Ongoing projects

Ongoing research projects

Ongoing PhD projects                            

                                                                                                                                                         

 

Ongoing research projects

Data-driven, self-tuning controllers for large scale deployment
More than 90% of industrial controllers are still based on proportional-integral-derivative (PID) and rule-based (RB) control algorithms, as no other algorithms match the simplicity, clear functionality, and applicability of these two types. However, during the lifetime of a system, due to aging or exchanging of some components of the system, the overall process conditions typically change. This leads to sub-optimal control performance, with direct or indirect operational costs and calls for the manual re-tuning of the controllers. More sophisticated controllers exist, such as ones based on model predictive control (MPC), but they typically require complex physics-based models for proper functioning, which can be quite costly to obtain for certain systems.
In this project we aim to develop new data-driven self-adaptive control strategies and compare them to the classical and state-of-the-art adaptive strategies. We will particularly look at the scalability potentials of these algorithms. Our current efforts involve the development of data-driven self-learning controllers for heat pump systems, room temperature control, and smart charging of bidirectional EVs when coupled to buildings and grids. 
Funding body: internal
Partners: ETH Zurich, EPFL, Industry
Contact: Bratislav Svetozarevic
Involved group: ehub

Data Predictive Control
The building sector is responsible for more than one third of the global final energy consumption. Heating and cooling of buildings requires approximately half of this energy. Improving the operation of heating and cooling systems has therefore a significant impact on the mitigation of the climate change. Model Predictive Control (MPC) has been shown to be an energy efficient approach for building climate control. However, the costs to generate the required first-principle based models might be a bottleneck for widespread industrial application of MPC in the building domain.
This project aims at the replacement of first-principle based building models by data-driven models in the MPC framework (called Data Predictive Control (DPC)). Recent experiments with data-driven models based on adapted Random Forests revealed the high potential of DPC for energy efficient climate control in residential buildings. Further comparisons with state-of-the-art controllers like conventional MPC as well as the implementation of the DPC algorithm into an embedded system, is part of this project. With the implementation into an embedded system, we are able to test and evaluate a thermostat retrofit case for residential buildings.
Funding body: internal
Partners: ETH Zurich
Contact: Benjamin Huber
Involved group: ehub

NCCR Automation
The latest advances in sensor technology, data generation and computing have the potential to profoundly change areas of our economic and daily lives. The complete automation and control of entire systems such as cities (smart cities), power grids (smart grids) or industrial processes (Industry 4.0) is increasingly becoming a reality in the course of digital transformation.
The aim of the Dependable Ubiquitous Automation National Centre of Competence in Research (NCCR) is to advance the methodological and technological bases for the large-scale implementation of such complex systems. By improving decision-making and control procedures and developing new algorithms and computer methods, the reliability and flexibility of intelligent systems can be improved. The new findings will be applied in the fields of energy management, mobility and advanced manufacturing. One of the NCCR’s key projects is to develop and implement a fully automated and decentralized energy management system at district or commune level. This will allow the economic potential and social impact of automated applications to be tested in real life.
Funding body: SNF
Partners: ETH Zurich, EPF Lausanne, FHNW
Contact: Philipp Heer
Involved groups: MES, ehub

RAPIDE
A major barrier limiting the installation of photovoltaics are voltage quality problems they cause in the distribution grids. Instead of expensive grid reinforcements, these issues can be solved by using the photovoltaics’ inverters to optimize the feed-in of reactive power. Today parameters controlling reactive power are pre-set at the factory and do not consider the situation in the local grid.
By using grid measurements and a self-learning algorithm the proposed method can optimize parameters in-situ without the need for a network model. The parameters can be updated regularly, i.e. to reflect different seasonal power flows or the addition of new plants.
In this project the performance of a developed algorithm is tested on the ehub infrastructure.
Funding body: Innosuisse
Partners: Fleco Power, CSEM
Contact: Philipp Heer
Involved group: ehub

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.
Funding body: Swiss Federal Office of Energy SFOE
Partners: HES-SO, SUPSI
Contact: Philipp Heer
Involved group: ehub

Aliunid
Existing infrastructure in buildings, such as heat pumps or domestic hot water heaters is usually operated to purely satisfy the thermal needs of residents. However, thanks to thermal storage capacities available in buildings' thermal mass or water tanks, there exists energy flexibility in when to charge these storages. These flexibilities can be coordinated to satisfy other interests, e.g. from other players in the electric or thermal distribution grid. In a joint project with aliunid AG, controllers that optimally utilize these flexibilities are developed and implemented in the NEST demonstrator. 
Funding body: Swiss Federal Office of Energy SFOE
Partners: aliunid AG, HSG, BFH
Contact: Martin Rüdisüli, Hanmin Cai
Involved groups: MES, ehub

Cofund
As part of the Horizon 2020 research and innovation programme, we have been granted a co-fund to construct a Swiss Building Energy Stock Modelling Platform, which will be used to evaluate the energy performance of buildings at different temporal and spatial scales. This work recognizes the vast amount of data available and a workflow is being developed to enable continual integration, updating and validation. To achieve these goals, we are applying semantic and linked data technologies to process and store the data. This will create a body of knowledge that can be queried for a deeper understanding of the underlying data.
Funding: EU
Contact: James Allan
Involved group: MES

Urban densification and its impact on energy use in Swiss cities
This research project investigates the potential of urban densification and their resulting influence on the total energy consumption of neighborhoods and districts. With different computational methods, different scenarios of redensification and the resulting energy performance of neighborhoods will be investigated and compared. Project results shall support decision makers in regional and urban development processes in Switzerland.
Funding: BFE
Partners: KCAP, Wagner-Vanzella Architects
Contact: Kristina Orehounig
Involved group: MES

Development of a Framework for Planning Clean Energy Access Solutions in Central America” – Seed Money Grant awarded by the Leading House for the Latin American Region,
University of St. Gallen

According to the World Bank (2019), the average rural electrification rate in Latin America and the Caribbean has successfully increased from 63% in 1995 to 94% in 2016. However, the region of Central America has some of the lowest rates, which are attributed to Nicaragua, Honduras and Guatemala, with 57%, 72% and 86% respectively. Due to the high investment required for infrastructure works to extend the electric grid to these areas, other solutions are required, such as the installation of stand-alone systems and micro or mini-grids. For any chosen solution, the starting point is to have an accurate knowledge of the energy demand of these areas, which is currently estimated through field studies, knowledge transfer and other modelling tools. Due to the lack of reliable data for these areas, these methods make general assumptions that do not represent the rural population, which results in an overestimation of the energy demand, consequently, in an overinvestment of resources (Howells, 2005).
In Switzerland, the Federal Institute of Technology Zurich (ETH Zurich) and the Swiss Federal Institute of Materials Science and Technology (Empa) are currently developing a methodology to model the current and future energy demand of rural households in developing countries in order to improve and support the planning of rural electrification projects. This methodology uses available data from household to national level in order to create a robust framework that can be generalized to a broad geographic scope. However, developing countries do not usually have available detailed geospatial data for rural areas, which is vital for the optimal performance of the model. In Guatemala, Universidad del Valle de Guatemala (UVG) had the initiative of creating detailed mapping of the existing infrastructure in the country, which application aims to enhance the rural development by improving the planning of future projects and policies to meet the basic needs of the population. Having access to this information is of great importance for the research community, as it can be used to provide potential solutions to critical problems and bringing societal benefits. The aim of this research is to combine both on-going projects to develop a framework to identify the potential sites for the deployment of rural electrification projects in Guatemala, providing as well solutions based on the availability of natural resources to promote the use of renewable energies to meet such purpose. This framework will be created in a way that can potentially be replicated in other countries of the region of Central America that count with similar geographic, climatic and socioeconomic conditions, which will lead to a larger future research cooperation between these institutions.
Funding: CLS HSG
Partners: ETH Zurich, Universidad del Valle de Guatemala
Contact: Cristina Dominguez
Involved group: MES

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.
Funding body: Empa Board
Contact: Andrew Bollinger
Involved groups: MES, ehub

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.
Funding body: Swiss Federal Office of Energy SFOE
Partners: Energy Science Center, ETH Zurich, PSI, SGS, Adaptricity, SCS, NI
Contact: Philipp Heer
Involved group: ehub

Renewable powered district heating networks – Repodh
Space heating accounts for around 70% of the final energy consumption in Swiss households. Therefore, as Switzerland looks towards its 2050 CO2 emission targets which require an 80% reduction in annual CO2 emissions per capita, there is a pressing need to increase the utilisation of energy efficient and renewable heating sources in the residential sector. It is claimed that district heating networks powered by local thermal energy sources like renewables (such as solar thermal energy, heat pumps, or waste heat) are considered a sustainable way to cover future heating and cooling demands in urban areas. However, existing types of district heating networks are not designed for decentralized renewable energy sources, and their integration becomes a challenging task. Existing networks are typically built in a branching configuration, whereas future renewable powered networks tend to be in ring topologies. Also, the efficiency of a thermal network is very much dependent on temperature levels of the thermal energy sources. These temperature levels can be easily controlled in networks that rely on centralized thermal energy generation sources like combined heat and power (CHP) or boiler units. However, temperature levels of non-dispatchable renewables cannot be controlled as easily as they are highly time variant. Also, the efficiency of a thermal network is strongly coupled to the supply and demand temperatures and flow rates of consumers connected to the network, and with the more frequent utilization of renewable energy sources it will become increasingly challenging to cover the temporal mismatch of demand and supply. Based on this background, a deeper knowledge is required in order to evaluate the potential of renewable energy in thermal networks. This project aims to deepen the knowledge by developing a holistic modelling framework to design and ideally operate renewable powered district heating networks (RePoDH). In this project a bi-level simulation approach is envisioned, which employs detailed dynamic modelling tools to evaluate the thermal performance and control of a network, and a simplified multi-energy modelling representation allowing to optimize the system design, for which dynamic tools are too complex, and computationally intensive. The two simulation approaches will be connected with a geographical information system, to evaluate potential network configurations using geo-referenced information. With the modelling framework we will assess how networks with a high share of renewable energy sources should be designed, in order to improve the operation of the network in terms of security and energy autarky. Moreover, we will evaluate what types of districts are suitable for RePoDH networks, and what types of networks should be used for which district in order to contribute to reaching future emission targets for our society. 
Funding: SNF
Contact: Kristina Orehounig, Danhong Wang
Involved group: MES

K3 - Handwerkcity
The overall objective of this project is to show a system-related contribution of the Swiss gas industry to the implementation of the Swiss Energy Strategy 2050. Empa will provide a quantification of energetic flexibilities and the free capacities. Additionally, we are conducting an optimization for future adaptions of operation. We evaluate the system stability based on measurement data, and we will evaluate economical factors related to electrical self-sufficiency in the K3 building complex.
K3, a commercially used building complex, will serve as validation area. Its energy system consists of roof and façade PV systems, air-to-water heat pumps, water-to-water heat pumps, a CHP unit and several hot- and cold-water storage units.
Funding body: FOGA
Partners: Die Werke Wallisellen, SVGW
Contact: Philipp Heer
Involved group: ehub

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.
Funding body: Innosuisse
Partners: ETH Zurich, PSI, EPFL, HSR
Contact: Philipp Heer
Involved groups: MES, ehub

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: Innosuisse – Swiss Innovation Agency
Partners: Empa, ETH Zurich, EPFL, HSLU, Uni Geneve, FHNW
Contact: Matthias Sulzer
Involved groups: BEST, MES, ehub

Swiss Competence Center for Energy Research “Heat and Electricity Storage” (SCCER HaE) – Phase II, WP1 Storage of Heat – Task 1 – Sorption-based Seasonal Heat Storage.
The major goal within this four years project is to increase the technology readiness level (TRL) of the technology from a 4 to 6-7. Development steps include lab-scale absorber testing, upscaling of lab-scale absorber to 5 kW, integration of upscaled absorber in hybrid pilot-scale plant from a former EU FP-7 research project COMTES and prototype storage plant installation in NEST/energy hub research facilities.
Funding: Innosuisse – Swiss Innovation Agency
Academic partners: Institute for Solar Technology SPF / University for Applied Sciences-HSR
Contact: Luca Baldini
Involved group: BEST

Swiss Competence Center for Energy Research “Efficiency of Industrial Processes” (SCCER EIP) – Phase II, WP4 Decentralized Wastewater Management – Task 4 – Wastewater Heat Recovery
A significant potential of wastewater heat recovery has been identified at household level, which shall be explored in the course of Empa’s contribution to this SCCER. It is the goal to evaluate different available technologies and novel system combinations through simulation and punctually through experimental evaluation within NEST. Further, different integration options for wastewater heat recovery in buildings along with possibilities for technology/system developments with industry involvement will be evaluated and finally, guidelines and recommendations shall be deduced and made available for planning engineers in the field.
Funding: Innosuisse – Swiss Innovation Agency
Academic partners: Eawag – Aquatic Research, University for Applied Sciences-HSR, University of Applied Sciences FHNW
Contact: Luca Baldini
Involved group: BEST

Swiss participation in IEA SHC/ECES Task 58/Annex 33 – Performance degradation in thermochemical energy storage from the material to the system scale.
This project is part of the newly started IEA SHC/ECES Task 58/Annex 33 „Materials and Component Development for Thermal Energy Storage“. The project contributes to the performance assessment of thermal energy storage materials and systems at different scales with the experience from the ongoing development of a seasonal thermal storage based on sodium hydroxide. Particular focus is laid on performance degradation during up-scaling from materials to pilot-scale systems.
Funding: Swiss Federal Office of Energy, Pilot and Demonstration Project
Contact: Luca Baldini
Involved group: BEST

Energy Efficient Spa Technologies
Wellness facilities are on the rise all over Europe. The technology used for saunas and steam baths nowadays is based entirely on electric resistance heating and is therefore extremely energy-intensive and costly to operate. The project develops concepts and technologies based on high-temperature CO2 heat pumps, which can be expected to save 70% of electricity. In a full-scale test installation within the research and demonstration platform NEST the effectiveness of the solutions developed is being verified and operation is being optimized.
Funding: Commission for Technology and Innovation (CTI)
Partners: University of Applied Sciences Buchs NTB, University of Applied Sciences Lucerne HSLU (Research), Suissetec (Industry assocoiation), other industry partners
Contact: Robert Weber
Involved group: BEST

 

Ongoing PhD projects

Felix Bünning
Building energy systems can be expected to undergo radical change in order to adapt to the needs of the future renewable energy environment. This includes the integration of renewable energy sources in the building itself (such as solar-thermal and PV), possibilities to interact with the electricity grid in smart-grids as a reserves provider, possible connection to novel district energy concepts such as combined heating and cooling networks, etc. Consequently, new concepts to control such systems are required, which leads to the following governing research questions:
What are the upcoming challenges in this field and how can they be tackled?
Novel technologies call for novel methods or the adoption of established control concepts. Electrical and thermal reserves through buildings, the integration of buildings in combined heating and cooling networks, renewable integration and other new topics are addressed by adapting existing control concepts to new problems and by developing new approaches based on data-driven methods and machine learning.
How can building and district energy control be made more real-life relevant, meaning cheaper and implementable?
Although proven effective, even conventional MPC for thermal zones has never found mass-adaption in the building energy industry so far, because the implementation is very complex and cost-inefficient. Thus, simplifications and new methods need to be found that allow intelligent control of district and building energy systems in real life applications.

Loris Di Natale
With the ratification of the Paris Agreement in 2015, many countries in Europe and in the world, including Switzerland, committed to ambitious greenhouse gas emission targets in 2050. In that context, electrification of the end-use services is required and developed around the globe.
In particular, the share of electrical vehicles (EVs) in the car market is growing faster than ever to replace classical Internal Combustion Engine (ICE) vehicles. Simultaneously, buildings are undergoing a similar transition to electricity, with heat pumps-based heating systems. Additionally, new constructions are now pushed to install on-site renewable generation capacity - typically through the installation of photovoltaic (PV) panels.
Taking all these changes into account, we can today use the growing share of EVs as an opportunity rather than a burden, due to the additional electricity demand required to charge them. Taking advantage of the introduction of EVs with bidirectional chargers and smart charging/discharging strategies, we can indeed simultaneously reduce the peak in the electricity demand and maximize the utilization of renewable energy production. The key is to consider EVs as energy storage systems as well as transportation means.
The following research question arises: What is the optimal use of electric vehicle batteries to maximize the utility from self-generated renewable energy and decrease the demand during peak-hours, thereby reducing the global energy costs of households? To answer it, in this project we aim to use state-of-the-art data-driven control techniques, like reinforcement learning, to tackle this problem.

Cristina Dominguez
Ensuring access to affordable, reliable, sustainable and modern energy for all was listed as one of the Sustainable Development Goals (SDG) proposed by the United Nations for the year 2030. Due to the strong link between electricity access and socioeconomic development, electrification projects are often listed as a top priority in developing countries. Still, according to the International Energy Agency, around 17% of the global population lack access to electricity, and 84% are located in rural areas from sub-Saharan Africa, Asia and Latin America. Due to the high investment required for infrastructure works to extend the electric grid to these areas, other potential solutions are developed, such as the installation of stand-alone systems and micro or mini-grids. For any chosen solution, the starting point is to have an accurate knowledge of the energy demand of these areas, which is currently estimated through field studies, knowledge transfer and other modeling tools. Due to the lack of reliable data for these areas, some of these methods make general assumptions and use macroeconomic drivers that do not represent the rural population, which results in an overestimation of the energy demand, consequently, in an overinvestment of resources.
The aim of this research is to develop a methodology to model the current and future energy demand of rural households in developing countries in order to improve and support the planning of rural electrification projects. This methodology is based on a hybrid approach combining bottom-up and top-down modeling techniques, utilizing available data from household to national level in order to create a robust framework that can be generalized to a broad geographic scope.

Emmanouil Thrampoulidis - Large-scale building retrofit towards more effective energy policies and strategies
The building sector accounts for more than 40% of the total energy consumption and  emissions in Europe. Building retrofit is of greatest importance to reduce the environmental footprint of the existing building stock. It may refer to two types of interventions: the first pertaining to interventions on the building envelope, for instance by enhancing the thermal insulation of a building’s walls, and the second one to building energy system replacements and renewable technologies integration. Even though building-specific solutions are important there is much more to gain if those are part of a coordinated large-scale retrofit plan. A more systematic and effective solution to derive energy policies, strategies and incentives is one of the benefits of such large-scale retrofit approaches.

Building retrofit is a complex process, which involves the use of highly heterogeneous building information (census data, 3D building data, weather data) and computationally intensive tools (multi-objective optimization, building simulation).  Usually, due to the limited timeline and investment of the retrofit projects the building process is extensively simplified, for instance by just performing some steady state calculations. Moreover, most large-scale retrofit projects are based on archetypes and arbitrary generalize. Eventually, such approaches might lead to results that highly deviate from reality.

Therefore, the aim of this research is to exploit the principled generalization ability of machine learning to develop a large-scale data-driven retrofit approach with the use of both simulation and real building data.  This approach can be more beneficial than the conventional ones in terms of: (i) generalization ability and adjustability, (ii) ease of application, (iii) retrofit selection time and (iv) computational cost. Last but not least, such a surrogate retrofit approach can contribute towards deriving more effective energy strategies and eventually accelerating the adoption of building retrofit measures.

Danhong Wang - Renewable powered district heating networks
website | poster

Space heating accounts for around 70% of the final energy consumption in Swiss households. Therefore, as Switzerland looks towards its 2050 CO2 emission targets which require an 80% reduction in annual CO2 emissions per capita, there is a pressing need to increase the utilisation of energy efficient and renewable heating sources in the residential sector. It is claimed that district heating networks powered by local thermal energy sources like renewables (such as solar thermal energy, heat pumps, or waste heat) are considered a sustainable way to cover future heating and cooling demands in urban areas. However, existing types of district heating networks are not designed for decentralized renewable energy sources, and their integration becomes a challenging task. Existing networks are typically built in a branching configuration, whereas future renewable powered networks tend to be in ring topologies. Also, the efficiency of a thermal network is very much dependent on temperature levels of the thermal energy sources. These temperature levels can be easily controlled in networks that rely on centralized thermal energy generation sources like combined heat and power (CHP) or boiler units. However, temperature levels of non-dispatchable renewables cannot be controlled as easily as they are highly time variant. Also, the efficiency of a thermal network is strongly coupled to the supply and demand temperatures and flow rates of consumers connected to the network, and with the more frequent utilization of renewable energy sources it will become increasingly challenging to cover the temporal mismatch of demand and supply. Based on this background, a deeper knowledge is required in order to evaluate the potential of renewable energy in thermal networks. This phd project aims to deepen the knowledge by developing a holistic modelling framework to design and ideally operate renewable powered district heating networks (RePoDH). In this project a bi-level simulation approach is envisioned, which employs detailed dynamic modelling tools to evaluate the thermal performance and control of a network, and a simplified multi-energy modelling representation allowing to optimize the system design, for which dynamic tools are too complex, and computationally intensive. The two simulation approaches will be connected with a geographical information system, to evaluate potential network configurations using geo-referenced information. With the modelling framework we will assess how networks with a high share of renewable energy sources should be designed, in order to improve the operation of the network in terms of security and energy autarky. Moreover, we will evaluate what types of districts are suitable for RePoDH networks, and what types of networks should be used for which district in order to contribute to reaching future emission targets for our society.