The Optimization of Energy Supply Systems by Sequential Streamflow Routing Method and Invasive Weed Optimization Algorithm; Case Study: Karun II Hydroelectric Power Plant

Document Type : Research Paper

Authors

Department of Civil Engineering, Environmental Sciences Research Center, Islamshahr Branch, Islamic Azad university, Islamshahr, Iran

Abstract

Among the major sources of energy supply systems, hydroelectric power plants are more common. Energy supply during peak hours and less environmental issues are some of the most important advantages of hydroelectric power plants. In this study, designing parameters to supply maximum amount of energy was determined by using the simulation-optimization perspective and combination of IWO-WEAP models. Subsequently, the developed model has been applied for designing the Karun II hydroelectric power plant. The sequential streamflow routing method has been developed for obtaining energy in WEAP water resources management software. In addition the optimization algorithm has been applied to optimize the invasive weeds. To verify the performance of this method, obtained results for the firm energy were compared to those of the total energy. Using this method, for 1398 GWY (Giga watt per your) firm energy, the minimum and normal levels of operation were 668 and 672 m.a.s.l (meters above sea level), respectively, and the installation capacity calculated around 498 MW as optimal value.

Keywords


  1. Basak, D. Maity, and S. Das, “A differential invasive weed optimization algorithm for improved global numerical optimization,” Applied Mathematics and Computation, vol. 219, no. 12, pp. 6645–6668, 2013.
  2. K. Barisal and R. C. Prusty, “Large scale economic dispatch of power systems using oppositional invasive weed optimization,” Applied Soft Computing, vol. 29, pp. 122–137, 2015.
  3. Ouyang, Z. Tang, K. Li, A. Sallam, and E. Sha, “Estimating parameters of Muskingum model using an adaptive hybrid PSO algorithm,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 28, no. 1, Article ID1459003, 29 pages,2014.
  4. R. Mehrabian and C. Lucas, “A novel numerical optimization algorithm inspired from weed colonization,” Ecological Informatics, vol. 1, no. 4, pp. 355–366, 2006.
  5. Saravanan, E. R. Vasudevan, and D. P. Kothari, “Unit commitment problem solution using invasive weed optimization algorithm,” International Journal of Electrical Power & Energy Systems, vol. 55, pp. 21–28, 2014.
  6. Abu-Al-Nadi I., O. M. Alsmadi, Z. S. Abo-Hammour, M. F. Hawa, and J. S. Rahhal, “Invasive weed optimization for model order reduction of linear MIMO systems,” Applied Mathematical Modelling, vol. 37, no. 6, pp. 4570–4577, 2013.
  7. Kundu, K. Suresh, S. Ghosh, S. Das, and B. K. Panigrahi, “Multi-objective optimization with artificial weed colonies,” Information Sciences, vol. 181, no. 12, pp. 2441–2454, 2011.
  8. M. Xu, L. Qiu, and S.-Y. Chen, “Estimation of nonlinear Muskingum model parameter using differential evolution,” Journal of Hydrologic Engineering, vol. 17, no. 2, pp. 348–353, 2012.
  9. Dezab Consulting Engineers Report on water and power resources planning of Karun II hydropower Project [Report]. - [s.l.] : Dezab, 2014.
  10. Diaz G.E and Fontane D.G Hydropower Optimization via Sequential Quadratic Programming [Journal] // Water Resources Planning and Management, ASCE, 115(6). - 1989. - pp. 715-733.
  11. Evenson, D. E; Moseley, J. C; Simulation/optimization techniques for multi-basin water resource planning [Journal] // JAWRA Journal of the American Water Resources Association. - 1970. - pp. 725–736.
  12. H. Karahan, G. Gurarslan, and Z. Geem, “Parameter estimation of the nonlinear muskingum flood-routing model using a hybrid harmony search algorithm,” Journal of Hydrologic Engineering, vol. 18, no. 3, pp. 352–360, 2013.
  13. J. Chu and L.-C. Chang, “Applying particle swarm optimization to parameter estimation of the nonlinear Muskingum model,” Journal of Hydrologic Engineering, vol. 14, no. 9, pp. 1024–1027, 2009.
  14. Luo and J. Xie, “Parameter estimation for nonlinear Muskingum model based on immune clonal selection algorithm,” Journal of Hydrologic Engineering, vol. 15, no. 10, Article ID 006010QHE, pp. 844–851, 2010.
  15. Ahmadi and H. Mojallali, “Chaotic invasive weed optimization algorithm with application to parameter estimation of chaotic systems,” Chaos, Solitons & Fractals, vol. 45,no. 9-10,pp. 1108–1120, 2012.
  16. M. Ghasemi, S. Ghavidel, E. Akbari, and A. A. Vahed, “Solving non-linear, non-smooth and non-convex optimal power flow problems using chaotic invasive weed optimization algorithms based on chaos,” Energy, vol. 73, pp. 340–353, 2014.
  17. Mehrabian A.R and Lucasc C A novel numerical optimization algorithm inspired from weed colonization [Journal] // Ecological Informatics. - 2006. - pp. 355–366.
  18. Najafpour, N., Emamgholizadeh, S., Torabi poudeh, H., Hamzeh Haghiabi, A., "Estimation of Sediment Transport Rate of Karun River (Iran)" Journal of Hydraulic Structures J. Hydraul. Struct., 2016; 2(2):74-84 DOI: 10.22055/jhs.2016.12874.
  19. R. Barati, “Parameter estimation of nonlinear Muskingum models using nelder-mead simplex algorithm,” Journal of Hydrologic Engineering, vol. 16, no. 11, pp. 946–954, 2011.
  20. Razi Khosroshahi M, Mousavi S j and Alizadeh H Upstream Effects on Aras Cascade Hydropower Plants System [Conference] // 10th International Congress on Civil Engineering. - University of Tabriz, Tabriz, Iran : [s.n.], 2015.
  21. S. Mohan, “Parameter estimation of nonlinear Muskingum models using genetic algorithm,” Journal of Hydraulic Engineering, vol. 123, no. 2, pp. 137–142, 1997.
  22. S. Peng, A. Ouyang, and J. J. Zhang, “An adaptive invasive weed optimization algorithm,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 29, no. 2, Article ID 1559004, pp. 1–20, 2015.
  23. Sieber J and Purkey D WEAP User Guide [Book]. - [s.l.] : Stockholm Environment Institute, U.S. Center, 2012.
  24. Tsoukalas I and Makropoulos C Hydrosystem Optimization with the Use of Evolutionary Algorithms: The Case of Nestos River [Journal] // 13th International Conference on Environmental Science and Technology (CEST2013), Athens, Greece. - 2013.
  25. S. Army Corps of Engineer Engineering and Design – Hydropower [Book]. - [s.l.] : Departmant of the Army, 1984.
  26. Wardlaw R and Sharif M Evaluation of Genetic Algorithms for Optimal Reservoir System Operation [Journal] // Journal of Water Resources Planning and Management., 125. - 1999. - pp. 25–33.
  27. Y. Zhou, H. Chen, and G. Zhou, “Invasive weed optimization algorithm for optimization no-idle flow shop scheduling problem,” Neurocomputing, vol. 137, pp. 285–292, 2014.
  28. Y.Zhou, Q. Luo, H. Chen,A.He, and J.Wu, “Adiscrete invasive weed optimization algorithm for solving traveling salesman problem,” Neurocomputing, vol. 151, no. 3, pp. 1227–1236, 2015.
  29. D. Zaharis, C. Skeberis, T. D. Xenos, P. I. Lazaridis, and J. Cosmas, “Design of a novel antenna array beamformer using neural networks trained by modified adaptive dispersion invasive weed optimization based data,” IEEE Transactions on Broadcasting, pp. 455–460, 2013.
  30. D. Zaharis, P. I. Lazaridis, J. Cosmas, C. Skeberis, and T. D. Xenos, “Synthesis of a near-optimal high-gain antenna array with main lobe tilting and null filling using taguchi initialized invasive weed optimization,” IEEE Transactions on Broadcasting, vol. 60, no. 1, pp. 120–127, 2014.
  31. W. Geem, “Parameter estimation for the nonlinear Muskingum model using the BFGS technique,” Journal of Irrigation and Drainage Engineering, vol. 132, no. 5, pp. 474–478, 2006.
  32. W. Geem, “Parameter estimation of the nonlinear Muskingum model using parameter-setting-free harmony search,” Journal of Hydrologic Engineering, vol. 16, no. 8, pp. 684–688, 2011.