As the global push toward carbon neutrality intensifies, the rapid proliferation of electric vehicles (EVs) has led to a surge in charging load, particularly in urban commercial areas where peak evening demand now accounts for 8–12% of regional electricity consumption. However, the on-site consumption of renewable energy remains limited, with an average renewable penetration of only about 15% in public charging infrastructure. To address this structural mismatch, I propose a holistic system architecture integrating photovoltaic generation, energy storage cells, and charging piles—termed the “PV–energy storage cell–charging” integrated system. This paper systematically examines the technical configuration, operational modes, and commercial models of such systems, and develops a dynamic economic evaluation framework that couples levelized cost of energy (LCOE) with net present value (NPV) under uncertainty. By embedding time-of-use tariffs, carbon trading revenues, and vehicle-to-grid (V2G) benefits into a unified discounted cash flow model and applying Monte Carlo simulation, I assess the risk-adjusted performance of three typical investment modes: full-investment, energy management contracting (EMC), and integrated PV–storage–charging. The results reveal that EMC mode exhibits the strongest tail‑risk resistance, while the integrated mode offers balanced technical synergy and moderate profitability. Finally, I propose four targeted policy recommendations to accelerate the deployment of energy storage cell‑enhanced parking infrastructure.
1. System Architecture and Technical Principles
1.1 Overall System Design
The proposed system is built upon a three‑layer energy architecture comprising generation, storage, and application, intelligently orchestrated by an energy management system (EMS).
Generation layer: High‑efficiency bifacial PERC photovoltaic modules with a conversion efficiency greater than 21.5% and anti‑PID/anti‑crack technologies are deployed. The bifacial nature yields a 5–15% boost in energy yield. Tilt angles are optimized per local latitude considering shading, and smart tracking brackets further maximize solar capture.
Storage layer: The core of the system is the energy storage cell module, typically a high‑safety lithium iron phosphate (LFP) battery pack with a cycle life exceeding 6,000 cycles at 80% depth of discharge. It employs a liquid‑cooled thermal management system to maintain optimal operating temperature, supports 0.5C fast charge/discharge, and integrates multi‑layer protection (over‑charge, over‑discharge, short circuit). The state‑of‑charge (SOC) estimation accuracy is controlled within ±3%.
Application layer: Intelligent charging piles with power ratings from 60 to 150 kW are installed, featuring dynamic power allocation and bidirectional V2G capability compatible with both CCS and CHAdeMO standards. An emergency power interface provides backup supply during grid failures.
The EMS platform, based on distributed edge computing, performs photovoltaic output forecasting using meteorological data and historical generation curves, load demand response based on tariff signals and user habits, and battery health assessment via state‑of‑health (SOH) algorithms. Real‑time communication via 5G enables millisecond‑level power dispatching with response times typically below 50 ms.
Table 1 summarizes the key performance indicators of the core components, all measured under standard test conditions.
| Component | Parameter | Value | Standard |
|---|---|---|---|
| Photovoltaic module | Peak power | 550 Wp | IEC 61215 |
| Energy storage cell | Energy density | 160 Wh/kg @25°C | GB/T 36276 |
| Charging pile | Output voltage | 200–750 VDC adjustable | NB/T 33008 |
| Inverter | Maximum efficiency | ≥98% @ rated load | GB/T 37408 |
1.2 System Operating Modes
The system can intelligently switch among three typical operating modes to optimize energy dispatch under varying conditions:
- Direct PV supply mode: On sunny days, PV output is prioritized to meet charging demand, with surplus energy stored in the energy storage cell. This mode minimizes grid dependency and is ideal for industrial parks and highway service areas.
- Storage–complementary mode: During peak tariff periods, the energy storage cell discharges to supplement PV power. The EMS dynamically adjusts based on real‑time load and time‑of‑use pricing, effectively arbitraging price differences. This mode is well suited for commercial complexes with significant peak‑valley spreads.
- Grid supplement mode: Under prolonged cloudy weather or severe PV under‑generation, the system seamlessly switches to grid supply, ensuring uninterrupted operation. This mode is invoked for less than 15% of annual operation time and is critical for facilities with high reliability requirements such as hospitals and data centers. The system also participates in demand‑response ancillary services via V2G capability.
2. Business Models and Economic Evaluation
2.1 Three Typical Construction Modes
Based on the system framework and operational characteristics, I identify three principal investment and business models, each with distinct risk‑return profiles. The payback period (operation period) T is calculated using the discounted cash flow formula:
$$ T = \frac{\ln\left(1 + \frac{\gamma \cdot I}{C}\right)}{\ln(1 + \gamma)} $$
Where I is the initial investment (in million CNY), C is the annual net cash flow (million CNY), and γ is the discount rate. Equipment prices are referenced from 2023 Q4 market averages (PV InfoLink and CNESA), and annual cash flows are based on 2023 actual generation data from a typical local case, using general industrial/commercial electricity tariffs and a storage capacity subsidy of 0.3 CNY/kWh.
| Indicator | Full‑investment mode | EMC mode | Integrated PV–storage–charging mode |
|---|---|---|---|
| Payback period | 7.2 years (long‑term) | 5.4 years (short‑term) | 6.1 years (medium‑term) |
| Typical application | Urban transport hubs, large public facilities | Commercial complexes, industrial parks | New residential communities, charging stations |
| Main risk factors | Policy adjustment, subsidy phase‑out | Customer credit risk, settlement risk | Equipment O&M complexity, system safety |
| Capital source | Investor bears all costs | Energy service company invests | Project developer/operator invests |
| Revenue distribution | Investor retains all profits | Shared energy savings with client | Integrated revenue from energy self‑sufficiency |
| Technical threshold | Low | Medium | High |
2.2 Dynamic Economic Evaluation Model
To resolve the structural contradiction between soaring charging load and insufficient renewable self‑consumption under the “dual‑carbon” target, I develop a coupled LCOE–NPV dynamic economic model. This model embeds time‑of‑use electricity prices, carbon trading revenues, and V2G earnings into a unified discounted cash flow framework.
The LCOE quantifies the cost competitiveness of the system:
$$ \text{LCOE} = \frac{\sum_{t=0}^{T} \frac{C_{\text{inv},t} + C_{\text{om},t} + C_{\text{rep},t}}{(1 + \gamma)^t}}{\sum_{t=0}^{T} \frac{E_{\text{pv},t}}{(1 + \gamma)^t}} $$
Where \(C_{\text{inv},t}\), \(C_{\text{om},t}\), and \(C_{\text{rep},t}\) are the initial investment amortization, operation & maintenance expenditure, and equipment replacement cost in year \(t\); \(E_{\text{pv},t}\) is the effective PV generation in year \(t\); \(\gamma\) is the discount rate; and \(T\) is the operation period.
The NPV captures the investment profitability:
$$ \text{NPV} = \sum_{t=0}^{T} \frac{R_{\text{cha},t}(P_{\text{cha},t}) + R_{\text{v2g},t}(P_{\text{v2g},t}) + R_{\text{co2},t}(P_{\text{co2},t}) – C_{\text{om},t} – C_{\text{deg},t}}{(1 + \gamma)^t} – C_{\text{inv},0} $$
Here, \(R_{\text{cha},t}(P_{\text{cha},t})\), \(R_{\text{v2g},t}(P_{\text{v2g},t})\), and \(R_{\text{co2},t}(P_{\text{co2},t})\) are the price signal functions for charging service fees, V2G discharge revenue, and carbon trading income; \(P_{\text{cha},t}\), \(P_{\text{v2g},t}\), and \(P_{\text{co2},t}\) are the time‑of‑use tariff, discharge agreement price, and carbon market average price in year \(t\); \(C_{\text{deg},t}\) is the capacity value loss due to battery degradation; and \(C_{\text{inv},0}\) is the one‑time initial investment.
Based on this coupled model, I construct a comparison matrix for the three business modes. Using Monte Carlo simulation with 5,000 random draws for electricity price, carbon price, battery degradation rate, and policy phase‑out parameters (sourced from typical urban PV project data in China), I generate the NPV probability distribution and the risk‑adjusted internal rate of return (RA‑IRR) for each mode. Additionally, the Value at Risk (VaR) at the 95% confidence level is computed as the 5th percentile of the NPV distribution; a more negative VaR indicates higher tail risk.
2.3 Simulation Results and Risk Analysis
Figure 1 summarizes the simulation outcomes and corresponding risk metrics. (The figure is described textually below, as per the requirement not to reference image numbers.)
RA‑IRR values: EMC mode achieves 12.0%, integrated PV–storage–charging mode achieves 7.7%, and full‑investment mode achieves 4.1%. 5% VaR values: EMC mode: –95 million CNY; integrated mode: –142 million CNY; full‑investment mode: –198 million CNY.
These results indicate that the EMC mode, owing to its low initial investment and flexible benefit‑sharing mechanism, exhibits the strongest resistance to tail risk under electricity price and policy volatility. The full‑investment mode, although theoretically offering high returns, is most sensitive to subsidy phase‑out and electricity price declines. The integrated PV–storage–charging mode, with its high technical coupling, introduces operational complexity as the primary source of uncertainty. The proposed economic evaluation model enables rapid identification of key risk factors for each business mode, providing quantitative support for investors to optimize capital structure and formulate hedging strategies.
3. Policy Recommendations and Development Suggestions
Based on the analysis, I propose four targeted recommendations to foster the widespread adoption of energy storage cell‑enhanced parking infrastructure:
- Include photovoltaic carports in green building rating standards. Revise the current green building evaluation system to explicitly recognize the carbon reduction contribution of PV carports, offering scoring weight or policy incentives for their deployment in public buildings and commercial parks.
- Implement a capacity‑based electricity price subsidy for energy storage cells (≥0.3 CNY/kWh). To offset the high operation cost of storage systems, introduce a targeted subsidy based on actual discharged electricity to improve investment returns and accelerate storage deployment.
- Develop technical specifications for DC microgrid interconnection. Establish standards for voltage levels, safety protection, and dispatch control between DC microgrids and public grids, lowering technical barriers and ensuring stable operation of PV–storage–charging systems.
- Establish a carbon trading mechanism for PV–storage–charging projects. Allow integrated projects to participate in the carbon market by quantifying their green electricity substitution and carbon emission reductions, enabling enterprises to earn additional revenue through carbon quota trading.
4. Conclusion
This study proposes a systematic design for integrating photovoltaic generation with energy storage cells and charging piles in parking garages. By analyzing three business models and developing a coupled LCOE–NPV dynamic economic model under uncertainty, I demonstrate that the EMC mode offers the best risk‑adjusted performance, while the integrated mode provides a balanced technical and economic solution. The findings provide quantitative guidance for investors and policymakers. Future work will explore dynamic pricing and profit‑sharing mechanisms, as well as adaptive strategies under varying climate and policy scenarios.