Investigation of Equilibrium Optimizer to Solve Economic Dispatch with Practical Constraints

Öz

Now a day’s power demand keep on increasing due to rapid changes occurs in power industry. For that purpose establishing new generating is costlier rather than effectively utilize the available generating stations. In order to properly planning of power system generation sharing Economic Dispatch (ED) plays vital role. In this paper, Equilibrium Optimizer (EO) is used to solve the ED problem with effect of valve-point, prohibited operating zones (POZs), ramp rate up/down limits and pollution like practical constraints. In order to analyze the capability of the EO algorithm, the algorithm is applied to four test systems with 6 unit systems and the results are compared with other optimization algorithms. The comparative results proven that EO is better optimization technique to solve ED problem with practical constraints.

Anahtar Kelimeler

• Power Industry
• Economic Dispatch
• Equilibrium Optimizer
• Valve-point
• Prohibited operating zones
• Ramp rate up/down limits.

Kaynakça

1. [1] Hardiansyah, Junaidi and Yohannes MS, “Solving Economic Load Dispatch Problem Using Particle Swarm Optimization Technique”, International journal of intelligent systems and applications, Vol.12, pp.12-18, 2012.
2. [2] K. Sudhakara Reddy and M. Damodar Reddy, “Economic Load Dispatch Using Firefly Algorithm”, International Journal of Engineering Research and Applications, Vol. 2, Issue4, pp.2325-2330, July-August 2012.
3. [3] Mohammad Moradi-Dalvand and Behnam Mohammadi-Ivatloo, “Continuous Quick Group Search Optimizer for Solving Non-Convex Economic Dispatch Problems”, Electric Power Systems Research (Elsevier Journal), Vol. 93, pp.93–105, 2012.
4. [4] A.Hima Bindu and M. Damodar Reddy, “Economic Load Dispatch Using Cuckoo Search Algorithm”, International Journal of Engineering Research and Applications, Vol. 3, Issue 4, pp. 498-502, Jul-Aug 2013.
5. [5] Shubham Tiwari, Ankit Kumar. 2013, Economic Load Dispatch Using Particle Swarm Optimization. IJAIEM, ISSN 2319 - 4847 Volume 2, Issue 4.
6. [6] Dexuan Zou, Steven Li. 2016, An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects. Applied Energy 181 375–390.
7. [7] Mohammed Azmi Al-Betar, Mohammed A. Awadallah. 2016, Tournament-based harmony search algorithm for non-convex economic load dispatch problem. Applied Soft Computing.
8. [8] Moumita Pradhan, Provas Kumar Roy, Tandra Pal. 2017, Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system. Ain Shams Engineering Journal

9. [9] Weerakorn, “Artificial intelligence in power system optimization”, CRC press, Taylor and Francis group, 2013.
10. [10] Tahir Nadeem Malik, Azzam ul Asar “A new hybrid approach for the solution of nonconvex ED problem with valve-point effects”, Electric Power Systems Research 80 (2010) 1128–1136.
11. [11] Celal Yas¸ “A new hybrid approach for nonconvex EDP with valve-point effect”, Energy. 36 (2011) 5838-45.
12. [12] Mostafa Modiri-Delshad, “Backtracking search algorithm for solving economic diapatch problems with valve-point effects and multiple fuel optins”, Energy 116 (2016) 637e649.
13. [13] Zwe-Lee Gaing “Particle Swarm Optimization to Solving the economic dispatch considering the generator constrints”, IEEE transactions on power systems, vol. 18, no. 3, august 2003.
14. [14] Wael Taha Elsayed, Yasser G. Hegazy “Improved Random Drift Particle Swarm Optimization With Self-Adaptive Mechanism for Solving the Power Economic Dispatch Problem”, IEEE transactions on industrial informatics, vol. 13, no. 3, june 2017.
15. [15] Naser Ghorbani and Ebrahim Babaei, “Exchange Market Algorithm for Economic Load Dispatch”, Electrical Power and Energy Systems (Elsevier Journal), Vol. 75, pp.19–27, 2016.
16. [16] Jian Zhao and Shixin Liu, “Modified Cuckoo Search Algorithm to Solve Economic Power Dispatch Optimization Problems”, IEEE/CAA journal of automatica sinica, Vol. 5, no. 4, pp,794-807, july 2018.
17. [17] MandaI, K., Chakraborty, N., “Differential Evolution based environmentally constrained Economic Dispatch”, IEEE, (2008).
18. [18] Nelson Jayakumar, D., Venkatesh, P., “Glowworm swarm optimization algorithm with topsis for solving multiple objective environmental economic dispatch problem”, Applied Soft Computing, 23: 375-386, (2014).
19. [19] M. A. Abido, “Multiobjective Evolutionary Algorithms for Electric Power Dispatch problem”, IEEE transactions on evolutionary computation, Vol. 10, no. 3, pp.315-330, june 2006.
20. [20] Qu, B.Y., Liang, J.J., “Economic emission dispatch problems with stochastic wind power using summation based multi-objective evolutionary algorithm”, Information Sciences, 351: 48-66 (2016).
21. [21] Mostafa Modiri-Delshad and Nasrudin Abd Rahim, “Multi-Objective Backtracking Search Algorithm for Economic Emission Dispatch Problem”, Applied Soft Computing (Elsevier Journal), Vol. 18, pp.1-15, 2015.
22. [22] B.Y. Qu, J.J. Liang, Y.S. Zhu, Z.Y. Wang and P.N. Suganthan, “Economic emission dispatch problems with stochastic wind power using summation based multi-objective evolutionary algorithm”, Information Sciences (Elsevier Journal), Vol.12, pp.1-19, 2016.
23. [23] Dexuan Zou, Steven Li, “A New Global Particle Swarm Optimization for the Economic Emission Dispatch with or without Transmission Losses”, Energy Conversion and Management (Elsevier Journal), Vol.139, pp.45-70, 2017.
24. [24] Faramarzi et al “Equilibrium Optimizer: A Novel Optimization Algorithm”, Elsevier 2019.