Hybrid Power Plant Prototype for Hill Areas: Integrating Solar and Wind Energy with Auto Switching and Monitoring Technology

Energy demand is rising due to population growth and technological advancements, while fossil fuel depletion and environmental concerns, such as carbon emissions, necessitate sustainable alternatives [1]. Renewable energy sources like solar and wind are critical for addressing these challenges, particularly in regions with abundant natural resources. Indonesia’s tropical location and hilly regions provide ideal conditions for harnessing solar and wind energy. The country’s wind energy potential is estimated at 978 MW [2], and hilly areas like Sukabumi offer 170 MW of renewable energy potential [3]. These regions experience consistent sunlight during the day and strong winds at night, making them suitable for hybrid power systems combining solar and wind energy. The unique advantages of hybrid systems, such as enhanced reliability through complementary energy sources and reduced dependency on unstable grid infrastructure, address the urgent need for sustainable power in mountainous areas where conventional energy access is limited and environmental conditions vary significantly.

Electrical energy demand in Indonesia is projected to grow significantly, reaching 2,214 TWh (Business as Usual), 1,918 TWh (Policy-Based), or 1,626 TWh (Renewable Knowledge) by 2050, a nearly ninefold increase from 254.6 TWh in 2018 [2]. The National Energy Policy (KEN) aims for 5% [4] of primary energy from renewable sources like solar and wind by 2025 [5]. However, challenges in managing renewable energy systems, such as variability in resource availability, require innovative solutions to ensure efficiency and sustainability.

Previous studies have explored solar and wind energy separately, such as solar-powered street lighting and wind speed monitoring [6, 7]. However, these efforts often fail to integrate both sources effectively. This study addresses this gap by developing a hybrid power plant prototype that combines solar and wind energy with an innovative Auto Switching system and real-time web-based monitoring. The Auto Switching system optimizes energy use by selecting the most efficient source based on environmental conditions, while the web application enables real-time performance tracking, enhancing system reliability and maintenance.

This research aims to create a prototype that introducing a novel integration of real-time Arduino-based Auto Switching logic—which dynamically selects the most efficient energy source—and a web-based monitoring application that enables real-time tracking of voltage, wind speed, and light intensity. This dual innovation addresses key challenges in hybrid system efficiency and management. The Auto Switching mechanism leverages real-time sensor data to determine optimal energy input, while the web monitoring system ensures transparent, remote-accessible performance oversight. Together, these features enhance the system’s adaptability to environmental changes, improve operational efficiency, and offer a scalable and cost-effective solution for energy delivery in remote or underserved hilly regions. It's integrates solar and wind energy, optimized by an Auto Switching system and monitored via a web-based application. It targets hilly regions with limited grid access, offering a sustainable and cost-effective energy solution. The system enhances energy efficiency, reduces waste, and supports Indonesia’s energy transition goals. Chapter 2 details the research methodology, Chapter 3 presents test results, Chapter 4 discusses findings, and Chapter 5 concludes with recommendations.

3.1 Voltage sensor value testing

The voltage sensor’s accuracy in measuring DC voltage outputs from the solar panel is evaluated to ensure reliable performance within the hybrid power plant system. The test involves connecting the solar panel to the voltage sensor and recording the output voltage readings. These measurements are systematically compared against those obtained from a calibrated voltmeter, which serves as the reference standard, to quantify any discrepancies and confirm the sensor’s precision.

The testing procedure involves connecting the solar panel to the voltage sensor in Table 1, recording the sensor’s voltage readings, and subsequently verifying these values using the voltmeter. The results, with an average discrepancy of 0.14 V (95% confidence interval: ±0.05 V), confirm the sensor’s high precision and reliability for hybrid power system applications.

Table 1. Voltage sensor test result

3.2 Solar panel voltage value testing

This test evaluates the voltage output of the solar panel and its impact on battery charging duration under varying light intensities. The solar panel was placed in an unobstructed area and connected to the prototype circuit, with light intensity measured using a GY-49 sensor. Data were collected at 10-minute intervals from August 13–17, 2021. Results, presented in Table 2, show a direct correlation between light intensity and voltage output. At a peak intensity of 71,937 lux (August 14, 12:30–13:00), the panel produced 22.01 V, charging a 12V, 7Ah battery in 30 minutes. At a minimum intensity of 34,240 lux (August 17, 15:30–16:48), the voltage was 18.06 V, requiring 78 minutes for a full charge. Morning tests (e.g., August 13, 07:30–08:42, 44,606 lux, 19.06 V) yielded moderate performance, confirming that higher light intensity enhances voltage output and reduces charging time, with optimal efficiency during midday conditions.

Table 2. Solar panel voltage test result

3.3 Wind turbine voltage value testing

This assessment evaluates the voltage output of the wind turbine under varying wind speeds to determine its adequacy for battery charging within the hybrid power plant prototype. The wind turbine is positioned in an unobstructed area and integrated into the prototype circuit, with wind speed data collected every 10 minutes using an anemometer. Results, presented in Table 3, reveal a direct correlation between wind speed and voltage output: on 17/08/2021, a mean wind speed of 16 m/s yielded a maximum voltage of 0.79 V, while on 14/08/2021, a wind speed of 2 m/s produced a minimum voltage of 0.18 V, insufficient for charging a 12V battery. Higher wind speeds, typically observed in the afternoon (e.g., 13/08/2021 and 15/08/2021), enhance voltage output, with improved performance noted during cloudy conditions due to increased wind velocity. However, the wind turbine’s voltage remains below system requirements, constrained by its design and generator efficiency, highlighting the need for optimization to improve energy conversion at low to medium wind speeds.

Table 3. Wind turbine voltage test result

3.4 Auto Switching component testing

This study assesses the functionality of the Auto Switching component within a hybrid power plant prototype, designed to optimize energy source selection between solar and wind inputs. The experiment involves supplying calibrated voltages from an adapter to the main board, ensuring compliance with the switching component’s specifications. Two distinct voltage inputs (Input 1 and Input 2) are provided, and the resulting output is measured to confirm the component’s ability to consistently select the highest voltage. As detailed in Table 4, the switching mechanism accurately identifies and delivers the maximum input voltage in all test scenarios, with the output voltage matching the selected input, free from voltage drops or transmission interference. These results validate the reliability and precision of the Auto Switching logic across varying voltage levels, ensuring efficient energy utilization in dynamic environmental conditions.

Table 4. Auto Switching test result

3.5 Battery life testing

This test evaluates the durability and performance of the battery within the hybrid power plant prototype under varying load conditions to assess its operational reliability. The experiment employs a 12V, 7Ah battery connected to multiple output devices, with results detailed in Table 5. Under a light load of three 3W bulbs (total 9W), the battery sustains operation for 63 minutes, while a medium load from a 35W cellphone charger yields a duration of 48 minutes. A high load of 65W, represented by a laptop charger, results in a 55-minute runtime. These findings indicate that increased power consumption correlates with reduced operational duration; however, the battery’s performance remains consistent with its rated capacity, suggesting effective energy management within the system’s design constraints. Higher loads reduce usage time, with system efficiency at approximately 85% for light loads.

Table 5. Battery life testing result

3.6 GY-49 sensor and anemometer feasibility testing

This evaluation assesses the performance of the GY-49 sensor in measuring light intensity (lux) and the anemometer in recording wind speed (m/s) across morning, afternoon, and evening periods from July 27 to 31, 2021, ensuring their suitability for environmental monitoring in the hybrid power plant system. As shown in Table 6, the GY-49 sensor recorded average light intensities of 16,181.4 lux (morning), 65,058.8 lux (afternoon), and 39,259.4 lux (evening), reflecting temporal variations due to solar positioning, with a notable decrease on July 30, 2021 (27,548 lux in the afternoon) under cloudy conditions, demonstrating the sensor’s sensitivity to meteorological changes.

Table 6. GY-49 sensor test result

Table 7 reveals the anemometer’s results, with average wind speeds of 0.6 m/s (morning), 10.2 m/s (afternoon), and 10.2 m/s (evening), driven by diurnal atmospheric heating patterns; elevated wind speeds (peaking at 10 m/s on July 30–31, 2021) during cloudy conditions highlight the instrument’s responsiveness to atmospheric dynamics. These findings validate the accuracy and reliability of both sensors in capturing environmental variations, with afternoon and evening wind speeds (10.2 m/s) indicating optimal periods for wind energy harnessing, supporting the system’s design for efficient renewable energy utilization in hilly regions.

Table 7. Anemometer’s test result

Table 7 reveals the anemometer’s results, with average wind speeds of 0.6 m/s (morning), 10.2 m/s (afternoon), and 10.2 m/s (evening), driven by diurnal atmospheric heating patterns; elevated wind speeds (peaking at 10 m/s on July 30–31, 2021) during cloudy conditions highlight the instrument’s responsiveness to atmospheric dynamics. These findings validate the accuracy and reliability of both sensors in capturing environmental variations, with afternoon and evening wind speeds (10.2 m/s) indicating optimal periods for wind energy harnessing, supporting the system’s design for efficient renewable energy utilization in hilly regions.

3.7 Data validation testing

This test verifies the accuracy and integrity of data transmission from the Arduino Mega to the web-based monitoring application within the hybrid power plant system, focusing on parameters such as solar voltage, light intensity (lux), wind voltage, and wind speed. Conducted on August 18, 2021, at 12:50:17, the test compares data from the Arduino’s serial monitor—recording solar voltage at 21.65 V, light intensity at 65,384.6 lux, wind voltage at 0.1 V, and wind speed at 1 m/s—with the corresponding values received by the monitoring application, as shown in Figure 4.

A paired t-test (p > 0.05) confirms no significant differences, with a 95% confidence interval (±0.02 V for voltage, ±100 lux for light intensity, ±0.1 m/s for wind speed), indicating precise alignment and zero data loss during transmission. The system demonstrates efficient and error-free interaction between hardware and software, ensuring reliable real-time monitoring critical for optimizing energy management in remote hilly regions.

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Figure 4. Validation testing of the monitoring system

This study successfully develops and tests a hybrid power plant prototype integrating solar and wind energy, equipped with Auto Switching technology and a web-based monitoring system, demonstrating its potential for sustainable energy generation in hilly regions. Solar energy achieves a maximum voltage of 21.65 V, significantly outperforming wind energy at 0.79 V, which proves insufficient for battery charging, underscoring the need for wind turbine optimization or a step-up converter.

The Auto Switching mechanism ensures stable output by selecting the highest voltage source, while data validation confirms seamless communication between the Arduino and the monitoring application, enabling real-time oversight of voltage, light intensity, and wind speed. Despite its efficacy, the system’s performance is constrained by environmental variability, particularly weather impacts on solar and wind energy yields.

Although wind energy output remains limited due to turbine design constraints, the system demonstrates strong potential for real-world deployment, particularly in mountainous or isolated regions where access to conventional electricity infrastructure is limited or non-existent. The modular and low-cost design, which utilizes widely available components such as Arduino and open-source web technologies, enhances its replicability and scalability across diverse geographic contexts.  The hybrid system offers a practical and sustainable solution to address energy poverty in rural highland communities by leveraging locally available natural resources—sunlight and wind. With further optimization (e.g., improved turbine efficiency, energy storage, and step-up voltage conversion), this system could significantly reduce dependency on fossil fuels, lower operational costs, and support socio-economic development through electrification.  Therefore, this research not only contributes academically to the field of hybrid renewable systems but also holds high practical value for deployment in national and community-based energy transition programs, particularly in developing countries with vast mountainous terrain like Indonesia. Future collaborations with local governments, NGOs, and rural cooperatives could accelerate the adoption and adaptation of this system to meet regional sustainability goals.

Future research should focus on enhancing wind turbine design for improved voltage output at low to medium wind speeds, integrating additional renewable sources such as micro hydro, and advancing the web-based monitoring system with AI-driven predictive analytics. A comprehensive economic feasibility study, evaluating initial costs, maintenance, and return on investment, is recommended to assess the system’s viability for widespread adoption in remote areas, contributing to a more reliable and sustainable energy solution.