Solar Power Plants Research Articles

Optimization of Electricity Supply in East Nusa Tenggara Through Communal Solar Power Plants: A Case Study of Kupang City Using PVSyst

Certain regions in Indonesia face challenges related to inadequate electricity supply. To address this, it is essential to model and simulate potential solutions before implementation. This study focuses on optimizing electricity provision to resolve a 4 MW deficit in Kupang, East Nusa Tenggara, particularly at night, through the development of a communal Solar Power Plant. PVsyst software is used for analysis to determine the most economical and optimal solar system configuration. The Oelnasi site, selected for its solar radiation levels, land availability, and proximity to the grid, will house 17,411 solar panels covering 28,360 m², with 196 inverters. The system will generate 4.005 kWp of PV power, with a maximum DC power of 3.583 kWDC, and an AC power of 2.940 kWAC. Simulation results indicate that the Oelnasi Solar Power Plant can supply stable nighttime electricity and reduce CO2 emissions by approximately 7,200 tons annually.

Green Management for Disasters Using an Inflatable Tent Prototype with a Solar Power Plant

Given the ever-increasing number of natural disasters, disaster management must be understood and implemented by all parties in a manner that includes green management and technology. This research begins with the problem of disaster preparedness management which currently uses less environmentally friendly facilities and infrastructure, including using tents with conventional structures and technological systems that are less flexible and require electricity with a gasoline-fueled generator. The aim of this research is to create a green technology-based disaster response facility in the form of an inflatable tent prototype for disaster preparedness using solar electricity. The method used in this study is experimental, designing and manufacturing prototype inflatable tents that use solar power plants. Tests are carried out on several variables: strength of tarpaulins, speed in construction and disassembly, comfort of air temperature, and performance of solar power generation. The results are of the experimental method are: the design and application of two prototype inflatable tents dimensions 6x9 square meters with a capacity of 20 patients, a 3,600 WP power plant using solar power, and a VRLA battery with a voltage capacity of 48 volts and a current of 200 Ah that is capable storing 4,800 watts of electricity. Thus, these devices meet electricity needs for air conditioning, lighting, and medical equipment in inflatable tents without using electricity from the state electricity company or gasoline generators. Keywords: disaster, inflatable tent, solar power generation

The Simulator of China General Nuclear Power Group (CGNPC) Delingha 50 MW Parabolic Trough Solar Thermal Power Plant

CGN Delingha 50MW parabolic trough solar power plant is China's first commercial trough solar power station. There are 190 heat collecting loops in the solar field of the power plant. Due to the mutual coupling of heat collecting loops layout location, environmental conditions, medium flow, working medium physical property changes and other factors, the hydrodynamic characteristics of the solar field are complex. In practical operation, balancing the flow of the heat absorbing medium in each loop of the large-scale trough solar thermal power plant and stabilizing the outlet medium temperature are difficult problems. Taking CGN Delingha 50MW parabolic trough solar power plant as the research object, this paper adopts the idea of modular modeling to establish the mathematical model of the main equipment in different systems of the power plant, to build the dynamic model of its solar field, steam generation system, heat storage system and steam turbine system. Based on the parallel computing function of the real-time dynamic simulation platform STAR-90, the real-time coupling calculation between different systems is realized. The error of the built model in steady-state is less than 2%, and the dynamic results of the model are in good agreement with the actual operation data. The modeling method in this paper is universal and can be a reference for dynamic modeling of other large-scale trough solar thermal power plants.

Comparison of Conventional and Microwave Heating

Carnot batteries (or Power-to-Heat-to-Power systems) are capable of increasing the use of electricity from photovoltaic or wind power plants to achieve decarbonisation of the electricity sector. To decouple electricity production from demand, these renewable power plants can be connected to the thermal energy storage system of a concentrated solar thermal power plant via electric heaters. As an alternative to electric heaters, microwaves are studied as a volumetric and selective heating method, avoiding the limitations of the Joule effect conventional heating when having solar salt (non-eutectic mixture of 60 wt % NaNO3 and 40 wt % KNO3), which is a storage medium with low thermal conductivity, working at a temperature very close to its affordable maximum temperature without degradation. In this work, the two heating methods are compared both experimentally and numerically. At experimental level, a microwave oven and a muffle furnace are used to record energy consumption and time for a common objective. Additionally, different crucible materials were used to test their suitability with microwaves. Only quartz glass was validated for microwave heating, while the porcelain and alumina based materials (Corundum and Alsint 99.7) failed while exposed to microwaves. Compared to conventional heating, heating solar salt with microwaves saved fifty minutes of time and ninety percent of consumption. These experiments were validated by numerical simulations, showing a different distribution in each experiment.

Augmenting a Concentrated Solar Power (CSP) Plant With a Solar PV Plant

The search for enhanced dispatchability at decreased prices has led to a surge in research on hybrid renewable energy systems. This paper explores the integration of Photovoltaic (PV) technology with Concentrating Solar Power (CSP) plants to enhance energy generation and economic feasibility, focusing on optimising the size of PV augmentation for existing CSP facilities. The study considers CSP plants with both Time-of-Day (ToD) and single tariffs, employing modelling techniques to assess the synergies between these two technologies. The cost of PV technology has significantly reduced, making it a cost-effective choice for electricity generation compared to CSP. CSP-PV augmentation promises to reduce online and offline auxiliary consumption, resulting in enhanced energy generation and a subsequent reduction in energy production costs. This study aims to explore the advantages of combining solar PV with a CSP system and determine the ideal PV size to maximise return, building upon previous studies to assess the technological and economic potential of integrating solar PV with CSP, focusing on South Africa. It utilises a rigorous analysis, combining the System Advisory Model and PVSyst modelling tool within an MS Excel framework. This economic assessment and sizing exercise aims to maximise profits while adhering to the limitations imposed by the power purchase agreement of a 100 MW CSP facility. The optimal size is 15 MWp for the ToD tariff and 16 MWp for the flat tariff. This highlights the potential for CSP-PV augmentation to improve energy dispatchability and financial viability, making it a compelling solution for existing CSP plants.

Porous Monolithic Perovskite Structures for High-Temperature Thermochemical Heat Storage in Concentrated Solar Power (CSP) Plants and Renewable Electrification of Industrial Processes

A novel approach towards thermal energy storage of surplus renewable energy (RE) is introduced via a hybrid thermochemical/sensible heat storage concept implemented with the aid of porous structures made of redox metal oxides, capable of reversible reduction/oxidation upon heating/cooling in direct contact with air, accompanied, respectively, by endothermic/ exothermic heat effects and demonstrating fully reversible dimensional changes under cyclic operation. The proposed modular storage units can be heated during the day to a level exceeding the metal oxide’s reduction onset temperature either by hot air streams from air-operated Concentrated Solar Power (CSP) tower plants or via surplus/cheap RE-electricity from photovoltaics, wind, or other renewable sources (“charging”/energy storage step). When this RE sources become non-available or upon demand, the fully charged system can transfer its energy to a controlled airflow that passes through the porous oxide block and initiates the exothermic oxidation of the reduced metal oxide. Thus, a hot air stream is produced which can be used to provide electricity or exploitable heat for industrial processes. The present work elaborates on the operating principles and the potential application of this concept and reports progress in the preparation and shaping of reticulated porous ceramics (RPCs also known as “ceramic foams”) from CaMnO3-based perovskite compositions and their preliminary testing with respect to cyclic reduction-oxidation.

DDPG‐based heliostats cluster control of solar tower power plant

AbstractThe control of heliostat is crucial for the development of solar tower power plant. Currently, most power plants use open‐loop control, which has low cost but low efficiency, closed‐loop control has high concentrating efficiency, but each heliostat requires sensors and has high cost, and Proportional‐Integral‐Derivative (PID) controller has good control effect, but the parameter adjustment is difficult and overshooting problem occurs. In this paper, we propose a DDPG‐based heliostat cluster control aimed at improving the heliostat control effect and reducing the control cost. A leader‐follower strategy is used to control the heliostat, where the whole heliostat field is divided into several groups, each group is assigned a leader heliostat, and the rest of the heliostats follow the leader heliostat to rotate. The leader acquires the control error by means of a photoelectric sensor or a camera device. The following heliographs rotate with the leader to obtain the control signal, so there is no need for sensors, which reduces the number of sensors and lowers the cost. To address the shortcomings of traditional PID, we propose a DDPG‐based PID control algorithm. The algorithm is trained to find out the optimal value at each moment, which ensures that the controller parameters are optimal at each moment. The results show that the tracking error is below 0.0001 rad for both cluster control and individual control. This ensures effective tracking performance while reducing the sensor cost. The controller based on the DDPG algorithm eliminates overshoots, reduces errors, and shortens the stabilization time by 0.5 seconds.

Thermal energy storage behaviour of 3D ceramic/molten salt structures under real concentrated solar radiation

Molten salts, phase change materials commonly employed in thermal energy storage (TES) systems, are widely known to enhance the efficient use and storage of solar energy in concentrated solar power (CSP) plants. Here, three-dimensional TES (3DTES) have been manufactured from highly porous (up to ∼90 %) 3D printed patterned vermiculite (V) and alumina (Al2O3) supports, which have been infiltrated with molten sodium nitrate salt (nn) and solar salt (ss). These 3DTES have been validated under real concentrated solar radiation in a parabolic solar furnace. Among the different 3DTES, those based on V-nn exhibits the best efficiency for the conversion of the incident solar radiation into heat; whereas Al2O3-nn transfers the heat more efficiently and allows a faster charging-discharging cyclability due to its higher thermal conductivity. This study confirms the benefits of additive manufacturing to develop a new class of innovative TES for CSP applications.

Experimental study of Lannea microcarpa seed oil as a heat transfer fluid or thermal energy storage material for medium-temperature applications

In this study, we investigated the suitability of Lannea microcarpa seed oil (LaMSO) as a heat transfer fluid (HTF) and thermal energy storage material (TESM) for medium-temperature applications, focusing on its performance in concentrated solar power (CSP) plants. LaMSO demonstrated higher specific heat capacity than both Dowtherm A and Xceltherm 600 across the temperature range of 30 °C to 300 °C. It also exhibited a volumetric thermal energy storage density similar to, or approximately 10 % greater than, that of Dowtherm A and Xceltherm 600 at 210 °C. The oil was subjected to isothermal aging at 210 °C for 500 h and 1000 h, followed by analysis of its thermophysical and chemical properties. Thermogravimetric analysis demonstrated excellent thermal stability, with less than 3 % mass loss after 1000 h of aging. The density remained relatively unchanged, indicating consistent performance as a HTF or TESM. Despite an increase in viscosity at 40 °C, the viscosity at 100 °C remained comparable between the new and aged LaMSO samples. Furthermore, the flash point also showed no significant change, remaining high even after 1000 h of aging. However, challenges arise from the increase in the melting point due to oil saturation, which poses a risk of solidification in pipelines or tanks in certain climates. From the perspective of thermal storage cost per kWh, LaMSO is cheaper than Dowtherm A and Xceltherm 600, being approximately 8 times and 3 times less expensive, respectively. Overall, the findings suggest that LaMSO holds promise as an effective and sustainable alternative for HTF and TESM applications across a wide temperature range, contributing to advancements in renewable energy technology.

Experimental study on the effects of integrating phase change material with a solar updraft tower

AbstractThe solar updraft tower power plant presents a promising renewable energy solution but faces limitations in thermal efficiency and energy production during nonsunny hours. This study addresses a critical gap in the literature by investigating the integration of latent heat storage systems utilizing paraffin wax as phase change materials (PCMs) to enhance solar updraft tower (SUT) performance. Two identical small‐scale SUTs were constructed, both using the same tank configuration; however, the PCM device was filled with paraffin wax as a PCM, while the non‐PCM device contained only atmospheric air. Three scenarios were explored, featuring varying PCM tank heights of 2, 4, and 6 cm (the full capacity of the PCM tank). Results indicated that, from 3 p.m. until sunset, the PCM‐integrated SUT in Case 3 (with 6 cm of PCM) achieved the best performance, with an average absorber temperature rise of 25°C, compared with 12°C for the non‐PCM device. During the same period, the average air velocity at the tower neck, where a wind turbine could be installed, was 1.21 m/s for the PCM device, versus 0.82 m/s for the non‐PCM device. Notably, the nocturnal operating time extended significantly with PCM use, rising from 90 min for the 2 cm case to 125 min for the 6 cm case. In contrast, the non‐PCM device exhibited no operational time after sunset. This research not only demonstrates the effectiveness of PCM in improving the thermal performance and operational longevity of SUTs but also introduces a novel tank configuration that allows for flexible PCM integration, representing a significant advancement in the development of more efficient SUT systems.

SPECIFICITY OF FOUNDATION OF SOLAR PANELS

Considering that the expansion of the construction of solar power plants in the world, Bosnia and Herzegovina tends to follow the global trend in the construction of solar power plants, that is, to provide as much electricity as possible from renewable sources. One of the solar power plants in the Tuzla Canton was built by the company Deling doo at the location of Bosanska poljana. The terrain represents an alluvial plain, a flat part of the relief that was created by the work and accumulation action of the Jala river. The morphological conditions of the locality in question are relatively favorable due to the plain character of the vast alluvial plain, the average altitude of about 190.00 m above sea level. The foundation of the solar panels requires a bearing capacity for uprooting from the wind, that is, the load on the ground can be variable in the vertical direction, in extreme situations the uprooting force from the wind is significantly greater than the gravitational force of the solar panels. Considering that it is a terrain where the effect of the Jala river is very pronounced, it was necessary to propose an adequate way of foundation of the solar panels in order to meet the stated conditions. The paper shows the way in which the depth of the panel foundation was determined, that is, the experimental determination of the depth of the load-bearing soil.

Solar Power Plant Optimization using Automatic Transfer Switch (ATS) and Low Voltage Disconnect (LVD)

The automatic system model uses a Solar Charge Controller (SCC) to turn off the solar panel system at the minimum battery point so that the battery is safe and durable and an Automatic Transfer Switch (ATS) to automatically transfer the electricity network from State Electricity Company or Grid or solar panels. In this study, Solar Panel Priority Grid electricity is used if the solar panel is insufficient and hybrid system with solar panel priority with 2 cut out battery usage. The results of this study are 450WP Solar Panel, 100A SCC MPPT, 12V/100Ah Battery, and 3000 Watt Modified Sine Wave Inverter, Miniature Circuit Breaker, Contactor, Relay Switch, Time Delay Relay and Indicators. The results of the switching process test between the Solar Power Plant source and grid with ATS control can run automatically in Grid Priority Mode, meaning Solar Power Plant as a backup, or Solar Power Plant Priority Mode where the grid source is used as a power backup system. In the Battery Capacity Optimization System, Low Voltage Disconnect (LVD) Protection can work well, namely it can cut off the voltage from the inverter if the battery is in a low voltage state at a rating below 10.8 Volts, Auto Cut Charging Protection testing can charge the battery up to 13.8 Volts and Cycle Use, namely the process of this system can work to store energy while releasing energy to run the load. The results of this study can be used as a reference for the best choice of the most efficient Solar Power Plant system.

Combined cycle gas turbine (CCGT) plants utilizing methane-hydrogen blends represent a significant element in Australia’s journey toward achieving net-zero emissions

To deliver 570 TWh of dispatchable electricity to the grid in Australia, it is necessary to build up wind and solar photovoltaic power plants generating capacity of 325 GW, and a long-term hydrogen energy storage system comprising 50–150 GW of electrolyzers, 50 TWh of hydrogen storage, and 165 GW of hydrogen power generation. While the addition of other renewable energy producers and energy storage technologies effective on shorter scales are certainly welcome and positively impact the above demand, the inclusion of inter-annual variability and safety negatively impacts these numbers. Fuel cells are currently less advanced compared to electrolyzers and hydrogen storage in salt caverns. Expanding hydrogen power generation will require exploring additional opportunities. Here we advocate for methane-hydrogen Combined Cycle Gas Turbine (CCGT) plants. Their adaptability to effortlessly integrate methane and green hydrogen mixtures positions them as a key player in the evolution toward net zero. This transition will be complete once a fully established wind and solar photovoltaic generation, coupled with an efficient hydrogen energy storage system, is in place. Notably, these CCGT plants consistently yield the lowest Life Cycle Analysis (LCA) carbon dioxide (CO2) emissions during the grid’s progression toward net zero.

Multiscale textured solar absorber coatings for next-generation concentrating solar power

A high-temperature stable solar absorber is crucial for next-generation (Gen3) concentrating solar power (CSP) plants, to enable high temperature operation, maximize power generation efficiency, and thereby reduce the levelized cost of energy. This study presents novel, fractal-textured solar absorber coatings whose multiscale feature scales effectively match the visible region of the solar spectrum, leading to superb absorptive properties. Single and bimetallic oxide coatings of copper, cobalt and manganese were prepared on Inconel 625 substrates using electrodeposition. The effect of electrodeposition parameters and annealing temperature are systematically studied to optimize the morphology and properties of the oxide coatings. Multiscale textured bimetallic coatings of copper manganese oxide and copper cobalt oxide demonstrate a superior absorptance of 0.985, a remarkably low emittance <0.45 and an exceptional optical efficiency of ∼96 % under Gen3 CSP operating conditions, surpassing previously reported values in the literature by about 5 %. Durability tests confirmed the coatings’ steadfast endurance of mechanical and environmental stress, making them suitable for long-term application in harsh CSP environments.

Framework for strategic deployment of hybrid offshore solar and wind power plants: A case study of India

Renewable energy sources are gaining prominence as eco-friendly and sustainable alternatives to fossil fuels due to their availability and minimal greenhouse gas emissions. Nonetheless, the critical challenge is the availability of renewable resources, which fluctuates with changes in climatic conditions. This limitation poses a consistent challenge to generating base load power if it relies solely on a single type of renewable resource. Addressing this, integrating multiple renewable sources into hybrid systems has emerged as a viable solution. This study presents a framework, integrating Geographic Information Systems (GIS) and Hybrid Multi-Criteria Decision Making (MCDM) techniques to identify plausible locations for the deployment of Hybrid Offshore Solar and Wind Power Plants (HOSWPP) and the developed framework is demonstrated considering Indian Exclusive Economic Zone (EEZ) as a study area. Using the proposed approach, Indian EEZ region is classified into five suitability classes. The effectiveness of regions within each class is further assessed in terms of complementarity measured using Kendall's coefficient. Findings suggested that Kendall's coefficient for highly suitable class is −0.41 indicating the regions identified in this study are the prime locations for installing HOSWPP. A total of twenty optimal sites for HOSWPP deployment, predominantly in the offshore regions of Tamil Nadu and Gujarat. Eighteen sites are located along Kanyakumari to Thisayanvilai in Tamil Nadu, including areas in the Gulf of Mannar and near Valinokkam are found plausible. The rest of the two sites are in the offshore regions of Gujarat. This study provides a strategic roadmap to increase the renewable footprint, contributing to the global transition towards cleaner energy sources.

On-Line Measurement of Tracking Poses of Heliostats in Concentrated Solar Power Plants.

The tracking pose of heliostats directly affects the stability and working efficiency of concentrated solar power (CSP) plants. Due to occlusion, over-exposure, and uneven illumination caused by mirror reflection, traditional image processing algorithms showed poor performances on the detection and segmentation of heliostats, which impede vision-based 3D measurement of tracking poses of heliostats. To tackle this issue, object detection using deep learning neural networks are exploited. An improved neural network based on YOLO-v5 framework has been designed to solve the on-line detection problem of heliostats. The model achieves a recognition accuracy of 99.7% for the test set, outperforming traditional methods significantly. Based on segmented results, the corner points of each heliostat are found out using Hough Transform and line intersection methods. The 3D poses of each heliostat are then solved out based on the image coordinates of specific feature points and the camera model. Experimental and field test results demonstrate the feasibility of this hybrid approach, which provides a low-cost solution for the monitoring and measurement of tracking poses of the heliostats in CSP.

A theoretical thermodynamic investigation on solar-operated combined electric power, heating, and ejector cooling cycle driven by an ORC turbine waste heat, tri-generation cycle system

The current study investigated a solar thermal power plant to simultaneously produce multiple energy outputs, including electric power, process heating, and cooling. This integrated approach aligns with the concept of a tri-generation system, where three different forms of energy are produced from a single source. The study involves a parametric analysis, where the impact of each influencing parameter including, turbine inlet temperature, and turbine inlet pressure is systematically varied with DNI ranging from 600 W/m2 to 1000 W/m2 to understand its effects on both first-law and second-law efficiencies. Further, the exergy analysis shows the irreversibility's occur within the system. Notably, the central receiver and heliostat are identified as major sources of exergy loss. The comparison to a combined power and heating (CPH) cycle without an ORC-ejector indicates that the additional technologies significantly contribute to improving the system's performance, approximately 3.97 % and 2.3 % for both efficiencies, respectively.

Optimization of Turbulent Flow Heat Transfer in a 3D Cubic Shell Heat Exchanger using Non-Mixture Multiphase Nanofluids

The present work investigates the turbulent flow heat transfer in a three-dimensional cubic shell and tube heat exchanger employing non-mixture multiphase nanofluid, specifically SiO₂-H₂O. Computational Fluid Dynamics (CFD) simulations were carried out to study the variation of both shell and tube inlet velocities and optimize the thermal performance of the exchanger. The Reynolds-averaged Navier-Stokes (RANS) solver and the k-epsilon turbulent model were employed, and the results were validated against experimental data and numerical results from the available literature. Key results showed that reducing the tube’s Reynolds number to 75% of the shell’s inlet velocity yields the highest total heat transfer rate of 26,692 Watt, marking an impressive 54% improvement over the baseline conditions. Lowering the tube inlet velocity improved the fluid residence time leading to enhanced heat absorption and higher performance. However, reductions in either fluid Reynolds number below 75% led to diminished heat transfer rates, attributed to reduced turbulent kinetic energy and less effective thermal mixing. These findings provide valuable insights into the role of nanofluids in boosting heat transfer efficiency, offering practical implications for industries seeking to enhance energy conservation and optimize thermal systems such as air conditioning and heating systems, thermal power plants, automotive and aerospace industries, as well as solar thermal power plants.

Predictive control technique for solar photovoltaic power forecasting

An accurate estimation of photovoltaic (PV) power production is crucial for organising and regulating solar PV power plants. The suitable prediction is often affected by the variable nature of solar resources, system location and some internal/external disturbances, such as system effectiveness, climatic factors, etc. This paper develops a novel strategy for applying a predictive control technique to PV power forecasting applications in a smart grid environment. The strategy develops the model predictive control (MPC) under demand response (DR) and some data-driven methods. It has been found that it is challenging to model an MPC for solar power forecasting regardless of its robustness and ability to handle constraints and disturbance. Thus, an optimal quadratic performance index-based MPC scheme is formulated to model a forecasting method for a PV power prediction. This strategy is then compared with some machine learning approaches. The developed strategies solve the problem of accurately estimating the direct current (DC) power yielded from the PV plant in a real-world implementation. The study also considers external disturbances to evaluate the significance of the developed methods for a suitable forecast. Therefore, this study optimally demonstrates that an accurate solar PV DC power prediction can relatively be estimated with an appropriate strategy, such as MPC and MLs, considering the system disturbances. This study also offers promising results for intelligent and real-time energy resource estimation that assist in developing the solar power sector.

Enhancing photovoltaic power prediction using a CNN-LSTM-attention hybrid model with Bayesian hyperparameter optimization

Improving the accuracy of solar power forecasting is crucial to ensure grid stability, optimize solar power plant operations, and enhance grid dispatch efficiency. Although hybrid neural network models can effectively address the complexities of environmental data and power prediction uncertainties, challenges such as labor-intensive parameter adjustments and complex optimization processes persist. Thus, this study proposed a novel approach for solar power prediction using a hybrid model (CNN-LSTM-attention) that combines a convolutional neural network (CNN), long short- term memory (LSTM), and attention mechanisms. The model incorporates Bayesian optimization to refine the parameters and enhance the prediction accuracy. To prepare high-quality training data, the solar power data were first preprocessed, including feature selection, data cleaning, imputation, and smoothing. The processed data were then used to train a hybrid model based on the CNN-LSTM-attention architecture, followed by hyperparameter optimization employing Bayesian methods. The experimental results indicated that within acceptable model training times, the CNN-LSTM-attention model outperformed the LSTM, GRU, CNN-LSTM, CNN-LSTM with autoencoders, and parallel CNN-LSTM attention models. Furthermore, following Bayesian optimization, the optimized model demonstrated significantly reduced prediction errors during periods of data volatility compared to the original model, as evidenced by MRE evaluations. This highlights the clear advantage of the optimized model in forecasting fluctuating data.