Integrated forecasting and deep reinforcement learning for price-based self-scheduling of PV-BESS: Utility-scale evidence in Chile.

ABSTRACT: Deep Reinforcement Learning (DRL) shows good performance for optimizing battery energy storage systems (BESS) coordinated operations with photovoltaic plants (PV), yet most studies rely on simulations. Bridging the gap to practical application requires validation using real-world operational data. This paper provides such empirical evidence by developing and rigorously evaluating an integrated forecast-and-control framework on three distinct utility-scale PV-BESS assets in Chile. The framework couples a Sequence-to-Sequence (Seq2Seq) LSTM for point forecaster embedded in a probabilistic scenario-generation pipeline of PV generation with nodal prices with DRL agents (Proximal Policy Optimization - PPO and Soft Actor-Critic - SAC) trained on 1,000 generated scenarios per site. Using two year

SUBMITTER: Perez J 

PROVIDER: S-EPMC12788681 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature