Deriving Operating Rules of Hydropower Reservoirs Using Gaussian Process Regression

Operating rules have been widely used to decide reservoir operations because they can help operators make an approximately optimal decision with limited runoff forecast information. As an effective alternative to explicit stochastic optimization (ESO) for considering hydrologic uncertainty, the implicit stochastic optimization (ISO) has been widely used to derive operating rules for the long-term operation of hydropower reservoirs. Within an ISO framework, operating rules extraction is a typical regression problem. In the past decades, various regression methods have been applied to derive operating rules, including artificial neural network (ANN), support vector regression (SVR) and so on, but these methods almost all are parametric regression method and there are few publications applying Bayesian regression method to derive operating rules. Therefore, Gaussian process regression (GPR), which is the representative Bayesian regression method, is introduced to derive operating rules for the first time in this paper and compared with ANN, SVR and conventional scheduling graph (CSG). China’s Three Gorges Reservoir (TGR) is selected as a case study, and four performance indexes are defined to evaluate different methods. The results show that (1) GPR, ANN and SVR can provide better performance than CSG method and are more practical than deterministic optimization operation; (2) GPR method can provide greater power generation benefits and higher reliability than ANN, SVR and CSG methods, and the average annual power generation increases from 88.481 billion kWh, 88.559 billion kWh and 87.563 billion kWh to 88.586 billion kWh, while the generation guarantee rate increase from 86.88%, 87.07% and 81.99% to 87.45%.

[1]  Honggang Zhang,et al.  A reservoir flood forecasting and control system for China / Un système chinois de prévision et de contrôle de crue en barrage , 2004 .

[2]  John D. C. Little,et al.  The Use of Storage Water in a Hydroelectric System , 1955, Oper. Res..

[3]  Chao Deng,et al.  Deriving adaptive operating rules of hydropower reservoirs using time‐varying parameters generated by the EnKF , 2017 .

[4]  C. Rasmussen,et al.  Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series Forecasting , 2002, NIPS.

[5]  Jianzhong Zhou,et al.  Study on guaranteed output constraints in the long term joint optimal scheduling for the hydropower station group , 2019, Energy.

[6]  Alexander Y. Sun,et al.  Monthly streamflow forecasting using Gaussian Process Regression , 2014 .

[7]  Quan J. Wang,et al.  A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites , 2009 .

[8]  Pan Liu,et al.  Dynamic control of flood limited water level for reservoir operation by considering inflow uncertainty , 2010 .

[9]  Chao Wang,et al.  Multiobjective Reservoir Operation Optimization Using Improved Multiobjective Dynamic Programming Based on Reference Lines , 2019, IEEE Access.

[10]  William W.-G. Yeh,et al.  Reservoir Management and Operations Models: A State‐of‐the‐Art Review , 1985 .

[11]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[12]  Omid Bozorg Haddad,et al.  Optimal Monthly Reservoir Operation Rules for Hydropower Generation Derived with SVR-NSGAII , 2015 .

[13]  Slobodan P. Simonovic,et al.  Reservoir Systems Analysis: Closing Gap between Theory and Practice , 1992 .

[14]  Wei Li,et al.  Deriving Reservoir Refill Operating Rules by Using the Proposed DPNS Model , 2006 .

[15]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[16]  Hui Qin,et al.  Long-term joint scheduling of hydropower station group in the upper reaches of the Yangtze River using partition parameter adaptation differential evolution , 2019, Eng. Appl. Artif. Intell..

[17]  Ting Zhou,et al.  Operating Rules Derivation of Jinsha Reservoirs System with Parameter Calibrated Support Vector Regression , 2014, Water Resources Management.

[18]  B. F. Sule,et al.  Stochastic dynamic programming models for reservoir operation optimization , 1984 .

[19]  Quan J. Wang,et al.  A Bayesian Approach to Predictor Selection for Seasonal Streamflow Forecasting , 2012 .

[20]  A. Tilmant,et al.  Assessing marginal water values in multipurpose multireservoir systems via stochastic programming , 2008 .

[21]  D. E. Rheinheimer,et al.  Parameter uncertainty analysis of reservoir operating rules based on implicit stochastic optimization. , 2014 .

[22]  Pan Liu,et al.  Identifying changing patterns of reservoir operating rules under various inflow alteration scenarios , 2017 .

[23]  Md. Shabbir Hossain,et al.  Intelligent Systems in Optimizing Reservoir Operation Policy: A Review , 2013, Water Resources Management.

[24]  Chuntian Cheng,et al.  Optimization of hydropower system operation by uniform dynamic programming for dimensionality reduction , 2017 .

[25]  John W. Labadie,et al.  Optimal Operation of Multireservoir Systems: State-of-the-Art Review , 2004 .

[26]  Chuntian Cheng,et al.  Comparison of Multiple Linear Regression, Artificial Neural Network, Extreme Learning Machine, and Support Vector Machine in Deriving Operation Rule of Hydropower Reservoir , 2019, Water.

[27]  Xiao Chen,et al.  Self-optimization system dynamics simulation of reservoir operating rules , 2018 .

[28]  Chunlong Li,et al.  Long-term generation scheduling of Xiluodu and Xiangjiaba cascade hydro plants considering monthly streamflow forecasting error , 2015 .

[29]  Duan Chen,et al.  Hierarchical Flood Operation Rules Optimization Using Multi-Objective Cultured Evolutionary Algorithm Based on Decomposition , 2018, Water Resources Management.

[30]  Alessandro Ancarani,et al.  A Neural Networks Approach for Deriving Irrigation Reservoir Operating Rules , 2002 .

[31]  Pan Liu,et al.  Deriving Optimal Refill Rules for Multi-Purpose Reservoir Operation , 2011 .

[32]  J. Platt.  Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[33]  Demetris Koutsoyiannis,et al.  Evaluation of the parameterization‐simulation‐optimization approach for the control of reservoir systems , 2003 .

[34]  Chuntian Cheng,et al.  Hydropower system operation optimization by discrete differential dynamic programming based on orthogonal experiment design , 2017 .

[35]  Nien-Sheng Hsu,et al.  Derived operating rules for a reservoir operation system: Comparison of decision trees, neural decision trees and fuzzy decision trees , 2008 .

[36]  Sen Wang,et al.  Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization , 2019, Journal of Hydrology.

[37]  Ahmed El-Shafie,et al.  Reservoir Optimization in Water Resources: a Review , 2014, Water Resources Management.

[38]  Deepti Rani,et al.  Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation , 2010 .

[39]  N. Bhaskar,et al.  Derivation of monthly reservoir release policies , 1980 .

[40]  Chao Ching Wang,et al.  Stochastic optimal operation of reservoirs based on copula functions , 2018 .

[41]  Reza Kerachian,et al.  Developing monthly operating rules for a cascade system of reservoirs: Application of Bayesian Networks , 2009, Environ. Model. Softw..

[42]  R. P. Oliveira,et al.  Operating rules for multireservoir systems , 1997 .

[43]  A. R. Senthil kumar,et al.  Application of ANN, Fuzzy Logic and Decision Tree Algorithms for the Development of Reservoir Operating Rules , 2013, Water Resources Management.

[44]  Ralph A. Wurbs.  Reservoir‐System Simulation and Optimization Models , 1993 .

[45]  Pan Liu,et al.  Derivation of Aggregation-Based Joint Operating Rule Curves for Cascade Hydropower Reservoirs , 2011 .

[46]  Shengzhi Huang,et al.  Monthly streamflow prediction using modified EMD-based support vector machine , 2014 .

[47]  G. K. Young.  Finding Reservoir Operating Rules , 1967 .

[48]  Chao Deng,et al.  Identifying Explicit Formulation of Operating Rules for Multi-Reservoir Systems Using Genetic Programming , 2014, Water Resources Management.

[49]  Matteo Sangiorgio,et al.  NN-Based Implicit Stochastic Optimization of Multi-Reservoir Systems Management , 2018 .