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.

Julie Rousseau - Probabilistic Prosumer Side Flexibilities for Multi-Use Case Applications
The electric energy demand of buildings is increasing due to the electrification of the transportation and heating systems which will lead to increasing numbers of electric vehicles (EVs) and heat pumps. While this puts an additional strain on the power grid, EVs and heat pumps are flexible loads that can be leveraged to keep the balance between generation and load. Coincidently, the building infrastructure for existing and new buildings are being modernized. This includes the deployment of smart meters and edge computing resources (i.e. building automation systems) as well as an increasing share of renewable energy sources such as rooftop or façade PV. The computing technology allows for a coordination of building loads, which are commonly oversized in their power- or storage-rating, in an interconnected fashion which can be exploited to optimize local needs, such as a high comfort level, low energy bills or maximizing the use of local generation while at the same time acting in a grid-supporting way. In other words, the load/production pattern of a building can be manipulated online to satisfy not only local needs (e.g. energy cost minimization or increased comfort), but at the same time provide upper-layer services.
In the literature that deals with the integration of demand-side flexibility into system operation, the building or load models are often oversimplified, i.e. using limits on the power and energy that should reflect the building’s flexibility in a concise way such that it can be integrated into the optimization problem. However, building energy systems are highly complex, dependent on the user’s behavior, the weather, characteristics of the building, etc. Hence, the focus of this project is to develop tools that allow for the identification and quantification of flexibility of a building that go beyond the device level models and/or the simplified generic models. Another important aspect is causality, i.e. that flexibility available at any time in the future is highly dependent on how flexibility is used between the present and that future point in time. It is further necessary to distinguish between available flexibility and accessible flexibility where the latter is a subset of the available flexibility taking into account restrictions that are imposed by the grid infrastructure and the behavior of other agents in the local grid.
In this project, we intend to leverage data available at Empa for building level loads to develop models for the quantification of flexibility. We intend to derive formulations that take into account the causality of available flexibility as well as include the fact that such availability will always be subject to quite a lot of uncertainty. Hence, our vision is to provide a probabilistic representation of available flexibility at the building level over a given time horizon, which is incrementally updated every time step as new data realizes. We will particularly investigate buildings that include EVs and heat pumps.
As a second step, we propose to investigate how to optimally provide the flexibility if requested, i.e. closing the loop from identification of available flexibility to actually providing it. This is dependent on the actual realization of the uncertainties and requires the coordination of various loads in a building. Given that flexibility should also be available at a later stage in the horizon, such availability should be ensured by including chance constraints in the formulation for the later time steps. We further envision to move towards a coordination among multiple buildings by the means of distributed optimization.


Leandro von Krannichfeld - Hybrid Digital Twins for Building Energy Systems
The operation of buildings is responsible for approximately 30% of energy consumption and 26% of CO2 emissions worldwide. Increasing digitalization and use of sensors hold great potential for constructing digital twins for building operation optimizations. A digital twin is a continuously monitored digital replica of the physical building, capable of forecasting future states and suggesting control actions for more efficient operation. The foundation of a digital twin for building energy optimization is based on the Building Energy Model (BEM), a digital model used for building energy analysis and prediction.
In building energy modeling, three major challenges related to data availability persist. First, data scarcity is a common issue for newly built or newly equipped buildings with sensors, as there may be insufficient data collected post-installation to develop a reliable model. Second, after model development, newly incoming sensor data can alleviate data scarcity but poses challenges for updating the model. Third, the unique sensor configuration in each building complicates a unified exploitation of traditional building energy modeling, given that buildings may not have the same number or types of sensors due to costs or equipment constraints.

This Ph.D. project aims to address these challenges by combining data-driven methodologies with physics-based modeling into hybrid models to exploit their respective advantages. Furthermore, we utilize knowledge from existing monitored buildings to benefit buildings with low data availability. To tackle the data scarcity problem in a new building, we propose leveraging a BEM created from a building with sufficient data. For this purpose, we use a hybrid model that incorporates various physical constraints and focuses on bridging the gap in building characteristics and operating condition between the buildings. To address the continuous model update problem, we propose to transfer a hybrid model created from a building with sufficient data to a target building experiencing data scarcity. Our focus here is on investigating different hybrid models and adjusting them for continuous adaptation from incoming sensor data streams. To address the unique sensor configuration challenge in buildings, we propose constructing a generalizable model from a diverse and large dataset. We concentrate on generating a dataset that covers variability in building characteristics, operating conditions, and weather conditions, as well as investigating strategies to adapt the model to the target building. The proposed methods will be evaluated using both simulated and real-world data from residential and office apartments from EMPA, assessing model performance, interpretability, and generalization ability.