Table 1 State-of-the-Art comparison.
Ref | Methodology | Optimization Approach | Key Findings | Limitations |
|---|---|---|---|---|
DER integration in grid management | Stochastic | Enhances resilience and flexibility | Requires advanced infrastructure | |
EV aggregation networks | Market participation | Facilitates grid participation for EV owners | Regulatory barriers | |
EV charging and grid stability | Deterministic | Improves energy efficiency and demand-side management | Limited scalability | |
Scenario-based stochastic optimization | Stochastic | Estimates power needs under uncertainty | High computational cost | |
Deterministic modeling of EV charging | Deterministic | Predicts energy consumption per EV | Ignores real-world uncertainty | |
Risk-averse chance constraints | Hybrid | Enhances robustness | Lacks adaptability to extreme conditions | |
V2G aggregator for grid support | Stochastic | Reduces charging costs and enhances frequency regulation | Battery degradation concerns | |
Multi-objective optimization for DSOs | Deterministic | Balances cost and efficiency | Limited real-time adaptability | |
Fuzzy cloud stochastic models | AI-Based | Enhances energy distribution and efficiency | Requires complex fuzzy logic tuning | |
Intelligent Energy Management Systems (EMS) | Hybrid | Reduces voltage fluctuations and network losses | High implementation complexity | |
Fuzzy logic-based EV selection models | AI-Based | Prioritizes cost-efficiency and minimizes battery degradation | Requires significant dataset training | |
Gaussian Process & Krill Herd Algorithm | AI-Based | Reduces prediction errors and operational costs | High computational demand | |
Hybrid control strategies | Hybrid | Optimizes EV charging efficiency | Needs real-time tuning and control | |
Multi-energy microgrids with EVs | Stochastic | Balances grid stability and economic factors | Complex integration requirements | |
Grid-interactive EVs in multi-microgrids | Hybrid | Enhances cost efficiency and stability | Needs large-scale coordination | |
Renewable-based EV charging stations | Advanced Control | Maximizes renewable energy utilization | Requires integration with existing grids | |
Stochastic EV integration in microgrids | Stochastic | Optimizes cost and demand response | Complexity in uncertainty modeling | |
Proposed Study | Multi-objective AI-enhanced optimization | Hybrid (AI + Stochastic) | Enhances computational efficiency, real-time adaptability, and grid stability | High initial computational cost, and dependency on data availability |
