Photovoltaic Generation Plants Research Articles

Evaluating the performance of the Anwaralardh photovoltaic power generation plant in Jordan: Comparative analysis using artificial neural networks and multiple linear regression modeling

The global energy demand is rising, driven by population growth, economic development, and industrialization. Shifting towards renewable energy, like solar energy, is gaining momentum worldwide because of ecological concerns and resource depletion. This paper aims to utilize Artificial Neural Networks (ANNs) and multiple linear regression (MLR) modeling techniques to evaluate the productivity of 11 MW photovoltaic (PV) solar power plant currently operational in Jordan. The case study reveals that both models can be used to predict the daily, monthly, and yearly average power produced and system efficiency with reasonable accuracy. The ANN model exhibited promising results, where the best value for the coefficient of determination (R2) and mean absolute percentage error (MAPE) for training were 95.85% and 0.59%, respectively. However, R2 was 93.7%, and MAPE was 1.27% for validation tests. All these results were achieved using a 7-6-1 model, with a sample ratio of 1:1 for the data allocated in training and validation. When using multiple linear regression, the R2 and standard error values were 93.42% and 0.17%. On the other hand, the results showed that the yearly output power for actual and predicted by both models over the year was 24,399 MWh, 24,538 MWh, and 24,401 MWh, respectively. This research showed valuable results in the monthly output power for solar cells at the Anwaralardh PV power system project, contributing to a better understanding of solar energy generation in arid desert climates and emphasizing the potential of solar power plants to play a crucial role in achieving SDG 7 objectives.

Technical-economic feasibility of installing a photovoltaic generation plant: a case study in a public university

The energy compensation system has attracted the interest of several sectors, including the public sector. In this context, this work presents a technical dimensioning and techno-economic feasibility of installing a photovoltaic generation plant, considering the service to a Brazilian public university campus. Four systems were designed using NREL's PVWatts and SAM tools with the NSRDB database, analyzing 6 economic viability situations. The results show a payback of 6 years and 3 months for a 545kWp system without energy storage and 10 years and 5 months for a 180kWp system with energy storage. The variations in the results depend on the implementation considerations of each of the proposed systems, such as the energy availability demand contract and the savings on the bill made possible by the implementation method.

Techno-economic feasibility of installing a photovoltaic power plant: a case study at a public university

The energy compensation system has attracted the interest of several sectors, including the public sector. In this context, this work presents a technical dimensioning and techno-economic feasibility of installing a photovoltaic generation plant, considering the service to a Brazilian public university campus. Four systems were designed using NREL's PVWatts and SAM tools with the NSRDB database, analyzing 6 economic viability situations. The results show a payback of 6 years and 3 months for a 545kWp system without energy storage and 10 years and 5 months for a 180kWp system with energy storage. The variations in the results depend on the implementation considerations of each of the proposed systems, such as the energy availability demand contract and the savings on the bill made possible by the implementation method.

Acceleration of potential-induced degradation in crystalline Si photovoltaic modules after a lightning impulse strike

The potential-induced degradation (PID) is one of the significant issues in realizing low-cost electricity from photovoltaic (PV) power generation plants. In this paper, we have investigated PID in crystalline Si (c-Si) PV modules with conventional p-type multicrystalline Si solar cells after the application of lightning impulse strikes. Lightning impulses with a voltage of −40 kV were applied to the module between the shorted electrodes of the c-Si cell and the mimic aluminum frame. It is confirmed that no degradation in the electrical characteristics of the c-Si cell occurs by applying the impulse only. We have found that the PID of c-Si PV modules was accelerated by applying the impulses between a c-Si cell and a metal frame. The acceleration of PID in the module applied with a lightning impulse might be caused by the migration of Na+ ions easily toward the c-Si cell owing to damage to the ethylene-vinyl acetate encapsulant by impulses.

A novel prediction and control method for solar energy dispatch based on the battery energy storage system using an experimental dataset

The high power generation growth by photovoltaic systems needs to forecast the power generation profile during a day. It is also required to evolve the high-efficient and optimal on-grid/off-grid photovoltaic power generation units. Furthermore, some advantages can be achieved by integrating photovoltaic systems with storage devices such as battery energy storage systems. Thus, optimizing the hybrid systems comprising photovoltaic and battery energy storage systems is needed to evaluate the best capacity. In the present work, a novel control and sizing scheme is proposed for the battery energy storage system in a photovoltaic power generation plant in one-hour ahead and one-day ahead during the dispatching phase. Then, the proposed prediction strategy is recommended for solar irradiation and power utilization. The control approach comprises a predictive control method concerning a Radial Basis Function network optimized by Levenberg-Marquardt back-propagation learning algorithm. Using the RBF network for simulation leads to a WAPE% =1.68 %.

Multi-Objective Dispatch of PV Plants in Monopolar DC Grids Using a Weighted-Based Iterative Convex Solution Methodology

The design of an efficient energy management system (EMS) for monopolar DC networks with high penetration of photovoltaic generation plants is addressed in this research through a convex optimization point of view. The EMS is formulated as a multi-objective optimization problem that involves economic, technical, and environmental objective functions subject to typical constraints regarding power balance equilibrium, thermal conductor capabilities, generation source capacities, and voltage regulation constraints, among others, using a nonlinear programming (NLP) model. The main characteristic of this NLP formulation of the EMS for PV plants is that it is a nonconvex optimization problem owing to the product of variables in the power balance constraint. To ensure an effective solution to this NLP problem, a linear approximation of the power balance constraints using the McCormick equivalent for the product of two variables is proposed. In addition, to eliminate the error introduced by the linearization method, an iterative solution methodology (ISM) is proposed. To solve the multi-objective optimization problem, the weighted optimization method is implemented for each pair of objective functions in conflict, with the main advantage that in this extreme the Pareto front has the optimal global solution for the single-objective function optimization approach. Numerical results in the monopolar version of the IEEE 33-bus grid demonstrated that the proposed ISM reaches the optimal global solution for each one of the objective functions under analysis. It demonstrated that the convex optimization theory is more effective in the EMS design when compared with multiple combinatorial optimization methods.

Electric Power Generation from Solar Photovoltaic Technology: Is It Marketable in Pakistan?

Solar photovoltaic systems are prohibitively expensive in terms of installation costs. Power from them is also available intermittently—only when energy from the sun is available. On the other hand, PV systems are free of the ever-rising costs of input fuel. They also incur much less operation and maintenance costs and are supposed to have a longer lifetime than, for example, a fossil fuel power plant. Thus using solar-PV power looks uneconomical in the short term, but may be profitable in the long term. It is, therefore, interesting to identify the factors that can make investment in solar PV power generation acceptable. This paper carries out a financial analysis of installing a 10 MW solar photovoltaic power generation plant for sale of electricity to a grid. It compares the levelised cost of this mode of energy generation as compared to a fossil fuel plant. It also calculates the cost of electricity generation and tariff for power from this plant. It then identifies the factors that can make the investment in a grid-scale solar PV plant more favourable than investment in other conventional and non-renewable sources.

Photovoltaic panels have altered grassland plant biodiversity and soil microbial diversity.

Human concerns about fossil fuel depletion, energy security and environmental degradation have driven the rapid development of solar photovoltaic (PV) power generation. Most of the photovoltaic power generation plants are concentrated in desert, grassland and arable land, which means the change of land use type. However, there is still a gap in the research of the PV panel layout on grassland plant species diversity and ecological function. In this study, Illumina high-throughput sequencing technology was used to investigate the effects of PV panel arrangement on grassland plant species diversity and soil microbial diversity. In view of the differences in the microclimate at different sites of the PV panels, quadrates were arranged in front edge (FE), beneath the center of each panel (BP), back edge (BE), the uncovered interspace adjacent to each panel (IS) and the undisturbed grassland around the PV panels (Control), respectively. PV panels (especially FE) significantly increased the total aboveground productivity (total AGB) and plant species diversity in grasslands. FE increased precipitation accumulation and plant species diversity directly and indirectly changed the diversity of soil bacterial and fungal communities. PV panels decreased the relative abundance of Actinobacteriota, while increased the relative abundance of Proteobacteria, Acidobacteriota, and Methylomirabilota. EC, Margalef' s richness and total AGB were the main factors affecting the composition of bacterial communities, while alkaline hydrolysis nitrogen (AN) and available phosphorus (AP) were the main factors affecting the composition of fungal communities. In conclusion, the arrangement of PV panels increased the plant species diversity and soil microorganisms in grassland. This study provides important information for further understanding the impact of PV panels on grassland ecosystem function and is of great significance for maintaining grassland ecosystem function.

Variable State of Charge-based Power Leveling Method using Multiple Energy Storage Systems

This paper proposes a power-leveling method in which a lithium-ion battery and a flywheel energy storage are installed in a photovoltaic power generation plant. The proposed method is a division method that focuses on the amplitude component of the compensating power required to level the generated power of photovoltaic power generation. Furthermore, by adding the flywheel state-of-charge automatic adjustment method to the proposed method, the coverage period of the flywheel is improved. The effectiveness of the proposed method is verified on an actual 40kW plant.

Novel control system applied in the modernization of public lighting systems in heritage cities: Case study of the City of Cuenca

Several heritage cities face the need to modernize public lighting systems that are installed on the facades of buildings. The legal obstacles to intervene in heritage infrastructures create a different scenario than other cities. This circumstance raises the question if it is possible to install a modern, innovative lighting system, isolated from the electrical distribution system. This research analyzes the feasibility of modernizing the system using 304 mini photovoltaic generation plants, implementing a novel lighting system control process that indirectly uses sky brightness measurements based on current research at the Catholic University of Cuenca. As a case study, the Historic Center of the City of Cuenca-Ecuador, a World Heritage Site, whose system is made up of 1,252 luminaires, is analyzed. The results conclude that it is feasible to modernize the lighting service with the adoption of the proposed technologies and for control, a novel system based on indirect measurement of sky clarity has been developed. Finally, the political implications are analyzed in the framework of energy efficiency and modernization of the systems, as well as the recommendation to the political decision-makers. The study can be replicated in other cities of the world with similar characteristics.

Integrated design of photovoltaic power generation plant with pumped hydro storage system and irrigation facility at the Uhuelem-Amoncha African community

— Seasonal and location dependence of renewable energy resources have limited their applications in power generation. Energy storage systems are promising solutions to the intermittence of renewable energy resources. Rural electricity grids are faced with economic sustainability challenges due to low power demand and poverty. A rural grid design around economic drivers like agriculture and micro industries can mitigate poverty and improve economic sustainability of rural grids. This paper presents an integrated design for photovoltaic power generation with a pumped hydro storage system for irrigation and community utilization. The design explored the natural availability of water body in an elevated settlement area that offers a natural storage height for hydro energy storage. A photovoltaic generation plant was designed to power a pump as a turbine system for water storage and generation. HOMER® energy simulation software was deployed in the simulation. The result shows a satisfactory net present cost for the possible integration of a pumped hydro storage system in a photovoltaic generation plant as the most viable option to provide power at a power supply probability of 99.9% and water for irrigation. The benefits from the irrigation and water supply will enhance the community's socio-economic development and sustain the investment.

Energy homeostasis management strategy for building rooftop nanogrids, considering the thermal model and a HVAC unit installed

This paper presents a case study on power control and energy management for a 60 apartments’ residential building with solar generation and energy storage tied to the grid in Santiago, Chile. A new energy management algorithm based on energy homeostasis is designed for a small electro thermal generation system (nanogrid), with smart metering. The test bed employs supervisory control with energy management that regulates the temperature inside a large room by the action of an HVAC (Heating/Ventilating/Air Conditioning) unit. The main objective of supervisory control is to allow temperature comfort for residents while evaluating the decrease in energy cost. The study considers a room with rooftop grid-tie nanogrid with a photovoltaic and wind turbine generation plant, working in parallel. It also has an external weather station that allows predictive analysis and control of the temperature inside the abode. The electrical system can be disconnected from the local network, working independently (islanding) and with voltage regulation executed by the photovoltaic generation system. Additionally, the system has a battery bank that allows the energy management by means of the supervisory control system. Under this scenario, a set of coordination and supervisory control strategies, adapted for the needs defined in the energy management program and considering the infrastructure conditions of the network and the abode, are applied with the aim of efficiently managing the supply and consumption of energy, considering Electricity Distribution Net Billing Laws 20.571 and 21.118 in Chile (https://www.bcn.cl/historiadelaley/historia-de-la-ley/vista-expandida/7596/), the electricity tariffs established by the distribution company and the option of incorporating an energy storage system and temperature control inside the room. The results show the advantage of the proposed tariffs and the overall energy homeostasis management strategy for the integration of distributed power generation and distribution within the smart grid transformation agenda in Chile.

Data-driven analysis and machine learning for energy prediction in distributed photovoltaic generation plants: A case study in Queensland, Australia

Understanding patterns and energy-related data in photovoltaic systems is one of the key tasks in energy generation and distribution. In fact, the use of data-driven tools and predictive learning models can support the government, power regulatory agencies, and the energy industry in improving their decision-making and operational activities. Bering this in mind, this paper presents a case study of data-driven analysis and machine learning to forecast the energy charge in the distributed photovoltaic power grid of Queensland, in Australia. Our analysis relies on a freely, open energy tracking platform and the design of three Machine Learning approaches built on the basis of Random Forest, Support Vector Machines, and Gradient Boosting methods. Experimental results with real data showed that the trained models allow for very consistent predictions while reaching a high forecasting accuracy (around 95%–93% in Generated - Exported prediction, respectively). Moreover, it was found that the Gradient Boosting-based model ensures robust behavior and low prediction errors, as endorsed by quality validation metrics. Another technical aspect observed is that the variables artificially created to boost the models substantially improve the post-analysis and overall accuracy of the results.

Local Energy Communities in Spain: Economic Implications of the New Tariff and Variable Coefficients

The European Union advocates for legislative support to local energy communities. Measures include the promotion of dynamic energy allocation and discriminatory electricity tariffs such as the recent Spanish framework. However, the impact of these normative changes is not yet evaluated. This paper inquires into the impact of dynamic allocation coefficient and different electricity tariffs on the profitability of local energy communities. To do so, a linear optimisation model is developed and applied to real consumer data in Spain around a variable capacity photovoltaic generation plant. Comparing the economic performance of the static or variable power allocation under the effect of changing electricity tariffs. While both measures are beneficial, the new electricity tariffs result in larger profitability increases than the planned variable coefficients. The combination of measures allows for profitability improvements of up to 25% being complementary measures. However, installations that maximise the potential for electricity generation are still not as profitable due to the low purchase price of surplus energy. While discriminatory electricity price tariffs and variable allocation coefficients are positive measures, further measures are needed for these communities to install generation plants as large as the potential that each case allows.

Hardware Design of a 13.8-kV/3-MVA PV Plus Storage Solid-State Transformer (PVS-SST)

Photovoltaic (PV) power generation plant with integrated battery energy storage (BES) is becoming increasingly attractive and necessary as the PV penetration increases. Traditional solutions involve two paralleled inverter systems at the same site. This increases the balance of the system cost and the control complexity. Furthermore, high-power step-up transformers are needed to connect to the distribution grid, further increasing the capital cost, land use, and installation cost. Combining PV and BES into a single inverter system without the 60-Hz step-up transformer is, therefore, very attractive as the next-generation technology for utility-scale solar. This effectively calls for the development of a PV+BES solid-state transformer (PVS-SST). This article proposes a 13.8-kV/3-MVA PVS-SST targeting a 13.8-kV grid connection on the ac side and a 1500-V PV farm on the dc side. The PVS-SST utilizes a modular SST concept with a total of 27 SiC submodules. Each submodule is based on 1700-V SiC power MOSFET devices and single-stage dual-active-bridge (DAB)-based dc/ac power conversion technology. In order to utilize the inevitable parasitic capacitances in the actual hardware, a novel turn-off loss model is proposed and experimentally verified. A comprehensive optimization strategy, including the proposed novel turn-off loss model, ZVS range, dead-time calculation, and minimum circulation current, is proposed. The medium-frequency transformer (MFT) design to achieve very high efficiency and isolation voltage is a grand challenge in the proposed PVS-SST and is discussed in detail. The power density of the SiC power submodule is 1.6 MW/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> (26 W/inch <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ). The PVS-SST control architecture is discussed with a focus on voltage balancing and real and reactive power controls.

Methodology based on higher‐order statistics and genetic algorithms for the classification of power quality disturbances

The present work proposes a methodology for the detection and classification of some transient power quality disturbances. It uses a genetic algorithm for estimating the amplitude, frequency, and phase of the fundamental component in an optimum way to suppress it from an electric signal. By using this pre-processing strategy, it is possible to remove a single frequency component instead of removing a whole frequency band as other methodologies do. Once the fundamental component is suppressed, it is possible to perform a better identification of an anomalous condition in a signal because its high energy does not hide the presence of a disturbance. After the suppression, the proposed methodology computes three higher-order statistics (variance, kurtosis, and sixth-order cumulant) from the resulting signal and uses them as inputs for a fuzzy-based classifier. Higher-order statistics are selected because they are insensible to the presence of Gaussian noise adding robustness to the proposed methodology. Experimentation is performed using both synthetic and real signals. Real signals come from a photovoltaic generation plant and a hospital facility, both located in Spain. Results prove that the proposed methodology allows enhancing the results delivered by other methodologies up to 30%.

Improved PV Forecasts for Capacity Firming

Some balancing authorities give owners of medium to large photovoltaic (PV) generation plants a choice between firming the production of their plants using battery energy storage or paying the balancing authority for the cost that these imbalances impose on the system. If the owner of a PV plant decides to do capacity firming, the net production of the PV plant and the battery must match a forecast value. A more accurate forecast of the PV production reduces the energy throughput of the battery and hence its degradation. This article compares capacity firming using persistence forecasts with predictions based on long short-term memory recurrent neural networks (LSTM-RNN), encoder-decoder LSTM-RNN and multi-layer perceptrons. This article also proposes to use the type-of-day, such as sunny, cloudy etc, which can be generated by clustering historical PV generation data according to the total daily PV generation, as a feature of the PV forecasting model. Results based on the Snohomish County Public Utility District's Arlington Microgrid show that the machine learning techniques perform significantly better than the persistence method in forecasting PV generation. In particular, encoder-decoder LSTM-RNN would reduce the yearly battery energy throughput by 29% and the number of battery cycles with a greater than 10% depth-of-discharge (DoD) by 51%. Including the day-type as a feature in PV forecasting reduces the battery energy throughput by 5.3% and the number of cycles with a DoD larger than 10% by 5.9%.

Voltage Stability Analysis in an IEEE 34 Nodes Test System with the Participation of Photovoltaic Generation Plants

Voltage Stability Analysis in an IEEE 34 Nodes Test System with the Participation of Photovoltaic Generation Plants

Non-linear least squares methodology for suppressing the fundamental frequency in the analysis of electric signals

Energy from the fundamental frequency component (FFC) in electrical signals is usually much higher than the energy from the rest of spectral components. This situation makes it difficult to obtain a proper analysis of the whole frequencies involved in a particular signal. To improve the spectral analysis of electrical signals, some works have proposed the use of filters and digital signal processing techniques for suppressing the influence of the FFC. However, these methodologies suppress a frequency band, introducing undesired effects in the frequencies close to the FFC. Thus, this paper proposes the use of non-linear least squares to identify the amplitude, frequency and phase that describe the FFC to suppress only one frequency component instead of a frequency band. The methodology is tested using synthetic signals and experimentation is performed with real electric signals from three different scenarios: current signals from two induction motors operating at two different frequencies (60Hz and 31Hz), and a voltage signal from a photovoltaic generation plant. Results show that the methodology can adequately recognize and subtract the FFC. This methodology aims to be a tool to enhance the results delivered by methodologies for condition monitoring of induction motors and power quality assessment.

Study of the harmonic and interharmonic content in electrical signals from photovoltaic generation and their relationship with environmental factors

The inclusion of renewable energies is associated with harmonic and interharmonic contamination due to the use of power inverters. Thus, several international standards have been developed to establish the admissible distortion levels associated with spectral contamination. However, these standards present limitations: interharmonics are not considered and only present limits for current signals and voltage are dismissed. This work assesses the spectral content introduced by photovoltaic generation in low voltage grids considering the existence of voltage harmonics and interharmonics. Additionally, a multiple linear regression analysis quantifies the effect of sun irradiance and photovoltaic cell temperature on harmonic and interharmonic distortion. The analysis is performed in real signals from a photovoltaic generation plant considering different weather conditions. The results prove that the spectral content in photovoltaic generation is variable throughout the day, and environmental factors can reasonably describe the behavior of harmonic and interharmonic distortion.