Two PhD Positions in Agent-Based Models for Residential Energy Transition (# of pos: 2)

We are hiring two doctoral candidates, who will work on the Technical System of a larger
NWA-ORC project- ALIGN4energy, together with a broad interdisciplinary consortium of universities, and public and private organizations. The 4-year project ALIGN4Energy, funded by the Netherlands Science Foundation (NWO), aims to contribute to deploying clean energy technologies in residential homes rapidly and at scale. The ALIGN4energy consortium provides a total of 11 PhD positions aimed to simultaneously address the citizens, policy, and business perspectives of the residential energy transition. These two PhD positions offered by TU/e will be hosted at the Department of Built Environment, in the group of Information Systems in the Built Environment. These two PhDs will be working on work package 2 on the Technical System of the ALIGN4energ project.

JobDescription

The energy and the climate crisis reinforced the urgency of energy efficiency in residential buildings in Europe. To meet its climate targets, the Netherlands needs to decarbonize the residential sector. This requires that a large number of distributed actors (households, owners associations, social housing associations, and citizen collectives) invest in clean energy technologies rapidly and at scale. Yet citizens' individual (homeowners, tenants) and collective (housing associations, homeowners' associations, etc) investments in energy-related home renovation will impact integrated energy systems, including correlations between energy grids, uncertainties and simultaneous peak loads.

On PhD project will focus on agent-based simulation models to quantify individuals' decision-making under uncertainties and its emerging impacts on energy systems. The model will be used to simulate citizens' preferences, investment decisions and demand response engagement in the quantification of the simultaneous load factors of future-proof smart energy systems.

The second PhD project will focus on agent-based simulation models to quantify collective decision-making with multi-objective optimization. The model will be used to simulate the interactions among the decision-makers, in which the simultaneous load factors for each energy network infrastructure (especially thermal energy and power) determine the required network capacity and investment cost at all spatial scales from the individual building level to the (neighbourhood) network scale.

Both PhDs will take into consideration the institutional, market and behavioural influences and focus on answering the following questions: How can we effectively model the expected emerging behaviour as a result of individual and collective decision-making processes affected by energy efficiency investments? How can we estimate citizens' demand response potential taking into account behavioural, social and psychological dimensions leading to specific decisions on energy efficiency investments? How can we best model citizens' preferences and demand response engagement to estimate future electricity and heating consumption? Once the simulation models are built, the two PhDs will further explore the optimal solution for gas-free neighbourhoods and support both individual and collective clean energy investments. The PhD projects will use a wide range of public data sources and data collected in WP1 human systems, to develop and validate models and align with other WPs of the ALIGN4energy project, to integrate the human-energy system models and learning algorithms to support clean energy investment decisions.