Optimal operating scenario for Polerood hydropower station to maximize peak shaving and produced profit

*Behzad Forouzi Feshalami -  Ferdowsi University of Mashhad, Iran, Islamic Republic of
Published: 15 Dec 2018.
Open Access Copyright (c) 2018 International Journal of Renewable Energy Development
Citation Format:
Article Info
Section: Original Research Article
Language: EN
Full Text:
Statistics: 1524 126

This paper deals with the optimization of the daily operation of Polerood hydropower station being constructed in the north of Iran. Dynamic Programming method (DP) is applied as the preferred optimization tool owing to the fact that it guarantees the optimal solution and is applicable to the present problem. Produced profit and peak-shaving are the two objectives considered separately in this study. The results show that the optimal water management of the case study through charging and discharging the reservoir at the appropriate times led to 4% increase in the produced profit. In another part of this study, the optimal performance strategies regarding to the two objectives (produced profit and peak-shaving) are compared. The observed similarity between the two performance strategies implies the substantial dependence of the electricity price and the network demand level. The paper ends with the profitability study of the project and the sensitivity analysis of the results to various economic parameters. 

Article History: Received December 15th 2017; Received in revised form April 18th 2018; Accepted September 16th 2018; Available online

How to Cite This Article: Feshalami, B.F. (2018) Optimal Operating Scenario for Polerood Hydropower Station to Maximize Peak Shaving and Produced Profit. International Journal of Renewable Energy Development, 7(3), 233-239.


Polerood Hydropower Station; Performance Optimization; Dynamic Programming Method; Profit; Peak Shaving

Article Metrics:

  1. Afshar, M. H. (2012). Large scale reservoir operation by Constrained Particle Swarm Optimization algorithms. Journal of Hydro-environment Research, 6(1), 75-87. http://www.sciencedirect.com/science/article/pii/S1570644311000979
  2. Amjady, N., & Soleymanpour, H. R. (2010). Daily Hydrothermal Generation Scheduling by a new Modified Adaptive Particle Swarm Optimization technique. Electric Power Systems Research, 80(6), 723-732. http://www.sciencedirect.com/science/article/pii/S037877960900282X
  3. Barten, H. (2010). International Energy Agency.
  4. Bellman, R. (2013). Dynamic programming, Courier Corporation.
  5. Belsnes, M. M., Wolfgang, O., Follestad, T., & Aasgård, E. K. (2016). Applying successive linear programming for stochastic short-term hydropower optimization. Electric Power Systems Research, 130, 167-180. http://www.sciencedirect.com/science/article/pii/S0378779615002576
  6. Borghetti, A., Ambrosio, C. D., Lodi, A., & Martello, S. (2008). An MILP Approach for Short-Term Hydro Scheduling and Unit Commitment With Head-Dependent Reservoir. IEEE Transactions on Power Systems, 23(3), 1115-1124. http://ieeexplore.ieee.org/abstract/document/4562139/
  7. Bozorg-Haddad, O., Azarnivand, A., Hosseini-Moghari, S.-M., & Loáiciga, H. A. (2017). Optimal operation of reservoir systems with the symbiotic organisms search (SOS) algorithm. Journal of Hydroinformatics, 19(4). http://jh.iwaponline.com/content/early/2017/03/15/hydro.2017.085
  8. Carter, R. G. Pipeline Optimization: Dynamic Programming After 30 Years, Pipeline Simulation Interest Group.
  9. Chatterjee, B., Howitt, R. E., & Sexton, R. J. (1998). The Optimal Joint Provision of Water for Irrigation and Hydropower. Journal of Environmental Economics and Management, 36(3), 295-313. http://www.sciencedirect.com/science/article/pii/S0095069698910476
  10. Chen, L., McPhee, J., & Yeh, W. W. G. (2007). A diversified multiobjective GA for optimizing reservoir rule curves. Advances in Water Resources, 30(5), 1082-1093. http://www.sciencedirect.com/science/article/pii/S0309170806001813
  11. Chen, Q., Chen, D., Li, R., Ma, J., & Blanckaert, K. (2013). Adapting the operation of two cascaded reservoirs for ecological flow requirement of a de-watered river channel due to diversion-type hydropower stations. Ecological Modelling, 252, 266-272. http://www.sciencedirect.com/science/article/pii/S0304380012001159
  12. Darmstader, P. D., Teitelbaum, P. D., & Polach, J. G. (1971). Energy in the world economy: a statistical review of trends in output, trade, and consumption since 1925, The Johns Hopkins Press,Baltimore; None.
  13. Delucchi, M. A., & Jacobson, M. Z. (2011). Providing all global energy with wind, water, and solar power, Part II: Reliability, system and transmission costs, and policies. Energy Policy, 39(3), 1170-1190. http://www.sciencedirect.com/science/article/pii/S0301421510008694
  14. Egré, D., & Milewski, J. C. (2002). The diversity of hydropower projects. Energy Policy, 30(14), 1225-1230. http://www.sciencedirect.com/science/article/pii/S0301421502000836
  15. Fang, W., Huang, Q., Huang, S., Yang, J., Meng, E., & Li, Y. (2017). Optimal sizing of utility-scale photovoltaic power generation complementarily operating with hydropower: A case study of the world’s largest hydro-photovoltaic plant. Energy Conversion and Management, 136, 161-172. http://www.sciencedirect.com/science/article/pii/S0196890417300122
  16. Feng, Z.-k., Niu, W.-j., Cheng, C.-t., & Zhou, J.-z. (2017). Peak shaving operation of hydro-thermal-nuclear plants serving multiple power grids by linear programming. Energy, 135, 210-219. http://www.sciencedirect.com/science/article/pii/S0360544217310915
  17. Finley, M. (2013). BP statistical review of world energy.
  18. Fu, X., Li, A., Wang, L., & Ji, C. (2011). Short-term scheduling of cascade reservoirs using an immune algorithm-based particle swarm optimization. Computers & Mathematics with Applications, 62(6), 2463-2471. http://www.sciencedirect.com/science/article/pii/S0898122111005864
  19. Gaudard, L., Avanzi, F., & De Michele, C. (2017). Seasonal aspects of the energy-water nexus: The case of a run-of-the-river hydropower plant. Applied Energy. http://www.sciencedirect.com/science/article/pii/S0306261917301174
  20. Gaudard, L., & Romerio, F. (2014). The future of hydropower in Europe: Interconnecting climate, markets and policies. Environmental Science & Policy, 43, 5-14. http://www.sciencedirect.com/science/article/pii/S1462901114001014
  21. Hakimi-Asiabar, M., Ghodsypour, S. H., & Kerachian, R. (2010). Deriving operating policies for multi-objective reservoir systems: Application of Self-Learning Genetic Algorithm. Applied Soft Computing, 10(4), 1151-1163. http://www.sciencedirect.com/science/article/pii/S1568494609001392
  22. Hosseini, S. E., & Wahid, M. A. (2013). Feasibility study of biogas production and utilization as a source of renewable energy in Malaysia. Renewable and Sustainable Energy Reviews, 19, 454-462. http://www.sciencedirect.com/science/article/pii/S1364032112006193
  23. Jahandideh-Tehrani, M., Bozorg Haddad, O., & Loáiciga, H. A. (2015). Hydropower Reservoir Management Under Climate Change: The Karoon Reservoir System. Water Resources Management, 29(3), 749-770. https://link.springer.com/article/10.1007/s11269-014-0840-7
  24. Jiekang, W., Zhuangzhi, G., & Fan, W. (2014). Short-term multi-objective optimization scheduling for cascaded hydroelectric plants with dynamic generation flow limit based on EMA and DEA. International Journal of Electrical Power & Energy Systems, 57, 189-197. http://www.sciencedirect.com/science/article/pii/S0142061513005139
  25. Koziel, S., & Yang, X.-S. (2011). Computational optimization, methods and algorithms, Springer.
  26. Labadie, J. W. (2004). Optimal operation of multireservoir systems: state-of-the-art review. Journal of water resources planning and management, 130(2), 93-111. http://ascelibrary.org/doi/abs/10.1061/(ASCE)0733-9496(2004)130:2(93)
  27. Lu, B., Li, K., Zhang, H., Wang, W., & Gu, H. (2013). Study on the optimal hydropower generation of Zhelin reservoir. Journal of Hydro-environment Research, 7(4), 270-278. http://www.sciencedirect.com/science/article/pii/S1570644313000038
  28. Ma, C., Lian, J., & Wang, J. (2013). Short-term optimal operation of Three-gorge and Gezhouba cascade hydropower stations in non-flood season with operation rules from data mining. Energy Conversion and Management, 65, 616-627. http://www.sciencedirect.com/science/article/pii/S0196890412003524
  29. Mahmoudimehr, J., Sorouri, A., & Feshalami, B. F. (2016). A novel map for deciding on the type of a hydro power plant. Proceedings of the Institution of Civil Engineers-Energy, 169(4), 161-178. http://www.icevirtuallibrary.com/doi/abs/10.1680/jener.15.00020
  30. Mantawy, A. H., Soliman, S. A., & El-Hawary, M. E. (2003). The long-term hydro-scheduling problem—a new algorithm. Electric Power Systems Research, 64(1), 67-72. http://www.sciencedirect.com/science/article/pii/S0378779602001463
  31. Marano, V., Rizzo, G., & Tiano, F. A. (2012). Application of dynamic programming to the optimal management of a hybrid power plant with wind turbines, photovoltaic panels and compressed air energy storage. Applied Energy, 97, 849-859. http://www.sciencedirect.com/science/article/pii/S0306261911008920
  32. Mariano, S. J. P. S., Catalao, J. P. S., Mendes, V. M. F., & Ferreira, L. A. F. M. (2007). Profit-Based Short-Term Hydro Scheduling considering Head-Dependent Power Generation. Power Tech, 2007 IEEE Lausanne.http://ieeexplore.ieee.org/abstract/document/4538514/
  33. Mariano, S. J. P. S., Catalão, J. P. S., Mendes, V. M. F., & Ferreira, L. A. F. M. (2008). Optimising power generation efficiency for head-sensitive cascaded reservoirs in a competitive electricity market. International Journal of Electrical Power & Energy Systems, 30(2), 125-133. http://www.sciencedirect.com/science/article/pii/S0142061507000853
  34. Martinot, E. (2005). Renewables 2005: Global status report. Washington, DC: Worldwatch Institute.
  35. Nautiyal, H., Singal, S., & Sharma, A. (2011). Small hydropower for sustainable energy development in India. Renewable and Sustainable Energy Reviews, 15(4), 2021-2027. http://www.sciencedirect.com/science/article/pii/S1364032111000219
  36. Pérez-Díaz, J. I., & Wilhelmi, J. R. (2010). Assessment of the economic impact of environmental constraints on short-term hydropower plant operation. Energy Policy, 38(12), 7960-7970. http://www.sciencedirect.com/science/article/pii/S0301421510007068
  37. Pérez-Díaz, J. I., Wilhelmi, J. R., & Sánchez-Fernández, J. Á. (2010). Short-term operation scheduling of a hydropower plant in the day-ahead electricity market. Electric Power Systems Research, 80(12), 1535-1542. http://www.sciencedirect.com/science/article/pii/S0378779610001537
  38. Sharma, V., Jha, R., & Naresh, R. (2007). Optimal multi-reservoir network control by augmented Lagrange programming neural network. Applied Soft Computing, 7(3), 783-790. http://www.sciencedirect.com/science/article/pii/S156849460600024X
  39. Simopoulos, D. N., Kavatza, S. D., & Vournas, C. D. (2007). An enhanced peak shaving method for short term hydrothermal scheduling. Energy Conversion and Management, 48(11), 3018-3024. http://www.sciencedirect.com/science/article/pii/S019689040700235X
  40. Xu, J., & Tao, Z. (2012). A class of multi-objective equilibrium chance maximization model with twofold random phenomenon and its application to hydropower station operation. Mathematics and Computers in Simulation, 85, 11-33. http://www.sciencedirect.com/science/article/pii/S0378475412002194
  41. Yang, T., Gao, X., Sellars, S. L., & Sorooshian, S. (2015). Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex. Environmental Modelling & Software, 69, 262-279. http://www.sciencedirect.com/science/article/pii/S1364815214003429
  42. Yoo, J.-H. (2009). Maximization of hydropower generation through the application of a linear programming model. Journal of Hydrology, 376(1), 182-187. http://www.sciencedirect.com/science/article/pii/S0022169409004193
  43. Zhang, R., Zhou, J., Ouyang, S., Wang, X., & Zhang, H. (2013). Optimal operation of multi-reservoir system by multi-elite guide particle swarm optimization. International Journal of Electrical Power & Energy Systems, 48, 58-68. http://www.sciencedirect.com/science/article/pii/S0142061512006849
  44. Zhao, G., & Davison, M. (2009). Optimal control of hydroelectric facility incorporating pump storage. Renewable Energy, 34(4), 1064-1077. http://www.sciencedirect.com/science/article/pii/S096014810800284X