The combination of carport photovoltaic and energy storage equipment requires the use of an energy management system (EMS) to achieve a dynamic balance of power generation, storage, and consumption. The core is to improve the flexibility and economy of power allocation through full-process control of data collection, strategy formulation, and equipment linkage. The system needs to take into account the volatility of photovoltaic output, the charging and discharging characteristics of energy storage equipment, and the power demand of users. While ensuring the reliability of power supply, it can achieve multiple goals such as peak-valley arbitrage and peak-to-valley filling, and promote the upgrade of the carport scene to an integrated "source-storage-load" smart microgrid.
The photovoltaic side uses a DC meter (accuracy 0.5S level) to collect component output power, voltage, current, and temperature data in real time. The energy storage side uses BMS (battery management system) to monitor the SOC (state of charge), SOH (health state), single cell voltage, and temperature distribution of the battery pack. The power consumption side uses a smart meter (supporting RS485 communication) to count the real-time power and cumulative power consumption of loads such as charging piles and lighting in the carport. In addition, meteorological sensors (light intensity, temperature, wind speed) are used to predict the short-term output of photovoltaic power, and a photovoltaic power prediction model (such as a prediction algorithm based on BP neural network, with an error rate of ≤5%) is established in combination with historical data to provide a basis for energy scheduling. All data are connected to the cloud platform through the edge computing gateway (supporting Modbus and MQTT protocols) to achieve millisecond-level data synchronization and visualization.
The carport photovoltaic energy management system needs to set a scheduling logic with clear priorities to balance safety, economy and user experience. The basic strategy follows the principle of "self-generation and self-use, surplus power storage": when the photovoltaic power is greater than the load demand, the load is powered first, and the remaining power is charged (start charging when SOC < 90%) or connected to the Internet according to the energy storage SOC state; when the photovoltaic power is insufficient, the energy storage discharge is used to supplement the power supply. If the energy storage SOC is < 20%, it is switched to the grid to avoid over-discharge of the battery. Advanced strategies can be combined with the peak-valley electricity price mechanism to purchase electricity from the grid at a low price during the valley period (such as 23:00-7:00), and supply power to the load from the energy storage during the peak period (such as 10:00-15:00), realizing the "low storage and high release" arbitrage. For electric vehicle charging scenarios, the "charging pile priority" mode can be set. When the vehicle charging demand is detected, the photovoltaic surplus power or energy storage power supply is prioritized to ensure charging efficiency while reducing dependence on the grid.
The energy storage converter (PCS) must have four-quadrant operation capabilities and support multiple modes such as constant current charging, constant voltage charging, and power control. During the charging stage, the charging current is dynamically adjusted according to the real-time photovoltaic power and load demand: when the photovoltaic power fluctuates greatly, the PI (proportional integral) control algorithm is used to smooth the charging power to avoid impact on the battery; when entering the high energy storage SOC range (such as 80%-90%), switch to the constant voltage charging mode to prevent overcharging and damage to the battery. During the discharge phase, the output strategy needs to be optimized in combination with the load characteristics. For impact loads such as charging piles, the dynamic response technology of PCS (adjustment time ≤ 20ms) is used to quickly compensate for the power gap to prevent voltage drops from affecting equipment operation. At the same time, the capacity difference between single cells is reduced through battery cluster-level balancing technology (active balancing current ≥ 5A), extending the overall life of the energy storage system (the number of cycles is increased to more than 6,000 times).
In the carport scenario, charging piles are the main power load, and the energy management system needs to be deeply connected with the charging pile management system (CMS) to achieve "light-storage-charging" linkage. Through V2G (vehicle-grid interaction) technology, when the power grid is at peak hours and the energy storage SOC is low, the electric vehicle battery can be dispatched to reverse power supply (the vehicle needs to support bidirectional charging) to supplement the energy storage or directly supply other loads to form a "mobile energy storage" network. At the same time, based on the user's charging reservation data (such as setting 18:00-20:00 as the centralized charging period), the system plans the energy storage discharge strategy in advance: using the photovoltaic surplus power or low-peak electricity price electricity to charge the energy storage before the reservation period, and giving priority to energy storage power supply during the reservation period to reduce the impact of charging peak on the power grid. For carports without energy storage installed, the decentralized charging piles can be aggregated into a virtual power plant (VPP) through load aggregation technology to participate in the power grid peak load auxiliary service and improve the flexibility of the power system.
The energy management system must have real-time monitoring, fault diagnosis and self-healing control functions. The monitoring interface needs to visualize the photovoltaic output curve, energy storage SOC changes, load power distribution and grid interaction status, and support historical data query (storage period ≥ 1 year) and abnormal alarms (such as overvoltage, overcurrent, battery high temperature). The fault diagnosis module identifies system anomalies through data mining algorithms (such as support vector machines (SVMs). For example, when it detects that the voltage difference of a cluster of energy storage batteries exceeds 50mV, it automatically triggers balanced charging and issues a maintenance prompt. When the photovoltaic inverter has an islanding effect, the grid-connected switch is cut off within 0.1 seconds, and the energy storage off-grid operation mode is started to ensure continuous power supply to key loads in the carport (such as emergency lighting). The self-healing control function can automatically switch the operation mode according to the fault type. For example, when the photovoltaic array is partially blocked and the output drops sharply, the system automatically adjusts the energy storage discharge power and limits the power consumption of non-critical loads to maintain the power balance of the system.
The energy management system needs to have a built-in economic calculation module to evaluate the benefits of different scheduling strategies in real time. By calculating the peak-valley electricity price difference benefits (such as the arbitrage space when the peak electricity price is 1.2 yuan/kWh and the valley is 0.3 yuan/kWh), the photovoltaic power abandonment rate (optimizing the energy storage charging strategy to make the power abandonment rate <5%), and the equipment depreciation cost (the energy storage system is calculated based on 10 years of depreciation), etc., the economically optimal scheduling plan is generated. At the same time, the reinforcement learning (RL) algorithm is used to train historical operation data to automatically optimize parameters such as energy storage charging and discharging thresholds and load priorities. For example, 90 days of data training can increase peak-valley arbitrage income by 15%-20%. In addition, the system can connect to the carbon trading platform to calculate the carbon emission reduction of carport photovoltaic (such as an annual power generation of 100,000 kWh is equivalent to a reduction of 60 tons of CO₂ emissions), increase additional income through carbon credit trading, and improve the project investment return model.
In order to adapt to future technology upgrades and multi-system integration, the energy management system needs to adopt modular design and standardized communication protocols. RS485, CAN, Ethernet and other interfaces are reserved at the hardware level to support docking with third-party equipment such as fire protection systems, meteorological warning systems, and electric vehicle cloud platforms; the software level follows IEC 61850, GB/T 34933 and other standards to achieve unified and interactive data formats. For example, when connected to the meteorological warning system, a strong wind forecast is obtained 2 hours in advance, and the system automatically adjusts the energy storage operation mode to "protection mode" (SOC is maintained at 50%±5%), reserving enough power to cope with post-disaster emergency power supply; after connecting to the electric vehicle cloud platform, the carport charging demand can be predicted according to the distribution of vehicles in the area, and the energy storage discharge strategy can be dynamically adjusted. The open architecture also supports access to the virtual power plant management platform, making the carport microgrid a flexible adjustment unit of the large power grid, participating in advanced applications such as demand response and frequency regulation.
The energy management system combining carport photovoltaic and energy storage needs to be data-driven, and a flexible and efficient power dispatch system is built through accurate prediction, intelligent scheduling, coordinated control and continuous optimization. With the in-depth application of the Internet of Things and artificial intelligence technologies, the future system will develop in the direction of intelligent autonomous decision-making and self-organized operation, promoting the carport scene from a single power generation unit to a comprehensive energy node with grid support capabilities, and providing core technical support for the large-scale application of distributed energy.
