A Neural Network Model for Estimation of Maximum Next Day Energy Generation Capacity of a Hydropower Station: A Case Study from Turkey

Yıl 2023, Cilt: 19 Sayı: 3, 197 - 204, 30.09.2023

Serkan İnal Sibel Akkaya Oy Ali Ekber Özdemir

https://doi.org/10.18466/cbayarfbe.1218381

Öz

Energy planning in a hydro power station (HPS) is essential for reservoir management, and to ensure efficient operation and financial usage. For robust energy planning, operators should estimate next day energy generation capacity correctly. This paper investigates use of a robust neural network model to estimate maximum next day energy generation capacity by using reservoir inflow rates for the previous four days, the current level of water in the reservoir, and the weather forecast for the Darıca-2 HPS in Ordu Province, Turkey. The generated energy in an HPS is directly dependent on the level of stored water in the reservoir, which depends on reservoir inflow. As the level of water in a reservoir varies during the year depending on climatic conditions, it is important to be able to estimate energy generation in an HPS to operate the HPS most effectively. This paper uses reservoir inflow data that has been collected daily during 2020 for the training phase of a neural network. The neural network is tested using a data set that has been collected daily during the first four months of 2021. Used neural network structure is called as LWNRBF (Linear Weighted Normalized Radial Basis Function) network, which is developed form of RBF network. In order to be able to be created valid model, LWNRBF network is trained with a two-pass hybrid training algorithm. After the training and testing stages, average training and testing error percentages have been obtained as 0.0012% and -0.0044% respectively.

Anahtar Kelimeler

hydro-electric power generation, hydropower generation, neural network, reservoir inflow, renewable energy sources

Kaynakça

• [1]. Saravanan, A, Senthil kumar, P, Vo, DVN, Jeevanantham, S, Bhuvaneswari, V, Anantha Narayanan, V, Yaashikaa, PR, Swetha, S, Reshma, B. 2021. A comprehensive review on different approaches for CO2 utilization and conversion pathways. Chemical Engineering Science; 236: 116515
• [2]. IEA (International Energy Agency) 2017. CO2 emissions from fuel combustion: Highlight. https://euagenda.eu/upload/publications/untitled-110953-ea.pdf/ (accessed at 12.01.2021).
• [3]. Kuriqi, A, Pinheiro, AN, Sordo-Ward, A, Bejarano, MD, Garrote, L. 2021. Ecological impacts of run-of-river hydropower plants—Current status and future prospects on the brink of energy transition. Renewable and Sustainable Energy Reviews; 142: 110833.
• [4]. IHA. Hydropower status report. 7 ed. London United Kingdom: International Hydropower Association (IHA). https://hydropower-assets.s3.eu-west-2.amazonaws.com/publications-docs/2020_hydropower_status_report.pdf (accessed at 08.12.2020).
• [5]. IRENA (International Renewable Energy Agency). Available online: https://www.irena.org/hydropower (accessed at 15.05.2021).
• [6]. Shi, Y, Zhou, J, Lai, X, Xu, Y, Guo, W, Liu, B. 2021. Stability and sensitivity analysis of the bending-torsional coupled vibration with the arcuate whirl of hydro-turbine generator unit. Mechanical Systems and Signal Processing; 149: 107306
• [7]. Bilgili, M, Bilirgen, H, Ozbek, A, Ekinci, F, Demirdelen, T. 2018. The role of hydropower installations for sustainable energy development in Turkey and the world. Renewable Energy; 126: 755-764.
• [8]. IEA Renewable Energy Essentials: Hydropower Available online: https://iea.blob.core.windows.net/assets/5b4df552-d99d-4bbb-b41e-c8ab4b6123b5/Hydropower_Essentials.pdf (16.05.2021).
• [9]. Jia, B, Zhou, J, Chen, X, He, Z, Qin, H. 2019. Deriving Operating Rules of Hydropower Reservoirs Using Gaussian Process Regression. IEEE Access; 7: 158170-158182.
• [10]. Feng, Z, Liu, S, Niu, W, Liu, Y, Lou, B, Miao, S, Wang, S. 2019. Optimal Operation of Hydropower System by Improved Grey Wolf Optimizer Based on Elite Mutation and Quasi-Oppositional Learning. IEEE Access; 7: 155513-155529.
• [11]. Li, B., Li, C., Cui, X., Lai, X., Ren, J., He, Q.: A Disassembly Sequence Planning Method with Team-Based Genetic Algorithm for Equipment Maintenance in Hydropower Station. IEEE Access. 8, 47538-47555 (2020).
• [12]. Shahryar Khalique, A, Hossain, F. 2019. A generic data-driven technique for forecasting of reservoir inflow: Application for hydropower maximization. Environmental Modelling & Software; 119: 147-165.
• [13]. Ahmad, A, El-Shafie, A, Razali, SFM. 2014. Reservoir Optimization in Water Resources: A Review. Water Resour Manage; 28: 3391–3405.
• [14]. Rahman, I., Mohamad-Saleh, J. 2018. Hybrid bio-Inspired computational intelligence techniques for solving power system optimization problems: A comprehensive survey. Applied Soft Computing; 69: 72-130.
• [15]. Chong, K.L., Lai, S.H., Ahmed, A.N., Zurina, W., Jaafar, W., El-Shafie, A.: Optimization of hydropower reservoir operation based on hedging policy using Jaya algorithm. Applied Soft Computing; 106, 107325.
• [16]. Azad, A., Rahaman, SA, Watada, J, Vasant, P, Vintaned, JAG. 2020. Optimization of the hydropower energy generation using Meta-Heuristic approaches: A review. Energy Reports; 6: 2230-2248.
• [17]. Mishra, S, Singal, SK, Khatod, DK. 2011. Optimal installation of small hydropower plant—A review. Renewable and Sustainable Energy Reviews; 15: 3862-3869.
• [18]. Ren, X, Zhao, Y, Hao, D, Sun, Y, Chen, S, Gholinia, F. 2021. Predicting optimal hydropower generation with help optimal management of water resources by Developed Wildebeest Herd Optimization (DWHO). Energy Reports; 7: 968-980.
• [19]. Huangpeng, Q, Huang, W, Gholinia, F. 2021. Forecast of the hydropower generation under influence of climate change based on RCPs and Developed Crow Search Optimization Algorithm. Energy Reports; 7: 385-397.
• [20]. Wang, Y, Liu, J, Han, Y. 2020. Production capacity prediction of hydropower industries for energy optimization: Evidence based on novel extreme learning machine integrating Monte Carlo. Journal of Cleaner Production; 272: 122824.
• [21]. Li, X, Liu, P, Gui, Z, Ming, B, Yang, Z, Xie, K, Zhang, X. 2020. Reducing lake water-level decline by optimizing reservoir operating rule curves: A case study of the Three Gorges Reservoir and the Dongting Lake. Journal of Cleaner Production; 264: 121676.
• [22]. Feng, ZK, Niu, WJ, Liu, S, Luo, B, Miao, SM, Liu, K. 2020. Multiple hydropower reservoirs operation optimization by adaptive mutation sine cosine algorithm based on neighborhood search and simplex search strategies. Journal of Hydrology; 590: 125223.
• [23]. Emami, M, Nazif, S, Mousavi, SF, Karami, H, Daccache, A. 2021. A hybrid constrained coral reefs optimization algorithm with machine learning for optimizing multi-reservoir systems operation. Journal of Environmental Management; 286: 112250.
• [24]. Li, J., Qin, H., Zhang, Z., Yao, L., Gul, E., Jiang, Z, Wang, Y., Mo, L, Pei. S.: Operation Rules Optimization of Cascade Reservoirs Based on Multi-Objective Tangent Algorithm. IEEE Access. 7, 161949-161962 (2019).
• [25]. Cai, X, Ye, F, Gholinia, F. 2020. Application of artificial neural network and Soil and Water Assessment Tools in evaluating power generation of small hydropower stations. Energy Reports; 6: 2106-2118.
• [26]. Yizi Shang, Yang Xu, Ling Shang, Qixiang Fan, Yongyi Wang, Zhiwu Liu, A method of direct, real-time forecasting of downstream water levels via hydropower station reregulation: A case study from Gezhouba Hydropower Plant, China, Journal of Hydrology, Volume 573, 2019, Pages 895-907,ISSN 0022-1694,
• [27]. Olofintoye, O, Otieno, F, Adeyemo, J. 2016. Real-time optimal water allocation for daily hydropower generation from the Vanderkloof dam South Africa. Applied Soft Computing; 47: 119-129.
• [28]. Yang, S, Yang, D, Chen, J, Zhao, B. 2019. Real-time reservoir operation using recurrent neural networks and inflow forecast from a distributed hydrological model. Journal of Hydrology; 579: 579124229.
• [29]. Hadiyan, PP, Moeini, R, Ehsanzadeh, E. 2020. Application of static and dynamic artificial neural networks for forecasting inflow discharges case study: Sefidroud Dam reservoir. Sustainable Computing: Informatics and Systems; 27: 100401.
• [30]. Karunanayake, C, Gunathilake, MG, Rathnayake, U. 2020. Inflow Forecast of Iranamadu Reservoir Sri Lanka under Projected Climate Scenarios Using Artificial Neural Networks. Applied Computational Intelligence and Soft Computing; 1-11.
• [31]. Cheng, CT, Niu, WJ, Feng, ZK, Shen, JJ, Chau, KW. 2015. Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization. Water; 7: 4232-4246.
• [32]. Xu ZX and Li JY. 2002. Short-term inflow forecasting using an artificial neural network model. Hydrol. Process; 16:2423-2439.
• [33]. Dampage, U, Gunaratne, Y, Bandara, O, Silva, SD, Waraketiya, V. 2020. Artificial Neural Network for Forecasting of Daily Reservoir Inflow: Case Study of the Kotmale Reservoir in Sri Lanka 2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Beijing China 19-21 June 2020.
• [34]. Ahmad, SK, Hossain, F. 2019. A web-based decision support system for smart dam operations using weather forecasts. Journal of Hydroinformatics; 21: 687-707.
• [35]. Liu, Y, Qin, H, Zhang, Z, Yao, L, Wang, Y, Li, J, Liu, G, Zhou, J. 2019. Deriving reservoir operation rule based on Bayesian deep learning method considering multiple uncertainties. Journal of Hydrology; 579: 124207.
• [36]. Energy Atlas of Turkey. https://www.enerjiatlasi.com/elektrik-uretimi/ (accessed at 23.05. 2021).
• [37]. Cobaner, M, Haktanir, T, Kisi, O. 2008. Prediction of Hydropower Energy Using ANN for the Feasibility of Hydropower Plant Installation to an Existing Irrigation Dam. Water Resour Manage; 22: 757–774.
• [38]. Kucukali, S, Bayatı, OA, Maraş, HH. 2021. Finding the most suitable existing irrigation dams for small hydropower development in Turkey: A GIS-Fuzzy logic tool. Renewable Energy; 172: 633-650.
• [39]. Koç, C. 2018. A study on operation problems of hydropower plants integrated with irrigation schemes operated in Turkey. International Journal of Green Energy; 15: 129-135.
• [40]. Mazroua, AA, Salama, MMA, Bartnikas, R. 1993. PD pattern recognition with neural networks using the multilayer perceptron technique. IEEE Transactions on Electrical Insulation; 28: 1082-1089.
• [41]. Lippmann, RP. 1993. Pattern classification using neural networks. IEEE Communications Magazine; 27: 47-50.
• [42]. Jang, JR. 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems Man and Cybernetics; 23: 665-685.
• [43]. Baldi, P. 1995. Gradient descent learning algorithm overview: a general dynamical systems perspective. IEEE Transactions on Neural Networks; 6: 182-195.
• [44]. Karnin, ED. 1990. A simple procedure for pruning back-propagation trained neural networks. IEEE Transactions on Neural Networks; 1: 239-242.
• [45]. Hagan, MT, Menhaj, MB. 1994. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks; 5: 989-993.
• [46]. Chen, S, Cowan, CFN, Grant, PM. 1991. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks; 2: 302-309.
• [47]. Kayhan, G, Özdemir, AE, Eminoğlu, İ. 2013. Reviewing and designing pre-processing units for RBF networks: initial structure identification and coarse-tuning of free parameters. Neural Computing and Applications; 22: 1655-1666.
• [48]. Ozdemir, AE. 2011. Algorithmic signal processing substructure improvement needed for control of multifunctional myoelectric prosthesis hand and arm. PHD thesis, Ondokuz Mayıs University, Samsun Turkey.