The age of the agent:
Alejandro Nuñez-Jimenez wins Ambizione fellowship to develop agent-based models for energy systems

Alejandro Nuñez-Jimenez, a senior researcher at ETH Zurich’s Group for Sustainability and Technology (SusTec), has secured an Ambizione fellowship from the Swiss National Science Foundation (SNSF). Over the next four years, Nuñez-Jimenez will lead a team in developing a modelling framework capable of simulating policies for net-zero emission technologies from a new perspective.

Alejandro Nuñez-Jimenez and colleagues
Alejandro Nuñez-Jimenez (middle) at ETH Week 2018.

Models have been Nuñez-Jimenez’s tool of choice for exploring how to advance policies for the energy transition throughout his career. During his doctorate, he used an agent-based model (ABM) to find out how different policies influence solar energy adoption. Later, as a postdoc at Harvard, he created an optimisation model to investigate the future of renewable hydrogen in Europe. Now, he has won the prestigious Ambizione fellowship from the Swiss National Science Foundation – a grant worth over CHF 800,000 – to continue advancing his research.

“I am delighted that Alejandro Nuñez-Jimenez has received the prestigious SNSF Ambizione grant,” says  Stephan Wagner, Head of D-MTEC. “It testifies that D-MTEC's energy policy research meets the highest scientific standards and is valued by industry experts and policymakers. The grant is also an indication that D-MTEC provides an excellent environment for young researchers to accelerate their careers. This is very important to me.”

Over the next four years, Nuñez-Jimenez will lead the “Zero-AMPS” project, which aims to build an agent-based model for policy simulation that integrates technical and behavioural aspects. “The new modelling approach promises more realistic insights into the effects different net-zero policies have and facilitates our efforts towards decarbonisation,” explains Volker Hoffmann, Head of SusTec.

Portrait Stephan Wagner
“I am delighted that Alejandro Nuñez-Jimenez has received the prestigious SNSF Ambizione grant. It testifies that D-MTEC's energy policy research meets the highest scientific standards and is valued by industry experts and policymakers.  ”
Portrait Stephan Wagner
Stephan Wagner, Head of D-MTEC

A fresh approach to energy policy

The Zero-AMPS project addresses one of the most pressing challenges of our time: mitigating climate change by accelerating the deployment of clean energy technologies such as renewable hydrogen. These emerging technologies require political support through regulations, subsidies or other policies before becoming competitive. Some, like hydrogen, also need policies to develop new infrastructures and markets. To estimate the impact of these interventions, policymakers use models of the energy system. Traditionally, these models rely on optimisation techniques that assume ideal circumstances, such as companies having perfect information and making decisions rationally. However, real-world energy systems are far from ideal, and optimisation models often fail to reflect how companies and individuals respond to policy changes.

This is where Nuñez-Jimenez’s approach comes into play. He plans to use ABM to simulate the decision-making processes of economic actors and combine it with optimisation modelling to account for the technical constraints of energy systems. “Our model will provide insights into how businesses and industries might react when different policies are introduced," says Nuñez-Jimenez. “The goal is to create a tool that policymakers can use to anticipate not only the scale of a policy’s intended effects but also to foresee its unintended consequences.” This nuanced understanding is crucial for crafting more effective policies, particularly in the rapidly evolving landscape of net-zero technologies.

What is agent-based modelling?

Agent-based modelling (ABM) is a simulation technique where autonomous agents interact within a system, following predefined rules. Unlike conventional models, ABM focuses on individual behaviours and interactions, allowing for heterogeneity and adaptability. Agents make decisions based on local information and can evolve over time. Key advantages include its ability to capture emergent phenomena and model complex systems where traditional approaches, which often rely on aggregate assumptions, struggle to represent dynamic, decentralised interactions.
 

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