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SUMMARYIn the framework of deregulated electricity markets, distributed generation and load management are emerging as new technologies. In this context, the microgrid concept adds value to distributed energy resources (DER) by aggregating them into autonomous grids that provide high levels of efficiency, security and controllability. In order to achieve its functions, a microgrid requires a control system in charge of operating the elements in it in an optimal way while the required quality parameters are fulfilled.This paper describes the general architecture of a microgrid energy management system (EMS) based on multi-agent system (MAS) technologies. MAS technologies are considered to be very well suited to systems that require plug and play capabilities, robustness, heterogeneous device integration, high scalability and extensibility. All of which are essential characteristics of a microgrid EMS. This paper also describes the implementation of a secondary control system based on the microgrid EMS architecture developed. This system is in charge of maintaining microgrid frequency and power exchange with the main grid close to predefined values. This is achieved in a way in which generation and consumption resources are optimally dispatched and able to operate in both grid connected and islanded modes. Finally, the control system developed is tested in an actual microgrid in order to assess its performance and demonstrate its applicability.

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Summary This paper describes a tool for an Aggregator to forecast the aggregated load demand response of a group of domestic customers subscribed to an indirect load control program based on price/volume signals. The tool employs a bottom‐up approach based on physical end‐use load models where the individual responses of a random sample of customers are combined in order to build the aggregated load demand response model. Simulation of the individual responses is carried out with an optimization algorithm based on mixed integer linear programming that minimizes the electricity bill whilst maintaining consumer's comfort level. To improve the performance of the model, a genetic algorithm for fitting the input parameters according to measured data is also provided. The tool is intended to allow the Aggregator rehearsing the impact of different control strategies and therefore choosing the most appropriate ones for market participation and portfolio optimization. To exemplify the methodological applicability of the developed algorithm, a case study based on an actual power system in eastern Spain is considered. Copyright © 2014 John Wiley & Sons, Ltd.

This chapter deals with the coupling of smart grid laboratories for joint experiments. Therefore, various possibilities are outlined and a reference implementation is introduced. Finally, the vision of a distributed, virtual research infrastructure is presented.