MW Photovoltaic Plant Research Articles

Iterative sizing methodology for photovoltaic plants coupled with battery energy storage systems to ensure smooth power output and power availability

Photovoltaic (PV) solar energy is a fundamental technology that will help transition from a fossil fuel–based energy mix to a future with high shares of renewable energy. To do so, PV plants coupled with energy storage systems can accumulate excess power and dispatch it when PV generation changes, performing PV smoothing. While coupling PV plants with battery energy storage systems (BESS) offers a solution, current methodologies often need to thoroughly describe the interplay between BESS energy capacity, power rating, and the long–term impacts of battery degradation. This paper addresses this gap by proposing a four–step methodology that optimizes BESS sizing for PV plants, accounting for both cycling and calendar aging effects on system performance and the economic implications of battery replacements. We use a model that considers the degradation of PV modules and two Li–ion battery types (LiFePO4, LFP, and LiNiMnCoO2, NMC). A 16.3 MW PV plant is simulated along with the BESS to test the methodology. The results indicate that an LFP–based BESS of 2.5 MWh with a rated power of 1.25 MW ensures a stable output power with variation below 10% of the rated PV plant 98 % of the time. In contrast, an NMC–based BESS, while effective in reducing non–compliance, incurs higher costs due to more frequent battery replacements, making it less economically viable. The methodology and results presented in this paper provide valuable insights for designing cost–effective and reliable energy storage solutions in PV plants, ensuring compliance with set power availability.

Prediction of rainy-day photovoltaic power generation based on Generative Adversarial Networks and enhanced Sparrow Search Algorithm

Accurate and timely photovoltaic (PV) power forecasting is crucial for the stable operation of power systems. To address the issue of sparse PV power data on rainy days, this paper proposes the use of Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to augment rainy-day data. A progressive gradient penalty strategy is introduced to avoid gradient vanishing. A Bidirectional Long Short-Term Memory network is employed for power forecasting, with adaptive hyperparameter optimization achieved through an improved Sparrow Search Algorithm (LSSA). The optimization capability is enhanced using a chaotic mapping strategy. Validation on data from a 1 MW PV plant demonstrates that the LSSA-optimized model achieves the best forecasting results. After augmenting the rainy-day dataset, the Mean Absolute Error and Root Mean Square Error decreased by 15.3 % and 6.15 %, respectively, indicating that WGAN-GP significantly improves forecasting accuracy on rainy days.

Analysis of environmental impacts in the construction of a photovoltaic plant at the Academic Unit of Cabo de Santo Agostinho, Brazil

The use of solar energy has been increasing its space in the energy matrix. Brazil has great potential for applications using photovoltaic energy. This potential is not fully explored. Due to this, this research aims to evaluate the environmental impacts caused by the production, implementation, operation and maintenance of a 1 MW photovoltaic plant implemented in the Academic Unit located in Cabo de Santo Agostinho, Pernambuco, Brasil, one of the branches of the of the Federal Rural University of Pernambuco. The Control List Method ("checklist") was used to identify and qualitatively evaluate the most relevant impacts. The environmental assessment of the plant identified that the biggest impact in the planning phase was the attraction of people, due to the publicity of the project. In the implementation phase, deforestation and soil movement stood out, while visual pollution and the increase in the supply of electricity were the main impacts in the operation phase.

Shading in Utility‐Scale Photovoltaic Plants: A Hemicube Approach for Efficient Shading Calculations

Accurate estimations of energy production in the planning and design stage of photovoltaic (PV) plants require advanced and efficient models to simulate shading effects. This is particularly important for plants located in complex terrain or plants that are affected by nearby shading obstacles. Herein, a hemicube approach is presented for calculating shading effects and assessing the model accuracy and performance for utility‐scale PV plants. The proposed model is thoroughly illustrated for common direct beam and diffuse irradiance shading effects occurring in solar plants. The article illustrates how the accuracy and computing needs of the model can be affected by the tuning of key parameters using as case studies several PV plants from 1 to 100 MW and different ground coverage ratios. The model is validated in a 16 MW PV plant located in a complex terrain. Finally, the results demonstrate how the proposed hemicube model can be set to leverage accurate and efficient computing performance. The proposed hemicube‐based shading model has been implemented in SolarFarmer, a commercial PV design software, to facilitate the PV industry, produce accurate energy yield assessments, and support the ongoing clean energy transition.

Renewable energy scenarios for water desalination and conveyance: Case of red sea-dead sea project

The objective of the Jordan Red Sea-Dead Sea (RSDS) project is to transport desalinated water from the Gulf of Aqaba to the Dead Sea. It aims to resolve two fundamental issues in Jordan: the lack of potable water in the country's center and the low water level in the Dead Sea. Desalinated seawater should be pumped from the Gulf of Aqaba to the Dead Sea as part of the RSDS project.This study is not only the first study to focus on the feasibility of using renewable energy sources to meet the project's energy needs, but the approach taken in this research is also novel. It employs a variety of software packages in order to accomplish the research goals. Thus, a number of operational scenarios for the RSDS project employing renewable energy technologies, as these resources are essential for sustainable development, are proposed. Based on the availability of renewable energy sources in the project region, only solar and wind technologies are included in the technology selection process. The selection of a suitable technology from renewable energy technologies was based on eight scenarios that were developed using several selection criteria. The social, technical, environmental, risk evaluation, and financial impact scenarios were investigated. Analytic hierarchy process and multi-criteria decision analysis were utilized to evaluate the electricity generation options for the project. The results indicated that the economic scenario is the optimal scenario for covering the entire load. Levelized Cost Of Energy (LCOE) and payback period are sub-criteria for this scenario. The primary goal of this scenario is to cover the load with the lowest LCOE and shortest payback period possible. The results indicated that a 945 MW Photovoltaic plant and a 630 MW wind energy plant are required to cover the total loads for desalination and water pumping. Energy storage is essential in any scenario where renewable energy sources are used to meet demand. Electric vehicles are employed in this research as a kind of energy storage to lessen the demand for more traditional storage methods. The number of 85 kWh electric vehicles needed to replace traditional storage medium is estimated. It is determined that 53,614 and 57,584 electric vehicles in the 24 hour and 12 hour operating times, respectively, are required under the economic scenario.

Virtual energy storage system for peak shaving and power balancing the generation of a MW photovoltaic plant

This article proposes a novel control of a Virtual Energy Storage System (VESS) for the correct management of non-programmable renewable sources by coordinating the loads demand and the battery storage systems operations at the residential level. The proposed novel control aims at covering two main gaps in current state-of-the-art VESSs. The first gap is considering a distributed battery storage system instead of a centralized one, the second gap is providing the electricity grid operator with two services instead of one. To this aim, the authors explore a VESS consisting of residential buildings where each apartment is equipped with an air conditioner but also with a battery storage system. The explored VESS provides the grid operator with both peak shaving and power balancing services for the generation of a megawatt photovoltaic plant located near the VESS. The goodness of the proposed coordinated control is demonstrated via numerical experiments and using real data, measured every 15 min in September 2019. The case study consists of a 1.4 MW photovoltaic plant located near a small town, 21 residential buildings with 168 apartments, each equipped with an air conditioner (continuous power is 1.5 kW) and battery energy storage systems (3 kW /2.5 kWh). The numerical results show that the battery energy storage systems are charged correctly during peak hours (the charging power is between 0.45 and 0.90 kW, and the state of charge varies from 20 % to 78 %) and that the residual photovoltaic plant generation resembles a horizontal line. Later, in the early afternoon, the reference temperature of the air conditioners and the charge/discharge of the battery storage systems are suitably adjusted by solving a mixed linear integer programming problem, to balance the reduction in photovoltaic plant generation, which lasts an hour and a half and peaks at 188 kW. Finally, the numerical results also show that the energy that remained in the batteries is entirely consumed by users in the late afternoon or evening and that the amplitude and the duration of the so-called “load rebound” are so slight that no compensation action (e.g., the bath returning or linear recovery strategy) is required for the considered case study.

Investigating the Impact of Grid-Tied Photovoltaic System in the Aljouf Region, Saudi Arabia, Using Dynamic Reactive Power Control

Due to uncertain photovoltaic (PV) power generation, analyzing the voltage stability of transmission networks with a large PV plant is challenging. The stability of PV output power is a critical factor in establishing PV penetration levels in active transmission networks when assessing loading capabilities. Therefore, this article contributes to enhancing the understanding of how a static synchronous compensator (STATCOM) could be used as part of the power system network. STATCOMs are often used to improve the static and transient voltage, maintain transmission limits, and reduce low-frequency disturbances. With the help of a STATCOM unit, a proper investigation and analysis of dynamic voltage stability in a transmission system are presented. The validity of the study is evaluated by the Saudi Arabia grid code (SAGC). The system being tested is comprised of a 300 MW PV plant in Sakaka, Saudi Arabia, integrated into a 17-bus transmission power network and a STATCOM, which is employed at one of the buses. PSAT software is used to evaluate a number of case studies to determine the transient voltage stability with and without the STATCOM and the PV plant. The simulation findings demonstrate how STATCOM functions to improve the quality of the power system in accordance with the SAGC. In addition, the analysis of voltage stability demonstrates that the power network’s resilience to short circuits at the PV system’s grid connection point significantly impacts the voltage’s dynamic behavior.

Life Cycle Assessment of an Integrated PV-ACAES System

The aim of this paper is to evaluate the overall life cycle environmental impact of an adiabatic compressed air energy storage (ACAES) system, which is designed to achieve the best match between the power production of a photovoltaic (PV) power plant and the power demand from the final user. The electrical energy demand of a small town, with a maximum power load of about 10 MW, is considered a case study. The ACAES system is designed with a compressor-rated power of about 10 MW and charging and discharging times of 10 and 24 h, respectively. Different sizes of the PV plant, ranging from 20 to 40 MWp, and two different solutions for the compressed air storage, an underground cavern, and a gas pipeline, are analyzed. The aim of this analysis is to compare the impacts on human health, ecosystem quality, climate change, and resource consumption of the PV power generation plant and the integrated PV-ACAES system with those of a reference scenario in which the end user demand is met entirely by the grid. The best results in terms of a reduction in environmental impact in comparison to the reference scenario are obtained for a small PV plant (20 MW) without the ACAES section, with reductions of about 85–95% depending on the category of impact. The integration of the ACAES system improves energy self-consumption but worsens the environmental impact, especially for air storage in gas pipelines. The best configuration in terms of environmental impact is based on a 30 MW PV plant integrated with an ACAES section using an underground cavern for air storage and allows for improvements in the energy self-consumption of between 38% and 61%, with a reduction in the environmental impact compared to the reference scenario of about 80–91% depending on the impact category.

A hybrid energy storage solution based on supercapacitors and batteries for the grid integration of utility scale photovoltaic plants

This paper presents a 2-level controller managing a hybrid energy storage solution (HESS) for the grid integration of photovoltaic (PV) plants in distribution grids. The HESS is based on the interconnection of a lead-acid battery pack and a supercapacitor pack through a modular power electronics cabinet. The inclusion of the HESS into the PV plant –and not an state-of-the-art energy storage system based on a single technology–, is motivated by the diversity of technical requirements for the provision of the services of grid peak power shaving and PV output power ramp limitation. The 2-level controller ensures a synergistic exploitation of the two storage technologies aiming for an optimal service level of the HESS and minimum battery degradation. The higher level of the controller is based on a mathematical optimization problem that solves with the optimal schedule of the storage technologies for peak power shaving purposes. The power setpoints of this optimization are then complemented by a real time controller managing PV plant output ramp limitation. The HESS performance and associated controller has been proved effective through two case studies. The first one adopts a 6.6 MW PV plant including a HESS solution combining a 5.5 MWh and 2.64 MW lead-acid battery pack with a 0.25 MWh and 1.32 MW supercapacitor pack. The second one reports experimental data from an analogous scenario scaled down to kW level and using a laboratory scale prototype for the HESS. All in all, the hardware and software solutions proposed in this paper contribute to a feasible exploitation of multi-purpose energy storages targeting the needs of renewables' and distribution system operators.

Long term operational performance and experimental on-field degradation measurement of 10 MW PV plant in remote location in India

The paper presents extensive monitoring, detailed long term operational performance and on-field degradation measurement of utility scale grid connected 10 MW solar photovoltaic (PV) plant in real operating conditions. Different performance indices are evaluated incorporating IEC standard 61724 guidelines based on actual plant monitoring data (incident solar irradiation, ambient and module temperature and generated electrical energy) from January 2016 to December 2020 (period of five years). The annual average reference, array and final yield, performance ratio, capacity utilization factor, array capture and system losses, PV array and system efficiency of the plant were found to be 6–6.7 h/day, 4.83–5.1 h/day, 4.69–4.86 h/day, 72–74%, 19–20%, 1.02–1.33 h/day, 0.15–0.156 h/day, 12% and 11% respectively. Degradation of fielded modules is assessed by visual inspection and I-V curve tracing. Correlation of I-V characterization with electrical parameters of visually degraded installed PV modules is also presented. The calculated linear degradation rates varied from 0.97 to 1.52%/year in modules with considerable bird dropping, 1.33 to 1.61%/year in modules with light EVA discoloration, 1.6 to 1.86%/year in modules with glass breakage, 2.31 to 3.94%/year in modules with glass breakage and dark EVA discoloration, and 2.97 to 5.8%/year in modules with glass breakage and cell cracks. The annual linear degradation rates were found to be in the range of 0.9 to 1.1%/year for normal fielded modules with no visible degradation and 0.97 to 2.9%/year for visually degraded modules with mean and median values of 1.8%/year and 1.6%/year with six years operational period. The levelized cost of electricity, simple and discounted payback period and return on investment were found to be 4.05 Rs, 5.5 years, 11 years and 62.22% respectively. Annual CO2 savings of the plant is around 16,000 tons and CO2 emission reduction potential of installed pc-Si technology is found to be 1.6 tons/kWp.

A robust risk assessment method for energy planning scenarios on smart islands under the demand uncertainty

Energy systems with a high share of variable renewable energy are more vulnerable to sudden changes in the system operation. This is especially emphasized on small systems such as energy systems on geographical islands. Because of these reasons, there is a need for quantifying the risk of energy scenarios of such systems. This paper presents a novel robust risk assessment method under demand uncertainty for energy planning scenarios for the islands. The method uses graph theory for the representation of power system topology. The Poisson distribution is used for calculating the probability of power system element failure. The robust modelling approach is applied by the introduction of auxiliary variables and compared to the deterministic model results. Four energy planning scenarios for Unije island are modelled and subjugated to several power system outages resulting in a risk vector calculated as the product of probability vector and damage matrix. The study also presents a zero-import risk energy planning scenario for Unije island that is achieved for a system of 0.5 MW photovoltaic plant and 3.55 MWh battery storage system.

Performance assessment of a 20 MW photovoltaic power plant in a hot climate using real data and simulation tools

The present study aims to evaluate the aptness of two commercial simulators, HOMER Pro and RETScreen Expert, as predictors of the performance of a large-scale photovoltaic power plant designed to deliver up to 20 MW in a hot climate, for which 26 months of real operational data are available. The power plant is located in the province of Adrar in the south of Algeria and classified as one of the hot regions worldwide. Performance parameters were reference yield, performance ratio, capacity factor, temperature loss and statistical indicators. The results showed that photovoltaic power plant performance depends on cell technology, insolation, and environmental conditions, especially temperature. The deviations between the simulation results and real monitoring data were found to be smaller in the case of HOMER Pro simulation tool. The total annual energy supplied in 2018 by the power plant was 36364MWh, whereas RETScreen Expert predicted 42339 MWh, or about 14% more and HOMER Pro predicted 34508 MWh or about 5.1% less. The influence of temperature on the power plant output was strong, causing a 40% drop during the summer, due to the limitations of the polycrystalline cell technology. This needs to be considered in the design of future photovoltaic power plants to be operated in hot climates. HOMER Pro and RETScreen Expert predicted an average annual final yield of 5.128 h/day, a module efficiency of 15% and an inverter efficiency of 98%. The t statistics were 3.75 for HOMER Pro and 6.12 for RETScreen Expert. The analysis shows that the 20 MW photovoltaic plant in hot climate experiences high losses compared to an equivalent plant based on thin-film photovoltaic cells.

Intelligent and Data-Driven Fault Detection of Photovoltaic Plants

Most photovoltaic (PV) plants conduct operation and maintenance (O&M) by periodical inspection and cleaning. Such O&M is costly and inefficient. It fails to detect system faults in time, thus causing heavy loss. To ensure their operations are at an ideal state, this work proposes an unsupervised method for intelligent performance evaluation and data-driven fault detection, which enables engineers to check PV panels in time and implement timely maintenance. It classifies monitoring data into three subsets: ideal period A, transition period S, and downturn period B. Based on A and B datasets, we build two non-continuous regression prediction models, which are based on a tree ensemble algorithm and then modified to fit the non-continuous characteristic of PV data. We compare real-time measured power with both upper and lower reference baselines derived from two predictive models. By calculating their threshold ranges, the proposed method achieves the instantaneous performance monitoring of PV power generation and provides failure identification and O&M suggestions to engineers. It has been assessed on a 6.95 MW PV plant. Its evaluation results indicate that it is able to accurately determine different functioning states and detect both direct and indirect faults in a PV system, thereby achieving intelligent data-driven maintenance.

Why Can Simple Operation and Maintenance (O&M) Practices in Large-Scale Grid-Connected PV Power Plants Play a Key Role in Improving Its Energy Output?

Existing megawatt-scale photovoltaic (PV) power plant producers must understand that simple and low-cost Operation and Maintenance (O&M) practices, even executed by their own personal and supported by a comparison of field data with simulated ones, play a key role in improving the energy outputs of the plant. Based on a currently operating 18 MW PV plant located in an under-developing South-Asia country, we show in this paper that comparing real field data collected with simulated results allows a central vision concerning plant underperformance and valuable indications about the most important predictive maintenances actions for the plant in analysis. Simulations using the globally recognized software PVSyst were first performed to attest to the overall power plant performance. Then, its energy output was predicted using existing ground weather data located at the power plant. Compared with the actual plant’s annual energy output, it was found that it was underperforming by −4.13%, leading to a potential monetary loss of almost 175,000 (EUR)/year. Besides, an analysis of the O&M power plant reports was performed and compared to the best global practices. It was assessed that the tracker systems’ major issues are the forerunner of the most significant PV power plant underperformance. In addition, issues in inverters and combiner boxes were also reported, leading to internal shutdowns. In this case, predictive maintenance and automated plant diagnosis with a bottom-up approach using low-cost data acquisition and processing systems, starting from the strings level, were recommended.

A novel framework for integration analysis of large-scale photovoltaic plants into weak grids

Developing countries can deploy large-scale photovoltaic (PV) plants extensively for supplying their rapidly growing electricity demand as PV plants are becoming more cost competitive. However, weak power grids are predominant in these emerging economies. Prior to integrating new PV plants into weak grids, the impacts of such plants on steady-state voltages of the grid should be studied comprehensively, and accurate energy analysis undertaken as part of the economic assessment of the plants. This study proposes a novel framework that uses distinct generation profiles for integration analysis of large-scale PV plants into weak grids. A probable maximum generation (PMG) profile based on clear-sky radiation is suggested for voltage evaluation while an average generation (AG) profile based on average radiation is used for energy analysis. The case study examined large-scale PV plant integration into one of the regional power systems in Mongolia. Load flow analysis was carried out every hour for a full year on a simplified 15-bus case network for 5, 10, 15 and 20 MW PV plant integration scenarios. Application of the PMG profile provided a more robust evaluation of the PV plants’ impact on the voltage profile of a weak grid and resulted in between 1032 and 16,011 possible voltage violations in the integration scenarios, while the AG profile resulted in 75 to 11,059 violations. Conversely, the AG profile outperformed the PMG profile for energy analysis, and total annual generations of PV plants were 7.4 to 29.7 GWh for the case study. The PMG profile overestimated the total annual PV generations by about 51%. The results show the proposed framework could help to improve the assessment of PV plant integration into weak grids by providing robust voltage and accurate energy estimations.

Risk Influence Analysis Assessing the Profitability of Large Photovoltaic Plant Construction Projects

The global energy system is in a phase of change for power generation technologies which involve traditional fossil fuel-based technologies to renewable energy-based systems, thanks to lower construction costs, mainly for photovoltaic energy, and changes in countries’ energy policies. In the case of Spain, both factors have led to a reactivation of renewable technologies, which can be found from the data on requests for access and connection to the electricity transmission network that are being processed in Red Eléctrica de España (REE). The requests that were granted access to the network exceeded 100 GW of power in November 2019 alone, and the companies which made the requests must commence electricity production by 2025. During the early stage of approval considerations, it is necessary to carry out an influence study of the risks that can already be identified, as this would enable determining the effects of these risks on the project’s main financial parameters. Based on a risk identification for similar prior projects, experts are typically asked to make their judgments on the influence of such risks on the main economic variables of a project, focusing on the project’s cost, time, and scope. By applying the fuzzy sets, these judgments can be transformed into triangular values that, through Monte Carlo simulation, allow us to assess the influence of these risks on the main financial parameters: the net present value (NPV), internal rate of return (IRR), and payback (PB); as a result of obtaining these parameters, a response to project risks can be planned. To check the functionality of the model, it was applied to a case study involving a construction project for a 250 MW photovoltaic plant located in Murcia (Spain). The application of this methodology allowed us to determine which evaluation criteria are most appropriate based on the philosophy of the PMO (Project Management Office) and the data that were obtained.

Performance Assessment of Large Photovoltaic (PV) Plants Using an Integrated State-Space Average Modeling Approach

Distributed power converters represent a technical solution to improve the performance of large or utility-scale photovoltaic (PV) plants. Unfortunately, evaluation of the yield obtained in large PV fields by using distributed converters is a difficult task because of recurring partial unavailability, inaccuracy of power analyzers, operating constraints imposed by the Power Plant Controller and so on. To overcome such issues in real operating scenarios, a new modeling strategy has been introduced and validated in terms of computational complexity and accuracy. This approach is based on the state-space averaging technique which is applied to large PV plants with multiple conversion stages by performing some elaborations in order to get a final integrated model. The new modeling strategy has been tested in MatLab Simulink environment using data coming from a 300 MW PV plant located in Brazil representing the case study of this work. In this plant, one subfield is equipped with central inverters while another is with string inverters. The proposed model, whose accuracy is in the range from 2.2 to 2.7% with respect to the measured energy, effectively supports data analysis leading to a consistent performance assessment for the distributed conversion system. Final results highlight that string inverters ensure a gain of about 2% in terms of produced energy.

Solar Potential Assessment using PVsyst Software in the Northern Zone of Morocco

The growing demand for solar photovoltaic (PV) installation systems as a renewable energy source is required in terms of accuracy to predict system efficiency. We have adopted in this paper a complete demonstration of the modelling and simulation of a 1 MW photovoltaic plant in one zone located in the north of Morocco, as a study; we will therefore use the PVsyst software for this purpose and evaluate its performance. The performance ratio and power losses such as temperature and electric power are calculated. As a result, the possibility of installing a 1 MW solar plant is examined by comparing the solar energy produced at certain sites in the northern zone of Morocco.

Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant

The inherently non-dispatchable nature of renewable sources, such as solar photovoltaic, is regarded as one of the main challenges hindering their massive integration in existing electric grids. Accurate forecasting of the power output of the solar plant might therefore play a key role towards this goal. In this paper, we compare several machine learning and deep learning algorithms for intra-hour forecasting of the output power of a 1 MW photovoltaic plant, using meteorological data acquired in the field. With the best performing algorithms, our data-driven workflow provided prediction performance that compares well with the present state of the art and could be applied in an industrial setting.

Economic Analysis and Performance of PV Plants: An Application in Kurdistan Region of Iraq

In this study, photovoltaic (PV) technology development over the years is reviewed. The use of PV power plants to solve inadequate power supply in Kurdistan is also analysed. PV power application is one of the most developed renewable energy applications but still not commercialized in somw countries. In this paper, three different locations will be evaluated in Kurdistan for PV plant installation. The research will investigate the best location for PV plant installation in Kurdistan, check the viability of the proposed plants and compare the performance of a fixed and a double axis tracking system. A 10 MW PV plant is developed and simulated based on economic terms. The results from the analysis shows that the simple payback period for a 10 MW PV plant in all the locations considered is between 6.8 and 7.2 years. Also, the installation with two-axis tracking system gave the lowest simple payback period (6.8 years). The PV plant is viable considering other economic indicators like; IRR, NPV, annual life cycle savings and BCR. The yearly savings of the system for one of the locations considered is US$1,573,327 with a dual axis tracking system. ©2019. CBIORE-IJRED. All rights reserved