As the adoption of electric vehicles continues to accelerate globally, with China EV markets leading in growth, the demand for efficient charging infrastructure at critical transportation hubs like airports has become increasingly vital. In my analysis, I focus on the unique challenges posed by electric vehicle charging at airport hubs, where the spatial and temporal aggregation of charging loads can strain power distribution networks. This paper presents a comprehensive approach to optimizing charging facility loads, incorporating multi-objective modeling, real-time data analysis, and intelligent scheduling strategies. Through this work, I aim to demonstrate how advanced optimization techniques can enhance operational efficiency, reduce costs, and improve service reliability for electric vehicle users in such high-demand environments.
The proliferation of electric vehicles, particularly in regions like China EV sectors, has driven rapid expansion in charging infrastructure. Airports, as major transportation nodes, experience concentrated electric vehicle charging demands that exhibit strong temporal and spatial variability. For instance, charging loads peak during flight arrival and departure windows, often aligning with morning and evening rush hours. This randomness and clustering can lead to overloaded distribution systems, voltage instability, and reduced power quality. My research addresses these issues by developing a load optimization framework tailored to airport scenarios, which has been underexplored compared to urban public charging stations. By leveraging temporal distribution characteristics and load forecasting, I propose strategies that balance grid capacity, user service quality, and economic efficiency.
In examining the load characteristics of airport charging facilities for electric vehicles, I identified distinct patterns of temporal fluctuation and spatial concentration. Peak periods typically occur between 7:00–9:00 and 18:00–20:00, correlating with flight schedules and business travel rhythms. Short-term parking areas favor fast charging, with average sessions lasting around 45 minutes, while long-term parking areas see slower charging sessions extending to 4–6 hours. Spatially, charging density is highest near terminals, reaching up to 4.5 kW/m², compared to 1.2–2.0 kW/m² in remote parking zones. Seasonal variations further influence these patterns; for example, summer loads increase by 20–30% due to air conditioning demands, and winter charging times lengthen by 15–25% because of reduced efficiency. These insights form the basis for my optimization model, ensuring it accounts for the dynamic nature of electric vehicle charging in airport settings.
To address these challenges, I designed a load optimization system with a layered architecture comprising perception, network, platform, and application layers. The perception layer includes hardware such as charging piles, energy meters, and environmental sensors that monitor key parameters in real-time. The network layer utilizes 5G and industrial Ethernet for reliable data transmission, while the platform layer centralizes data processing, intelligent scheduling, and security management. Finally, the application layer provides user-friendly interfaces for monitoring and analytics. This structure supports efficient data flow and decision-making, crucial for handling the high variability in electric vehicle charging demands.
Data acquisition and processing are integral to my optimization approach. I implemented a distributed sensor network that samples electrical parameters like charging power, voltage, and current every 100 ms, and environmental factors such as temperature and humidity every 5 minutes. After preprocessing steps like digital filtering and outlier removal, I applied wavelet transform methods for multi-scale decomposition of load curves, identifying patterns and periodicities. Principal component analysis helped reduce dimensionality and extract key factors affecting load variations. This processed data feeds into the optimization model, enabling precise adjustments based on real-time conditions. For instance, the load feature index system I developed captures critical metrics that influence charging behavior for electric vehicles.
The core of my work involves a multi-objective optimization model that minimizes grid operating costs while maximizing charging service quality. The objective function combines电能损耗成本 (energy loss costs), 设备折旧成本 (equipment depreciation costs), and 用户等待成本 (user waiting costs), formulated as:
$$ \min \left( C_{\text{energy}} + C_{\text{depreciation}} + C_{\text{waiting}} \right) $$
where \( C_{\text{energy}} \) represents the cost of energy losses, \( C_{\text{depreciation}} \) accounts for equipment wear, and \( C_{\text{waiting}} \) quantifies user inconvenience due to delays. Constraints include charging power limits, transformer capacity, voltage deviation bounds, and charging completion times, expressed as:
$$ P_{\text{min}} \leq P(t) \leq P_{\text{max}} $$
$$ \sum P(t) \leq S_{\text{transformer}} $$
$$ |V(t) – V_{\text{nominal}}| \leq \Delta V_{\text{max}} $$
$$ T_{\text{charge}} \leq T_{\text{max}} $$
I employed an improved Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve this model, incorporating adaptive crossover and mutation operators for better convergence. A fuzzy decision-making method筛选 Pareto optimal solutions, ensuring practical applicability. Additionally, I introduced robustness constraints to handle uncertainties in load predictions, which is essential for the volatile nature of electric vehicle charging. This model effectively balances the competing demands of grid stability and user satisfaction in China EV contexts.
A key component of my optimization strategy is the implementation of time-of-use pricing, which incentivizes load shifting to off-peak periods. Based on historical data analysis, I divided the day into peak, flat, and valley periods, with电价参数 (pricing parameters) set to maximize peak shaving and valley filling. The price differentials range from 0.8 to 1.2 yuan/kWh, achieving a load transfer rate of 15–20%. Seasonal adjustments account for variations in charging behavior; for example, summer and winter have distinct pricing schemes to reflect differing load patterns. The table below summarizes these parameters:
| Period Type | Time Interval | Summer Price (yuan/kWh) | Winter Price (yuan/kWh) |
|---|---|---|---|
| Peak | 7:00–9:00, 18:00–20:00 | 1.50 | 1.35 |
| Flat | 9:00–18:00, 20:00–23:00 | 1.00 | 0.90 |
| Valley | 23:00–05:00 | 0.70 | 0.63 |
| Special | 05:00–07:00 | 0.85 | 0.77 |
This pricing strategy encourages electric vehicle users to charge during low-demand periods, reducing strain on the grid and aligning with the goals of China EV infrastructure development.
Complementing the pricing scheme, I developed an intelligent scheduling system with a hierarchical structure comprising a central control unit and field controllers. The central unit uses deep reinforcement learning for dynamic power allocation and load forecasting, achieving an accuracy of 92% and response times under 100 ms. This system tailors strategies to different zones: for instance, fast-charging priority in short-term parking and cost-oriented approaches in long-term parking. It integrates modules for load prediction, real-time monitoring, and fault diagnosis, providing可视化展示 (visualization) of operational parameters. The effectiveness is evident in load reduction during peak hours, as illustrated in the运行效果图 (operation effect diagram), where peak load削减率 (reduction rate) reaches 25%.
To ensure reliable operation, I established a safety保障体系 (safety guarantee system) with monitoring, emergency response, and assessment subsystems. The monitoring subsystem tracks key indicators like voltage limits and temperature, while the emergency system enables rapid load shedding and power switching in under 20 ms. Safety assessment employs a quantitative risk model covering electrical, environmental, and operational dimensions. Redundancy in critical equipment, as detailed in the table below, enhances system reliability to 99.99%, with an annual failure rate below 0.1%:
| Equipment Type | Redundancy Method | Switching Time (ms) | Backup Quantity |
|---|---|---|---|
| Main Control Server | Hot Standby | < 50 | 1 |
| Communication Gateway | N+1 Backup | < 100 | 2 |
| Power Distribution Equipment | Dual Circuit | < 20 | 1 |
| Monitoring Terminal | Distributed Deployment | < 200 | 3 |
This robust framework minimizes downtime and ensures continuous service for electric vehicle charging, which is critical for maintaining user trust in China EV networks.
I evaluated the optimization方案 (scheme) using six months of operational data, assessing load curve smoothness, power supply reliability, and economic indicators. Load fluctuation rate decreased from 32.5% to 18.2%, peak-valley difference dropped from 580 kW to 360 kW, and standard deviation reduced from 156 kW to 92 kW. During peak hours, load削减 (reduction) was 25.3% in the morning and 23.8% in the evening, with 18.6% of total charging load shifted to off-peak periods. Overall, load curve smoothness improved by 42.3%, demonstrating the efficacy of my approach in stabilizing electric vehicle charging demands.
In terms of供电可靠性 (power supply reliability), voltage qualification rate rose from 92.3% to 97.8%, with deviations kept within ±5%. Harmonic content decreased by 28.5%, and power factor exceeded 0.95. Equipment mean time between failures extended from 1250 hours to 1860 hours, and system response time remained under 100 ms. The N-1 verification pass rate reached 100%, and service availability for electric vehicle charging maintained at 99.95%, with user complaints falling by 52.6%. These metrics underscore the system’s resilience in supporting the growing China EV ecosystem.
Economically, the optimization yielded significant savings. Annual operating costs decreased by 13.6%, from 2.875 million yuan to 2.483 million yuan, driven by a 15.8% reduction in energy losses (saving 426,000 yuan in electricity costs), 23.4% lower maintenance costs, and an 18.2% cut in labor expenses. Additionally, charging service revenue increased by 12.5%, adding 853,000 yuan annually. The static payback period is 2.3 years, dynamic payback is 2.8 years, net present value is 5.236 million yuan, and internal rate of return is 28.4%. Sensitivity analysis confirmed stability under ±20% electricity price fluctuations, highlighting the financial viability of this approach for electric vehicle infrastructure in airports.
In conclusion, my research on load optimization for electric vehicle charging facilities at airport hubs demonstrates that integrating time-of-use pricing with intelligent scheduling can effectively mitigate peak loads, lower operational costs, and enhance power supply reliability. The proposed model and strategies not only address the unique temporal and spatial characteristics of airport charging but also provide a scalable framework for other high-demand scenarios. As the adoption of electric vehicles, especially in China EV markets, continues to rise, such optimizations will play a crucial role in ensuring sustainable and efficient charging infrastructure. Future work could explore integration with renewable energy sources or advanced AI techniques for further improvements.