Double weighted particle swarm optimization to non-convex wind penetrated emission/economic dispatch and multiple fuel option systems

Kheshti, Mostafa; Ding, Lei; Ma, Shicong; Zhao, Bing

doi:10.1016/j.renene.2018.03.024

Cited by 51 publications

(26 citation statements)

References 60 publications

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“…e transmission loss P L depends on the output of each generated unit, grid structure, and line parameter. Generally, the power system loss is defined as a function of active power at each power unit, and then, the resultant calculation formula is expressed as follows [17]: (14) where B ij , B i0 , and B 00 are the line loss coefficients (the P Gi in the formula should be replaced by w j when the generated power is from wind turbine).…”

Section: Problem Constraintsmentioning

confidence: 99%

“…It is biologically inspired by intelligent social behaviors like a flock of birds. In recent decades, PSO is widely applied to optimization solution in terms of its efficiency, simplicity, and effectiveness [5,6,8,[14][15][16][17][18]. In the basic PSO, the population consists of a group of particles which represent potential solutions to the given problem moving through the D-dimensional searching space.…”

Section: Gravitational Particle Swarm Optimization Algorithm (Gpsoa)mentioning

confidence: 99%

“…Without a doubt, some heuristic approaches have been also reported in the literature to solve the CEED problems. Among these heuristic techniques such as evolutionary programming (EP) [11], refined genetic algorithm (RGA) [12], nondominated sorting genetic algorithms (NSGA) [13], particle swarm optimization (PSO) [14][15][16][17], differential evolution (DE) [18], gravitational search algorithm (GSA) [19,20], ant colony optimization [21], bacterial foraging algorithm [22], opposition-based harmony search [23], biogeography-based optimization algorithm [24], artificial physical optimization [25], and glowworm swarm optimization [26] have been widely investigated to find the optimal solution of the CEED problem.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: Problem Constraintsmentioning

confidence: 99%

Section: Gravitational Particle Swarm Optimization Algorithm (Gpsoa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…This paper proposed an incorporated cost model, which combines the valve-point loadings and the fuel changes into one frame. Therefore, the cost function should combine (4) with (5), and can be realistically represented as [3]: Complication of the actual PED problem is due to the incorporated cost model composed of both valve-point effects and multiple fuels. Hence, an algorithm that overcomes these complexities has to be evolved.…”

Section: Problem Formulationmentioning

confidence: 99%

“…However, many generating units, particularly those which are supplied with multi-fuel sources (coal, nature gas, or oil), lead to the problem of determining the most economic fuel to burn. Some studies of the PED problems, such as an efficient crisscross optimization (CSO) [4], double weighted particle swarm optimization (DWPSO) [5], lightning flash algorithm (LFA) [6], numerical method (NM) [7], shuffled frog leaping algorithm and global-best harmony search (SFLA-GHS) [8], adaptive Hopfield neural network (AHNN) [9], evolutionary programming technique (EP) [10], augmented Lagrange Hopfield network initialized quadratic programming (QP-ALHN) [11], and an improved particle swarm optimization with a dynamic search space squeezing strategy [12] have considered the multiple-fuel-constrained generation scheduling of power systems.…”

Section: Introductionmentioning

confidence: 99%

Quantum-behaved Particle Swarm Optimization for Multiple-fuel-constrained Generation Scheduling of Power System

Chiang¹

2018

Proceedings of the 2018 International Conference on Information Technology and Management Engineering (ICITME 2018)

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This research proposes a quantum-behaved particle swarm optimization with a multiplier updating technique (QPSO-MU) for the multiple-fuel-constrained generation scheduling of power system. The quantum-behaved particle swarm optimization (QPSO) equips with a migration can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to avoid deforming the augmented Lagrange function and resulting in difficulty of solution searching. The proposed algorithm integrates the QPSO and the MU that has merits of automatically adjusting the randomly given penalty to a proper value and requiring only a small-size population for the power economic dispatch problem of the multiple-fuel-constrained generation scheduling. Numerical results of two test systems indicate that the proposed algorithm is more suitable than previous approaches in the practical economic dispatch for the multiple-fuel-constrained generation scheduling of power system.

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Quantum‐Behaved Particle Swarm Optimization Based on Concentration Selection Probability Assignment Weights for Power System Economic Dispatch

Wang,

Cao,

2024

IEEJ Transactions Elec Engng

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The economic dispatch (ED) problem is very important in the economics of power systems, and a suitable dispatch algorithm can help power plants save huge amounts of money and use energy efficiently. To ensure the economics of the power system dispatch strategy, this paper proposes a quantum‐behaved particle swarm improvement algorithm for solving the ED problem. First, the algorithm addresses the problem that the classical quantum‐behaved particle swarm optimization (QPSO) tends to be premature and falls into local optimality. We propose a method to calculate the mean best position by assigning weights to different particles, by adopting the idea of concentration selection in the immune concentration regulation mechanism, and designing a Weight‐Concentration selection probability function, so that those particles located outside the local optimal solution region but with smaller fitness value and better evolutionary trend can be retained. Second, to make the algorithm focus on global search at the beginning of the iteration and more focused search at the end to enhance the neighborhood search capability at the later stage, a decreasing function of the slope K in the Weight‐Concentration selection probability function is designed so that the influence of the weighting term on the calculation of the mean best position decreases gradually. The algorithm is evaluated with four power systems, and the results show that the overall performance of the improved algorithm is better than other algorithms in solving the ED problem in terms of minimum solution, convergence, and stability. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Double weighted particle swarm optimization to non-convex wind penetrated emission/economic dispatch and multiple fuel option systems

Cited by 51 publications

References 60 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

Quantum-behaved Particle Swarm Optimization for Multiple-fuel-constrained Generation Scheduling of Power System

Quantum‐Behaved Particle Swarm Optimization Based on Concentration Selection Probability Assignment Weights for Power System Economic Dispatch

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