Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets

Reddy, K.S.; Panwar, Lokesh Kumar; Panigrahi, B. K.; Kumar, Rajesh

doi:10.1080/0305215x.2018.1463527

Cited by 77 publications

(49 citation statements)

References 33 publications

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“…By applying the combination of FC-PG-PSO and the newly updated position model, PPSO is first introduced in the paper. In summary, PPSO method applies Formulas (21) and 23to update new velocity and then applies Formulas (29) and (30) to update new position.…”

Section: The Proposed Pso Methodsmentioning

confidence: 99%

“…However, if customers do not use the reserved power, thermal power plants must suffer non-benefit [19]. Optimal operation of thermal power plants in competitive electricity market has been widely and successfully studied [20][21][22][23][24][25][26][27][28][29][30][31]. Among the studies, start-up fuel cost has been concerned in some studies [20][21][22][23][24][25][26][27][28] while this cost has not been taken into account in remaining studies.…”

Section: Introductionmentioning

confidence: 99%

“…Among the studies, start-up fuel cost has been concerned in some studies [20][21][22][23][24][25][26][27][28] while this cost has not been taken into account in remaining studies. Different methods have been proposed for the problem such as Lagrange function-based evolutionary programming (LFEP) [19], Tabu search algorithm (TSA) [20], muller approach (MA) [21], memetic optimization algorithm (MOA) [22], modified artificial bee colony optimization algorithm (MABCOA) [23], ant colony optimization algorithm (ACOA) [24], multi-agent model algorithm (MAMA) [25], binary fish optimization algorithm (BFOA) [26], Lagrange function-based invasive weed optimization algorithm (LFIWOA) [27], sine function and cosine function-based algorithm (SCBA) [28], binary whale optimization algorithm (BWOA) [29], expanded Lagrange function-based Hopfield network method (ELF-HNM) [30] and five Lagrange function-based Hopfield neuron network (LF-HNN) methods [31]. The main difference between studies [1][2][3][4][5][6][7][8][9][10][11][12][13] and studies [20][21][22][23][24][25]…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Maximizing Total Profit of Thermal Generation Units in Competitive Electric Market by Using a Proposed Particle Swarm Optimization

et al. 2020

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In the paper, a proposed particle swarm optimization (PPSO) is implemented for dealing with an economic load dispatch (ELD) problem considering the competitive electric market. The main task of the problem is to determine optimal power generation and optimal reserve generation of available thermal generation units so that total profit of all the units is maximized. In addition, constraints, such as generation limit and reserve limit of each unit, power demand and reserve demand, must be exactly satisfied. PPSO is an improved version of conventional particle swarm optimization (PSO) by combining pseudo gradient method, constriction factor and a newly proposed position update method. On the other hand, in order to support PPSO to reach good results for the considered problem, a new constraint handling method (NCHM) is also proposed for determining maximum reserve generation and correcting reserve generation. Three test systems with 3, 10 and 20 units are employed to evaluate the real performance of PPSO. In addition to the comparisons with previous methods, salp swarm optimization (SSA), modified differential evolution (MDE) and eight other PSO methods are also implemented for comparisons. Through the result comparisons, two main contributions of the study are as follows: (1) NCHM is very effective for PSO methods to reach a high success rate and higher solution quality, (2) PPSO is more effective than other methods. Consequently, NCHM and PPSO are the useful combination for the considered problem.

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Section: The Proposed Pso Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Maximizing Total Profit of Thermal Generation Units in Competitive Electric Market by Using a Proposed Particle Swarm Optimization

et al. 2020

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show abstract

“…Besides the aforementioned conventional methods, various metaheuristic algorithms like Tabu search (TS) [73,74], GA [75], simulated annealing (SA) [76], evolutionary programming (EP) [77], PSO [78], nodal ant colony optimization (NACO) [79], multiagent modeling (MAM) [80], improved teaching-learning-based algorithm (TLBO) [81], binary fireworks algorithm (BFWA) [82], imperialist competitive algorithm (ICA) [83], parallel artificial bee colony (PABC) [84], Benders decomposition (BD) [85], binary fish swarm algorithm [86], binary whale optimization algorithm (BWOA) [87], and gravitational search algorithm (GSA) [88] have also been implemented to solve the UC problems. Typically, metaheuristic algorithms for solving UC problems search both local and global solutions.…”

Section: Overview Of Algorithms For Solving Uc Problemmentioning

confidence: 99%

Energy Storage System Analysis Review for Optimal Unit Commitment

et al. 2019

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Energy storage systems (ESSs) are essential to ensure continuity of energy supply and maintain the reliability of modern power systems. Intermittency and uncertainty of renewable generations due to fluctuating weather conditions as well as uncertain behavior of load demand make ESSs an integral part of power system flexibility management. Typically, the load demand profile can be categorized into peak and off-peak periods, and adding power from renewable generations makes the load-generation dynamics more complicated. Therefore, the thermal generation (TG) units need to be turned on and off more frequently to meet the system load demand. In view of this, several research efforts have been directed towards analyzing the benefits of ESSs in solving optimal unit commitment (UC) problems, minimizing operating costs, and maximizing profits while ensuring supply reliability. In this paper, some recent research works and relevant UC models incorporating ESSs towards solving the abovementioned power system operational issues are reviewed and summarized to give prospective researchers a clear concept and tip-off on finding efficient solutions for future power system flexibility management. Conclusively, an example problem is simulated for the visualization of the formulation of UC problems with ESSs and solutions.

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“…Nazari-Heris et al [26], Zhao et al [27], Sameer et al [28], Reddy et al [29], Stützle et al [30], Pijarski and Piotr [31], Yapici and Cetinkaya [32], Dede et al [33], Grzywiński et al [34], Kaveh et al [35], Kaveh et al [36] and Kaveh and Ilchi Ghazaan [37]. Following a description of the EVPS algorithm with a brief description of six other metaheuristic algorithms including VPS, GWO, HS, SSA, ECBO and GOA algorithms, is presented in the following subsections.…”

Section: Optimization Algorithmsmentioning

confidence: 99%

Optimal Design of Steel Curved Roof Frames by Enhanced Vibrating Particles System Algorithm

Kaveh¹,

Vaez²,

Hosseini³

et al. 2019

Period. Polytech. Civil Eng.

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The paper presents an optimal design of steel curved roof frames with its roof being part of a circular arc. The elements of frames are tapered I-section members. In the objective function for optimization, two factors affecting the weight of frames are considered simultaneously. First, the roof slope angle as an effective variable on the values of the structural loading and second, the cross-section of members that are considered as continuous and discrete variables, respectively. In the range of 3 to 70 degrees, the optimum range of roof slope angles for steel curved roof frames, as well as precise value of the best roof slope angle, will be reported. Enhanced Vibrating Particles System (EVPS) algorithm is utilized for the optimal design of steel curved roof frames with tapered members. The performance and efficiency of the EVPS algorithm is compared with six other recently developed optimization algorithms including VPS, GWO, HS, SSA, ECBO and GOA algorithms. The effectiveness and performance of EVPS algorithm is proven. Frames design are performed using ANSI/AISC 360-05 speciﬁcations which strength, displacement and stability constraints are imposed on the frames.

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Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets

Cited by 77 publications

References 33 publications

Maximizing Total Profit of Thermal Generation Units in Competitive Electric Market by Using a Proposed Particle Swarm Optimization

Maximizing Total Profit of Thermal Generation Units in Competitive Electric Market by Using a Proposed Particle Swarm Optimization

Energy Storage System Analysis Review for Optimal Unit Commitment

Optimal Design of Steel Curved Roof Frames by Enhanced Vibrating Particles System Algorithm

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