Residential demand response of thermostatically controlled loads using batch Reinforcement Learning

Driven by recent advances in batch Reinforcement Learning (RL), this paper contributes to the application of batch RL to demand response. In contrast to conventional model-based approaches, batch RL techniques do not require a system identification step, making them more suitable for a large-scale implementation. This paper extends fitted Q...

Efficient balancing by effort-based activation of demand response services

We present a unified model for flexibility services in the power system, identify two existing categories (ramping and loading) and introduce a new category (stalling). Each service is characterised by duration, capacity and effort, with associated prices. We show that the effort of stalling, measurable in kWh2, is a significant cost component...