Improved multiple-objective dynamic programming model for reservoir operation optimization

Reservoirs are usually designed and operated for multiple purposes, which makes the multiple-objective issue important in reservoir operation. Based on multiple-objective dynamic programming (MODP), this study proposes an improved multiple-objective DP (IMODP) algorithm for reservoir operation optimization, which can be used to solve multiple-objective optimization models regardless whether the curvatures of trade-offs among objectives are concave or not. MODP retains all the Pareto-optimal solutions through backward induction, resulting in the exponential increase of computational burden with the length of study horizon. To improve the computational efficiency, this study incorporates the ranking technique into MODP and proposes an efficient IMODP algorithm. We demonstrate the effectiveness of IMODP through a hypothetical test and a real-world case. The hypothetical test includes three cases in which the trade-offs between objectives are concave, convex, and neither concave nor convex. The results show that IMODP satisfactorily captures the Pareto front for all three cases. The real-world test focuses on hydropower and analyzes the trade-offs between total energy and firm energy for Danjiangkou Reservoir. IMODP efficiently identifies the Pareto-optimal solutions and the trade-offs among objectives.

[1]  S. Yakowitz.  Dynamic programming applications in water resources , 1982 .

[2]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[3]  Marcello Restelli,et al.  Tree-based fi tted Q-iteration for multi-objective Markov decision processes in water resource management , 2013 .

[4]  M. Janga Reddy,et al.  Multiobjective Differential Evolution with Application to Reservoir System Optimization , 2007 .

[5]  Taesoon Kim,et al.  Single-reservoir operating rules for a year using multiobjective genetic algorithm , 2008 .

[6]  Avi Ostfeld,et al.  State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management , 2010 .

[7]  Jin-Hee Lee,et al.  Stochastic optimization of multireservoir systems via reinforcement learning , 2007 .

[8]  Lingfeng Wang,et al.  Environmental/economic power dispatch using a fuzzified multi-objective particle swarm optimization algorithm , 2007 .

[9]  Andrea Castelletti,et al.  Interactive response surface approaches using computationally intensive models for multiobjective planning of lake water quality remediation , 2011 .

[10]  J. Antenucci,et al.  A multiobjective response surface approach for improved water quality planning in lakes and reservoirs , 2010 .

[11]  L. Grüne,et al.  Using dynamic programming with adaptive grid scheme for optimal control problems in economics , 2004 .

[12]  Rodolfo Soncini-Sessa,et al.  Combining metamodelling and stochastic dynamic programming for the design of reservoir release policies , 2010, Environ. Model. Softw..

[13]  J. Gero,et al.  REDUCING THE PARETO OPTIMAL SET IN MULTICRITERIA OPTIMIZATION(With Applications to Pareto Optimal Dynamic Programming) , 1985 .

[14]  Ximing Cai,et al.  Improved Dynamic Programming for Reservoir Operation Optimization with a Concave Objective Function , 2012 .

[15]  Ximing Cai,et al.  Optimality conditions for a two-stage reservoir operation problem , 2010 .

[16]  J. Wayland Eheart,et al.  Reservoir management to balance ecosystem and human needs: Incorporating the paradigm of the ecological flow regime , 2006 .

[17]  J. Lund,et al.  Derived Operating Rules for Reservoirs in Series or in Parallel , 1999 .

[18]  George F. McMahon,et al.  Reallocation of Federal Multipurpose Reservoirs: Principles, Policy, and Practice , 2004 .

[19]  Pan Liu,et al.  Deriving multiple near‐optimal solutions to deterministic reservoir operation problems , 2011 .

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

[21]  Marcello Restelli,et al.  Tree‐based reinforcement learning for optimal water reservoir operation , 2010 .

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

[23]  Chuntian Cheng,et al.  Flood control management system for reservoirs , 2004, Environ. Model. Softw..

[24]  S. Jamshid Mousavi,et al.  Stochastic multiobjective reservoir operation under imprecise objectives: multicriteria decision-making approach , 2010 .

[25]  John Rust.  Using Randomization to Break the Curse of Dimensionality , 1997 .

[26]  J. Lund,et al.  Optimal Hedging and Carryover Storage Value , 2004 .

[27]  Jianshi Zhao,et al.  Improved Dynamic Programming for Hydropower Reservoir Operation , 2014 .

[28]  Robert R. Inman,et al.  Multiobjective dynamic programing with application to a reservoir , 1979 .

[29]  Juan I. Pérez-Díaz,et al.  Optimal short-term operation schedule of a hydropower plant in a competitive electricity market , 2010 .

[30]  H. G. Daellenbach,et al.  Note on Multiple Objective Dynamic Programming , 1980 .

[31]  Lyn C. Thomas,et al.  An aggregate stochastic dynamic programming model of multireservoir systems , 1997 .

[32]  Ximing Cai,et al.  Finding multiple optimal solutions to optimal load distribution problem in hydropower plant , 2012 .