Advanced three-stage pseudo-inspired weight-improved crazy particle swarm optimization for unit commitment problem

Shukla, Abhishek; Singh, S. N.

doi:10.1016/j.energy.2015.12.046

Cited by 43 publications

(19 citation statements)

References 36 publications

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“…Numerical optimization methods are simple and fast but most of them may suffer from numerical convergence and solution quality problems [12]. Hence heuristics methods such as a genetic algorithm [13], particle swarm optimization [14], or memetic algorithm have been proposed for the UC problem. In [15], a hybrid evolutionary framework based on hybridization of genetic algorithm and differential evolution is proposed to solve UC.…”

Section: Introductionmentioning

confidence: 99%

“…Genetic algorithm is more capable of handling binary variables and differential evolution is better in real parameter optimization, hence the performance is enhanced by combining the two algorithms. Shukla [14] uses a three stages approach to get the optimum solution of UC. In the first two stages, operating status of units are obtained by predefined priority while the generation of each unit is obtained by a weight-improved crazy particle swarm optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

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A New Multi-Objective Unit Commitment Model Solved by Decomposition-Coordination

et al. 2019

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Multi-objective unit commitment (MOUC) considers concurrently both economic and environmental objectives, then finds the best trade-off with respect to these objectives. This paper proposes a novel model for MOUC, and a decomposition coordination approach is presented to solve the model. The economic objective is to reduce the fuel cost while the environmental objective is to reduce the CO 2 emission. The MOUC model considers these objectives by minimizing the distance to the Utopian point, which avoids generating Pareto optimal solutions. The model is solved by a decomposition coordination approach, which decomposes the whole system into subsystems and performs an iterative process. During each iteration step, the tie-line power flow is updated based on the margin price in connected subsystems, then, each subsystem is solved by branch and bound method, and the result is improved during iterations as shown in case studies. Besides, as the process does not require uploading units parameters, it protects the privacy of generating companies. Numerical case studies conducted using the proposed multi-objective model are applied to illustrate the performance of the approach.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Multi-Objective Unit Commitment Model Solved by Decomposition-Coordination

et al. 2019

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“…The load curve in [27] is used, and shown in Table 4. For each hour, UC is carried out considering the corresponding load factor.…”

Section: Seven-day Unit Schedulingmentioning

confidence: 99%

A Hybrid Unit Commitment Approach Incorporating Modified Priority List with Charged System Search Methods

Wu¹,

Chih-Cheng²,

Lin³

et al. 2017

SGRE

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This paper presents a new hybrid approach that combines Modified Priority List (MPL) with Charged System Search (CSS), termed MPL-CSS, to solve one of the most crucial power system's operational optimization problems, known as unit commitment (UC) scheduling. The UC scheduling problem is a mixed-integer nonlinear problem, highly-dimensional and extremely constrained. Existing meta-heuristic UC solution methods have the problems of stopping at a local optimum and slow convergence when applied to largescale, heavily-constrained UC applications. In the first step of the proposed method, initial hourly optimum solutions of UC are obtained by Modified Priority List (MPL); however, the obtained UC solution may still be possible to be further improved. Therefore, in the second step, the CSS is utilized to achieve higher quality solutions. The UC is formulated as mixed integer linear programming to ensure the tractability of the results. The proposed method is successfully applied to a popular test system up to 100 units generators for both 24-hr and 168-hr system. Computational results show that both solution cost and execution time are superior to those of published methods.

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“…However, the significantly increased system complexity due to the introduction of a number of new players into the power system in recent years, such as distributed reneable generations brings considerable computational requirement on these convetional approaches, making them technically less appealing. On the other hand, intelligent mix-coded meta-heuristic algorithms such as genetic algorithm (GA) [9], harmony search (HS) [10], particle swarm optimisation (PSO) [11], firefly algorithm [12] and teaching learning based optimisation (TLBO) [13] have been employed to solve UC problems and achieved some good results. These methods however require a large number of iterations to ensure the algorithm convergence, which may reduce their computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

A hybrid meta-heuristic method for unit commitment considering flexible charging and discharging of plug-in electric vehicles

Yang

2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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Advanced three-stage pseudo-inspired weight-improved crazy particle swarm optimization for unit commitment problem

Cited by 43 publications

References 36 publications

A New Multi-Objective Unit Commitment Model Solved by Decomposition-Coordination

A New Multi-Objective Unit Commitment Model Solved by Decomposition-Coordination

A Hybrid Unit Commitment Approach Incorporating Modified Priority List with Charged System Search Methods

A hybrid meta-heuristic method for unit commitment considering flexible charging and discharging of plug-in electric vehicles

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