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In this paper, we present a two-stage model of zonal electricity markets with day-ahead market clearing and real-time re-dispatch and balancing that accounts for transmission line switching at both stages. We show how the day-ahead problem with switching can be formulated as an adaptive robust optimization problem with mixed integer recourse and present a new algorithm for solving the adversarial max-min problem that obeys the structure of an interdiction game. We apply the model on a realistic instance of the Central Western European system and comment on the impacts of both proactive and reactive transmission switching on the operating costs of the system. Part I presents day-ahead models of a short-term zonal electricity market with switching, and describes our algorithmic approach for solving these models efficiently. Part II describes variants of the real-time model, and presents the results of our case study on the Central Western European market. [ABSTRACT FROM AUTHOR]

In this paper, we present a two-stage model of zonal electricity markets with day-ahead market clearing, and real-time re-dispatch and balancing that accounts for transmission line switching at both stages. We show how the day-ahead problem with switching can be formulated as an adaptive robust optimization problem with mixed integer recourse, and present a new algorithm for solving the adversarial max-min problem that obeys the structure of an interdiction game. We apply the model on a realistic instance of the Central Western European system, and comment on the impacts of both proactive, and reactive transmission switching on the operating costs of the system. Part I presents day-ahead models of a short-term zonal electricity market with switching, and describes our algorithmic approach for solving these models efficiently. Part II describes variants of the real-time model, and presents the results of our case study on the Central Western European market. [ABSTRACT FROM AUTHOR]

This paper deals with the design of robust model-based fault detection and isolation (FDI) systems for atmospheric reentry vehicles. This work draws expertise from actions undertaken within a project at the European level, which develops a collaborative effort between the University of Bordeaux, the European Space Agency, and European Aeronautic Defence and Space Company Astrium on innovative and robust strategies for reusable launch vehicles (RLVs) autonomy. Using an H∞/H- setting, a robust residual-based scheme is developed to diagnose faults on the vehicle wing-flap actuators. This design stage is followed by an original and specific diagnosis-oriented analysis phase based on the calculation of the generalized structured singular value. The latter provides a necessary and sufficient condition for robustness and FDI fault sensitivity over the whole vehicle flight trajectory. A key feature of the proposed approach is that the coupling between the in-plane and out-of-plane vehicle motions, as well as the effects that faults could have on the guidance, navigation, and control performances, are explicitly taken into account within the design procedure. The faulty situations are selected by a prior trimmability analysis to determine those for which the remaining healthy control effectors are able to maintain the vehicle around its center of gravity. Finally, some performance indicators including detection time, required onboard computational effort, and CPU time consumption are assessed and discussed. Simulation results are based on a nonlinear benchmark of the HL-20 vehicle under realistic operational conditions during the autolanding phase. The Monte Carlo results are quite encouraging, illustrating clearly the effectiveness of the proposed technique and suggesting that this solution could be considered as a viable candidate for future RLV programs. [ABSTRACT FROM AUTHOR]

The countries of Central and Eastern Europe moved from a previously centrally planned economy to a modern transition economy with strong market aspects. This paper proposes project management as an answer to this transition. Traditional Cost-Benefit analysis does not respond to this purpose. Indeed Cost-Benefit analysis is only interested in one specific project and not in a competition between projects. In addition all goals (objectives) have to be translated into money terms, leading sometimes to immoral consequences. On the contrary Multi-Objective Optimization takes care of different objectives, whereas the objectives keep their own units. However different methods exist for the application of Multi-Objective Optimization. The authors tested them after their robustness resulting in seven necessary conditions. MOORA (Multi-Objective Optimization by Ratio analysis) and MULTIMOORA (MOORA plus Full Multiplicative Form), assisted by Ameliorated Nominal Group and Delphi Techniques, satisfy the seven conditions, although in a theoretical way. A simulation exercise illustrates the use of these methods, ideals to be strived for as much as possible. [ABSTRACT FROM AUTHOR]