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The purpose of this study is toward a better understanding of the interplay between the design and the operation under uncertainties of a simple distributed energy system (DES), by analyzing the sensitivity of the operation strategy over the size of the assets.

A two-step framework is developed in this work: first, the equipment sizes are obtained solving an integrated design approach where an operation strategy F^d is embedded in a design loop; then, once the sizes have been fixed, the DES is evaluated with an operation strategy F^a (which can be the same as F^d). The operation strategies F^d and F^a are not necessarily the same, so the objective of the paper is to study the interplay between the design and the operation by varying the optimality level of the operation strategies in both phases.

The results show that the design of DES cannot be approached without considering its close relationship with the operation strategy. Indeed, the design method needs to be chosen according to the performance of the operation policy finally used in real life: no matter if the operation strategies are the same in both phases but they must lead to a similar level of optimality in terms of operating performance.

The originality of this work is to shed light on the importance of the operation strategy in the design procedure as it seems rarely addressed, to the best of the authors’ knowledge, in the literature. Indeed, most of the paper dealing with stochastic design of DES solves single large two-stage problems without discussing the way power flows are finally controlled in real life. The optimal design and operation of DES is rarely addressed conjointly, this study aims at bridging the gap between these two isolated scientific communities.

The effectiveness of full passive wind turbine (WT) systems has been recently demonstrated. Such low cost and reliable structures without active control and with a minimum number of sensors can be efficient only if the system design parameters are mutually adapted through an integrated optimal design approach. The purpose of this paper is to analyze the effectiveness of a passive WT with regard to the variations of PMSG electrical parameters.

This work is more specifically devoted to the sensitivity analysis of a passive WT system according to the electrical variable variations of a Permanent Magnet Synchronous Generator (PMSG). It also investigates the interest of a robust design approach for reducing the sensitivity of the WT efficiency to the most influencing variables.

It is shown that efficiency of the passive WT system is rather sensitive to the variation of the stator flux and DC voltage at the system output.

A robust design approach is investigated in order to reduce the passive WT sensitivity to the stator flux and DC voltage variations.

A wide number of applications requires classifying or grouping data into a set of categories or clusters. The most popular clustering techniques to achieve this objective are K‐means clustering and hierarchical clustering. However, both of these methods necessitate the a priori setting of the cluster number. The purpose of this paper is to present a clustering method based on the use of a niching genetic algorithm to overcome this problem.

The proposed approach aims at finding the best compromise between the inter‐cluster distance maximization and the intra‐cluster distance minimization through the silhouette index optimization. It is capable of investigating in parallel multiple cluster configurations without requiring any assumption about the cluster number.

The effectiveness of the proposed approach is demonstrated on 2D benchmarks with non‐overlapping and overlapping clusters.

The proposed approach is also applied to the clustering analysis of railway driving profiles in the context of hybrid supply design. Such a method can help designers to identify different system configurations in compliance with the corresponding clusters: it may guide suppliers towards “market segmentation”, not only fulfilling economic constraints but also technical design objectives.

Servitization of manufacturing is characterized by very complex decision processes within strongly unstable and uncertain decision contexts. Decision-makers are face situations of lack of internal and external information. The purpose of this paper is to develop a decision aid approach to support the management of servitization decision-making processes.

The scientific orientation of this research consists in working at improving the efficiency of the servitization decision-making process, by identifying factors of non-reliability, in order to propose remediation actions for the whole process. Improving the final decisions taken by the managers is considered as a consequence of the improvement of the decision-making process reliability. The method, based on modeling and evaluation, requires the specification of a decision process model for servitization, used as a basis to assess decision process reliability and diagnose the enterprise’s servitization decision system. Improving the final decisions made by the managers is considered as a consequence of the improvement of the decision-making process reliability.

Key added values: first, to formalize a servitization decision-making reference model; second, to specify a reliability assessment applied to the decision system; and third, to define a decision process reliability diagnosis procedure for servitization, illustrated in a case study.

A direct perspective is to complete the focus on procedural reliability, by taking into consideration the subjective rationality of decision-makers in the reliability assessment procedure. Additionally, this reliability assessment method and diagnosis could become the basis of a larger risk management approach for servitization.

The diagnosis procedure proposed in the paper is dedicated to generating practical results for enterprise decision-makers, consisting in recommendations for decision process improvements, in the context of servitization. The approach is illustrated through an industrial SME case study. The practical implications are highly contextualized.

The key originality of this research is to tackle servitization complexity with a decision system modeling and diagnosis orientation, including the formalization of the notion of “decision process reliability,” and the specification and implementation of a quantitative assessment procedure.