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

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

     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.

Abstract

Introduction

Methods

Results

Discussion

Conclusion

References

Abstract

     The industry 4.0 has created a paradigm shift in how industrial equipment could be monitored and diagnosed with the help of emerging technologies such as artificial intelligence (AI). AI-driven troubleshooting tools play an important role in high-efficacy diagnosis and monitoring processes, especially for systems consisting of several components including wind turbines (WTs). The utilization of such approaches not only reduces the troubleshooting and diagnosis time but also enables fault prevention by predicting the behavior of different components and calculating the probability of near future failure. This not only decreases the costs of repair by providing constant component’s monitoring and identifying faults’ causes but also increases the efficacy of the apparatus by lowering the downtimes due to the AI-driven early warning system. This article evaluated, compared, and contrasted eight different artificial neural network (ANN) models for diagnosis and monitoring of WTs that predict the machinery’s system failure based on internal components’ sensor signals and generation temperature. This article employed a machine learning model approach with two hidden layers using multilayer linear regression to achieve its objective. The developed system predicted the output of the WT’s generator temperature with an accuracy of 99.8% with 2 months in advance measurement prediction.

Introduction

     Industry 4.0 introduced a new paradigm to the machineries’ monitoring and diagnosis procedure. Enabled by advances in artificial intelligence (AI) and notions such as Internet of Things (IoT) in recent years, Industry 4.0 has proved to be an effective and reliable trend toward digitization and automation (Haag and Anderl 2018). Industry 4.0 is the fourth paradigm shift and major breakthrough in industrial revolution made possible by the advancements in electronics and information technology; a continuation of the evolution of automation commenced from the invention of steam engines and mass production as a result of assembly lines and standardization (Xu, Xu, and Li 2018).

     The wind power industry and the whole renewable energy sector could benefit significantly from the employment of industry 4.0. Most of the machineries including wind turbines (WTs) produce a huge amount of data related to power consumption, current, voltage, vibration, and environmental factors that are not necessarily utilized. The information processed from these gathered data could be used to improve the troubleshooting, monitoring, and maintenance procedures. Sensors attached to different parts of WTs will provide important data of the health state of the apparatus, which require interpretation and processing.

Conclusion

     Eight different ML models were developed with different sensors’ data based on SCADA data collected from nine WTs over 10 years received from the Westmill Windfarm located in Swindon, United Kingdom, to predict the generator failure in WTs in advance using pattern recognition based on historical data. The results of each model’s accuracy in terms of minimum, maximum, and standard deviation offsets between the predicted and actual generator temperature values were compared and contrasted, and the effect of the input sensor data was explored. Overall, this research showed the possibility of utilizing ML-driven regression algorithms to predict WTs’ generator failure caused by heat, lowering the maintenance costs related to downtime and staff,and, at the same time, improving the operational availability of the apparatuses. For the future works, the authors of this article aim to explore the possibility of implementing transfer learning for fast adaptation and deployment of the trained models to new WTs, allowing quick training of new assets and lowering the readiness time required for the model.

Abstract

1. Introduction

2. Methods

3. Results

4. Conclusions

Code availability

CRediT authorship contribution statement

Declaration of competing interest

Acknowledgements

References

Abstract

     Urban sustainability is a significant factor in combating climate change. Replacing polluting by renewable energies is fundamental to reduce the emission of greenhouse gases. Photovoltaic (PV) facilities harnessing solar energy, and particularly self-consumption PV facilities, can be widely used in cities throughout most countries. Therefore, locating spaces where photovoltaic installations can be integrated into urban areas is essential to reduce climate change and improve urban sustainability. An open-source software (URSUS-PV) to aid decision-making regarding possible optimal locations for photovoltaic panel installations in cities is presented in this paper. URSUS-PV is the result of a data mining process, and it can extract the characteristics of the roofs (orientation, inclination, latitude, longitude, area) in the urban areas of interest. By combining this information with meteorological data and characteristics of the photovoltaic systems, the system can predict both the next-day hourly photovoltaic energy production and the long-term photovoltaic daily average energy production.

Introduction

     Cities have become a determining factor in climate change, as they are the place where much energy is consumed (64% of global primary energy use) and high levels of greenhouse gases emitted (70% of the global total), due to the use of fossil fuels as energy sources (International Energy Agency, 2016). There is a great opportunity for citizens to reduce these emissions. Replacing polluting energy sources by renewable energies that respect the environment and do not compromise future generations is one of the essential requirements to achieve energy-sustainable cities and favour the fight against climate change. Additionally, switching to renewable energy sources as a detriment to polluting energies could improve health and quality of life. Precisely, one of the goals proposed in the 2030 Agenda for Sustainable Development by UN is making cities inclusive, safe, resilient and sustainable (United Nations, 2015).

     Solar energy has seen a large increase among renewable energies. According to the International Energy Agency (IEA), there was a 22% growth up to 720 TWh (representing 3% of global electricity generation) in 2019 (International Energy Agency, 2020). Although large photovoltaic infrastructures are away from cities, there has been an exponential rise in distributed installations in buildings, industry and houses in Europe, the United States and Japan (International Energy Agency, 2020). This is very important since local production reduces transportation losses and enhance citizen’s responsibility because of inspiration for searching for energy self-sufficiency. In recent years, a new type of building, based on that type of installation, has been proposed as an evolution of Zero Energy Building (ZEB): the Positive Energy Building (PEB) (Magrini et al., 2020).

Conclusions

     An open-source tool, URSUS-PV, has been built to estimate potential electricity that can be generated in photovoltaic (PV) facilities in the short-term (one-day-ahead) and in the long-term (daily average) in an urban area of interest (neighbourhoods, streets, complex buildings). It could be potentially useful for multiple types of users, including municipalities, public administrations, companies in the photovoltaic sector, cooperatives or neighbourhood communities.

     One of the tools’ most significant benefits is the automation of the complex process. It initially had to be performed manually to obtain global results in urban areas of interest to produce short-term or long-term photovoltaic energy estimations. After conducting a data mining process, such manual processing has now been computerized. CRISP-DM methodology has supported the process. We have executed all the steps from the business understanding to the final deployment, which includes models discovered in intermediate phases. Therefore, following such methodology, once the data sources (meteorological and geographical data) have been identified, its acquisition and integration are automated. Transformations done in the preprocessing stage are also programmed, meaning that segmentation of the roofs available in urban areas of interest and extraction of their features can be easily computed. The calculating photovoltaic energy potential phase has also been fully automated using meteorological data of the city and the configuration of PV facility that could be integrated into each roof.

Abstract

Introduction

Methodology

Data collecti

Performance of ANN forecaster

Performance of hybrid-ANN forecaster

Performance of hybrid-ANN forecaster-S

Conclusion

Nomenclature

Acknowledgments

Disclosure statement

References

Abstract

     The integration of solar photovoltaic (SPV) system to the grid has introduced a new source of intermittency in the grid, and the grid has to react smartly to the changes that occur in the penetration of SPV power. Accurate modeling of weather-dependent SPV power will be helpful in forecasting the penetration of SPV power into the grid. An SPV power output forecasting model has been developed based on artificial neural network (ANN) approach. Two forecasters, namely ANN forecaster and two-stage hybrid-ANN forecaster, are developed with operational and weather parameters. The historical data of SPV power (P), hours of operation of SPV system (to), daily global solar radiation (H), and ambient temperature(T) are used as modeling parameters. The combination of modeling parameters {P, H, T, to} is identified as the best combination that influences the forecasting of day-ahead power output. A relative root mean square error (RRMSE) of 5.74% was obtained with the combination of {P, H, T, to}. An RRMSE of 6.04% was observed with the combination of {P, H, T} as inputs, and the hours of operation of the SPV plant could be ignored in the model. The historical power data of the SPV plant is identified as the crucial parameter in the SPV power forecast model and has given an RRMSE of 7.25%.

Introduction

In recent times, the consumption of energy from conventional sources has been increased, which causes the rapid depletion of fossil fuel-based resources. The emission of greenhouse gases has increased because of the utilization of conventional sources over the past few decades (Fikru and Gautier 2015). In addition to that, the energy demand has been increasing enormously in both developed and developing countries due to the increase in population, urbanization, and industrialization. The portion of this energy demand can be met by renewable energy sources (RES) such as wind and sun. Solar energy can be captured by installing solar collectors, solar ponds, solar chimneys, and solar photovoltaics (SPVs). Out of all these installations, the SPV installation growth rate has increased enormously. The generation of electricity using SPV is widely accepted (Larson, Nonnenmacher, and Coimbra 2016). PV power generation is ecofriendly because it produces no toxic emission, of low maintenance, of noiseless operation, and it is abundantly available (Yan et al. 2021). Up to now, several thousands of SPV systems of 1 kW to several hundreds of megawatts have been installed and integrated into the grid worldwide (Tina, Scavo, and Gagliano 2020). The integration of SPV systems to the grid has introduced a new resource of intermittency in the grid (Sepasi et al. 2017). This is due to the sensitivity of the SPV system to local weather conditions and variation in solar radiation availability. The SPV power output is uncertain since the parameters like irradiance level, ambient temperature, surface temperature of the panel, dust deposits, wind speed, cloud cover, and relative humidity etc., will have an impact on the SPV power generation. Accurate modeling of SPV power is required by the utilities to control the high instabilities of the electric grid due to unpredictable PV power penetrations (Wan et al. 2015).

Conclusion
A day ahead SPV power forecast models are developed by using ANN approach. Daily GSR (Hm, Hp), ambient temperature, hours of operation of SPV system, and historical data of SPV power are used as input parameters in the modeling of the day ahead forecast models. The trial and error method has revealed that the ANN model with three neurons in the hidden layer with linear-log-sigmoid transfer function has shown the best performance.

     The effect of each parameter and the combination of parameters on the model accuracy is identified by developing various forecast models, and a total of 21 forecast models are developed. Out of the 21 developed forecast models, ANN forecaster-4 with the combination of inputs {P, H, T, to} has shown the best performance. The combination has shown a strong influence on the SPV power output. The MAPE of 4.18% and the RRMSE of 5.74% are derived with 4-3-1 ANN architecture. Historical data of measured power is the best suitable modeling parameter to predict the SPV power. Historical power measurements will not be available before the installation of the SPV system, and in such cases, ANN forecaster-2 with {Hm, T} could be used as a forecast model.

Abstract

     The use of renewable energy technologies is a key factor for sustainable development but their selection from several alternatives is a difficult task that relies on the careful assessment of relevant criteria. While Multiple Criteria Decision Making (MCDM) methods have been used successfully in various renewable energy technology selection problems, the decision process becomes more challenging when preferential judgements are made on the basis of non-homogenous and imprecise input data, and when there is uncertainty due to disparities among decision makers. This paper presents a hybrid MCDM method capable of overcoming these problems by taking into account quantitative and qualitative data under a probabilistic environment in the context of group decision making. In this method, qualitative data is fuzzified and used along with quantitative data to develop a hybrid model. A coefficient factor allows decision makers to vary the weight of each quantitative model so that the resultant criteria weights and overall alternatives’ scores consider both subjective considerations and objective information. An example is presented to showcase the usability of the method developed for ranking and evaluating renewable energy technologies in the mining industry. In addition, the impact of different coefficient factors on the final results was assessed by means of sensitivity analysis. The results indicate that the method developed is able to minimise the loss of valuable objective information, caused by the subjective bias of qualitative weights during the evaluations, by adjusting the coefficient factors of the hybrid model during the calculations.

Introduction

     Energy-generating technologies that depend on non-renewable fossil fuels result in significant environmental challenges, such as increasing greenhouse gas (GHG) emissions, which lead to climate change (Disli et al., 2016, Li et al., 2020). In response to these challenges, it is important to better exploit renewable energy technologies (e.g. wind and solar), which are low-cost, clean and sustainable (Cunden et al., 2020, Dincer, 2000).

     The selection of renewable energy technologies is a complex and multidisciplinary problem that mainly refers to the performance of the technologies concerning multiple criteria such as environmental, social, technical and economic (Wu et al., 2018). In order to evaluate holistically and select the technologies that have a higher performance appropriately, decision makers need to have methodological tools that incorporate both quantitative and qualitative analyses of the multiple criteria. Decision makers should, therefore, make use of the best tools available to evaluate the performance criteria of renewable energy technologies. Choosing the best renewable energy technology to use among various alternatives considering conflicting criteria is thus considered a Multiple Criteria Decision Making (MCDM) problem (Büyüközkan & Güleryüz, 2017).

Conclusions

     A hybrid MCDM method was proposed and was applied to the selection of renewable energy technologies in the mining industry, which faces an increase in energy demand as high grade ores are depleted and the demand for metals and minerals, including those required for renewable energy technologies, increases. The large scale of mining operations makes it very important to consider renewable energy options in order to contribute to the sustainability of the operations.

     Three renewable technology alternatives, namely onshore wind, concentrated solar power, and solar photovoltaic, were assessed taking into account both subjective considerations and objective information with respect to five sustainability criteria. The selected criteria were potential total power generation, GHG emissions, area requirement, levelised energy cost, and prospective jobs creation. An objective weight was obtained using data compiled from the literature, whereas a subjective weight was obtained from the judgements and preferences of four experts. The proposed method was then employed to compute the criteria weights and the alternatives’ scores.