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Building the future of decentralized energy with AI-driven markets and grid-aware systems.
I design market mechanisms, multi-agent coordination, and optimization for distributed energy resources—bridging future electricity markets with technical grid realities.
At a glance
Technical depth
From mathematical models to working software
I design the methods and build the systems that run them—market clearing algorithms, learning agents, and optimization engines, packaged into the open EUnix Nexus simulation ecosystem.
Formulating and solving scheduling and dispatch problems for DERs, batteries, and industrial loads.
- MILP
- Model predictive control
- Degradation-aware BESS scheduling
- Multi-commodity planning
Learning agents and forecasters that make predictive market decisions under uncertainty.
- Q-learning
- DQN
- PPO
- LSTM / GRU forecasting
Modular, reproducible simulation software that couples markets, grids, and aggregators.
- Multi-agent frameworks
- Python APIs
- Distributed ledger markets
- Modular simulation stack
Market and grid realities built into every model, from local trading to balancing services.
- Day-ahead & intraday
- FCR / aFRR / mFRR
- Congestion & voltage limits
- VPP revenue stacking
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Research, projects, and publications
Every section is one tap away—structured like a modern academic portfolio.
EUnix Nexus
Simulation stack for multi-market energy coordination
Local markets, grid constraints, optimization, and VPP strategies in one integrated toolchain.
Weekly Energy Insight
A weekly read on energy markets, flexibility, and AI
Short, practical analysis of what is moving in electricity markets, grid flexibility, and the technology behind the energy transition—published every week.