Optimal power flow using artificial bee colony algorithm with global and local neighborhoods

Bansal, Jagdish Chand; Jadon, Shimpi Singh; Tiwari, Ritu; Kiran, D. Shashi; Panigrahi, Bijaya Ketan

doi:10.1007/s13198-014-0321-7

Cited by 21 publications

(10 citation statements)

References 54 publications

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“…14 [236]. • ARTIFICIAL BEE COLONY The principle of an artificial bee colony (ABC) is based on the natural foraging act of honey bees [251]. The implementation process of the ABC algorithm is illustrated in Fig.…”

Section: ) Population-based Algorithmsmentioning

confidence: 99%

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

et al. 2021

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Buildings account for a significant amount of energy consumption leading to the issues of global emissions and climate change. Thus, energy management in a building is increasingly explored due to its significant potential in reducing the overall electricity expenses for the consumers and mitigating carbon emissions. In line with that, the greater control and optimization of energy management integrated with renewable energy resources is required to improve building energy efficiency while satisfying indoor environment comfort. Even though actions are being taken to reduce the energy consumption in buildings with several optimization and controller techniques, yet some issues remain unsolved. Therefore, this work provides a comprehensive review of the conventional and intelligent control methods with emphasis on their classification, features, configuration, benefits, and drawbacks. This review critically investigates the different optimization objectives and constraints with respect to comfort management, energy consumption, and scheduling. Furthermore, the review outlines the different methodological approaches to optimization algorithms used in building energy management. The contributions of controller and optimization in building energy management with the relation of sustainable development goals (SDGs) are explained rigorously. Discussions on the key challenges of the existing methods are presented to identify the gaps for future research. The review delivers some effective future directions that would be beneficial to the researchers and industrialists to design an efficiently optimized controller for building energy management toward targeting SDGs.INDEX TERMS Building energy management, controller, optimization, scheduling, sustainable development goals I. INTRODUCTIONPresently, buildings take the lead in consuming a substantial amount of energy, indicating about 40% of global energy consumption, which is responsible to release one-third of greenhouse gas (GHG) emissions [1], [2]. Another report demonstrates that buildings hold 49% of the total energy worldwide in which 60% of the energy is consumed for heating and cooling purposes [3], [4]. The poor management and ineffective control approach of appliances used in the building may result in a significant loss of energy in a

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Section: ) Population-based Algorithmsmentioning

confidence: 99%

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

et al. 2021

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“…The proposed method GSA [14] TLBO [15] At the same time, it is seen that the fuel cost curve of the proposed method in Figure 5 is less convergent and has lower fuel cost than the standard GSA and TLBO algorithms. [15] 799.0715 N/A N/A N/A N/A PSO [9] 800.41 N/A N/A N/A N/A ABC [13] 799.264 N/A N/A N/A N/A ABCGLN [17] 800.4464 800.5607 N/A N/A 18.0 SFLA-SA [16] 801.79 N/A N/A N/A 18.93…”

Section: Variablesmentioning

confidence: 99%

“…In order to minimize the fuel cost in the EDP, the work performed by hybrid methods is as follows: the hybrid FAPSO-NM method with combinations of fuzzy adaptive (FA), PSO algorithm, and Nelder-Mead (NM) methods [8]; the G-TLBO algorithm combined with GA and TLBO algorithms [10]; simulated annealing with shuffle frog leaping algorithm (SFLA-SA) method [16]; ABCGLN method modifying the ABC global and local neighborhoods [17]; hybrid MICA-TLA with combinations of the modified imperialist competitive algorithm (MICA) and teaching learning algorithm [18] and CMICA [19]; the EADE hybrid method, which is a combination of evolving ant direction search with DE algorithm [20]; and the FHSA method, an integration of fuzzy logic system and harmony search (HS) algorithm [21].…”

Section: Introductionmentioning

confidence: 99%

A new hybrid gravitational search-teaching-learning-based optimization method for the solution of economic dispatch of power systems

Tefek¹,

Uğuz²

2019

Turk J Elec Eng & Comp Sci

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The economic dispatch problem (EDP) is a complex, constrained, and nonlinear optimization problem. In the EDP, the active power bus should operate between the minimum and maximum bus limits to minimize the fuel cost. In this study, a fast, efficient, and reliable hybrid gravitational search algorithm-teaching learning based optimization (GSA-TLBO) method was proposed for the purpose of solving the EDP in power systems. The proposed method separates the search space into two sections as global and local searching. In the first part, searching was carried out by GSA method effectively to form the second search space. In the second part, the optimum solution was sought in the local search space by the TLBO method. The proposed method was implemented to a constrained benchmark G01 problem. The proposed hybrid method was then applied to the constrained EDP in IEEE 30-bus and IEEE 57-bus test systems and Turkey's 22-bus power system to minimize the fuel cost. Obtained results were compared with other methods. Experimental results show that the proposed method results in shorter, more reliable, and efficient lowest fuel cost solutions. It has been found that the proposed method can be used to solve constrained optimization problems.

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“…Among the bio-inspired methods, this article is focused on the ABC algorithm, which is a swarm intelligence algorithm that is widespread and inspired by the social behavior of honeybees [27]. The ABC algorithm has been widely applied in distinct fields of electrical engineering such as: in fault diagnosis [28], [29], optimized PID controller design [30], optimal power dispatching [31], automatic voltage regulator system [32], MPPT power extraction [23], [33],and antenna design [34], [35].…”

Section: Introductionmentioning

confidence: 99%

MPPT Algorithm Based on Artificial Bee Colony for PV System

et al. 2021

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Energy structures from non-conventional energy source has become highly demanded nowadays. In this way, the maximum power extraction from photovoltaic (PV) systems has attracted the attention, therefore an optimization technique is necessary to improve the performance of solar systems. This paper proposes the use of ABC (artificial bee colony) algorithm for the maximum power point tracking (MPPT) of a PV system using a DC-DC converter. The procedure of the ABC MPPT algorithm is using data values from PV module, the P-V characteristic is identified and the optimal voltage is selected. Then, the MPPT strategy is applied to obtain the voltage reference for the outer PI control loop, which in turn provides the current reference to the predictive digital current programmed control. A real-time and high-speed simulator (PLECS RT Box 1) and a digital signal controller (DSC) are used to implement the hardwarein-the-loop system to obtain the results. The general system does not have a high computational cost and can be implemented in a commercial low-cost DSC (TI 28069M). The proposed MPPT strategy is compared to the conventional perturb and observe method, results show the proposed method archives a much superior performance. INDEX TERMS Maximum power point tracking, Photovoltaic system, Artificial bee colony, Hardware in the loop testing. ABBREVIATION Term Description ABC Artificial bee colony algorithm. ACO Ant colony optimization. ADE Adaptive differential evolution. ANFIS Adaptive neuro-fuzzy inference system. ANNs Artificial neural networks. BA Bat algorithm. BI Bio-inspired methods. COA Coyote optimization algorithm. DSC Digital signal controller. ESC Extremum-seeking control. FL Fuzzy logic algorithm. FPA Flower pollination algorithm. FPGA Field-programmable gate arrays. GA Genetic algorithm. HIL Hardware-in-the-loop. INC Incremental conductance algorithm. MOA Moth-flame optimization algorithm.

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Optimal power flow using artificial bee colony algorithm with global and local neighborhoods

Cited by 21 publications

References 54 publications

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

Intelligent Controllers and Optimization Algorithms for Building Energy Management Towards Achieving Sustainable Development: Challenges and Prospects

A new hybrid gravitational search-teaching-learning-based optimization method for the solution of economic dispatch of power systems

MPPT Algorithm Based on Artificial Bee Colony for PV System

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