Solution of the Multi-objective Economic and Emission Load Dispatch Problem Using Adaptive Real Quantum Inspired Evolutionary Algorithm

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Jitender Singh
Ashish Mani
H.P. Singh
Dinesh Singh Rana


Economic load dispatch is a complex and significant problem in power generation. The inclusion of emission with economic operation makes it a Multi-objective economic emission load dispatch (MOEELD) problem. So it is a tough task to resolve a constrained MOEELD problem with antagonistic multiple objectives of emission and cost. Evolutionary Algorithms (EA) have been widely used for solving such complex multi-objective problems. However, the performance of EAs on such problems is dependent on the choice of the operators and their parameters, which becomes a complex issue to solve in itself. The present work is carried out to solve a Multi-objective economic emission load dispatch problem using a Multi-objective adaptive real coded quantum-inspired evolutionary algorithm (MO-ARQIEA) with gratifying all the constraints of unit and system. A repair-based constraint handling and adaptive quantum crossover operator (ACO) are used to satisfy the constraints and preserve the diversity of the suggested approach. The suggested approach is evaluated on the IEEE 30-Bus system consisting of six generating units. These results obtained for different test cases are compared with other reputed and well-known techniques.

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How to Cite
Singh, J. ., A. . Mani, H. . Singh, and D. S. . Rana. “Solution of the Multi-Objective Economic and Emission Load Dispatch Problem Using Adaptive Real Quantum Inspired Evolutionary Algorithm”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 1s, Jan. 2023, pp. 01-12, doi:10.17762/ijritcc.v11i1s.5989.


Singh, Pragya & Priya, Aayushi. (2017). A Comprehensive Review on Economic Load Dispatch using Evolutionary Approach. INTERNATIONAL JOURNAL ONLINE OF SCIENCE. 3. 7. 10.24113/ijoscience.v3i2.172.

Tiwari, Satyam & Singh, Nidhi & Ansari, Momtaj & Yadav, Dilip & Singh, Nivedita. (2020). Economic Load Dispatch Using PSO. 10.1007/978-981-15-2329-8_6.

Fahim, Khairul & Roy, Tanmoy & Yassin, Hayati & Rihan, Naahi & Silva, Liyanage & Tanvir, Mohammad. (2022). Jaya Algorithm-a practical algorithm for solving economic load dispatch problems. 10.13140/RG.2.2.32969.01123/1.

Aribowo, Widi & Rahmadian, Reza & Widyartono, Mahendra & Hermawan, Aditya & Wardani, Ayusta & Kartini, Unit. (2022). Tasmanian Devil Optimization For Economic Load Dispatch. 169-173. 10.1109/ICVEE57061.2022.9930370.

Tahir, Barzan & Rashid, Tarik & Rauf, Hafiz Tayyab & Bacanin, Nebojsa & Chhabra, Amit & Shanmuganathan, Vimal & Yaseen, Zaher. (2022). Improved Fitness-Dependent Optimizer for Solving Economic Load Dispatch Problem. Computational Intelligence and Neuroscience. 2022. 1-16. 10.1155/2022/7055910.

Singh, Manmohan & Dhillon, J.S. & Kaur, Avneet. (2022). Economic Load Dispatch using HCGSA. 10.1109/ICEPES52894.2021.9699770.

Ghanizadeh, Rasool & Hojber Kalali, Seyed & Farshi, Hatef. (2019). Teaching–learning-based optimization for economic load dispatch. 851-856. 10.1109/KBEI.2019.8734963.

Kumar, Mohit & Dhillon, J.S.. (2018). Hybrid artificial algae algorithm for economic load dispatch. Applied Soft Computing. 71. 10.1016/j.asoc.2018.06.035.

Spea, Shaimaa & Spea, S.. (2020). Solving practical economic load dispatch problem using crow search algorithm. International Journal of Electrical and Computer Engineering. 10. 3431-3440. 10.11591/ijece.v10i4.pp3431-3440.

Ramesh, R. & Ramachandran, V.. (2007). Grid service model for real time economic load dispatch. Iranian Journal of Electrical and Computer Engineering. 6. 26-29.

Athab, Falah & Saeed, Wafaa. (2020). Solving Economic Load Dispatch with Reliability Indicators. Journal of Engineering and Sustainable Development. 24. 103-114. 10.31272/jeasd.24.6.9.

Swain, Rajkishore & Sarkar, Pallab & Meher, Krishna & Chanda, Chandan. (2017). Population variant differential evolution-based multiobjective economic emission load dispatch. International Transactions on Electrical Energy Systems. 27. e2378. 10.1002/etep.2378.

Sinha, Nidul & Purkayastha, Bipul & Purkayastha, Biswajit. (2007). Optimal Combined Non-convex Economic and Emission Load Dispatch Using NSDE. 1. 473-480. 10.1109/ICCIMA.2007.373.

MA, Xin. (2012). Economic Emission Load Dispatch Based on Bacterial Colony Chemo-taxis Algorithm. 10.1007/978-1-4419-8849-2_179.

Allah, Abd & Mousa, A.. (2014). Hybrid ant optimization system for multiobjective economic emission load dispatch problem under fuzziness. Swarm and Evolutionary Computation. 18. 10.1016/j.swevo. 2014.06.002.

Moraes, Nadime & Bezerra, Ubiratan & Moya Rodríguez, Jorge & Nascimento, Manoel & Leite, Jandecy. (2018). A new approach to economic-emission load dispatch using NSGA II. Case study. International Transactions on Electrical Energy Systems. 28. e2626. 10.1002/etep.2626.

Purkayastha, Biswajit & Sinha, Nidul. (2010). Optimal Combined Economic and Emission Load Dispatch using Modified NSGA-II with Adaptive Crowding Distance. Internatiol Journal of Information Technology and Knowledge Management. 2.

Panigrahi, Bijaya & Pandi, V. & Sharma, Renu & Das, Swagatam & Das, Sanjoy. (2011). Multiobjective bacteria foraging algorithm for electrical load dispatch problem. Energy Conversion and Management. 52. 1334-1342. 10.1016/j.enconman.2010.09.031.

Mousa, A & Kotb, Kotb. (2012). Hybrid Multiobjective Evolutionary Algorithm Based Technique for Economic Emission Load Dispatch Optimization Problem. Scientific Research and Essays. 7. 10.5897/SRE11.197.

Rajesh, Kummari & Visali, N. (2020). Hybrid method for achieving Pareto front on economic emission dispatch. International Journal of Electrical and Computer Engineering (IJECE). 10. 3358. 10.11591/ijece.v10i4.pp3358-3366.

Singh, Nagendra & Kumar, Yogendra. (2015). Multiobjective Economic Load Dispatch Problem Solved by New PSO. Advances in Electrical Engineering. 2015. 1-6. 10.1155/2015/536040.

Kumar, Vineet & Naresh, Ram & Sikarwar, Shiv. (2018). Multi-area Economic Dispatch Using Dynamically Controlled Particle Swarm Optimization. 10.1007/978-981-13-0662-4_14.

Zhang Y., Gong D.-W., and Ding Z. (2012) ‘A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch’, Information Sciences, vol. 192, no. 1, pp. 213 – 227.

Ah King R.T.F., Rughooputh H.C.S. and Deb K. (2004) ‘Evolutionary multi-objective environmental/economic dispatch: stochastic versus deterministic approaches’, KanGAL Rep. 2004019, 2004, pp. 1–15.

Mani A. and Patvardhan C. (2010) ‘A Hybrid Quantum Evolutionary Algorithm for solving Engineering Optimization Problems’, International Journal of Hybrid Intelligent System, Vol. 7, No. 3, pp. 225-235.

Mani A.and Patvardhan C. (2011) ‘Financial Portfolio Design by Multiobjective Adaptive Quantum inspired Evolutionary Algorithm’, In Proc. International Conference on Practice and Research in Management, PRIM-2011, Department of Management, DEI, Dayalbagh, Agra, 18-20 Feb. 2011.

Ah King R. T. F., Rughooputh H. C. S., Deb K. (2005) ‘Evolutionary Multi-objective Environmental/Economic Dispatch: Stochastic Versus Deterministic Approaches’, Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science, Vol. 3410, 2005, pp 677-691.

Abido, Mohammed. (2003). Abido, M.A.: A Novel Multiobjective Evolutionary Algorithm for Environmental / Economic Power Dispatch. Electric Power Systems Research 65(1), 71-81. Electric Power Systems Research. 2. 71-81. 10.1016/S0378-7796(02)00221-3.

Abido, Mohammed. (2003). A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch. International Journal of Electrical Power & Energy Systems. 25. 97-105. 10.1016/S0142-0615(02)00027-3.

Abido M.A. (2006) ‘Multiobjective evolutionary algorithms for electric power dispatch problem’, IEEE Transactions on Evolutionary Computation, vol. 10, No. 3, pp. 315–329.

Das, D.B. & Patvardhan, C.. (1998). New multi-objective stochastic search technique for economic load dispatch. Generation, Transmission and Distribution, IEE Proceedings-. 145. 747 - 752. 10.1049/ip-gtd:19982367.

Brodesky S.F., Hahn R.W. (1986) ‘Assessing the influence of power pools on emission constrained economic dispatch’, IEEE Transactions on Power Systems PWRS, vol. 1, no. 1, pp. 57–62.

Narayanan A. and Moore M. (1996) ‘Quantum-inspired genetic algorithms’, in Proceedings of IEEE International Conference on Evolutionary Computation, pp. 61-66, 20-22 May, 1996.

Gexiang Zhang. Quantum-inspired evolutionary algorithms: a survey and empirical study.J Heuristics (2011) 17: 303–351, DOI 10.1007/s10732-010-9136-0.

Ashish Mani and C. Patvardhan. An Adaptive Quantum Evolutionary Algorithm for Engineering Optimization Problems. ©2010 International Journal of Computer Applications (0975 - 8887) Volume 1 – No. 22.

Kuk-Hyun Han and Jong-Hwan Kim. Introduction of Quantum-inspired Evolutionary Algorithm. 2002 FIRA Robot Congress Seoul, Korea.

Ashish Mani and C. Patvardhan. An Improved Model of Ceramic Grinding Process and its Optimization by Adaptive Quantum inspired Evolutionary Algorithm. ©2010 International Journal of Simulations: Systems Science and Technology, Vol. 11, No. 6, pp. 76-85.

Nija Mani, Gurusaran, A.K. Sinha and Ashish Mani. Taguchi-Based Tuning of Rotation Angles and Population Size in Quantum-Inspired Evolutionary Algorithm for Solving MMDP. Advances in Intelligent Systems and Computing, February 2014. DOI: 10/1007/978-81-322-1602-5_12.

G. Manikanta, Ashish Mani, H. P. Singh, and D. K Chaturvedi. Placing Distribution Generators in Distribution System using Adaptive Quantum inspired Evolutionary Algorithm. 978-1-5090-1047-9/16/$31.00© 2016 IEEE.

Goptisetti Manikanta, Ashish Mani, H. P. Singh, and D. K. Chaturvedi. Distribution Network Reconfiguration with Different Load Models using Adaptive Quantum inspired Evolutionary Algorithm. 978-1-5386-5866-6/18/$31.00 ©2018 IEEE.

G. Manikanta, Ashish Mani, H.P. singh, and D.K Chaturvedi. Minimization of Power Losses in Distribution Systems with Variation in Loads using Adaptive Quantum inspired Evolutionary Algorithm. 978-1-5386-1379-5/17/$31.00 ©2017 IEEE.

G. Manikanta, Ashish Mani, H. P. Singh, and D.K Chaturvedi. Simultaneous application of distributed generator and network reconfiguration for power loss reduction using an adaptive quantum inspired evolutionary algorithm. International Journal of Energy Technology and Policy, Vol. 17 (2), pp. 140-179.

Goptisetti Manikanta, Ashish Mani, Hemender Pal Singh, and Devendera Kumar Chaturvedi. simultaneous placement and sizing of DG and Capacitor to minimize the power losses in radial distribution network. Chapter · January 2019, Pp. 605-619, soft computing: theories and applications, advances in intelligent system and computing and applications, DOI: 10.1007/978-981-13-0589-4_56.

Goptisetti Manikanta, Ashish Mani, H. P. Singh, and D. K. Chaturvedi. Sitting and Sizing of Capacitors in Distribution system using Adaptive Quantum inspired Evolutionary Algorithm. 978-1-5090-4530-3/16/$31.00 ©2016 IEEE.

Goptisetti, Manikanta, Ashish Mani, Hemender Pal Singh, and Devendera Kumar Chaturvedi. Adaptive Quantum-Inspired Evolutionary Algorithm for Optimizing Power Losses by Dynamic Load Allocation on Distributed Generators. Serbian Journal of Electrical Engineering, Vol. 16, No. 3, pp. 325-357, October 2019.

DanilomVasconcellos Vargas, Junichi Murata and Hirotaka Takano. Tackling Unit Commitment and Load Dispatch Problems Considering all Constraints with Evolutionary Computation”, arXiv: 1903.09304v1 [cs.CY] 6 Mar 2019.

Yun- Won Jeong, Jong-Bae Park, Joong-Rin Shin, and Kwang Y.Lee. A Thermal Unit Commitment Approach Using an Improved Quantum Evolutionary Algorithm. in Electric Power Components and System, 37:770-786,2009, ISSN: 1532-5008 Print/1532-5016 online, DOI: 10:1080/15325000902762331.

Hichem Talbi, Amar Draa, and Mohamed Batouche. A novel quantum-inspired evolutionary algorithms for multi-sensor image registration. in the international Arab journal of information technology, Vol. 3, No. 1, January 2006.

Shantanu Ckakraborty, Takayuki Ito, and Tomonobu Senjyu. Optimal Economic Operation of Smart Grid by Fuzzy Advanced Quantum Evolutionary method. in 3rd IEEE PES innovative Smart Grid Technologies Europe (ISGT Europe), Berlin, 978-1-4673-2597-4/12/$31.00© 2012IEEE.

John.G.Vlachogiannis and Kwang Y.Lee. Quantum- Inspired Evolutionary Algorithm for Real and Reactive Power Dispatch.IEEE Transactions on Power Systems, Vol. 23, NO. 4, November 2008, 0885-8950/$25.00© 2008 IEEE.

Yehoon Kim, Jong-Hwan Kim, and Kuk-Hyun Han. Quantum-inspired Multiobjective Evolutionary Algorithm for Multiobjective 0/1 Knapsack Problems. In 2006 IEEE Congress on Evolutionary Computation Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, July 16-21, 2006, 0-7803-9487-9/06/$20.00/©2006 IEEE.

T.W. Lau, C.Y.Chung, K.P.Wong, T.S. Chung, and S.L.Ho. Quantum-Inspired Evolutionary Algorithm Approach for Unit Commitment. IEEE Transactions on Power Systems, Vol. 24, NO. 3, August 2008, 0885-8950/$25.00© 2009 IEEE.

JERZY BALICKI. An Adaptive Quantum-based Evolutionary Algorithm for Multiobjective Optimization. WSEAS transactions on systems and control, Issue 12, Volume 4, December 2009, ISSN: 1991-8763.

Bin Ji, Xiaohui Yuan, Xianshan Li, Yuehua Huang, Wenwu Li. Application of quantum-inspired binary search algorithm for thermal unit commitment with wind power integration.Energy Conversion and Management 87 (2014) 589–598, 0196-8904/_ 2014.

Deepika Joshi, Anjali Jain, and Ashish Mani. Solving Economic Load Dispatch problem with value loading Effect using Adaptive Real Coded Quantum-inspired Evolutionary Algorithm. 978-1-4673-9080-4/16/$31.00©2016 IEEE.

Jitender Singh, Ashish Mani, H. P. Singh, and D. S. Rana. Towards Multipartite Adaptive Binary & Real Coded Quantum-Inspired Evolutionary Algorithm for Solving Multi-Objective Unit Commitment Problem with Thermal Units and Wind Farm. In 2021 4th International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE), pp.592-597.IEEE, 2021. DOI:10.1109/RDCAPE52977.2021.9633584

Jitender Singh, Ashish Mani, H. P. Singh, and D. S. Rana. Towards multipartite adaptive binary-real quantum inspired evolutionary algorithm for scheduling wind-thermal units. In AIP Conference Proceedings 2494, 020004 (2022)

Kramer, Oliver. (2010). A Review of Constraint-Handling Techniques for Evolution Strategies. Applied Comp. Int. Soft Computing. 2010. 10.1155/2010/185063.

Mallipeddi, Rammohan & Suganthan, Ponnuthurai. (2010). Ensemble of Constraint Handling Techniques. IEEE Trans. Evolutionary Computation. 14. 561-579. 10.1109/TEVC.2009.2033582.

Liang, Ximing & Long, Wen & Haoyu, Qin & Li, Shanchun. (2009). A Novel Constraint-Handling Method Based on Evolutionary Algorithm. 10.1109/ICICTA.2009.40.

Mezura-Montes, Efrén & Coello, Carlos. (2011). Constraint-handling in nature-inspired numerical optimization: Past, present, and future. Swarm and Evolutionary Computation. 1. 173-194. 10.1016/j.swevo.2011.10.001.

Ashish Mani and C. Patvardhan. A novel hybrid constraint handling Technique for Evolutionary Optimization. 978-1-4244-2959-2/09/$25.00© 2009 IEEE.

Deb K. (2000) ‘An efficient constraint-handling method for genetic algorithms’, Comput. Methods Appl. Mech. Eng., vol. 186, no. 2–4, pp. 311–338, doi: 10.1109/ISMS.2010.19.

B.G. W. Craenen, A. E. Eiben, and E. Marchiori. How to handle constraints with evolutionary algorithms.In Practical Handbook of Genetic Algorithms. Chapman & Hall/CRC, 2001, pp. 341–361.

Dosoglu, M. & Guvenc, Ugur & Duman, Serhat & Sonmez, Yusuf & Kahraman, Hamdi Tolga. (2018). Symbiotic organisms search optimization algorithm for economic/emission dispatch problem in power systems. Neural Computing and Applications. 29. 10.1007/s00521-016-2481-7.

Bhattacharya, Aniruddha & Chattopadhyay, P.. (2010). Application of Biogeography-based Optimization for Solving Multi-objective Economic Emission Load Dispatch Problems. Electric Power Components and Systems - ELECTR POWER COMPON SYST. 38. 340-365. 10.1080/15325000903273296.

Song, Y.H. & Wang, G.S. & Wang, P.Y. & Johns, A.T.. (1997). Environmental/economic dispatch using fuzzy logic controlled genetic algorithms. Generation, Transmission and Distribution, IEE Proceedings-. 144. 377 - 382. 10.1049/ip-gtd:19971100.

Rajasomashekar, S. & Aravindhababu, P.. (2012). Biogeography based optimization technique for best compromise solution of economic emission dispatch. Swarm and Evolutionary Computation. 7. 47-57. 10.1016/j.swevo.2012.06.001.