Demand Response

Battery. At 5 p.m., the grid wants help. The evening peak is approaching, the aggregator needs 3 kW of flexibility between 17:00 and 19:00, and one household in the portfolio looks promising. In the old demand-response world, this might become a price alert, an app notification, or a silent automated command. The household either complies, ignores it, or discovers later that the “smart” system has made a decision that feels less smart when dinner, laundry, or comfort is involved. ...

TL;DR for operators HVAC optimisation is not really about “setting the right temperature”. That is the version suitable for brochure copy and mildly insulting procurement decks. The harder problem is deciding when comfort, occupancy, outdoor conditions, and electricity prices should overrule one another. The paper behind this article proposes a human-in-the-loop reinforcement learning controller for HVAC systems.1 Its main idea is simple enough to be useful: when occupants override the system, that feedback should not merely fix the current moment. It should also teach the controller what went wrong, so future decisions require fewer overrides. ...