Addressing Terminal Constraints in Data-Driven Demand Response Scheduling
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866913129073475584 |
|---|---|
| author | Bloor, Maximilian White, Martha Chanona, Ehecatl Antonio del Rio Tsay, Calvin |
| author_facet | Bloor, Maximilian White, Martha Chanona, Ehecatl Antonio del Rio Tsay, Calvin |
| contents | Electrified chemical processes are incentivized by exposure to time-varying electricity markets to operate flexibly, but participating in demand response schemes can require satisfying terminal constraints over long horizons. Specifically, terminal constraints may be required when computing optimal schedules in order to preserve dynamic stability. Model-based optimization methods are computationally costly, and data-driven scheduling via reinforcement learning (RL) faces severe credit-assignment challenges. We integrate Goal-Space Planning (GSP) with Deep Deterministic Policy Gradient (DDPG), using learned temporally abstract models over discrete subgoals to propagate value across extended horizons. Using a simulated air separation benchmark, we demonstrate the proposed approach improves sample efficiency over standard DDPG while satisfying terminal storage constraints, mitigating myopic control behavior. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14741 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Addressing Terminal Constraints in Data-Driven Demand Response Scheduling Bloor, Maximilian White, Martha Chanona, Ehecatl Antonio del Rio Tsay, Calvin Systems and Control Artificial Intelligence Electrified chemical processes are incentivized by exposure to time-varying electricity markets to operate flexibly, but participating in demand response schemes can require satisfying terminal constraints over long horizons. Specifically, terminal constraints may be required when computing optimal schedules in order to preserve dynamic stability. Model-based optimization methods are computationally costly, and data-driven scheduling via reinforcement learning (RL) faces severe credit-assignment challenges. We integrate Goal-Space Planning (GSP) with Deep Deterministic Policy Gradient (DDPG), using learned temporally abstract models over discrete subgoals to propagate value across extended horizons. Using a simulated air separation benchmark, we demonstrate the proposed approach improves sample efficiency over standard DDPG while satisfying terminal storage constraints, mitigating myopic control behavior. |
| title | Addressing Terminal Constraints in Data-Driven Demand Response Scheduling |
| topic | Systems and Control Artificial Intelligence |
| url | https://arxiv.org/abs/2605.14741 |