Sensing Cloud Optimization applied to a non-convex constrained economical dispatch

Fonte, P. M.; Monteiro, Cláudio; Barbosa, F. P. Maciel

doi:10.1109/iecon.2013.6699466

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…As heuristic methods may not converge exactly to the same solution at each run due to their stochastic behaviour, their performances could not be judge by the results of a single trial [3]. Due to that, all cases were performed 20 times keeping the average, maximum and minimum reached values.…”

Section: Swarm Behavior In Optimizationmentioning

confidence: 99%

“…A compromise must be obtained between a high number of particles, which may reach a better solution but take more processing resources, and a low number of particles, which can use lesser processing resources but also reach a poor solution. Due to that, using as starting point some authors [3], [6], [8] and [9] and with some trial and error, in all tests were used 30 particles.…”

Section: Swarm Behavior In Optimizationmentioning

confidence: 99%

“…Over the past decade, many methods have been developed to solve the ED problem. There are the traditional methods used in optimization packages such as Gradient, Lagrangean function, Lambda-iteration method, Dynamic Programming, Newton's method, Linear Programming and Interior Point method, among others [2] [3]. However, some generation cost functions of recent thermal units can be not continuous, not convex, neither differentiable.…”

Section: Introductionmentioning

confidence: 98%

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Solving non-Convex and Restricted Problems Using Swarms – Economic Dispatch Case

Pontes¹,

Fonte²,

Pestana³

2014

Procedia Technology

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In this paper three heuristic techniques based in swarm intelligence are studied, namely its ability to solve non-convex, nondifferentiable and highly restricted optimization problems. The performances of Particle Swarm Optimization, Bee Colony Optimization and Cockroach Swarm Optimization solving a set of benchmark functions will be compared. These techniques will also be applied to an electrical engineering problem, to be precise, the economic dispatch with non-convex cost functions. The results obtained up to now, have been demonstrating that these techniques are able to reach good results in the benchmark functions as well as in the problem of economic dispatch.

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Section: Swarm Behavior In Optimizationmentioning

confidence: 99%

Section: Swarm Behavior In Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Solving non-Convex and Restricted Problems Using Swarms – Economic Dispatch Case

Pontes¹,

Fonte²,

Pestana³

2014

Procedia Technology

View full text Add to dashboard Cite

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“…From the work developed up to now, it has demonstrated precise results as well as the capacity to deal with large quantity of variables [16]. In this paper is intended to continue the research solving an ED of units with valve-point effects and multi-fuels.…”

Section: Introductionmentioning

confidence: 99%

“…In [14] and [16], SCO showed appropriate heuristic characteristic to solve not convex, not differentiable and highly constrained optimisation problems. It is characterized by two distinct steps, the cloud particles fitness evaluation and a statistical analysis to determine the cloud's direction and dimension.…”

Section: Algorithmmentioning

confidence: 99%

Sensing Cloud Optimization to Solve ED of Units with Valve-Point Effects and Multi-fuels

Fonte

Monteiro

Barbosa

2013

IFIP Advances in Information and Communication Technology

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Abstract. In this paper a solution to an highly constrained and non-convex economical dispatch (ED) problem with a meta-heuristic technique named Sensing Cloud Optimization (SCO) is presented. The proposed meta-heuristic is based on a cloud of particles whose central point represents the objective function value and the remaining particles act as sensors "to fill" the search space and "guide" the central particle so it moves into the best direction. To demonstrate its performance, a case study with multi-fuel units and valve-point effects is presented.

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A comprehensive review of soft computing algorithms for optimal generation scheduling

et al. 2020

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Summary Strategic planning for optimal operation of modern power systems has always been a significant task to satisfy the techno‐social aspects in the sustainable energy scenario. In the light of restructured power systems and decentralized generation, the economic dispatch (ED) problem has remained the utmost important economic assignment for the network operators. The conventional techniques have failed to address the nonlinear characteristics of generators for solving the practical ED problem. The complications identified in the literature are valve point loading, prohibited operating zones, multiple fuels, ramp rate limits, and spinning reserve. Therefore, a diverse range of traditional and modern optimization techniques have been adopted to tackle those complexities and nonlinearities. With the recent advancements in centralized optimization techniques, several research works were reported in the past decade to accomplish better results. This review paper endeavors to cover the pioneering research on centralized approaches as well as present‐day research trends on decentralized and distributed approaches by focusing on the economic aspects. First, a detailed survey of several deterministic, stochastic, nature‐inspired, and meta‐heuristic based centralized approaches for solving the ED problem is reviewed. A brief analysis based on the performance evaluation of centralized algorithms on six standard test systems is presented. Then, the recent literature on decentralized and distributed optimization approach and the consensus protocols with respect to various network topologies is briefly reviewed. The prime objective of this review is to summarize the centralized, decentralized, and distributed approaches to solve the classic dispatch, dynamic dispatch, economic emission dispatch, and multi‐area ED problems.

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Sensing Cloud Optimization applied to a non-convex constrained economical dispatch

Cited by 5 publications

References 11 publications

Solving non-Convex and Restricted Problems Using Swarms – Economic Dispatch Case

Solving non-Convex and Restricted Problems Using Swarms – Economic Dispatch Case

Sensing Cloud Optimization to Solve ED of Units with Valve-Point Effects and Multi-fuels

A comprehensive review of soft computing algorithms for optimal generation scheduling

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