Addressing Terminal Constraints in Data-Driven Demand Response Scheduling
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 extende...
| Authors | Maximilian Bloor, Martha White, Ehecatl Antonio del Rio Chanona et al. |
|---|---|
| Primary category | eess.SY |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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