# DER Allocation without Load Prediction via Reinforcement Learning

arXiv: https://arxiv.org/abs/2608.15977 · HTML: https://mlsanity.com/paper/2608.15977/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 3 of 3)

- [A tractable formulation for multi-period linearized optimal power flow in presence of thermostatically controlled loads](https://mlsanity.com/paper/1908.09167/) (2019)
- [Linear Dynamics meets Linear MDPs: Closed-Form Optimal Policies via Reinforcement Learning](https://mlsanity.com/paper/2508.17185/) (2025)
- [Model Predictive Control for Joint Ramping and Regulation-Type Service from Distributed Energy Resource Aggregations](https://mlsanity.com/paper/2405.02813/) (2024)

## Abstract (arXiv)

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.