Multiobjective thermal power load dispatch using adaptive predator–prey optimization

Singh, Nirbhow Jap; Dhillon, J.S.; Kothari, D.P.

doi:10.1016/j.asoc.2018.02.006

Cited by 17 publications

(5 citation statements)

References 51 publications

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“…The ABC‐PO algorithm has been initialized with control parameters such as population size N s of 20, a number of dimensions D as 5, the maximum number of iterations

{IT}^{\max}

is considered as 500. The predator‐related constants such as

c_{p}

, a , and b have been fixed to 2.0, 0.1, and 10 respectively as suggested by 47 …”

Section: Resultsmentioning

confidence: 99%

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System sizing of hybridsolar‐fuel cell batteryenergy system using artificial bee colony algorithm with predator effect

Singh

Gupta

2021

Intl J of Energy Research

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Summary In current years, hydrogen‐based power generation has emerged as an alternative energy source for transportation and electricity. Hydrogen‐based power generation along with solar photovoltaic (PV) is a matter of current research. In this manuscript, a hybrid solar, fuel cell, battery with an electrolyzer and hydrogen tank has been developed to cater the electricity demand of a small academic complex. The economic analysis of the project has been performed with detailed mathematical modeling of the system. The hydrogen and energy management is performed on a one‐year complete data. To deduce the sizing of the components, a novel artificial bee colony algorithm with predator effect (ABC‐PO) has been proposed by the authors. The optimal sizing results obtained from the proposed ABC‐PO have been compared with other optimization algorithms, that is, conventional ABC, particle swarm optimization (PSO), and hybrid ABC‐PSO. The results show that electricity demand of 39.4 MWh/yr could be met with a combination of 27 kW solar PV, 24 kW electrolyzer, 10 kW fuel cell, and 57 kWh batteries with a 131 kg of hydrogen tank. The designed system provides a levelized cost of electricity of $0.279/kWh with a minimum loss of power supply probability. A detailed hydrogen and energy management has been discussed to show the efficacy of the system.

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“…The ABC‐PO algorithm has been initialized with control parameters such as population size N s of 20, a number of dimensions D as 5, the maximum number of iterations

{IT}^{\max}

is considered as 500. The predator‐related constants such as

c_{p}

, a , and b have been fixed to 2.0, 0.1, and 10 respectively as suggested by 47 …”

Section: Resultsmentioning

confidence: 99%

“…b have been fixed to 2.0, 0.1, and 10 respectively as suggested by. 47 The proposed system has been studied for a smallscale academic building situated on the University campus. It comprises a few labs and faculty offices; the electrical load has been calculated per five working days.…”

Section: Resultsmentioning

confidence: 99%

System sizing of hybridsolar‐fuel cell batteryenergy system using artificial bee colony algorithm with predator effect

Singh

Gupta

2021

Intl J of Energy Research

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“…Singh et al [29] handled the problem of multi-objective optimization exploiting the weighting method. They then used an adaptive predator-prey optimization to solve it.…”

Section: Related Workmentioning

confidence: 99%

“…Various factors, operational issues related to stability and design, make it impossible to operate from some power generation ranges of generating unit. The operating zones of the ath unit are presented as below [29]:…”

Section: Prohibited Operating Zonesmentioning

confidence: 99%

Environmental/Economic Dispatch Using a New Hybridizing Algorithm Integrated with an Effective Constraint Handling Technique

Nourianfar

Abdi

2022

Sustainability

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This work tackles a relatively new issue in power system operation, known as the Environmental/Economic Dispatch problem. For this purpose, the combination of two powerful heuristic algorithms, namely, the Exchange Market Algorithm (EMA) and Adaptive Inertia Weight Particle Swarm Optimization (AIWPSO), was employed. Additionally, the Multiple Constraint Ranking (MCR) technique was used to address the system constraints such as prohibited operating zones and ramp rate limits. Furthermore, the mutation operator was used to improve the performance of the global search mechanism. The main purpose of combining these two algorithms was utilizing the EMA’s high performance to explore the global optimum and local exploitation ability of AIWPSO. The algorithm performance was evaluated on six standard benchmark functions and was scrutinized on several different test systems, including 6–40 units. By using the proposed method, the minimum values of the reduction in annual costs, with equal or less emissions, compared to other methods, were USD 17,520, 8760 and 10,801,080, respectively, for the 6-unit, 10-unit, and 40-unit test systems (assuming the same load profile throughout the year). Similarly, in the 14-unit test system for 1750, 2150, and 2650 (MW) load demands, these values were USD 229,879, 148,438, and 4483, respectively.

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“…In the past decade, one of the most im-portant concepts of nature, Predator-prey behavior has been mimicked as search algorithms and presented in the literature viz. adaptive predator prey optimization [19], integrated predator optimization [20], spatial predator-prey approach [21], real coded predator prey genetic algorithm [22], synergic predator prey optimization [23] etc. These algorithms have been highly recommended as solution procedure for the diverse engineering problems.…”

Section: Introductionmentioning

confidence: 99%

Artificial bee colony with predator effect algorithm for PID controller tuning

Singh

Chopra

Pandey

et al. 2022

Preprint

Self Cite

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This paper proposed a novel artificial bee colony with predation effect (ABCPE) algorithm for tuning a proportional integral derivative (PID) controller. The mathematical model of ABCPE algorithm to introduce predator effect in the foraging behavior of artificial bees colony algorithm has been formulated. The proposed algorithm has been tested on tuning problems of different process models. The simulation results reveal that the closedloop responses are relatively fast and non-oscillatory as compared to the Ziegler-Nichols criteria. Further, the results obtained using ABCPE are also compared with other evolutionary algorithms. The exhaustive analysis shows that the ABCPE based solution approach leads to a set of tuning parameters having smaller overshoot, less setting time and rise time compared to other solution approaches. The stability analysis using Nichols plot reveals that the phase margin of proposed algorithm is higher as compared to other tuning methods.

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Multiobjective thermal power load dispatch using adaptive predator–prey optimization

Cited by 17 publications

References 51 publications

System sizing of hybridsolar‐fuel cell batteryenergy system using artificial bee colony algorithm with predator effect

System sizing of hybridsolar‐fuel cell batteryenergy system using artificial bee colony algorithm with predator effect

Environmental/Economic Dispatch Using a New Hybridizing Algorithm Integrated with an Effective Constraint Handling Technique

Artificial bee colony with predator effect algorithm for PID controller tuning

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