Modelling Optimization of Energy Efficiency in Buildings for Urban Sustainability

MOEEBIUS introduces a Holistic Energy Performance Optimization Framework that enhances current modelling approaches and delivers innovative simulation tools which deeply grasp and describe real-life building operation complexities in accurate simulation predictions that significantly reduce the “performance gap” and enhance multi-fold, continuous optimization of building energy performance as a means to further mitigate and reduce the identified “performance gap” in real-time or through retrofitting.

 


MOEEBIUS objectives: Enabling the efficient Integration of distributed and intermittent energy resources into the Smart Grid and enhancing reliability and security of energy supply.


Peak-load management holds a significant position in MOEEBIUS, since it will enable making the most out of intermittent distributed energy sources through properly managing the flexibility offered by the demand side (non-critical loads controlled to regulate comfort at acceptable boundary levels) and optimally coordinating highly flexible district-wide systems (District Heating Systems). Accurate forecasting of weather conditions and, subsequently, RES outputs, along with aggregation of forecasted demand flexibility will enable the application of optimal control strategies at the district level, thus allowing for

(a) maximum penetration of RES into the energy mix,

(b) optimized scheduling and progressive retirement of emission-intensive primary energy sources,

(c) significant peak demand reduction,

(d) enhanced security of energy supply and

(e) significant associated monetary costs for prosumers (energy cost savings and avoidance of high energy charges during peak periods, incentives, rebates, etc.) and Aggregators (trading an inexpensive and highly competitive commodity – demand flexibility – in the balancing and ancillary services markets).

Sunday, April 30, 2017

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EU  This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 680517.