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-This paper introduces an approach to decision support systems in service-oriented automation control systems, which considers the knowledge extracted from the Petri nets models used to describe and execute the process behavior. Such solution optimizes the decision-making taking into account multi-criteria, namely productive parameters and also energy parameters. In fact, being manufacturing processes typically energy-intensive, this allows contributing for a clean and saving environment (i.e. a better and efficient use of energy). The preliminary experimental results, using a real laboratorial case study, demonstrate the applicability of the knowledge extracted from the Petri nets models to support real-time decision-making systems in service-oriented automation systems, considering some energy efficiency criteria.

This paper introduces a novel approach to the real-time decision-making in service-oriented manufacturing systems, addressing the myopia problem usually presented in such systems. The proposed decision method considers the knowledge extracted from the Petri nets models used to describe the services process behavior, mainly the T-invariants, combined with a multi-criteria function customized according to the system's particularities and strategies. An experimental laboratorial case study was used to demonstrate the applicability of the proposed real-time decision-making approach in service-oriented manufacturing systems, considering some productivity and energy efficiency criteria.

To enable the use of smart metering historical information of energy measurements in real time network operation, in this paper is proposed the generation of pseudo-measurements, which can be combined with real-time SCADA measurements and feed an online state estimation procedure. Hence increasing the network operator's situational awareness. The goal is to obtain a better representation of the network operation points, voltage values, than the one that is possible to obtain with the direct use of smart metering data, which is based on average values, by increasing the amount of available real time data points.