Optimal operation of multi-reservoir system by multi-elite guide particle swarm optimization

Abstract With increasingly scarce of fossil fuel resources required for energy demand, hydropower has become one of the most important energy resources. As a renewable and sustainable energy, hydropower system has developed rapidly during recent decades. The unprecedented rate of expansion and development scale of hydropower has posed a challenge to the operation of multi-reservoir system (OMRS). The optimal operation of multi-reservoir system is a large-scaled, non-linear and multi-stage problem which subject to a series of hydraulic constraints. The main objective of OMRS is to find out the optimal hourly water discharge rate of each hydro station in the multi-reservoir system to minimize the power deficit and distribute the uniformly deficit if any. In order to solve OMRS problem effectively, in this paper, a multi-elite guide particle swarm optimization (MGPSO) is proposed by introducing archive set into standard particle swarm optimization. External archive set which can preserve elite solutions along the evolution process is employed to provide multi-elite flying directions for particles. Meanwhile, an effective constrain handling method is proposed to handle the operational constraints of OMRS problem. This proposed method is applied to a multi-reservoir system consisting of 10 cascaded hydro plants for case study. Compared with several previous methods, the simulation results of MGPSO can get better solutions with smaller energy deficit, which proves it is an alternative method to deal with OMRS problem.

[1]  Xiaohui Yuan,et al.  Application of enhanced PSO approach to optimal scheduling of hydro system , 2008 .

[2]  M. Afshar.  Large scale reservoir operation by Constrained Particle Swarm Optimization algorithms , 2012 .

[3]  L. Bodin,et al.  Multireservoir Operation Studies , 1970 .

[4]  M. E. El-Hawary,et al.  Optimal Economic Operation of Electric Power Systems , 2012 .

[5]  Chuntian Cheng,et al.  Knowledge management system on flow and water quality modeling , 2002, Expert Syst. Appl..

[6]  R. Jha,et al.  Optimal multi-reservoir network control by augmented Lagrange programming neural network , 2007, Appl. Soft Comput..

[7]  Veena Sharma,et al.  Optimal multi-reservoir network control by two-phase neural network , 2004 .

[8]  Jianzhong Zhou,et al.  Multi-objective optimization of hydrothermal energy system considering economic and environmental aspects , 2012 .

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

[10]  Ruey-Maw Chen,et al.  Particle swarm optimization with justification and designed mechanisms for resource-constrained project scheduling problem , 2011, Expert Syst. Appl..

[11]  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.

[12]  Didem Cinar,et al.  Development of future energy scenarios with intelligent algorithms: Case of hydro in Turkey , 2010 .

[13]  Chuntian Cheng,et al.  Comparison of particle swarm optimization and dynamic programming for large scale hydro unit load dispatch , 2009 .

[14]  Frank T.-C. Tsai,et al.  Optimization of Large-Scale Hydropower System Operations , 2003 .

[15]  M. Papageorgiou.  Optimal Multireservoir Network Control by the Discrete Maximum Principle , 1985 .

[16]  Huifeng Zhang,et al.  An efficient multi-objective adaptive differential evolution with chaotic neuron network and its application on long-term hydropower operation with considering ecological environment problem , 2013 .

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

[18]  J. Sharma,et al.  Short term hydro scheduling using two-phase neural network , 2002 .

[19]  Jianzhong Zhou,et al.  An adaptive artificial bee colony algorithm for long-term economic dispatch in cascaded hydropower systems , 2012 .

[20]  Amita Mahor,et al.  Short term generation scheduling of cascaded hydro electric system using novel self adaptive inertia weight PSO , 2012 .

[21]  Li Mo,et al.  Short-term hydrothermal generation scheduling using differential real-coded quantum-inspired evolutionary algorithm , 2012 .

[22]  Joao P. S. Catalao,et al.  Scheduling of head-dependent cascaded reservoirs considering discharge ramping constraints and start/stop of units , 2010 .

[23]  Ibrahim Yuksel,et al.  Hydropower for sustainable water and energy development , 2010 .

[24]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

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

[26]  Gianfranco Rizzo,et al.  Application of dynamic programming to the optimal management of a hybrid power plant with wind turbines, photovoltaic panels and compressed air energy storage , 2012 .

[27]  Abbas Afshar,et al.  Fuzzy rule-based model for hydropower reservoirs operation , 2011 .

[28]  Chuntian Cheng,et al.  Operation challenges for fast-growing China's hydropower systems and respondence to energy saving and emission reduction , 2012 .