Optimization of economic/emission load dispatch for hybrid generating systems using controlled Elitist NSGA-II

Abul’Wafa, Ahmed R.

doi:10.1016/j.epsr.2013.07.006

Cited by 53 publications

(32 citation statements)

References 17 publications

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“…Nowadays, this algorithm is very used to solve the EELD and other problems related to power system optimization . In Deb et al, the NSGA II was compared with other algorithms when applied to 4 problems selected from literature.…”

Section: Results Analysismentioning

confidence: 99%

A new approach to economic-emission load dispatch using NSGA II. Case study

Moraes

Bezerra

Rodríguez³

et al. 2018

Int Trans Electr Energ Syst

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Summary Economic‐emission load dispatch optimization problem is an essential task in power plants with internal combustion engines. In power plants, in addition to electricity, a lot of air pollution by exhaust gases is generated. There are many international standards that establish the permissible limits of different substances but still have not developed an expression to evaluate the environmental impact caused by all components of the exhaust gases as a whole. A new method to evaluate this impact is developed in this paper. The developed mathematical expression was called “emission index.” To get a better idea of the environmental impact of each type of engine, the “specific emission index”, which is the emission index divided by the power delivered by the engine. This paper also presents a mathematical model for a multiobjective optimization of economic‐emission load dispatch using nondominated sorting genetic algorithm II.

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Section: Results Analysismentioning

confidence: 99%

A new approach to economic-emission load dispatch using NSGA II. Case study

Moraes

Bezerra

Rodríguez³

et al. 2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…This algorithm has the advantage on large coverage of the solution set and a fast convergence rate, and has the ability to increase the population diversity under lower fitness value [37].…”

Section: Genetic Algorithmmentioning

confidence: 99%

A Multi-Objective Chance-Constrained Programming Approach for Uncertainty-Based Optimal Nutrients Load Reduction at the Watershed Scale

Liu

Zhang

et al. 2017

Water

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Abstract:A multi-objective chance-constrained programming integrated with Genetic Algorithm and robustness evaluation methods was proposed to weigh the conflict between system investment against risk for watershed load reduction, which was firstly applied to nutrient load reduction in the Lake Qilu watershed of the Yunnan Plateau, China. Eight sets of Pareto solutions were acceptable for both system investment and probability of constraint satisfaction, which were selected from 23 sets of Pareto solutions out of 120 solution sets. Decision-makers can select optimal decisions from the solutions above in accordance with the actual conditions of different sub-watersheds under various engineering measures. The relationship between system investment and risk demonstrated that system investment increased rapidly when the probability level of constraint satisfaction was higher than 0.9, but it reduced significantly if appropriate risk was permitted. Evaluation of robustness of the optimal scheme indicated that the Pareto solution obtained from the model provided the ideal option, since the solutions were always on the Pareto frontier under various distributions and mean values of the random parameters. The application of the multi-objective chance-constrained programming to optimize the reduction of watershed nutrient loads in Lake Qilu indicated that it is also applicable to other environmental problems or study areas that contain uncertainties.

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“…The authors of [6,7] have taken PV modules as a priority source and these generations have been dealt with as negative loads. Modern soft computing techniques, like DSM (Direct Search Method) [8], dynamic programming [9,10], mixedinteger linear program [11], DER-CAM [12], linear programming [13], Bayesian particle filtering [14], genetic algorithms (GA) [15], Controlled elitist NSGA-II [16], stochastic mixed integer program [17] etc. have successfully been applied by many previous researchers to obtain optimal solution on ELD, EELD and related topics in their works.…”

Section: Introductionmentioning

confidence: 99%

“…The authors of [12] have taken minimization of cost of combination of CHP equipments and renewable sources for commercial building, whereas the authors of [13] have suggested minimization of equivalent yearly cost of taking care of a given energy demand profiles, both electricity and heat. Multi-objective EELD cases have also found applications in previous works [10,14,16]. Some authors [16] used Pareto optimality for best compromised solution, others [10] used dynamic programming based algorithm to achieve bargain between fuel cost and CO 2 emission.…”

Section: Introductionmentioning

confidence: 99%

Microgrid: Planning of Solar PV Incorporation to the Optimal CHP-System–An Evolutionary Algorithmic Approach

Basu

2019

Technol Econ Smart Grids Sustain Energy

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The author has planned to introduce the solar PV priority sources to the existing 14-bus optimal self-sufficient CHP-microgrid to reduce the use of scarce fuel resources and their emission of pollutants like hazardous NOx. The Priority PV output is analyzed as negative demand and modifies, directly, the profile demand of the microgrid. The hourly change of kW output of PV modules at the site is obtained from the website of Surface Meteorology and Solar Energy, sponsored by NASA, using HOMER software. It is observed that at any solar insolation power output from the solar module is directly proportional to the module capacity. To plan the best economic solar PV capacity incorporation in the system, PV output is varied over a suitable range and in discrete steps, depending on hour of profile electric demand and availability of solar insolation at the site. At each modified tracking of hourly electrical demand, Pareto-biobjective EELD optimization is performed, simultaneous with compromised reduction between the cost of fuel and NO X emission of the DERs. The sets of results of best optimal output of sharing of strategically deployed DER-mix could be obtained and these results are kept within their respective capacity limits. For optimization both differential evolution (DE) and PSO algorithms is used for comparison of results. Hourly heat demand requires to be met by waste heat recovery from DERs and, if required, by stand-by boiler. An array is formed of these best solutions per annum for each solar PV capacity change. These results, in comparison with the No-PV base case results, are used for economic analyses based on benefit-to-cost ratio (BCR). It is found that best BCR value of 1.416 occurs at PV capacity of 130 kW and so, the best PV capacity size is decided as 130 kW.

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Optimization of economic/emission load dispatch for hybrid generating systems using controlled Elitist NSGA-II

Cited by 53 publications

References 17 publications

A new approach to economic-emission load dispatch using NSGA II. Case study

A new approach to economic-emission load dispatch using NSGA II. Case study

A Multi-Objective Chance-Constrained Programming Approach for Uncertainty-Based Optimal Nutrients Load Reduction at the Watershed Scale

Microgrid: Planning of Solar PV Incorporation to the Optimal CHP-System–An Evolutionary Algorithmic Approach

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