Economic emission dispatch of thermal generating units using genetic algorithm technique

2011 ◽  
Vol 4 (4) ◽  
pp. 344
Author(s):  
H. Vennila ◽  
B.G. Malini ◽  
V. Evangelin Jeba ◽  
T. Ruban Deva Prakash
Keyword(s):  
Genetic Algorithm ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

scholarly journals Dynamic Combined Economic Emission Dispatch Including Wind Generators by Real Coded Genetic Algorithm

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Genetic Algorithm ◽  
Comparative Analysis ◽  
Optimization Technique ◽  
Electric Power System ◽  
Fuel Cost ◽  
Renewable Source ◽  
Wind Generators ◽  
Real Coded Genetic Algorithm ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

With the growing environmental depletion, the shift in the focus towards minimizing the emissions of gases released in the conventional generators and further incorporation of a cleaner alternate renewable source of energy such as wind or solar to the existing system is of utmost importance. The research paper aims to build an environmentally resilient electric power system. Real coded genetic algorithm- powerful optimization technique is employed to solve the dynamic combined economic emission dispatch i.e. DCEED strategy for two proposed algorithm. The first proposed DCEED algorithm includes fuel cost of only conventional generators while in the second algorithm along with conventional generators, wind powered generators with varying power output characteristic is added. A comparative analysis of both the algorithms in terms of total combined cost, emission level and fuel cost is taken into account and it is observed that in spite of wind uncertainty the proposed method is more economical.

scholarly journals Implementation and Comparison of Particle Swarm Optimization and Genetic Algorithm Techniques in Combined Economic Emission Dispatch of an Independent Power Plant

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2037 ◽  
Author(s):  
Shahbaz Hussain ◽  
Mohammed Al-Hitmi ◽  
Salman Khaliq ◽  
Asif Hussain ◽  
Muhammad Asghar Saqib
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Power Plant ◽  
Particle Swarm ◽  
Computational Time ◽  
Fuel Cost ◽  
Swarm Optimization ◽  
Penalty Factor ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

This paper presents the optimization of fuel cost, emission of NOX, COX, and SOX gases caused by the generators in a thermal power plant using penalty factor approach. Practical constraints such as generator limits and power balance were considered. Two contemporary metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA), have were simultaneously implemented for combined economic emission dispatch (CEED) of an independent power plant (IPP) situated in Pakistan for different load demands. The results are of great significance as the real data of an IPP is used and imply that the performance of PSO is better than that of GA in case of CEED for finding the optimal solution concerning fuel cost, emission, convergence characteristics, and computational time. The novelty of this work is the parallel implementation of PSO and GA techniques in MATLAB environment employed for the same systems. They were then compared in terms of convergence characteristics using 3D plots corresponding to fuel cost and gas emissions. These results are further validated by comparing the performance of both algorithms for CEED on IEEE 30 bus test bed.

scholarly journals LSA-GA: A Hybrid Algorithm for Solving Economic Emission Dispatch Problem

2020 ◽  
Vol 8 (5) ◽  
pp. 4661-4669
Keyword(s):  
Genetic Algorithm ◽  
Wind Power ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Hybrid Strategy ◽  
Proposal A ◽  
Artificial Neural Network Ann ◽  
Emission Dispatch ◽  
Reproduction Stage ◽  
Economic Emission Dispatch

In this proposal, a hybrid algorithm is conveyed for unraveling Economic Emission Dispatch (EED) issue of the hybrid warm wind power age framework. The hybrid philosophy is a mix of Lightning Search Algorithm (LSA) with Genetic Algorithm (GA). In this, the consolidated endeavor of LSA-GA is utilized for upgrading the warm generators blends dependent on the vulnerability states of wind power. For catching the vulnerability states of wind power, Particle Swarm Optimization (PSO) with Artificial Neural Network (ANN) is utilized, so framework guaranteed the breeze power usage at higher. In this manner, the proposed philosophy is utilized for streamlining of the hybrid warm wind power age framework and limited the all out expense of activity. For assessing the adequacy of the proposed hybrid strategy, the six and the ten units of warm age is examined initially without wind power and besides with wind power. The two clashing goals for example fuel cost and outflow are streamlined at a similar interim of time. The proposed procedure is actualized in MATLAB/reproduction stage and results are analyzed by contrasting the got outcome and the consequence of Genetic Algorithm (GA). The examination uncovers that proposed approach has ability to deal with multi-target issues of advancement of electrical force frameworks, more efficiently.

scholarly journals Multi-Region Combined Heat and Power Economic Emission Dispatch

2020 ◽  
Vol 9 (3) ◽  
pp. 2305-2311
Keyword(s):  
Genetic Algorithm ◽  
Combined Heat And Power ◽  
Planning Problem ◽  
Fuel Cost ◽  
Nsga Ii ◽  
Strength Pareto Evolutionary Algorithm ◽  
Emission Dispatch ◽  
Nondominated Sorting ◽  
Economic Emission Dispatch ◽  
Power Economic

Multi-Region Combined Heat and Power Economic Emission Dispatch (MRCHPEED) is an important chore in operational and planning problem. The valve point impact and restricted useful zone of regular thermal generators have been contemplated. In this work, Nondominated Sorting Genetic Algorithm-II (NSGA-II) is proposed for illuminating confounded MRCHPEED problem where power and heat generations have been distributed amongst the all committed units so that fuel cost and outflow echelon have been streamlined in chorus though gratifying every single operational requirement. The research consequence of a two-region investigation framework achieved from the prescribed technique are coordinated up to those acquired from Strength Pareto Evolutionary Algorithm 2 (SPEA 2).

Sign in / Sign up

Export Citation Format

Share Document