Electricity Markets
Market design, price formation, short-term market behavior, and cross-border trading mechanisms.
Research
Methods and themes behind the work
This work is driven by a long-term ambition: improving power-market efficiency and helping build a more sustainable energy system through AI, market design, and trading practice.
I pay particular attention to short-term power markets and market-facing energy assets, where rule changes, renewable uncertainty, price formation, bidding behavior, and operational constraints meet most directly.
Research Themes
Core domains
Energy Decision Agents
AI systems that connect rules, forecasts, strategies, execution, risk control, and post-event learning into continuous decision loops.
Storage and Flexibility
Battery dispatch, flexibility assets, and market participation under operational and regulatory constraints.
Energy Digitalization
Turning models, workflows, knowledge bases, and risk boundaries into decision systems that real energy organizations can deploy and use.
Methods
What I work with
Modeling Stack
Time-series forecasting, feature engineering, probabilistic modeling, optimization, and scenario analysis.
Decision Stack
Constraint-aware decision models, reinforcement learning, agent workflows, and safe AI for energy systems and market operations.
Selected Public Projects
Open research repositories
Public GitHub repositories connected to my research on reinforcement learning, energy storage dispatch, and energy-system scheduling.
RL-ADN
A high-performance deep reinforcement learning environment for optimal energy storage systems dispatch in active distribution networks.
Open on GitHubOptimal Energy System Scheduling
Source code for the paper on combining mixed-integer programming and deep reinforcement learning for energy management and safe scheduling.
Open on GitHubDRL for Energy Systems Optimal Scheduling
Code for comparing deep reinforcement learning algorithms on energy-system optimal scheduling problems.
Open on GitHubRelated research line
This code layer connects to broader work on storage flexibility, local energy systems, campus-scale energy digitalization, and decision-support methods.