Work

Comparative Analysis of Optimal Control and Reinforcement Learning Methods for Energy Storage Management Under Uncertainty

Year: 2025

Type: article

Abstract: The challenge of optimally controlling energy storage systems under uncertainty conditions, whether due to uncertain storage device dynamics or load signal variability, is well established. Recent research works tackle this problem using two primary approaches: optimal control methods, such as stochastic dynamic programming, and data-driven techniques. This work’s objective is to quantify the inherent trade-offs between these methodologies and identify their respective strengths and weaknesses across different scenarios. We evaluate the degradation of performance, measured by increased operational costs, when a reinforcement learning policy is adopted instead of an optimal control policy, such as dynamic programming, Pontryagin’s minimum principle, or the Shortest-Path method. Our study examines three increasingly intricate use cases: ideal storage units, storage units with losses, and lossy storage units integrated with transmission line losses. For each scenario, we compare the performance of a representative optimal control technique against a reinforcement learning approach, seeking to establish broader comparative insights.

Source: Energy storage and applications

DOI: 10.3390/esa2040014

Authors: Elinor Ginzburg-Ganz, Itay Segev, Yoash Levron, Juri Belikov, Dmitry Baimel, Sarah Keren

Institutions: Technion – Israel Institute of Technology, Tallinn University of Technology, Sami Shamoon College of Engineering

Language: English

Topic: Electric Power System Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $1,207