Large-scale Solar Photovoltaic Power Research Articles

Performances Analysis of Three Grid-Tied Large-Scale Solar PV Plants in Varied Climatic Conditions: A Case Study in Algeria

Currently, for the determination of the suitable and optimal PV power plant according to the climate conditions of the concerned region, researchers focus on the estimation of certain performance factors, which are reported to be the key parameters for the analysis of the performances of grid-connected photovoltaic (PV) power systems. In this context, this paper focuses on on-site real-time analysis of the performance of three solar photovoltaic plants: Sidi-bel-Abbés (12 MWp), Laghouat (60 MWp), and Ghardaïa (1.1 MWp). These plants are located in different regions experiencing diverse climatic conditions in Algeria. The analysis was carried out by the standardized norms of IEC 61724, using monitoring data collected over one year. The photovoltaic power plants were evaluated in terms of performance factors, such as the reference yield (Yr), final yield (Yf), performance ratio (PR), and capacity factor (CF). On the other side, based on real data collected at the concerned sites, two linear functions depending on solar irradiance and the PV module temperature for each site are proposed for the evaluation of the generated alternative power output (PAC) for the three PV plants. The obtained results based on the study presented in this paper can help designers of PV power plants of different technologies and different climate conditions to precisely decide the convenient technology that allows the best production of the electrical energy for grid-tied PV systems. Furthermore, this study can contribute in giving a clear vision of the implementation of upcoming large-scale solar PV power plants in Algeria within the studied area and other areas.

Spatial modelling the location choice of large-scale solar photovoltaic power plants: Application of interpretable machine learning techniques and the national inventory

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.

LVRT and Reactive Power/Voltage Support of Utility-Scale PV Power Plants during Disturbance Conditions

This paper proposes a control technique for a large-scale grid-connected photovoltaic (PV) plant that maintains the connection of an inverter to the grid voltage under different types of faults, while injecting a reactive power to accommodate the required grid connection. This control strategy is suggested to improve the low-voltage ride-through (LVRT) capability of grid-connected PV power generation plants. A 20 MW solar PV power plant is modeled and simulated using Matlab/Simulink. The power plant is composed of 10 parallel groups of arrays with a power rating of 2 MWp. The solar PV arrays are connected to a medium-voltage side-rated 22 KV to the utility grid. A dynamic analysis of the grid-connected large-scale solar PV power plant is introduced. This analysis is accomplished in order to determine the impact of three-phase short-circuits at the point of common-coupling (PCC), where the solar PV power station is connected to ensure a practical voltage level by injecting active and reactive power. The reactive power support allows for faster restoration of voltage values at the PCC. When subjected to transient disturbances, the stability of the studied system relies on both the type of the disturbance and the initial operating situation. The disturbance may be either small, resulting from electrical load changes, or large, such as from a transmission line short-circuit (fault) and significant generator loss.

Robust reconfiguration strategies for maximum output power from large-scale photovoltaic arrays under partial shading conditions

Extraction of maximum power from large scale solar photovoltaic power systems is the most challenging and demanding research in the current scenario. Solar photovoltaic panels are highly susceptible to a phenomenon known as partial shading. Partial shading increases mismatch losses and reduces the output of the solar photovoltaic system The output reduction in the partially shaded array is proportional to the shaded area, shaded panel’s placement within the array, panel connections, shade geometry, etc. There are several approaches for reducing Partial shading effects in the literature. The most efficient approach to mitigating the mismatch losses due to Partial shading in large-scale solar photovoltaic systems is the reconfiguration technique, which distributes shaded panels more evenly and increases the maximum power output. The current work utilizes a set of reconfiguration rules for selecting the location of shaded panels within an array that allows for multiple reconfiguration options. The results show that the proposed reconfiguration has obtained an improved Performance enhancement ratio of 25% in one shading pattern i.e. short wide shading, Performance enhancement ratio of 6.4% in short narrow and centre shading and Performance enhancement ratio of 5.9% in long narrow shading. The proposed reconfiguration was found to be the most suitable, simple, and cost-effective solution for large size of solar photovoltaic system under all shading conditions.

Computational solar energy – Ensemble learning methods for prediction of solar power generation based on meteorological parameters in Eastern India

The challenges in applications of solar energy lies in its intermittency and dependency on meteorological parameters such as; solar radiation, ambient temperature, rainfall, wind-speed etc., and many other physical parameters like dust accumulation etc. Hence, it is important to estimate the amount of solar photovoltaic (PV) power generation for a specific geographical location. Machine learning (ML) models have gained importance and are widely used for prediction of solar power plant performance. In this paper, the impact of weather parameters on solar PV power generation is estimated by several Ensemble ML (EML) models like Bagging, Boosting, Stacking, and Voting for the first time. The performance of chosen ML algorithms is validated by field dataset of a 10kWp solar PV power plant in Eastern India region. Furthermore, a complete test-bed framework has been designed for data mining as well as to select appropriate learning models. It also supports feature selection and reduction for dataset to reduce space and time complexity of the learning models. The results demonstrate greater prediction accuracy of around 96% for Stacking and Voting EML models. The proposed work is a generalized one and can be very useful for predicting the performance of large-scale solar PV power plants also.

Intelligent monitoring of photovoltaic panels based on infrared detection

With the continuously increasing application of photovoltaic (PV) panels, how to effectively manage these valuable facilities has become an issue of concern. To date, some methods have been developed to meet this purpose. However, to date, a satisfactory solution has not been achieved for managing large-scale solar PV power plants. To address this issue, a new PV panel condition monitoring and fault diagnosis technique is developed in this paper. The new technique uses a U-Net neural network and a classifier in combination to intelligently analyse the PV panel’s infrared thermal images taken by drones or other kinds of remote operating systems. The novelties of this research include: (1) a U-net neural network is developed and trained to carry out image segmentation, thereby significantly improving the efficiency of image processing; (2) the condition monitoring and fault diagnosis is based on the contour features in the ‘mask’ of true colour infrared images. As compared to the true colour images, their mask images have little interference information, the reliability and accuracy of condition monitoring and fault diagnosis based on mask images can be guaranteed to a large extent. In the research, 295 infrared images were taken first from the PV panels in different health states, and then their ‘masks’ were manually created using the software LabelMe. Secondly, enlarge the number of image samples using three image expansion methods (i.e. mirroring left and right, flipping up and down, and cropping and then zooming in) to establish the image sample database for training and testing the U-net neural network. Thirdly, use 1852 infrared images and their mask images stored in the database to train a U-Net neural network until the trained U-net neural network can create mask images as accurately as the software LabelMe does. The trained neural network will guarantee that the achieved segmentation accuracy can be as high as 95.2%. Finally, four potential criteria (i.e., Contour area, Perimeter, Aspect ratio and Ratio of contour area to the area of contour outer rectangle) are proposed to characterise the contour of mask images and then based on the calculation results of these four criteria, different types of PV panel faults are diagnosed with the aid of three kinds of classifiers. The classifiers are Decision tree, K-Nearest Neighbours algorithm (KNN), and Support-vector machine (SVM). The research results have shown that the combined use of a well-trained U-Net neural network and Decision tree can diagnose the PV panel faults with 99.8% accuracy. Therefore, it may arguably provide a promising intelligent tool for condition monitoring the PV panels.

Economic Energy Allocation of Conventional and Large-Scale PV Power Plants

During the past few decades, rapid progress in reducing the cost of photovoltaic (PV) energy has been achieved. At the megawatt (MW) to gigawatt (GW) scale, large PV systems are connected to the electricity grid to provide power during the daytime. Many PVs can be installed on sites with optimal solar radiation and other logistical considerations. However, the electricity produced by the PV power plant has to be transmitted and distributed by the grid, which leads to more power losses. With the widespread commissioning of the large-scale solar PV power plants connected to the grid, it is crucial to have an optimal energy allocation between the conventional and the PV power plants. The electricity cost represents the most significant part of the budget in the power distribution companies, which can reach in many countries billions of dollars. This optimal energy allocation is used to minimize the electricity cost from buyers’ (distribution companies) point of view rather than sellers’ (owners of power plants, i.e., investors) point of view. However, some constraints have to be considered and met, such as water demand, network limitations, and contractual issues such as minimum-take energy. This paper develops a model for the economic energy allocation of conventional and large-scale PV power plants, which considers both the operational aspects and the contractual provisions. The model can be used either in the design or operation phases to minimize the operating cost. Moreover, the proposed model can be used for budgeting tasks. The developed model is entirely generic and can be used for any country or electricity system regardless of the PV energy contribution. Furthermore, the Al-Karsaah power plant located in Qatar is discussed as a case study to validate the claimed contribution.

GIS Analysis of Solar PV Locations and Disaster Risk Areas in Japan

Following the global trend of climate change mitigation, Japan has been rapidly increasing its share of renewable energy, in particular, its share of solar energy. However, Japan has limited flat land area that is suitable for solar photovoltaic (PV) power generation and a high risk of natural disasters. There is a possibility that some of its newly built solar power plants are located in areas where landslides and floods are likely to occur. Therefore, it is important to study the locations for solar PV from the perspective of disaster risk management. Previous studies have reported a number of incidents where solar PV installations were damaged as a result of natural disasters. One study utilized geographical analysis technology to reveal the overlapping of solar PV powerplant locations and disaster-prone areas in Fukuoka prefecture in Japan. However, to our best knowledge, no previous research about the solar PV locations' hazard risks has been done on a national scale. This paper investigates the risks stemming from landslides and floods for the existing solar PV power plants in Japan. We compare the geographical data of disaster risks in Japan with the location data of solar PV power plants to investigate the number of solar PV power plants located in disaster risk areas. Our results show that the shares of medium and large-scale solar PV power plants located in areas where landslides and floods are likely to occur are about 8.5 and 9.1% respectively.

Impact assessment of simultaneous operation of photovoltaic and cogeneration power plants on industrial distribution system

<p>Industries with co-generation plants face unprecedented problems due to penetration of renewable energy systems such as solar power on their existing distribution networks. These problems are caused by intermittent solar power. To this end, this paper provides a detailed investigation of the effects due to sudden changes in solar power on an existing industrial distribution network connected to co-generation plants. Moreover, the case studies in this work consider simultaneous operation of a large industry having co-generation captive power plant and large scale solar photovoltaic power plant. The real-time field data for the past three years are used to check the performance of solar photovoltaic power plant, load management, power quality and other concerning issues on the distribution network. In addition to the real-time data, the simulations were performed for the solar power output under different solar irradiance conditions. Moreover, these simulations are used to assess photovoltaic integration effects on a distribution system having a co-generation captive power plant. Finally, this paper put forward photovoltaic integration guidelines to industries and policymakers interested to carry out the integration studies in the future.</p>

Modular Multilevel Converters for Large-Scale Grid-Connected Photovoltaic Systems: A Review

The use of photovoltaic (PV) systems as the energy source of electrical distributed generators (DG) is gaining popularity, due to the progress of power electronics devices and technologies. Large-scale solar PV power plants are becoming the preferable solution to meet the fast growth of electrical energy demand, as they can be installed in less than one year, as compared to around four years in the case of conventional power plants. Modular multilevel inverters (MMIs) are the best solution to connect these large-scale PV plants to the medium-voltage (MV) grid, due to their numerous merits, such as providing better power quality, having higher efficiency, providing better reliability, and their scalability. However, MMIs are still progressing and need some improvement before they can be implemented safely in the industrial, medium, and high voltage networks. The main purpose of this paper is to review the present MMIs topologies when used in PV applications. The review aims to present a comprehensive study of the various recent submodule circuits associated with MMI topologies. Maximum power point tracking (MPPT) control schemes for PV inverters will be explored extensively. Then, the different control strategies of PV MMIs will be presented and compared to give a holistic overview of the submodules balancing techniques, ranges, and capabilities under balanced and unbalanced grid conditions. In addition, the paper will discuss the future of PV MMIs systems in electricity networks.

Investigation of Structural Voltage Stability in Tunisian Distribution Networks Integrating Large-Scale Solar Photovoltaic Power Plant

This research shows a structural voltage stability analysis of a distribution network incorporating large-scale solar photovoltaic power plant. Detailed modeling of the transmission network and photovoltaic systems is presented and a differential-algebraic equations model is developed. The resulting system state and load-flow Jacobian matrix are reorganized according to the type of the bus system in place of the standard injected complex power equations arrangement. The interactions among system buses for loading tests and solar photovoltaic power penetration are structurally scrutinized. Two-bus bifurcations are revealed to be a predecessor to system voltage collapse. The investigation is carried out by using bifurcation diagrams of photovoltaic generation margin, load-flow analysis, short-circuits, photovoltaic farm disconnections and loading conditions. Furthermore, evaluation of voltage stability reveals that the dynamic component of the voltage strongly depends on fault short-circuit capacity of the power system at the bus, where, the solar system is integrated. The overall result, which encompasses the views from the presented transmission network integration studies, is a positive outcome for future grid integration of solar photovoltaic in the Tunisian system. Tunisia’s utilities policies on integration of solar photovoltaic in distribution network is expected to benefit from the results of the presented study. Moreover, given the huge potential and need for solar photovoltaic penetration into the transmission network, the presented comprehensive analysis will be a valuable guide for evaluating and improving the performances of national transmission networks of other countries too.

Impacts Generated by a Large-Scale Solar Photovoltaic Power Plant Can Lead to Conflicts between Sustainable Development Goals: A Review of Key Lessons Learned in Madagascar

In a context of energy transition towards renewable energies, this case study situated in Madagascar allows us to verify the extent to which an on-grid photovoltaic solar power plant represents a vector for sustainable development. The article proposes a model for assessing sustainability from a qualitative multi-criteria perspective. This analysis fits into the theoretical question of the science of sustainability by challenging the theory of endogenous development. The innovation of this research is based on the use of a qualitative approach to a technological issue filling a literature gap in the major issue of the effective sustainability of renewable energy (particularly in the context of an island state). The study emphasizes that the plant can only represent a vector for sustainable development with the collaboration of the concerned parties, which implies considering the electrification needs at the local level. The article confirms that the impacts generated by the power plant can lead to conflicts between different sustainable development goals. Theoretically, the study emphasizes that the evaluation of the sustainability of solar power plants should follow a process that: (i) uses a preferably qualitative methodology likely to understand the local conditions of the communities in which they are established; (ii) identifies dissociated indicators while taking into account the context; and (iii) analyzes the possible negative interactions between the impact areas by highlighting the key areas linked to land management and the well-being of women within a poverty reduction approach.

Verification of Utility-Scale Solar Photovoltaic Plant Models for Dynamic Studies of Transmission Networks

In recent years, there has been a growing need for accurate models that describe the dynamics of renewable energy sources, especially photovoltaic sources and wind turbines. In light of this gap, this work focuses on the validation of standard dynamic models developed by the Western Electricity Coordinating Council (WECC), using actual measurements from the Western Texas and Southern California transmission networks. The tests are based on the North American Electric Reliability Corporation compliance standards and include dynamic stability tests for volt-varcontrol and primary frequency response. Through an extensive set of field tests, we show that the WECC generic models can be used to simulate real dynamic phenomena in large-scale solar photovoltaic power plants, and we propose guidelines for correct usage of these models. The results show that the WECC models are especially accurate when the photovoltaic system is connected with a low impedance to the main network. We also show that the tested WECC models successfully predict the frequency response of an actual grid event that occurred in the Electric Reliability Council of Texas and which resulted in a loss of nearly 1.365 GW. This result supports the use of these models in the study of large-scale dynamic phenomena that include renewable energy sources.

Integrated Power Flow Analysis with Large-scale Solar Photovoltaic Power Systems Employing N-R Method

Abstract A method is proposed to modify the conventional load flow programme to accommodate large-scale Solar PhotoVoltaics (SPV) power plant with series power specifications. The programme facilitates easy handling of any number of SPV systems with standard control strategies such as pf-control and voltage-control, considering solar inverter’s power constraints. In this method, the non-linear equations related to SPV systems, located at multiple locations, are solved with the main load flow equations in an integrated fashion, considerably reducing the implementation task. This task is achieved by augmenting the inverter buses to the existing power system network in such a way that the changes required in the conventional programme are minimal. To show the effectiveness of the proposed method, it is compared with the alternate-iteration method popularly followed in the literature. The workability of the proposed method has been demonstrated by using a Single Machine Infinite Bus (SMIB) system and the IEEE14-bus power system with SPV systems. Various test cases pertaining to meteorological variables and control strategies are also presented.

Economic feasibility of developing large scale solar photovoltaic power plants in Spain

In 2017, electricity generation from renewable sources contributed more than one quarter (30.7%) to total EU-28 gross electricity consumption. Wind power is for the first time the most important source, followed closely by hydro power. The growth in electricity from photovoltaic energy has been dramatic, rising from just 3.8 TWh in 2007, reaching a level of 119.5 TWh in 2017. Over this period, the contribution of photovoltaic energy to all electricity generated in the EU-28 from renewable energy sources increased from 0.7% to 12.3%. During this period the investment cost of a photovoltaic power plant has decreased considerably. Fundamentally, the cost of solar panels and inverters has decreased by more than 50%. The solar photovoltaic energy potential depends on two parameters: global solar irradiation and photovoltaic panel efficiency. The average solar irradiation in Spain is 1,600 kWh m-2. This paper analyzes the economic feasibility of developing large scale solar photovoltaic power plants in Spain. Equivalent hours between 800-1,800 h year-1 and output power between 100-400 MW have been considered. The profitability analysis has been carried out considering different prices of the electricity produced in the daily market (50-60 € MWh-1). Net Present Value (NPV) and Internal Rate of Return (IRR) were estimated for all scenarios analyzed. A solar PV power plant with 400 MW of power and 1,800 h year-1, reaches a NPV of 196 M€ and the IRR is 11.01%.

Performance Evaluation of Two Similar 100MW Solar PV Plants Located in Environmentally Homogeneous Conditions

With recent steep decrement of Photovoltaic (PV) module prices, many utilities around the world are investing in large scale solar PV power plants to meet their energy needs. Countries with an ample amount of deserted areas tend to utilize it for the purpose of energy generation. This article reviews two equal power rated solar PV power plants with similar environmental conditions located next to each other with similar installed equipment but different output energy generation. Various factors affecting the generation of these technically similar power plants such as PV module tilt angle, inter row spacing, annual degradation effect, the negative temperature coefficient of power and other causes are explored evaluate the performance along with the assessment of reasons for deviation in the performance. The energy output trend and the percentage difference for each month for a complete year are graphed for analysis with and without considering the degradation effect to give a level playing field for both the PV plants under review. The efficient design of tilt angle, inter row spacing for the area of installation with the help of sun charts and shading occurrence diagram, is of utmost importance to maximize the energy yield. Any laxity in designing these parameters result in heavy financial losses to the investor which multiply over the life cycle of the project. Similarly, an improved and proper design can increase the energy output and have a positive impact on the financial savings of the investor which in this case is USD 0.85 million per annum.

Comparison of zero‐sequence injection methods in cascaded H‐bridge multilevel converters for large‐scale photovoltaic integration

Photovoltaic (PV) power generation levels in the three phases of a multilevel cascaded H-bridge (CHB) converter can be significantly unbalanced, owing to different irradiance levels and ambient temperatures over a large-scale solar PV power plant. Injection of a zero-sequence voltage is required to maintain three-phase balanced grid currents with unbalanced power generation. This study theoretically compares power balance capabilities of various zero-sequence injection methods based on two metrics which can be easily generalised for all CHB applications to PV systems. Experimental results based on a 430 V, 10 kW, three-phase, seven-level cascaded H-bridge converter prototype confirm superior performance of the optimal zero-sequence injection technique.

The cost Benefit Analysis of Large Scale Solar Photovoltaic Power Plant under New Market Environment in China

A newly constructed 10MW solar photovoltaic power plant in Qinghai province China is examined for the latest cost benefits evaluation in this research, the RETScreen analysis software tool is used for the energy production estimation, financial feasibility analysis and environmental benefit analysis. Initial results show high solar PV potential in Qinghai province China, 20GWh power electricity generation can reduce more than 18,000 tons of CO2 emissions annually. Although current LCOE of PV power is still double than fossil fuel electricity, but the investment of solar PV is motivated by governmental subsidy policy, the payback of the solar PV project became attractive under the new market environment, the IRR could reach more than 20%, with further material cost decrease, efficiency improvement and continues subsidy policy, the solar PV power will be a trend of renewable energy in China. The GHG emissions reduction is another driving reason to encourage solar PV power development in China.

The costs and benefits of large-scale solar photovoltaic power production in Abu Dhabi, United Arab Emirates

The potential for a 10 MW photovoltaic power plant in Abu Dhabi is examined in this paper using RETScreen modeling software to predict energy production, financial feasibility and GHG emissions reductions. Initial results show high energy production potential, generating 24 GWh and saving over 10,000 tons of GHG emissions annually, but poor financial prospects yielding a net present value (NPV) of −$51 million. Benefits of reducing GHG and air pollution emissions by replacing natural gas with PV generation are calculated to have a net present value of $47 million, with a large range of possible values. Results show that the high initial costs and low expected price for electricity generated are driving reasons why photovoltaic systems are not being implemented in Abu Dhabi. A feed-in tariff rate of $0.16/kWh is recommended to make large-scale PV systems profitable.