DER Allocation without Load Prediction via Reinforcement Learning
TL;DR
A forecast-free reinforcement learning (RL) framework for DERA allocation that learns optimal policies directly from operational data, which preserves the interpretability and constraint satisfaction of DER model while adapting to stochastic demand variations through data-driven updates.
Abstract
The growing variability of renewable generation increases the need for fast and flexible grid-balancing mechanisms. Existing frameworks for distributed energy resource aggregations (DERAs) rely on short-term forecasts of net demand, making their performance highly sensitive to prediction errors. In this paper we present a forecast-free reinforcement learning (RL) framework for DERA allocation that learns optimal policies directly from operational data. We model the DERA dynamics as a deterministic linear system and the exogenous net load as a feature-based linear Markov process, capturing short-range temporal dependencies without explicit forecasting. We derive a closed-form expression for the optimal policy, which is learned through a least-squares value iteration (LSVI) algorithm using data collected across episodes. The proposed framework preserves the interpretability and constraint satisfaction of DER model while adapting to stochastic demand variations through data-driven updates. Numerical experiments on real California Independent System Operator (CAISO) net-demand data demonstrate that the learned controller achieves high tracking accuracy and stable regulation across heterogeneous DER aggregators without requiring any demand prediction.