Gaussian Particle swarm optimization for combined economic emission dispatch

Varma, Sajan; Murthy, K. S. R.; Kondamudi, Srichandan

doi:10.1109/iceets.2013.6533581

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…By using Weighted Sum Method (WSM), the best method can be found with the best resolution for the objective conflict that be expressed by Min Y in equation (7). The penalty factor, h is formed from the ratio of maximum cost and maximum emission level of corresponding generator [22].…”

Section: Static Combined Economic and Emission Dispatch (Sceed)mentioning

confidence: 99%

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Optimal Economic and Emission Dispatch of Photovoltaic Integrated Power System Using Firefly Algorithm

Abdullah

Salim

2022

IJIE

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The main purpose of Economic Load Dispatch (ELD) is to determine the optimal output of generating units to meet the power demand at lowest possible cost and subjected to the operational constraints. Various ELD optimization methods have been developed in order to deal with the challenges of continuous and sustainable power at optimal cost. The deficiency of fossil fuel reserve and rapid increase of fuel prices to generate electricity have encouraged the use of Renewable Energy (RE). Furthermore, the concerns over environmental pollution also become a factor to incorporate the RE and fossil fuel in generating electricity. This paper proposes the Firefly Algorithm (FA) to solve Economic and Emission Load Dispatch (EELD) problems by considering the photovoltaic integration power systems. The FA algorithm is used to determine the optimal cost and emission level of power generation. The test case considered in this paper is Static Combined Economic and Emission Dispatch (SCEED) that been simulated for each hour. The test system with 6 units of thermal generator and 13 units of PV generator are used to optimize SCEED problem by using FA. The Weight Sum Method (WSM) approach is used to determine the best compromise solution among the cost and emission. It found that FA can provide the fast convergence in finding the global minima value. Itcan be concluded that FA can solve the problem of economic and emission dispatch accurately.

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Section: Static Combined Economic and Emission Dispatch (Sceed)mentioning

confidence: 99%

“…There are two constraint that will be considered and need to be satisfied in this system which are equality and inequality constraint. The equality constraint is formulated as equation below [22]:…”

Section: Problem Constraintmentioning

confidence: 99%

Optimal Economic and Emission Dispatch of Photovoltaic Integrated Power System Using Firefly Algorithm

Abdullah

Salim

2022

IJIE

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“…In fact, (non-stochastic) function approximation has been shown to improve evolutionary algorithms, as the surrogate can be used to evaluate additional candidate solutions within a local neighborhood, while keeping the number of function calls to f unchanged (Regis, 2014a). Examples are surrogate-assisted variants of Gaussian PSO (e. g. Krohling, 2004;Melo & Watada, 2016;Varma et al, 2013;Barman et al, 2016;Liu et al, 2013;Gao et al, 2020), Bayesian PSO (e. g. Zhang et al, 2015;Chen & Yu, 2017;Kang et al, 2018), and modified PSO (e. g. Tian & Shi, 2018;Liu et al, 2015). However, surrogate-assisted algorithms are primarily used to speed up runtime (due to fewer evaluations f ) but with similar convergence characteristics.…”

Section: Surrogate-assisted Psomentioning

confidence: 99%

“…This is because, for instance, complex industrial simulations are involved (e. g. Hong et al, 2018). This is the setting to which our algorithms are tailored, and, hence, our evaluations compare the efficiency of the algorithms after 100 D function evaluations following Liu et al (2013); Varma et al (2013). For reasons of space, we report the results for ten dimensional (D = 10) objective functions.…”

Section: Setupmentioning

confidence: 99%

Directed particle swarm optimization with Gaussian-process-based function forecasting

Jakubik¹,

Binding²,

Feuerriegel³

2021

Preprint

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Particle swarm optimization (PSO) is an iterative search method that moves a set of candidate solution around a search-space towards the best known global and local solutions with randomized step lengths. PSO frequently accelerates optimization in practical applications, where gradients are not available and function evaluations expensive. Yet the traditional PSO algorithm ignores the potential knowledge that could have been gained of the objective function from the observations by individual particles. Hence, we draw upon concepts from Bayesian optimization and introduce a stochastic surrogate model of the objective function.That is, we fit a Gaussian process to past evaluations of the objective function, forecast its shape and then adapt the particle movements based on it. Our computational experiments demonstrate that baseline implementations of PSO (i. e., SPSO2011) are outperformed. Furthermore, compared to, state-of-art surrogate-assisted evolutionary algorithms, we achieve substantial performance improvements on several popular benchmark functions. Overall, we find that our algorithm attains desirable properties for exploratory and exploitative behavior.

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“…ELD problems have been recently solved by Particle Swarm Optimization (PSO) approaches [23][24][25][26][27]. The PSO originally [23][24][25] in 1995, which is a population based stochastic algorithm. Literature survey shows that particle swarm optimization technique is very simple optimization technique and easier to understand from any other techniques.…”

Section: Introductionmentioning

confidence: 99%

An analysis of economic load dispatch with ramp-rate limit constraints using different algorithms

Dasgupta¹,

Banerjee²,

Chanda

2015

2015 International Conference on Industrial Instrumentation and Control (ICIC)

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The economic load dispatch (ELD) plays an important role in power system operation and control. Different techniques have been used to solve these problems. Recently, the soft computing techniques have widely used in practical applications. This paper shows successful implementation of four evolutionary algorithms, namely particle swarm optimization (PSO), particle swarm optimization with constriction factor approach (PSOCFA), particle swarm optimization with inertia weight factor approach (PSOIWA) and particle swarm optimization with constriction factor and inertia weight factor approach (PSOCFIWA) algorithms. Here ramp-rate limit constraints are considered to solve this problem. Here three cases have been considered. Four algorithms have been applied for each case. Optimum fuel cost is taken from each algorithm is compared for each cases (Case study I, II and lIlY.

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Gaussian Particle swarm optimization for combined economic emission dispatch

Cited by 7 publications

References 6 publications

Optimal Economic and Emission Dispatch of Photovoltaic Integrated Power System Using Firefly Algorithm

Optimal Economic and Emission Dispatch of Photovoltaic Integrated Power System Using Firefly Algorithm

Directed particle swarm optimization with Gaussian-process-based function forecasting

An analysis of economic load dispatch with ramp-rate limit constraints using different algorithms

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