An Improved ABC Algorithm for Energy Management of Microgrid

Gao, Ren; Wu, Juebo; Hu, Wen; Zhang, Yun

doi:10.15837/ijccc.2018.4.3143

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…The results shows that the proposed algorithm has better performance for solving MIVFSP compared to GA and PSO in terms of convergence iteration, convergence time and run time. However, considering other improved ABC algorithms [7,27,36] in solving MIVFSP problem is a worthwhile area of research that could be the scope for the future work. Finally, as future works, we can focus on extending the proposed algorithm and method to other systems such as transportation problems [4,14,15,17], new product development projects [43], seru production system [44], manufacturing and supply chain [45], workforce scheduling [46] and so on.…”

Section: Discussionmentioning

confidence: 99%

Modified artificial bee colony algorithm for solving mixed interval-valued fuzzy shortest path problem

Ebrahimnejad

Enayattabr

Motameni

et al. 2021

Complex Intell. Syst.

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In recent years, numerous researchers examined and analyzed several different types of uncertainty in shortest path (SP) problems. However, those SP problems in which the costs of arcs are expressed in terms of mixed interval-valued fuzzy numbers are less addressed. Here, for solving such uncertain SP problems, first a new procedure is extended to approximate the summation of mixed interval-valued fuzzy numbers using alpha cuts. Then, an extended distance function is introduced for comparing the path weights. Finally, we intend to use a modified artificial bee colony (MABC) algorithm to find the interval-valued membership function of SP in such mixed interval-valued fuzzy network. The proposed algorithm is illustrated via two applications of SP problems in wireless sensor networks and then the results are compared with those derived from genetic and particle swarm optimization (PSO) algorithms, based on three indexes convergence iteration, convergence time and run time. The obtained results confirm that the MABC algorithm has less convergence iteration, convergence time and implementation time compared to GA and PSO algorithm.

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Section: Discussionmentioning

confidence: 99%

Modified artificial bee colony algorithm for solving mixed interval-valued fuzzy shortest path problem

Ebrahimnejad

Enayattabr

Motameni

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…In [39], a variant of SSA was developed to address an economic load dispatch issue, which determines the optimum scheduling of electricity generator, taking into account fuel consumption and power unit generator restrictions. Improved ABC also resolved this problem in [80].…”

Section: Commercial and Industrialmentioning

confidence: 99%

Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges

Nguyen

Jung

et al. 2020

Sustainability

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Sustainable energy development consists of design, planning, and control optimization problems that are typically complex and computationally challenging for traditional optimization approaches. However, with developments in artificial intelligence, bio-inspired algorithms mimicking the concepts of biological evolution in nature and collective behaviors in societies of agents have recently become popular and shown potential success for these issues. Therefore, we investigate the latest research on bio-inspired approaches for smart energy management systems in smart homes, smart buildings, and smart grids in this paper. In particular, we give an overview of the well-known and emerging bio-inspired algorithms, including evolutionary-based and swarm-based optimization methods. Then, state-of-the-art studies using bio-inspired techniques for smart energy management systems are presented. Lastly, open challenges and future directions are also addressed to improve research in this field.

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“…This approach also maximizes the comfort level of customers with restricted total energy consumption. In [19], the authors discussed optimal operation methods for a micro-grid. They used the improved adaptive evolutionary algorithm and swarm optimization algorithm.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Energy Management in Office Using Bio-Inspired Energy Optimization Algorithms

et al. 2019

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Energy is one of the valuable resources in this biosphere. However, with the rapid increase of the population and increasing dependency on the daily use of energy due to smart technologies and the Internet of Things (IoT), the existing resources are becoming scarce. Therefore, to have an optimum usage of the existing energy resources on the consumer side, new techniques and algorithms are being discovered and used in the energy optimization process in the smart grid (SG). In SG, because of the possibility of bi-directional power flow and communication between the utility and consumers, an active and optimized energy scheduling technique is essential, which minimizes the end-user electricity bill, reduces the peak-to-average power ratio (PAR) and reduces the frequency of interruptions. Because of the varying nature of the power consumption patterns of consumers, optimized scheduling of energy consumption is a challenging task. For the maximum benefit of both the utility and consumers, to decide whether to store, buy or sale extra energy, such active environmental features must also be taken into consideration. This paper presents two bio-inspired energy optimization techniques; the grasshopper optimization algorithm (GOA) and bacterial foraging algorithm (BFA), for power scheduling in a single office. It is clear from the simulation results that the consumer electricity bill can be reduced by more than 34.69% and 37.47%, while PAR has a reduction of 56.20% and 20.87% with GOA and BFA scheduling, respectively, as compared to unscheduled energy consumption with the day-ahead pricing (DAP) scheme.

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An Improved ABC Algorithm for Energy Management of Microgrid

Cited by 12 publications

References 8 publications

Modified artificial bee colony algorithm for solving mixed interval-valued fuzzy shortest path problem

Modified artificial bee colony algorithm for solving mixed interval-valued fuzzy shortest path problem

Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges

An Efficient Energy Management in Office Using Bio-Inspired Energy Optimization Algorithms

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