Hybrid discrete PSO and OPF approach for optimization of biomass fueled micro-scale energy system

Gonzalez, MA; Lopez, Adolfo R.

doi:10.1016/j.enconman.2012.07.029

Cited by 22 publications

(5 citation statements)

References 18 publications

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“…Recent attempts to overcome the limitations of mathematical programming approaches include the application of meta-heuristic algorithm i.e. modified teaching-learning based optimization (MTLBO) [8], gravitational search algorithm (GSA) [9], improved harmony search method [10], discrete PSO [11], League Championship Algorithm (LCA) [12], etc. However, none of the above-mentioned frameworks has considered all the practical constraints on TUs such as POZs, multi-fuel options and valve-point effects.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Although, there is a vast body of literature on proposing a solution methodology for DOPF including [6][7][8][9][10][11][12], and on modeling the uncertainties and correlations between IRVs of the POPF problem which comprise [13][14][15][16][17][18][19][20][21][22][23], little attention has been paid to accomplish the goal of contributing all these essential issues in a well-organized POPF considering both the technical and economical restrictions.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Optimal power flow based TU/CHP/PV/WPP coordination in view of wind speed, solar irradiance and load correlations

Azizipanah‐Abarghooee

Niknam

Malekpour

et al. 2015

Energy Conversion and Management

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Optimal power flow based TU/CHP/PV/WPP coordination in view of wind speed, solar irradiance and load correlations

Azizipanah‐Abarghooee

Niknam

Malekpour

et al. 2015

Energy Conversion and Management

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“…More studies for different biomass system will be needed to make a generic conclusion regarding the advantage of heuristics. Several studies compared the computational time of different heuristics and concluded that the performance of PSO in reaching the near‐optimal solution surpassed other types of heuristics (Gómez‐González et al, 2013; López et al, 2008; Vera et al, 2010), with an exception that the GA performs better than PSO in optimizing the net present value of the given BSC scenario (Rafiel & Zadeh, 2013).…”

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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“…The methodology includes Binary Honey Bee Foraging (BHBF) Approach which based on particles swarm. Results are compared with those of Binary PSO and GA. Gómez-González et al (2013) developed a hybrid heuristic approach to find the best location and size of biomass fuelled electricity generation facilities. To this aim, PSO is used to search a range of location combinations in the distribution network and Optimal Power Flow (OPF) is utilized to define available capacity for each combination.…”

Section: Studies With Single Objectivementioning

confidence: 99%

Biomass to Energy Supply Chain Network Design: An Overview of Models, Solution Approaches and Applications

Balaman

Selim

2015

Handbook of Bioenergy

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Energy production from biomass is an alternative and additive way to fossil fuel based energy production to reduce the dependency on limited fossil fuel sources and mitigate the harmful environmental impacts of these systems. One of the major challenges in establishing efficient renewable energy systems is the complex supply chain structure in an uncertain decision environment, various decisions to be made and different conflicting criteria/objectives. This study describes the key issues in decision making for biomass to energy supply chains such as decision levels, uncertainty and sustainability concepts. It also provides a comprehensive review and systematic classification of the current literature on decision making approaches for design, management and operation of biomass to energy supply chains. This study allows readers to identify the decision making methods that satisfy the problem specific requirements and offer a clear vision of the advances in the field.

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Hybrid discrete PSO and OPF approach for optimization of biomass fueled micro-scale energy system

Cited by 22 publications

References 18 publications

Optimal power flow based TU/CHP/PV/WPP coordination in view of wind speed, solar irradiance and load correlations

Optimal power flow based TU/CHP/PV/WPP coordination in view of wind speed, solar irradiance and load correlations

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

Biomass to Energy Supply Chain Network Design: An Overview of Models, Solution Approaches and Applications

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