Microgrid Fuel Cost Optimization Considering Economic Emission using Whale Cat Hybrid Technique

doi:10.23940/ijpe.22.07.p5.502511

Abstract

Abstract: Several nations have systematically developed a number of programmes to reduce the amount of pollution emitted by fossil fuels during power generation. The main focus of economic emission dispatch (EED) strategy is to find the most suitable solution by balancing the cost and emissions for a microgrid(MG) network which satisfy the operational needs of the generators. The electricity network in rural areas needs a significant investment in terms of infrastructure. As a result, the economic benefits of deploying a microgrid powered by renewable energy sources(RES) are remarkable. In order to come up with the most efficient EED, a combination of the cost and emission functions is integrated. A PV-thermal-wind-based framework has been employed to address the real-time combined EED problem effectively in a microgrid network. This study introduces a multi-objective load dispatch problem(LDP) using hybrid Nature-inspired algorithms. The proposed hybrid Whale Cat Optimization (WCO) outperforms other four optimization approaches, such as PSO, GWO, and WOA, in a series of experiments by considering two fitness functions (Microgrid cost and emission).

Key words: cat optimization, economic emission dispatch, Microgrid, whale optimization

Eshan Misra and Vibhuti. Microgrid Fuel Cost Optimization Considering Economic Emission using Whale Cat Hybrid Technique [J]. Int J Performability Eng, 2022, 18(7): 502-511.

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References

1. Nwulu, N.I. and Xia, X.Optimal Dispatch for a Microgrid Incorporating Renewables and Demand Response. Renewable energy, vol. 101, pp. 16-28, 2017.
2. Dey B., Roy S.K., andBhattacharyya B.Solving Multi-objective Economic Emission Dispatch of a Renewable Integrated Microgrid Using Latest Bio-inspired Algorithms. Engineering Science and Technology, an International Journal, vol. 22, no. 1, pp. 55-66, 2019.
3. Kumano T.A Functional Optimization Based Dynamic Economic Load Dispatch Considering Ramping Rate of Thermal Units Output. In2011 IEEE/PES Power Systems Conference and Exposition, IEEE, pp. 1-8, 2011.
4. Anastasiadis A.G., Konstantinopoulos S.A., Kondylis G.P., Vokas G.A., andPapageorgas P.Effect of Fuel Cell Units in Economic and Environmental Dispatch of a Microgrid with Penetration of Photovoltaic and Micro Turbine Units. International Journal of Hydrogen Energy, vol. 42, no. 5, pp. 3479-3486, 2017.
5. Dinkara P.V.C., Prasad, R., and Reddy, T., Optimal Renewable Resources Placement in Distribution International Journal of Electrical Power Energy System, vol. 28, pp. 669-678, 2017.
6. Augustine N., Suresh S., Moghe P., andSheikh K.Economic Dispatch for a Microgrid Considering Renewable Energy Cost Functions. In2012 IEEE PES Innovative Smart Grid Technologies (ISGT), IEEE, pp. 1-7, 2012.
7. Rawat G.S.Modeling and Performance Analysis of Renewable Sources under Islanded DC Microgrid. In2018 International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE, pp. 498-503, 2018.
8. Truong, D.N., Thi, M.S.N., Le, H.G., Do, V.D., Ngo, V.T., and Hoang, A.Q. Dynamic Stability Improvement Issues with a Grid-connected Microgrid System. In2019 International Conference on System Science and Engineering (ICSSE), IEEE, pp. 214-218, 2019.
9. Zhu J., andLiang S., Dynamic Economic Dispatch of Microgrid with Biomass Generation, 6th International Conference on Energy and Environment Protection (ICEEP) Advance in Engineering Research(AER), vol. 143, pp. 2352-5401, 2017.
10. Augustine N., Suresh S., Moghe P., andSheikh K.Economic Dispatch for a Microgrid Considering Renewable Energy Cost Functions. In2012 IEEE PES Innovative Smart Grid Technologies (ISGT), IEEE, pp. 1-7, 2012.
11. Qais M.H., Hasanien H.M., andAlghuwainem S.Whale Optimization Algorithm-based Sugeno Fuzzy Logic Controller for Fault Ride-through Improvement of Grid-Connected Variable Speed Wind Generators. Engineering Applications of Artificial Intelligence, vol. 87, pp. 103328, 2020.
12. Zhan. J.P.Fast lambda-iteration Method for Economic Dispatch, IEEE Trans. Power Syst. Vol.29, no.2, pp 990-991, 2014
13. Chen, S.D. and Chen, J.F.A Direct Newton-raphson Economic Emission Dispatch. International journal of electrical power & energy systems, vol. 25, no. 5, pp. 411-417, 2003.
14. Bishe H.M., Kian A.R., andEsfahani M.S.A Primal-dual Interior Point Method for Solving Environmental/Economic Power Dispatch Problem. International Review of Electrical Engineering, vol. 6, no. 3, 2011.
15. McLarty, D., Panossian, N., Jabbari, F., and Traverso, A. Dynamic Economic Dispatch Using Complementary Quadratic Programming. Energy, vol. 166, pp. 755-764, 2019.
16. Prabakaran S.Application of Genetic Algorithm to Solve Emission Constrained Economic Dispatch Problems, International Journal for Research in Applied Science and Engineering Technology, vol. 6, no. 6, pp. 2086-2092, Jun. 2018.
17. Mariakuttikan A., Rajaram M., Ananthan B., Subramanian K., andMurugesan K.P.Multi-objective Economic Dispatch of Distributed Generation Using Differential Evolution Algorithm. In IOP Conference Series: Materials Science and Engineering, IOP Publishing, vol. 1084, no. 1, pp. 012016, 2021.
18. Karthikeyan R.Combined Economic Emission Dispatch Using Grasshopper Optimization Algorithm. Materials Today: Proceedings, vol. 33, pp. 3378-3382, 2020.
19. Olang’o S.A., Musau P.M., andOdero N.A.Multi Objective Multi Area Hydrothermal Environmental Economic Dispatch Using Bat Algorithm. In2018 International Conference on Power System Technology (POWERCON), IEEE, pp. 198-203, 2018.
20. Karakonstantis, I. and Vlachos, A.Ant Colony Optimization for Continuous Domains Applied to Emission and Economic Dispatch Problems. Journal of Information and Optimization Sciences, vol.36, no. 1-2, pp. 23-42, 2015.
21. Rahmani Dashti, D., Ghabeli, A., and Hosseini, S.M. Solving Static Economic Load Dispatch Using Improved Exponential Harmony Search Optimisation. Australian Journal of Electrical and Electronics Engineering, vol. 13, no. 2, pp. 142-150, 2016.
22. Aydin D., Özyön S., Yaşar C., andLiao T.Artificial Bee Colony Algorithm with Dynamic Population Size to Combined Economic and Emission Dispatch Problem. International journal of electrical power & energy systems, vol. 54, pp. 144-153, 2014.
23. Chandrasekaran K., Simon S.P., andPadhy N.P.Cuckoo Search Algorithm for Emission Reliable Economic Multi-objective Dispatch Problem. IETE Journal of Research, vol. 60, no. 2, pp. 128-138, 2014.
24. Ab Ghani, M.R., Hussein, S.T., Ruddin, M., Mohamad, M.T., and Jano, Z. An Examination of Economic Dispatch Using Particle Swarm Optimization. MAGNT Res. Rep, vol. 3, no. 8, pp. 193-209, 2015.
25. Chopra N., Kumar G., andMehta S.Multi-objective Economic Emission Load Dispatch Using Grey Wolf Optimization. International Journal of Advanced Engineering Research and Science, vol. 3, no. 11, pp. 236901, 2016.
26. Mishra, S.K. and Mishra, S.K.Solution of the Combined Environmental Economic Dispatch Problem Using Multi-objective Cat Swarm Optimization. International Journal on Electrical Engineering & Informatics, vol. 13, no. 2, 2021.
27. Faseela, C.K. and Vennila, H.Economic and Emission Dispatch Using Whale Optimization Algorithm (WOA). International Journal of Electrical and Computer Engineering, vol. 8, no. 3, pp. 1297, 2018.
28. Qais M.H., Hasanien H.M., andAlghuwainem S.Enhanced Whale Optimization Algorithm for Maximum Power Point Tracking of Variable-speed Wind Generators. Applied Soft Computing, vol. 86, pp. 105937, 2020.
29. Elazab O.S., Hasanien H.M., Elgendy M.A., andAbdeen A.M.Parameters Estimation of Single-and Multiple-diode Photovoltaic Model Using Whale Optimisation Algorithm. IET Renewable Power Generation, vol. 12, no. 15, pp. 1755-1761, 2018.
30. Grigg C., Wong P., Albrecht P., Allan R., Bhavaraju M., Billinton R., Chen Q., Fong C., Haddad S., Kuruganty S., andLi W.The IEEE Reliability Test System-1996. A Report Prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee. IEEE Transactions on power systems, vol. 14, no. 3, pp. 1010-1020, 1999.
31. Hassan N.M., Swief R.A., Kamh M.Z., Hasanien H.M., andAbdelaziz A.Y.Centralized/Decentralized Optimal Load Flow Based on Tuned Whale Optimization Algorithm. In2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE), IEEE, pp. 352-358, 2020.
32. Chu S.C., Tsai P.W., andPan J.S.Cat Swarm Optimization. In Pacific Rim international conference on artificial intelligence, Springer, Berlin, Heidelberg, pp. 854-858, 2006.
33. Chu, S.C. and Tsai, P.W.Computational Intelligence Based on the Behavior of Cats. International Journal of Innovative Computing, Information and Control, vol. 3, no. 1, pp. 163-173, 2007.

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