1. M.Ahrabi, H.Abedi, M. Nafisi, A. Mirzaei, B. Mohammadi-Ivatloo, and M. Marzband, (2021). "Evaluating the effect of electric vehicle parking lots in transmission-constrained AC unit commitment under a hybrid IGDT-stochastic approach," International Journal of Electrical Power & Energy Systems, vol. 125, p. 106546. [ DOI:10.1016/j.ijepes.2020.106546] 2. Aghdam, F. H., Javadi, M. S., & Catalão, J. P. (2023). "Optimal stochastic operation of technical virtual power plants in reconfigurable distribution networks considering contingencies". International Journal of Electrical Power & Energy Systems, 147, 108799. [ DOI:10.1016/j.ijepes.2022.108799] 3. Al-Dhaifallah, M., Alaas, Z., Rezvani, A., Le, B. N., & Samad, S. (2023). "Optimal day-ahead economic/emission scheduling of renewable energy resources based microgrid considering demand side management". Journal of Building Engineering, 76, 107070. [ DOI:10.1016/j.jobe.2023.107070] 4. Chen, Y., Lu, X., Zhang, H., Zhao, C., & Xu, Y. (2023). "Optimal configuration of integrated energy station using adaptive operation mode of combined heat and power units." International Journal of Electrical Power & Energy Systems, 152, 109171. [ DOI:10.1016/j.ijepes.2023.109171] 5. G.Cavazzini,.Benato,A.,Pavesi,G.,Ardizzon,G., (2021).Techno-economic benefits deriving from optimal scheduling of avirtual power plant: Pumped hydro combined with wind farms. J. Energy Storage. 37, 102461. http://dx.doi.org/ 10.1016/j.est.2021.102461. [ DOI:10.1016/j.est.2021.102461] 6. Coath and K. Halgamuge, (2003)."A comparison of constraint-handling methods for the application of particle swarm optimization to constrained nonlinear optimization problems," in The 2003 Congress on Evolutionary Computation. ,CEC'03., 2003, pp. 2419-2425. [ DOI:10.1109/CEC.2003.1299391] 7. C.Cheng. C.-W. Liu, and C.-C. Liu, (2000) "Unit commitment by Lagrangian relaxation and genetic algorithms," IEEE transactions on power systems, vol. 15, pp. 707-714. [ DOI:10.1109/59.867163] 8. D.Dasgupta. and D. R. McGregor, (1994)."Thermal unit commitment using genetic algorithms," IEE Proceedings-Generation, Transmission and Distribution, vol. 141, pp. 459-465. [ DOI:10.1049/ip-gtd:19941221] 9. M.Emarati, , Keynia, F., & Rashidinejad, M. (2019). A two‐stage stochastic programming framework for risk‐based day‐ahead operation of a virtual power plant. International Transactions on Electrical Energy Systems, 30(3), e12255. [ DOI:10.1002/2050-7038.12255] 10. El-Gallad.A, A. Sallam, and M. El-Hawary, (2001) "Swarming of intelligent particles for solving the nonlinear constrained optimization problem," International journal of engineering intelligent systems for electrical engineering and communications, vol. 9, pp. 155-164. 11. H.Farahbakhsh, Pourfar, I., & Lashkar Ara, A. (2024). Virtual power plant operation using an improved meta-heuristic optimization algorithm considering uncertainties. Journal of Operation and Automation in Power Engineering. vol.12, no.4, Pages:312-325. 12. Heredia.F and N. Nabona, (1995). "Optimum short-term hydrothermal scheduling with spinning reserve through network flows," IEEE Transactions on Power Systems, vol. 10, pp. 1642-1651. [ DOI:10.1109/59.466476] 13. Jiang, X., Lin, Z., He, T., Ma, X., Ma, S., & Li, S. (2020). "Optimal path finding with beetle antennae search algorithm by using ant colony optimization initialization and different searching strategies". IEEE Access, 8, 15459-15471. [ DOI:10.1109/ACCESS.2020.2965579] 14. Jain, A., Yamujala, S., Gaur, A., Das, P., Bhakar, R., & Mathur, J. (2023). "Power sector decarbonization planning considering renewable resource variability and system operational constraints". Applied Energy, 331, 120404. [ DOI:10.1016/j.apenergy.2022.120404] 15. Khalafian, F., Iliaee, N., Diakina, E., Parsa, P., Alhaider, M. M., Masali, M. H, (2024). "Capabilities of compressed air energy storage in the economic design of renewable off-grid system to supply electricity and heat costumers and smart charging-based electric" vehicles. Journal of Energy Storage, 78, 109888. [ DOI:10.1016/j.est.2023.109888] 16. A.Lorca, A., & Sun, X. A. (2016). Multistage robust unit commitment with dynamic uncertainty sets and energy storage. IEEE Transactions on Power Systems, 32(3), 1678-1688. [ DOI:10.1109/TPWRS.2016.2593422] 17. Li, J., Fang, Z., Wang, Q., Zhang, M., Li, Y., & Zhang, W. (2024). "Optimal Operation with Dynamic Partitioning Strategy for Centralized Shared Energy Storage Station with Integration of Large-Scale Renewable Energy". Journal of Modern Power Systems and Clean Energy, PP.1-13, IEEE (Early Access). [ DOI:10.35833/MPCE.2023.000345] 18. Lin, W. T., Chen, G., & Li, C. (2021). Risk-averse energy trading among peer-to-peer based virtual power plants: A stochastic game approach. International Journal of Electrical Power & Energy Systems, 132, 107145. [ DOI:10.1016/j.ijepes.2021.107145] 19. Maifeld.T and Sheble, (1996). "Genetic-based unit commitment algorithm," IEEE Transactions on Power systems, vol. 11, pp. 1359-1370. [ DOI:10.1109/59.536120] 20. Nazari, M. E., & Ardehali, M. M. (2017). Profit-based unit commitment of integrated CHP-thermal-heat only units in energy and spinning reserve markets with considerations for environmental CO2 emission cost and valve-point effects. Energy, 133, 621-635. [ DOI:10.1016/j.energy.2017.05.164] 21. Ouyang.Z and S. Shahidehpour, (1991). "An intelligent dynamic programming for unit commitment application," IEEE Transactions on power systems, vol. 6, pp. 1203-1209. [ DOI:10.1109/59.119267] 22. Pinson.P and H. Madsen, (2014). "Benefits and challenges of electrical demand response: A critical review," Renewable and Sustainable Energy Reviews, vol. 39, pp. 686-699. [ DOI:10.1016/j.rser.2014.07.098] 23. Poncelet.K, E. Delarue, and W. D'haeseleer, (2020). "Unit commitment constraints in long-term planning models: Relevance, pitfalls and the role of assumptions on flexibility," Applied Energy, vol. 258, p. 113843. [ DOI:10.1016/j.apenergy.2019.113843] 24. Pappala, V. S., & Erlich, I. (2008). A new approach for solving the unit commitment problem by adaptive particle swarm optimization. In 2008 IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century (pp. 1-6). IEEE. [ DOI:10.1109/PES.2008.4596390] 25. Papavasiliou and S. S. Oren, (2013). "Large-scale integration of deferrable demand and renewable energy sources," IEEE Transactions on Power Systems, vol. 29, pp. 489-499. [ DOI:10.1109/TPWRS.2013.2238644] 26. Richter C. W. and G. B. Sheble, (2000). "A profit-based unit commitment GA for the competitive environment," IEEE Transactions on Power systems, vol. 15, pp. 715-721. [ DOI:10.1109/59.867164] 27. Rawa, M., Al-Turki, Y., Sedraoui, K., Dadfar, S., & Khaki, M. (2023). "Optimal operation and stochastic scheduling of renewable energy of a microgrid with optimal sizing of battery energy storage considering cost reduction". Journal of Energy Storage, 59, 106475. [ DOI:10.1016/j.est.2022.106475] 28. Sudhakaran.M and P. Raj, (2010). "Integrating genetic algorithms and tabu search for unit commitment problem," International Journal of Engineering, Science and Technology, vol. 2, pp. 57-69 [ DOI:10.4314/ijest.v2i1.59085] 29. Sisworahardjo.S and A. El-Keib, (2002). "Unit commitment using the ant colony search algorithm," in LESCOPE'02. Large Engineering Systems Conference on Power Engineering. Conference Proceedings, pp. 2-6. [ DOI:10.1109/LESCPE.2002.1020658] 30. Swarup.S and S. Yamashiro, (2002). "Unit commitment solution methodology using genetic algorithm," IEEE [ DOI:10.1109/59.982197] |
