A methodology for criticality analysis in integrated energy systems

Benjamin, Michael Francis D.; Tan, Raymond R.; Razón, Luis F.

doi:10.1007/s10098-014-0846-0

Cited by 30 publications

(20 citation statements)

References 63 publications

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“…Three main steps can be differentiated: (1) problem identification and structuring, (2) model building and application of the model to inform and challenge thinking, and (3) determination of a plan of action (Belton and Stewart 2002). The first step starts with defining the strategic objective in order to develop a common understanding of the problem.…”

Section: Development Of Risk-mitigation Strategiesmentioning

confidence: 99%

“…A criterion is 'a particular perspective according to which decision alternatives may be compared, usually representing a particular interest, concern or point of view' (Belton and Stewart 2002). According to Stewart (1992), criteria commonly have a hierarchical structure, starting from something general and leading to something specific.…”

Section: Development Of Risk-mitigation Strategiesmentioning

confidence: 99%

“…Moreover, they must be exclusive. That is, the decision makers can choose only one of the alternatives (Belton and Stewart 2002). For the development of riskmitigation strategies for the seaweed supply chain in Indonesia, appropriate alternatives need to be formulated.…”

Section: Development Of Risk-mitigation Strategiesmentioning

confidence: 99%

“…For example, within the application of Multi-Attribute Utility Theory (MAUT)/multi-attribute value theory (MAVT), utility/value functions are applied. In contrast, the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) procedure makes use of preference functions (Belton and Stewart 2002;Geldermann and Rentz 2005). This makes it necessary to compile the method-specific information as utility or preference functions, with corresponding thresholds where needed.…”

Section: Development Of Risk-mitigation Strategiesmentioning

confidence: 99%

“…To analyze the stability of the result or important parameters, it is essential to conduct a sensitivity analysis. The impact of changes in the weight values assigned to criteria, for example, can be analyzed with such an analysis (Belton and Stewart 2002).…”

Section: Development Of Risk-mitigation Strategiesmentioning

confidence: 99%

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Managing risks in the Indonesian seaweed supply chain

Mulyati

Geldermann

2016

Clean Techn Environ Policy

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Seaweed supply chains in Indonesia, especially carrageenan and agar products, are subject to risks arising both inside the participating companies and in their external networks. Uncertainties in yield, quality, price, and infrastructure in one part of the supply chain can affect the whole chain. To ensure a sustainable seaweed industry, an appropriate supply chain risk management (SCRM) is needed. There are four critical steps in SCRM: identifying seaweed supply chains, identifying and categorizing risks, assessing risks, and mitigating risks. To identify seaweed supply chains, we conducted field research, in-depth interviews, and literature studies. The field survey was conducted in the Provinces of South Sulawesi, West Java, East Java, Banten, and West Nusa Tenggara. The seaweed supply chains were modeled by the software Umberto to get a better understanding of material and energy flows between the key members. To identify and categorize the risks, we started with the risks mentioned in the existing literature works, and then applied the Delphi method to analyze the potential risk sources, their causes, and their impacts. To assess risks, we used a semiquantitative approach through the face-to-face interviews to generate a risk map showing the likelihood, and impact of adverse events. Afterward, the risk intensity was categorized based on the value of the responses and classified into five categories: negligible, marginal, critical, most critical, and catastrophic risks. The mitigation strategies considered sustainability (environment, economy, and social) and risks criteria. Multi-criteria decision analysis was used to evaluate these strategies.

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Section: Development Of Risk-mitigation Strategiesmentioning

confidence: 99%

Section: Development Of Risk-mitigation Strategiesmentioning

confidence: 99%

Section: Development Of Risk-mitigation Strategiesmentioning

confidence: 99%

Section: Development Of Risk-mitigation Strategiesmentioning

confidence: 99%

Section: Development Of Risk-mitigation Strategiesmentioning

confidence: 99%

See 3 more Smart Citations

Managing risks in the Indonesian seaweed supply chain

Mulyati

Geldermann

2016

Clean Techn Environ Policy

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Design of energy systems with redundancy allocation for unit operations based on supply reliability

Ling

Andiappan

Chew

2021

Intl J of Energy Research

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Summary Several mathematical models have been developed in the past to consider reliability when designing energy systems. However, these models focus on the system reliability which only considers the operability of the system. Hence, a reliability indicator called supply reliability is introduced to explicitly assess an energy system's reliability meeting the desired energy demand. Besides, previous models can only allocate equipment with identical sizes and equipment reliability. This could overdesign systems and ignore the possibility of allocating a combination of different equipment sizes and reliability. This paper presents a combinatory matrix methodology for allocating equipment with different sizes and reliabilities to improve supply reliability of an energy system design. This combinatory matrix method is a novel feature that allows decision‐makers to efficiently evaluate various configurations of equipment based on size and reliability. A case study consisting of a power distribution network is first presented and solved as a pedagogical example to illustrate the methodology and features of the proposed approach. Then, a large‐scale power system case study in Malaysia combining existing conventional power equipment and a palm‐based bioelectricity supply chain is solved. Based on the optimized results, the redundancy allocated increased the energy generated from the bioelectricity supply chain by 74.14% and cost by 82.96% to achieve the desired energy demand at high supply reliability. This allows decision‐makers to determine the cost associated with improving supply reliability for an energy system.

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