Short term generation scheduling of cascaded hydro electric system using novel self adaptive inertia weight PSO

Hydro power plants are multipurpose projects, which are not only generating power but also responsible for the fulfillment of irrigation requirements of nearby zone. To harness the potential energy of released water Canal Head Power Houses (CHPHs) are also located at irrigation canals of the multipurpose projects. The present paper proposed a Novel Self Adaptive Inertia Weight Particle Swarm Optimization (NSAIW_PSO) approach to determine the optimal generation schedule of real operated cascaded hydroelectric system located at Narmada river in Madhya Pradesh, India. Here generation scheduling problem has been formulated in two cases. Case one considers natural inflows, evaporation losses and irrigation requirements assuming no generation through CHPHs. Whereas second case considers all above factors along with generation through CHPHs. Results show that in case two the amount of water discharge through all hydro power plants is less in comparison to case one to fulfill the same load demand, which shows the importance of CHPHs.

[1]  A. Immanuel Selvakumar,et al.  Anti-predatory particle swarm optimization : Solution to nonconvex economic dispatch problems , 2008 .

[2]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[3]  Tharam S. Dillon,et al.  Stochastic optimization and modelling of large hydrothermal systems for long-term regulation , 1980 .

[4]  Leandro dos Santos Coelho,et al.  Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches , 2008 .

[5]  S. J. Huang,et al.  Enhancement of Hydroelectric Generation Scheduling Using Ant Colony System-Based Optimization Approaches , 2001, IEEE Power Engineering Review.

[6]  Renato A. Krohling,et al.  Gaussian particle swarm with jumps , 2005, 2005 IEEE Congress on Evolutionary Computation.

[7]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[8]  Malabika Basu,et al.  A simulated annealing-based goal-attainment method for economic emission load dispatch of fixed head hydrothermal power systems , 2005 .

[9]  Sakti Prasad Ghoshal,et al.  A novel crazy swarm optimized economic load dispatch for various types of cost functions , 2008 .

[10]  M. Pandit,et al.  Self-Organizing Hierarchical Particle Swarm Optimization for Nonconvex Economic Dispatch , 2008, IEEE Transactions on Power Systems.

[11]  Leandro dos Santos Coelho,et al.  PSO-E: Particle Swarm with Exponential Distribution , 2006, 2006 IEEE International Conference on Evolutionary Computation.

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

[13]  Gaofeng Wang,et al.  The inertia weight self-adapting in PSO , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[14]  P. K. Chattopadhyay,et al.  Evolutionary programming techniques for economic load dispatch , 2003, IEEE Trans. Evol. Comput..

[15]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[16]  Whei-Min Lin,et al.  An Improved Tabu Search for Economic Dispatch with Multiple Minima , 2002, IEEE Power Engineering Review.

[17]  Bijaya Ketan Panigrahi,et al.  Adaptive particle swarm optimization approach for static and dynamic economic load dispatch , 2008 .

[18]  Niladri Chakraborty,et al.  Particle swarm optimization technique based short-term hydrothermal scheduling , 2008, Appl. Soft Comput..

[19]  P. Sriyanyong,et al.  Solving economic dispatch using Particle Swarm Optimization combined with Gaussian mutation , 2008, 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.