (2006). Co-evolutionary particle swarm optimization using gaussian distribution for solving constrained optimization problems, IEEE Transactions on Systems, and Cybernetics—Part b: Cybernetics. DOI:10.1109/TSMCB.2006.873185