Modeling and Optimization of AI-Based Centralized Energy Management for a Community PV-Battery System Using PSO

Sree Devi, Sree Lekshmi Reghunathan Pillai; Krishnan, Chinmaya; Panikkar, Preetha Parakkat Kesava; Santhi Bhavan, Jayesh

doi:10.3390/en19020439

Open AccessArticle

Modeling and Optimization of AI-Based Centralized Energy Management for a Community PV-Battery System Using PSO

by

Sree Lekshmi Reghunathan Pillai Sree Devi

^1,*

,

Chinmaya Krishnan

^2,*

,

Preetha Parakkat Kesava Panikkar

¹

and

Jayesh Santhi Bhavan

³

¹

Department of Electrical and Electronics Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam 690525, India

²

Department of Mechanical Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam 690525, India

³

School of Mechanical Engineering, Coventry University, Coventry CV1 5RW, UK

^*

Authors to whom correspondence should be addressed.

Energies 2026, 19(2), 439; https://doi.org/10.3390/en19020439

Submission received: 29 November 2025 / Revised: 24 December 2025 / Accepted: 26 December 2025 / Published: 16 January 2026

Download

Browse Figures

Versions Notes

Abstract

The rapid rise in energy demand, urban electrification, and the increasing prevalence of Electric Vehicles (EV) have intensified the need for reliable and decentralized energy management solutions. This study proposes an AI-driven centralized control architecture for a community-based photovoltaic–battery energy storage system (PV–BESS) to enhance energy efficiency and self-sufficiency. The framework integrates a central controller which utilizes the Particle Swarm Optimization (PSO) technique which receives the Long Short-Term Memory (LSTM) forecasting output to determine optimal photovoltaic generation, battery charging, and discharging schedules. The proposed system minimizes the grid dependence, reduces the operational costs and a stable power output is ensured under dynamic load conditions by coordinating the renewable resources in the community microgrid. This system highlights that the AI-based Particle Swarm Optimization will reduce the peak load import and it maximizes the energy utilization of the system compared to the conventional optimization techniques.

Keywords: community PV-BESS; centralized controller; energy management system; LSTM forecasting; Particle Swarm Optimization

Graphical Abstract

Share and Cite

MDPI and ACS Style

Sree Devi, S.L.R.P.; Krishnan, C.; Panikkar, P.P.K.; Santhi Bhavan, J. Modeling and Optimization of AI-Based Centralized Energy Management for a Community PV-Battery System Using PSO. Energies 2026, 19, 439. https://doi.org/10.3390/en19020439

AMA Style

Sree Devi SLRP, Krishnan C, Panikkar PPK, Santhi Bhavan J. Modeling and Optimization of AI-Based Centralized Energy Management for a Community PV-Battery System Using PSO. Energies. 2026; 19(2):439. https://doi.org/10.3390/en19020439

Chicago/Turabian Style

Sree Devi, Sree Lekshmi Reghunathan Pillai, Chinmaya Krishnan, Preetha Parakkat Kesava Panikkar, and Jayesh Santhi Bhavan. 2026. "Modeling and Optimization of AI-Based Centralized Energy Management for a Community PV-Battery System Using PSO" Energies 19, no. 2: 439. https://doi.org/10.3390/en19020439

APA Style

Sree Devi, S. L. R. P., Krishnan, C., Panikkar, P. P. K., & Santhi Bhavan, J. (2026). Modeling and Optimization of AI-Based Centralized Energy Management for a Community PV-Battery System Using PSO. Energies, 19(2), 439. https://doi.org/10.3390/en19020439

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Modeling and Optimization of AI-Based Centralized Energy Management for a Community PV-Battery System Using PSO

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI