Combined Economic Emission Dispatch Using Perfectly Convergent Particle Swarm Optimization

The majority of electricity is generated by fossil fueled thermal power plants which results in environmental pollutants such as CO2, SO2 and NOx. As a result researchers started focusing to multi-objective power dispatch. This paper presents perfectly convergent Particle swarm optimization (PCPSO) for solving combined economic and multiple emissions dispatch problems while taking into account the impacts of various pollutants with seven price penalty factors using cubic functions. Cubic cost function are more accurate and shows the actual response of all thermal units. This algorithm has better search capabilities with strong convergence characteristics that minimizes the cubic cost and cubic multiple emissions functions at various load demands with minimum transmission losses for an IEEE 30 bus,6 generators test system. The simulation test results were compared with Lagrange method, simulated annealing, PSO, Bio-geography based method, grasshopper optimization algorithm, Artificial ecosystem based optimization, Multi objective 4th chaotic function Artificial ecosystem based optimization, Quantum Particle swarm optimization and Sine-cosine algorithm. This algorithm is fast, robust, accurate and takes less computational time with better results for solving such non-convex problems.

[1]  Wei-guo Zhao,et al.  Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm , 2019, Neural Computing and Applications.

[2]  Dimitrios Gonidakis,et al.  A new sine cosine algorithm for economic and emission dispatch problems with price penalty factors , 2019, Journal of Information and Optimization Sciences.

[3]  Pandian Vasant,et al.  A quantum‐inspired particle swarm optimization approach for environmental/economic power dispatch problem using cubic criterion function , 2018 .

[4]  Mauridhi Hery Purnomo,et al.  ADVANCE OPTIMIZATION OF ECONOMIC EMISSION DISPATCH BY PARTICLE SWARM OPTIMIZATION (PSO) USING CUBIC CRITERION FUNCTIONS AND VARIOUS PRICE PENALTY FACTORS , 2014 .

[5]  Qing Guo Wei,et al.  Cultural-Based Multi-Objective Particle Swarm Optimization for EEG Channel Reduction in Multi-Class Brain-Computer Interfaces , 2012 .

[6]  R. Tzoneva,et al.  Impact of price penalty factors on the solution of the combined economic emission dispatch problem using cubic criterion functions , 2012, 2012 IEEE Power and Energy Society General Meeting.

[7]  Raynitchka Tzoneva,et al.  Comparative analyses of Min-Max and Max-Max price penalty factor approaches for multi criteria power system dispatch problem using Lagrange's method , 2011, 2011 INTERNATIONAL CONFERENCE ON RECENT ADVANCEMENTS IN ELECTRICAL, ELECTRONICS AND CONTROL ENGINEERING.

[8]  Ming Zeng,et al.  The Multi-Objective Optimization Model of Energy-Efficient Scheduling Based on PSO Algorithm , 2010, 2010 Asia-Pacific Power and Energy Engineering Conference.

[9]  Jan K. Sykulski,et al.  Solution of Different Types of Economic Load Dispatch Problems Using a Pattern Search Method , 2008 .

[10]  R. Balamurugan,et al.  A Simplified Recursive Approach to Combined Economic Emission Dispatch , 2007 .

[11]  M. Sydulu,et al.  Particle Swarm Optimisation for Economic Dispatch with Cubic Fuel Cost Function , 2006, TENCON 2006 - 2006 IEEE Region 10 Conference.

[12]  M. Sydulu,et al.  Fast and Effective Algorithm for Economic Dispatch of Cubic Fuel Cost Based Thermal Units , 2006, First International Conference on Industrial and Information Systems.

[13]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[14]  Salah Kamel,et al.  Developing Chaotic Artificial Ecosystem-Based Optimization Algorithm for Combined Economic Emission Dispatch , 2021, IEEE Access.

[15]  U. Nangia,et al.  Perfectly convergent particle swarm optimisation in multidimensional space , 2021, Int. J. Bio Inspired Comput..

[16]  Farid Benhamida,et al.  Simulated annealing algorithm for combined economic and emission power dispatch using max/max price penalty factor , 2016, Neural Computing and Applications.

[17]  Deepak Mishra,et al.  OR-Neuron Based Hopfield Neural Network for Solving Economic Load Dispatch Problem , 2006 .