Robust Optimization Framework Tames Uncertainty in Smart EV Parking Lot Energy Management

Electric vehicle parking lots are becoming active nodes in modern power grids, but managing their energy flows is far from simple. A new study published in Energy Informatics proposes a hybrid multi-objective optimization framework, called Hybrid Self-Improved Seagull Optimization Algorithm with Differential Evolution (H-SI-SOA-DE), for the short-term energy management of smart EV parking lot systems operating under renewable generation, electricity price, and EV mobility uncertainties. The work, authored by Manvir Kaur and Alok Jain, was published as open access research on 15 October 2026.

The framework tackles four conflicting objectives simultaneously: total operational cost, voltage deviation, the average state-of-charge (SOC) of the EV fleet, and peak grid power dependency. To handle uncertainty, the researchers developed a chance-constrained optimization formulation that incorporates variability in wind generation, electricity pricing, and EV arrival patterns while maintaining system operational feasibility. This approach reflects the growing challenge posed by increasing penetration of electric vehicles, renewable energy sources, and uncertain electricity market conditions.

Algorithmically, H-SI-SOA-DE combines adaptive exploration mechanisms from the Self-Improved Seagull Optimization Algorithm with Differential Evolution-based refinement, aiming to balance global exploration of the solution space with local exploitation of promising regions. The method was evaluated on the IEEE-33 bus distribution system and benchmarked against established multi-objective techniques, including NSGA-III, MOPSO, and the classical Seagull Optimization Algorithm.

The reported results come from 20 independent simulation trials. The proposed framework reduced operational cost to ₹5.22 × 10³, minimized voltage deviation to 0.051 p.u., improved average EV fleet SOC to 91.5%, and lowered peak grid power dependency to 1.88 MW. Relative to a non-optimized case, this corresponds to a 17.7% reduction in operational cost, a 21.5% reduction in voltage deviation, an 11.5% improvement in EV fleet SOC, and a 36.3% reduction in grid dependency.

Beyond the headline metrics, the study includes statistical robustness analysis, sensitivity evaluation, a scalability assessment with increasing EV penetration, and a computational performance analysis, which the authors say demonstrate the reliability and adaptability of the framework under uncertain operating conditions. Because the findings are simulation-based on a standard test distribution system rather than a deployed parking lot, real-world performance would still need to be validated in practice.

The authors position the framework as an effective and flexible solution for short-term smart EV parking lot energy management, with potential for practical smart grid scheduling applications.

Subject of Research: A hybrid multi-objective optimization framework for short-term energy management of smart EV parking lots under renewable, price, and mobility uncertainties.

Article Title: A robust optimization framework for energy management in smart EV parking lots

Article References: Kaur, M., & Jain, A. (2026). A robust optimization framework for energy management in smart EV parking lots. Energy Informatics. https://doi.org/10.1186/s42162-026-00695-w

Image Credits: AI Generated

DOI: 10.1186/s42162-026-00695-w

Keywords: electric vehicles, smart parking lots, energy management, robust optimization, multi-objective optimization, chance-constrained programming, renewable energy, IEEE-33 bus system, smart grid, differential evolution, voltage deviation, state of charge