As global demand for renewable energy and environmental protection intensifies, EV charging stations with integrated photovoltaic and storage systems are emerging as innovative energy solutions, gradually becoming a mainstream choice for urban energy futures. These systems efficiently convert solar energy into electricity through high-performance photovoltaic panels and intelligently store it in batteries to meet building power demands. This technology not only reduces reliance on traditional energy sources but also effectively alleviates grid pressure and enhances energy utilization efficiency. Based on this, I aim to delve into the energy management strategies of such EV charging stations, analyzing their system architecture, operational principles, and the formulation and optimization of energy management strategies, to provide theoretical support and practical guidance for green energy solutions in smart cities.
The core of these systems lies in their ability to harness solar power and manage energy flow dynamically. In this article, I will explore the architecture design, including photovoltaic panels, storage batteries, charging systems, and energy management systems, followed by a detailed analysis of energy flow and management strategies. By incorporating mathematical models and optimization techniques, I demonstrate how these EV charging stations can achieve high efficiency and cost-effectiveness. The integration of tables and formulas will help summarize key concepts and relationships, providing a comprehensive understanding of the topic.
In the system architecture, photovoltaic panels are installed on rooftops and other locations with optimal sunlight exposure to maximize solar energy collection. The collected solar energy is then adjusted by efficient DC-DC power conversion modules, which regulate the DC output to suitable voltage levels for the system. This regulated DC power is directly connected to a DC bus system, providing stable power supply to various DC loads. Unlike traditional photovoltaic energy generation methods, this approach avoids the complex process of inverting DC to AC and grid connection, followed by drawing power back from the grid. Traditional methods involve multiple energy conversion steps, leading to energy losses during inversion and grid integration. In contrast, this solution creates an all-DC system, enabling “instant use” of energy through local consumption. This simplifies the energy conversion process, reduces energy losses, and significantly improves energy utilization efficiency. For EV charging stations, this means a more reliable and efficient power source, directly supporting the charging infrastructure without intermediate conversions.
The storage component in EV charging stations addresses the need for high instantaneous power to meet building demands, while considering spatial constraints and land resource scarcity. Accompanying storage systems must not only have strong power output capabilities but also high energy density storage to store sufficient energy in limited spaces. Lithium-ion batteries are the preferred choice due to their excellent cycle life and high energy/power density. Among various lithium-ion batteries, lithium iron phosphate batteries stand out for their outstanding thermal stability and safety performance. They excel during charging and discharging processes, responding quickly to the high-power demands of EV charging stations and ensuring safe and stable energy output. Compared to other electrochemical storage technologies, such as lead-acid batteries and vanadium flow batteries, lithium iron phosphate batteries have significant advantages in integrated “photovoltaic-storage-charging” systems. Although lead-acid battery technology is mature, its energy density and cycle life are low. Vanadium flow batteries have high energy density but come with high costs and complex systems. In contrast, lithium iron phosphate batteries not only have good charging and discharging performance but also offer long cycle life and high safety, making them the ideal energy storage solution for “photovoltaic + storage + charging” systems. In this architecture, connecting the energy storage battery to the DC bus ensures bus voltage stability, simplifies the system structure, and improves energy transmission efficiency. Additionally, by connecting the bus to power converters, the storage battery can flexibly exchange energy with the grid and photovoltaic arrays, enhancing the overall functionality of the EV charging station.
The charging system in traditional DC EV charging stations typically consists of multiple AC-to-DC charging power modules connected in parallel, which draw power from the grid and convert it to DC for building power needs. The core control circuit board in the system manages charging strategies, enables human-machine interaction, performs energy metering, insulation monitoring, and provides electrical safety protection to ensure safe and efficient operation of the EV charging station. With the development of energy technology and the widespread application of green energy, this charging system has been innovatively upgraded based on traditional AC/DC charging modules. In addition to maintaining the original DC fast-charging function, it adds DC/DC charging modules connected to the DC bus, providing new pathways for obtaining energy from photovoltaic generation and storage devices. During peak electricity demand periods when grid prices are high, operators of EV charging stations can choose to use clean energy from storage batteries to power indoor related electrical equipment, which not only helps reduce operating costs but also decreases reliance on the traditional grid, promoting the application of green energy.
The energy management system plays a crucial role in regulating power supply and demand among the grid, storage devices, and EV charging stations. It precisely controls the flow of electricity between the grid, storage equipment, and charging stations, and optimizes scheduling during peak and off-peak hours, thereby significantly improving energy use efficiency. From a hardware configuration perspective, the core of the energy management system is a powerful local controller. This controller primarily uses various performance processors, such as microcontrollers, digital signal processors, and programmable logic controllers, to achieve precise control of local power converters, while supporting functions like energy metering, fault monitoring, and protection. Furthermore, the energy management system has remote communication capabilities, allowing seamless connection to cloud platforms. Through this connection, upper-level controllers can obtain real-time system status and send commands, enabling remote monitoring and management of the system. In terms of communication protocol selection, local communication employs mature and reliable protocols such as RS485 and CAN bus, combined with high-voltage electrical isolation solutions to effectively enhance system stability and anti-interference performance. This design ensures efficient and stable data transmission between local devices, providing support for precise control of the EV charging station.
To better understand the energy flow in EV charging stations, I analyze the power modules and energy directions. In the system, circles represent power converters, and based on system power balance conditions, the following equations can be derived:
$$P_a + P_b + P_d = P_c$$
$$P_a = P_1 + P_2$$
$$P_c = P_1 + P_3$$
$$P_b = P_4$$
$$P_d = P_3 – P_2 – P_4$$
Here, $P_a$ represents the grid input power, $P_b$ is the photovoltaic input power, $P_c$ is the EV charging station output power, and $P_d$ is the storage output power. The power converters 1 to 4 are real-time output powers, denoted by $P_1$ to $P_4$ respectively. Additionally, each source, load, or conversion device should satisfy the following constraint:
$$P_X \in [P_{X_{\text{min}}}, P_{X_{\text{max}}}], \quad X = a, b, c, d, 1, 2, 3, 4$$
To maximize economic benefits, the optimization objective is set to maximize profit, calculated as:
$$\max \sum_t (P_{ct} \cdot P_c – P_{at} \cdot P_a) \cdot \Delta t$$
In this equation, $P_{at}$ and $P_{ct}$ represent the electricity price and EV charging station selling price at time $t$, respectively, and $\Delta t$ is the duration of the current time period. This formulation helps in scheduling energy flow for EV charging stations to achieve cost savings.
In model analysis and simplification, the model varies with different variables, so using a multi-variable model for optimization求解 can effectively handle various complex computational scenarios. After analysis, it is evident that energy flow paths and conversion efficiencies differ. The conversion efficiency path is typically transformed by upper-level power modules and obtained by multiplying the average working efficiencies. Since the photovoltaic storage side has more conversion环节, the corresponding losses are higher. For instance, in this project, when power is supplied from the photovoltaic storage端, the path $a-2-3-c$ has one more环节 than $a-1-c$, resulting in lower efficiency. Therefore, to equivalent the net flow simultaneously passing through paths $P_2$ and $P_3$, it depends on the magnitude of the net flow in the photovoltaic storage system $(P_b + P_d)$.
When there is a net inflow to photovoltaic storage, $(P_b + P_d) < 0$, the energy flow is distributed as shown in one scenario. When there is a net outflow from photovoltaic storage, $0 < (P_b + P_d) < P_c$, the distribution changes. And when the net outflow from photovoltaic storage is fed into the grid or used for charging, $(P_b + P_d) > P_c$, another distribution applies. This analysis helps in optimizing the operation of EV charging stations by adjusting energy paths based on real-time conditions.
For photovoltaic storage management from an optimal economic perspective, several factors must be considered. First, since photovoltaic storage can be used as a clean renewable energy source, it incurs no electricity cost. Second, given the significant differences in peak and valley electricity prices, electricity costs vary considerably. Utilizing photovoltaic storage batteries to charge during valley periods and discharge during peak periods can yield substantial economic benefits, thus achieving the best economic outcome. Based on these two points, to ensure maximum utilization of photovoltaic power generation, photovoltaic power converters are controlled based on maximum power point tracking, and their output paths depend on actual solar irradiation conditions. Although this has randomness and uncontrollability, the data can be measured in real-time and treated as known. In this system, the building’s power demand is determined by actual indoor needs, which are random and uncontrollable but can also be considered as已知 conditions. By controlling the charging and discharging power of photovoltaic storage, the real-time power state of the system can be calculated, enabling the maximization of economic benefits. Additionally, photovoltaic storage can adopt a strategy of two charges and two discharges per day, adjusted solely based on peak and valley periods, aiming to fully charge during valley periods and discharge during peak periods. This provides a planned charging and discharging duration for setting the charging and discharging power of photovoltaic storage in EV charging stations.
To summarize the key parameters and relationships in EV charging stations, I present the following tables. Table 1 outlines the power variables and their descriptions, while Table 2 shows typical efficiency values for different conversion paths.
| Variable | Description | Unit |
|---|---|---|
| $P_a$ | Grid input power | kW |
| $P_b$ | Photovoltaic input power | kW |
| $P_c$ | EV charging station output power | kW |
| $P_d$ | Storage output power | kW |
| $P_1$ to $P_4$ | Power converter outputs | kW |
| Conversion Path | Description | Average Efficiency |
|---|---|---|
| $a-1-c$ | Grid to EV charging station via converter 1 | 0.98 |
| $a-2-3-c$ | Grid to EV charging station via converters 2 and 3 | 0.95 |
| $b-4-3-c$ | Photovoltaic to EV charging station via converters 4 and 3 | 0.94 |
| $d-3-c$ | Storage to EV charging station via converter 3 | 0.96 |
Furthermore, the optimization of EV charging stations involves solving the profit maximization problem subject to constraints. The objective function is:
$$\max \sum_{t=1}^{T} (P_{ct} \cdot P_c – P_{at} \cdot P_a) \cdot \Delta t$$
subject to:
$$P_a + P_b + P_d = P_c$$
$$P_a = P_1 + P_2$$
$$P_c = P_1 + P_3$$
$$P_b = P_4$$
$$P_d = P_3 – P_2 – P_4$$
$$P_X \in [P_{X_{\text{min}}}, P_{X_{\text{max}}}], \quad X = a, b, c, d, 1, 2, 3, 4$$
This formulation ensures that the EV charging station operates within safe limits while maximizing revenue. By implementing such strategies, EV charging stations can reduce operational costs and enhance sustainability.
In conclusion, EV charging stations with integrated photovoltaic and storage represent an innovative charging solution, and the formulation and optimization of their energy management strategies are crucial for improving energy utilization efficiency and reducing operating costs. Through in-depth research into the system architecture, operational principles, and energy management strategies of these EV charging stations, I find that economically principled photovoltaic storage management strategies can effectively balance the electricity supply and demand among the grid, photovoltaic storage facilities, and charging stations, achieving high-efficiency energy use. As technology advances and costs decrease, the application and promotion scope of EV charging stations are expected to expand further, providing more possibilities for green energy solutions in smart cities. Therefore, I recommend continuing to strengthen research and practice on energy management strategies for EV charging stations to drive their development towards higher efficiency, intelligence, and environmental friendliness.