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

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0

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.

In this paper, grasshopper optimization algorithm is presented to resolve the combined economic emission dispatch (CEED) problem involving cubic functions considering power flow constraints. Electric power system wants to satisfy its customers load demand with minimum fuel cost and emission. Fuel cost and emission has instantly association with energy cost. In CEED problem, the price penalty factor occupies a cardinal role to fetch the optimal results. The various types of price penalty factor available in the literature are analyzed to determine the optimal one for the test cases considered. The test systems used in this CEED problem are 3 unit system considering transmission loss and 13 unit system considering valve point effects. The leading requirement in both the test cases is to optimize the total cost, fuel cost and emission. The numerical and statistical results affirm the high degree of the solution founded by GOA and its superiority is compared with already existing algorithms employed in solving CEED problems


Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2037 ◽  
Author(s):  
Shahbaz Hussain ◽  
Mohammed Al-Hitmi ◽  
Salman Khaliq ◽  
Asif Hussain ◽  
Muhammad Asghar Saqib

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.


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).


Author(s):  
Alok Ranjan Biswal ◽  
Tarapada Roy ◽  
Rabindra Kumar Behera

The current article deals with finite element (FE)- and genetic algorithm (GA)-based vibration energy harvesting from a tapered piezolaminated cantilever beam. Euler–Bernoulli beam theory is used for modeling the various cross sections of the beam. The governing equation of motion is derived by using the Hamilton's principle. Two noded beam elements with two degrees of freedom at each node have been considered in order to solve the governing equation. The effect of structural damping has also been incorporated in the FE model. An electric interface is assumed to be connected to measure the voltage and output power in piezoelectric patch due to charge accumulation caused by vibration. The effects of taper (both in the width and height directions) on output power for three cases of shape variation (such as linear, parabolic and cubic) along with frequency and voltage are analyzed. A real-coded genetic algorithm-based constrained (such as ultimate stress and breakdown voltage) optimization technique has been formulated to determine the best possible design variables for optimal harvesting power. A comparative study is also carried out for output power by varying the cross section of the beam, and genetic algorithm-based optimization scheme shows the better results than that of available conventional trial and error methods.


2018 ◽  
Vol 7 (4.30) ◽  
pp. 443 ◽  
Author(s):  
Ainul, H.M.. Y ◽  
Salleh, S. M ◽  
Halib, N ◽  
Taib, H. ◽  
Fathi, M. S

System identification is a method to build a model for a dynamic system from the experimental data. In this paper, optimization technique was applied to optimize the objective function that lead to satisfying solution which obtain the dynamic model of the system. Real-coded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. Hence, the model of the plant was represented by the transfer function from the identified parameters obtained from the optimization process. For performance analysis of toothbrush rig parameter estimation, there were six different model orders have been considered where each of model order has been analyzed for 10 times. The influence of conventional genetic algorithm parameter - generation gap has been investigated too. The statistical analysis was used to evaluate the performance of the model based on the objective function which is the Mean Square Error (MSE). The validation test-through correlation analysis was used to validate the model. The model of model order 2 is chosen as the best model as it has fulfilled the criteria involved in selecting the accurate model. Generation gap used was 0.5 has shorten the algorithm convergence time without affecting the model accuracy.


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

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