Investment decisions considering economic, environmental and social factors: An actors' perspective for the electricity sector of Mexico

Jano-Ito, Marco A.; Crawford‐Brown, Douglas

doi:10.1016/j.energy.2017.01.016

Cited by 29 publications

(8 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…With regards to the EPDM categories, most of the studies analyzed in the sample (94%) reffer to the C1.4-Energy evaluation and assessment category, followed by C1.5-Site selection (32%), C1.1-Energy planning (26%), C1.2-Energy policy (17%), and last C1.3-Environmental impact analysis (10%). In this context, the first category (C1.1) covers a wide range of important strategic EPDM problematics (e.g., investments [88,189,207], sustainability assessment of energy systems, sources, technologies and options [34,42,93], power generation scenarios [97,205], production pathways [21,105,185], etc.) whereas the remaining categories are more like topic-specific (i.e., site/location, policy, environmental impacts, and strategic planning).…”

Section: Results and In-depth Analysismentioning

confidence: 99%

Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support

Sellak¹,

Ouhbi²,

Frikh

et al. 2017

Renewable and Sustainable Energy Reviews

View full text Add to dashboard Cite

Achieving sustainable energy planning and development involves complex decision-making processes. The energy planning decision-making (EPDM) field relies on a plethora of decision analysis methods that offered many solutions to process a variety of energy management and strategic decision-making problems. However, current EPDM solutions are unable to overcome the increasing complexity of strategic energy planning situations involving a large number of stakeholders in uncertain, dynamic, and distributed environments. This raises significant new challenges for researchers in both decision sciences and renewable and sustainable energy planning. On the basis of a representative assortment of peer-reviewed related literature selected by querying multiple electronic databases and indexed in Scopus and Web of Science databases domain journals over the last 12 years, this paper exhaustively highlights and discusses limitations of existing strategic EPDM solutions. The analysis is based on a classification specially developed by holistically harmonizing important domain concepts to categorize the considered representative sample of the field of interest. Additionally, this paper integrates results and conclusions from some recent and most cited literature reviews to (i) formulate essential evidence as well as practical and conclusive literature's supportalongside with the formulated representative sample -to this paper's subsequent insights and statements, and (ii) guarantee that no relevant articles have been excluded. A total of 78 related works is gathered and analyzed to provide a general view and discussion on major complexities found in classical/traditional strategic EPDM solutions and challenges for next-generation EPDM solutions. Moreover, a comparative analysis of the two solutions and a set of "quality indexes" of a next-generation EPDM solution were identified and some proposals were made to improve future applicative research. As an original result coming from the "quality indexes" identified through the review process, an intelligent expert-based framework for next-generation EPDM solutions is developed for enhanced renewable and sustainable energy planning.

show abstract

Section: Results and In-depth Analysismentioning

confidence: 99%

Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support

Sellak¹,

Ouhbi²,

Frikh

et al. 2017

Renewable and Sustainable Energy Reviews

View full text Add to dashboard Cite

show abstract

“…MAUT has been extensively used in multiple fields, e.g. for projects in investment planning (Jano-Ito & Crawford-Brown, 2017; Pujadas, Pardo-Bosch, Aguado-Renter, & Aguado, 2017), for health care (Claudio & Okudan, 2009;Sun, 2016), and for performance and budget assessment of water works (Ismaeel, 2016). A few studies in the context of infrastructure management are also found in literature that refer to MAUT as a base for mathematical modelling, for inspection and maintenance (de Almeida et al, 2015;Garmabaki, Ahmadi, & Ahmadi, 2016), for bridge sustainability assessment (Dong, Frangopol, & Sabatino, 2015), for maintenance investment decisionmaking (Arif, Bayraktar, & Chowdhury, 2015) and for bridge network level optimisation (Frangopol & Liu, 2007).…”

Section: Application Steps Of Multi-attribute Utility Theory (Maut)mentioning

confidence: 99%

Network level bridges maintenance planning using Multi-Attribute Utility Theory

Bukhsh

Stipanović

Klanker

et al. 2018

Structure and Infrastructure Engineering

View full text Add to dashboard Cite

Bridge infrastructure managers are facing multiple challenges to improve the availability and serviceability of ageing infrastructure, while the maintenance planning is constrained by budget restrictions. Many research efforts are ongoing, for the last few decades, ranging from development of bridge management system, decision support tools, optimisation models, life cycle cost analysis, etc. Since transport infrastructures are deeply embedded in society, they are not only subject to technical requirements, but are required to meet the requirements of societal and economic developments. Therefore, bridge maintenance planning should accommodate multiple performance goals which need to be quantified by various performance indicators. In this paper, an application of Multi-Attribute Utility Theory (MAUT) for bridge maintenance planning is illustrated with a case study of bridges from the Netherlands road network. MAUT seeks to optimise multiple objectives by suggesting a trade-off among them and finally assigns a ranking to the considered bridges. Moreover, utility functions of MAUT appropriately account for the involved uncertainty and risk attitude of infrastructure managers. The main contribution of this study is in presenting a proof-of-concept on how MAUT provides a systematic approach to improve the decisionmaking of maintenance planning by making use of available data, accommodating multiple performance goals, their uncertainty, and preferences of infrastructure managers.

show abstract

“…Typically, the portfolio approach is based on the solution of problems with one objective function seeking to minimise either the cost or the risk of the portfolio, subject to different constraints, also considering that real electricity generation assets can be defined in terms of cost or return and economic risk, for each alternative technology (deLlano-Paz et al, 2017). The most exhaustive and complete reviews on the application of MPT in energy planning are found in the studies of Delarue et al (2011) andJano-Ito andCrawford (2017). On more topics related to the principles of portfolio theory, the reader is referred to the papers of Awerbuch and Berger (2003), Awerbuch and Yang (2007), Elton and Gruber (1997).…”

Section: Introductionmentioning

confidence: 99%

Identifying optimal technological portfolios for European power generation towards climate change mitigation: A robust portfolio analysis approach

et al. 2019

View full text Add to dashboard Cite

In this paper, an integrative approach is proposed to link integrated assessment modelling results with a novel portfolio analysis framework for robust modelling. The approach is applied for identifying optimal technological portfolios for power generation in the EU towards climate change mitigation, in a timescale until 2050. The technologies considered include photovoltaics, concentrated solar power, wind, nuclear, biomass and carbon capture and storage. The proposed approach links data from the Global Change Assessment Model (GCAM), namely subsidy curves for emissions reduction and energy security for the six power generation technologies until 2050, with other decision support methods, in the aim of managing the inherent uncertainty and assessing the robustness of the optimal portfolios. The modelling results are then integrated in a bi-objective evaluation model for portfolio analysis. The model treats uncertainty stochastically, using a Monte Carlo simulation algorithm and the Iterative Trichotomic Approach, and defines specific portfolios of electricity generation technologies as the most robust. The results are presented and discussed, mainly in terms of highlighting the robustness of the Pareto optimal solutions, which is essential for policymakers to be more confident when selecting technology portfolios that feature a high degree of uncertainty, regarding their vulnerability to different future developments. By aggregating the results to one robust technological portfolio, the proposed approach features the potential to subsequently be linked to a deterministic model.

show abstract

Investment decisions considering economic, environmental and social factors: An actors' perspective for the electricity sector of Mexico

Cited by 29 publications

References 38 publications

Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support

Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support

Network level bridges maintenance planning using Multi-Attribute Utility Theory

Identifying optimal technological portfolios for European power generation towards climate change mitigation: A robust portfolio analysis approach

Contact Info

Product

Resources

About