Degradation-Aware Reinforcement Learning for Smarter Energy Management at IJCCI 2025, Marbella (Spain)

 

Towards Sustainable and Intelligent Microgrids

At the International Joint Conference on Computational Intelligence (IJCCI 2025), held in Marbella (Spain) from 22 to 24 October, CIPARLABS presented new research on degradation-aware energy management for residential microgrids. The paper, authored by Danial Zendehdel, Gianluca Ferro, Enrico De Santis, and Antonello Rizzi, introduces a reinforcement learning framework that intelligently manages battery usage to balance economic efficiency and battery longevity.

Residential microgrids and Renewable Energy Communities are redefining the energy landscape by promoting decentralized and cooperative energy production. Within these systems, lithium-ion batteries play a crucial role in storing excess solar power and ensuring supply continuity. Yet, their performance is constrained by degradation processes that reduce both capacity and lifespan.

Traditional control strategies often neglect this degradation, focusing only on short-term cost optimization. The work presented by the CIPARLABS team moves beyond that limitation by embedding battery health awareness directly into the control policy.

Reinforcement Learning Meets Battery Physics

The study proposes a Reinforcement Learning (RL) framework based on the Soft Actor-Critic (SAC) algorithm, implemented with Stable Baselines3, to learn optimal energy dispatch policies in real time.

What sets this approach apart is the explicit integration of battery State of Health (SoH) feedback into the agent’s learning loop. The RL agent learns to maximize long-term economic rewards while implicitly minimizing degradation, guided by a simplified but empirically calibrated degradation model derived from NASA’s Li-ion cell datasets.

The microgrid environment modeled in the study includes photovoltaic generation, household consumption profiles, and time-of-use electricity tariffs. The RL agent decides, at every timestep, whether to charge or discharge the battery or exchange energy with the grid. Its performance was benchmarked against a Model Predictive Control (MPC) strategy based on Mixed-Integer Linear Programming.

Learning to Preserve Energy and Battery Life

Simulations were conducted using high-resolution load and solar data from the Pecan Street Inc. Dataport. Over both one-year and ten-year scenarios, the RL-based controller demonstrated remarkable improvements over the MPC baseline.

• For a typical household, the RL agent extended battery life by up to 6.3 % compared to the MPC benchmark.

• It reduced energy purchased from the grid by 45–60 %, while maintaining or improving economic performance.

• Over long-term simulations, the degradation-aware SAC agent lowered total battery wear cost by 6.4 %, reflecting more efficient use of the storage system without compromising availability.

These outcomes reveal that the RL framework not only optimizes daily dispatch decisions but also learns non-linear, context-dependent policies that capture the intricate balance between short-term gain and long-term sustainability.

Implications and Future Work

The results suggest a promising pathway for deploying AI-driven, degradation-aware control systems in residential and community microgrids. Such systems could operate autonomously, adapting to changing conditions and maximizing both user savings and battery lifespan.

The research team plans to extend this work through real-world validation and multi-agent reinforcement learning experiments, where multiple prosumers within a Renewable Energy Community coordinate energy exchanges. The framework is being developed within the MOST – Sustainable Mobility Center and supported by European Union Next-GenerationEU funding under the Italian PNRR program.

Danial Zendehdel at IJCCI 2025

This work continues CIPARLABS’ mission to merge computational intelligence and sustainable energy research, paving the way for smarter, more resilient energy ecosystems.

Cite as:

@inproceedings{Zendehdel2026IJCCI,
  author    = {Danial Zendehdel and Gianluca Ferro and Enrico De Santis and Antonello Rizzi},
  title     = {Degradation-Aware Energy Management in Residential Microgrids: A Reinforcement Learning Framework},
  booktitle = {Proceedings of the 17th International Joint Conference on Computational Intelligence (IJCCI 2025)},
  year      = {2026},
  address   = {Marbella, Spain},
  month     = {October 22--24},
  publisher = {SCITEPRESS -- Science and Technology Publications},
  keywords  = {Reinforcement Learning, Battery Management System, Energy Management, Lithium-ion Batteries, Degradation Modeling, Microgrids},
  note      = {(Presented at IJCCI 2025, Marbella, Spain)},
}