Abstract
While renewable energy generation from time variable sources keeps increasing, end-user interactions through smart grid development and the adoption of smart appliances lead to significant changes in consumer behavior. Hence, renewable energy generation must be curtailed more frequently when the expected demand is surpassed. Likewise, demand side measures should be considered more thoroughly so that appropriate capacity limits for new generation units can be defined. An analysis of load shifting is performed for Sao Miguel Island, Azores, and indicates that through defined rules of load shifts the base load limit can be elevated and new limits for the maximum installed capacity can be set. The effects of load shifts are crucial for decision makers since investments in additional renewable energy capacities can be limited and back-up capacities can be reduced. (C) 2016 The Authors. Published by Elsevier Ltd.

Abstract
In this paper, operation management of microgrids is performed. To do so, some contingencies including outage of distributed generators (DG), energy storage (ES) and the upstream network are considered. Since the microgrids have suitable capabilities in terms of control and communication, demand response reserve can be applied to improve the operation management. Using Monte Carlo simulation method and Markov chain, several scenarios are generated to show the possible contingencies in various hours. Then, a scenario reduction method is used for reducing the number of scenarios. Finally, a two-stage stochastic model is applied to solve a day-ahead scheduling problem in mixed-integer linear programming by GAMS. Consequently, the effect of demand response in the reduction of operation cost is demonstrated.

Abstract
In recent years, we have been witnessing a real explosion of information, due in large part to the development in Information and Knowledge Technologies (ICTs). As in-formation is the raw material for the discovery of knowledge, there has been a rapid growth, both in the scientific community and in ICT itself, in the approach and study of the phenomenon called Big Data (BD) [1]. The concept of Smart Grids (SG) has emerged as a way of rethinking how to produce and consume energy imposed by economic, political and ecological issues [2]. To become a reality, SGs must be sup-ported by intelligent and autonomous IT systems, to make the right decisions in real time. Knowledge needed for real-time decision-making can only be achieved if SGs are equipped with systems capable of efficiently managing all the information sur-rounding their ecosystem. Multi-Agent systems have been increasingly used from this purpose. This work proposes a system for the management of information in the context of agent based SG to enable the monitoring, in real time, of the events that occur in the ecosystem and to predict upcoming events.

Abstract
Predicting the inherent traffic behaviour of a network is an essential task, which can be used for various purposes, such as monitoring and managing the network's infrastructure. However, the recent surge of dynamic environments, such as Internet of Things and Cloud Computing have hampered this task. This means that the traffic on these networks is even more complex, displaying a nonlinear behaviour with specific aperiodic characteristics during daily operation. Traditional network traffic predictors are usually based on large historical data bases which are used to train algorithms. This may not be suitable for these highly volatile environments, where the strength of the force exerted in the interaction between past and current values may change quickly with time. In light of this, a taxonomy for network traffic prediction models, including the review of state of the art, is presented here. In addition, an analysis mechanism, focused on providing a standardized approach for evaluating the best candidate predictor models for these environments, is proposed. These contributions favour the analysis of the efficacy and efficiency of network traffic prediction among several prediction models in terms of accuracy, historical dependency, running time and computational overhead. An evaluation of several prediction mechanisms is performed by assessing the Normalized Mean Square Error and Mean Absolute Percent Error of the values predicted by using traces taken from two real case studies in cloud computing.

Abstract
The PERFoRM project, an innovation action promoted within the scope of the EU Horizon 2020 program, advocates the use of an Industrie 4.0 compliant system architecture for the seamless reconfiguration of robots and machinery. The system architecture re-uses the innovative results from previous successful R&D projects on distributed control systems domain, such as SOCRADES, IMC-AESOP, GRACE and IDEAS. This paper, after describing the main pillars of the PERFoRM system architecture, focuses on mapping the system architecture into four industrial use cases aiming to validate the system architecture design before its deployment in the real environments. © Springer International Publishing AG 2017.

Abstract