Energy-based stochastic MPC for integrated electricity-hydrogen VPP in real-time markets
| Autor: | Han Wang, Jorge Angel Gonzales Ordiano, Timm Faulwasser, Veit Hagenmeyer, Riccardo Remo Appino, Ralf Mikut, Pierluigi Mancarella |
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| Rok vydání: | 2021 |
| Předmět: |
Power to gas
Flexibility (engineering) Mathematical optimization business.industry Computer science 020209 energy 05 social sciences Energy Engineering and Power Technology 02 engineering and technology Optimal control Renewable energy 0502 economics and business 0202 electrical engineering electronic engineering information engineering Stochastic optimization Electricity Electrical and Electronic Engineering business Energy (signal processing) 050205 econometrics Quantile |
| Zdroj: | Electric Power Systems Research. 195:106738 |
| ISSN: | 0378-7796 |
| DOI: | 10.1016/j.epsr.2020.106738 |
| Popis: | Virtual Power Plants (VPPs) comprising renewables and hydrogen production through power-to-gas technologies can help to increase renewable penetration and to improve operational flexibility and economic performance. However, the uncertainty inherent to forecasts of renewable generation and energy prices renders cost effective operation difficult. The present paper approaches the issue by means of receding-horizon stochastic optimization (i.e. by stochastic Model Predictive Control (MPC)). Differently from previous works, we do not tackle computational tractability with a sampling-based approach, but by mapping quantile forecasts of virtual energy profiles to the mode of operation that has the highest probability of being optimal. This way, we reduce the computational load and the forecasting burden. Furthermore, simulation studies show that the proposed algorithm can attain a significant percentage of the revenue of optimal control with perfect forecasts. |
| Databáze: | OpenAIRE |
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