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

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

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.

Abstract

     The rapid increase in the world population along with the rising trend of global economic growth triggers serious problems related to energy consumption. In this regard, a comprehensive analysis of the current and potential energy situation in the light of demographic trends, main economic indicators, technical conditions, social and environmental concerns has become a significant issue for developing well-planned global and regional energy strategies. In this study, a multi-objective decision-making model is constructed for renewable energy planning to determine the most appropriate resource diversity for Turkey by focusing on five renewable energy sources, namely solar, wind, geothermal, hydroelectric, and biomass. There are four objective functions presented in the proposed multi-objective model including maximization of the technical score of regions, maximization of the job creation, maximization of the environmental score, and minimization of the cost. As a solution methodology, a two-phase fuzzy goal programming approach is performed. The proposed model is solved using LINGO software as a comprehensive solver for model optimization. The appropriateness of the proposed method is analyzed by comparing it with other multi-objective methods. Results reveal that the majority of the energy demand should be met via solar and hydroelectric energies for the satisfaction of the indicated objectives.

Introduction

     The ongoing growth in the world population and the global economic development of countries together with the effects of industrialization, urbanization, and technological developments, have caused a dramatic increase in the amount of resource consumption. In the meantime, the level of consumption, the most inevitable of which is the need for energy, maintains its rise in many ways despite the fact that the world’s fossil resources are finite and rapidly depleting. Since the world already has limited reserves in terms of fossil fuel resources, and these resources are not evenly distributed between countries, it is not possible to meet the energy demand with the existing fossil resources in the short term. Moreover, the use of fossil fuels causes damage to the environment and living conditions, especially human health, animal life, and nature. This situation is also considered as another threat. Hence, it seems inevitable that the energy problem will be one of the major challenges humanity must deal with in the short term since energy is considered one of the most important subjects on the agenda of policymakers. In these circumstances, decision-makers have started to find out alternative sources to meet the energy needs. Energy sources in the renewable form are considered fundamental primary sources of alternative energy that require strategic planning in terms of sustainable development. Therefore, reducing the use of fossil resources and replacing them with renewable energy sources in a controlled manner are both critical for energy sustainability and vital for energy planning in the long term.

Conclusion

     Real-world problems in planning activities require consideration of multiple conflicting objectives simultaneously. In recent years, renewable energy planning has become a very important issue, which poses itself as a multi-objective problem due to its impact on economic, social, and environmental concerns. As the number of options increases, it becomes more difficult for decision-makers to determine specific renewable energy technology among alternatives [103]. Therefore, the proposed model for the prioritization of renewable energy sources based on twenty-one electricity distribution zones in Turkey has been developed in a multi-objective structure.

     The advantage of the proposed multi-objective model is that it can be further modified by integrating particular considerations such as national renewable energy targets and local incentive support schemes. In this way, the model enables the evaluation of several objectives simultaneously and better accommodates various parameters together in order to contribute to the process of determining the most suitable renewable energy technologies in future energy planning. Thus, this study presents a framework that provides a useful roadmap for decision-makers in the development and implementation of sustainable energy plans by analyzing renewable energy resources from a regional perspective to utilize energy potential efficiently. In addition, another contribution made through this study is the use of efficient solution methods comparatively in multi-objective analysis, which allows decision-makers to decide on the most appropriate evaluation method in the planning process. Hence, the two-phase fuzzy goal programming method is utilized for the evaluation procedures in comparison with three other multi-objective methods including lexicographic optimization, goal programming with absolute deviation, and goal programming with percentage deviation.

Abstract

     The utilization of renewable energy (RE) is a meaningful way to realize the low-carbon transformation of energy systems. However, due to the imbalance of resources, economy, technology, society, and environment among regions, the coordinated development of regional RE may be restricted by different factors, which brings challenges to the formulation of relevant development policies. This paper focuses on the development of RE in 30 provinces in China from 2011 to 2019. It uses the AHP-EM integrated evaluation model to evaluate the constructed multilayer indicator system for the comprehensive development of RE. The characteristics of the coupling and coordination relationship between indicators are explored, and the critical driving factors affecting the coordinated development and change in RE in different regions are quantitatively identified through the logarithmic mean Divisia index method. The results show that the comprehensive development level of RE in each province is relatively low, and the relatively high-level areas gradually move eastward in terms of spatial distribution. The degree of coupling and coordination between indicators is still in a low-level coupling stage, and RE in each region has not achieved coordinated development. In addition, the comprehensive development of regional RE is consistent with the spatial evolution characteristics of the degree of coordination among indicators, emphasizing the importance of coordinated development among indicators for RE. These findings will provide broader insights for improving the comprehensive development level of regional RE and formulating differentiated policies.

Introduction

     The development of renewable energy (RE) is closely related to the issue of carbon emission reduction, which has attracted widespread attention. From a long-term perspective, the development of RE has become a key measure to address global climate change and achieve carbon emission reduction [1,2], as well as an essential means to promote the low-carbon development of the energy structure [3]. On a global scale, RE (wind energy, solar energy) has become a necessary force to replace fossil energy due to its nonpolluting and environmentally friendly nature, and excellent resource potential [4,5]. It is estimated that RE in the EU and the US has received sufficient attention as advocates of a global low-carbon energy system. It is manifested in the rapid growth of wind power and solar installed capacity, increased investment in the RE industry [6–8], and an increase in the proportion of RE power generation [9]. However, with the scale development and widespread use of RE, issues such as social acceptance [10–12], infrastructure construction [13,14], and grid transmission technology [15,16] related to the RE industry also follow.

Conclusions and Policy Implications

     For the low-carbon transformation of China’s current energy system, RE has become a necessary force to replace fossil energy with its advantages of being nonpolluting and environmentally friendly. Based on the panel data of 30 provinces in China from 2011 to 2019, this study constructed a multidimensional comprehensive development evaluation system for RE. It calculated the comprehensive degree of RE development in each region. This paper explores the coupling and coordination relationship between various dimensions. It quantitatively identifies the dominant factors that affect the changes in the coupling and coordination relationship of the comprehensive development of RE in different regions. This provides directions for further improving the development level of RE in various regions. The specific conclusions are as follows:

     The overall development of China’s RE is at a relatively low level (0.3558), and the development of the RE industry is still in its infancy. The spatial distribution of provinces with a high level of comprehensive regional RE development shifted to the east. Moreover, the development potential difference between regions is still apparent. As of 2019, the leading provinces in the comprehensive development of RE (>0.40) include Qinghai, Beijing, Jiangsu, Guangdong, Zhejiang, and Shanghai and play the role of leaders in the comprehensive development of RE. These regions benefited from resource endowment and social development, with an average contribution of 2.20% and 1.50%, respectively. At the same time, limited by environmental sustainability, the average contribution is −0.47%. The number of provinces with relatively backward comprehensive RE development (<0.30) decreased, including only Heilongjiang, Hunan, Liaoning, Sichuan, and Xinjiang. Among them, Heilongjiang, Liaoning, and Sichuan are mainly limited by economic benefits (−2.38%; −2.92%; and −5.51%), Hunan is mainly limited by environmental sustainability (−0.62%), and Xinjiang is mainly limited by technical support (−31.52%).

ABSTRACT

1. Introduction

3. Research methodology

4. Sample data, sources, and characteristics

5. Empirical results

6. Conclusions

Declarations

References

Abstract

This paper examines whether the 1997 Asian financial crisis affected the renewable energy/carbon dioxide (CO2) emissions relationship differently when compared to the 2008 global financial crises. Using the Dynamic Panel Data Model, we examine separately the impact of the 1997 crisis and the 2008 crises on the stated relationship for annual data between the 1987–2018 period for a group of high, upper-middle, and lower middle-income countries. Our findings suggest that the results were crisis and country specific. For the overall sample, the relationship between the two variables was positive (and significant post-1997 and pre-2008 crises) but negative post-2008 crisis. In contrast, the positive relationship remained unchanged for the lower middle-income subsample through the two crises. We also find evidence that the 1997 Asian crisis altered the relationship differently than the 2008 financial crisis especially for the upper and middle-income groups. Clearly, reduction of CO2 emissions may not be guaranteed even if host countries adopt renewable energy sources since country income levels and the nature of the crisis may matter. Future research may consider how the degree of pollution controls and differential costs of renewable energy adoption in countries may alter this relationship.

1. Introduction

Research examining the renewable energy consumption/carbon dioxide (CO2) emission links for developing countries are extensive and generally recommend policies to ratchet up the local renewable energy infrastructure to encourage renewable energy consumption (Pao and Tsai, 2011; Shahbaz et al., 2013; Zhu et al., 2016), particularly for lower income countries to allow them to attract foreign direct investment (FDI) aimed at inflows of technology transfer (Omri and Kahouli, 2014; Doytch and Narayan, 2016). Extant literature also documents links between global financial crises and the transmission of technological innovation to recipient countries. For instance, Colombo et al., 2016, Zouaghi and Sánchez, 2016, and Zouaghi et al., 2018 show the firms devise survival and growth strategies designed to overcome global financial crises by developing innovation products. Although there is extensive work on the factors that affect the renewable energy adoption/CO2 emissions relationship, there is scant work (with few recent exceptions) on how a crisis will alter this relationship. The exception includes recent work by Dong et al., 2020a who show that countries switching to renewable energy sources reduced CO2 emissions (but not statistically significantly) post-2008 crisis 1 versus pre-2008 crisis levels for a sample of 120 countries.

In this paper, we add to the emerging literature by examining whether the renewable energy/pollution links were also similarly altered during the 1997 crisis. We conjecture that the impact of the 2008 crisis affected global economies differently than the 1997 crisis. Hence, we conjecture that the differential effects on country macroeconomic variables can imply differences in the relationship between renewable energy adoption and CO2 emissions pre and post each crisis. If the pre/post link changes were different for the 1997 crisis versus the 2008 crisis, then policy prescriptions useful for one crisis may not work for other crises. We don’t believe that this issue has been investigated in the extant literature. Specifically, we conjecture that the renewable energy consumption – CO2 emission relationship was altered differently by the 1997 crisis than by the 2008 crisis and may also be a function of the level of economic development of host countries.

Abstract

     This paper evaluates the impact of climate policy uncertainty on renewable and non-renewable energy consumption in the United States over the quarterly data from 2000Q1 to 2021Q3. Economic growth and crude oil prices are added to the energy consumption functions as control variables. The paper considers several approaches to model both renewable and non-renewable energy demand. It is found that crude oil prices promote non-renewable energy demand and climate policy uncertainty reduces it. Surprisingly, the impact of economic growth on non-renewable energy consumption is positive but insignificant. It is also observed that economic growth promotes renewable energy demand, and crude oil prices reduce it. Furthermore, climate policy uncertainty positively affects renewable energy demand in the long run. Some policy implications are provided for reducing non-renewable energy consumption and promoting renewable energy use in the United States through climate policy implementation.

Introduction

     Climate change is one of the leading problems in the 21st century. Scholars have demonstrated that risks and uncertainties related to climate change can affect various dimensions of the global economic system [1,2]. Climate change is attributed to global warming, environmental pollution, and caused by greenhouse gas emissions. Also, CO2 emissions are driven by fossil fuels and non-renewable energy sources [3,4]. Therefore, the transition from fossil fuels to green renewable energy is an important policy tool to transfer economies to the low carbon economy. It is suggested that higher renewable energy consumption can slow down the negative consequences of climate change on the economic system [5,6]. Similarly [7], argued that promoting renewable energy usage in consumption and production activities can also reduce the adverse impact of climate change and global warming on living human beings and natural habitats in the long run. Therefore, the determinants of the non-renewable and renewable energy demand are crucial for designing climate change policies in developing and developed economies.

Conclusion

     The determinants of energy consumption patterns have been an important issue in achieving sustainable environmental quality in the current context of energy depletion, climate change, and global warming. The following facts motivated us to explore the topic. The first fact is that the concept of “green recovery” or “green economic growth” in 2020 has reignited the debate among scholars about the influence of climate policy uncertainty, economic growth and crude oil prices on the pattern of energy consumption (non-renewable and renewable) pressure. The second fact is that the relevant empirical findings are inconclusive when they are based on the influences of economic growth and crude oil prices on the energy consumption pattern. This evidence suggests that we should use climate policy uncertainty as to the key factor in non-renewable and renewable energy consumption functions, which need to be further investigated empirically using the right methods. The third fact is that advanced economies like the United States are the leading energy consumers and second-best pollution creating countries globally; experiencing the factual findings can provide effective policy recommendations for their authorities and departments to adopt energy sustainability measures. This result can promote sustainable environmental quality for the United States and other growing economies in the long run.

     In this connection, we argue that this is the first study that motivates us to examine the effects of climate policy uncertainty on renewable and non-renewable energy consumption in the United States over the quarterly data from 2000Q1 to 2021Q3. As control variables, economic growth and crude oil prices are added to non-renewable and renewable energy functions. This objective is modelled by applying the ARDL bounds test to cointegration. This model helps us to estimate the long-run and short-run results. Moreover, the DOLS and the FMOLS techniques are used as results of robust checking purposes.