An optimal renewable energy management strategy with and without hydropower using a factor weighted multi-criteria decision making analysis and nation-wide big data - Case study in Iran

Ifaei, Pouya; Faridhosseini, Alireza; Yoo, ChangKyoo

doi:10.1016/j.energy.2018.06.043

Cited by 58 publications

(10 citation statements)

References 47 publications

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“…In 2013, Iran was among the countries responsible for two-thirds of global carbon dioxide emissions, where the ratio of CO 2 (kg) to GDP was 2.17, very high compared to the world average of 0.57 [26]. Still, Iran donates diverse potential scenarios of renewable energy generation due to climatic diversity [27]. New government policies have been developed to expand scientific knowledge on renewable energy sources to meet energy demand, reducing dependence on fossil fuels [28].…”

Section: Preliminary Findingsmentioning

confidence: 99%

Renewable Energy Problems: Exploring the Methods to Support the Decision-Making Process

et al. 2020

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In the current scenario of increasing energy demand and encouraging sustainable development in countries, the energy sector’s planning has become more complex, involving multiple factors, such as technical, economic, environmental, social, and political. The decision process plays a vital role in structuring and evaluating complex decision situations related to the sector, considering various criteria and objectives, encouraging adopting policies to promote energy efficiency actions by increasing research on renewable energy sources and strategic energy decisions. The high number of multi-criteria decision support methods (MCDM) available and their efficiency in solving highly complex problems results in an impasse with their selection and application in specific decision situations. Thus, the scientific community requires methodological approaches that help the decision-maker select the method consistent with his problem. Accordingly, this paper conducts a Systematic Literature Review (SLR) of renewable energy problems associated with MCDM methods based on a final set of 163 articles. We identified five categories of problems solved by MCDM techniques: Source selection, location, sustainability, project performance, and technological performance. We separate the MCDM process into five evaluation steps (alternative selection, criteria selection, criteria weighting, evaluation of alternatives, and post-assessment analyzes), and we extract the methods used in each MCDM step from papers. This paper’s main contribution is identifying the most common MCDM methods in the renewable energy area and the energy problem they solve. Accordingly, this manuscript helps energy decision-makers, entrepreneurs, investors, and policy-makers to improve their ability to choose the proper MCDM methods to solve energy problems.

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Section: Preliminary Findingsmentioning

confidence: 99%

Renewable Energy Problems: Exploring the Methods to Support the Decision-Making Process

et al. 2020

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“…In other studies of W / PV systems, TOPSIS has been applied in its basic form (Perera et al, 2015), or with FL defining the fuzzy TOPSIS tool (Theodosiou et al, 2015). For their part, Ifaei et al (2018), applied the same tool to simulate a RES composed of PV / W / BG / HY / BM, as a pilot unit to evaluate the energy potential of Iran. Bakhtiari & Naghizadeh (2016), propose the SFLA / ε-constrain (Shuffled Frog Leaping Algorithm / Restrictions Ipsilon) tool, for the design of a RES W / PV / FC / Elec / H2 / Batt, with an intermittent demand profile.…”

Section: Background (Roots)mentioning

confidence: 99%

Use of the multicriteria analysis methods to optimize sustainable energy systems based on renewable sources

Urbano-Arcila

Danguillecourt

Galvez

et al. 2020

JURRE

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Sustainable The sustainable energy development implies to look for balanced technological alternatives, economically, socially, and environmentally. Therefore, it is a complex process which solution is commonly supported on mathematic tools, whether multi-objective optimization (MODM) or multicriteria analysis (MCDM). In this work, an analysis of these tools in the field of the renewable energy is carried out, with the purpose of identify the hybrid renewable energy systems design under a sustainable view perspective, which implies the use of MCDM or MODM tools. The methodology used consists of identifying the references related to the design of hybrid renewable energy systems and the sustainability term, using the WOS (Web of Science) search engine, which were analyzed in chronological order to identify the basic forms of the methods, the application foundations, and finally, the perspectives, reflected in the most recent publications. MCDM tools were identified as the most used with the 71% of the analyzed references, above the multiobjective methodologies; being the most popular the Hierarchical Analytical Process (AHP) and the Order of Preference for Similarity with the Ideal solution (TOPSIS). It is important that the decision makers use such kind of tools, both to design and to plan sustainable energy systems.

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“…Among the portioned clustering methods, the k-means clustering algorithm is a mature and effective clustering approach [30,31]. The k-means algorithm is a well-known approach that clusters the observations or variables according to the highest similarities and lowest dissimilarities among them [33,34]. k-means clustering starts by selecting a random number of centroids for desired clusters.…”

Section: K-means Clustering Approachmentioning

confidence: 99%

An Efficient Burst Detection and Isolation Monitoring System for Water Distribution Networks Using Multivariate Statistical Techniques

et al. 2019

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Detection and isolation of burst locations in water distribution networks (WDN) are challenging problems in urban management because burst events cause considerable economic, social, and environmental losses. In the present study, a novel monitoring and sensor placement approach is proposed for rapid and robust burst detection. Accordingly, a hybrid principal component analysis (PCA) and standardized exponential weighted moving average (EWMA) system is proposed for WDN monitoring and management. In addition, the optimal sensor configuration is obtained using PCA, k-means clustering, and a sensitivity analysis considering the diurnal patterns and the noises of pressure and flowrate data in the WDN. The proposed system is applied to a branched WDN, and the results are compared to those obtained with conventional monitoring systems. The results show that the proposed system detected the burst occurrence regardless of noise size with a detection rate of 93%. Compared to conventional systems, the isolation ratio improved by 10%, indicating that the bursts were isolated more accurately. In addition, the corresponding sensor configuration was 40% less expensive than the conventional systems.

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An optimal renewable energy management strategy with and without hydropower using a factor weighted multi-criteria decision making analysis and nation-wide big data - Case study in Iran

Cited by 58 publications

References 47 publications

Renewable Energy Problems: Exploring the Methods to Support the Decision-Making Process

Renewable Energy Problems: Exploring the Methods to Support the Decision-Making Process

Use of the multicriteria analysis methods to optimize sustainable energy systems based on renewable sources

An Efficient Burst Detection and Isolation Monitoring System for Water Distribution Networks Using Multivariate Statistical Techniques

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