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The uncertain and time-variant nature of renewable energy results in the need to deal with peaks in the production of energy. One approach is to achieve a load shift and thereby help balancing the grid by using thermally Activated Building Systems (TABS). Control systems currently in place do not exploit the full potential of TABS. This paper reviews how Model Predictive Control can possibly reduce the fluctuations of the demand and supply of (renewable) energy as it enables the TABS to react to the dynamics of weather and its impact on the grid at any time.
This paper focuses on appropriately measuring the accuracy of forecasts of load behavior and renewable generation in micro-grid operation. Common accuracy measures like the root mean square of the error are often difficult to interpret for system design, as they describe the mean accuracy of the forecast. Micro-grid systems, however, have to be designed to handle also worst case situations. This paper therefore suggests two error measures that are based on the maximum function and that better allow understanding worst case requirements with respect to balancing power and balancing energy supply.
Innovative technologies and concepts will emerge as we move towards a more dynamic, service-based, market-driven infrastructure, where energy efficiency and savings can be facilitated by interactive distribution networks. A new generation of fully interactive Information and Communication Technologies (ICT) infrastructure has to be developed to support the optimal exploitation of the changing, complex business processes and to enable the efficient functioning of the deregulated energy market for the benefit of citizens and businesses. The architecture of such distributed system landscapes must be designed and validated, standards need to be created and widely supported, and comprehensive, reliable IT applications will need to be implemented. The collaboration between a smart house and a smart grid is a promising approach which, with the help of ICT can fully unleash the capabilities of the smart electricity network.