4.7 Article

Biogascluster: A clustering algorithm to identify potential partnerships between agribusiness properties

期刊

RENEWABLE ENERGY
卷 206, 期 -, 页码 982-993

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2023.02.121

关键词

Partnerships; Biogas; Clustering; Hierarchical algorithm; Renewable energy

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The lack of partnerships between biomass generating producers is a major obstacle to efficient biogas production. This paper proposes a new clustering algorithm, BiogasCluster, to determine the optimal number of clusters for generating a renewable energy community. The algorithm was implemented in R language and demonstrated its efficiency in generating stable clusters in various scenarios. This study provides important guidance for creating strategic partnerships in bioenergy generation and assessing the potential for establishing clusters of rural properties.
The lack of partnerships between biomass generating producers is one of the main barriers to efficient biogas production. Partnerships can help producers properly dispose of waste, assist in the supply of biodigesters, allow the union of properties with different potential biomass generators into clusters, and optimize operating costs. In this sense, new measures must be adopted with regard to the establishment of strategic partnerships between producers. Therefore, in this paper, we propose a new clustering algorithm, BiogasCluster, to decide the optimal number of clusters for generating a renewable energy community. To test the proposed algorithm's applicability, we implemented the algorithm in R language. Experiments with real databases showed that the BiogasCluster proved to be efficient in generating stable clusters for all considered scenarios. It was also found that the filters and criteria used in the proposed algorithm have not been used in previous studies. This study provides important guidance to creating strategic partnerships within bioenergy generation in addition to obtaining a preliminary assessment of the possibility of establishing clusters of rural properties with the aim of achieving energy security and adding value to their businesses.

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