For each photovoltaic power plant, it is extremely important to perform an analysis of its efficiency, as well as an analysis of all parameters that may affect efficiency. The electric energy of the ...photovoltaic system, which is delivered to the electric power system on a daily basis, is determined through the average daily insolation, the surface of the panel and the average efficiency value. One of the parameters that affect the conversion efficiency of a photovoltaic power plant is a decrease in the conversion efficiency due to an increase in panel temperature. In this paper an example is a real photovoltaic power plant with a nominal power of 50 kW, which is installed on the rooftop of the building of the Institute "Mihajlo Pupin", located in Zvezdara forest, Belgrade, Serbia. The correlation analysis of the estimated temperature of the photovoltaic panel was performed using two models and the measured temperature of the photovoltaic panel. The temperature of the photovoltaic panel was estimated using models, one of which does not take into account, and the other takes into account the influence of wind speed on the temperature of the panel.
Governments in the region are actively trying to decentralize production in the energy sector by encouraging the construction of power plants using renewable energy resources. The Network Code
and ...the Connection Regulation
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define the procedure for issuing connection conditions to the power transmission network in Bosnia and Herzegovina. This paper will demonstrate the necessary approach to assessing the possibilities of connection and placement of the produced power and energy at a specific connection point in the early stages of planning, using the example of an 80 MW photovoltaic power plant planned for construction in the municipality of Tomislavgrad. The connection of the power plant is planned on a 110 kV voltage level network, by connecting it to the target 110 kV overhead transmission lines, following the principle of input-output.
This paper develops a fractional-order sliding mode based extremum seeking controller (FO SM-ESC) for the optimization of nonlinear systems. The proposed FO SM-ESC, involving an FO derivative ...function 0Dtqsgn(e),0≤q<1 is shown to achieve faster tracking and higher control accuracy than the integer-order (IO) SM-ESC. The tradeoff between control performance and parameters selection is analyzed and visualized. The comparison between the FO/IO SM-ESC is given to reveal the potential advantages of the FO controller over the IO controller. Simulation and experimental results show that the FO SM-ESC can have a faster convergence speed and a smaller neighborhood around the optimal operational point.
•Machine learning is used in spatial location choices of solar power plants.•Random Forest model presents the better performance.•Vegetation index and distance to power grid are the dominant ...predictors.•National-scale solar photovoltaic installations probability map is produced.
The optimum site selection of solar photovoltaics power plant across a given geographic space is usually assessed by using the geographic information system based multi-criteria decision making methods with various restriction criteria, while such evaluation results vary with criteria weights and are difficult to be validated in real life practices. To address this issue, this paper uses a national inventory dataset of large-scale solar photovoltaics installations (the land coverage area ≥ 1 hm2) to investigate the spatial location choices of solar power plants with the aids of interpretable machine learning techniques. A total of 21 geospatial conditioning factors of solar energy development are considered. The location choices of solar photovoltaics installation are then modeled with the multi-Layer perceptron, random forest, extreme gradient boosting models for each land cover type (e.g. cropland, forest, grassland, and barren). The SHapley additive explanation and variable importance measure methods are adopted to identify key criteria and their influences on the solar photovoltaics installation location selection. Results indicate that the random forest model presented the better performance among three machine learning models. The relative importance of conditioning factors revealed that the vegetation index and distance to power grid were always the most important predictors of solar photovoltaics installation location. Furthermore, topographical factors and transportation convenience may have a moderate impact on the spatial distribution of solar photovoltaics power stations. Unexpectedly, most of resources endowment and socio-economic factors play a negligible role in determining the optimal siting of solar power farms. Simulated solar photovoltaics installations probability maps illustrated that the most suitable regions account for 4.6 % of China’s total land area. The evidence-based method proposed in this research can not only help identify suitable solar photovoltaics farm locations in terms of various decision-making criterion, but also provide a robust planning tool for sustainable development of solar energy sources.
The installation of large scale photovoltaic power plants connected at transmission level has increased during the last years. There are some challenges that these power plants have to overcome ...regarding the operation and control while dealing with the solar energy variability and uncertainty. Today, few countries are aware of the importance of this source of energy as part of the utility system and how it can affect the operability. Thus, this paper discusses about the trend of large scale photovoltaic power plants around the world and the importance of the development of grid codes for its integration. Then, the paper addresses a comparison of the grid codes of Germany, US, Puerto Rico, Romania, China and South Africa considering: fault ride through capability, frequency and voltage regulation, as well as active and reactive power support. In addition, a broad discussion about the challenges that the large scale photovoltaic power plants have to overcome is presented together with the compliance technology and future trend.
This paper presents an online energy management tool that suggests the most suitable size of a hybrid photovoltaic-battery energy storage system (PV-BESS) to residential prosumers based on their ...self-sufficiency expectations. An offline analysis of electricity generation and consumption expected from 128 residential prosumers has been carried out at first in order to find out their self-sufficiency map with different sizes of PV and BESS; this is carried out by the genetic algorithm based energy management (GA) presented in a previous work. Subsequently, a number of clusters have been defined, each of which groups prosumers that share similar self-efficiency maps; particularly, clustering has been carried out and refined by identifying the most significant features of prosumers belonging to the same cluster, as well as those that differentiate prosumers belonging to different clusters. As a result, it has been revealed that the habit of usage of some appliances, such as Heat Ventilation Air Conditioning system (HVAC) and water heater, and peak electricity consumption represent the most important features influencing clustering. Based on these outcomes, the proposed online energy management tool is able to assign a prosumer to the most suitable cluster just based on the answers to a few simple questions related to energy consumption habits, providing the corresponding self-efficiency map almost immediately. The results achieved by the proposed tool, which is currently running online, are promising and show that significant self-sufficiency increases can be obtained, allowing the proper choice of PV-BESS depending on specific prosumer’s needs and expectations.
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•Hourly-based electricity generation and consumption profiles of 128 residential prosumers have been analyzed.•Self-sufficiency maps with different combinations of sizes of hybrid Photovoltaic-Battery Energy Storage System (PV-BESS) have been found.•Similar self-sufficiency maps have been clustered by identifying the most significant features of their prosumers.•Results show that Heat Ventilation Air Conditioning (HVAC) systems, water heaters, and peak electricity consumption represent the most important features influencing clustering.•An online energy management tool that recommends PV-BESS sizes according to the answers to a few simple questions related to user’s energy consumption habits has been proposed.
Renewable energy is clean sources and has a much lower environmental impact than other energy sources. In Turkey, solar energy investments have been developed rapidly in recent years. Site selection ...for solar farms is a critical issue for large investments because of quality of terrain, local weathering factors, proximity to high transmission capacity lines, agricultural facilities and environmental conservation issues. Multi criteria evaluation methods are often used for different site selection studies. The purpose of this study was to determine suitable site selection for solar farms by using GIS and AHP in the study area. The final index model was grouped into four categories as “low suitable”, “moderate”, “suitable” and “best suitable” with an equal interval classification method. As a result, 15.38% (928.18km2) of the study area has low suitable, 14.38% (867.83km2) has moderate suitable, 15.98% (964.39km2) has suitable and 13.92% (840.07km2) has best suitable for solar farms area. 40.34% (2434.52km2) of the study area is not suitable for solar farm areas.
The aim of this study is to assess the economic potentials of power and hydrogen generation via solar and wind energy resources at locations in Northern Germany and California, both of these ...(geographical) regions being pioneers in terms of a sustainable energy transition. Based on extensive research and electrolyzer manufacturer interviews, green hydrogen generation plants are modeled in a MATLAB® environment. All relevant inputs and outputs of the systems studied (wind power plants, photovoltaic power plants, and polymer electrolyte membrane electrolyzers, among others) are considered for different locations and framework conditions. In contrast to the existing literature, special attention is paid to the part-load behavior of electrolysis systems, which becomes particularly relevant in their interplay with volatile renewables. Metrics such as the levelized costs of electricity (LCOE) of the wind and photovoltaic power plants and the resulting levelized costs of hydrogen (LCOHy) are determined. With the help of the developed model, dimensioning of a whole system's components can be determined for different locations. A case study is conducted for a Northern German site and another one for a site in the Californian Mojave Desert. Both the optimal dimensioning of the renewable energy plants and the ratio of installed wind and photovoltaic power plant capacity are strongly location-dependent. In Northern Germany, for example, lower LCOHy can be generated at higher wind power plant capacity shares and, in the Mojave Desert, be produced at higher photovoltaic power plant capacity shares. In general, with larger plants and correspondingly larger polymer electrolyte membrane electrolyzers, LCOHy are lower due to scaling effects. Following this dimensioning recommendation, the LCOHy vary between 4.5 €/kg and 5.2 €/kg in the Northern German case study and between 4.6 US$/kg and 5.3 US$/kg in the Californian one. With costs of 1–2 €/kg, gray hydrogen is still economically superior.
•Hydrogen production is modeled based on electrolyzers, wind and PV power plants.•Due to the volatile power supply, a focus is on part-load behavior of electrolyzers.•The optimal sizing of the system components is determined for different locations.•Two case studies are conducted, for Northern Germany and the Mojave Desert, CA.•Levelized costs of hydrogen are found to vary between 4.5 and 5.2 €/kg (4.6–5.3 US$/kg).
► We assess forecasting techniques for PV power production without exogenous inputs. ► We test five different methods: Persistent, ARIMA, kNN, ANN and GA optimized ANN. ► ANN and ANN optimized models ...perform better than persistent, kNN and ARIMA. ► Forecasting error depends strongly on the season. ► GA method efficiently determines ANN inputs and general topology.
We evaluate and compare several forecasting techniques using no exogenous inputs for predicting the solar power output of a 1MWp, single-axis tracking, photovoltaic power plant operating in Merced, California. The production data used in this work corresponds to hourly averaged power collected from November 2009 to August 2011. Data prior to January 2011 is used to train the several forecasting models for the 1 and 2h-ahead hourly averaged power output. The methods studied in this work are: Persistent model, Auto-Regressive Integrated Moving Average (ARIMA), k-Nearest-Neighbors (kNNs), Artificial Neural Networks (ANNs), and ANNs optimized by Genetic Algorithms (GAs/ANN). The accuracy of the models is determined by computing error statistics such as mean absolute error (MAE), mean bias error (MBE), and the coefficient of correlation (R2) for the differences between the forecasted values and the measured values for the period from January to August of 2011. This work also addresses the accuracy of the different methods as a function of the variability of the power output, which depends strongly on seasonal conditions. The findings show that the ANN-based forecasting models perform better than the other forecasting techniques, that substantial improvements can be achieved with a GA optimization of the ANN parameters, and that the accuracy of all models depends strongly on seasonal characteristics of solar variability.
Saharan sand dust events present notable challenges to solar energy systems, particularly in regions with prevalent solar photovoltaic (PV) deployment. Experimental measurements and simulations using ...PVsyst software were conducted on two identical PV power plants—one cleaned and the other exposed to sandstorms. Empirical findings, supported by simulations, indicate that sand dust accumulation has negative effects on energy and output power, with a soiling rate of 0.25 %/day. Monthly power generation, energy generation, PV efficiency, and performance ratio decreased with increasing soiling percentages. Simulation results show that in July, for stationary PV power stations, monthly energy generation decreased from 661 kWh to 633 kWh and 576 kWh, with soiling percentages of 5 % and 15 %, respectively. Similarly, monthly power generation for two-axis solar tracking systems decreased from 984 kWh to 946 kWh and 865 kWh under the same soiling conditions. Simulation results indicate that in July, for fixed PV systems, the monthly PV efficiency could have declined from 10.96 % to 10.55 % and 9.55 % under soiling percentages of 5 % and 15 %, respectively. Similarly, for two-axis solar tracking systems, the monthly PV efficiency was found to decrease from 10.87 % to 10.45 % and 9.56 % under the same soiling conditions. In July, simulation results indicated that for fixed PV systems, the monthly performance ratio could have decreased from 73.53 % to 70.43 % and 63.98 % under soiling percentages of 5 % and 15 %, respectively. Similarly, the monthly performance ratio for two-axis solar tracking systems was found to decrease from 73.18 % to 70.34 % and 64.34 % under the same soiling conditions. The study highlights the importance of understanding soiling effects for investors, PV engineers, and researchers to develop mitigation strategies and maintenance protocols for PV power plants in dust-prone regions to sustain high performance.
•Experimental measurements and simulations using PVsyst software were conducted on two identical PV power plants—one cleaned and the other exposed to sandstorms. Empirical findings, supported by simulations, indicate that sand dust accumulation has negative effects on energy and output power, with a soiling rate of 0.25%/day.•Monthly power generation, energy generation, PV efficiency, and performance ratio decreased with increasing soiling percentages.•Simulation results show that in July, for stationary PV power stations, monthly energy generation decreased from 661 kWh to 633 kWh and 576 kWh, with soiling percentages of 5% and 15%, respectively. Similarly, monthly power generation for two-axis solar tracking systems decreased from 984 kWh to 946 kWh and 865 kWh under the same soiling conditions.•Simulation results indicate that in July, for fixed PV systems, the monthly PV efficiency could have declined from 10.96% to 10.55% and 9.55% under soiling percentages of 5% and 15%, respectively. Similarly, for two-axis solar tracking systems, the monthly PV efficiency was found to decrease from 10.87% to 10.45% and 9.56% under the same soiling conditions.•In July, simulation results indicated that for fixed PV systems, the monthly performance ratio could have decreased from 73.53% to 70.43% and 63.98% under soiling percentages of 5% and 15%, respectively. Similarly, the monthly performance ratio for two-axis solar tracking systems was found to decrease from 73.18% to 70.34% and 64.34% under the same soiling conditions.
