Deep integration of renewable energies into the electricity grid is restricted by the problems related to their intermittent and uncertain nature. These problems affect both system operators and renewable power plant owners since, due to the electricity market rules, plants need to report their production some hours in advance and are, hence, exposed to possible penalties associated with unfulfillment of energy production. In this context, energy storage systems appear as a promising solution to reduce the stochastic nature of renewable sources. Furthermore, batteries can also be used for performing energy arbitrage, which consists in shifting energy and selling it at higher price hours. In this paper, a bidding optimization algorithm is used for enhancing profitability and minimizing the battery loss of value. The algorithm considers the participation in both day-ahead and intraday markets, and a sensitivity analysis is conducted to check the profitability variation related to prediction uncertainty. The obtained results highlight the importance of bidding in intraday markets to compensate the prediction errors and show that, for the Iberian Electricity Market, the uncertainty does not significantly affect the final benefits.

Information and communication technology (ICT) applications are increasingly being applied in industry. Indeed, in the last years, initiatives like Industry 4.0, Factories of the Future and Industrial Internet of things, pushed by governments and industrial leaders, refer to ICT as the future of manufacturing. To meet this trend, machine tool industry needs to include all these advances. This chapter provides a state of the art of ICT applied in machine tool industry and serves as a reference of all the developments of twin-control project.

This paper presents a method for selecting the best battery sizing based on an optimal market participation strategy in a hybrid renewable power plant. The proposed formulation considers different scenarios to ensure the reserve provision compliance, and includes two terms. The first one maximizes the day-ahead and automatic frequency restoration reserve (aFRR) market revenues. The second minimizes the battery degradation, accounting for both cycling and calendar ageing effects. The Iberian Electricity Market is selected as the study case, and two different market scenarios are compared: the current market scenario and the upcoming one, in which the upward and downward aFRR band offers are independent. The operation of a set of batteries is simulated until their end of life, and results reveal that energy/power ratios higher than 3 h for the current market scenario, and than 4 h for the upcoming one, are less cost-effective. Regarding the best BESS sizing, the selection varies considering incomes or profitability criteria. Moreover, keeping the current prices, in the upcoming market scenario hybrid renewable power plants are encouraged to participate more in the downward than in the upward aFRR market.

This paper focusses on the dynamic modeling of the machine tool including its Computer Numeric Control (CNC), and its interaction with the machining process. To properly simulate modern machine tools in machining condition, which show close interaction between the dynamic behavior of the mechanical structure, drives, and the CNC, we use an integrated methodology that combines control and MBS capabilities in a nonlinear FEA solver called SAMCEF Mecano. To fully capture the dynamic behavior of the machine, force interactions between the cutting tool and the workpiece are also considered. A strong coupling between the mechatronic model of the machine tool and a machining simulation tool is implemented. A specialized cutting force element has been developed. It considers the dynamics of the tool tip combined with the tool workpiece engagement to generate cutting forces. The use of such digital twin model is demonstrated considering some industrial machining operations.

Manufacturing process monitoring is showing great advances thanks to increasing sensor availability and the development of edge to cloud IoT systems. However, the application of this technology in industry is slowed down due to cyber security policies, the coexistence of old manufacturing systems, with limited monitoring capabilities, with newer and fully monitored ones, and the lack of application-oriented functionalities. In this paper, a fast and automated machine tool characterization procedure, called Fingerprint, is presented, that allows determining useful Key Performance Indicators of the status of machine tools based on IoT technologies. The paper also presents the implementation of this technology in industrial environment.

Twin-Control (http://twincontrol.eu/) is a new concept for machine tool and machining process performance optimization. It is based on a new simulation model that integrates the different aspects that affect machine tool and machining performance. This holistic approach will allow a better estimation of machining performance than single featured simulation packages, including lifecycle concepts like energy consumption and end-life of components.This theoretical representation of the machine is complemented with real process data by the monitoring of the most important variables of the machining process and machine condition. This monitored information, combined with the developed models, is used at machine level to perform model-based control actions and/or warn about damaged components of the machine tool. In addition, a fleet-level data management system is used for a proper health management and optimize the maintenance actions on the machine tools.