A novel approach for economic dispatch of hydrothermal system via gravitational search algorithm

The daily economic dispatching of hydrothermal system (DHS), which is a large-scale dynamic nonlinear constrained optimization problem, plays an important role in economic operation of electric power systems. This paper proposes a novel enhanced gravitational search algorithm (EGSA) to solve DHS problem. In the proposed method, the improvements mainly include three aspects. Firstly, particle swarm optimization (PSO) that acts complementary is integrated into gravitational search algorithm for update of agent's velocity. Secondly, heuristic search strategies based random selected dependent discharge of hydro plants and average full-load cost priority list of thermal units are adopted to deal with equality constraints of DHS problem. Thirdly, feasibility-based selection comparison techniques are devised to effectively handle inequality constraints in EGSA, which do not require penalty factors or extra parameters and can guide the agent to the feasible region quickly. The feasibility and effectiveness of the proposed EGSA method is verified by a hydrothermal test system and the simulation results are compared with those of differential evolution, PSO, genetic algorithm, classical evolutionary programming, fast evolutionary programming, and improved fast evolutionary programming algorithm. From the results, it clearly shows that the proposed method gives better quality solutions than other methods.

[1]  L. Bayón,et al.  A constrained and non-smooth hydrothermal problem , 2009, Appl. Math. Comput..

[2]  Weerakorn Ongsakul,et al.  Improved merit order and augmented Lagrange Hopfield network for short term hydrothermal scheduling , 2009 .

[3]  Xiaohui Yuan,et al.  Application of cultural algorithm to generation scheduling of hydrothermal systems , 2006 .

[4]  P. K. Chattopadhyay,et al.  Fast evolutionary programming techniques for short-term hydrothermal scheduling , 2003 .

[5]  K. Shanti Swarup,et al.  Hybrid DE–SQP algorithm for non-convex short term hydrothermal scheduling problem , 2011 .

[6]  Xiaohui Yuan,et al.  Application of enhanced discrete differential evolution approach to unit commitment problem , 2009 .

[7]  Yanbin Yuan,et al.  Unit commitment problem using enhanced particle swarm optimization algorithm , 2011, Soft Comput..

[8]  Secundino Soares,et al.  A second order network flow algorithm for hydrothermal scheduling , 1995 .

[9]  T. G. Werner,et al.  An evolution strategy for short-term operation planning of hydrothermal power systems , 1999 .

[10]  Peter B. Luh,et al.  Hydrothermal scheduling via extended differential dynamic programming and mixed coordination , 1995 .

[11]  Serhat Duman,et al.  Optimal reactive power dispatch using a gravitational search algorithm , 2012 .

[12]  Mitra Basu,et al.  Artificial immune system for fixed head hydrothermal power system , 2011 .

[13]  S. M. Shahidehpour,et al.  Hydro-thermal, scheduling by tabu search and decomposition method , 1996 .

[14]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[15]  Malcolm Irving,et al.  A genetic algorithm modelling framework and solution technique for short term optimal hydrothermal scheduling , 1998 .

[16]  J.P.S. Catalão,et al.  Hydro energy systems management in Portugal: Profit-based evaluation of a mixed-integer nonlinear ap , 2011 .

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

[18]  L. Bayón,et al.  A Bolza problem in hydrothermal optimization , 2007, Appl. Math. Comput..

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

[20]  Serhat Duman,et al.  Optimal power flow using gravitational search algorithm , 2012 .

[21]  D. P. Kothari,et al.  Multiobjective fixed head hydrothermal scheduling using integrated predator-prey optimization and Powell search method , 2012 .

[22]  Joao P. S. Catalao,et al.  Optimal hydro scheduling and offering strategies considering price uncertainty and risk management , 2012 .

[23]  R. Naresh,et al.  Two-phase neural network based solution technique for short term hydrothermal scheduling , 1999 .

[24]  P. K. Chattopadhyay,et al.  Fast Evolutionary Progranuning Techniques for Short-Term Hydrothermal Scheduling , 2002, IEEE Power Engineering Review.

[25]  M. Ghalambaz,et al.  Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm) , 2011 .

[26]  Mohammad Reza Ahmadi.  Comment on “Hybrid DE–SQP algorithm for non-convex short term hydrothermal scheduling problem” by S. Sivasubramani and K. Shanti Swarup [Energy Convers. Manage. 52 (2011) 757–761] , 2015 .

[27]  J. Waight,et al.  Experiences with Mixed Integer Linear Programming-Based Approaches in Short-Term Hydro Scheduling , 2001, IEEE Power Engineering Review.

[28]  Xiaohui Yuan,et al.  Hydrothermal scheduling using chaotic hybrid differential evolution , 2008 .

[29]  Stefan Preitl,et al.  Novel Adaptive Gravitational Search Algorithm for Fuzzy Controlled Servo Systems , 2012, IEEE Transactions on Industrial Informatics.