Comment on essd-2022-16

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.

Photovoltaic (PV) technology is becoming more popular due to climate change because it allows for replacing fossil-fuel power generation to reduce greenhouse gas emissions. Consequently, many countries have been attempting to generate electricity through PV power plants over the last decade. Monitoring PV power plants through satellite imagery, machine learning models, and cloud-based computing systems that may ensure rapid and precise locating with current status on a regional basis are crucial for environmental impact assessment and policy formulation. The effect of fusion of the spectral, textural with different neighbor sizes, and topographic features that may improve machine learning accuracy has not been evaluated yet in PV power plants’ mapping. This study mapped PV power plants using a random forest (RF) model on the Google Earth Engine (GEE) platform. We combined textural features calculated from the Grey Level Co-occurrence Matrix (GLCM), reflectance, thermal spectral features, and Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) from Landsat-8 imagery and elevation, slope, and aspect from Shuttle Radar Topography Mission (SRTM) as input variables. We found that the textural features from GLCM prominent enhance the accuracy of the random forest model in identifying PV power plants where a neighbor size of 30 pixels showed the best model performance. The addition of texture features can improve model accuracy from a Kappa statistic of 0.904 ± 0.05 to 0.938 ± 0.04 and overall accuracy of 97.45 ± 0.14% to 98.32 ± 0.11%. The topographic and thermal features contribute a slight improvement in modeling. This study extends the knowledge of the effect of various variables in identifying PV power plants from remote sensing data. The texture characteristics of PV power plants at different spatial resolutions deserve attention. The findings of our study have great significance for collecting the geographic information of PV power plants and evaluating their environmental impact.

Models for the calculation of the production of large photovoltaic (PV) power plants with different types of modules (monofacial or bifacial) have been developed in detail in the literature. In addition, detailed models have been developed for PV power plants that have fixed modules as well as PV power plants with tracking the azimuth angle of the Sun and/or the tilt angle of the PV module. On the other hand, due to the increasing installation of PV power plant, their construction is often imposed on the field with complex geometry, so it is interesting to develop models that can optimize the spatial layout and geometric elements of panels on sloping surface in relation on horizontal. Namely, during various analyses in the literature, production models for PV power plants that are planned on sloping terrains have not been developed. Accordingly, in this paper, a procedure was developed for the calculation of the production of PV power plants that are placed on terrain that is not flat, but oriented towards the arbitrary side of the World and with an arbitrary tilt angle.

The modes of the electrical network have not been calculated for the photovoltaic power plants if they are used for small and medium-sized applications. It is known that the voltage at the point of installation of a photovoltaics power plant may exceed the critical limit voltage and as a result it may cause the emergency with the electrical equipment. According to the existing methods, it is possible to find the voltage only by full electrical network modes calculating. In practice such calculation is not convenient, especially for 0.4 kV distribution networks. The suggestion is to find the most probable limit voltage at the point of installation of a photovoltaic power plant without calculating the mode of the electrical network using instead the information about the voltage value in the power supply centre. The voltage at the installation point of the photovoltaic power plant mainly depends on the generated power of the photovoltaics power plant, line resistance, load power connected to a voltage of 0.4 kV and bus voltages of 0.4 kV of the power supply. The voltage hardly depends on the power of the power transformer, the parameters of 10 kV lines and the parameters of 0.4 kV lines, except for the line "power centre–photovoltaic power plant". The highest voltage at the point of installation of the photovoltaics power plant will be in the case when the load on the line is absent. The suggested methods allow finding analytically the maximum value of voltage at the point of installation of a photovoltaic power plant without using of the successive approximations methods. As a rule the photovoltaics power plant operates in the mode of delivery of the maximum active power. By the range of various analytical formulas, the value of voltage at the point of installation of a photovoltaic power plant has been determined. The accuracy of finding the voltage by different formulas has also been determined. It is established that in order to achieve the required accuracy, the dependence of the current at the output of the photovoltaics power plant on the voltage at the connection point should be taken into account. Modification of formulas for the purpose of simplification of their application is considered. The conditions for finding the maximum possible voltage value at the point of installation of a photovoltaics power plant are considered. This requires that the argument of the voltage drop vector on the line be equal to the argument of the voltage vector on the busbars of the power supply. This is achieved by the fact that the photovoltaic power plant generates active and reactive power in a certain ratio. It is shown the inexpediency of finding the maximum possible voltage value at the point of installation of a photovoltaic power plant due to a small difference with the most possible voltage value with a significant complication of calculations.

Performance study on a grid connected 20 kWp solar photovoltaic installation in an industry in Tiruchirappalli (India)

Western China has good conditions for constructing large-scale photovoltaic (PV) power stations; however, such power plants with large fluctuations and strong randomness suffer from the long-distance power transmission problem, which needs to be solved. For large-scale PV power stations that do not have the conditions for simultaneous hydropower and PV power, this study examined long-distance delivery mode and energy storage optimization. The objective was to realize the long-distance transmission of electrical energy and maximize the economic value of the energy storage and PV power storage. For a large-scale PV power station, the energy storage optimization was modelled under a given long-distance delivery mode, and the economic evaluation system quantified using the net present value (NPV) of the battery was based on the energy dispatch optimization model. By contrast, a lithium battery performance model was developed. Therefore, further analysis of the economics of the energy storage and obtaining the best capacity of the energy storage battery and corresponding replacement cycle considered battery degradation. The case study of Qinghai Gonghe 100 MWp demonstration base PV power station showed that the optimal energy storage capacity was 5 MWh, and the optimal replacement period was 2 years. Therefore, the annual abandoned electricity was reduced by 3.051 × 10 4 MWh compared with no energy storage. The utilization rate of both the PV power station and quality of the delivered electricity were modelled to realize a long-distance transmission to the grid net. This will have an important guiding significance to develop and construct large-scale single PV power stations.

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.

This study developed a rational approach to the consideration of solar photovoltaic (PV) power plants applied to electric utility systems, then performed a requirements assessment and preliminary impact and penetration analyses by studying PV generation in three actual utility systems. Conventional utility loss-of-load probability and production simulation methods were used, along with a PV plant performance model developed for the study.Evaluations were based on comparison of total utility generation system costs with and without PV plants. The study is reported under the following sections: 1) geographic areas for photovoltaic power plants; 2) siting of photovoltaic power plants; 3) electric utility characteristics; 4) photovoltaic power plant characteristics; 5) methods of study; 6) study inputs; 7) results; 8) cost and performance goals for viability; 9) reference PV selection; 10) reference PV plant definitions; 11) comparison with conventional generation; 12) requirements for PV applications; 13) preliminary impact analysis; and 14) preliminary penetration analysis. (WHK)

Characterization and mapping of photovoltaic solar power plants by Landsat imagery and random forest: A case study in Gansu Province, China

This paper presents an algorithm for the economical design of a utility-scale photovoltaic (PV) power plant via compromising between the cost of energy and the availability of the plant. The algorithm inputs are the plant peak power and the price of inverters with respect to their power ratings. The outputs are the optimum inverter ratings and the interconnection topology of PV panels. This paper introduces the effective levelized cost of energy (LCOE) (ELCOE) index as the core of the proposed design algorithm. ELCOE is an improved index based on the conventional LCOE that includes the availability of a power plant in economical assessments. The conventional LCOE index determines centralized topology (e.g., 1-MW inverter for a 1-MW PV power plant) for minimizing the energy generation cost, whereas based on ELCOE, a multistring topology (e.g., a 1-MW PV plant consists of fifty 20-kW inverters) despite of higher investment cost becomes the economically winning topology. Given the price of commercially available PV inverters at present, the case studies in this paper show that, for 0.1-100-MW PV power plants, the economical ratings of inverters range from 8 to 100 kW. The recently installed PV power plants confirm the feasibility of the calculations based on the suggested algorithm.

Performance analysis of a grid connected photovoltaic park on the island of Crete

The favorable climate conditions of the island of Crete and the recent legislation for the utilization of renewable energy sources, provide a substantial incentive for the installation of photovoltaic power plants. In this paper, the grid-connected photovoltaic park (PV park) of C. Rokas SA in Sitia, Crete is presented and its performance is evaluated. The photovoltaic park has a peak power of 170 kWp, and has been in operation since 2002. The park is suitably monitored for a one year internal and the performance ratio and the various power losses (temperature, soiling, internal network, power electronics, grid availability & interconnection) are calculated. The PV Park supplied 230 MWh to the grid during 2007, ranging from 335 kWh to 870 kWh per day. The final yield (YF) ranged from 2 h/d to 5 h/d and the performance ratio (PR) ranged from 58 % to 73 %, giving an annual PR of 67,36 %.

Land reclamation of previously mined regions has been incorporated in the mining process as awareness of environmental protection has grown. In this study, we used the open-pit coal mine Oslomej in R. N. Macedonia to demonstrate the activities related to the monitoring process of the study area. We combined the Google Earth Engine (GEE) computing platform with the Landsat time-series data, Normalized Difference Vegetation Index (NDVI), Random Forest (RF) algorithm, and the LandTrendr algorithm to monitor the mining impacts, land reclamation, and the solar farm growth of the coalfield region between 1984 and 2021. The data from the sequential Landsat archive that was used to construct the spatiotemporal variability of the NDVI over the Oslomej mine site (1984-2021) and the pixel-based trajectories from the LandTrendr algorithm were used to achieve accurate measurements and analysis of vegetation disturbances. The different land use/land cover (LULC) classes herbaceous, water, mine, bare land, and solar farm in the Oslomej coalfield area were identified, and the effects of LULC changes on the mining environment were discussed. The RF classification algorithm was capable of separating these LULC classes with accuracies exceeding 90 %. We also validated our results using random sample points, field knowledge, imagery, and Google Earth. Our methodology, which is based on GEE, effectively captured information on mining, reclamation, and solar farm change, providing annual data (maps and change attributes) that can help local planners, policymakers, and environmentalists to better understand environmental influences connected to the ongoing conversion of the mining areas.

Effect of land-based solar power development on ecosystem functions in global terrestrial ecosystems.

Here, the impacts of biogas power plants and photovoltaic (PV) power plants on the power quality in the distribution network are presented and analysed. The biogas power plant's influence on the current harmonics is explored, as a synchronous generator powers the biogas power plant, and the PV plants are connected to the distribution network via the inverter. All the analyses are based on actual measurements that are performed in the last 10 years. Results of the analysis of these numerous measurements were a challenge for investigation how large share of PV systems in distribution network with non‐linear loads influence on THD from one side and how biogas power plant with a synchronous generator can improve THD from the other side. In order to investigate interaction of the biomass power plant and PV power plants on the power quality in distribution network, computer model was developed in DIgSILENT Power Factory where PV power plant and household were modelled as a source of current harmonic injections and biomass power plant was modelled as synchronous machine, as a source of voltage harmonics. Analysis of harmonic behaviour in different scenarios is examined through the performed simulations.