Centrum Wiskunde & Informatica (CWI) in Amsterdam has a vacancy for
PhD student position in AI-based methods for integrated hydrogen-electricity markets (m/f/x)Job description Our energy systems are undergoing rapid changes, with increasing adoption of distributed renewable generation (from PVs and wind turbines), new forms of demand (from EV charging, heating) and storage. This poses significant challenges for our power networks, as seen in the significant network congestion issues emerging in many regions of the Netherlands. In recent years, hydrogen has emerged as a significant energy vector, with potential to address many of the challenges posed by the energy transition. The Netherlands national hydrogen research programme on hydrogen, Groenvermogen NL (GVNL,
https://groenvermogennl.org/) was established to perform fundamental and practical research required for realizing and accelerating the hydrogen economy.
WP7 of Groenvermogen (called Hy-SuCCESS: Social, User aCCeptable, Economically Sustainable Systems for hydrogen) studies the development of hydrogen systems from a socio-technical perspective. It considers both the economic and business cases of hydrogen systems, but also the system integration aspects, legal aspects and the user acceptability of hydrogen solutions in different application areas. It is based on a highly interdisciplinary research consortium, that brings together economists, business school scientists, system modeling and optimization researchers, computer scientists, legal experts and social scientists working on energy topics.
Description of the PhD project The project of the PhD student based at CWI in Amsterdam will study integrated hydrogen-electricity markets. In particular techniques from Artificial Intelligence and multi-agent systems for modelling new types of markets are particularly relevant for this position. From an application perspective, in most application areas where hydrogen is deployed, it has to inter-operate with other energy carriers and consumers, in particular electricity, transport, gas, heating etc. For example, a hydrogen based-fuel cell or electrolyser will have to supply a number of services, ranging from supplying local transport or industry needs, to acting as a source of storage and flexibility for the power networks. Here, flexibility is the capability to change some individual energy supply or demand in time, size, or location: this can relieve power network congestion, which is an increasingly important problem in the Netherlands. From a market-based perspective, this means hydrogen assets will have to participate simultaneously in a number of markets, including local hydrogen and electricity markets, but also flexibility markets related for managing network congestion.
Some specific topics that are relevant for this PhD position include (non-exclusive list):
- Automated bidding strategies for participating in multiple markets (hydrogen, electricity) over different time scales, such as short-term forward markets and spot markets (operating in real-time). This can be achieved through a variety of optimization, machine learning or AI-based heuristics.
- Optimization of revenue stacking models for hydrogen assets that have to supply a number of market-based services simultaneously (e.g. deliver hydrogen for transport, or act as a form of energy storage for electricity system)
- Design of new types of short-term electricity markets, that incentivize the participation of hydrogen assets, in combination with other types of power system assets (batteries, etc.). From a research perspective, this can involve techniques from algorithmic game theory and AI-based mechanism design.
- Validation of new types of markets (both those designed above and others) through principled multi-agent simulations, complex systems analysis or other data-driven simulation methods.
Fundamental AI techniques relevant to this project include a wide range of computational intelligence and ML techniques, distributed, multi-agent optimization, market-based coordination between agents, automated negotiation and algorithmic game theory approaches. In terms of the optimization aspects, relevant topics include: single and multi-criteria optimization, dynamic constraints, uncertainty and risks, costs of actions, and economic/technical trade-offs.
The PhD student will start with a survey identifying promising alternatives and then delve deeper into specific solutions and approaches and real case studies and data. The projects will involve regular discussion and presentation of the results to other consortium members from other universities, HBOs and companies. Given this is a large, interdisciplinary project, partners may have potentially, different expertise backgrounds. Hence, effective communication skills are required.