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Rue, Switzerland

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Grant
Agency: European Commission | Branch: H2020 | Program: IA | Phase: EE-06-2015 | Award Amount: 5.56M | Year: 2016

The growing share of variable renewable energy necessitates flexibility in the electricity system, which flexible energy generation, demand side participation and energy storage systems can provide. SIMBLOCK will develop innovative demand response (DR) services for smaller residential and commercial customers, implement and test these services in three pilot sites and transfer successful DR models to customers of Project partners in further European countries. The pilot sites are blocks of highly energy efficient buildings with a diverse range of renewable and cogeneration supply systems and requisite ICT infrastructure that allows direct testing of DR strategies. SIMBLOCKs main objectives are to specify the technical characteristics of the demand flexibility that will enable dynamic DR; to study the optimal use of the DR capability in the context of market tariffs and RES supply fluctuations; and to develop and implement market access and business models for DR models offered by blocks of buildings with a focus on shifting power to heat applications and optimization of the available energy vectors in buildings. Actions toward achieving these objectives include: quantifying the reliability of bundled flexibility of smaller buildings via pilot site monitoring schemes; combining innovative automated modelling and optimization services with big data analytics to deliver the best real time DR actions, including motivational user interfaces and activation programs; and developing new DR services that take into account the role of pricing, cost effectiveness, data policies, regulations, and market barriers to attain the critical mass needed to effectively access electricity markets. SIMBLOCKs approach supports the Work Program by maximizing the contribution of buildings and occupants and combining decentralized energy management technology at the blocks of building scale to enable DR, thereby illustrating the benefits achievable (e.g. efficiency, user engagement, cost).


Grant
Agency: European Commission | Branch: FP7 | Program: CP-IP | Phase: EeB.NMP.2012-1 | Award Amount: 9.84M | Year: 2012

To solve the energy dilemma, research effort was concentrated on stand-alone buildings weakly tight to their immediate environment. Specifically in each building only key sub-systems were considered individually. Such individual systems were made more effective by becoming pervasive into any building area (the reign of boxes and controllers). Importing parameters from other sub-systems allowed better regulations (Access or Occupancy management systems). Energy Usage Analysis tools are fairly new on the market. They provide the capability to analyse energy profiles of scattered buildings of large corporations. Energy usage analysis and planning on a district level is inexistent. Neither is Energy Usage Modelling leveraged in control schemes. This leaves an unexplored area for more effective building control schemes and suggests a potential of each smart building to contribute to District level energy optimization schemes, thanks to appropriate behavioural and stochastic models. In parallel, efforts made many more renewable and cogeneration energy sources available as high capacity energy storage systems. Such systems now increasingly enter into the planning of large districts, but still timidly penetrate individual buildings. Thus, energy flows (electrical or thermal) can be managed through energy usage schemes, planned in time for significant savings. To reach this aim, it is proposed to play in real time adaptive and predictive behavioural models of buildings and districts, exposed to weather conditions, human presence, energy-efficient materials and technologies. Such models will allow finding optimal supply/demand balancing. Building energy management systems will be turned in real-time configurable ones, bringing flexibility to the building. Thus, buildings will establish, in real-time, energy schemes with the district energy management and information system (DEMIS). AMBASSADORs vision: Flexible buildings to make eco-friendly districts.


Lindelof D.,Neurobat AG | Afshari H.,Neurobat AG | Alisafaee M.,Neurobat AG | Biswas J.,Neurobat AG | And 3 more authors.
Energy and Buildings | Year: 2015

Conventional weather-compensated heating controllers are often configured to deliver more heating than necessary, resulting in energy losses. Furthermore, they cannot take into account future climate conditions, and yield less than optimal thermal comfort. We have developed a non-invasive add-on module for existing heating controllers that implements an adaptive, model-predictive heating control algorithm. This algorithm helps the heating controller deliver a heating energy just sufficient for maintaining thermal comfort, resulting in energy savings. In this paper we report on the energy savings measured on ten buildings equipped with this device. By monitoring the space heating energy during the 2013-2014 heating season, and by periodically alternating between the new controller and the reference controller, we establish the energy signature of all buildings with both controllers. The comparison of the energy signatures yields the relative energy savings achievable with the new controller. These energy savings are positive for all test sites, with a mean of 28 ± 4% (standard error of the mean). © 2015 Elsevier B.V. All rights reserved.


A heating control unit for a building with a heating system, which includes heat exchangers supplied by a boiler, a loop for circulating the heating fluid including a mixing valve, radiators, a return loop, and a control circuit which receives information on the indoor and outdoor conditions in order to control the mixing valve. The control unit includes elements for predicting and optimizing the heating needs of the users of the building and for providing the control circuit with modified information on the outdoor temperature likely to adjust the parameters of the heating system to the needs of the users and minimize power consumption.

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