Optimal Control Strategies for Seasonal Thermal Energy Storage Systems with Market Interaction
| Autor: | Gowri Suryanarayana, Ecem Sogancioglu, Jesus Lago, Bart De Schutter |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
model predictive control (MPC)
Computer science 020209 energy seasonal storage systems 02 engineering and technology Energy transition 7. Clean energy Demand response optimal control All institutes and research themes of the Radboud University Medical Center electricity markets 0202 electrical engineering electronic engineering information engineering Electricity market Reinforcement learning Electrical and Electronic Engineering Flexibility (engineering) reinforcement learning (RL) Environmental economics 021001 nanoscience & nanotechnology Optimal control Model predictive control Control and Systems Engineering Profitability index 0210 nano-technology Rare cancers Radboud Institute for Health Sciences [Radboudumc 9] |
| Zdroj: | IEEE Transactions on Control Systems Technology, 29, 1891-1906 IEEE Transactions on Control Systems Technology, 29(5) IEEE Transactions on Control Systems Technology, 29, 5, pp. 1891-1906 |
| ISSN: | 1063-6536 |
| Popis: | Seasonal thermal energy storage systems (STESSs) can shift the delivery of renewable energy sources and mitigate their uncertainty problems. However, to maximize the operational profit of STESSs and ensure their long-term profitability, control strategies that allow them to trade on wholesale electricity markets are required. While control strategies for STESSs have been proposed before, none of them addressed electricity market interaction and trading. In particular, due to the seasonal nature of STESSs, accounting for the long-term uncertainty in electricity prices has been very challenging. In this article, we develop the first control algorithms to control STESSs when interacting with different wholesale electricity markets. As different control solutions have different merits, we propose solutions based on model predictive control and solutions based on reinforcement learning. We show that this is critical since different markets require different control strategies: MPC strategies are better for day-ahead markets due to the flexibility of MPC, whereas reinforcement learning (RL) strategies are better for real-time markets because of fast computation times and better risk modeling. To study the proposed algorithms in a real-life setup, we consider a real STESS interacting with the day-ahead and imbalance markets in The Netherlands and Belgium. Based on the obtained results, we show that: 1) the developed controllers successfully maximize the profits of STESSs due to market trading and 2) the developed control strategies make STESSs important players in the energy transition: by optimally controlling STESSs and reacting to imbalances, STESSs help to reduce grid imbalances. |
| Databáze: | OpenAIRE |
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