A Method for Particle Swarm Optimization and its Application in Location of Biomass Power Plants

López, P. Reche; García-Galán, S.; Ruiz-Reyes, N.

doi:10.1080/15435070802107165

Cited by 25 publications

(8 citation statements)

References 22 publications

(16 reference statements)

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“…Many studies combined traditional optimization methods with heuristic algorithms. Several studies highlighted the reduction of computational costs by using heuristics (Asadi et al, 2018; Kumar et al, 2015; Lopez et al, 2008; López et al, 2008), especially when solving BSC models with a large number of variables and constraints (Asadi et al, 2018). Kumar et al reported that adaptive large neighborhood search (ALNS), a heuristic algorithm, took 140 s while CPLEX took ~3 h to find the solution for the same BSC problem (Kumar et al, 2015).…”

Section: Applications Of Artificial Intelligence To Bioenergy Systemsmentioning

confidence: 99%

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Ming-yang

Yao

2021

GCB Bioenergy

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Bioenergy is widely considered a sustainable alternative to fossil fuels. However, large‐scale applications of biomass‐based energy products are limited due to challenges related to feedstock variability, conversion economics, and supply chain reliability. Artificial intelligence (AI), an emerging concept, has been applied to bioenergy systems in recent decades to address those challenges. This paper reviewed 164 articles published between 2005 and 2019 that applied different AI techniques to bioenergy systems. This review focuses on identifying the unique capabilities of various AI techniques in addressing bioenergy‐related research challenges and improving the performance of bioenergy systems. Specifically, we characterized AI studies by their input variables, output variables, AI techniques, dataset size, and performance. We examined AI applications throughout the life cycle of bioenergy systems. We identified four areas in which AI has been mostly applied, including (1) the prediction of biomass properties, (2) the prediction of process performance of biomass conversion, including different conversion pathways and technologies, (3) the prediction of biofuel properties and the performance of bioenergy end‐use systems, and (4) supply chain modeling and optimization. Based on the review, AI is particularly useful in generating data that are hard to be measured directly, improving traditional models of biomass conversion and biofuel end‐uses, and overcoming the challenges of traditional computing techniques for bioenergy supply chain design and optimization. For future research, efforts are needed to develop standardized and practical procedures for selecting AI techniques and determining training data samples, to enhance data collection, documentation, and sharing across bioenergy‐related areas, and to explore the potential of AI in supporting the sustainable development of bioenergy systems from holistic perspectives.

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Section: Applications Of Artificial Intelligence To Bioenergy Systemsmentioning

confidence: 99%

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Ming-yang

Yao

2021

GCB Bioenergy

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“…whereas, for the slack bus, the voltage is assumed to be 1∟0. Now that the objective and constraints are defined clearly, the exhaustive search method is used in this paper to get the optimal PP as in [20].…”

Section: Optimization Problemmentioning

confidence: 99%

A novel branch current flow-based construction of microgrids

Sivakumar

Jayashree

Danasagaran³

2021

IJEECS

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<span>An efficient procedure for defining the boundaries of microgrids in smart distribution systems during distributed generation expansion planning becomes an important consideration in constructing smart grids. A novel approach named "Modified Reverse Current Flow Method" is proposed in this paper to split a large radial distribution system into a required number of self-adequate microgrids. This cluster of microgrids will be capable of utilizing maximum power output of the distributed renewable energy generators and will act as highly reliable zones, during both islanded and grid-connected modes. This method is based on the trend reversal of the flow of current in the various parts of the circuit. This paper uses the practical machine operating curves of the distributed generators to calculate their reactive power output. For the considered distribution system, the proposed method is applied to find the optimal point of operation and the boundaries of microgrids. To bring out the superiority of this novel method, the improvement in reliability indices and economic savings of this method are compared with the results obtained using a similar method available in the literature. This method has several notable merits, namely, increased accuracy in the calculation of annual energy losses and the voltage profile. </span>

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“…López et al [120] presented a binary PSO-based method to accomplish optimal location of biomass-fuelled systems for distributed power generation with forest residues as biomass source, and the results outperformed those obtained by a GA when maximizing a profitability index taking into account technical constraints. In [121] the authors also applied a PSO algorithm for the optimal location and supply area for biomass-based power plants. There are also a number of researches reported on the application of PSO in the design and control of hybrid photovoltaic systems [122][123][124][125][126].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Bio-Inspired Optimization of Sustainable Energy Systems: A Review

Zheng

Chen

Yao

et al. 2013

Mathematical Problems in Engineering

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Sustainable energy development always involves complex optimization problems of design, planning, and control, which are often computationally difficult for conventional optimization methods. Fortunately, the continuous advances in artificial intelligence have resulted in an increasing number of heuristic optimization methods for effectively handling those complicated problems. Particularly, algorithms that are inspired by the principles of natural biological evolution and/or collective behavior of social colonies have shown a promising performance and are becoming more and more popular nowadays. In this paper we summarize the recent advances in bio-inspired optimization methods, including artificial neural networks, evolutionary algorithms, swarm intelligence, and their hybridizations, which are applied to the field of sustainable energy development. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.

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A Method for Particle Swarm Optimization and its Application in Location of Biomass Power Plants

Cited by 25 publications

References 22 publications

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

A novel branch current flow-based construction of microgrids

Bio-Inspired Optimization of Sustainable Energy Systems: A Review

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