Data Augmented Rule-based Expert System to Control a Hybrid Storage System

Hybrid storage systems that combine high energy density and high power density technologies can enhance the flexibility and stability of microgrids and local energy communities under high renewable energy shares. This work introduces a novel approach integrating rule-based (RB) methods with evolutionary strategies (ES)-based reinforcement learning. Unlike conventional RB methods, this approach involves encoding rules in a domain-specific language and leveraging ES to evolve the symbolic model via data-driven interactions between the control agent and the environment. The results of a case study with Li-ion and redox flow batteries show that the method effectively extracted rules that minimize the energy exchanged between the community and the grid.