This paper presents the economics analysis of 140 Wp photovoltaic (PV) stand-alone system by using a generic excel model. The main components of PV stand-alone system consist of 140 Wp PV module, 150 W inverter, and two different types of battery as lithium-ion and lead-acid battery. The economic analysis of this paper presents the cost of energy (COE), benefit cost ratio (BCR), and simple net present value (SNPV). From the results of this study show that the COE, BCR, and SNPV of PV standalone system, which using lithium-ion battery are 0.13, 34.93 baht/kWh and 145,927 baht, respectively. For the COE, BCR, and SNPV of PV stand-alone system, which using lead-acid battery are 0.19, 23.30 Baht/kWh and 89,143 Baht, respectively.Although the economic parameters show that the PV stand-alone which using lead-acid batteries is suitable than PV stand-alone that used lithium-ion battery. However, lithium-ion batteries have many advantages when compare with lead-acid battery technology as high energy density, low maintenance and the number of lifecycle is higher compared with lead-acid battery

This paper presents a fuzzy logic based algorithm developed for Battery Storage Power Management (BSPM) and Demand Side Power Management (DSPM) for the SERT-Smart Grid at Naresuan (SSG) University in Phitsanulok, Thailand. This algorithm enables lower cost operation of the SSG by utilizing the Time of Use electricity-pricing concept, and thus is called the FLO (Fuzzy Low-cost Operation) Algorithm. After applying the FLO Algorithm to the SSG, BSPM can strategically handle fluctuating PV production by intelligently alternating between absorbing power during high solar irradiation periods and discharging power to the load during peak consumption times. The FLO Algorithm also empowers the SSG’s DSPM to decrease the peak load by using SERT’s Building Energy Management System to disconnect noncritical load as desired, for example lighting, air conditioners, and dummy heater load (used for research testing purposes). Research results show that the FLO Algorithm successfully decreased SERT’s annual electricity bill by 17.58 %, equivalent to a grand total of $ 9,148 in annual savings. Of the 17.58 % in savings, 5.78 % came from modifications to the BSPM and 11.81 % came from modifications to the DSPM. The average monthly savings correspond to $762 ($250 from BSPM and $512 from the DSPM), significant savings that without the FLO Algorithm would not be available.

Solar collector is an important component of solar hot water system. The purpose of this research is design, fabricate and test thermal efficiency of the heat pipe evacuated tube with compound parabolic concentrating (CPC) solar collector. The advantage of this system is it produces hot water over a period of time without adjusting the direction of the CPC to track the sun. The thermal performance of solar collector is critically important for the end users in order to ensure the efficiency of the system. The testing of CPC according to ISO 9806 - 1 found that the thermal efficiency was equal to 78%. The heat loss coefficient of the solar collector a1 and a2 are equal to 3.55 and 0.06 W/m2-oC respectively. The mathematical model was developed to determine the energy production base on solar radiation and ambient temperature data of Phitsanulok province. It was found that the monthly average energy of the CPC produced throughout the year is equal to 286.16 kWh or equal to 3,433.87 kWh/year

The use of photovoltaic systems has increased in recent years due to their decreasing costs and improved performance. However, these systems can be susceptible to faults that can reduce efficiency and energy yield. To prevent and reduce these problems, preventive or predictive maintenance and effective monitoring are necessary. PV health monitoring systems and automatic fault detection and diagnosis methods are critical for ensuring PV plants’ reliability, high-efficiency operation, and safety. This paper presents a new framework for developing fault detection in photovoltaic (PV) systems. The proposed approach uses machine learning algorithms to predict energy power production and detect anomalies in PV plants by comparing the predicted power from a model and the measured power from sensors. The framework utilizes historical data to train the prediction model, and live data is compared with predicted values to analyze residuals and detect abnormal scenarios. The proposed approach has been shown to accurately distinguish anomalies using constructed thresholding, either static or dynamic thresholds. The paper also reports experimental results using the Matthews correlation coefficient, a more reliable statistical rate for an imbalanced dataset. The proposed approach leads to a reasonable anomaly detection rate, with an MCC of 0.736 and a balanced ACC of 0.863.

Rise in PV module temperature (TPV) majorly drops the electrical output of the PV system. This research presents a novel cylindrical tube PCM matrix that is not in physical contact with the PV module back surface unlike the existing PCM based PV module cooling techniques. This contactless PCM matrix prevents the PV module from thermal and physical stress, also it blocks thermal energy re-conduction from PCM to PV module. While stored thermal energy from PCM retransferred to the PV module during off-sunshine hours and also when the PCM turns to liquid TPV starts to rise abruptly, this contactless PCM matrix minimizes these issues as PCM matrix receives thermal energy by the mode of radiation and convection; Besides, PCM matrix surface area is not enclosed with the PV module back surface area that reduces the thermal stress and re-conduction. Developed PCM matrix is integrated beneath the PV module at particular distances of 6 mm, 9 mm and 12 mm to optimize the spacing between PV module and PCM matrix. It is found that 6 mm spacing PCM matrix reduced the TPV maximum of 2.5 °C compared to 9 mm and 12 mm spacing. This TPV reduction enhanced the PV module electrical output by 0.2 % than PV without PCM and it is observed that 6 mm is an optimal spacing for the radiation source PCM matrix.