OBJECTIVE OPTIMIZATION OF POWER SYSTEM ECONOMIC DISPATCH PROBLEM BASED ON RANDOM CHAOS BAT ALGORITHM

Tingxi Wang

View Full Paper

References

  1. [1] A. Raihan, “Green energy and technological innovation towarda low-carbon economy in bangladesh,” Green and Low-CarbonEconomy, vol. 3, no. 2, pp. 171–181, 2025.
  2. [2] J. Li, Y. Peng, Z. Yang, and J. Pan, “Virtual power plant eco-nomic dispatch model based on parallel molecular differentialevolution algorithm,” International Journal of Power and En-ergy Systems, vol. 45, no. 2, pp. 64–77, 2025.
  3. [3] D. Wang, N. Gao, D. Liu, J. Li, and F. L. Lewis, “Recentprogress in reinforcement learning and adaptive dynamic pro-gramming for advanced control applications,” IEEE/CAA Jour-nal of Automatica Sinica, vol. 11, no. 1, pp. 18–36, 2023.
  4. [4] J. Lin and Q. Sun, “A low carbon economic optimal dispatch-ing model for comprehensive energy system based on improvedwhale algorithm,” International Journal of Power and EnergySystems, vol. 44, pp. 1–12, 2024.
  5. [5] R. Fei, Y. Guo, J. Li, B. Hu, and L. Yang, “An improved bpnnmethod based on probability density for indoor location,” IE-ICE Transactions on Information and Systems, vol. 106, no. 5,pp. 773–785, 2023.
  6. [6] S. Sobhanayak, “Mohba: Multi-objective workflow schedulingin cloud computing using hybrid bat algorithm,” Computing,vol. 105, no. 10, pp. 2119–2142, 2023.
  7. [7] M. H. Seifipour and H. Abdi, “Solving the economic dispatchproblem by using wild horse optimizer algorithm,” Research andTechnology in the Electrical Industry, vol. 3, no. 2, pp. 361–372,2024.
  8. [8] S. P. M. Lialestani, D. Parcerisa, M. Himi, and A. A. Shahri, “Anovel modified bat algorithm to improve the spatial geothermalmapping using discrete geodata in catalonia-spain,” ModelingEarth Systems and Environment, vol. 10, no. 3, pp. 4415–4428,2024.
  9. [9] Y. K. Saheed, T. O. Kehinde, M. A. Raji, and U. A. Baba,“Feature selection in intrusion detection systems: A new hybridfusion of bat algorithm and residue number system,” Journal ofInformation and Telecommunication, vol. 8, no. 2, pp. 189–207,2024.
  10. [10] S. Heddam, S. Kim, A. D. Mehr, M. Zounemat-Kermani,M. Ptak, A. Elbeltagi, and Y. Tikhamarine, “Bat algorithm op-timised extreme learning machine (bat-elm): A novel approachfor daily river water temperature modelling,” The GeographicalJournal, vol. 189, no. 1, pp. 78–89, 2023.
  11. [11] Q. Luo, F. Garcia-Menendez, H. Yang, R. Deshmukh, G. He,J. Lin, and J. X. Johnson, “The health and climate benefitsof economic dispatch in china’s power system,” EnvironmentalScience & Technology, vol. 57, no. 7, pp. 2898–2906, 2023.
  12. [12] Q. Liu, G. Xiong, X. Fu, A. W. Mohamed, J. Zhang, M. A.Al-Betar, and S. Xu, “Hybridizing gaining–sharing knowledgeand differential evolution for large-scale power system economicdispatch problems,” Journal of Computational Design and En-gineering, vol. 10, no. 2, pp. 615–631, 2023.
  13. [13] F. She, F. Li, H. Cui, J. Wang, Q. Zhang, and R. Bo, “Virtualinertia scheduling (vis) for real-time economic dispatch of ibr-penetrated power systems,” IEEE Transactions on SustainableEnergy, vol. 15, no. 2, pp. 938–951, 2023.
  14. [14] S. Lei, S. Bu, Q. Wang, Q. Chen, L. Yang, and Y. Chi, “Look-ahead rolling economic dispatch approach for wind-thermal-bundled power system considering dynamic ramping and flexibleload transfer strategy,” IEEE Transactions on Power Systems,vol. 39, no. 1, pp. 186–202, 2023.
  15. [15] B. Pan, B. Hu, C. Shao, L. Xu, K. Xie, Y. Wang, and A. Anvari-Moghaddam, “Reliability-constrained economic dispatch withanalytical formulation of operational risk evaluation,” IEEETransactions on Power Systems, vol. 39, no. 2, pp. 4422–4436,2023.
  16. [16] K. A. Khatri, K. B. Shah, J. Logeshwaran, and A. Shrestha,“Genetic algorithm based techno-economic optimization of anisolated hybrid energy system,” CRF, vol. 8, no. 4, pp. 1447–1450, 2023.
  17. [17] M. Mesquita-Cunha, J. R. Figueira, and A. P. Barbosa-P´ovoa,“New ϵ-constraint methods for multi-objective integer linearprogramming: A pareto front representation approach,” Euro-pean Journal of Operational Research, vol. 306, no. 1, pp. 286–307, 2023.
  18. [18] L. Yang and N. Engelhardt, “The complexity of learning(pseudo) random dynamics of black holes and other chaotic sys-tems,” Journal of High Energy Physics, vol. 2025, no. 3, pp. 1–65, 2025.
  19. [19] R. K. Tipu, V. Batra, Suman, K. S. Pandya, and V. R. Panchal,“Predicting compressive strength of concrete with iron waste:A bpnn approach,” Asian Journal of Civil Engineering, vol. 25,no. 7, pp. 5571–5579, 2024.
  20. [20] T. Sabharwal and R. Gupta, “Human face identification af-ter plastic surgery using surf, multi-knn and bpnn techniques,”Complex & Intelligent Systems, vol. 10, no. 3, pp. 4457–4472,2024.11
  21. [21] J. O. Agushaka, A. E. Ezugwu, L. Abualigah, S. K. Alharbi,and H. A. E. W. Khalifa, “Efficient initialization methodsfor population-based metaheuristic algorithms: A comparativestudy,” Archives of Computational Methods in Engineering,vol. 30, no. 3, pp. 1727–1787, 2023.
  22. [22] C. Yin, Q. Fang, H. Li, Y. Peng, X. Xu, and D. Tang, “Anoptimized resource scheduling algorithm based on ga and acoalgorithm in fog computing,” The Journal of Supercomputing,vol. 80, no. 3, pp. 4248–4285, 2024.

Important Links:

Go Back