Non-dominated Sorting Disruption-based Gravitational Search Algorithm with Mutation Scheme for Multi-objective Short-Term Hydrothermal Scheduling

Abstract The short-term economical/environmental hydrothermal scheduling problem is formulated as a non-linear and non-convex constrained multi-objective optimization problem considering transmission losses and valve point loading effects. This article presents a non-dominated sorting disruption-based gravitational search algorithm with mutation to solve fixed-head and variable-head short-term economical/environmental hydrothermal scheduling problems. In this solution technique, a set of non-dominated solutions are obtained by using the concept of non-dominated sorting and an external archive. Thereafter, a fuzzy decision-making approach has been applied to achieve a suitable and the best compromising solution from the non-dominated solution set. Finally, the non-dominated sorting disruption-based gravitational search algorithm with mutation approach is demonstrated on three test systems: fixed-head two hydro and two thermal plants, two hydro and four thermal plants, and variable-head cascaded four hydro and three thermal plants. Simulation results obtained from this approach are compared with the other methods reported in the literature and it has been found that the proposed approach yields better solutions and is efficient for solving short-term economical/environmental hydrothermal scheduling problems.

[1]  Xiaohui Yuan,et al.  Multi-objective optimization of short-term hydrothermal scheduling using non-dominated sorting gravitational search algorithm with chaotic mutation , 2014 .

[2]  Hossein Nezamabadi-pour,et al.  Disruption: A new operator in gravitational search algorithm , 2011, Sci. Iran..

[3]  M. Rouhani,et al.  A Multi-objective Gravitational Search Algorithm , 2010, 2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks.

[4]  Malabika Basu,et al.  Economic environmental dispatch of hydrothermal power system , 2010 .

[5]  Niladri Chakraborty,et al.  Daily combined economic emission scheduling of hydrothermal systems with cascaded reservoirs using self organizing hierarchical particle swarm optimization technique , 2012, Expert Syst. Appl..

[6]  Chao-Lung Chiang,et al.  Optimal economic emission dispatch of hydrothermal power systems , 2007 .

[7]  Malabika Basu,et al.  An interactive fuzzy satisfying method based on evolutionary programming technique for multiobjective short-term hydrothermal scheduling , 2004 .

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

[9]  Mahdi Nikusokhan,et al.  A Multi-Objective Gravitational Search Algorithm Based on Non-Dominated Sorting , 2012, Int. J. Swarm Intell. Res..

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

[11]  Jingrui Zhang,et al.  Small Population-Based Particle Swarm Optimization for Short-Term Hydrothermal Scheduling , 2012, IEEE Transactions on Power Systems.

[12]  Songfeng Lu,et al.  Short-term combined economic emission hydrothermal scheduling using improved quantum-behaved particle swarm optimization , 2010, Expert Syst. Appl..

[13]  Malabika Basu,et al.  Goal-Attainment Method Based on Simulated Annealing Technique for Economic-Environmental-Dispatch of Hydrothermal Power Systems with Cascaded Reservoirs , 2004 .

[14]  Z. Ibrahim,et al.  Vector Evaluated Gravitational Search Algorithm (VEGSA) for Multi-objective Optimization Problems , 2012, 2012 Fourth International Conference on Computational Intelligence, Modelling and Simulation.

[15]  Peng Lu,et al.  Short-term economic environmental hydrothermal scheduling using improved multi-objective gravitational search algorithm , 2015 .

[16]  Sakti Prasad Ghoshal,et al.  A novel opposition-based gravitational search algorithm for combined economic and emission dispatch problems of power systems , 2012 .

[17]  Ferial El-Hawary,et al.  A summary of environmental/economic dispatch algorithms , 1994 .

[18]  Carlos A. Coello Coello,et al.  A particle swarm optimizer for multi-objective optimization , 2005 .

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

[20]  J. T. Wood,et al.  Potential impacts of clean air regulations on system operations , 1995 .

[21]  Ajoy Kumar Chakraborty,et al.  Solution of optimal power flow using nondominated sorting multi objective gravitational search algorithm , 2014 .

[22]  N. Chakraborty,et al.  Short-term combined economic emission scheduling of hydrothermal power systems with cascaded reservoirs using differential evolution , 2009 .

[23]  Songfeng Lu,et al.  An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling , 2010 .

[24]  Aniruddha Bhattacharya,et al.  Multi-objective economic emission load dispatch solution using gravitational search algorithm and considering wind power penetration , 2013 .

[25]  Niladri Chakraborty,et al.  Short-term combined economic emission scheduling of hydrothermal systems with cascaded reservoirs using particle swarm optimization technique , 2011, Appl. Soft Comput..

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

[27]  Mousumi Basu,et al.  Economic environmental dispatch of fixed head hydrothermal power systems using nondominated sorting genetic algorithm-II , 2011, Appl. Soft Comput..

[28]  Songfeng Lu,et al.  Quadratic approximation based differential evolution with valuable trade off approach for bi-objective short-term hydrothermal scheduling , 2011, Expert Syst. Appl..

[29]  Songfeng Lu,et al.  Lu, S.: Short-term combined economic emission hydrothermal scheduling using improved quantum-behaved particle swarm optimization. Expert Systems with Applications 37, 4232-4241 , 2010 .

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