Extracting Optimal Policies of Hydropower Multi-Reservoir Systems Utilizing Enhanced Differential Evolution Algorithm

Deriving the optimal policies of hydropower multi-reservoir systems is a nonlinear and high-dimensional problem which makes it difficult to achieve the global or near global optimal solution. In order to optimally solve the problem effectively, development of optimization methods with the purpose of optimizing reservoir operation is indispensable as well as inevitable. This paper introduces an enhanced differential evolution (EDE) algorithm to enhance the exploration and exploitation abilities of the original differential evolution (DE) algorithm. The EDE algorithm is first applied to minimize two benchmark functions (Ackley and Shifted Schwefel). In addition, a real world two-reservoir hydropower optimization problem and a large scale benchmark problem, namely ten-reservoir problem, were considered to indicate the effectiveness of the EDE. The performance of the EDE was compared with the original DE to solve the three optimization problems. The results demonstrate that the EDE would have a powerful global ability and faster convergence than the original DE to solve the two benchmark functions. In the 10-reservoir optimization problem, the EDE proved to be much more functional to reach optimal or near optimal solution and to be effective in terms of convergence rate, standard deviation, the best, average and worst values of objective function than the original DE. Also, In the case of two-reservoir system, the best values of the objective function obtained 93.86 and 101.09 for EDE and DE respectively. Based on the results, it can be stated that the most important reason to improve the performance of the EDE algorithm is the promotion of local and global search abilities of the DE algorithm using the number of novel operators. Also, the results of these three problems corroborated the superior performance, the high efficiency and robustness of the EDE to optimize complex and large scale multi-reservoir operation problems.

[1]  S. Yakowitz,et al.  Constrained differential dynamic programming and its application to multireservoir control , 1979 .

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

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

[4]  Kumaraswamy Ponnambalam,et al.  An application of Karmarkar's interior-point linear programming algorithm for multi-reservoir operations optimization , 1989 .

[5]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[6]  Kevin E Lansey,et al.  OPTIMAL MULTIRESERVOIR HYDROPOWER OPERATIONS BY DECOMPOSITION , 1992 .

[7]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[8]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

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

[10]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[11]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[12]  R. Wardlaw,et al.  EVALUATION OF GENETIC ALGORITHMS FOR OPTIMAL RESERVOIR SYSTEM OPERATION , 1999 .

[13]  Robin Wardlaw,et al.  Multireservoir Systems Optimization Using Genetic Algorithms: Case Study , 2000 .

[14]  Leon S. Lasdon,et al.  Piece-by-piece approach to solving large nonlinear water resources management models , 2001 .

[15]  Miguel A. Mariño,et al.  Coupled Reservoir Operation-Irrigation Scheduling by Dynamic Programming , 2002 .

[16]  Kourosh Behzadian,et al.  An Evolutionary Model for Operation of Hydropower Reservoirs , 2003 .

[17]  Li Chen,et al.  REAL CODED GENETIC ALGORITHM OPTIMIZATION OF LONG TERM RESERVOIR OPERATION 1 , 2003 .

[18]  Jouni Lampinen,et al.  A Trigonometric Mutation Operation to Differential Evolution , 2003, J. Glob. Optim..

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

[20]  Amit Konar,et al.  Two improved differential evolution schemes for faster global search , 2005, GECCO '05.

[21]  Arup Kumar Sarma,et al.  Genetic Algorithm for Optimal Operating Policy of a Multipurpose Reservoir , 2005 .

[22]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[23]  M. Janga Reddy,et al.  Ant Colony Optimization for Multi-Purpose Reservoir Operation , 2006 .

[24]  Omid Bozorg Haddad,et al.  Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization , 2006 .

[25]  Barry J. Adams,et al.  Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation , 2007, J. Frankl. Inst..

[26]  A. Vasan,et al.  Application of Differential Evolution for Irrigation Planning: An Indian Case Study , 2007 .

[27]  M. Janga Reddy,et al.  Multipurpose Reservoir Operation Using Particle Swarm Optimization , 2007 .

[28]  Sh. Momtahen,et al.  Direct Search Approaches Using Genetic Algorithms for Optimization of Water Reservoir Operating Policies , 2007 .

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

[30]  Miguel A. Mariño,et al.  Multi-Colony Ant Algorithm for Continuous Multi-Reservoir Operation Optimization Problem , 2007 .

[31]  Miguel A. Mariño,et al.  Design-Operation of Multi-Hydropower Reservoirs: HBMO Approach , 2008 .

[32]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[33]  R. Moeini,et al.  Partially and Fully Constrained Ant Algorithms for the Optimal Solution of Large Scale Reservoir Operation Problems , 2008 .

[34]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[35]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[36]  Ju-Hwan Yoo,et al.  Maximization of hydropower generation through the application of a linear programming model , 2009 .

[37]  Ville Tirronen,et al.  Recent advances in differential evolution: a survey and experimental analysis , 2010, Artificial Intelligence Review.

[38]  Amit Konar,et al.  Differential Evolution Using a Neighborhood-Based Mutation Operator , 2009, IEEE Transactions on Evolutionary Computation.

[39]  M. Afshar Elitist mutated particle swarm optimisation algorithms: application to reservoir operation problems , 2009 .

[40]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[41]  Mohamed Hedi Louati,et al.  Application of a Genetic Algorithm for the Optimization of a Complex Reservoir System in Tunisia , 2011 .

[42]  Omid Bozorg Haddad,et al.  Multireservoir optimisation in discrete and continuous domains , 2011 .

[43]  Qingfu Zhang,et al.  Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters , 2011, IEEE Transactions on Evolutionary Computation.

[44]  V. Jothiprakash,et al.  Optimal Reservoir Operation for Hydropower Generation using Non-linear Programming Model , 2012 .

[45]  Ali Wagdy Mohamed,et al.  Constrained optimization based on modified differential evolution algorithm , 2012, Inf. Sci..

[46]  Michael G. Epitropakis,et al.  Evolving cognitive and social experience in Particle Swarm Optimization through Differential Evolution: A hybrid approach , 2012, Inf. Sci..

[47]  Ardeshir Bahreininejad,et al.  Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems , 2012 .

[48]  Mohammad Hadi Afshar,et al.  Extension of the constrained particle swarm optimization algorithm to optimal operation of multi-reservoirs system , 2013 .

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

[50]  Henrik Madsen,et al.  Optimization of Conventional Rule Curves Coupled with Hedging Rules for Reservoir Operation , 2014 .

[51]  Omid Bozorg Haddad,et al.  Application of the Water Cycle Algorithm to the Optimal Operation of Reservoir Systems , 2015 .

[52]  Omid Bozorg-Haddad,et al.  Development and Application of the Bat Algorithm for Optimizing the Operation of Reservoir Systems , 2015 .

[53]  Iman Ahmadianfar,et al.  Optimizing Multireservoir Operation: Hybrid of Bat Algorithm and Differential Evolution , 2016 .

[54]  Omid Bozorg-Haddad,et al.  Modified Firefly Algorithm for Solving Multireservoir Operation in Continuous and Discrete Domains , 2016 .

[55]  Miguel A. Mariño,et al.  Application of the Firefly Algorithm to Optimal Operation of Reservoirs with the Purpose of Irrigation Supply and Hydropower Production , 2016 .