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High-quality short-term forecasts of electrical energy generation in solar power plants are crucial in the dynamically developing sector of renewable power generation. This article addresses the issue of selecting appropriate (preferred) methods for forecasting energy generation from a solar power plant within a 15 min time horizon. The effectiveness of various machine learning methods was verified. Additionally, the effectiveness of proprietary ensemble and hybrid methods was proposed and examined. The research also aimed to determine the appropriate sets of input variables for the predictive models. To enhance the performance of the predictive models, proprietary additional input variables (feature engineering) were constructed. The significance of individual input variables was examined depending on the predictive model used. This article concludes with findings and recommendations regarding the preferred predictive methods.

Solar photovoltaic power plants typically consist of rows of solar panels, where the accurate estimation of solar irradiance on inclined surfaces significantly impacts energy generation. Existing practices often only account for the first row, neglecting shading from subsequent rows. In this work, ten transposition models were assessed against experimental data and a transposition model for inner rows was developed and validated. The developed model incorporates view factors and direct and circumsolar irradiances shading from adjacent rows, significantly improving global tilted irradiance (GTI) estimates. This model was validated against one-minute observations recorded between 14 April and 1 June 2022, at Évora, Portugal (38.5306, −8.0112) resulting in values of mean bias error (MBE) and root-mean-squared error (RMSE) of −12.9 W/m2 and 76.8 W/m2, respectively, which represent an improvement of 368.3 W/m2 in the MBE of GTI estimations compared to the best-performing transposition model for the first row. The proposed model was also evaluated in an operational forecast setting where corrected forecasts of direct and diffuse irradiance (0 to 72 h ahead) were used as inputs, resulting in an MBE and RMSE of −33.6 W/m2 and 169.7 W/m2, respectively. These findings underscore the potential of the developed model to enhance solar energy forecasting accuracy and operational algorithms’ efficiency and robustness.

This paper deals with improving the feed-in tariff policy for green energy advancement in Ukraine’s households based on assessing the economic efficiency of investment project implementation under the current state support mechanisms. This study was conducted for solar and wind power plants with different installed capacities. The Levelized Cost of Electricity and the payback period for such power plants were calculated considering the ongoing feed-in tariffs and discount rates determined by various equity and debt capital ratios. The results showed that the state support provides attractive payback periods for solar and wind power plants with an installed capacity of ≥30 kW. In comparison, 5 kW solar power plants and wind power plants with a capacity of up to 10 kW are not paid off during the power plants’ lifecycle. It confirmed that the ongoing energy policy in Ukraine’s residential sector is still designed to obtain profits by the owners of generating facilities by selling the excess electricity. In the meantime, its main goal—providing households energy independence—has been levelled. To resolve the issues caused by such a state support model, a methodical approach to improve the feed-in tariff calculation is proposed. In addition, recommendations for reconsidering other policy measures to ensure effective renewable energy development in the residential sector have been made.

Among the various renewable energy generation systems, the solar photovoltaic occupies a leading position today due to its simple structure. However, increasing the efficiency of solar photovoltaic systems is a highly researched topic. In this study, possible connection failures in maximum power inverters and other failures, which decrease the efficiency in solar power plants, are examined. Furthermore, the possible consequences of these losses and their effects on the performance of solar power plants are explained. Some missing-failure processes were identified and corrected in the field analysis of the solar power plant in Turkey. Detected missing failures include connection failures of solar inverters, incorrect network configuration of camera system, fixing lighting time settings. The inverter string connection failure made during the projecting and assembly phase was eliminated and the maximum output was determined as 584.25 kW after the DC string arrangement. An increase of approximately 10% was achieved in production. In the project and application phase, the connection details of the inverters should be drawn and given to the field application personnel as a full-fledged project. In this way, incorrect connections that are not shown in the project and made in the field are prevented. This ensures that the installed power plant operates more efficiently, and the budgetary payback period of the investments made is shortened.

In the process of creating a prediction model using artificial intelligence by utilizing a deep neural network, it is of utmost significance to know the amount of insolation that has an absolute effect on the quantity of power generation of a solar cell. To predict the power generation quantity of a solar power plant, a deep neural network requires previously accumulated power generation data of a power plant. However, if there is no equipment to measure solar radiation in the internal facilities of the power plant and if there is no record of the existence of solar radiation in the past data, it is inevitable to obtain the solar radiation information of the nearest point in an effort to accurately predict the quantity of power generation. The site conditions of the power plant are affected by the geographical topography which acts as a stumbling block while anticipating favorable weather conditions. In this paper, we introduce a method to solve these problems and predict the quantity of power generation by modeling the power generation characteristics of a power plant using a neural network. he average of the error between the actual quantity and the predicted quantity for the same period was 1.99, that represents the predictive model is efficient to be used in real-time.

The implementation of a dual electric system that is capable of operating with either constant current and variable voltage, or constant voltage and variable current appliances, is one of the possible options to solve low-intensity stochastic energy utilization problems from renewable energy sources. This research paper analyzes the potential benefit of a novel three-phase dual system power inverter over the conventional inverter used in a solar power plant. The concept of such a power inverter is explained, and the digital twin model is created in a MATLAB Simulink environment. The efficiency characteristic of the simulated inverter is compared to the efficiency characteristic of a real conventional inverter. A standalone data logging system and an additional data acquisition system were used to collect and process data from the real inverter. Comparison of the digital twin inverter and the real conventional inverter shows the potential benefit of this novel inverter technology. It is shown that the novel inverter can operate in a wider range of DC input power. The potential economic benefit is also presented and discussed in the paper.

Following the recent development trend in the struggle for cleaning the earth’s environment, solar is the one of most promising area that can partially be used as a replaceable energy from non-renewable fuel sources. As such, it plays a significant role in protecting the environment from global warming. As solar power does not emit harmful gases into the atmosphere, its production, distribution, setup, and operation are vital should the production remain constant. Even solar energy waste emissions are small; when compared to current energy sources, the amount of harmful gases is negligible. This paper presented an integrated approach for site of solar plants by using data envelopment analysis (DEA) and Fuzzy Analytical Network Process (FANP). Furthermore, these integrated methodologies, incorporated with the most relevant parameters of requirements for solar plants, are introduced. First, the paper considers an integrated hierarchical DEA and FANP model for the optimal geographical location of solar plants in Mekong Delta Region, Vietnam. Using the proposed model for implementation would allow the renewable energy policy makers to select and control the optimal location for allocating and constructing a solar energy power plant in Vietnam. This is the preferred strategy for location optimization problems associated with solar plant units in Vietnam and around the world.

The ongoing industrialization and modernization period has increased the demand for energy in Viet Nam. This has led to over-exploitation and exhausts fossil fuel sources. Nowadays, Viet Nam’s energy mix is primarily based on thermal and hydro power. The Vietnamese government is trying to increase the proportion of renewable energy. The plan will raise the total solar power capacity from nearly 0 to 12,000 MW, equivalent to about 12 nuclear reactors, by 2030. Therefore, the construction of solar power plants is needed in Viet Nam. In this study, the authors present a multi-criteria decision making (MCDM) model by combining three methodologies, including fuzzy analytical hierarchy process (FAHP), data envelopment analysis (DEA), and the technique for order of preference by similarity to ideal solution (TOPSIS) to find the best location for building a solar power plant based on both quantitative and qualitative criteria. Initially, the potential locations from 46 sites in Viet Nam were selected by several DEA models. Then, AHP with fuzzy logic is employed to determine the weight of the factors. The TOPSIS approach is then applied to rank the locations in the final step. The results show that Binh Thuan is the optimal location to build a solar power plant because it has the highest ranking score in the final phase of this study. The contribution of this study is the proposal of a MCDM model for solar plant location selection in Viet Nam under fuzzy environment conditions. This paper also is part of the evolution of a new approach that is flexible and practical for decision makers. Furthermore, this research provides useful guidelines for solar power plant location selection in many countries as well as a guideline for location selection of other industries.

In solar thermal power plants molten salt is often used to store and transport the energy that is collected during the day. The external pipe temperature is measured to activate an electric heating system if the temperature approaches the melting point. However, salt solidification cannot be completely excluded from the plant management. Once occurred, the location of a salt blockage is very complex due to the high temperature of the pipe. Therefore, when this problem arises, power plants have to stop production with the consequences in time and cost that this entails. Electro-magnetic acoustic transducers can be used as non-destructive testing systems for this application. A method for salt blockage detection is proposed that is applicable in straight sections of pipes by employing torsional guided waves that are generated with magnetostrictive transducers. The present paper deals with the transducer conception and the design of the power supply to activate it. Two alternatives are proposed and compared to determine the improvement in the amplitude/noise ratio. Finally, the experimental results show the performance of the equipment in a small prototype, thus validating the technique presented.