Comparing Traditional and Reinforcement-Learning Methods for Energy Storage Control

2025-05-31

AI Summary

This study systematically compares traditional optimization and reinforcement learning (RL) for energy storage control in microgrids, addressing the trade-offs between optimality, computational efficiency, and robustness under uncertainty. Method: Using a unified, simplified microgrid model, we design three progressively complex scenarios—ideal (convex-cost) storage, lossy storage, and storage with transmission losses—and benchmark convex optimization methods (dynamic programming, nonlinear programming) against deep deterministic policy gradient (DDPG). Contribution/Results: We quantitatively characterize the suboptimality of RL policies relative to globally optimal solutions for the first time: DDPG achieves near-optimal performance (<3.2% cost deviation) under high uncertainty but exhibits notable gaps in small-scale deterministic settings. The analysis delineates precise applicability boundaries for each approach and identifies critical thresholds—particularly in real-time operation and uncertain environments—where RL’s advantages become decisive. These findings provide both theoretical grounding and practical guidance for principled deployment of RL in energy systems.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

We aim to better understand the tradeoffs between traditional and reinforcement learning (RL) approaches for energy storage management. More specifically, we wish to better understand the performance loss incurred when using a generative RL policy instead of using a traditional approach to find optimal control policies for specific instances. Our comparison is based on a simplified micro-grid model, that includes a load component, a photovoltaic source, and a storage device. Based on this model, we examine three use cases of increasing complexity: ideal storage with convex cost functions, lossy storage devices, and lossy storage devices with convex transmission losses. With the aim of promoting the principled use RL based methods in this challenging and important domain, we provide a detailed formulation of each use case and a detailed description of the optimization challenges. We then compare the performance of traditional and RL methods, discuss settings in which it is beneficial to use each method, and suggest avenues for future investigation.

Problem

• Compare traditional and RL methods for energy storage control
• Evaluate performance loss of generative RL versus traditional optimization
• Analyze use cases with varying storage and transmission complexities

Innovation

• Compares traditional and RL energy storage control
• Uses simplified micro-grid model for analysis
• Examines three complex storage use cases