With the rapid growth in the adoption of electric vehicles, particularly in the context of the expanding China EV market, airport transportation hubs have become critical public service facilities where the construction and management of charging infrastructure are increasingly vital. As an integral part of modern urban mobility, the proliferation of electric cars necessitates efficient charging solutions to support sustainable transportation. In this analysis, I focus on the load characteristics of charging facilities at airports, establishing a multi-objective optimization model that considers charging demand, grid capacity, and user service quality. By incorporating spatiotemporal distribution analysis and load forecasting methods, I model operational data from an international airport’s charging facilities and propose load optimization strategies based on time-of-use pricing and intelligent scheduling. The findings demonstrate that the optimized approach can reduce the peak-to-valley load difference by 37.9%, lower power supply costs by 13.6%, and achieve a charging service availability rate of 99.95%. This research underscores the importance of tailored solutions for electric car charging in specialized environments like airports, contributing to the broader goals of energy efficiency and reliability in the China EV ecosystem.
The load distribution of electric car charging facilities at airports exhibits significant spatiotemporal fluctuations and aggregation. Peak periods typically occur between 7:00–9:00 and 18:00–20:00, aligning closely with flight schedules and business travel patterns. In short-term parking areas, charging demands are dominated by fast charging, with an average charging duration of approximately 45 minutes, whereas long-term parking areas see a prevalence of slow charging, lasting 4 to 6 hours per session. Spatially, charging facilities near terminal buildings experience the highest utilization rates, with load densities reaching up to 4.5 kW/m², while remote parking areas show more dispersed loads, fluctuating between 1.2 and 2.0 kW/m². Seasonal variations further influence these patterns; for instance, summer conditions, compounded by air conditioning loads, can increase peak charging loads by 20% to 30%, while winter efficiency reductions extend charging times by 15% to 25%. These characteristics provide a foundational basis for designing optimization strategies that address the unique demands of electric car charging in airport settings. The following table summarizes key load parameters across different parking zones:
| Parking Zone Type | Average Charging Duration (minutes) | Load Density (kW/m²) | Peak Load Increase (%) |
|---|---|---|---|
| Short-Term Parking | 45 | 4.5 | 20-30 |
| Long-Term Parking | 240-360 | 1.2-2.0 | 15-25 |
| Remote Parking | 180-300 | 1.0-1.8 | 10-20 |
To quantify the load variability, I employ mathematical formulations that capture the temporal dynamics. The load at time \( t \) can be expressed as \( L(t) = \sum_{i=1}^{N} P_i(t) \cdot \delta_i(t) \), where \( P_i(t) \) represents the power demand of the \( i \)-th electric car charger, and \( \delta_i(t) \) is a binary variable indicating charging activity. The aggregate load profile follows a probability distribution influenced by arrival rates, which I model using a Poisson process: \( \lambda(t) = \lambda_0 \cdot f(t) \), where \( \lambda_0 \) is the base arrival rate and \( f(t) \) is a time-dependent function accounting for flight peaks. This approach allows for precise forecasting, essential for optimizing the China EV charging infrastructure in high-demand areas like airports.
The load optimization system is structured into four hierarchical layers: perception, network, platform, and application. The perception layer comprises hardware components such as charging piles, energy metering devices, and environmental sensors, which continuously monitor critical parameters like charging power, voltage, and current. For example, sampling occurs every 100 ms for electrical data and every 5 minutes for environmental factors like temperature and humidity. This real-time data acquisition is crucial for managing the dynamic load patterns of electric car charging. The network layer utilizes 5G and industrial Ethernet technologies to ensure reliable data transmission, facilitating seamless communication between devices and control centers. As the core of the system, the platform layer includes data processing units, intelligent scheduling modules, and security management components, which perform advanced analytics and strategy optimization. Finally, the application layer provides user-friendly interfaces for operational staff and electric car users, offering features such as visual monitoring, smart reservations, and statistical analysis. This integrated architecture supports the efficient operation of charging facilities, particularly in the context of the growing China EV market, by enabling responsive and adaptive management.
Data processing involves several techniques to enhance accuracy and utility. After collection, raw data undergoes preprocessing steps like digital filtering and outlier removal to eliminate noise. I then apply wavelet transform methods for multi-scale decomposition of load curves, identifying characteristic patterns and periodicities. This can be represented as \( W(a,b) = \frac{1}{\sqrt{a}} \int L(t) \psi\left(\frac{t-b}{a}\right) dt \), where \( \psi \) is the wavelet function, and \( a \) and \( b \) are scaling and translation parameters, respectively. Principal component analysis (PCA) is used to reduce dimensionality, extracting key factors affecting load variations. The resulting feature set includes metrics such as average load, variance, and peak indicators, which form the basis for the optimization model. The table below outlines the data processing parameters and their functions:
| Processing Step | Method | Parameters | Output |
|---|---|---|---|
| Data Filtering | Digital Filter | Cut-off Frequency: 0.1 Hz | Smoothed Load Data |
| Feature Extraction | Wavelet Transform | Scales: 1-10 | Multi-scale Load Components |
| Dimensionality Reduction | PCA | Components: 5 | Key Load Factors |
The optimization model is formulated as a multi-objective problem aimed at minimizing grid operational costs while maximizing the quality of service for electric car users. The objective function includes terms for energy loss costs, equipment depreciation costs, and user waiting costs, expressed as \( \min \left[ C_{\text{loss}} + C_{\text{dep}} + C_{\text{wait}} \right] \), where each cost component is derived from historical data and predictive analytics. Constraints encompass charging power limits, transformer capacity, voltage deviations, and charging completion times, formulated as \( P_i(t) \leq P_{\text{max}} \), \( \sum P_i(t) \leq S_{\text{transformer}} \), \( |V(t) – V_{\text{nominal}}| \leq \Delta V_{\text{max}} \), and \( T_{\text{charge},i} \leq T_{\text{max},i} \). To solve this, I implement an improved non-dominated sorting genetic algorithm (NSGA-II) with adaptive crossover and mutation operators, enhancing convergence efficiency. The algorithm’s fitness function is defined as \( F = \sum w_k \cdot f_k \), where \( w_k \) are weights assigned to each objective, and \( f_k \) are the normalized cost functions. Additionally, fuzzy decision-making is applied to select the optimal solution from the Pareto front, ensuring robustness against load prediction errors inherent in electric car charging behaviors. This model effectively addresses the stochastic nature of China EV charging loads, providing a reliable framework for airport applications.
In designing the load optimization scheme, time-of-use pricing parameters are calibrated based on airport charging load characteristics and power supply cost structures. The day is divided into peak, flat, and valley periods, with pricing set to maximize peak shaving and valley filling effects. Through machine learning analysis of historical data, I determine optimal price differentials; for instance, a peak-to-valley price difference of 0.8–1.2 CNY/kWh achieves a load transfer rate of 15–20%. Seasonal adjustments are incorporated, with differentiated pricing for summer and winter to account for varying load patterns. The table below details the time-of-use pricing parameters, which are tailored to align with flight densities and electric car charging behaviors:
| Period Type | Time Interval | Summer Price (CNY/kWh) | Winter Price (CNY/kWh) |
|---|---|---|---|
| Peak Period | 7:00–9:00, 18:00–20:00 | 1.50 | 1.35 |
| Flat Period | 9:00–18:00, 20:00–23:00 | 1.00 | 0.90 |
| Valley Period | 23:00–5:00 | 0.70 | 0.63 |
| Special Period | 5:00–7:00 | 0.85 | 0.77 |
The intelligent scheduling system adopts a hierarchical structure comprising a central control unit and field controllers. Leveraging deep reinforcement learning algorithms, the system dynamically allocates charging power and predicts loads with an accuracy of 92%. Response times are kept under 100 ms to meet real-time operational demands. Scheduling strategies are differentiated by charging zones and user types; for example, fast-charging priority is applied in temporary parking areas, while economic considerations guide long-term parking allocations. The system integrates modules for load forecasting, real-time monitoring, and fault diagnosis, providing visualizations of device status and operational parameters. The effectiveness of this scheduling is evident in a 25% reduction in peak load during high-demand periods. The optimization can be mathematically described using a reinforcement learning framework, where the state \( s_t \) includes current load and queue lengths, actions \( a_t \) involve power adjustments, and the reward \( r_t \) is based on cost savings and service levels: \( r_t = -\left( C_{\text{operation}}(t) + \alpha \cdot W(t) \right) \), where \( W(t) \) represents waiting times and \( \alpha \) is a weighting factor. This approach ensures that the scheduling adapts to the evolving patterns of electric car usage, supporting the scalability of China EV infrastructure.
A comprehensive safety assurance system is established, consisting of monitoring and warning, emergency response, and safety assessment subsystems. The monitoring system tracks the operational status of charging facilities in real time, with twelve categories of alarm indicators, such as voltage limits and overtemperature conditions. The emergency response system includes functions like rapid load shedding and power source switching, achieving switchover times of less than 20 ms. Safety assessment employs state evaluation models to quantify operational risks, covering electrical, environmental, and operational safety dimensions. System reliability metrics reach 99.99%, with an annual failure rate below 0.1%. Redundant configurations for key equipment are outlined in the following table, ensuring continuous monitoring and automatic failover:
| Equipment Type | Redundancy Method | Switchover 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 |
Evaluation of the optimization scheme’s effectiveness involves analyzing load curve stability, power supply reliability, and economic indicators over a six-month period from September 2023 to February 2024. Load fluctuation rates decrease from 32.5% to 18.2%, while the peak-to-valley difference drops from 580 kW to 360 kW, and the standard deviation reduces from 156 kW to 92 kW. During peak hours (7:00–9:00 and 18:00–20:00), load reductions of 25.3% and 23.8% are observed, respectively, with 18.6% of total charging energy shifting to off-peak periods (10:00–16:00 and 22:00–4:00). Overall, load curve stability improves by 42.3%, demonstrating the efficacy of the strategies in managing electric car charging loads. The load profile after optimization can be modeled as \( L_{\text{opt}}(t) = L_{\text{base}}(t) – \Delta L(t) \), where \( \Delta L(t) \) represents the load reduction achieved through scheduling and pricing incentives.
Power supply reliability is assessed through voltage quality and equipment performance metrics. Post-optimization, voltage qualification rates rise from 92.3% to 97.8%, with deviations controlled within ±5%, harmonic content reduced by 28.5%, and power factor elevated above 0.95. Equipment-wise, the mean time between failures for charging facilities extends from 1250 hours to 1860 hours, and system response times are maintained under 100 ms. The power network passes N-1 contingency checks at a 100% rate, and critical user supply reliability reaches 99.99%. Charging service availability remains at 99.95%, with user complaint rates falling by 52.6%. These outcomes highlight the robustness of the optimization in supporting the reliable operation of electric car infrastructure, which is paramount for the continued expansion of the China EV sector.
Economic performance is evaluated based on annual operational data from 2023, showing a reduction in equipment operating costs from 2.875 million CNY to 2.483 million CNY, a saving of 13.6%. Energy losses decrease by 15.8%, resulting in annual electricity cost savings of 426,000 CNY, while maintenance costs drop by 23.4% and labor costs by 18.2%. Additionally, charging service revenue increases by 12.5%, generating an additional 853,000 CNY per year. The scheme’s static payback period is 2.3 years, with a dynamic payback period of 2.8 years, a net present value of 5.236 million CNY, and an internal rate of return of 28.4%. Sensitivity analysis confirms that economic indicators remain stable even with ±20% fluctuations in electricity prices. The net present value is calculated as \( \text{NPV} = \sum_{t=1}^{T} \frac{R_t – C_t}{(1 + r)^t} – I_0 \), where \( R_t \) and \( C_t \) are revenues and costs in year \( t \), \( r \) is the discount rate, and \( I_0 \) is the initial investment. This economic viability reinforces the strategic importance of optimizing electric car charging facilities, particularly in high-traffic hubs like airports, to foster sustainable growth in the China EV industry.
In conclusion, through in-depth analysis of load characteristics and optimization modeling for airport charging facilities, I have developed a comprehensive load optimization scheme tailored to the airport environment. The results indicate that combining time-of-use pricing guidance with intelligent scheduling strategies effectively reduces peak-to-valley load differences, decreases operational costs, and enhances power supply reliability. This optimized approach ensures that user charging demands are met while providing strong support for grid stability and efficiency. The success of this research underscores the potential for scalable solutions in the electric car charging domain, contributing to the advancement of smart infrastructure for the China EV market. Future work could explore integration with renewable energy sources and further advancements in AI-driven scheduling to accommodate the evolving landscape of electric mobility.