A novel gravitational acceleration enhanced particle swarm optimization algorithm for wind–thermal economic emission dispatch problem considering wind power availability

Jiang, Shanhe; Ji, Zhicheng; Wang, Yan

doi:10.1016/j.ijepes.2015.06.014

Cited by 75 publications

(24 citation statements)

References 51 publications

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“…e results are compared with those obtained by the existing algorithms to demonstrate the superiority performance of the suggested algorithm. e line data, bus data, and fuel cost and emission coefficients for standard IEEE 30-bus test systems are taken from [46,47] and the B-loss coefficients from [48].…”

Section: Description Of the Case Studymentioning

confidence: 99%

Combined Economic and Emission Dispatch Problem of Wind‐Thermal Power System Using Gravitational Particle Swarm Optimization Algorithm

Jiang

Zhang

et al. 2019

Mathematical Problems in Engineering

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In this paper, a novel hybrid optimization approach, namely, gravitational particle swarm optimization algorithm (GPSOA), is introduced based on particle swarm optimization (PSO) and gravitational search algorithm (GSA) to solve combined economic and emission dispatch (CEED) problem considering wind power availability for the wind-thermal power system. e proposed algorithm shows an interesting hybrid strategy and perfectly integrates the collective behaviors of PSO with the Newtonian gravitation laws of GSA. GPSOA updates particle's velocity caused by the dependent random cooperation of GSA gravitational acceleration and PSO velocity. To describe the stochastic characteristics of wind speed and output power, Weibull-based probability density function (PDF) is utilized. e CEED model employed consists of the fuel cost objective and emission-level target produced by conventional thermal generators and the operational cost generated by wind turbines. e effectiveness of the suggested GPSOA is tested on the conventional thermal generator system and the modified wind-thermal power system. Results of GPSOA-based CEED problems by means of the optimal fuel cost, emission value, and best compromise solution are compared with the original PSO, GSA, and other state-of-the-art optimization approaches to reveal that the introduced GPSOA exhibits competitive performance improvements in finding lower fuel cost and emission cost and best compromise solution.

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Section: Description Of the Case Studymentioning

confidence: 99%

Combined Economic and Emission Dispatch Problem of Wind‐Thermal Power System Using Gravitational Particle Swarm Optimization Algorithm

Jiang

Zhang

et al. 2019

Mathematical Problems in Engineering

Self Cite

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“…This kind of research is inspired by natural behavior, and it has yielded algorithms such as artificial bee colony (ABC) [2], ant colony optimization (ACO) [3], cuckoo search (CS) [4], differential evolution (DE) [5], firefly algorithm (FA) [6], gravitational searching algorithm (GSA) [7], and particle swarm optimization (PSO) [8]. In addition, various applications have been proposed for such metaheuristics algorithms in areas such as in of bioinformatics [9], clustering [10], deep learning [11], DNA fragment assembly [12], flow-shopscheduling [13], feature selection [14], geographical information systems [15], image segmentation [16], job-shop scheduling [17], power system [18], traveling salesman [19], vector quantization [20], and the water reactor problem [21].…”

Section: Id:p0075mentioning

confidence: 99%

CPO: A Crow Particle Optimization Algorithm

Huang¹,

Wu²

2019

IJCIS

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Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms and is inspired by the social behavior of bird flocking. However, the PSO algorithm converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. Recently, a new metaheuristic algorithm called the crow search algorithm (CSA) was proposed. The CSA is similar to the PSO algorithm but is based on the intelligent behavior of crows. The main concept behind the CSA is that crows store excess food in hiding places and retrieve it when needed. The primary advantage of the CSA is that it is rather simple, having just two parameters: flight length and awareness probability. Thus, the CSA can be applied to optimization problems very easily. This paper proposes a hybridization algorithm based on the PSO algorithm and CSA, known as the crow particle optimization (CPO) algorithm. The two main operators are the exchange and local search operators. It also implements a local search operator to enhance the quality of the best solutions from the two systems. Simulation results demonstrated that the CPO algorithm exhibits a significantly higher performance in terms of both fitness value and computation time compared to other algorithms.

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“…Jiang et al established a wind-thermal economic emission dispatch (WTEED) model, where the wind power cost was considered as a part of the optimization objective function. The gravitational acceleration enhanced particle swarm optimization (GAEPSO) algorithm was applied to optimize the costs and emission objectives [22]. Hooshmand et al proposed a new approach based on a hybrid algorithm consisting of genetic algorithm (GA), pattern search (PS) and sequential quadratic programming (SQP) techniques to solve the well-known power system economic dispatch problem (ED) [23].…”

Section: Introductionmentioning

confidence: 99%

Economic Dispatch of the Low-Carbon Green Certificate with Wind Farms Based on Fuzzy Chance Constraints

Wang

Tian³

et al. 2018

Energies

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Abstract:As the low-carbon economy continues to expand, wind power, as one form of clean energy, promotes the low-carbon power development process. In this paper, a multi-objective environmental economic dispatch (EED) model is proposed considering multiple uncertainties of the system. Carbon trading costs and green certificate trading costs are introduced into the economic costs. Meanwhile, the objective function of pollutant emissions is taken into account in the model, which can further promote the reduction of pollutant emissions in the system scheduling. The output of wind turbines is uncertain and volatile, so it brings new challenges to the power system EED once the large-scale wind power accesses the power grid. For the multiple uncertainties of the system, fuzzy chance-constrained programming is introduced, and the output of the wind turbines and the load are regarded as fuzzy variables. We use the clear equivalence forms to clarify the fuzzy chance constraints. The improved multi-objective standard particle swarm optimization (SPSO) algorithm is used to solve the optimization problem effectively. The feasibility and effectiveness of the proposed model and algorithm are verified by an example of a 10-unit system with two wind farms.

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A novel gravitational acceleration enhanced particle swarm optimization algorithm for wind–thermal economic emission dispatch problem considering wind power availability

Cited by 75 publications

References 51 publications

Combined Economic and Emission Dispatch Problem of Wind‐Thermal Power System Using Gravitational Particle Swarm Optimization Algorithm

Combined Economic and Emission Dispatch Problem of Wind‐Thermal Power System Using Gravitational Particle Swarm Optimization Algorithm

CPO: A Crow Particle Optimization Algorithm

Economic Dispatch of the Low-Carbon Green Certificate with Wind Farms Based on Fuzzy Chance Constraints

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