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Integrating small and large-scale photovoltaic (PV) solar systems into electrical distribution systems has become mandatory due to increased electricity bills and the concern for limiting greenhouse gases. However, the reliable and efficient operation of PV-based distribution systems can be confronted by the intermittence and high variability of solar sources and their consequential faults. In this regard, this article suggests a moderated fault-clearing strategy based on the incremental conductance–maximum power point tracking (IC–MPPT) technique and artificial neural networks (ANNs) to enhance fault detection, localization, and restoration processes in PV-based distribution systems. The proposed strategy leverages IC–MPPT to ensure optimal power generation from the PV solar system, even in the presence of faults. By tracking the maximum power point, the algorithm maintains the performance of the system and mitigates against the impact of faults on the output power. Furthermore, an ANN is employed to improve fault detection and localization accuracy. The developed ANN-based moderated fault-clearing strategy is trained using historical data and fault scenarios, enabling it to recognize fault patterns and make informed decisions through extensive simulations and comparisons with traditional fault-clearing methods. To accomplish this study, benchmarks in PV-based distribution systems are constructed and employed using the MATLAB®/Simulink® software package. Moreover, to validate the efficacy of the developed ANN-based moderated fault-clearing strategy, a real case study of a 1-MW PV-based distribution system in an industrial field located in Giza governorate, Egypt, is tested and investigated. The obtained results demonstrate the effectiveness of the IC–MPPT and ANN-based moderated fault-clearing strategy in achieving faster fault detection, precise fault localization, and efficient restoration in PV solar-based distribution systems while preserving maximum power extraction under small and large system disturbances. Furthermore, IC–MPPT based on an ANN achieves an average power of 98.556 kW and 299.632 kWh energy availability, whereas the IC–MPPT based on a proportional–integral controller achieves 95.7996 kW and 283.4036 kWh, and the classic perturb-and-observe MPPT algorithm achieves 92.2657 kW and 276.8014 kWh. [ABSTRACT FROM AUTHOR]

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Till now the conversion efficiency of the commercial photovoltaic (PV) solar modules is in the range of 14 to 20%. Therefore, PV power plants need very large area to achieve the desired output power. This paper presents some proper calculations to estimate land area occupied by the PV array. Calculations for the minimum and the maximum land area for a range of PV array with power capacity from 1 to 250 kW for different latitudes in the northern hemisphere were presented. Six different PV modules were selected for the calculation. A group of correlations are presented to roughly estimate both the land area and the number of PV modules per row as a function of the total power capacity required. In addition, correlations suggested to calculate the spacing between the rows in the PV array as a function of local latitude.

Photovoltaic (PV) systems need measurements of incident solar irradiance and PV surface temperature for performance analysis and monitoring purposes. Ground-based network sensor measurement is preferred in many near real-time operations such as forecasting and photovoltaic (PV) performance evaluation on the ground. Hence, this study proposed a Fuzzy compensation scheme for temperature and solar irradiance wireless sensor network (WSN) measurement on stand-alone solar photovoltaic (PV) system to improve the sensor measurement. The WSN installation through an Internet of Things (IoT) platform for solar irradiance and PV surface temperature measurement was fabricated. The simulation for the solar irradiance Fuzzy Logic compensation (SIFLC) scheme and Temperature Fuzzy Logic compensation (TFLC) scheme was conducted using Matlab/Simulink. The simulation result identified that the scheme was used to compensate for the error temperature and solar irradiance sensor measurements over a variation temperature and solar irradiance range from 20 to 60 °C and from zero up to 2000 W/m2. The experimental results show that the Fuzzy Logic compensation scheme can reduce the sensor measurement error up to 17% and 20% for solar irradiance and PV temperature measurement.

Renewable energy sources (including hydropower, wind, biomass, geothermal, tidal, wave and solar energy sources) can satisfy the present and future energy demands, with minor environmental impact respect to traditional sources of energy. Solar energy is the cheapest and widely available renewable energy and solar cooling systems are a green cold production technology that produce minor CO 2 emissions due to use of heat pumps. Infect, absorption refrigeration is a mature technology that has proved its applicability with the possibility to be driven by low grade solar energy. In this contest, thermal storage systems contribute to balance the disadvantages of the intermittent nature of solar energy and the variation in cooling demand, receiving more attention of researchers in the recent years. This paper presents an innovative and comprehensive literature review on solar systems integrated with environmental-friendly processes as absorption heat pumps and thermal energy storages. A study about processes regarding the renewable and sustainable energy according the European policy is carried out. Photovoltaic and photovoltaic/thermal solar systems can be used to this purpose and are the most promising technologies growing very fast in recent years. Researches show that one, half, two effect of absorption heat pumps integrated with solar systems and thermal energy storages can be an attractive alternative in cooling systems, meeting the demands for energy conservation and environmental protection. Showing various systems, a discussion about the improvement of cooling applications respecting the environment is also presented in this paper. [ABSTRACT FROM AUTHOR]

Abstract: In the case of photovoltaic (PV) systems acting as distributed generation (DG) systems, the DC energy that is produced is fed to the grid through the power-conditioning unit (inverter). The majority of contemporary inverters used in DG systems are current source inverters (CSI) operating at unity power factor. If, however, we assume that voltage source inverters (VSI) can replace CSIs, we can generate reactive power proportionally to the remaining unused capacity at any given time. According to the theory of instantaneous power, the inverter reactive power can be regulated by changing the amplitude of its output voltage. In addition, the inverter active power can be adjusted by modifying the phase angle of its output voltage. Based on such theory, both the active power supply and the reactive power compensation (RPC) can be carried out simultaneously. When the insolation is weak or the PV modules are inoperative at night, the RPC feature of a PV system can still be used to improve the inverter utilisation factor. Some MATLAB simulation results are included here to show the feasibility of the method. [Copyright &y& Elsevier]

[Display omitted] • Among the scenarios, employing full feed-in strategy (FFS) has the most benefits. • The optimum tilt angle for FFS in Tehran, as the case study, is 31°. • Performance ratio of 78.6 % for FFS reveals high performance from energy viewpoint. • Internal rate of return of 21.8 % for FFS shows it is economically justifiable. • Environmental indicators for FFS are lower than supply from network. A way to find the best solution to utilize photovoltaic solar panels for residential buildings in urban areas is presented here. Three scenarios, namely, connecting to the grid with and without batteries, and full feed-in, are considered. After obtaining the optimal solution for each item, the best one is selected through a comprehensive comparison by considering energy, economic, and environmental criteria which are taken into account as the objective of the conducted multi-objective optimization. The combination of non-dominated sorting genetic algorithm II (NSGA-II) and technique for order preference by similarity to ideal solution (TOPSIS) is utilized for determination of the optimal answer for each scenario. A residential building complex in Tehran, Iran, as one of the biggest cities in the world is chosen as the case study. The results show that the full feed-in strategy offers the best condition among the alternatives. The optimal answer of this scenario enjoys the high performance ratio of 78.6 % (Performance ratio indicates the ratio of the actual to the theoretical energy output). Moreover, it provides levelized cost of electricity, payback period, and the internal rate of return of 0.15 $.(kWh)−1, 7.3 years, and 21.8 %, while it has the values of 487 $, 0.101 species.year, and 0.023 DALY for damage to resources, ecosystem, and human health, respectively. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems need measurements of incident solar irradiance and PV surface temperature for performance analysis and monitoring purposes. Ground-based network sensor measurement is preferred in many near real-time operations such as forecasting and photovoltaic (PV) performance evaluation on the ground. Hence, this study proposed a Fuzzy compensation scheme for temperature and solar irradiance wireless sensor network (WSN) measurement on stand-alone solar photovoltaic (PV) system to improve the sensor measurement. The WSN installation through an Internet of Things (IoT) platform for solar irradiance and PV surface temperature measurement was fabricated. The simulation for the solar irradiance Fuzzy Logic compensation (SIFLC) scheme and Temperature Fuzzy Logic compensation (TFLC) scheme was conducted using Matlab/Simulink. The simulation result identified that the scheme was used to compensate for the error temperature and solar irradiance sensor measurements over a variation temperature and solar irradiance range from 20 to 60 °C and from zero up to 2000 W/m2. The experimental results show that the Fuzzy Logic compensation scheme can reduce the sensor measurement error up to 17% and 20% for solar irradiance and PV temperature measurement. [ABSTRACT FROM AUTHOR]