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Electricity prices have been exceptionally volatile in recent years. In 2022, we witnessed a sharp increase in electricity prices in many countries. Several factors contributed to this, including reduced electricity production by hydropower plants due to climatic conditions and geopolitical situations around the world, amongst other factors. The research presented in this paper aims to analyze electricity price data and determine the parameters CPS (Capacity Payment Scheme) and CRS (Capacity Remuneration Scheme). These parameters are calculated from hourly data on a monthly basis from 2018 to the present. Determining these parameters provides a clearer understanding of the efficiency of utilizing photovoltaic power plants. However, the results indicate that identical CRS values can signify different situations in the electricity market. Given the current support for projects utilizing photovoltaic energy, the research findings may offer insights into the future direction of photovoltaic energy utilization. If CRS and CPS show positive trends (e.g., increasing values), this may suggest that current political measures (subsidies, supports, etc.) are effective. Policymakers could then strengthen or expand these measures. The research outcomes could shape the geopolitical situation in individual states. The research results show how differences in electricity prices in 2022 compared to pre-2022 affected CSP and CRS parameters. The findings also indicate that electricity prices vary across different countries, which naturally impacts the calculation of CPS and CRS. In 2024, a significant decline in CRS was observed across all analyzed countries, which may indicate issues with integrating solar energy into the market, market saturation, or changes in market dynamics. [ABSTRACT FROM AUTHOR]

This paper presents a series of enhancements to a previously proposed mixed-integer linear programming (MILP) model for investment decisions and operational planning in distributed generation (DG) systems. The main contribution of this study consists of integrating a wind generation system and multiple loads at different buses in a network. The model considers dynamic weather data, energy prices, costs related to photovoltaic and wind systems, storage systems, operational and maintenance costs, and other pertinent factors, such as efficiencies, geographical locations, resource availability, and different load profiles. The simulation results obtained through implementation in Julia's programming language illustrate that the MILP formulation maximizes the net present value, and four configurations for hybrid power generation systems in Mexico are analyzed. The objective is to enable profitability assessment for investments in large-capacity DG systems in two strategic zones of Mexico. The results show that the configurations in the NE zone, especially in Tamaulipas, are the most cost-effective. Case 1 stands out for its highest net present value and shortest payback time, while Case 2 offers the highest energy savings. In addition, Cases 3 and 4, which incorporate storage systems, exhibit the longest payback periods and the lowest savings, indicating less favorable economic performance compared with Cases 1 and 2. Moreover, the sales of two case studies, one without a storage system and the other with a storage system, are shown. The model also incorporates instruments for buying or selling energy in the wholesale electricity market, including variables that depict the injected energy into the electrical grid. This comprehensive approach provides a detailed overview of optimal energy management. [ABSTRACT FROM AUTHOR]

As the construction of photovoltaic power plants continues to expand, investors have placed great importance on the suitability assessment of site selection. In this study, we have developed a multi-level evaluation system and proposed an AHP–XGBoost–GIS comprehensive evaluation model for assessing site suitability in the Beijing–Tianjin–Hebei region. The findings revealed that approximately 48,800 km2 (21.59% of the total area) constituted a suitable area in this region, surpassing previous research results. Generally suitable areas accounted for the largest proportion at 35.89%. Suitable areas in Beijing and Tianjin were relatively limited, with most of them concentrated in Baoding City, Zhangjiakou City, and Chengde City on the northwest side of the "Yanshan-Taihang Mountains", where there are ample wastelands with gentle slopes conducive to constructing photovoltaic power plants southeast of the "Yanshan-Taihang Mountains", and solar radiation conditions were better. However, ecological and economic factors restricted suitable areas resulting in smaller coverage including cities such as Handan, Xingtai, Qinhuangdao, and Tangshan, among others. This study successfully overcame limitations associated with traditional subjective evaluation methods by providing decision support for regional energy allocation planning and spatial planning efforts, as well as environmental protection endeavors. [ABSTRACT FROM AUTHOR]

Large-scale photovoltaic power plants located in highland mountainous areas are vulnerable to landslides due to extreme rainfall, posing a significant threat to the normal operation of photovoltaic power plants. However, limited research has been conducted on landslide risk assessment specifically tailored to large photovoltaic power plants, with most studies focusing on static assessments that lack long-term sustainability in risk assessment and prediction. In this paper, a dynamic study on landslide risk at a large photovoltaic power plant project under extreme rainfall conditions is conducted. Firstly, the factors in landslide susceptibility assessment based on typical landslide characteristics in the study area are selected and an assessment index database using mapping units to extract the relevant factors is established. Subsequently, the ANP-FBN model is employed to evaluate the landslide susceptibility of large photovoltaic power plant sites. Furthermore, an assessment index system for landslide hazard vulnerability is developed by considering population, economic, and material vulnerabilities, and the AHP method is adapted to assess landslides vulnerability in the study area. Finally, the landslide rainfall threshold with the susceptibility and vulnerability assessment results are coupled to achieve a dynamic assessment of landslide hazard risk at large photovoltaic power plant sites under extreme rainfall conditions. The findings highlight that the central valley and the eastern steep slope of the study area are the primary "high" and "very high" risk areas. Moreover, with the increase in rainfall duration, the risk level of landslide hazards in the study area also rises. [ABSTRACT FROM AUTHOR]

With the steady increase in the use of renewable energy sources in the energy sector, new challenges arise, especially the unpredictability of these energy sources. This uncertainty complicates the management, planning, and development of energy systems. An effective solution to these challenges is short-term forecasting of the output of photovoltaic power plants. In this paper, a novel method for short-term production prediction was explored which involves continuous photography of the sky above the photovoltaic power plant. By analyzing a series of sky images, patterns can be identified to help predict future photovoltaic power generation. A hybrid model that integrates both a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) for short-term production forecasting was developed and tested. This model effectively detects spatial and temporal patterns from images and power output data, displaying considerable prediction accuracy. In particular, a 74% correlation was found between the model's predictions and actual future production values, demonstrating the model's efficiency. The results of this paper suggest that the hybrid CNN-LSTM model offers an improvement in prediction accuracy and practicality compared to traditional forecasting methods. This paper highlights the potential of Deep Learning in improving renewable energy practices, particularly in power prediction, contributing to the overall sustainability of power systems. [ABSTRACT FROM AUTHOR]

The use of solar trackers can help increase the production and time period of electricity generation in photovoltaic power plants. Different types of trackers in terms of rotation mechanisms and sun tracking systems have been used in these types of power plants in recent years. In this article, a comparison has made between the electricity produced of fixed and tracking structures in a number of power plants which are located in different cities of Iran but in similar geographical locations. Following this, software modeling is used to evaluate various sun tracking scenarios in the design of sample power plants. Finally, a techno-economic analysis has been made to evaluate the decision-making regarding the construction of such power plants in Iran using this technology. Using east-west detectors has had a positive effect on increasing the production of power plants, especially in summer. Due to the higher initial costs of using this technology in the power plants, as well as the higher maintenance costs, the economization of the power plants' business plan is extremely dependent on other economic conditions governing the project. Using trackers alone cannot lead to a better situation in a project's lifetime. From the national point of view, if domestic companies can produce east-west single-axis tracking technology at an acceptable cost and provide related services in the long term, it would be beneficial for the power grid. One of the attractive proposals for the power plants under construction will be the use of this technology, because of additional electricity production in peak hours in summer. Annual experimental data from different tracking/fixed PV power plant have been used for the first time in this techno-economic investigations, they are validated with the simulation process, and the results have been predicted for a wide range of tracking scenarios. Techno-economic analyses of this type of power plant are an essential need for policy makers and investors in Iran's energy market. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems are dependent on solar irradiation and environmental temperature to achieve their best performance. One of the challenges in the photovoltaic industry is performing maintenance as soon as a system is not working at its full generation capacity. The lack of a proper maintenance schedule affects power generation performance and can also decrease the lifetime of photovoltaic modules. Regarding the impact of environmental variables on the performance of PV systems, research has shown that soiling is the third most common reason for power loss in photovoltaic power plants, after solar irradiance and environmental temperature. This paper proposes a new statistical predictor for forecasting PV power generation by measuring environmental variables and the estimated mass particles (soiling) on the PV system. Our proposal was based on the fit of a nonlinear mixed-effects model, according to a log-logistic function. Two advantages of this approach are that it assumes a nonlinear relationship between the generated power and the environmental conditions (solar irradiance and the presence of suspended particulates) and that random errors may be correlated since the power generation measurements are recorded longitudinally. We evaluated the model using a dataset comprising environmental variables and power samples that were collected from October 2019 to April 2020 in a PV power plant in mid-west Brazil. The fitted model presented a maximum mean squared error (MSE) of 0.0032 and a linear coefficient correlation between the predicted and observed values of 0.9997 . The estimated average daily loss due to soiling was 1.4 % . [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are currently popular, and their number is growing; therefore, they have an impact on the power system. In this paper, the impact of electric vehicles on a power line connected to photovoltaic (PV) power plants regarding the power losses and voltage level is presented. The study is performed for a power line located in Mureș County, Romania, to which two photovoltaic power plants are connected. Three supply options are available for the power line, which supplies 15 loads. The power demand of the loads is determined with the power meters installed at the load premises. Electric vehicles are also considered to be connected in different points along the power line at the buses with the lowest voltage level. The results give the power losses and voltage levels for the case of when the PV power plants and EVs are connected to the power line, compared with the case when the EVs are not connected to the power line. The power losses were 400% higher in the case where the EVs were connected when the power demand was higher, while the voltage level was 2% lower if the EVs were connected. [ABSTRACT FROM AUTHOR]

Despite large amounts of available roof space, long pay-back periods for investments in photovoltaic (PV) power plants often hinder PV installations in industrial parks. Photovoltaic citizen participation initiatives (PV-CPI) are an alternative way of financing PV power plants that add non-financial benefits to these investments. This paper analyzed the feasibility of the installation of PV power plants focused on high rates of self-consumption financed by citizen participation initiatives on the roofs of five companies located in the Austrian Ennshafen industrial business park based on the net present value and the discounted pay-back period and compared it to a standard financing scheme, assuming a predetermined interest rate for participants as well as economies of scale with respect to the specific installation costs due to a joint purchase of the PV power plants. To calculate the feasibility, site-specific data and literature input have been used. The results show that despite an interest rate above the current interest rates of conservative forms of investments provided to (small-scale) investors, a payback-period of 17–23 years can be reached while the joint purchase can lead to a competitive feasibility of the PV-CPI compared to an individual purchase of PV power plants. [ABSTRACT FROM AUTHOR]

Over the last two decades, variable renewable energy technologies (i.e., variable-speed wind turbines (VSWTs) and photovoltaic (PV) power plants) have gradually replaced conventional generation units. However, these renewable generators are connected to the grid through power converters decoupled from the grid and do not provide any rotational inertia, subsequently decreasing the overall power system's inertia. Moreover, the variable and stochastic nature of wind speed and solar irradiation may lead to large frequency deviations, especially in isolated power systems. This paper proposes a hybrid wind–PV frequency control strategy for isolated power systems with high renewable energy source integration under variable weather conditions. A new PV controller monitoring the VSWTs' rotational speed deviation is presented in order to modify the PV-generated power accordingly and improve the rotational speed deviations of VSWTs. The power systems modeled include thermal, hydro-power, VSWT, and PV power plants, with generation mixes in line with future European scenarios. The hybrid wind–PV strategy is compared to three other frequency strategies already presented in the specific literature, and gets better results in terms of frequency deviation (reducing the mean squared error between 20% and 95%). Additionally, the rotational speed deviation of VSWTs is also reduced with the proposed approach, providing the same mean squared error as the case in which VSWTs do not participate in frequency control. However, this hybrid strategy requires up to a 30% reduction in the PV-generated energy. Extensive detailing of results and discussion can be also found in the paper. [ABSTRACT FROM AUTHOR]

Considering the recent drop (up to 86%) in photovoltaic (PV) module prices from 2010 to 2017, many countries have shown interest in investing in PV plants to meet their energy demand. In this study, a detailed design methodology is presented to achieve high benefits with low installation, maintenance and operation costs of PV plants. This procedure includes in detail the semi-hourly average time meteorological data from the location to maximise the accuracy and detailed characteristics of different PV modules and inverters. The minimum levelised cost of energy (LCOE) and maximum annual energy are the objective functions in this proposed procedure, whereas the design variables are the number of series and parallel PV modules, the number of PV module lines per row, tilt angle and orientation, inter-row space, PV module type, and inverter structure. The design problem was solved using a recent hybrid algorithm, namely, the grey wolf optimiser-sine cosine algorithm. The high performance for LCOE-based design optimisation in economic terms with lower installation, maintenance and operation costs than that resulting from the use of maximum annual energy objective function by 12%. Moreover, sensitivity analysis showed that the PV plant performance can be improved by decreasing the PV module annual reduction coefficient. [ABSTRACT FROM AUTHOR]

The amplitude and phase angle of the fault current in photovoltaic power plants (PVPPs) are significantly influenced by the control system of the grid-connected inverters, unlike in a conventional synchronous source. Hence, PVPPs may adversely affect the performance of the current differential protection designed for synchronous sources-based power grids. In order to study the performance problem of current differential protection on AC transmission lines, an analytical expression of the fault current in the PVPPs was deduced, and the fault current characteristic was extensively analyzed. Based on this analysis, the ratio of differential current over restraint current was initially derived in this study; this ratio is observed to be affected by the control system parameters, power grid system parameters, fault resistance, and fault types. Moreover, the dynamic characteristics of this ratio can be clearly observed based on a three-dimensional diagram. Furthermore, the operating performance of the current differential protection was analyzed under different influencing factors. The mathematical analysis presents that the amplitude ratio of the fault current on both sides of the line is larger than nine and that current differential protection will operate reliably in any case. Meanwhile, the theoretical analysis and simulation results show that the current phase angle difference may become an obtuse angle in case of an ungrounded fault, which will cause inaccurate operation of the current differential protection. The results of this study will provide guidance for the engineering application of current differential protection in case the PVPPs are connected to a power grid. [ABSTRACT FROM AUTHOR]

In this paper we study and compare the environmental efficiency of 118 photovoltaic (PV) plants in China. Drawing on the nonparametric data envelopment analysis (DEA) method, our study takes the initiative to take the insolation, annual sunshine duration, and covering area as input variables into account, as well as the installed capacity, annual electricity generation, CO2 emission reduction, and coal saving as output variables, to provide a unified measure of environmental efficiency of PV plants in China. We find widespread inefficiencies in roughly 95% of the PV plants, and the performance of different economic zones and types of PV plants are quite different. Specifically, those PV plants in eastern China are the least satisfying performers among three different economic zones. The surprising result indicates that eastern China has room for improvement by overcoming the inefficiencies caused by serious aerosol pollution and the high urbanization rate. We also find rooftop PV plants have the highest efficiencies among the four types of PV plants due to very little power loss. However, complementary PV plants have the lowest efficiencies most likely because of high operating temperatures during the process of power generation. [ABSTRACT FROM AUTHOR]