## 1 Introduction  
Recent developments in sustainable energy supply can be directly related to the energy transition to renewable sources, since these are not only relatively clean sources, but can also be used in a decentralized manner [1]. In recent years, clean energy technologies have gained ground, causing a decrease in the demand for fossil fuels such as coal, natural gas,and oil. Projections suggest that this trend will continue, with fossil fuel demand declining by an average of 3 exajoules (EJ) per year until 2050. The peak demand is expected to occur around 2030 [2]. For a higher transition from fossil fuels to renewable sources, this process should be affordable. Addressing the initial expenses associated with clean energy technologies will be crucial in promoting their adoption, particularly among households with lower incomes [2]. To optimize the performance of renewable energy generators, such as photovoltaic panels, it is essential to gather detailed information on how they operate under various conditions. This allows real-time data collection over extended periods, with easy storage and accessibility [3; 4]. While measuring voltage and current effectively provides information about the power of photovoltaic panels (PV), fully characterizing these devices requires varying the load they are subjected to. By doing so, the characteristic curves of the panel can be obtained. However, due to rapidly changing and unpredictable field conditions, reproducing the characteristics of individual or groups of PV panels becomes challenging. To address this issue, it is necessary to rapidly vary the load (resistance) across the entire range [5]. Manufacturers of photovoltaic panels typically provide current-voltage (I-V) curves measured under laboratory conditions. However, these conditions may not accurately represent real operational environments, where temperature and irradiance variations affect the I-V characteristics and, consequently, the module's power output. To properly characterize these devices, measurement systems known as I-V curve tracers are used. These systems perform a voltage sweep on the PV module while simultaneously measuring the output current delivered to a connected load [6]. Commercial I-V curve tracers are available on the market, as referenced in [7; 8], offering reliable measurements ensured by the manufacturer. However, they also present some disadvantages, such as high cost, limited flexibility for modifications, and control software that is not designed for automated experimental measurement campaigns [6]. To address these limitations, low-cost data acquisition systems (DAQs) present an accessible alternative for obtaining operational data from electric generators without relying on proprietary software [9]. Among these, the ArduinoTM platform, which consists of a series of microcontroller boards, provides a versatile tool for developing DAQs using open-source code. With dedicated libraries and drivers, ArduinoTM allows users to program and interact with each device directly via a standard USB connection, eliminating the need for additional hardware [10; 11]. Numerous research papers highlight the utilization of ArduinoTM for equipment control and data acquisition systems. Notable examples include using Arduino to control load cells [12], applications in automobile dynamics [13], nuclear and particle physics [10], fault detection in fused deposition modeling [14], and water flow measurement [15]. When it comes to photovoltaic panels, ArduinoTM has been used as a data acquisition system in several works [16; 17; 18; 19], where it has been used mainly for panel voltage and current readings. When compared with a mathematical model of photovoltaic cells, an ArduinoTM-based DAQ proved to be very close to the values obtained from modeling [17; 19]. Table 1 lists several studies in the literature that have developed I-V curve tracers using different measurement and load control approaches.