Algorithms & Tools for Control of Micro Generation and Energy Storage Components

WP Leader: TU Wien
Participants: UCC-NUIC, CARTIF

Objective
Utilization of existing buildings' monitoring data and the integrated analysis of this data can support the efforts to improve energy efficiency through the optimization of buildings' operational regime. In this work package, we collaborate with industrial partners to address gaps and deficits in current control strategies and algorithms and seeking new solutions.
Novel approaches toward optimised integration of energy generation and storage systems are developed for the demonstration campuses. In-depth knowledge of energy demand and occupancy patterns is required in order to optimise the operational scenarios for different types of energy harvesting and energy storage systems.

Approach

Optimized Control at the Building Level
Advanced building-level control algorithms for micro generation from renewable systems in UCC ERI demonstrator and for intelligent facade in UCC CEE demonstrator. The figure below shows the energy flows in ERI building that have been modelled according to the building systems schema and have been used in the optimizer.


The optimization model in UCC ERI building deploys predictions (gas price, electricity price, solar irradiation, DHW use and outdoor temperature) to find the least cost model to operate the building. The output values of the model are the required mode and power output for the geo thermal heat pump and the boiler, the filter factor used by blinds, and the power split between the different output flows.

The optimization model in UCC CEE building uses predicted occupancy, computer usage, solar radiation and outdoor temperature to find the cost optimal schedule of the heating for a given occupancy pattern and to compare different schedules. Output values for UCC CEE Demonstrator are the filter factor for the blinds, heating mode and power settings for the rooms, and the distribution of heating power amongst different rooms.

Optimized Control at the Zone Level
For the creation of a zone controller in UCC ERI demontrator, we utilize a model-based control approach. This approach is based on the creation of  virtual zone models (using HAMBase and Radiance simulation tools) for the ongoing control of building automation systems.
For a given set of monitored conditions, the model is used for calculating the predicted effects of different operational scenarios. These scenarios are then compared in order to identify a single preferable operational mode to be used for the deployment of a new control schedule.
The following figure (left) shows the general room model for the test setup of a predictive zone control. The figure on the right illustrates a rendered image of the test room generated using Radiance.


Weather and Occupancy Predictors
To predict the diffuse fraction of solar irradiance, a number of models were examined and implemented. The following figure shows the correlation between monitored and predicted diffuse horizontal irradiance for Cork using three different models.


Moreover, a stochastic occupancy model has been developed to capture the probabilistic nature of occupancy and to address its variance over time. The following figure shows the probabilistic distributions, which have been derived from the monitored data, to be used for predicting the daily occupancy.

Achievements

D5.1: Specification of Systems Architecture and Concept for Integration and Up-scaling.

Most recent, selected publications:

  • Realization of ICT potential in improving the energy efficiency of buildings: The CAMPUS 21 project, ECPPM2012, Reykjavík, Island.
  • Optimization-based simulation model calibration using sensitivity analysis,  IBPSA-CZ 2012.
  • The potential and challenges of monitoring-supported energy efficiency improvement strategies in existing buildings, ICEBO 2012, Manchester, UK.
  • A High-level Generative Scheme for Distributed Building System Control Logic, CLIMA 2013, Prague, Czech Republic.

WP Leader:   
Prof. Ardeshir Mahdavi, Dr. Matthias Schuss
Department of Building Physics and Building Ecology, Vienna University of Technology, Vienna, Austria
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