Particle swarm optimization with crazy particles for nonconvex economic dispatch

Chaturvedi, Krishna Teerth; Pandit, Manjaree; Srivastava, Laxmi

doi:10.1016/j.asoc.2008.11.012

Cited by 67 publications

(47 citation statements)

References 11 publications

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“…The particles move throughout the search space until they find the excellent solution. Therefore particles endeavor to improve it's locations using the current velocity and distance from P best and P Gbest , [23,18]. The corrected velocity and location of each particle for competence evaluation in the future iteration are calculated using the flowing equations:…”

Section: Gbestmentioning

confidence: 99%

Modified iteration particle swarm optimization procedure for economic dispatch solving with non-smooth and non-convex fuel cost function

Zare¹,

Bolandi²

2014

3rd IET International Conference on Clean Energy and Technology (CEAT) 2014

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This paper proposes a novel technique named modified iteration particle swarm optimization for finding possible optimal solution in the economic load dispatch problem considering non-convex and non-smooth fuel cost function. Many constrains such as ramp rate limits, transmission losses and prohibited operating zones are considered. The valve point effects with nonlinear cost function in non-convex economic dispatch are also considered. Classical particle swarm optimization (CPSO) highly depends on its parameters and often suffers from being involved in local optima. The proposed approach incorporates two new index named "first iteration best" and "second iteration best" in CPSO. It results that searching behavior is enhanced and solution quality is promoted. To ensure the efficiency of the proposed approach, it is applied for four test system including 3, 6, 13 and 15 units. From the simulation results, the capabilities and effectiveness of the proposed approach compared with existing methods is confirmed.

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Section: Gbestmentioning

confidence: 99%

Modified iteration particle swarm optimization procedure for economic dispatch solving with non-smooth and non-convex fuel cost function

Zare¹,

Bolandi²

2014

3rd IET International Conference on Clean Energy and Technology (CEAT) 2014

View full text Add to dashboard Cite

show abstract

“…One possible path to the improvement has been the hybridization of PSO with evolutionary algorithms [8,29,30]. A good example of this the technique is proposed in [31]. EPSO [10] can be seen as a self-adaptive evolutionary algorithm where the recombination is replaced by an operation called particle movement.…”

Section: State Of the Artmentioning

confidence: 99%

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

Soares

Sousa

Morais

et al. 2013

Applied Soft Computing

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This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem.The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intel-ligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence charac-teristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered.The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Program-ming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

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“…He implemented such optimization model on IEEE 30 and 118 bus test system and results demonstrated superior capability to solve best solution under constrained above mentioned conditions. [13] presented an idea to real time/online economical allocation of power demand at the generators. Modern generating units have cost curves that are nonlinear.…”

Section: Optimization Barrier Of Economic Dispatch Algorithmsmentioning

confidence: 99%

Multi Criteria Optimization Algorithm for Economic Dispatch Complications for Sustainable Interconnected Power System

Shaikh¹,

Shaikh²,

Mirani³

et al. 2012

IJCA

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Power system engineers are facing difficulty in economic operation of the large interconnected power system. Economic dispatch of large interconnected power system is complex, constrained and nonlinear problem which require rigorous and complex calculations before taking any decision apropos to power dispatch. Evolutionary algorithms have been nowadays frequently used for optimum performance of nonlinear and complex systems. This paper proposed to develop an evolutionary algorithm which considers all constraints of power dispatch for economic operation of the power system. General TermsEconomic Dispatch Problems, Computational Methods, Evolutionary Algorithms et. al.

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Particle swarm optimization with crazy particles for nonconvex economic dispatch

Cited by 67 publications

References 11 publications

Modified iteration particle swarm optimization procedure for economic dispatch solving with non-smooth and non-convex fuel cost function

Modified iteration particle swarm optimization procedure for economic dispatch solving with non-smooth and non-convex fuel cost function

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

Multi Criteria Optimization Algorithm for Economic Dispatch Complications for Sustainable Interconnected Power System

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