Investigating Economic Emission Dispatch Problem Using Improved Particle Swarm Optimization Technique

De, M.; Das, Gourab; Mandal, Sourav; Mandal, Kamal Krishna

doi:10.1007/978-981-10-3953-9_5

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…By then, the cost varies with respect to the renewable power generation magnitude and fuel-based sources with nonlinear characteristics of cost. In solving CEED problem, the utilization of PSO was developed by De et al (2017). While maintaining operational constraints, the EED considers two major objectives they were cost minimization and emission minimization.…”

Section: Hybrid Techniquementioning

confidence: 99%

Multi-objective-based robust unit commitment using hydro-thermal-wind: a hybrid technique

Jain¹,

Huddar

2019

IJESM

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Purpose The purpose of this paper is to solve economic emission dispatch problem in connection of wind with hydro-thermal units. Design/methodology/approach The proposed hybrid methodology is the joined execution of both the modified salp swarm optimization algorithm (MSSA) with artificial intelligence technique aided with particle swarm optimization (PSO) technique. Findings The proposed approach is introduced to figure out the optimal power generated power from the thermal, wind farms and hydro units by minimizing the emission level and cost of generation simultaneously. The best compromise solution of the generation power outputs and related gas emission are subject to the equality and inequality constraints of the system. Here, MSSA is used to generate the optimal combination of thermal generator with the objective of minimum fuel and emission objective function. The proposed method also considers wind speed probability factor via PSO-artificial neural network (ANN) technique and hydro power generation at peak load demand condition to ensure economic utilization. Originality/value To validate the advantage of the proposed approach, six- and ten-units thermal systems are studied with fuel and emission cost. For minimizing the fuel and emission cost of the thermal system with the predicted wind speed factor, the proposed approach is used. The proposed approach is actualized in MATLAB/Simulink, and the results are examined with considering generation units and compared with various solution techniques. The comparison reveals the closeness of the proposed approach and proclaims its capability for handling multi-objective optimization problems of power systems.

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Section: Hybrid Techniquementioning

confidence: 99%

Multi-objective-based robust unit commitment using hydro-thermal-wind: a hybrid technique

Jain¹,

Huddar

2019

IJESM

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“…At each time interval, the amount of fuel supplied to all units must be less than or equal to the fuel supplied by the seller, i.e., the fuel delivered to each unit in each interval should be within its lower limit F min,i and its upper limit F max,i so that [21]. (9) where F i,m is the fuel supplied to the engine i at the interval m, F i,min is the minimum amount of fuel supplied to i generator, and F max,i is the maximum amount of fuel supplied to i generator.…”

Section: Fuel Delivery Constraintmentioning

confidence: 99%

“…The solution of EELD problem is to minimize the total cost of fuel consumption and carbon emissions [7], considering power demand and operational restrictions [8]. Several techniques such as particle swarm optimization [9,10], linear programming [11,12], ant colony optimization [13], biogeography-based optimization [14], genetic algorithms (GA) [15,16], Tabu search algorithm [17], simulated annealing (SA) [1], neural networks [18], differential evolution (DE) [19], harmony search algorithm [20], Lagrange functions [7], and others [19] have been used to fix the problem of EELD. In spite that all of them have been used, few are used with cost and emission functions in a multi-objective optimization.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization Techniques to Solve the Economic Emission Load Dispatch Problem Using Various Heuristic and Metaheuristic Algorithms

Júnior¹,

Nunes²,

Nascimento³

et al. 2018

Optimization and Control of Electrical Machines

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The main objective of thermoelectric power plants is to meet the power demand with the lowest fuel cost and emission levels of pollutant and greenhouse gas emissions, considering the operational restrictions of the power plant. Optimization techniques have been widely used to solve engineering problems as in this case with the objective of minimizing the cost and the pollution damages. Heuristic and metaheuristic algorithms have been extensively studied and used to successfully solve this multi-objective problem. This chapter, several optimization techniques (simulated annealing, ant lion, dragonfly, NSGA II, and differential evolution) are analyzed and their application to economic-emission load dispatch (EELD) is also discussed. In addition, a comparison of all approaches and its results are offered through a case study.

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“…The fuel cost and the emission functions of the generating unit must be minimized simultaneously to have the minimal cost and emissions when this unit is in operation to supply the power demand scheduled for the plant . Because of the simultaneous minimization of 2 functions, this problem is considered as a multiobjective optimization problem …”

Section: Introductionmentioning

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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Investigating Economic Emission Dispatch Problem Using Improved Particle Swarm Optimization Technique

Cited by 5 publications

References 15 publications

Multi-objective-based robust unit commitment using hydro-thermal-wind: a hybrid technique

Multi-objective-based robust unit commitment using hydro-thermal-wind: a hybrid technique

Multi-Objective Optimization Techniques to Solve the Economic Emission Load Dispatch Problem Using Various Heuristic and Metaheuristic Algorithms

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

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