研究目的
To describe a novel method that uses the battery to reduce the daily peaks to a defined target power in a grid-connected PV-battery system.
研究成果
The peak shaving method applied on the grid-connected PV-battery system achieved an average daily reduction of about 15% in 2017 compared to standard strategies. It helps save money by smoothing the load during high power consumption periods, with savings depending on costs. Future improvements require more dynamic threshold adjustments and better data handling capabilities.
研究不足
The method suffers from inaccurate load forecasts when load fluctuates a lot, leading to unsuitable charge/discharge of the BESS. The processor capacity memory at the field point is limited, making it difficult to handle large amounts of data for prediction. Threshold values need to be more dynamic with hourly updates for better results.
1：Experimental Design and Method Selection:
The study involves a grid-connected PV-battery system with a 31 kWp PV array and a 76 kWh Li-Ion battery. A peak shaving method is applied using threshold values based on load and State of Charge (SOC) of the battery, with a control algorithm running at 5 Hz.
2：Sample Selection and Data Sources:
The system is installed at Helmholtz Institute Ulm (HIU) in Germany, operational since May
3：Data includes power measurements from PV, grid, and battery, collected every 200 ms. List of Experimental Equipment and Materials:
20 PV array (31 kWp), Li-Ion battery (76 kWh, 60 kW peak power), field point controller for system control, sensors for power measurement.
4：Experimental Procedures and Operational Workflow:
The battery is discharged during daytime peaks (7:30 to 19:30) when grid power exceeds a threshold, and charged at night (19:30 to 7:30) with a constant power of 10 kW. Threshold values are adjusted based on average SOC and load consumption over the last two hours.
5：Data Analysis Methods:
Power reduction is calculated using the difference between maximum load and maximum grid power per day. A PID algorithm in LabVIEW is used for control optimization.
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