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International Journal of Energy for a Clean Environment
SJR: 0.195 SNIP: 0.435 CiteScore™: 0.74

ISSN Imprimer: 2150-3621
ISSN En ligne: 2150-363X

International Journal of Energy for a Clean Environment

Précédemment connu sous le nom Clean Air: International Journal on Energy for a Clean Environment

DOI: 10.1615/InterJEnerCleanEnv.2014007281
pages 69-89

DEMAND SHIFTING USING MODEL-ASSISTED CONTROL

Giorgos D. Kontes
School of Production Engineering & Management, Technical University of Crete, Chania, Greece
Georgios I. Giannakis
School of Production Engineering & Management, Technical University of Crete, Chania, Greece
Dimitrios Rovas
Technical University of Crete

RÉSUMÉ

Increasing energy demand and more strict environmental regulations have led to a turn to renewable energy generation sources, thus enabling the transition from traditional centralized electric grids to smart grids where the existing power grid is enhanced by distributed, small-scale renewables-based energy generation systems. In this new, complex landscape, buildings equipped with dedicated renewables are tasked to properly shape their thermal loads in order to consume as much power as possible from the renewables during peak-demand periods−a behavior enforced by Time-of-Use tariffs communicated from the grid. Under this perspective, in the present work, the ability to explore different operation strategies for the building systems to shape the thermal loads while accounting for the stochastic production profile of the renewables and maintaining comfortable building interiors is ensured by designing Building Energy Management Systems optimized for a specific building and targeted to the microclimate conditions of each area, maximizing the renewables' energetic benefits while preserving indoor comfort requirements. This is achieved utilizing a detailed thermal simulation model of the building, along with weather and occupancy forecasts and a response surface-based stochastic optimization algorithm. The potential of the proposed approach is demonstrated on example building located in Athens, Greece, but the generality of the method allows application in any building and in any test area, regardless of constructional, geographical, and climatic variations.