Stochastic Data Envelopment Analysis applied to the 2015 Brazilian energy distribution benchmarking model

Costa, Marcelo Azevedo; Salvador, Cláudio Vítor Maquiné; Silva, Aline Veronese da

doi:10.1016/j.dajour.2022.100061

Cited by 7 publications

(2 citation statements)

References 26 publications

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“…A recent study, on offshore wind and wave energy integration sites in the China Sea, was evaluated by Chen et al (Li et al, 2022), meta‐evaluation based on qualitative and quantitative indicators by using an unstructured‐grid finite‐volume surface wave model (FVCOM‐SWAVE). In addition, problems with the choice of locations for renewable energy extraction equipment are found by some DEA models (Costa et al, 2022; Kouaissah & Hocine, 2022). Besides the DEA method, it can be extended with PT for evaluation.…”

Section: Literature Reviewmentioning

confidence: 99%

A prospect theory extension of data envelopment analysis model for wave‐wind energy site selection in New Zealand

Wang,

Nguyen,

Nhieu

et al. 2023

Manage Decis Econ

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Renewable energy sources have been utilized by countries worldwide, but offshore energy exploitation projects remain highly promising for both the world and New Zealand specifically. The selection of equipment installation locations can be challenging and requires significant investment, prompting the need for optimization in offshore energy exploitation. Wind and wave location (WWL) integrated sites offer a highly potential solution for reducing installation costs and enhancing efficiency in renewable energy exploitation. However, the decision‐making process for selecting equipment locations is crucial and directly affects the outcome. This study aims to evaluate and consider sustainable and effective equipment installation sites based on both quantitative indicators and behavioral psychology of decision making. To achieve this goal, the study utilizes the prospect theory Data Envelopment Analysis (PT‐DEA) model, which builds on PT to analyze and evaluate the effectiveness of offshore installation site selection with consideration for human psychology. By combining the results of quantitative indicators and decision‐making behavior, the study identifies sustainable and effective offshore installation sites, recommending appropriate equipment installation locations WWL. The study is also compared and referenced with relevant plans, projects, and development policies in New Zealand for confirmation.

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Section: Literature Reviewmentioning

confidence: 99%

A prospect theory extension of data envelopment analysis model for wave‐wind energy site selection in New Zealand

Wang,

Nguyen,

Nhieu

et al. 2023

Manage Decis Econ

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“…An AHP-Technique for Order of Preference by Similarity to Ideal Solution (AHP-TOPSIS) has been used for identifying the best location for the implantation of large-scale photovoltaic plants [3]. Costa et al [4] used a stochastic data envelopment analysis (DEA) as an alternative for estimating efficient costs, thus providing a much simpler alternative. Bertoncini et al [5] applied the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) II method for the comparison of energy requalification strategies to design post-carbon cities.…”

Section: Introductionmentioning

confidence: 99%