Passive congregation based particle swam optimization (pso) with self-organizing hierarchical approach for non-convex economic dispatch

Chaturvedi, Krishna Teerth; Patidar, N. P.; Kolhe, Mohan Lal; KHOBARAGADE, TEJASWITA

doi:10.1109/icpre.2017.8390599

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…In contrast with other nonconvex optimization techniques, the PSO has several appealing advantages (Al Bahrani and Patra, 2017; Al-Bahrani and Patra, 2018; Ding et al, 2019; Mirjalili et al, 2020): (1) the relatively independent calculation mode of particles in PSO has natural advantages when combined with parallel computing and asynchronous update, contributing to the improvement of computational efficiency; (2) the PSO only has a few parameters and requires less hyperparameter tuning; (3) the PSO can easily handle the inequality constraints; (4) the PSO has a few or no restrictions on the form of the objective function; (5) the PSO has a simple concept and is easy to implement. However, the PSO also bears several drawbacks on solving the ED problem (Chaturvedi et al, 2017; Chopra et al, 2020; Panda and Padhy, 2008). First, the PSO often fails to strike a favorable balance between exploration and exploitation, resulting in the premature convergence problem.…”

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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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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Economic Load Dispatch Using Hybrid DE-Firefly Algorithm

Samal

Chandrakar

Chattopadhyay

2018

2018 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT)

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Techno-Economic Power Dispatching of Combined Heat and Power Plant Considering Prohibited Operating Zones and Valve Point Loading

2022

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Co-generation units (i.e., combined heat and power plants—CHPs) are playing an important role in fulfilling the heat and power demand in the energy system. Due to the depletion of fossil fuels and rising carbon footprints in the environment, it is necessary to develop some alternatives as well as energy efficient operating strategies. By utilising the waste heat from thermal plants, cogeneration units help to decrease energy generation costs as well as reduce emitted pollutants. The combined heat and power economic dispatch (CHPED) operation strategy is becoming an important optimisation task in the energy efficient operation of a power system. The optimisation of CHPED is quite complex due to the dual dependence of heat and power in the cogeneration units. The valve point loading effect and prohibited operating zones of a thermal power unit make the objective function non-linear and non-convex. In this work, to address these issues more effectively, the viable operational area of the CHP and power system network losses are considered for the optimal allocation of power output and heat generation. A metaheuristic optimisation algorithm is proposed to solve the CHPED to minimize the fuel supply, thus satisfying the constraints. To handle equality and inequality constraints, an external penalty factor-based constraint handling technique is used. The success of the proposed CHPED algorithm is tested on three considered cases. In all the cases, the results show the effectiveness in terms of solution accuracy and better convergence.

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Passive congregation based particle swam optimization (pso) with self-organizing hierarchical approach for non-convex economic dispatch

Cited by 3 publications

References 27 publications

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

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

Economic Load Dispatch Using Hybrid DE-Firefly Algorithm

Techno-Economic Power Dispatching of Combined Heat and Power Plant Considering Prohibited Operating Zones and Valve Point Loading

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