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Research questionHow can utilities optimize demand-response decisions when customers adapt to price signals during volatile markets?Historical smart-meter and wholesale-price data do not capture how customers respond to a utility’s pricing signals over time. This missing feedback makes it difficult to learn policies for demand-response programs during volatile market conditions.
AI
Economics
Machine Learning
Reinforcement Learning
Research Paper
Technology
Latest papersRecent research connected to this question, newest first.Towards Affordable Energy: A Gymnasium Environment for Electric Utility Demand-Response ProgramsThe source concerns an open-source, online Gymnasium-compatible environment for training and evaluating demand-response from the electric utility’s perspective. It models regime-switching wholesale prices calibrated to extreme events, physics-based building demand, and configurable multi-objective rewards; baseline strategies and data snapshots demonstrate that the simulated settings are realistic and learnable, but do not establish performance in live utility deployments.research paper · Sep 2, 2026
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