Solar Power Generation Research Articles

Optimization and analysis of methanol production from CO2 and solar-driven hydrogen production: A Danish case study

This paper presents a comprehensive techno-economic analysis of the world's first large-scale commercial Power-to-X (PtX) plant in Kassø, Denmark. This innovative facility combines solar photovoltaic (PV), an electrical grid, green hydrogen production, methanol synthesis, and district heating in an integrated system. We conduct an in-depth analysis to explore optimal operating conditions, the potential for sector coupling within the power and heat sectors, and the economic feasibility of the integrated system. In particular, we study different scenarios considering changes in solar PV power generation, electricity market price, CO2 price, and methanol price to explore their impacts on the system performance. Our results show that the electrolyzer's operation, a pivotal component of the system, heavily depends on the electricity spot price as well as the methanol price. Based on the green production of methanol, a premium price of 4800 DKK/t (0.64 €/t) is considered in this study (selling price of 8400 DKK/t). The results show that in case of high electricity prices (2022 spot market) or no premium price for methanol, the optimum annual production of methanol decreases by 58% and 67%, respectively. Furthermore, we observe that the variability of solar power output significantly impacts the plant's performance and economic viability, as it supplies about 30% of the plant's power consumption. Although the results are unique to the Kassø plant, the study's techniques and insights can be applied to other comparable systems around the world. The study also highlights how crucial it is to take market, climate, and technical factors into account when developing and running integrated energy systems.

Optimized energy management of a solar battery microgrid: An economic approach towards voltage stability

As the adoption of renewable energy sources (RESs) continues to surge, and the concept of microgrids (MGs) gains widespread recognition, the need for efficient battery energy storage system (BESS) management within MGs has become increasingly prominent. This paper introduces a novel Energy Management System (EMS) designed for grid-connected MGs, with the primary objectives of enhancing the energy economics of the MG, minimizing BESS degradation, and ensuring that the point of common coupling (PCC) voltage remains within prescribed limits. The proposed EMS utilizes a finite-horizon Model Predictive Control (MPC) framework to seamlessly coordinate BESS operations, encompassing charging, discharging, and reactive power provision. This coordination is done in conjunction with solar power generation, load demand, electricity pricing, and feed-in tariffs. Furthermore, the EMS accounts for uncertainties related to load and solar demand using a Monte Carlo simulation approach. The optimization goal of the proposed EMS is to minimize the expected total operational cost of the MG over a 24-hour prediction horizon. This cost encompasses both economic costs and battery degradation costs. The optimization process adheres to a set of constraints that consider BESS limitations, such as state of charge (SoC) constraints and charging/discharging limits, as well as inverter capacity limits and PCC voltage requirements. Conducting rigorous case studies on the MATLAB Simscape Electrical platform and validating them in real-time using the OPAL-RT simulator demonstrate the effectiveness of the proposed EMS across diverse operational scenarios and pricing models. The findings of these case studies provide valuable insights into the effectiveness of the EMS in real-world MG applications.

Low-carbon scheduling of electricity consumption in wastewater treatment plant by using photovoltaic system

Sewage treatment as a high energy consumption industry, its electricity consumption accounts for 3 % of the total electricity consumption of society. That means significant greenhouse gas emissions. In the context of China's goal of “reaching carbon peak by 2030 and achieving carbon neutrality by 2060”, reducing the energy consumption of wastewater treatment systems has emerged as an important issue in recent years. In this paper, the GPS-X simulation software was employed to conduct a simulation study of a modified Anoxic-Aerobic-Oxic wastewater treatment plant (WWTP) in Wuhan, and the response surface methodology (RSM) was utilized to ascertain the interactive effects of DO, IRF, ERR, and SD on the effluent quality, thereby identifying the operational parameters that minimize energy consumption while maintaining satisfactory effluent quality. Additionally, the PVsyst software was employed to design the solar power generation system of the WWTP and analyze its power generation potential. On this basis, through the coupling of photovoltaic power, electricity load, time-of-use pricing, and the water quality simulation model, and taking the WWTP data in September as a case study, the electricity usage strategies under various illumination conditions were formulated. The aim is to maximize the use of photovoltaic power to reduce the cost and carbon emissions of the WWTP. The results show that the optimal combination of operational parameters, including an external reflux ratio of 0.3, the internal recycle flow of 50,000 m3/d, and the sludge discharge of 448 m3/d, resulted in a reduction in power of 208.5 kW, and after the combination optimization of operational parameters and electricity utilization, the operation cost of the WWTP in September was reduced by 40 % ∼ 60 %, and the carbon emission attributable to electricity was reduced by 30 % ∼ 50 %.

A novel optimal identification of various solar PV cell parameters by using MRDT controller

At present, Renewable Energy Sources (RES) utilization keeps on increasing because of their merits are more availability in the atmosphere, easy energy harvesting, less maintenance expenses, plus more reliability. Here, the solar power generation systems are utilized for supplying the energy to the local consumers. The accurate, and efficient solar power supply to the customers is a very important factor to meet the peak load demand. The accurate power generation of the sunlight system completely depends on its accurate parameters extraction. In this work, a Modified Rao-based Dichotomy Technique (MRAODT) is introduced to identify the actual parameters of the different PV cells which are PWP 201 polycrystalline, plus RTC France. The proposed MRAODT method is compared with the other existing algorithms which are the teaching and learning algorithm, African vultures, plus tuna intelligence algorithm. Finally, from the simulation results, the MRAODT gives superior performance when associated with the other controllers in terms of parameters extraction time, accuracy in the PV cells parameters identification, plus convergence time of the algorithm.

Solar photovoltaic automatic tracking device based on IoT and BDS

Solar photovoltaic automatic tracking device based on IoT and BDS

Surface temperature and power generation efficiency of PV arrays with various row spacings: A full-scale outdoor experimental study

Quantifying the relationship between surface temperature and power generation efficiency of solar photovoltaics (PV) is critical to their practical implementation. Although empirical models have been developed on this, they were mainly based on indoor laboratory tests, ignoring a practically significant arrangement factor. Therefore, a combined outdoor experimental and empirical study on PV array systems considering various row spacings was undertaken through this study. Experimental results indicated that solar irradiance shows a relatively more prominent effect than row spacing and wind speed in outdoor environments. During the practical arrangement, it is only necessary to ensure that there is no shadow between the adjacent rows of PV arrays in practical applications, and the possible cooling effect brought by excessive row spacing may not be pursued. However, this does not mean that row spacing can be ignored when predicting surface temperature and power generation efficiency. Based on the data from our long-term experimental tests, empirical models to predict solar PV's surface temperature and power generation efficiency were developed, considering various row spacings. Comparison showed that the models developed in this study can reduce the mean relative error of the previous model's predictions from 20% to 5%, which proves the necessity of including row spacing in the models. This study's developed models and research outcomes pave the way for future large-scale practical analysis of solar PV arrays.

Renewable Energy Development in Vietnam - Current Situation and Solutions

Vietnam’s renewable energy sector has witnessed notable advancements in recent years, particularly in wind and solar power generation. However, challenges persist, hindering the sector’s full potential. This study evaluates the current status of renewable energy development in Vietnam from 2015 to the present, identifying existing obstacles and proposing effective solutions. Achievements in harnessing wind, solar, biomass, hydropower, and waste-to-energy sources are highlighted. Challenges such as inadequate grid infrastructure, high initial investment costs, complex administrative procedures, and energy losses are addressed. The paper proposes a comprehensive set of solutions, including infrastructure upgrades, financial incentives, institutional reforms, technology transfers, and research promotion. Implementing these solutions synchronously holds the promise of accelerating Vietnam’s renewable energy development, contributing significantly to sustainable development goals. The findings provide insights for policymakers and stakeholders to enhance the efficiency of green energy deployment in Vietnam.

Deep-Reinforcement-Learning-Based Vehicle-to-Grid Operation Strategies for Managing Solar Power Generation Forecast Errors

This study proposes a deep-reinforcement-learning (DRL)-based vehicle-to-grid (V2G) operation strategy that focuses on the dynamic integration of charging station (CS) status to refine solar power generation (SPG) forecasts. To address the variability in solar energy and CS status, this study proposes a novel approach by formulating the V2G operation as a Markov decision process and leveraging DRL to adaptively manage SPG forecast errors. Utilizing real-world data from the Korea Southern Power Corporation, the effectiveness of this strategy in enhancing SPG forecasts is proven using the PyTorch framework. The results demonstrate a significant reduction in the mean squared error by 40% to 56% compared to scenarios without V2G. Our investigation into the effects of blocking probability thresholds and discount factors revealed insights into the optimal V2G system performance, suggesting a balance between immediate operational needs and long-term strategic objectives. The findings highlight the possibility of using DRL-based strategies to achieve more reliable and efficient renewable energy integration in power grids, marking a significant step forward in smart grid optimization.

Artificial intelligence based succored power prediction in stand alone PV system

ABSTRACT Accurate forecasting of solar power generation is crucial for effective management of renewable energy resources. This literature review emphasises the effectiveness of Artificial Neural Networks (ANNs) in predicting solar energy potential. While traditional ANNs with single hidden layers and predefined neuron configurations have shown success in various applications, they are not suitable for predicting nonlinear time-series databases in the context of this research. The limitations of traditional ANNs in predicting solar power potential necessitate the exploration of alternative approaches. Configuring the structure of traditional ANNs for solar power prediction is a time-consuming process that often requires manual or trial-and-error methods. To address this issue, optimisation techniques are incorporated into the ANN model. Various traditional methods have been explored in this research, but none has achieved satisfactory performance. Drawing from experiences with traditional techniques, a novel Evolution of Cub to Predator (ECP) technique is proposed. The investigation results demonstrate that the incorporation of optimisation techniques yields superior performance compared to traditional ANNs. The proposed ECP technique achieves an impressive prediction accuracy of 97.2%, surpassing existing optimisation techniques. These findings highlight the potential of the ECP technique for accurate solar power prediction and its contribution to optimising renewable energy resource management.

Characterization of different darken sand particles and behavior under fluidized and irradiated conditions

The use of solid particles in a fluidized bed with concentrated solar irradiation from the top is a promising technology for the next generation of concentrated solar power (CSP) plants. Sand is an inexpensive and abundant material easy to fluidize, but it has a low absorptivity, which is around 0.5 according to different previous works. This optical property is a key parameter for CSP applications with solid particles. This work presents a novel methodology to induce darkening of the sand surface by inducing solid state diffusion of Mn in SiO2, rendering a stable material resistant to abrasion upon the fluidization process. For this study, two different samples considering different MnCO3:SiO2 weight ratios (1:30 and 1:50) were analyzed. The objective is to compare the two samples and optimize the concentration of MnCO3 to get the desired darkening of the sand and high absorptivity. First, the main properties of the particles were analyzed (particle size, morphology, color and absorptivity). Second, the samples were tested in a lab-scale fluidized bed directly irradiated from the top with a beam-down 4kW Xe lamp. Both samples were tested under three different fluidized velocities: 1.5, 2.0, and 2.5 times the minimum fluidization velocity (Umf). In both cases, there is a significant increase in the maximum temperature reached during the process, with temperatures exceeding 260 °C. This is clearly higher than the case of raw sand, which reaches 230 °C under the same conditions. Furthermore, these values exceed the highest temperature reached by SiC in the same facility, which is 250 °C, and it is considered as one of best tested materials for CSP applications.

Preface

Available on the latest research progress of clean energy and power generation technology, this paper volume gathers a bunch of papers collected from 2023 8th International Conference on Clean Energy and Power Generation Technology (CEPGT 2023), held via virtual form in Zhuhai, China during December 15th to 17th, 2023.The Conference was attended by about 100 delegates around the globe. In the opening ceremony speech, all academicians, experts and scholars were welcomed to attend the Conference and to share insightful speeches and presentations. Then in the keynote speech part, Prof. Marc A. Rosen (Ontario Tech University, Canada), Prof. Haiping Yang (Huazhong University of Science and Technology, China), Prof. Qinmin Yang (Zhejiang University, China), Prof. Qiang Lu (North China Electric Power University, China) and other professors made wonderful academic reports on international frontier hot spots. Apart from the keynote speeches, oral and poster presentation parts were also held and displayed by various scholars, leading to a warm atmosphere of academic discussion.We received a number of submissions from the Conference. After rigorous review and review rebuttal process by related top experts, various excellent papers were accepted and included in this paper volume. These papers cover many topics of the Conference theme, including Energy Security and Clean Use, Energy Conservation and Emission Reduction, Biomass Energy Engineering, Hydroelectric Power Generation, Solar Photovoltaic Power Generation System, etc. The works of this volume can promote the development of clean energy and power generation technology, and thereby enhance scientific information interchange among scholars from top universities, research centers and high-tech enterprises working all around the world.Featuring the most cutting-edge research directions and achievements related to clean energy and power generation technology, this Conference provided the most comprehensive research in related fields and a more comprehensive understanding of the latest results of cross researches in the fields. Meanwhile, it also helped researchers and engineers understand the research frontier, as well as discover the solutions to potential problems.We would like to acknowledge the authors for their contributions and the reviewers for their time to review the submissions. We are thankful to all the committee members and advisors of this volume. Finally, our appreciation also goes to the editors and members of Journal of Physics: Conference Series for their efforts in publishing this volume.We hope that it will serve as a reference for researchers and practitioners in academia and industry in the areas related to clean energy and power generation technology.The Committee of CEPGT 2023List of Committee Member is available in this pdf.

Design of maximum power point energy storage and inverter for photovoltaic power generation

With the increasing tension of fossil energy in the world and the damage to the environment caused by the use of fossil energy, new energy led by solar energy has been paid much more attention by countries around the world. If the efficiency of solar energy is improved, it will bring breakthrough changes to the world’s energy structure. Based on the related applications of solar photovoltaic power generation, this paper designs an independent photovoltaic power generation system with energy storage device. In the form of DC/DC conversion, the system uses the maximum power point tracking technology of photovoltaic cells to realize the efficient use of solar energy during the charging process. The system inverts the DC voltage after the storage, realizing the transformation of DC voltage to 220 V AC voltage, to realize the utilization of energy in various aspects.

A Parameter Optimization Design and Numerical Calculation Method for Circular Heliostat Field

Solar energy is a low-carbon, eco-friendly, clean energy source that is easily converted into heat and electricity, making the study of solar thermal power generation technology of great significance and with broad application prospects. To improve the performance of solar power plants, it is necessary to reasonably arrange the light reflectors. Both denser heliostats are needed to ensure enough light can be concentrated into the solar collector, thereby improving the power of the power station; at the same time, waste of material must be prevented, striving to increase output thermal power per unit mirror area. This paper is based on the background of the 2023 CUMCM Problem A. Firstly, it analyzes parameters such as the optical efficiency of the original heliostat field, the annual average output power, and the annual average thermal power output per unit mirror area. Secondly, based on the data provided in the appendix, it analyzes the distribution pattern of the heliostat field and establishes an optimization design model for the heliostat field. Next, the optimization design model of the heliostat field is solved using discrete numerical calculation methods. Finally, according to the results of the model solution, the placement positions of the heliostats, the size of the mirrors, and the installation height are adjusted to improve the annual average thermal power output per unit mirror area, thereby achieving an optimized design of the heliostat field.

Fault diagnosis study of ship solar photovoltaic power generation system

In this paper, a fault diagnosis method of ship photovoltaic (PV) power generation system based on a convolutional neural network (CNN) is proposed to ensure the smooth navigation of ships. Firstly, to understand the normal operation and fault state of the generation system, a simulation model of the photovoltaic power generation system is constructed using MATLAB / Simulink. Then, the model is simulated to extract the fault data under different fault conditions, and the eigenvalue extraction of the fault data is carried out by using the fast Fourier transform method. Following that, a fault diagnosis model is built using the convolutional neural network and trained accordingly. The final diagnostic results show that the established simulation model and convolutional neural network can provide support for the fault diagnosis of ship photovoltaic power generation system, and can autonomously detect the type of faults and locate the location of the faults after the faults occur in the generation system, to ensure the stable and safe operation of ship power generation system.

Design and hardware verification of photovoltaic converter based on adaptive P&O with snubber and BJT circuits

The efficient utilization of renewable energy is a key technology area of high domestic and international concern, and the research and development of DC–DC photovoltaic converters with high conversion efficiency is of great significance for improving performance and reducing the cost of solar power generation systems. In order to improve the conversion efficiency of solar energy, this paper proposes the design of a high-efficiency photovoltaic DC–DC converter with a non-isolated DC–DC converter as the object of the study. Solar power conversion is accomplished by designing a simple and reliable snubber circuit and a triode auxiliary circuit and tracking the maximum power point by combining the perturbation observation method with variable step size. The snubber circuit can effectively suppress the problems of pulse spikes and oscillations, and the triode auxiliary circuit prevents the reverse current and improves the switching speed, which reduces the switching loss and combines with the perturbation observation method with variable step size for fast and stable tracking of the maximum power point. In order to verify the feasibility of the converter, a 600 W prototype is designed. The experimental results show that using the snubber circuit reduces the pulse spike by 4.2 V and the overshoot is reduced by 4.3%. The maximum conversion efficiency is increased by 0.88% with the use of the transistor-assisted circuit, and the tracking efficiency of the MPPT is still stable under cloudy conditions. The maximum conversion efficiency of the prototype is finally measured to be up to 98.12%.

Advancing solar PV panel power prediction: A comparative machine learning approach in fluctuating environmental conditions

Solar photovoltaic (PV) panels play a crucial role in sustainable energy generation, yet their power output often faces uncertainties due to dynamic weather conditions. In this study, a comparative machine learning approach is introduced, utilizing multivariate regression (MR), support vector machine regression (SVMR), and Gaussian regression (GR) techniques for precise solar PV panel power prediction. The investigation into the impact of environmental factors—solar radiation, ambient temperature, and relative humidity—on PV panel output reveals the superior predictive capabilities of SVMR models. With a mean squared error (MSE) of 0.038, a mean absolute error (MAE) of 0.17, and an R2 value of 0.99, SVMR outperforms GR and MR models. Conversely, Gaussian regression demonstrates comparatively weaker performance, yielding an R2 of 0.88, an MSE of 0.49, and an MAE of 0.63.This research underscores the reliability and enhanced accuracy of the proposed SVMR model in forecasting solar PV panel output. The outcomes presented herein carry significant implications for promoting the widespread adoption of PV panels in electricity generation, particularly in challenging environmental conditions. The findings offer valuable insights into optimizing solar PV deployment, ultimately contributing to the expansion of solar power generation in the national energy landscape. Moreover, the comparative analysis provides insights into how anticipated PV power generation can adapt to varying weather conditions, encompassing factors such as temperature, humidity, and solar radiation.

Corrosion behaviour of FeCoNiCrAl high-entropy alloy in molten NaCl–KCl–MgCl2 chloride salt

Molten NaCl–KCl–MgCl2 is becoming a potential heat transfer fluid (HTF) and thermal energy storage (TES) material in the third generation of concentrated solar energy power (CSP) stations due to its excellent heat storage-capacity and thermal stability. However, the strong corrosion of the molten chloride salt at high temperature limits its industrial application. In this study, the corrosion behaviour of FeCoNiCrAl HEA with BCC + FCC phase structure in molten NaCl–KCl–MgCl2 (24.5–20.55–54.95 wt-%) at 650 °C under argon was investigated by a combination of weight loss and electrochemical methods. The results show that, the corrosion of FeCoNiCrAl HEA in molten chloride salt mainly manifests as the selective dissolution of Fe and Cr. With the further increase of immersion time, oxides gradually crack and slightly flake, resulting in large weight loss of FeCoNiCrAl HEA.

Analysis of the Problems and Reasons for the Development of Suzhou Hydrogen Energy Industry and Industrial Policies

In recent years, although there have been some policy plans related to hydrogen energy development in China, a complete fiscal policy system has not yet been established. In policies related to hydrogen energy, we cannot keep up with the times. Only some policies are specifically targeted at solar photovoltaic and wind power generation, and some policies are even temporary. These fiscal policies are severely lagging and lack uniformity, which not only weakens the overall effect of the money supply, but also to some extent hinders the healthy and rapid development of the hydrogen energy industry. In addition, the national development plan, including national and local plans, cannot be well connected, resulting in some policies not forming strong support for hydrogen energy. In these industrial plans, production capacity targets account for the majority, but there is a lack of refinement of utilization targets and little mention of the development balance of the industrial chain.

Solar Power Generation System at Household Scale

Solar power plants are renewable energy systems that utilize sunlight as a power source to generate electricity. The conversion of light energy into electrical energy is achieved through the photovoltaic effect. The process of converting solar energy into electrical energy is made possible by photovoltaic solar panels, which convert solar light energy into electricity. This technology began to be widely known and began to be widely used. Its use is widespread, ranging from large factories, office buildings, commercial buildings, shopping centers, hospitals, hotels, and households The purpose of this study is to know and analyze household-scale solar power generation systems. The method used in this study is a literature review, data used in the form of articles in electronic databases such as Google Scholar, in the period 2006 to 2023. The results showed that solar power plants (PLTS) are the right choice to overcome the energy crisis in Indonesia. Solar Power Plant Energy (PLTS) is abundant sunlight. Solar Power Plant System (PLTS) at the household level using Solar Home System (SHS) which is increasingly adopted in Indonesia (especially in households). The implication in this study is to provide a deeper understanding of household-scale solar systems, including the associated advantages and challenges. This can help individuals, institutions, and governments make better decisions regarding the use of renewable energy.

Economic indicators evaluation to study the feasibility of a solar agriculture farm: A case study

The 209 kWp solar agricultural farm at Dayalbagh Educational Institute's Dairy Campus in Agra, India, is the subject of this economic analysis. This study analyzes economic indicators relevant to agriculture food production, such as gross financial margins, farm profits, and cost-benefit ratios. Other measured variables include NPV (net present value), PB (payback period), and LCOE (levelized cost of electricity) of solar power generation to determine the economic viability of this agrivoltaics (production of solar energy and agriculture on the same land) APV project. Farm profit and gross financial margins both are positive values of 161, 907 INR (Indian Rupees) and 316, 907 INR, respectively. The cost-benefit ratio was 1.5.The economic factors of solar energy generation were studied for two systems: surface-mounted or ground-mounted solar systems and an elevated solar system with agriculture production on the same ground (APV or agrivoltaics). Both systems had the same production capacity. Two different hypothetical situations were used for each system. In the first situation (Case Study A), the assumption was that solar power performance would remain constant during operation. In the second situation (Case Study B), the assumption was that annual performance would be reduced by 0.5 %.The results show that APV is better than the surface-mounted system because there is no significant difference between the payback periods of APV and surface-mounted systems; the NPV of APV is greater than the surface-mounted solar system for both the study case A and case B and the LCOE of APV is 55 % less than the LCOE of the surface-mounted solar system.