Owners and operators of solar hybrid systems need to monitor and control the operation and performance of the system, which usually requires the collection of large data sets. Data visualization techniques can simplify monitoring and enable quick identification of problems, IEA-PVPS says.
To assess whether PV hybrid systems are achieving these goals, system data must be monitored and analyzed. The data sets are typically large and complex, but they can be analyzed in a short time with the help of suitable visualization tools. The IEA-PVPS Task 18 report defines an easy-to-use, standardized data visualization for PV hybrid systems.
The method enables a quick review of the systems’ performance in order to measure the success or shortcomings of the optimization system or to compare the performance of different systems in the fleet. This standardization and the possibility to compare systems enable the identification of weak points in the operation and management strategy of individual systems. In this way, operators can improve the efficiency of the systems.
Data visualization focuses on battery data, as this is the most cost-intensive component of any PV hybrid system. Optimizing battery usage and extending battery life is always key to reducing the overall operating cost of solar hybrid systems. To implement the proposed data visualization, it is necessary to monitor various battery metrics such as voltage, current and temperature. The visualization draws temperature heatmaps, temperature profiles and daily energy profiles and other Combinatorial metric visualizations needed to analyze load and supply sources.
Using these images, it is possible to match and match multiple charge and discharge setpoints, timed generator start and stop points, or any other applied control strategy to the actual operation of a given system. This is often more valuable than pre-planned strategies, because in many systems the reality of use differs greatly from planned and scheduled activity.
This article focuses on specific visualizations of PV hybrid systems to demonstrate their utility. More information can be found in the report, which reviews different monitoring and visualization systems for several types of systems, including pico solar, classic solar home systems (50 W – 500 W), inverter-based systems (500 W – 5 kW), PV hybrid systems (5 kW – 250 kW) and larger microgrid systems (over 250 kW).
For example, to analyze the operation strategy of an energy management system, it makes sense to visualize the battery current and state of charge (SOC) behavior depending on the battery voltage. For this, a chart can be created as shown below. The x-axis represents the normalized battery voltage, while the battery current or SOC is placed on the y-axis. Displaying all data sets results in a point cloud.
The voltage is shown as normalized battery voltage (V/cell) and represents the x-axis in both of the scatter plots above. The current is normalized by the battery capacity (A/Ah) and reflects the y-axis in the left graph. With a 100Ah battery, the classification means that a current of 10A is shown in the given example as 10A/100Ah = 0.1A/Ah. 5A/100Ah = 0.05A/Ah. Positive values are charge currents, while negative values are discharge currents.
The example on the left side of the image (red dots) shows:
- The absolute values of the maximum charge and discharge currents are the same. This indicates reasonable system sizing as the used generators, including PV, can recharge in approximately the same time as it was discharged.
- The highest charge and discharge currents are seen at very low and very high voltages. In the example shown, currents that charge and discharge the battery in a short time occur quite often.
The example on the right side of the image (blue dots) shows:
- The SOC distribution is weighted relatively evenly across the cloud in the high voltage and SOC window. Typical battery hysteresis can be seen as the vertical ends of the blue dot cloud. Compared to a good battery SOC situation, the area between these two “lines” is relatively wide, which gives information about the accuracy of the SOC calculation in the system. In this case, the performance is low.
- It can also be seen that often low battery voltages occur together with very high SOC values. This indicates that there are high discharge currents in the system. This means that the selected battery capacity was quite small compared to the used inverters and leads to a short battery autonomy time.
- This type of chart also gives an idea of the battery’s remaining capacity and its age. If a good SOC algorithm is used, the width of the SOC window is small. As the battery ages, the SOC window expands more and more. The density of points with a high SOC rating and low battery voltage also increases. At the same time, the number of points with low SOC value and high battery voltage increases.
For an experienced system designer or operator, the insights gleaned from the above data visualizations are obvious and meaningful. A system that works as expected produces data visualizations that “look” nominal, while poorly performing systems or improperly designed systems look non-standard.
Visualizations that can provide immediate feedback to project stakeholders can help optimize the system and make changes for future plans. IEA-PVPS Task 18 hopes that its report on PV hybrid system data visualizations can help system operators optimize their systems.
This article is part of the IEA’s PVPS program monthly column. It was facilitated by IEA-PVPS Task 18 – Off-Grid and Edge-of-Grid PV Systems. More information can be found in the recent report of Task 18: PV-Hybrid System Data Visualization Recommendations.
By Christopher Martell, GSES, Australia