Aligning citizens and systems - Combining digital citizen engagement and personalised behavioural interventions to enable system-optimal clean energy investments at scale (ALIGN4energy)

The project aims at the development, application and dissemination of an integrated decision support system for energy use of buildings and transport, ensuring maximum efficiency with respect to supply and distributed energy generation.

ALIGN4energy unites scientists from the humanities and social sciences (economics, psychology, political sciences) and technical sciences (computer science, energy systems modelling), as well as companies, municipalities, and NGOs to jointly work towards a quick and low-cost transformation to natural gas-free homes in the Netherlands.

The consortium develops an online platform that serves as an adaptive digital decision-support system for citizens and policymakers, to help them make energy-related investment decisions that are simultaneously optimal for each individual citizen and for the energy system. Citizens will receive information that will simplify investment decisions and help them coordinate with neighbours for collective investments.

Consortium: VU Amsterdam, Eindhoven University of Technology, Erasmus University Rotterdam, Delft University of Technology, CWI - Centrum Wiskunde & Informatica, Netherlands Environmental Assessment Agency (PBL), Amsterdam Institute for Advanced Metropolitan Solutions, TNO, Ministry of the Interior and Kingdom Relations (BZK), Municipality of Rotterdam, Municipality of The Hague, Municipality of Haarlemmermeer, Municipality of Eindhoven, Alliander, Waternet, Rabobank, Deelstroom Delft, Building Blocks Energy, TheEarlybirds, Het Groene Brein, VVE Belang, Woonbond, 75INQ, Stichting !WOON, University of Cologne (DE), Urgenda.

Sub-project 1: Navigating the Energy Transition: Modeling Collective Energy Efficient Investments in Energy Communities

Residential buildings account for significant global energy consumption and greenhouse gas emissions. Promoting residents’ energy-efficient investments is crucial to achieving sustainability goals. However, collective decision-making can be complex due to diverse interests or varying priorities. The project aims to explore and simulate the decision-making process of residential collectives concerning energy-efficient investments. It focuses on understanding the factors that influence these decisions and the interactions between members of the community.

The project considers the context of energy communities, such as apartments, housing complexes, or neighborhoods, where multiple stakeholders collaborate to make decisions regarding renovations. There is a need to develop a simulation model that incorporates social factors and balances multiple community goals to inform and support stakeholders in making optimal choices for energy-efficient renovations.

Keywords: Energy Community; Energy-efficient Investment; Decision-making

Sub-project 2: Agent-based simulation models to quantify individuals’ investment decision-making under uncertainties and its emerging impacts on energy systems

The adoption of energy-efficient technologies by households is crucial for reducing greenhouse gas emissions and achieving a decarbonized society in the Netherlands. As a partner of a multi-disciplinary project funded by NWO, we leverage agent-based modeling (ABM) to quantify individuals' energy-efficient investment decisions and their impacts on energy systems under uncertain conditions.

ABM allows us to simulate the decision-making processes and interactions of individuals, overcoming the limitations of traditional models. We will categorize agents (energy consumers and potential energy-efficient adopters at the household level) based on characteristics such as building types, demographics, and ownership status. Initially, we will develop a conceptual ABM to predict household adoption patterns for different energy-retrofit measures ( e.g., solar panels, heat pumps, and insulation).
This model will be refined using insights from project partners, resulting in an empirical stochastic model.

Keywords: Agent-based modeling, Uncertainty, Decision support systems, Energy efficiency retrofit, energy transition

Project information

  • Category Energy transition
  • Authors Xinyi Mu & Saeed Akbari
  • Project date 2023 -