AI-Enabled Battery Storage and Electric Vehicle Flexibility for Power-Quality-Constrained Photovoltaic Integration: A Critical Review
5 affiliations
- 1IRC for Sustainable Energy Systems (IRC-SES), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
- 2School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
- 3Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
- 4Electrical Power and Machines Engineering Department, Faculty of Engineering, Tanta University, Tanta 31511, Egypt
- 5Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
Abstract
High photovoltaic (PV) penetration in distribution networks can be limited by voltage rise, unbalance, harmonics, thermal loading, and converter-current constraints before the available solar resource is fully utilized. Battery energy storage systems (BESS) and electric vehicle (EV) flexibility can relieve some of these limits, but their contribution depends on battery state, degradation, converter headroom, mobility, communications, and point of common coupling requirements. This critical review asks when artificial intelligence (AI) improves the use of stationary and mobile battery flexibility under those constraints. The synthesis compares these engineering dimensions qualitatively and includes a source-level comparison of 30 selected sources, together with focused comparisons of battery technologies and AI constraint-enforcement methods. Across the reviewed evidence, fast reactive and waveform-support services are often constrained by converter current more directly than by battery energy, whereas energy shifting and vehicle-to-grid operation are strongly affected by state of charge, availability, efficiency, and aging. AI is most defensible as a forecasting, estimation, and supervisory layer; it does not replace electrical feasibility or battery safety limits. Usable PV energy, meaning energy delivered after curtailment, conversion losses, and battery constraints, is not the same quantity as a larger nominal hosting-capacity rating. The review therefore recommends reporting net recovered PV energy together with compliance margin, converter utilization, battery throughput, aging assumptions, uncertainty, fallback behavior, and validation maturity.
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BibTeX
@article{zalhaf2026aienabled,
title = {{AI-Enabled Battery Storage and Electric Vehicle Flexibility for Power-Quality-Constrained Photovoltaic Integration: A Critical Review}},
author = {Amr S. Zalhaf and Hongzhi Jiang and Yang Han and Mohamed Mohandes and Ali Al-Shaikhi and Abdelaziz M. Gebril and Suliman Aladhadh},
journal = {Batteries},
year = {2026},
month = nov,
doi = {10.3390/batteries12110456},
url = {https://www.mdpi.com/2313-0105/12/11/456},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - AI-Enabled Battery Storage and Electric Vehicle Flexibility for Power-Quality-Constrained Photovoltaic Integration: A Critical Review
AU - Amr S. Zalhaf
AU - Hongzhi Jiang
AU - Yang Han
AU - Mohamed Mohandes
AU - Ali Al-Shaikhi
AU - Abdelaziz M. Gebril
AU - Suliman Aladhadh
PY - 2026
DA - 2026/11/02
JO - Batteries
DO - 10.3390/batteries12110456
UR - https://www.mdpi.com/2313-0105/12/11/456
AB - High photovoltaic (PV) penetration in distribution networks can be limited by voltage rise, unbalance, harmonics, thermal loading, and converter-current constraints before the available solar resource is fully utilized. Battery energy storage systems (BESS) and electric vehicle (EV) flexibility can relieve some of these limits, but their contribution depends on battery state, degradation, converter headroom, mobility, communications, and point of common coupling requirements. This critical review asks when artificial intelligence (AI) improves the use of stationary and mobile battery flexibility under those constraints. The synthesis compares these engineering dimensions qualitatively and includes a source-level comparison of 30 selected sources, together with focused comparisons of battery technologies and AI constraint-enforcement methods. Across the reviewed evidence, fast reactive and waveform-support services are often constrained by converter current more directly than by battery energy, whereas energy shifting and vehicle-to-grid operation are strongly affected by state of charge, availability, efficiency, and aging. AI is most defensible as a forecasting, estimation, and supervisory layer; it does not replace electrical feasibility or battery safety limits. Usable PV energy, meaning energy delivered after curtailment, conversion losses, and battery constraints, is not the same quantity as a larger nominal hosting-capacity rating. The review therefore recommends reporting net recovered PV energy together with compliance margin, converter utilization, battery throughput, aging assumptions, uncertainty, fallback behavior, and validation maturity.
SN - 2313-0105
ER -