Economic Load Dispatch Using Intelligent Particle Swarm Optimization

Bansal, Nayan; Gautam, Rahul; Tiwari, Rishabh; Thapa, Surendrabikram; Singh, Alka

doi:10.1007/978-981-15-8443-5_8

Cited by 5 publications

(7 citation statements)

References 13 publications

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“…In this section, we apply three meta-heuristic algorithms including particle swarm optimization (PSO) [26], jellyfish search algorithm (JS) [27] and salp swarm algorithm (SSA) [28] to determine the optimal results of for two systems. This work is conducted on a personal computer with a 2.2 GHz central processing unit alongside 8GB of random memory access.…”

Section: Resultsmentioning

confidence: 99%

“…In this study, we implement particle swarm optimization (PSO) [26], jellyfish search algorithm (JS) [27], and salp swarm optimization (SSA) [28] to search the optimal solutions of ELD and MELD problems. In ELD problem, the constraint about prohibited operation zone (POZ) of thermal power plants is taken into account to investigate the outstanding performance of applied methods.…”

Section: Issn: 2252-8938 mentioning

confidence: 99%

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Jellyfish search algorithm for economic load dispatch under the considerations of prohibited operation zones, load demand variations, and renewable energy sources

Trong,

Nguyen,

Nguyen

2024

IJ-AI

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<span lang="EN-US">This paper suggests a modified version of the former economic load dispatch (MELD) problem with the integration of wind power plant (WPP) and solar power plants (SPP) into thermal units (TUs). The target of the whole study is to cut the total producing electricity cost (TPEC) as much as possible. Three meta-heuristic algorithms, including particle swarm optimization (PSO), jellyfish search (JS) and salp swarm algorithm (SSA), are applied to solve the MELD. The real performance of these optimization tools is tested on the first system with six thermal units considering prohibited zones, and the second system with the combination of the first system and one solar, and two WPPs. In addition, the variation of load demand in 24 hours per day is also taken into account in the second system. JS is proved to be the most effective method for dealing with MELD. Furthermore, JS can also reach lower or the same TPEC as other previous algorithms. Hence, JS is a recommended to be a strong computing method for dealing with the MELD problem. </span>

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Section: Resultsmentioning

confidence: 99%

Section: Issn: 2252-8938 mentioning

confidence: 99%

Jellyfish search algorithm for economic load dispatch under the considerations of prohibited operation zones, load demand variations, and renewable energy sources

Trong,

Nguyen,

Nguyen

2024

IJ-AI

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“…All these considerations are aimed at reaching the goal of minimizing EFE as much as possible. The results from DO are compared to other implemented algorithms, the salp swarm algorithm (SSA) [26] and particle swarm optimization (PSO) [27]. In summary, the contributions of the study can be summarized as follows: − Successfully implement a novel meta-heuristic algorithm developed in early 2022 and two earlier algorithms, SSA and PSO, to find the optimal solutions for EELD.…”

Section: Introductionmentioning

confidence: 99%

Solutions of economic load dispatch problems for hybrid power plants using Dandelion optimizer

Nguyen

Pham

2023

Bulletin EEI

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In this paper, an economic load dispatch problem (ELD) is solved for reaching optimal power output of hybrid systems in addition to cost minimization. The systems consider the forbidden working zones (FWZs), dynamic load demand, wind farms, and solar photovoltaic fields (SPs). The cost minimization solutions for the ELD problem are found by applying the Dandelion optimizer (DO), the salp swarm algorithm (SSA), and the particle swarm optimization (PSO). In the study case, the power system consists of six thermal power plants (TPs), two wind farms, and two SPs. In addition, the variation of load demand over 24 hours of one day is applied. DO and SSA can achieve the best cost of $15443.0753 for the first system, but PSO cannot. However, DO is the most stable method reaching the standard deviation of 0.0184 for fifty runs but that of SSA and PSO is about 1.0439 and 8.9664. For the second system, DO can reach smaller cost than PSO by $11.17, $137.74 and $323.09 for the best, mean and worst solutions among fifty found solutions. As a result, DO is strongly recommended for solving the ELD problem.

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“…Third, the computational burden increases rapidly as the scale of the systems increases. Many improved version of PSO has been proposed to tackle these problems (Bansal et al, 2021; Barati and Sadeghi, 2018; Elsayed et al, 2017; Gholamghasemi et al, 2019). Nevertheless, a few approaches are able to address these three drawbacks simultaneously.…”

Section: Introductionmentioning

confidence: 99%

A diversity-based parallel particle swarm optimization for nonconvex economic dispatch problem

Xin

Wang

et al. 2022

Transactions of the Institute of Measurement and Control

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The economic dispatch (ED) problem aims to minimize the total generation cost while satisfying certain constraints, such as valve-point effects, multi-fuel options, prohibited operating zones, transmission losses, and ramp rate limits. In this paper, these constraints are considered simultaneously for the first time, resulting in a complex nonconvex ED problem. A diversity-based parallel particle swarm optimization (DPPSO) is proposed to solve the nonconvex ED problem, where the implementation details—such as evaluation function design, particle definition, and equality and inequality handling strategies—have been carefully discussed. In our approach, the population of DPPSO is divided into different groups to maintain diversity in particles so that the optimization capacity can be enhanced. An asynchronous information–sharing mechanism (AISM) helps decrease the population size. Hence, the computational burden is reduced. Moreover, information in different groups is calculated parallelly and updated asynchronously to improve computational efficiency. Benchmark functions are employed to demonstrate the effectiveness of the proposed method. Furthermore, three nonconvex ED problems are resolved by the proposed method, and state-of-the-art performance has been achieved. In addition, the proposed algorithm is highly modular, making it easy to unite other salient variants of particle swarm optimization (PSO) to improve its performance.

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Economic Load Dispatch Using Intelligent Particle Swarm Optimization

Cited by 5 publications

References 13 publications

Jellyfish search algorithm for economic load dispatch under the considerations of prohibited operation zones, load demand variations, and renewable energy sources

Jellyfish search algorithm for economic load dispatch under the considerations of prohibited operation zones, load demand variations, and renewable energy sources

Solutions of economic load dispatch problems for hybrid power plants using Dandelion optimizer

A diversity-based parallel particle swarm optimization for nonconvex economic dispatch problem

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