New self‐adaptive bat‐inspired algorithm for unit commitment problem

Niknam, Taher; Bavafa, Farhad; Azizipanah‐Abarghooee, Rasoul

doi:10.1049/iet-smt.2013.0252

Cited by 27 publications

(13 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This indicates that the BSA is more competent while considering several constrains. It is evidently realized from the results that the BSA attains high worthy solutions than other techniques such as SABA [23]. The basic reason behind this event is the superior capability of the BSA along with the proposed redefining binary on/off variables as well as up/down ramping rate handling.…”

Section: -Case Study 1: Single Objective Uc Without Wind Powermentioning

confidence: 77%

“…The first case is provided for the base 10 TUs without including wind power generators to illustrate the effectiveness of the suggested BSA with respect to other well-known methods such as integer-coded GA (ICGA) [13], dynamic programming (DP) [20], Lagrangian relaxation (LR) [20], GA [20], self-adaptive bat-inspired algorithm (SABA) [23], and intelligent quantum inspired evolutionary algorithm (IQEA) [24]. The results are shown in Table I.…”

Section: -Case Study 1: Single Objective Uc Without Wind Powermentioning

confidence: 99%

See 1 more Smart Citation

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

Bavafa

Niknam

Azizipanah‐Abarghooee

et al. 2017

IEEE Trans. Ind. Inf.

Self Cite

View full text Add to dashboard Cite

Abstract-Large penetration of wind generating units in power systems necessitates a flexible unit commitment tool to handle the intermittent nature of these units as well as demand. Moreover, power system operators face not only the risks of wind power curtailments, but also probable unit outages. Therefore, assessing a trade-off between operational costs and such risks is very important. In the proposed approach, the probability of the residual demand falling within the up-and-down spinning reserve imposed by n-1 security criterion is converted into a risk index. A new bi-objective probabilistic risk/cost based unit commitment model is proposed to simultaneously minimize both the operational costs and risk. The novel formulation presented provides a new power re-dispatch process to satisfy up-and-down ramp rate constraints. A new operational cycles based unit commitment algorithm is developed. The approach profits from a new non-dominated sorting backtracking search optimization algorithm for extracting the Pareto optimal set. The proposed approach is shown to provide superior results when applied to two test systems: a) 10-unit and b) IEEE 118-bus, 54-unit system.

show abstract

Section: -Case Study 1: Single Objective Uc Without Wind Powermentioning

confidence: 77%

Section: -Case Study 1: Single Objective Uc Without Wind Powermentioning

confidence: 99%

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

Bavafa

Niknam

Azizipanah‐Abarghooee

et al. 2017

IEEE Trans. Ind. Inf.

Self Cite

View full text Add to dashboard Cite

show abstract

“…An efficient multi objective strategy based differential evolution considering multi FACTS technology is proposed in [11], a biogeography optimization [12] applied for solving multi-constraint optimal power flow with emission and non-smooth cost function, in [13] a multi-objective harmony search adapted and applied for solving the OPF problem, a multi-objective evolutionary algorithms [14] applied to enhance the solution of the combined economic and environment dispatch, authors in [15] proposed a multi objective optimal location of a combined shunt-series FACTS controllers for power system operation planning, in [16], A novel self-adaptive learning charged system search algorithm proposed for solving unit commitment problem, in [17] authors proposed a modified shuffled frog leaping algorithm for multi-objective optimal power flow with FACTS devices, a modified differential evolution approach based on cultural algorithm and diversity measure proposed in [18] to solving the economic dispatch considering valve point effect, in [19] a combined method using chaotic self adaptive differential harmony search algorithm is proposed to solving the OPF problems, in [20] a semidefinite programming is adapted and applied for solving multi-objective economic dispatch, A novel adaptive modified harmony search algorithm proposed in [21] to solve multi-objective environmental/economic dispatch, in [22] the OPF in Micro-grids is solved considering the energy storage. The dynamic economic dispatch considering practical generator constraints is widely studied by researchers and many standard and new hybrid methods proposed and applied with success such as: Improved chaotic particle swarm optimization algorithm [23], adaptive hybrid differential evolution algorithm [24], A hybrid multi-agent based particle swarm optimization algorithm [25], an enhanced cross-entropy method [26], Quantum genetic algorithm [27], artificial immune system [28], an improved PSO [29], Quadratically constrained quadratic program method [30], High-speed real-time [31], A New fast self-adaptive modified firefly algorithm [32], A fuzzyoptimization approach [33], Enhanced adaptive particle swarm optimisation algorithm [34], adaptive modified particle swarm optimization [35], a ...…”

Section: Introductionmentioning

confidence: 99%

Solving practical power system problems using hierarchical interactive PSO strategy considering SVC controllers

Mahdad

Srairi

Benbouzid³

2014

Journal of Intelligent &Amp; Fuzzy Systems

View full text Add to dashboard Cite

This paper presents a hierarchical interactive particle swarm optimization (HIPSO) for solving the static multi objective optimal power flow (OPF) considering multi SVC controllers and dynamic economic dispatch (DED). This study introduces a variant of PSO using new mechanism search based dynamic adjustment of PSO parameters coordinated with a hierarchical strategy for reactive power planning stage to improve the performances of the original PSO in terms of solution quality and convergence characteristics. The HIPSO algorithm optimizes a combined control variables such as: active power generations, voltage control of generators, tap transformer and the reactive power of multi SVC Controllers installed at a specified buses to minimize fuel cost, total power loss and voltage deviation while satisfying the generators constraints and power system security. The proposed approach examined and applied to many practical test systems: IEEE 57-Bus, IEEE 118-Bus, 40 generating units and 10 generating units to solving; the static multi objective OPF with quadratic fuel cost function, the economic dispatch considering valve point effects and the dynamic economic dispatch considering both valve point effects and ramp rate limits. Simulation results compared to Matpower, to the standard PSO and others recent metaheuristic methods confirm the effectiveness of the proposed combined approach.plex when practical constraints related to generating units (valve point effect, ramp rate limits) and also when FACTS devices are integrated as non-linear control variables [2]. In recent years many types of FACTS Controllers FACTS; shunt Controllers (SVC, STAT-COM), series Controllers (TCSC, SSSC) and hybrid controllers (UPFC) modeled and integrated in the electricity market to improve the performances of power system planning and control [3,4]. The role of FACTS technology is not only to bring a system under control and to transmit power as ordered by the control centers, but also to increase the usable transmission capacity to its thermal limits [5]. Various mathematical methods have been used largely for solving the OPF

show abstract

“…In 2010, a novel meta-heuristic optimization algorithm called the bat algorithm (BA) was proposed by Yang [7] . At present, the bat algorithm is in-depth studied by many scholars [8][9][10] .However bat algorithm is always falling into local optimum or lacking of search activity. So a self-adaptive bat algorithm (SABA) is proposed to overcome these shortcomings.…”

Section: Camera Calibration By Improved Self-adaptive Bat Algorithmmentioning

confidence: 99%

“…We define the fitness function as follows: r are set to the big and small values in the first iteration and then decreased and increased slowly during the optimization process to find the prey and raise the attack, respectively [10]. 0 i r is the emission pulse rate at time t=0.…”

Section: Camera Calibration By Improved Self-adaptive Bat Algorithmmentioning

confidence: 99%

A Self-adaptive Bat Algorithm for Camera Calibration

Liu¹,

Qi²

2016

Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)

View full text Add to dashboard Cite

Abstract. To obtain more accurate results of optimization calibration, an improved self-adaptive bat algorithm of camera calibration was proposed. Firstly, step parameters were set adaptively so that the objective function could avoid local minima. Secondly, the improved bat algorithms used in non-linear camera calibration did not need initial estimation values. So the proposed method could solve the problem that traditional optimization algorithms were sensitive to initial value. Furthermore, the self-adaptive bat algorithm combined with the process of camera calibration was used to optimize the camera's intrinsic parameters and the coefficient of radial distortion. Finally, the average re-projection error was analyzed, and the mean absolute error and the standard deviation were also calculated on the cases of different noise level. The experimental evaluation demonstrates that the proposed method was more efficient and accurate.

show abstract

New self‐adaptive bat‐inspired algorithm for unit commitment problem

Cited by 27 publications

References 45 publications

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

A New Biobjective Probabilistic Risk-Based Wind-Thermal Unit Commitment Using Heuristic Techniques

Solving practical power system problems using hierarchical interactive PSO strategy considering SVC controllers

A Self-adaptive Bat Algorithm for Camera Calibration

Contact Info

Product

Resources

About