Eco-friendly and Ageing-aware Energy Management Software for Li-ions Battery (ECOBATTEM)
- The BSS will allow to increase the energy self-consumption and consequently reduce the global CO2 emissions
- An ageing-aware strategy for BSS deployment allows for maximizing the lifetime of the BSS itself with a consequent large renewable energy self-consumption and CO2 reduction
- A BSS with a minimum state of ageing can be deployed by utility/DSO in order to provide ancillary services to the power grid (such as peak-shaving and frequency/voltage control)
Coherent Energy Demonstrator Assessment (CEDA)
In Switzerland, six energy demonstrators serve as research platforms for different technologies, systems, and scaling. In their analysis, CEDA will standardize these demonstrators in order to show the impact of existing technologies on nationwide implementation in Switzerland. For this purpose, Switzerland-wide communication and collaboration between four SCCERs will be carried out with a total of six demonstrators. The harmonized data collection and modelling of state-of-the-art technologies makes it possible to plan their use in industry more effectively. Case studies will be carried out in order to obtain a direct benefit from the foundation that has been developed. Empa's ehub provides ideal conditions for other demonstrators still in the planning or construction phase.
Efficient Tethered Drones for Airborne Wind Energy (T10)
TwingTec develops together with Empa the next generation of wind energy using a tethered drone that flies like a kite. During this project a full scale tethered drone prototype will be developed and tested. The goal of this project is to design, build and test a full scale tethered drone prototype for a 10kW pilot system. This prototype will address the two critical remaining challenges before development of an upscaled system can begin: efficiency and energy autonomy.
Algorithmic Regulation & Control: A Novel Hybrid Data-driven Approach for Enhancing Building Performance along the Life Cycle (ARC)
In recent years, performance monitoring of buildings has become more common and the volume and resolution of data produced has increased significantly. This data opens up new possibilities for systematically improving building performance throughout the life cycle. Unlocking the potential for large-scale, data-driven performance improvement, however, necessitates the realization of effective feedback loops across different timescales (seconds to years) and spatial scales (building, city, canton, etc.). These feedback loops must be driven by a combination of rich, real-time data describing the state of the building stock and intelligent learning algorithms capable of effectively identifying and adapting feedback signals.
The aim of this project is to develop a transferrable methodology for algorithmic regulation and control of buildings and districts. A prototype ARC system will be implemented in NEST, the ehub facilities and the emerging dhub infrastructure. The project will result in the methodological elaboration and validation of the ARC approach, and a quantification of its potential to improve the energy performance of the Swiss building stock.
Renewable Management and Real-Time Control Platform (ReMaP)
The multidisciplinary demonstration project ReMaP will develop a flexible, software- and hardware-based, modular research platform for assessing potential energy system solutions for the neighborhood of the future. ReMaP will enable the testing, analysis and optimization of multi-component, multi-energy carrier systems on the distribution level, fostering the collaboration of multidisciplinary research teams from both academia and industry, and will furthermore provide a control and communication infrastructure for the joint operation of existing platforms and demonstrator sites. A large number of institutes at ETH Zürich, Empa, and PSI have committed to carrying out research projects using the platform through projects that feature inherent commonalities and that set ideal conditions for fostering further collaboration between these research groups.
Ecobilan Dynamique des Bâtiments (EcoDynBat)
The aim of the EcoDynBat project is to study the influence of temporal variability when calculating the environmental impacts of consumed electricity in buildings. This work will consider the temporal fluctuations of 1) national electricity generation, 2) electricity imports, 3) network losses and conversions, 4) decentralized electricity generation and 5) electricity demand within buildings.
Adaptive Controller Tuning
Heat pumps are essential devices for the realization of future energy efficient HVAC systems. However, controlling them is challenging due to their nonlinear dynamics, highly changing disturbances, and time delays, which typically occur in heating systems installations. In practice, manually tuned PID controllers are implemented, but there are several drawbacks to this approach: there are no guarantees for finding optimal parameters and stability of the dynamics, the performance is degraded when process conditions change (e.g. aging, replacing some part of the HVAC installation), and its implementation is expensive in personal costs and time. Another approach to controlling a heat pump system that can achieve better performance than manually tuned PID controller is model predictive control, but it requires complex modeling and system identification for each installation, which results in even higher personal costs and longer realization time. On the other hand, autotuning controllers based on adaptive machine learning (ML) algorithms can outperform the best manually tuned PID controllers or classical model predictive controllers in the presence of unknown disturbances and change of operative conditions while requiring substantially less implementation time and costs.
The goal of this project is to implement a controller autotuning method using recently developed learning algorithms such as safe Bayesian optimization and safe reinforcement learning and compare them to classical autotuner approaches.
This project aims at the replacement of linear building models by data-driven models for Model Predictive Control (MPC). State and output equations in MPC are replaced by a data-driven oracle based on adapted random forests (then called DPC – data predictive control) is going to be tested on a real system and compared to conventional MPC. For this, a unit in the NEST building is going to be selected in which the temperature in some thermal zones should be controlled by MPC/DPC by controlling a radiator or floor heating. To train the random forest and to build the MPC model, previously measured high quality data of the NEST building is used.
With the help of this project the possibility of optimal control of thermal zones in residential buildings can be investigated. Especially for private customers, the use of MPC is often not beneficial as the cost to model the thermal behavior of the building might outweigh the saved energy costs through better control. The use of data-driven methods such as ANN or random forest could significantly lower modelling cost and thus make optimal control real-life tractable for residential buildings. This would again have a significant impact on CO2 emissions.
Swiss Competence Center for Energy Research – Future Energy Efficient Buildings & Districts SCCER FEEB&D
The vision of the Swiss Competence Center for Energy Research on Future Energy Efficient Buildings & Districts (SCCER FEEB&D) is to develop solutions for the Swiss building stock which will lead to a reduction of the environmental footprint of the sector by a factor of three by 2035 thanks to efficient, intelligent and interlinked buildings.
The SCCER FEEB&D is addressing this challenge in a combined effort by leading Swiss and international partners from academia, industry and the public sector.
Funding body: Innosuisse
Partners: Empa, ETH Zurich, EPFL, HSLU, Uni Geneve, FHNW
Contact: Matthias Sulzer