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Ongoing research projects

Ongoing PhD projects                            

                                                                                                                                                         

 

Ongoing research projects

SWEET: Decarbonisation of Cooling and Heating in Switzerland (DecarbCH)
The DeCarbCH project addresses the colossal challenge of decarbonization of heating and cooling in Switzerland within three decades and it prepares the grounds for negative CO2 emissions. The overall objective of the project (with the ultimate target of net zero emissions) is to facilitate, speed up and de-risk the implementation of renewables for heating and cooling in the residential sector (for various scales and degrees of urbanization) as well as for the service and the industry sector - by providing guidance on which combinations of technologies to implement where, to which extent and when - by developing, piloting and demonstrating combinations of commercially viable technologies thereof, consequently helping to drive down the cost of renewable heating and cooling in all sectors - by conducting model-based analyses that support planning, inter alia by the development of scenarios representing the supply, distribution and demand of renewable heating and cooling services - by quantifying the value of both renewable heating and cooling as well as of negative CO2 emissions - by providing evidence-based guidance on how to enable the implementation of renewable heating and cooling by policies and by legal measures as well as by engaging with the relevant actors and ensuring the necessary level of acceptance. The DeCarbCH project focusses on three main components, i.e. i) advanced renewable energy and transformation technologies, ii) thermal grids (for heating and cooling) and iii) energy storage. For these, we establish optimal combinations (in technical, economic and environmental terms) as well as necessary and desirable conditions for their implementation. A solution-oriented, interdisciplinary approach is applied for the project as a whole and within each work package. The work packages deal with subsystems (e.g. WP3 on grids in combination with renewables and energy storage, WP4/WP10 on industry and WP5/WP11 on primarily standalone renewable energy-driven system solutions), they represent case studies (WP6 for Zurich and WP7 for Romandie) or they apply specific approaches (legal and socio-economic integration in WP2/WP9 and energy system modelling in WP1/WP8), leading to recommendations for policy makers and other stakeholders.
Funding body: BFE, Industry
Partners: UniGE, ETH, , HSLU, UniGE, CREM, ZHAW, HEIG-VD, SUPSI, INDP, Industry
Contact: Luca Baldini

SWEET: Improve renewable energy system efficiency through flexibility and sector coupling (PATHFNDR)
In the PATHFNDR project, we will investigate how to incorporate a much higher share of renewable energy sources into the energy system while striving to achieve a more efficient Swiss energy system with the goal to reach a net-zero greenhouse gas emission-society by 2050, as set out by the Federal Council. To fulfill this goal, feasible pathways will be studied in particular through enabling flexibility providers across various sectors, along different temporal and spatial scales ranging from the European perspective over the country level to municipalities and individual companies, buildings and technologies.
In order to exploit the energetic flexibility potential under the national and international scenarios, single technologies cannot be analyzed individually. Rather, multiple technologies need to be put into the context of existing or novel use cases such that they can be evaluated following a holistic approach. Based on the systemic interaction of individual technologies, a real and usable flexibility potential can be quantified and exploited based on measurement data of bottom-up technologies installed in demonstrators like NEST, move and ehub, as well as participating districts and cities in Switzerland.
Funding body: BFE, Industry
Partners: ETH, PSI, HSLU, UniGE, Industry
Contact: Philipp Heer

Energy and comfort optimization in living and working environments through a user-centered predictive control (UC-DPC)
User need for comfort is one of the most critical barriers for energy efficient living and working. Recent advancements in automatic optimization of room climate successfully foster energy efficiency, but usually ignore comfort needs. In this project, we will introduce an approach, which aims at maximizing energy efficiency and user comfort at the same time. For this purpose, Empa NEST is used as a testbed. Machine learning based state of the art energy optimization algorithms (Reinforcement Learning) are combined with real time user feedback. Our previous work suggests an energy saving potential of around 20%.
Funding body: BFE
Partners: mehr als wohnen, Bouygues, Empa BEMC, Empa AMP, Empa NEST
Contact: Bratislav Svetozarevic

Data Predictive Control
The building sector is responsible for more than one third of the global final energy consumption. Heating and cooling of buildings require around half of this energy. Improving the operation of heating and cooling systems has therefore a significant impact on mitigating climate change. Model Predictive Control (MPC) has proven to be an energy efficient approach to climate control in buildings. However, the costs associated with the identification of first-principle models represent a major challenge for the widespread application of MPC in the residential building sector. Digitization opens now the possibility of replacing first-principle based with data-driven models, thereby model identification can be automated. This approach is called Data Predictive Control (DPC).
Recent experiments with DPC at NEST showed the high potential for energy-efficient climate control in residential buildings. In order to obtain statistical evidence for the improved performance with DPC, a field test is carried out in this project. It is also planned that the control problem will be expanded with additional components (supply temperature optimization, blind control, etc.).
Funding body: Lynus AG, internal
Partners: Lynus AG
Contact: Benjamin Huber

BFE S-DSM
The aim of this project is to develop and practically test a building automation system that regards the CO2 footprint (carbon footprint, CF) of the Swiss grid electricity and thus supports a sustainable operation of buildings. Two control concepts shall be compared. On the one hand a concept aiming at distribution network operators (VNB), on the other hand a concept to be implemented directly at prosumers, i.e. the Building. Since today's building operations do not take the CF profile into account, we assume that there is great potential for savings of CO2 emissions. An estimate based on historical data shows a potential saving of 22% for a poorly insulated two-family house, which can lead to a saving of one tonne of CO2 equivalent per year. This exclusively by adjusting the operation of the plant. In this project, this potential is to be analysed and exploited by means of an office/commercial use (K3 superstructure, Wallisellen) and a residential use (NEST Demonstrator, Dübendorf) by quantifying and validating in a practical test. At both locations, the building operation is extended in such a way that the practicability of the developments can be demonstrated and expected reductions of the CF can be assigned on the basis of measurement data.
Funding body: BFE
Partners: die werke Wallisellen
Contact: Philipp Heer

Superblocks: automated identification and evaluation of urban greening potentials
Superblocks have been proposed in Barcelona as an innovative and unconventional urban transformation strategy to create pedestrian-centric neighborhoods. Assessing unconventional urban-design approaches are a necessity due to the manifold challenges today’s cities all over the world are facing due to climate change, urban heat island effects, air or noise pollution. Superblocks are one promising strategy to tackle multiple problems in high-density living areas. The goals of the proposed research project are to explore the potential of superblocks for different case studies and to develop an automated data-driven procedure for their geospatial identification and evaluation of urban greening potentials. Whereas the concept of superblocks originates from Barcelona, it will need to be translated for other urban morphologies. This conceptual transfer to different urban morphologies shall be explored for Switzerland first before analyzing other global cities. The result of this research will be a differentiated quantification of superblocks and greening potentials, which could prove vital in supporting the implementation of current densification strategies for achieving more sustainable cities.
Funding body: SNF
Partners: -
Contact: Sven Eggimann
Involved group: MES

Carbon footprint optimization of electricity in smart buildings (CAPITAL)
In Switzerland, ~50% of our final energy demand is linked to the building stock with an increasing electricity demand that depends on a national mix with significant variations of its carbon footprint (CF). Traditional load profiles of buildings are not coordinated with nationwide renewable energy production meaning that electricity with a low CF is not utilized optimally.
Recent research activities on the dynamic life cycle assessment (DLCA) of energy flows in Swiss buildings, and on data-driven control of buildings, should be combined to tackle the reduction of present and future GHG emissions from electricity uses in buildings. This potential reduction can then be optimized with data-driven operationalization of energy flexibility that has been developed within recent research activities.
It is envisioned that up to 20% of indirect emissions can be reduced due to a sustainability aware smart building Operation.
Funding body: Empa Board
Involved Labs: Empa UESL, Empa TSL, Empa NEST
Contact: Philipp Heer

ERA-Net: Underground Sun Conversion – Flexible Storage (USC FlexStore)
The ERA-Net “Underground Sun Conversion – Flexible Storage” (USC FlexStore) project aims at developing a large-scale, seasonal storage solution for erratic renewable energy. This involves injecting CO2 and H2 produced from renewable surplus electricity into a porous underground gas storage facility (a depleted gas reservoir), where microbial conversion of CO2 and H2 to methane (CH4), the main component of natural gas, takes place. Technology-wise, “Underground Sun Conversion” (USC has already been demonstrated at a test site in Pilsbach (Austria) by RAG Austria AG . The aim of the "USC FlexStore" project is now to take the USC technology to a next level and also provide a first estimation of its potential in Switzerland. Investigations will centre on the technological, commercial, energy-sector and legal requirement. Our lab's contribution is to quantify the specific needs for seasonal storage of renewable electricity to cover the expected renewable energy demand in Switzerland.
Funding body: BFE (ERA-Net)
Partners: Energie 360°, OST, Uni Bern, RAG Austria AG, IFA-Tulln BOKU, WIVA P&G
Contact: Martin Rüdisüli
Involved groups: MES

Dynamic CO2 emission model of cities and its influence on the climate, building design and energy system
Cities worldwide are trying to tackle the task of reducing their CO2 emissions in support of the Paris Climate Agreement. The city of Zurich, in particular, has adapted the targets of the 2000 Watt Society, which proposes an 82% reduction in emissions by 2050 compared to 2005 levels. However, tracking progress towards these reduction targets requires consistent, reliable, and timely information on CO2 concentrations and emissions.
The goal of this project is to develop a CO2 emission monitoring system for Zurich and to better understand the interactions between the weather and the CO2 emissions from buildings and other sectors. This involves the integration of top-down emission estimates deduced from atmospheric CO2 concentration measurements with bottom-up estimates derived from a dynamic urban building simulation model, traffic counts, and industrial sources. The approach builds on an existing dense CO2 sensor network (Carbosense). The relationship between atmospheric concentrations and emissions will be established with building-resolved atmospheric dispersion simulations. A dynamic bottom-up emission model will be developed that combines data from the detailed CO2 emission inventory of Zurich with near-real time activity data on buildings and traffic. Emissions of buildings and their energy systems will be captured by a dynamic building stock model (CESAR-P), which provides heating, cooling and electricity consumption, including their CO2 emissions.
The model will predict CO2 emissions from stacks and district level energy systems, and include activity information on traffic and people. Natural fluxes of CO2 will be simulated with the vegetation photosynthesis and respiration model (VPRM). The resulting prior bottom-up emission estimates per category will be optimized with the measurements from the CO2 sensor network in a Bayesian framework to identify main sources and assess their trends against the reduction targets.
Funding body: internal
Partners: Empa, Laboratory for Air pollution/Environmental technology
Contact: Kristina Orehounig, Fazel Khayatian
Involved group: MES

EU EcoQube
ECO-Qube’s smart cooling system, will effectively use Computational Fluid Dynamics (CFD) simulations to adapt cooling system and IT devices for the best airflow and cooling performance in small data centres with minimum energy consumption.
The ECO-Qube project will introduce innovative cooling of edge data centers by zonal heat management. The system will exploit the big data formed by the monitoring of the CPU utilization, temperature and power consumption of the IT devices to operate the zonal heat management system, which predicts zonal temperature rises in advance. In this framework, ECO-Qube instantaneously reads the cooling requirements of the data centre, making the data available for the artificial intelligence supported cooling system and control over the cooling & energy management systems and IT devices in order to obtain an energy efficient operation of the whole integrated facility. To obtain this efficient and dynamic management of the facility, ECO-Qube will develop a customized smart energy management system that interfaces with the building’s energy management system (BEMS). Furthermore, ECO-Qube will demonstrate strategies to achieve a high share of the ICT energy consumption covered by sustainable energy sources: data centers can be greener with successful RES integration.
The development and testing of ECO-Qube smart cooling system will be accompanied with the integration of renewable energy and waste heat valorization solutions in 3 pilot sites including the Empa NEST demonstrator.
Funding body: EU
Partners: LANDE, D&S TECH, SDIA, helio, VATTENFALL AB, LULEA TEKNISKA UNIVERSITET, STICHTING GREEN IT, GIROA, Endoks
Contact: Philipp Heer

Energy System Modeling for the Real World: Transforming Modeling Approaches for Sustainable and Resilient Urban Development (MEASURES)
Energy system models are vital decision support tools. However, developing countries grapple with socio-technical and economic factors rarely represented in conventional energy modeling approaches; these include power sector failures, informal economies, and suppressed energy demand, for example. Developing cities will also experience climate change impacts more acutely than industrialized nations; thus, maximizing their resilience is essential. This study pioneers methods to address these gaps in energy system models.
Our team consists of multiple experts across disciplines and geographic boundaries. We employ the expertise of economists, climate change experts, and energy modelers across Switzerland and Africa in order to identify synergies and integration opportunities between modeling methods. We will build up-on existing open-source optimization/simulation models and demonstrate developed methods through a case study city committed to sustainable energy planning. The developed methods will describe how to represent various socio-technical, economic, and climate change impacts on developing cities through scenario and modeling framework advancements.
We aim to ensure knowledge transfer, especially to member cities of transnational municipal networks (e.g., the Global Covenant of Mayors, consisting of over 10000 cities), by sharing all models, data, methods, and findings on open-source, web-based platforms and through workshops. In this way, cities around the world can apply demonstrated models and methods to their own sustainable energy strategy development. The case study municipality also directly benefits from this work in developing their climate strategy.
Funding body: SNF (SPIRIT)
Partners: The Brew-Hammond Energy Center at the Kwame Nkrumah University of Science and Technology (KNUST); Department of Economics at KNUST; Regional Centre for Energy and Environmental Sustainability at the University of Energy and Natural Resources (UENR); ECOWAS Centre for Renewable Energy and Energy Efficiency (ECREEE); Energy Commission of Ghana (EC); Municipality of Accra (MA)
Contact: Mashael Yazdanie
Involved group: MES

Zukunftsfiliale
In cooperation with a large food retailer, a sustainable energy strategy is being developed. The far more than 100 Swiss stores together have the energy consumption of a medium-sized Swiss city. The right technology can save a corresponding amount of energy.
Together with the Empa spin-off Sympheny, the UESL is analysing the energy consumption and the interaction of the various systems such as heating, ventilation and cooling, product cooling and freezers, the photovoltaic systems and the e-charging stations. On the one hand, concrete optimisation measures are derived from the findings of the study. These can be easily applied to existing branches and help to reduce energy consumption and emissions. On the other hand, alternative energy systems are tested by means of computer simulations, for example an expansion of photovoltaics, the use of energy storage systems or an optimised use of waste heat from freezers and ovens.
Funding body: Industry
Partners: Sympheny
Contact: Curdin Derungs
Involved group: ehub

KiG - Connectivity in Buildings
The project is to focus on the topics of 1) interoperability, 2) data protection, and 3) cyber security in buildings. In the topic area of interoperability, Empa is to support the project management. The project will focus on the need for all players to interact in a "digital building world" and to promote the corresponding understanding of this.
The result will be a jointly defined target state and the derivation of appropriate solutions based on the needs analysis and the existing fundamentals.
In a first phase, the existing standards will be identified with the aim of detect overlaps, contradictions and gaps. Based on this, the current point of view of the mentioned stakeholders is compiled and differences to the current state are compared. Solutions are discussed and defined with all participants to define solutions that will help establish a synergetic interaction between stakeholders.
The project is intended to bundle the interests of the players involved. The work on this will be divided into the topics of interoperability (protocol, interface, data model, etc.), data protection and cyber security.
Funding body: BFE
Partners: EnergieCluster, INEXTR
Contact: Philipp Heer

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

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

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

PACE-REFITS - Policies for accelerating renewables and efficient building & district retrofits
Energy demand and CO2 emissions from buildings can be drastically reduced with state-of-the-art renewable and energy-efficient technologies on the building and district scales.  For new buildings, these technologies have been implemented widely, however for retrofits they are far from standard.  Focusing on large-scale investors (LSIs), this project analyses their motivation and barriers, and which regulatory conditions support their investment in renewable and energy-efficient retrofitting technologies on the building scale and for in-stock district-level renewable energy systems.  To assess their economic performance, we apply static and dynamic modelling and evaluation techniques.
Funding: BFE
Academic partners: ETHZ-Group for Sustainability and Technology
Contact: Andrew Bollinger

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

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

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

 

Ongoing PhD projects

Varsha N. Behrunani - Decentralized control of multi-energy systems
Multi-energy system perspectives in district and/or urban areas are core to carbon neutral societies. These systems incorporate electrical, thermal and chemical processes that follow individual demand and supply patterns under distinct time scales, i.e. from sub-second (electrical) to weekly/monthly (thermal) and seasonal (chemical) applications. Coordination of the related technologies in the domain of energy systems can only be achieved by optimally controlling the envisioned technology setup, under current or future stakeholder interests. This includes decentral control setups with restricted information propagation between stakeholders, working with measured data for modelling, controller synthesis and showcasing developments on operating real-life systems. In addition to sector specific technological limitations, the systemic coupling of energy carriers needs to account for the coupling of different time scales as well as different production and consumption patterns. A decentralized technological landscape facilitates faster computation and reduces the need for extending large-scale infrastructure, such as electrical grid reinforcements or international imports of energy. A decentral setup also results in more stakeholders being involved in the energetic supply chain that can benefit if they share information on their capabilities and intended production and consumption, such that they can be matched locally. 
The goal of this project is to develop novel distributed control methods for operational decision making in multi objective optimization of joint thermal and electrical sectors under current and future stakeholder setups that will be fully developed, customized to the multi-energy setting and extended in two directions. Firstly, we address the coupling of multiple energy streams in urban areas during runtime and optimizing their operational behavior using data-driven control algorithms that can adapt to local specificities by the use of locally gathered measured data and improve system wide optimal behavior in terms of local preferences and privacy. Secondly, we investigate the coupling of agents in these energy grids and the balance between privacy of information and economic/environmental performance using a game theoretic approach for coordinating the decisions of multiple stakeholders. The ultimate goal of this project is to advance widespread acceptance of the coordinated operation of energy system technologies in districts and cities, a key enabling technology in the dynamic next generation multi-energy management systems.

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.