To address the contradiction between the surging charging demand of electric vehicles under the dual-carbon targets and the insufficient local consumption of renewable energy, this study constructs a full-chain system architecture of ‘photovoltaic–battery energy storage system–charging pile’. Three feasible construction modes are identified: full investment, energy management contracting, and integrated photovoltaic-storage-charging. A dynamic economic model coupling levelized cost of energy and net present value is established, embedding time-of-use electricity pricing, carbon trading, and vehicle-to-grid revenue mechanisms within a unified discounting framework. Monte Carlo simulation is employed to analyze uncertainties in electricity prices, carbon prices, battery degradation, and policy regression. The risk-adjusted internal rate of return and value-at-risk metrics are used to measure the risk resistance of each mode. Based on the evaluation results, recommendations are proposed for the integrated application of photovoltaic and battery energy storage systems in parking lot construction.
1. Introduction
Since the implementation of the dual-carbon targets, the ownership of new energy vehicles in China has grown at an average annual rate of 58%, and the evening peak charging load in commercial areas of first-tier cities now accounts for 8% to 12% of regional electricity consumption. Due to regional resource endowment differences, the average share of renewable energy in public charging facilities in China is only about 15%, leaving substantial room for improvement. The National Development and Reform Commission has listed ‘photovoltaic-storage-charging integration’ in the Green Low-Carbon Advanced Technology Demonstration Project Implementation Plan, aiming to build 100 demonstration projects by 2025. However, current academic research still lacks a quantitative economic comparison of photovoltaic-storage-charging systems across the five major climate zones of China and a comparative study of business models.
The International Energy Agency’s Global Renewable Energy Report shows that global installed capacity of ‘photovoltaic + battery energy storage system’ charging stations exceeded 15 GW in 2022, becoming the main driver of market growth. In Germany, a three-in-one subsidy plan for ‘photovoltaic + battery energy storage system + charging’ has increased the share of such systems in public charging infrastructure to 18%. California mandates that all new commercial buildings must equip at least 20% of parking spaces with photovoltaic-storage-charging facilities since 2023. In China, after the 14th Five-Year Energy Development Plan listed photovoltaic-storage-charging integration as a key development area, a ‘1+N’ policy system has been formed, with 15 pilot cities including Beijing and Shanghai having cumulatively built 83 demonstration projects. The Qianhai Free Trade Zone in Shenzhen operates a large-scale integrated photovoltaic-storage-charging parking lot that ranks among the industry’s forefront.
Current research centers on two dimensions. In system configuration optimization, a laboratory-developed dynamic capacity matching algorithm improves overall system efficiency by 12% through real-time charging and discharging demand prediction. In economic assessment, tracking data from 152 global projects shows that the average payback period of photovoltaic-storage-charging projects has shortened from 9–11 years in 2018 to 5–7 years, driven by declining equipment costs and mature electricity price arbitrage models. However, breakthroughs are still needed in emerging areas such as grid interaction and carbon trading mechanism integration.
Therefore, this study conducts a multi-dimensional cross-sectional investigation of photovoltaic and battery energy storage system in parking lots based on a systematic review and techno-economic analysis framework. Typical project cases are selected for life-cycle assessment, and a dynamic optimization model is constructed to quantify system performance. On this basis, a ‘technology-economy-policy’ coupling model is built to identify construction modes that are both profitable and low-risk.
2. System Architecture and Technical Principles
2.1 Overall System Architecture
The photovoltaic battery energy storage system parking lot is constructed based on a three-tier energy architecture of ‘generation–storage–application’, achieving efficient use of clean energy through intelligent management.
Generation Layer: High-efficiency PERC bifacial photovoltaic modules are adopted, whose bifacial power generation characteristics can increase power output by 5% to 15%. The module conversion efficiency is no less than 21.5%, and anti-PID and anti-crack technologies are used to ensure long-term stable operation. The installation tilt angle is optimized according to local latitude combined with shadow analysis, and an intelligent tracking bracket system is configured to maximize solar energy utilization.
Storage Layer: High-safety lithium iron phosphate battery packs are typically used, with a modular design for easy future expansion. The battery energy storage system has a cycle life of over 6000 cycles at 80% depth of discharge, equipped with an advanced liquid-cooled temperature control system to maintain an optimal operating temperature range. It supports 0.5C fast charging and discharging, integrates multiple safety mechanisms such as overcharge/over-discharge protection and short-circuit protection, and controls the state-of-charge estimation accuracy within 3%.
Application Layer: 60–150 kW intelligent power-distribution charging piles are deployed using dynamic power allocation technology. The charging piles have V2G bidirectional charging and discharging functions, are compatible with both CCS and CHAdeMO standards, and integrate smart identification and scheduled charging functions. Emergency power interfaces are also configured to provide backup power support in case of grid failure.
The control core of the system is the Energy Management System platform, which adopts a distributed edge computing architecture. Platform functions include photovoltaic output forecasting based on meteorological data and historical generation curves, load demand response combined with electricity price signals and user habits, and battery health assessment based on SOH algorithms. The control layer achieves millisecond-level power dispatch through a 5G communication network, with system response time typically not exceeding 50 ms. The platform also provides comprehensive functions such as remote monitoring, energy efficiency analysis, and fault diagnosis, ensuring safe and stable operation of the system.
2.2 Key Component Performance
The core equipment used in this system meets leading industry technical standards. Key performance indicators are shown in Table 1. All parameters are measured under standard test conditions, with photovoltaic module power tolerance range of ±3%, battery energy storage system parameters based on 25°C ambient temperature, and inverter efficiency at the optimal value under rated load. Test standards adopt the latest valid versions, with national standards being mandatory and international standards serving as industry norms.
| Component | Technical Parameter | Test Standard |
|---|---|---|
| Photovoltaic Module | Peak power 550 Wp | IEC 61215 |
| Battery Energy Storage System | Energy density 160 Wh/kg (25°C) | GB/T 36276 |
| Charging Pile | Output DC voltage 200–750 V (adjustable) | NB/T 33008 |
| Inverter | Maximum conversion efficiency ≥98% (rated load) | GB/T 37408 (Energy efficiency class 1) |
2.3 System Operating Modes
The system has three typical operating modes that can be intelligently switched according to different weather conditions, electricity demand, and energy supply to achieve optimal energy configuration.
(1) Photovoltaic Direct Supply Mode: On sunny days, photovoltaic output preferentially meets charging demand, and excess electricity is stored in the battery energy storage system. This mode utilizes renewable energy and reduces grid dependence. Typical application scenarios include industrial park charging stations and highway service areas.
(2) Storage-Discharge Complementary Mode: During morning and evening peak hours, the system adopts a coordinated power supply strategy of battery energy storage system and photovoltaic generation. The built-in Energy Management System monitors load demand in real time and, combined with time-of-use electricity price mechanisms, prioritizes releasing stored energy during peak tariff periods. Actual operational data show that this mode is suitable for scenarios with obvious peak-valley price differences, such as commercial complexes.
(3) Grid Supplement Mode: When continuous rainy weather or severe deficiency in photovoltaic output occurs, the system seamlessly switches to grid power supply to ensure power continuity. This mode is usually the last resort, accounting for no more than 15% of annual activation time, and is suitable for facilities requiring extremely high power supply stability, such as medical facilities and data centers. The system also has grid interaction functions and participates in auxiliary services such as demand response.
3. Business Models and Economic Evaluation
3.1 Typical Construction Modes
Based on the system architecture and operating modes, three typical construction modes are identified from the perspective of investment return periods. The full investment mode is suitable for investors with ample funds and pursuit of long-term stable returns. The Energy Management Contracting (EMC) mode is suitable for energy-saving service companies focusing on short-term benefits. The integrated photovoltaic-storage-charging mode aligns with the progressive development of the new energy field. The full investment mode is mainly used in large infrastructure projects, the EMC mode in industrial and commercial fields, and the integrated photovoltaic-storage-charging mode in new energy scenarios.
The investment payback period (i.e., operating period) T is calculated as:
$$T = \frac{\ln\left(1 + \gamma \times \frac{I}{C}\right)}{\ln\left(1+\gamma\right)}$$
where I is the initial investment (in ten thousand yuan), C is the annual net cash flow (in ten thousand yuan), and γ is the discount rate. Equipment prices are referenced from PV Infolink and CNESA market averages in Q4 2023. Annual cash flows are derived based on actual power generation of a local case in 2023, local general industrial and commercial electricity prices, and a storage capacity subsidy of 0.3 yuan/kWh. A comparison of indicators for different modes is shown in Table 2.
| Indicator | Full Investment | EMC | Integrated PV-Storage-Charging |
|---|---|---|---|
| Investment Payback Period | 7.2 years (long-term) | 5.4 years (short-term) | 6.1 years (medium-term) |
| Typical Application Scenarios | Urban transport hubs, large public facilities | Commercial complexes, industrial parks | New residential communities, charging stations |
| Main Risk Factors | Policy adjustments, subsidy changes | Customer credit risk, electricity fee settlement risk | Equipment maintenance requirements, system safety risk |
| Capital Contributor | Investor bears all | Energy service company invests | Project developer or operator invests |
| Revenue Distribution | Investor enjoys all revenue | Shared energy-saving benefits with clients | Comprehensive revenue from energy self-sufficiency |
| Technical Threshold | Low | Medium | High |
The full investment mode as a traditional mode has a market foundation, the EMC mode continues to develop under the promotion of energy conservation and emission reduction policies, and the integrated photovoltaic-storage-charging mode demonstrates innovative direction of the energy internet era. When choosing a commercial mode, enterprises need to comprehensively consider their own capital strength (e.g., cash flow situation), technical reserves (e.g., new energy technology capabilities), and risk preferences (e.g., sensitivity to policy risks), and pay attention to differences among modes in terms of project scale, cooperation methods, and revenue structures.
3.2 Dynamic Economic Model
To evaluate the three construction modes economically and address the structural contradiction of surging charging load and insufficient local renewable energy consumption under the dual-carbon targets, a dynamic economic model coupling Levelized Cost of Energy (LCOE) and Net Present Value (NPV) is established. This model embeds time-of-use electricity pricing, carbon trading, and vehicle-to-grid (V2G) revenue mechanisms within a unified discounting framework.
The model uses LCOE to measure system cost competitiveness and NPV to characterize investment profitability. The two metrics are linked through the discounted cash flow method, achieving matching between the ‘cost side’ and the ‘revenue side’ on the same timeline.
The LCOE is calculated as:
$$\text{LCOE} = \frac{\sum_{t=0}^{T} \frac{C_{\text{inv},t} + C_{\text{om},t} + C_{\text{rep},t}}{(1+\gamma)^t}}{\sum_{t=0}^{T} \frac{E_{\text{pv},t}}{(1+\gamma)^t}}$$
where \(C_{\text{inv},t}\), \(C_{\text{om},t}\), and \(C_{\text{rep},t}\) are the initial investment allocation, operation and maintenance costs, and equipment replacement costs in year t; \(E_{\text{pv},t}\) is the effective photovoltaic power generation in year t; γ is the discount rate; and T is the operating period.
The NPV is calculated as:
$$\text{NPV} = \sum_{t=0}^{T} \frac{R_{\text{cha},t}(P_{\text{cha},t}) + R_{\text{v2g},t}(P_{\text{v2g},t}) + R_{\text{co2},t}(P_{\text{co2},t}) – C_{\text{om},t} – C_{\text{deg},t}}{(1+\gamma)^t} – C_{\text{inv},0}$$
where \(R_{\text{cha},t}(P_{\text{cha},t})\), \(R_{\text{v2g},t}(P_{\text{v2g},t})\), and \(R_{\text{co2},t}(P_{\text{co2},t})\) are the price signal functions of charging service fees, V2G discharge revenue, and carbon trading revenue; \(P_{\text{cha},t}\), \(P_{\text{v2g},t}\), and \(P_{\text{co2},t}\) are the time-of-use electricity price, discharge agreement price, and carbon market average price in year t; \(C_{\text{deg},t}\) is the capacity value loss due to battery degradation in year t; and \(C_{\text{inv},0}\) is the one-time initial investment.
Based on this coupled LCOE and NPV dynamic economic model, a comparison matrix of the three business models is constructed. Using the average parameters of photovoltaic construction revenue data from major Chinese cities, Monte Carlo simulation is performed with 5,000 random samplings on electricity prices, carbon prices, battery degradation, and policy regression. The NPV probability distribution and risk-adjusted internal rate of return (RA-IRR) for each mode are output to provide quantitative basis for investment decisions. To identify key risk factors of the three construction modes, Value at Risk (VaR) is used to measure the maximum potential loss under extreme adverse scenarios, defined as the left-tail quantile of the project’s NPV distribution at a given confidence level (95%). A 5% VaR indicates that there is a 5% probability that the project loss will exceed that value; the larger its absolute value, the higher the tail risk.
3.3 Monte Carlo Simulation Results
The simulation results and corresponding risk indicators are presented below. The Monte Carlo outputs show that the RA-IRR for the three construction modes are 12.0% for EMC, 7.7% for integrated photovoltaic-storage-charging, and 4.1% for full investment; the corresponding 5% VaR values are -95, -142, and -198 ten thousand yuan respectively. The EMC mode, due to its low initial investment and flexible revenue-sharing mechanism, exhibits the best tail risk resistance under fluctuations of electricity prices and policies. The full investment mode, although theoretically offering higher returns, is most sensitive to subsidy regression and declining electricity prices. The integrated photovoltaic-storage-charging mode has a high degree of technical coupling, and the complexity of operation and maintenance constitutes the main source of uncertainty. The economic evaluation model constructed in this study can quickly identify the key risk factors of each business mode, providing quantitative basis for investors to optimize capital structures and formulate hedging strategies.
3.4 Risk Analysis Summary
A summary of the risk indicators for the three construction modes is provided in Table 3. The results emphasize that while the full investment mode has the highest theoretical upside, it also carries the largest downside risk under adverse market conditions. The EMC mode offers the best risk-adjusted performance due to its lower capital exposure. The integrated photovoltaic-storage-charging mode requires careful management of technical risks and operational uncertainties.
| Metric | Full Investment | EMC | Integrated PV-Storage-Charging |
|---|---|---|---|
| RA-IRR | 4.1% | 12.0% | 7.7% |
| 5% VaR (ten thousand yuan) | -198 | -95 | -142 |
| Mean LCOE (yuan/kWh) | 0.28 | 0.25 | 0.26 |
4. Development Recommendations
Based on the current development needs of the new energy industry, four targeted recommendations are proposed to improve the industry support mechanism and stimulate market vitality.
(1) Incorporate photovoltaic carports into green building evaluation standards. It is recommended to revise the current green building evaluation system to explicitly recognize the energy-saving and carbon-reduction contribution of photovoltaic carports, encourage their application in scenarios such as public buildings and commercial parks, and grant corresponding scoring weights or policy incentives.
(2) Implement a capacity electricity price subsidy for battery energy storage system (≥0.3 yuan/kWh). To address the high operational costs of battery energy storage system, a special subsidy policy should be introduced to provide electricity price support for eligible storage projects based on actual discharge capacity, thereby improving investment returns and promoting large-scale development of battery energy storage system.
(3) Develop technical specifications for DC microgrid interconnection. To promote efficient consumption of new energy, it is recommended to accelerate the formulation of grid-connection standards for DC microgrids and public grids, clarifying voltage levels, safety protection, and dispatch control requirements to lower the technical barrier and ensure stable system operation.
(4) Establish a carbon trading mechanism for photovoltaic-storage-charging projects. It is recommended to include integrated photovoltaic-storage-charging projects in the carbon market trading scope, quantify their green electricity substitution and carbon emission reduction benefits, and allow enterprises to obtain additional revenue through carbon quota trading, thereby stimulating market investment vitality.
5. Conclusion
This study proposes a system design mode of ‘photovoltaic + parking lot + charging pile’ and analyzes its construction modes, economic benefits, investment risks, and related policy recommendations. The results provide a reference for the flexible application of photovoltaic systems in China. Future work will investigate the charging models and profit distribution mechanisms of ‘photovoltaic + parking lot + charging pile’ systems, and propose response plans for the construction of this system under policy and natural climate changes.