Advanced control algorithms for future energy systems
The power-to-gas-to-power (P2G2P) path is a suitable candidate for long-term energy storage and could help closing the mismatch between renewable energy power generation and local electricity demand on a seasonal level (e.g. storing power produced by sun during summer to be used during winter). The technical elements are an electrolyzer, a storage tank for the hydrogen, and a fuel cell.
Due to the relatively low overall efficiency of P2G2P, the optimal control of the system is of fundamental importance. Model Predictive Control (MPC) is a suitable candidate to achieve this. We aim at developing suitable models for the electrolyzer and fuel cell behavior at the Energy System Integration (ESI) platform of PSI and the Move demonstrator of EMPA. We also develop forecasts for solar power production, wind power production, electricity price and CO2 intensity of electricity by exploring different artificial neural network architectures.
The developed models and forecasts are used in different optimal control problems, both in a deterministic and in a stochastic framework: from price arbitrage and optimal bidding strategies, to cost or CO2 emissions minimization during operations, to stochastic multiperiod peak shaving and demand side management. In the ESI and Move platforms, we are able to implement our algorithms in a unique setting, providing experimental validation to our methods.