Abstract

In recent years, floating photovoltaic (FPV) systems have emerged as a promising technology for generating renewable energy using the surface of water bodies such as reservoirs, lakes, and oceans. FPV systems offer several advantages over traditional land-based solar arrays, including increased land-use efficiency, reduced water evaporation, and improved cooling and maintenance. However, like all solar power systems, FPVs are subject to variability and intermittency due to changes in weather, seasons, and time of day. The environmental impact is discussed along with the deployment consideration and the feasibility for a better understanding of the system. Challenges associated with this are addressed by progressed research suggesting the integration of FPVs with various energy storage and hybrid systems. The most promising areas researched in this paper look at hybrid FPV hydropower plants (HPP), in parts of the world experiencing droughts HPP is not working to its optimum capacity. A review of available literature has been conducted on the topic of offshore and onshore floating solar electricity generation using floating solar photovoltaics to identify the challenges and opportunities presented. This work looks at a variety of other hybrid FPV energy sources with varying technology readiness levels. This paper concludes with the possibility of integrating different renewable technologies with existing FPVs and highlights the boons of doing so with some examples. Ultimately, current as well as future perspectives have been provided which consolidate the current research being done and give recommendations for future research work.

Introduction

One of the biggest challenges that is faced by the world is global warming due to which both humans and the planet are suffering as a whole, this is directly linked to the burning of the fossil fuels that we rely on to facilitate our daily life. The Intergovernmental Panel on Climate Change (IPCC) set out the impacts of global warming 1.5 °C above preindustrial levels [1]. We are not currently on course to limit 21st-century global warming to under 1.5 °C or even 2 °C [2]. Another key issue is the lack of clean electrification for a vast population which is hampering the development of the planet in tackling the climate change issue as many rely on the burning of fossil fuel for various daily requirements from generating electricity to cooking. It has been estimated that about 675 million people are still forced to live in the dark most of them belong to sub-Saharan Africa according to 2021 data. Though there has been an increase in the rate of access to electricity from 87% in 2015 to 91% in 2019, this has provided electricity to nearly 800 million people [3]. This is where solar PV can play a substantial role, solar PV has the benefit of being a renewable energy source, producing electricity from solar irradiance without any greenhouse emission [4].

However, there are challenges that must be addressed in order to fully realize the potential of solar energy and traditional photovoltaics [5]. These challenges include land usage, intermittency, storage, and integration into existing energy grids. One promising and upcoming alternative to traditional land-based photovoltaics is Floating Photovoltaics (FPV) or flotavoltaics [6]. The majority of renewable energy sources, such as biomass, solar, and others, take considerable footprint areas to generate electricity on a larger scale, which restricts the use of land for agriculture [[7], [8], [9]]. This sparked the discussion over whether land should be used for food production or energy production [10,11], encouraging research into offshore renewable technologies [12], and led to the creation of the floating photovoltaic (PV) array concept for the production of commercial electricity [13]. FPV technology is a concept in which solar panels are placed on platforms that float on water bodies such as natural lakes, man-made reservoirs, and the seas and oceans [14]. Fig. 1 shows a typical standalone floating photovoltaic system with all the components including an inverter, pontoons, solar panels, and cables connected to the grid.

Conclusion

This review article has examined the current state of research on the integration of floating photovoltaics with different storage and hybrid systems, including batteries, pumped hydro storage, compressed air energy storage, hydrogen storage and mixed energy storage options as well as the hybrid systems of FPV wind, FPV aquaculture, and FPV hydrogen production. The findings suggest that such systems have the potential to significantly increase the efficiency and reliability of renewable energy generation, as well as provide additional flexibility in managing electricity supply and demand.

FPV has many benefits over ground-mounted such as reduced land costs, increased efficiency, reduced soiling, reduced shading, reduced evaporation, and lower visual impact. Hybrid FPV has all of these benefits while being able to reduce its lifetime costs when paired with another technology a reduction in O&M costs, grid infrastructure and cable pooling. Overall, hybrid FPV has all the benefits of FPV plus reduced costs, while enhancing another technology. While FPV is set to rise this will initially be in land first with offshore following, the priority should be covering HPP first due to its complementary nature and benefits for both technologies. Countries, which have HPP reservoirs, should be looking into all the benefits of combining FPV with it. As FPV becomes more common it will be seen moving offshore, as the offshore wind increases it creates the opportunity for FPV to become a hybrid system.

Abstract

The fast transition to the electrification of the energy system, combined with an exponential growth of the market share of electric vehicles, is leading to a tight interrelation between electric energy production and transportation, two prominent sectors in fossil fuels consumption and greenhouse gas emissions. Accelerating this process, the management of electric fluxes, aiming at optimizing production and demand coupling, plays a crucial role in reaching the net-zero emission target. The proposed software platform is designed to optimally manage the energy fluxes for a solar powered parking lot, serving a fleet of electric vehicles; the real-time knowledge of energy production and demand, in conjunction with forecasted power generation, allows the maximization of renewable energy self-consumption, thus reducing the exchange with the external grid. The software platform can work either in design mode, allowing the dimensioning of the various parking lot components, or in real-time mode managing instantaneously the energy balance. As a case study, it is tested on the 2019 parking lot mobility data of a research center, assuming a complete transformation of the then existing fleet of employees’ cars to electric vehicles. A comparison of the resulting energy flows with those projected by an established commercial tool is performed, as well as a preliminary economic evaluation. Both consistency of the simulation results and favorable economics validate the presented smart charging algorithm and Internet of Things platform for the real-time energy management of a solar parking lot.

Introduction

The steep growth of anthropogenic greenhouse gas (GHG) emissions related to the use of fossil resources requires fast and challenging solutions; transportation and electricity production sectors are responsible for around two thirds of global CO2 emissions. According to the numbers published by the U.S. Energy Information Administration (EIA) in its monthly energy review of August 2022 [1], the total consumption of primary energy in the U.S.A. in the year 2021 was 28.5 Million terawatt-hours (TWh) or 2.4546 Billion tonnes of oil equivalent (toe), 7.9 Million TWh (0.6782 Billion toe) of which are used by the transportation sector and 10.8 Million TWh (0.9267 Billion toe) by the electric power sector. 94% of the total energy used for transportation came from petroleum and natural gas and 6% from biofuels and electricity [2]. In 2021, according to EIA estimates [3], cars, light trucks, and motorcycles accounted for the largest shares of total U.S. transportation sector energy consumption, with light-duty vehicles (cars, small trucks, vans, sport utility vehicles, and motorcycles) accounting for 54.2%, commercial and freight trucks 4.5%, jets, planes, and other aircraft 8.7%, boat, ships, and other watercraft 4.6%, trains and buses 2.6%, the military sector 2.0%, pipelines 2.8%, lubricants 0.5%. Globally, carbon dioxide emissions associated with transportation accounted in 2021 for 7620 Mt; 600 Mt less than pre pandemic level by 2019 [4].

Since the onset of the industrial revolution, ever increasing amounts of GHG have been released into the atmosphere. While the energy mix and corresponding supply chains shifted from coal to oil and more recently towards natural gas, the prosperity of humankind still relies on fossil fuels and energy conversion systems based on fuels combustion. Even today, transportation and electricity production are heavily reliant on CO2 emitting technologies. But there is a silver lining, as technological improvements coupled with a strong cost reduction enabled the fast growth of the total installed energy capacity of renewable energy sources for electricity production (RES-E). Together with powerful battery energy storage systems (BESSs), they will impose drastic changes in these two historically conservative sectors. Today, the fast growth of electric vehicles (EVs) related technologies, as well as their forecasted market penetration, reaching the record of 6.6 million vehicles sold in 2021 [5], generates new scenarios impacting the electric energy production and transportation market. Based on existing climate-focused policy pledges and announcements, the International Energy Agency predicts for 2030 EVs to represent more than 30% of vehicles sold globally across all modes (excluding two- and three-wheelers) and the global electricity demand for EVs to reach 1100 TWh; then about 4% of the total final electricity demand [5].

Conclusions

This paper presented the SPEM IoT platform, capable of simulating, designing and real time managing the smart charging of EV fleets in a RES-E driven parking lot.

A deterministic rule-based algorithm was introduced for the fine-grained management of the energy fluxes between EVs and the different electric components of a solar parking lot. Since the algorithm does not utilize any assumption on the future mobility and energy demand it can handle unforeseen changes avoiding critical situations.

As it was shown, intra-day deferral serves to shift EV charging demand towards the time of maximum production, guided by MPC, constantly analyzing the momentary situation of 1) intermittent solar production, 2) stored energy and energy storage availability, 3) charging station occupancy and 4) solar production forecast.

An extensive case study was conducted, where the design mode of SPEM was applied to the historic situation of an Italian Research Center in 2019, assuming the substitution of all employee’s cars by EVs. As a benchmark for the validation of SPEM, HOMER® Grid, a commercial software for the modeling, design and simulation of electrical infrastructure, was used.

Abstract

     This work suggests an optimal strategy to sort and recycle plastic waste as a renewable energy resource with maximizing economic feasibility and mitigating environmental pollution. To derive the optimal sorting and recycling strategies of plastic waste, a novel optimization model is developed; it calculates the overall profit by subtracting the profit of recycling plastic from the total annualized cost. Then the model is used to identify the optimal strategy to sort and recycle plastic waste as a renewable energy resource in mixed-integer nonlinear programming that maximizes the overall profit. In the derived optimal sorting and recycling strategy, high-density polyethylene is recycled to produce downgrade plastic; low-density polyethylene, polypropylene, and polystyrene are recycled as pyrolysis oil; and polyethylene terephthalate is recycled to produce refuse plastic fuel. The derived optimal case can significantly increase the overall profit by about 3,137% (i.e., 35 US$/1 kg of recycled plastic), and 492% (i.e., 29 US$/1 kg of recycled plastic) compared to conventional case in South Korea and Japan respectively.

Introduction

     The demand for plastics has rapidly increased in many industries because of their versatility, and easy production. Consequently, plastic waste is discharged in massive quantities; estimates of plastic waste discharged into rivers, lakes, and seas is 9–23 million t per year globally (Borrelle et al., 2020; Masuda et al., 2001). Thus, the importance of plastic waste recycling is increasing (Shah et al., 2015; Zhang et al., 2020). Plastic waste is a mixture of different types, so it must be sorted into before it is recycled (Gundupalli et al., 2017; Hearn and Ballard, 2005; Lim and Cho, 2003). Economically-viable sorting and recycling of plastic will yield a cheap and abundant source of valuable chemicals and renewable energy (Gadaleta et al., 2020). However, current systems to sort and recycle plastic waste are not optimized, so their costs are high. Thus, only 27.2 wt% of plastic waste is recycled, whereas 36.4 wt% is landfilled, and 36.4 wt% is incinerated (Vieira et al., 2022). Therefore, the soil and air pollution according to the landfilled plastic and the significant amount of SOx, NOx, and CO2 emitted in the incineration of waste plastic is serious. The types of recycled plastic have different uses depending on the product (e.g., downgrade plastic, pyrolysis oil, and refuse plastic fuel) that is produced (Kim et al., 2020; Krauklis et al., 2021; Shaha et al., 2020; Yaqoob et al., 2021). Methods to recycle plastic waste are classified into material, chemical, and thermal types (Zhuo and Levendis, 2014). They have very different capital and operating costs according to the throughput of the plastic waste of each method (Huang et al., 2002). Therefore, to improve plastic waste recycling, it is crucial to derive optimal sorting and recycling strategy for plastic waste that indicate which plastics will be sorted to be recycled and how the plastic will be recycled according to each recycling method considering economic feasibility.

Conclusion

     This study found an optimal strategy to sort and recycle plastic waste as a renewable energy resource for maximizing economic feasibility and mitigation of environmental pollution caused by landfilled plastic and air pollutants according to incineration of plastic. This study makes two major contributions to the literature. First, to the best of the author’s knowledge, it is the first attempt to optimize the plastic waste sorting and recycling system by deriving an optimal sorting and recycling strategy to improve the recycling of for plastic waste as a renewable energy resource maximizing economic feasibility and mitigating of environmental pollution. Also, the results allow us to increase the recycling of plastic waste by maximizing the overall profit of the plastic waste sorting and recycling system. In the derived optimal sorting and recycling strategy, HDPE is recycled to produce downgrade plastic; LDPE, PP and PS are recycled as pyrolysis oil; and PET is recycled to produce refuse plastic fuel. The derived optimal case can increase overall profit by ∼ 3,137% compared to CCS, and 492% compared to CCJ, and also emission of air pollutants. Thus, this study provides valuable insight into the many recycling industries of waste plastics to achieve clean production, cost-effectiveness, and environmental protection. In many literatures, strategies such as operating condition optimization were proposed to reduce the cost of each sorting method. Thus, in further study, it is crucial to consider the detailed proposed strategies in deriving the optimal solution to maximize economic feasibility.

Abstract

     Economic development and the comfort-loving nature of human beings in recent years have resulted in increased energy demand. Since energy resources are scarce and should be preserved for future generations, optimizing energy systems is ideal. Still, due to the complexity of integrated energy systems, such a feat is by no means easy. Here is where computer-aided decision-making can be very game-changing in determining the optimum point for supply and demand. The concept of artificial intelligence (AI) and machine learning (ML) was born in the twentieth century to enable computers to simulate humans’ learning and decision-making capabilities. Since then, data mining and artificial intelligence have become increasingly essential areas in many different research fields. Naturally, the energy section is one area where artificial intelligence and machine learning can be very beneficial. This paper uses the VOSviewer software to investigate and review the usage of artificial intelligence and machine learning in the energy field and proposes promising yet neglected or unexplored areas in which these concepts can be used. To achieve this, the 2000 most recent papers in addition to the 2000 most cited ones in different energy-related keywords were studied and their relationship to AI- and ML-related keywords was visualized. The results revealed different research trends in recent years from the basic to more cutting-edge topics and revealed many promising areas that are yet to be explored. Results also showed that from the commercial aspect, patents submitted for artificial intelligence and machine learning in energy-related areas had a sharp increase.

Introduction

     Economic development and increasing welfare are always entangled with the rising consumption of energy resources. Increasing energy generation as the default answer to how to cope with this additive energy consumption may not be the best answer. From an energy justice perspective, it’s not acceptable to deplete energy reservoirs that belong to the next generations [1]. Although retrofitting existing equipment and minimizing energy usage by combining (or cascading) multiple systems for increased efficiency may be an answer to this challenge, increasing efficiency may not be the ideal solution. It’s valid that efficiency leads to lower consumption, but it should be noted that many of these systems might also be able to be turned off or operate on lower loads. So, a more dynamic approach may be a better solution.

     Artificial intelligence and machine learning are relatively new concepts in energy that can be promising tools to operate systems by implementing past and predicted futures to increase the effectiveness of systems.

Conclusion

     The concept of artificial intelligence (AI) and machine learning (ML) is for computers to simulate humans’ learning and decision-making capabilities. With advances in computing systems, AI and ML have become increasingly important areas in many different branches of science and industry. The energy section is also one of the areas that can benefit from AI and ML. To investigate the current standing point of AI and ML in energy-related areas, we used VOSviewer software to investigate and review the relatively new usage of AI and ML in the energy field and propose promising or neglected areas in which these concepts can be used.

     The results showed that from 2000 AI gains an increasing focus, especially after 2014 when the number of articles in ML skyrocketed. And in 6 years become 10 times more compared to 2014. Although there have been many papers in different fields of energy that introduced new usages of AI and ML in that section, due to the vastness of energy usage and respected fields of it, obviously, in no way the available articles could cover all these areas.

Abstract

Keywords

Introduction

Smart electric pole

Sustainable power unit

Conclusion

Data Availability Statement

Declaration of Competing Interest

References

Abstract

With the effective result of IoT architecture in all research areas, we propose IoT framework based energy efficient smart and intelligent street road lighting system that consist of IoT sensor bases smart electric pole with controller for tuning LED lamps. In our work we replace traditional metal halide lamps with mesophic design LED lamps based on human eye sensitivity and it provides significant saving of energy too. Based on the sensor unit information about traffic flow and presence and absence of occupant smart and intelligent decision making module compute intensity level that generate different width pulse using PWM dimming system which triggers LED power switch via DALI controller mounted inside LED Light Controller. For efficient use of sustainable energy resources we use sustainable power systems that consist of PV solar panel units, Battery storage systems and smart electric power grids. For the charging battery system we use MPPT based dynamic battery charging algorithm. From experiment and simulated result, we observe that proposed energy efficient Smart Street lighting saves considerable amount of energy during peak and off peak hours not only at highway roads but at residential and sub-urban pedestrian areas also.

Introduction

Sustainable energy is the method of utilizing energy in an efficient way and at the same time satisfying the needs of the modern world. Sustainable energy can be used in economic, social and environmental issues. An efficient sustainable energy management system can be achieved by using renewable resources in all the possible ways, using sustainable energy resources, using alternative energy resources, making use of smart systems, implementing IoT systems, implementing green technologies. The smart system is the technique, which is used to embed the modern technology into functions already used by the world. In terms of smart system, the information about utility system and traffic flow of the large cities is extracted with the help of sensor also it analysis the pattern of information for prediction. Then, the ability to transfer data over a network without the help of human-to-computer or human-to-human interaction is called IoT or internet of things system and the green technology is used to explain the functions of science and technology to develop products as environmental friendly. Some of the examples of green technology is water purification, conserve natural resources, clan energy creation and waste recycle management. It is also used in digital and mechanical machines, objects and in correlated computing devices.

Abstract

Keywords

1. Introduction

2. Related work

3. Application research of Internet of Things in solar photovoltaic power station monitoring and management system

4. Development of integrated computer-aided design system for solar photovoltaic buildings

5. Conclusion

Declaration of Competing Interest

References

Abstract

At the same time of economic development, the increasing scarcity of energy has had a great impact on people’s development. People’s production and life demand for electricity is also increasing rapidly, and solar power technology has received more and more attention. As a new energy source, solar energy has the advantages of environmental protection and sustainability, and it has no regional restrictions, can be used on-site, and designed to scale. Solar power generation is an important way to use solar energy. As the main component of the grid-connected power generation system, solar grid-connected inverters complete the tracking problem of the maximum power point in the photovoltaic array and transmit electrical energy to the grid through a set of control algorithms. The electrical energy is transmitted to the grid through the inverter, consistent with solar energy. In order to solve the problems of low integration, low energy efficiency, low reliability, high power consumption and lack of effective monitoring measures for solar energy devices. This article starts with the design of the solar cell integrated system, and through detailed analysis of the solar production system and building integrated planning, establishes the shadow radiant energy model of the solar cell system building electrical and solar cell system based on the Internet of Things, and designs an object-based Networked comprehensive auxiliary platform.

1. Introduction

Today’s society is an information society centered on the Internet. Digitization, informationization and world economics are the hallmarks of the current era. With the continuous rapid development of information technology, the process of world economics has been continuously strengthened. The use of computer technology, communication data and the Internet to complete e-logistics and Internet commerce has been widely used in society. The continuous improvement of information technology and information storage is just the prelude to the transformation of the IT industry. The cost of information dissemination is getting less and less, and it is bound to usher in the era of the Internet of Things dominating. As a post-IP product, the Internet of Things is developing rapidly and will become popular in the future. This is a major plan to realize the intelligentization of the world.

Abstract

INTRODUCTION

COMPUTATIONAL METHODS

DPD Method

Drift-diffusion Model

RESULTS AND DISCUSSION

One-step Quench

Two-step Quench

Comparison with Existing Experimental Observations

CONCLUSIONS

REFERENCES

Abstract

     The mixing morphology control plays a crucial role in photovoltaic power generation, yet this specific effect on device performances remains elusive. Here, we employed computational approaches to delineate the photovoltaic properties of layered heterojunction polymer solar cells with tunable mixing morphologies. One-step quench and two-step quench strategies were proposed to adjust the mixing morphology by thermodynamic and kinetic effects. The computation for the one-step quench revealed that modulating interfacial widths and interfacial roughness could significantly promote the photovoltaic performance of layered heterojunction polymer solar cells. The two-step quench can provide a buffer at a lower temperature before the kinetic quenching, leading to the formation of small-length-scale islands connected to the interface and a further increase in photovoltaic performance. Our discoveries are supported by recent experimental evidence and are anticipated to guide the design of photovoltaic materials with optimal performance.

Introduction

     Polymer solar cells (PSCs) are a kind of potential clean-energy technology, which holds promise for manufacturing lightweight and highly flexible devices such as portable electronic products and building-integrated photovoltaics.[1−5] Although the power conversion efficiency was dramatically improved recently, it has not yet broken through the requirements of commercial markets due to the limitation on the mechanism of charge carrier generation and transport.[6,7] To date, a considerable effort has been devoted to creating novel π-conjugated polymers to improve photovoltaic performance. In contrast, understanding the morphology effect on PSC performance and formulating basic rules that guide morphology optimization need to be further enhanced.

     Optimizing the morphology is indispensable for the successful preparation of PSCs with outstanding performance.[8−12] The PSC performance can be quantitatively correlated with phase purity and Flory-Huggins parameters.[13] Insufficient phase separation in PSCs can lead to performance deteriorations. However, a larger repulsion between donors and acceptors can lead to over-purification of mixed domains and decreased PSC performance.[14] For example, Ade et al. observed that the average power conversion efficiency shows a substantial drop as the composition of the amorphous mixed domains is below the percolation threshold.[15] Ye et al. recently found that such a problem can be resolved by kinetically quenching the mixed domains to an optimal composition close to the percolation threshold.[16] Despite this, fundamental guidelines are still required to optimize PSC performances with optimal mixing morphologies. Combining thermodynamic effects and kinetic controls can assist the design of heterojunctions with varied mixing morphologies.[16] The thermodynamics can drive the phase separation of donors and acceptors in PSCs, and the quench by kinetic control can “lock-in” instantaneous phase-separated morphologies. Recent attention has been paid to the stability of such mixing morphologies.[17−20] However, little is known about the kinetic route to control mixing morphologies and the influence of mixing morphologies on PSC performances. Quantifying the impact of mixing morphology on device performance by developing kinetic control rules is the key to optimizing the heterojunction structure and promoting power conversion efficiency.

Conclusion

     The DPD method, coupled with the drift-diffusion model, was employed to study the mixing morphology effect on the photovoltaic performance of layered heterojunction PSCs. We performed an in silico layer inter-diffusion experiment and designed two quench approaches to regulating the mixing morphology of layered heterojunctions, that is, one-step quench and two-step quench. In the one-step quench, the layered heterojunction with intermediate interfacial width exhibits an optimal photovoltaic performance. We further enhanced the power conversion efficiency of the layered heterojunction by modulating the mixing morphology with a two-step quench method. We discovered that the interfacial width, interfacial roughness, and small-length-scale island structures formed within acceptor- and donor-rich domains combinedly affect the power conversion efficiency. Our work delineates the effect of quenching processes on photovoltaic performance, which could be beneficial to the design and quantitative optimization of active layers.

Abstract

     The paper studies the performance of Offshore Wind Turbines (OWTs) founded on Suction Bucket Jackets (SBJs) in clay under combined wind and seismic loading. A detailed 3D FE model of the soil–foundation–structure (SFS) system is developed and used as a benchmark to assess the efficiency of an enhanced Winkler-based “Caisson-on-Winkler-Soil” (CWS) model, where the soil is replaced by nonlinear hysteretic elements. The proposed CWS model captures residual deformations and hysteresis and offers physical coupling between vertical and moment loading. It allows excellent prediction of the  failure envelope in the most relevant first quadrant of  space, where the loads act in the same direction. Despite its successful application for the simulation of inertial loading, the CWS model fails to reproduce the dual shearing mechanism that develops at the caisson shaft during shaking, stemming from the combination of kinematic soil shearing due to the vertically propagating shear waves, and shearing due to the superstructure inertial response, thus underpredicting the co-seismic caisson settlements. For the prediction of the latter, the research utilizes spectrum compatible input motions and 3D FE models of varying geometric and material properties to derive linear regression equations that correlate the co-seismic dimensionless settlement of caissons ) with characteristic dimensional variables of the problem under investigation and the Arias Intensity  of the surface ground motion. As a final step, the paper proposes a hybrid method for performance-based assessment of SBJ OWTs. The proposed method employs the simplified CWS model to calculate the VHM loads and approximately estimate horizontal displacements and rotations at the jacket legs, followed by a preliminary assessment of caisson settlements using the correlations of  with  on the basis of spectrum-compatible input motions.

Introduction

     Following the ambitious energy targets set by countries worldwide, the offshore wind sector has seen impressive growth over the last decade, transforming from niche technology to a global industry. As of 2019, Europe alone has 22 GW of installed offshore wind capacity – enough to cover 2.3% of its electricity consumption [1], while similar or even more potent growth is observed in many countries around the world, such as China, the USA, and India. Part of this energy transition is led by technology-driven innovation, with offshore wind turbines reaching new heights and larger capacities, while wind farms are moving deeper into the sea to harness the increased energy potential. Giventhis new “deep-water” environment, traditional OWT support structures are gradually being replaced by more cost-effective and agile foundation systems in a quest for reduction of investment costs.

Conclusions

     The paper has developed a simplified performance-based assessment technique for SBJ OWTs founded in clay under the combined action of wind () and earthquake () loading. Using an example of an 8 MW jacket-supported OWT, installed at 60 m depth in the Adriatic Sea, system performance was assessed employing a detailed 3D FE model of the soil–foundation–structure (SFS) system (global 3D FE model). After deriving insights on system performance, the global 3D FE model was used as a benchmark to assess the efficiency of an enhanced Winkler-based “Caisson-on-Winkler-Soil” (CWS) model. Soil–suction caisson interaction is represented by nonlinear hysteretic elements, capturing residual deformations and hysteresis. The proposed CWS model offers physical coupling between vertical and moment loading by introducing distributed vertical hysteretic elements along the caisson shaft, simultaneously contributing to vertical and moment shaft resistance.

Abstract

1. Introduction

2. PV cell model and design parameters

3. Fuzzy Distribution of PV cell parameters

4. Global sensitivity analysis of PV cell parameters

5. Analysis and discussion

6. Conclusion

CRediT authorship contribution statement

Declaration of competing interest

Acknowledgments

References

Abstract

     Photovoltaic cells can directly convert solar energy into electrical energy, which is environmentally friendly and has a wide range of applications. In practical engineering, the uncertainty of photovoltaic cell parameters has an important impact on cell performance. In order to study the influence of parameter uncertainty on the output performance of photovoltaic cells, a photovoltaic cell model is established and five parameters are selected: irradiation intensity, photovoltaic cell surface temperature, temperature coefficient, equivalent series resistance and equivalent parallel resistance. The influence of these parameters on the output power and conversion efficiency of photovoltaic cells is studied by using the global sensitivity analysis method based on fuzzy theory. The principal and total global sensitivity indexes describing the influence of each parameter on the output performance were therefore calculated and ranked. The key parameters influencing the performance of photovoltaic cells were determined to be related to cell surface temperature and equivalent parallel resistance. In actual production and life, the output performance of photovoltaic cells can be improved by controlling these two factors. The results of this study can help to guide the performance analysis and parameter optimization of photovoltaic cells, accelerating the development and improving the adoption of photovoltaic systems.

Introduction

     With the aggravation of the global energy crisis and increasing environmental pollution, the development of solar energy occupies a critical position in the energy structure (Wang et al., 2021b, Xiong et al., 2022). As photovoltaic (PV) cells directly convert solar energy into electric energy, they represent an increasingly popular source of renewable energy. The number of deployed PV power generation systems has significantly increased worldwide (Wang et al., 2021a, Wu et al., 2022), and many countries have introduced grid pricing policies to accelerate investment in PV power generation. Therefore, research into the performance of PV cells is of great significance for ensuring their continued adoption (Sharma et al., 2021, Sadan and Dwivedi, 2020). To date, many experts and scholars have studied the parameters of PV cells (Chehreh Ghadikolaei, 2021, Chen et al., 2020, Chandran et al., 2021, Ganesh Pardhu and Kota, 2021, Ji et al., 2021). For example, Chen et al. (2020) used variables reduction and improved shark optimization technique to extract photovoltaic cells parameters, Chandran et al. (2021) proposed an improved distribution estimation algorithm to estimate the optimal parameters of fuel cells and solar cells, and Ganesh Pardhu and Kota (2021) proposed a new random radial shift optimization algorithm based on a group algorithm to extract the unknown parameters of solar PV cells. The confirmation and extraction of parameters of photovoltaic cells and their components is of great significance for evaluating the performance of photovoltaic cell systems and preventing aging and failure of systems and components.

Conclusion

     To address the instability of PV cell output performance according to the inherent fluctuation of design parameters, this study applied the global sensitivity index based on fuzzy theory to measure the influence of inherent design parameter fluctuation on the stability of PV cell output power  and conversion efficiency . The Monte Carlo method was used to calculate the principal and global sensitivity indexes of the design variables based on the credibility variance. The results show that compared with the cell surface temperature  and equivalent parallel resistance , the total global sensitivity index values of the short-circuit current temperature coefficient , irradiation intensity , and equivalent series resistance  are minimal for both the output power  and conversion efficiency , and the effects of their variation can therefore be ignored. Therefore, the key design parameters of PV cells are related to the working environment () and material (). In actual production and application, the output performance of PV cells can therefore be improved by controlling material selection(ensure a consistent equivalent parallel resistance ) and expected working environment temperature . The results of this study can provide guidance for the design and optimization of future PV cells, and further improve their application and utility to increase adoption.

Abstract

     It is important to unearth the role of structural change factors for sustainable growth in developed economies. Economic complexity, export product quality, and institutional indicators represent the overall structural change and transformation in an economy. This paper probed the influence of export product quality and economics of complexity on economic growth for 28 OECD (Organization for Economic Co-operation and Development) countries using the data from 1990 to 2019. In doing so, the study employs three-panel data econometric models to investigate the role of economic complexity, export quality, and institutional quality on economic development. The paper performs the empirical tests, including the cross-sectional related dependency methods and cointegration techniques. As per the results emerging from fully modified OLS (FMOLS) and dynamic OLS (DOLS) estimation, export quality and financial development positively and significantly strongly impact growth in the long run. The empirical conclusions report detailed discussion on sustainable growth and discuss novel implications concerning product quality and institutional performance. The conclusions stress the practical implications for sustainable economic growth of OECD economies.

Introduction

     Over the last decade, there has been rising research in describing and analyzing countries’ productive structures. New measures of the economic complexity index have been developed to determine the sophisticating nature of the procedure of production making and gains from trade. According to Hidalgo and Hausmann (2009), the economic complexity helps to analyze countries’ productive capacities and the product quality of exports. Since they are more effective in estimating economic growth than traditional predictors, like GDP per capita, they are now widely used (Hausmann et al., 2014). Economic complexity occurs in a country if it can produce diversified products and export products have a more complex production structure. More specifically, economic complexity is the expanse of productive knowledge a country possesses that impacts economic performance. According to Lapatinas et al. (2019), it has been observed empirically that countries with higher complexity tend to gain additionally from trade and have a high degree of trade openness. Countries with advanced levels of economic-based complexity have higher economic capabilities since they utilize more intensive technology and high skilled labor (Hartmann et al., 2017).

Concluding remarks and implications

     International trade and industrial activities are conducive to the economic development of emerging and developed economies. However, the bolster economic objectives come with the repercussions of environmental externalities and structural transformation challenges. The on-hand research study is an effort to examine the heterogeneous impacts of structural change and institutional quality on economic growth. In doing so, the current study draws novel conclusions and implications regarding the sustainable development goals and sustainable growth in developed economies. This research attempted to explore the nexus among economic growth, economic complexity, export quality, financial development, human capital, renewable energy, and institutional quality for 28 OECD economies. In doing so, the study employed the annual data from 1990 to 2019 and tested three model specifications using extensive empirical analysis such as cross-sectional dependency tests by Pesaran (2015), Westerlund cointegration tests, and Pedroni cointegration tests, FMOLS, and DOLS.

     The empirical results highlight that greener and renewable energy have an important role to play for industrial activities, economic development and it can act as an alternative to achieve sustainable economic growth. Export quality leads to strengthening the trade by improving the quality of products, ultimately it can lead towards the economic progress of countries. Further, it can be interpreted that overall economic complexity encompasses financial development and product complexity, both of which are conducive for the growth of the economy.

Abstract

     With the advent of new technologies and globalization of business, supply chains have turned into indispensable tools for gaining competitive advantage. The application of new technologies like blockchain can benefit sustainable energy supply chains by improving chain and logistics operations in the areas of trust, transparency and accountability, cooperation, information sharing, financial exchanges, and supply chain integration. However, the efforts to adopt such technologies in supply chains tend to face many challenges and challenges, which can seriously threaten their success. Therefore, it is crucial to carefully examine the challenges to blockchain technology application. This research focuses on identifying the criteria and challenges to the application of blockchain in renewable energy supply chains and also ranks the identified challenges in terms of their capacity to disrupt the process. The applicability of the suggested structure is examined in a case study of the renewable energy supply chain of Iran. In this study, the challenges are evaluated and ranked by the hybrid developed methods by the integration of the concept of gray numbers into the gray stepwise weight assessment ratio analysis (SWARA-Gray) and the gray evaluation based on distance from average solution (EDAS-Gray). Another group of hybrid methods including the gray weighted sum method (WSM-Gray), the gray complex proportional assessment (COPRAS-Gray), and the gray technique for order of preference by similarity to ideal solution (TOPSIS-Gray) is used to validate the results. The rankings obtained from all of these techniques show high degree of correlation. Among the identified challenges, “high investment cost” is found to be the most important challenge to the application of blockchain in sustainable energy supply chains.

Introduction

     In today’s ever-changing world, supply chain networks tend to have diverse needs in terms of information assessment and risk management, which could be difcult to address (Ivanov et al., 2018). Indeed, the evolution of information and communications technologies (ICT) is constantly changing the requirements and capabilities of logistics and supply chain management (Goldsby & Zinn 2016). In recent years, blockchain has been hailed as a ground-breaking technology capable of transforming the form and size of businesses and how they conduct transactions (Behnke & Janssen 2020). Blockchain and Bitcoin were introduced by the person (or persons) using the pseudonym Nakamoto in 2008 (Nakamoto, 2008). Since its advent, this decentralized currency and transaction technology has been a major driver of change in business and is expected to play an even larger role in both the industry and service sectors (Iansiti 2017). The impetus behind the invention of blockchain was the lack of a transparent and sustainable independent decentralized fnancial system (Christidis & Devetsikiotis 2016). Blockchain prevents forgery and fake transactions by building encrypted blocks of all transaction records and storing them across the web such that they are constantly validated. Furthermore, the use of intelligent agreements in blockchain to control operations improves transaction transparency and increases trust among users (Constantinides et al. 2018). It has been argued that blockchain technology can modify numerous sectors of the economy, including supply chain management (Hofmann & Rüsch 2017), and may have far-reaching economic, social, and environmental impacts (Mathivathanan et al. 2021).

Conclusions and recommendations

     The advent of new technologies in recent decades has transformed how people do business across the world. This also applies to supply chains, and especially energy generation chains, which have undergone signifcant changes in the past decades. The application of new technologies like blockchain can beneft sustainable energy supply chains by improving chain and logistics operations in the areas of trust, transparency and accountability, cooperation, information sharing, fnancial exchanges, and supply chain integration. Blockchain technology can indeed have extensive economic, social and environmental impacts and hinder or accelerate sustainable development. Therefore, it is important to carefully consider adopting this technology in sustainable energy supply chains. However, as with all innovations, early adopters of this technology tend to face some resistance as well as unforeseen challenges. Blockchain has several features that can create challenges to its practical application. A survey of the research literature showed that the greatest studies on the application of blockchain have only provided case examples for this process in diferent contexts. Also, only a few studies have tried to systematically identify and evaluate the criteria and challenges to the application of blockchain in supply chains. To discuss this space in the literature, this research frst identifed the criteria of and practical challenges to the application of blockchain in a renewable energy supply chain and then ranked the identifed challenges. The ranking was conducted by the combined use of gray numbers and SWARA and EDAS. Also, WSM-G, COPRAS-G, and TOPSIS-G techniques were used to validate the fndings.