Search

This paper investigates the impacts of economic (in)stability on carbon emissions and the role of renewable energy in these effects at the crossroads of accelerated climate change and rising global economic instability. The paper hypothesizes that these implications should be examined not by direct impacts across variables, but rather via indirect effects serially mediated by renewable energy consumption and economic growth. This paper analyzes the United States from January 1980 and April 2022 using manifest structural equation modeling. The paper reaches two major conclusions. First, there are clear distinctions between (i) the direct and indirect effects of economic stability, surrogated by inflation and policy interest rate, on carbon emissions, (ii) the symmetric and asymmetric indirect effects, and (iii) the roles of aggregate renewable energy and its components, such as biomass and hydropower energy use, in mediating these symmetric and asymmetric indirect effects. Second, renewable energy significantly reduces the carbon-increasing effects of inflation and interest rates. The paper contains proper policy suggestions based on the findings. [ABSTRACT FROM AUTHOR]

With further penetration of low-carbon energy conversion, microgrids (MGs) have become a necessary tool for expanding the consumption of renewable energies. In this paper, an optimal operation model for a microgrid-based multi-agent system is proposed. The goal is to save the total energy cost, which is expressed as a sum of locally observable convex functions. Therefore, improving the operational efficiency of microgrids is the key to promoting renewable energy development. This paper develops a three-layer multi-agent system model considering energy storage system and power thermal load demand response to solve the energy management problem of microgrids. In order to investigate the effect of energy storage system and demand response in microgrids, this paper designs three simulation cases, namely infrastructure case, energy storage case and demand response case. In order to prove the effectiveness of the proposed method, this paper uses the proposed method to solve three cases and compare the result with other meta-heuristic algorithms. The comparison results show that: (1) the multi-agent system model can realize the joint optimization of "resource, network, load and storage". (2) The introduction of energy storage and demand response system in microgrids can stabilize the output. Renewable energy units promote the use of renewable energy and reduce the overall operating cost of microgrids. Moreover, the results clearly demonstrate that the proposed algorithm has far better performance than other optimization methods. Also, the analysis obtained from the results shows that the cost is reduced by 1.82%. PV and WT output increased by 14.54% and 2.42%. In addition, their standard deviation decreases after ESS participation. The proposed approach is very effective through a simulation case study, which shows high potential for applications. [ABSTRACT FROM AUTHOR]

The present paper analyses the evolution of renewable energy generation in the European Union through a decomposition analysis based on the Index Decomposition Analysis together with the Logarithmic Mean Divisia Index within the EU27 between 2000 and 2020. A four factors decomposition approach is considered for decomposing the total RES generation and a five factors model is employed to decompose the carbon emissions. Furthermore, a focus on wind and solar photovoltaic generation is introduced through a four factors decomposition calculated in the period 2010–2020. The most influential factors affecting the RES generation trend in the period 2000–2020 are the RES share and energy efficiency wich determined an increase of 1841 TWh and a decrease of 635 TWh respectively. Variations of carbon emissions are mostly affected by the energy intensity, namely the amount of energy necessary to produce one unit of GDP, which determined a reduction of 1040 Mt. Through the decomposition analysis, the paper successfully identifies the specific drivers supporting RES development and controlling carbon emissions, thus adequate policy measures can be designed to reach planned targets. • LMDI methodology is used to study decomposition of RES generation in EU. • Technical and socio-economic factors are highlighted by using 4 or 5 factors identity. • Technical factors have the main impact on the increase in RES generation. [ABSTRACT FROM AUTHOR]

This paper probes the effectiveness of competitive industrial performance (CIP) and renewable energy in enhancing load capacity factor in top-10 CIP economies for the period 1990–2018. We run panel econometric techniques that address cross-sectional dependence, endogeneity and heterogeneity to provide policy recommendations with the applications of industry, innovation, and infrastructure (SDG 9), which is linked to affordable and clean energy (SDG 7) and climate action (SDG 13). In the empirical analysis, two different environmental quality indicators such as CO 2 emissions and load capacity factor are used. While cointegration between variables cannot be detected in the emissions model, evidence for the existence of cointegration is presented in the load capacity factor model. The findings in the paper suggest that economic growth and competitive industrial performance adversely impact environmental quality, while renewable energy consumption and human capital can improve load capacity factor. The result shows that human capital and renewable energy consumption can be strong tools for stimulating load capacity factor in top-10 CIP economies. Countries with high competitive industrial performance need to return to a green economy without losing their competitiveness. • SDG-based environmental policies was presented for the top-10 CIP economies. • A battery of second generations methods was employed. • Investigating the impacts of Competitive industrial performance on environmental quality. • Competitive industrial performance has a negative impact on environmental quality. [ABSTRACT FROM AUTHOR]

This paper collects monthly data for the United States from January 2005 to April 2021 and develops a time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model to investigate the time-varying relationship between climate policy uncertainty, oil prices, and renewable energy consumption. In addition, this paper discusses the dynamic impact of climate policy uncertainty on five types of renewable energy consumption. Our results show that the relationship between them is time-varying. In most periods, climate policy uncertainty positively affects oil prices in the short and medium term, and positively affects total renewable energy consumption in the short and long term. In addition, the impact of climate policy uncertainty on the consumption of the five types of renewable energy is heterogeneous. The results of the robustness tests indicate that our results are robust and our study has important implications for the development of relevant policies. [ABSTRACT FROM AUTHOR]

This research investigates the asymmetric impact of renewable electricity energy usage and industrialization on the environmental sustainability of South Africa. The load capacity factor (LF) is a more extensive measure of environmental deterioration when compared to carbon emissions and the ecological footprint, which cover a range of both the demand on and supply of nature. The series dataset used for the empirical analysis spans between 1990 and 2019. The study also investigates the effect of human capital, industrialization, and economic growth on load capacity factor. The result of the NARDL estimator reveals that the increase in renewable electricity energy usage increases LF in the long and short term. However, the decline in renewable electricity energy usage has a neutral impact on LF. Furthermore, the results indicate that industrialization and economic growth impact LF negatively. Lastly, human capital improves environmental quality due to the increasing effect on LF. The spectral Granger causality approach outcomes show that all the parameters can forecast load capacity factors in the long term. Some policy implications, restrictions, and potential future study pathways have been explored in light of this paper's results. • The nonlinear autoregressive distributive lag is employed. • Asymmetric impact of renewable electricity consumption on environmental sustainability. • A broad environmental indicator is used, load capacity factor. • The positive change in renewable electricity consumption promotes environmental sustainability. [ABSTRACT FROM AUTHOR]

As the solar energy utilization technology for renewable clean energy, actual parabolic trough receivers are subject to tracking errors and absorber tube installation errors, and the bellows and Kovar ring under these errors have a significant impact on the receiver's safety. Based on the MCRT-FVM-FEM principles in this paper, a receiver's optical-thermal-stress mathematical model is modeled and the thermal-stress performance and safety of the receiver's key components under two errors are evaluated. The correlation between the bellows and Kovar ring's performance and the two errors is analyzed. The results demonstrate that the lower tracking errors and larger installation errors markedly increment the absorber's deformation and have a negative impact on the receiver safety. Bellows and Kovar ring's asymmetrical temperature distribution causes variable swell and uneven stress under tracking and installation errors. Temperature gradient variation of bellows is more sensitive under errors than Kovar rings. Kovar ring's compressive stress rises substantially as the Y-directional installation error varied from −15 mm to 15 mm compared to other errors. With tracking errors exceeding 8 mrad, bellows' tensile stress and Kovar ring's compressive stress diminish under installation error. Thus, larger tracking errors significantly reduce the threat to the receiver when installation errors exist. [ABSTRACT FROM AUTHOR]

With the high penetration of renewable energy, its intermittency and volatility also bring challenges to traditional power system such as maintaining reliable operation of system and improving the utilization of renewable energy. Under the background of Energy Internet (EI), energy router (ER) emerges as the times require. Aiming to improve the ability of support of energy storage units to DC buses and suppressing power shocks both inside and outside the ER, in this paper, an ER based on multi-hybrid energy storage system (MHESS) is proposed. As the principle of maximizing the utilization of renewable energy, a corresponding energy coordinated management strategy is proposed. Charging/discharging timespan optimization model is established to ensure MHESS as available as possible. The reference power of MHESS is allocated based on the proposed optimization model. Simulation analysis of different working conditions in 4 operation scenarios are carried out and the results show that the power oscillation on different buses of ER can be dampened within 0.1s and the bus voltages fluctuate within 2% while operation scenarios are switched, which verifies the feasibility and effectiveness of the proposed energy coordinated management strategy. [ABSTRACT FROM AUTHOR]

Renewable energy is regarded as an effective method to alleviate environmental pollution and accelerate the green transition in China. This paper systematically investigates the impact of financial inclusion on renewable energy demand by using S-GMM model based on provincial data in the green finance era. This paper also analyzes the asymmetry, heterogeneity, and mediating mechanism in the nexus between financial inclusion and renewable energy demand, and highlights the following findings: (1) Improved financial inclusion can promote renewable energy development in China and present the characteristics of green finance; (2) the impact of financial inclusion on renewable energy demand is asymmetric and heterogeneous; specifically, this impact is much higher in northern China and the regions with higher renewable energy development; and (3) wind and photovoltaic power generation are effective mediators between financial inclusion and renewable energy industry, while the mediation effects of hydropower and nuclear power are not significant. New evidence in this study is presented for the achievement of green transition in China, as well as practical policy implications for improving financial inclusion and increasing renewable energy consumption. For instance, fiscal and tax incentives and green investment subsidies in the renewable energy sector need to be further improved. • This study explores the impact of financial inclusion (FI) on renewable energy demand (RED) in China. • Asymmetry, heterogeneity, and the mediating mechanism are further discussed. • Increased FI can promote RED , which presents the characteristics of green finance. • The impact of FI on RED is asymmetric and heterogeneous. • Wind and photovoltaic power are effective mediators between FI and RED. [ABSTRACT FROM AUTHOR]

Despite their economic success, the BRICS countries have yet to achieve environmental sustainability. On the other hand, these countries will not be able to maintain their economic expansion if they do not protect the natural environment. In this regard, this paper intends to assess the effects of financial integration (globalization) on ecological footprint. The paper also considers other ecological footprint drivers, including economic expansion, urbanization, renewable energy and technological innovation. The research utilizes a dataset spanning from 1990 to 2018. The study employed the method of moments quantile regression (MMQR) approach, which is robust to cross-sectional dependence and slope homogeneity issues. The outcomes from the MMQR disclose that the effect of financial globalization on ecological footprint is positive across all quantiles (0.1–0.90), thus validating the pollution haven hypothesis (PHH) in the BRICS nations. Furthermore, both renewable energy and technological innovation curb ecological footprint across all quantiles (0.1–0.90). Moreover, technological innovation is proven to impact the environment positively across all quantiles (0.1–0.90) via the path of urbanization. As a result, technological innovation is projected to help the BRICS nations achieve sustainable urbanization. The DOLS, FMOLS, FE-OLS outcomes also validate the outcomes of MMQR. Based on these results, policies were proposed. • MMQR applied to integrate asymmetric effects. • Renewable energy consumption and technological innovation derive ecological sustainability. • Financial integration-led Pollution-haven-hypothesis is confirmed. • Economic growth and urbanization lead to higher ecological footprints. • Technology innovation neutralizes the negative impact of urbanization on ecological sustainability. [ABSTRACT FROM AUTHOR]

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. • We examine the impact of the Climate Policy Uncertainty (CPU) on renewable and non-renewable energy consumption. • We use the quarterly data from 2000Q1 to 2021Q3 in the United States. • The CPU reduces non-renewable energy demand. • The CPU positively affects renewable energy demand in the long run. • Promoting renewable energy use through climate policy implementation is discussed. [ABSTRACT FROM AUTHOR]

Process steam is an important product in the industrial sector but the traditional steam boiler will lead to tremendous fossil fuel consumption and carbon emissions. The non-concentrating solar collectors have a huge potential for process steam generation but their application potential has not been fully explored. In this paper, a high-efficient evacuated flat plate solar collector used for direct steam generation is proposed. Its thermal performance in both direct steam generation mode and pressurization water mode is presented via experiment test and a validated numerical model. The results indicate that the direct steam generation mode can obtain a 10% enhancement (absolute value) of the thermal efficiency compared with the pressurization water mode. The thermal radiant losses of the solar collector can be suppressed and the heat transfer coefficient of the working fluid can also be elevated. Furthermore, a dual-mode evacuated flat plate solar system is proposed to produce thermal energy for space heating in the heating season and generate steam in the non-heating season, thereby solving the solar seasonal mismatch problem for the traditional solar thermal heating system. The comparison with the state-of-the-art study manifests that the dual-mode system proposed in this paper has better performance all year round. [Display omitted] [ABSTRACT FROM AUTHOR]

Developing new technologies is a key to achieve renewable energy utilization. The near-field thermo-photovoltaics (NFTPV), an emerging power generation device, is investigated from the energy perspective in this paper. A novel oxy-enrich NFTPV system is proposed, and a thermo-physical model is established for sustainable fuel gas based on energy balance. The effects of parameters on system performance are numerically explored, including the furnace size, oxygen ratio, voltage and vacuum gap, etc. Moreover, special attention is paid to the effects of oxygen ratio on system performance under 2 atm of O 2 /N 2 and O 2 /CO 2. Besides, exergy analysis is conducted. The results show that it is more appropriate to control the power density of furnace at 50 kW/m2 in system design. The efficiency of NFTPV system is about twice greater than that of far-field TPV system. The power density of NFTPV system increases by more than 2.5 times when the oxygen ratio increases; the TPV volume can increase by 2–3 times and the efficiency is also improved. The results indicate that oxy-enrich combustion with higher oxygen ratio matches a larger NFTPV system, which reduces the manufacturing difficulty and is significant in engineering. This study provides new ideas and references for NFTPV practical application. [ABSTRACT FROM AUTHOR]

The increasing penetration of photovoltaic technology in the electricity market requires the development of a methodology that facilitates the optimisation of photovoltaic plants with single-axis trackers. This paper presents an optimisation methodology that takes into account the most important design variables of single-axis photovoltaic plants, including irregular land shape, size and configuration of the mounting system, row spacing, and operating periods (for backtracking mode, limited range of motion, and normal tracking mode). Equations for the determination of the optimal row spacing and operating periods have been developed and is presented in detail. A packing algorithm that takes into account the irregular land shape and the possible configurations of the mounting systems is also presented. The objective function is the total area of the photovoltaic field and the optimisation is performed by a packing algorithm. As the economic aspect of energy generation also plays a key role in decision-making, the levelised cost of energy has been used to assess the economic viability of the optimal layout of the mounting systems. The results show that the proposed methodology and packing algorithm are able to optimise the photovoltaic plant with single-axis solar tracking and provide reliable results after a reasonable computation time. The methodology was demonstrated in detail for a Spanish photovoltaic plant (Granjera photovoltaic power plant), including the optimal layout of the mounting systems and the cost analysis for this layout. The optimal layout of the mounting systems could increase the amount of energy captured by 91.18% in relation to the current of Granjera photovoltaic power plant. The mounting system configuration used in the optimal layout is the one with the best levelised cost of energy efficiency, 1.09. The presented optimisation methodology can be utilised to facilitate the optimal design of commercial photovoltaic plants with single-axis trackers. Therefore, questions such as: what is the optimal distribution of mounting systems?, how much energy will this distribution produce?, and at what cost will it produce it?, can be answered by using the proposed methodology. • The optimal layout of single-axis solar trackers in large-scale PV plants. • A detailed analysis of the design of the inter-row spacing and operating periods. • The optimal layout of the mounting systems increases the amount of energy by 91%. • Also has the best levelised cost of energy efficiency, 1.09. [ABSTRACT FROM AUTHOR]

The increase of Renewable Energy (RE) production to fight the climate crisis is posing new technological and financial challenges, due to the availability and variability of RE Sources (RES). These challenges can be addressed by selecting the most suitable mix of RES to optimise power production, to assure grid resilience and to promote local energy use. To facilitate the selection of such combination, this paper presents an original methodology that allows to compare mixing scenarios with different RES, also in presence of batteries and backup system. It simultaneously optimises the energy surplus with respect to the eventual external electrical load and the missing energy with respect to the same electrical load. This method, which can cope with isolated or plugged-to-grid systems, is here applied to a novel case study, an oil&gas platform under decommissioning, located in the Adriatic Sea (Italy). The RE production from wind, wave and solar panels is supposed to support other activities for the platform reuse, such as aquaculture, monitoring and mineral deposition. In this case, solar energy is providing the greatest contribution to the optimal mix in terms of production, while wave energy assures the most relevant contribution in terms of continuity. [ABSTRACT FROM AUTHOR]

The focal length of the Fresnel lens changing with the light incident angle will decrease Fresnel lens solar concentrators' energy efficiency and daily working hours, which seriously hinders its commercial application. Existing solutions, mainly including single-axis tracking, two-dimensional tracking, and passive tracking have their limitations and fail to realize the maximum application potential of the Fresnel lens. This paper proposes a novel elastic Fresnel lens solar concentrator, whose focal length can be regulated by tensile deformation. The mechanical and optical coupling simulations are conducted to reveal its concentrating behavior during tensile deformation. An exemplary elastic Fresnel lens with an aperture width of 200 mm and an original focal length of 360 mm is designed and studied. The simulation results illustrate that the optimal base thickness of the elastic Fresnel lens is 1 mm when its prism thickness is 2 mm. Under the optimal thickness parameters combination, the elastic Fresnel lens has an effective elongation rate r about 15%, which increases the focal length by 163 mm and 47% to the original focal length. The focus width only increases from 16 mm to about 20 mm when adjusting the focal length from 360 mm to 523 mm. Besides, the step-zoom strategy increases the exemplified elastic Fresnel lens' acceptable angle from 30° to 45°. The tensile deformation caused zoom method increases the working hours of Fresnel lens by 1.5 times, demonstrating the effectiveness of this method. [ABSTRACT FROM AUTHOR]

This paper investigates conversion of a Nordic oil-heated townhouse into carbon-neutral by different energy efficiency (EE) improvements and an off-grid system including solar photovoltaics (PV), wind power, and battery and hydrogen energy storage systems (BESS and HESS). A heat-pump-based heating system including waste heat recovery (WHR) from the HESS and an off-grid electrical system are dimensioned for the building by applying models developed in MATLAB and Microsoft Excel to study the life cycle costs (LCC). The work uses a measured electrical load profile, and the heat generation of the new heating system and the power generation are simulated by commercial software. It is shown that the EE improvements and WHR from the HESS have a positive effect on the dimensioning of the off-grid system, and the LCC can be reduced by up to €2 million. With the EE improvements and WHR, the component dimensioning can be reduced by 22%–41% and 13%–51% on average, respectively. WHR can cover up to 57% of the building's annual heat demand, and full-power dimensioning of the heat pump is not reasonable when WHR is applied. Wind power was found to be very relevant in the Nordic conditions, reducing the LCC by 32%. [Display omitted] • Energy efficiency improvements notably reduce dimensioning of system components. • Waste heat from FC and electrolyzer covers up to 57% of the annual heat demand. • A full-power-dimensioned GSHP does not reduce the fuel cell dimensioning. • Seasonal storage on a small scale makes the system not economically viable. • Small-scale wind power is expensive, but it significantly reduces the system LCC. [ABSTRACT FROM AUTHOR]

In this paper, we explore the co-movements between renewable and non-renewable energy sources and CO 2 emissions and GDP per capita in Saudi Arabia, respectively. Using the spectral Granger causality approach, our results suggested that non-renewable energy sources lead to carbon emission expansion in the frequency domain. Taken together, a significant causal relationship from fossil to economic growth is perceived over the long-term horizon while renewable energy source drives economic growth over different frequencies and considerably contributes to carbon emissions reduction. The wavelet plots revealed strong discrepancies in all variables in the time-frequency space. While non-renewable energy sources indorsed the economic growth across short-and medium-term scales and contribute to a polluted environment, renewable energy consumption has a substantial effect on carbon emission drop and on economic growth in the short-term. The multiple wavelet outcomes show that renewable energy may not only stimulate the economic growth but also promote sustainable and cleaner environment. The combined effects of non-renewable energy sources rise the carbon emissions. Thus, Saudi's government is counselled to promote the green energy investments and accelerate the transition to renewable energy production as its use as a substitute of fossil fuels carries advantages and opportunities alternating from environment to social-economic. [Display omitted] [ABSTRACT FROM AUTHOR]

Obtaining reliable and high-quality wind speed probability forecast results is of great significance to wind energy utilization and power system management. In this paper, multi-network deep ensemble method, which combines the intelligent optimization algorithm and deep ensemble method, is proposed to deal with the probabilistic prediction problems. This method can effectively integrate a variety of different deep learning neural networks and provide reliable uncertainty estimates for prediction. Furthermore, spatiotemporal multi-network deep ensemble model, which employs multi-network deep ensemble method, is proposed to deal with the probabilistic spatiotemporal wind speed forecasting problems. In the model, three advanced convolutional recurrent neural networks are integrated to capture spatiotemporal information from the underlying meteorological variables. Intelligent optimization algorithm is used to assign weights to each network in the ensemble. In addition, an uncertainty quantification method, which quantify the uncertainty by adjusting the network structure and optimize the uncertainty by utilizing the truncated negative log-likelihood scoring rule, is introduced to provide reliable probability forecasts. The proposed model is applied to a real-world case in the United States. The test results demonstrate that spatiotemporal multi-network deep ensemble model can not only provide high-precision point prediction results, but also provide suitable interval predictions and reliable probability prediction results. Moreover, the impact of input features on model prediction results is also evaluated. [ABSTRACT FROM AUTHOR]

Global warming is one of the most important environmental problems in the world. One might state that the most potential considerable cause of global warming is the increase in greenhouse gas emissions in the atmosphere. The commonly suggested method used to struggle with this potential problem is to consume renewable energy sources instead of fossil fuels. In today's World, where the rate of urbanization is very high, the research on the effect of the energy demand of the residential sector on global warming has become a crucial research topic in the energy and climatic literature. Among different solar sectors, the residential sector is a major energy consumer. Considering the residential, commercial, and industrial solar sectors, the residential solar sector has become a major component of total solar energy. It accounts for approximately 43% of total solar. On the other hand, to the best of my knowledge, there exists no empirical study in the literature considering the influence of residential solar consumption on environmental quality. Thus, determining the effect of the most used energy source, residential solar, energy consumption on the environmental quality has arisen the key research problem of this paper. This research analyzes the effects of residential solar energy usage on CO 2 emissions for the period 1989:1–2020:1 in the USA. It has been observed throughout Morlet wavelet analyses that the use of residential solar energy has a strong effect on mitigating CO 2 emissions in the 1–2 year frequency. Eventually, this paper provides relevant potential energy policies that can enhance the efficiency of residential solar energy consumption. Partial wavelet coherence (RSolar-CO 2 || Coal, Hydro, IP), 1989:1–2020:1. [Display omitted] • Considers the impact of residential solar consumption (RSolar) on CO 2 emissions. • Follows the data for the USA for the monthly period 1989:1–2020:1. • Launches wavelet models in which RSolar, CO 2 , Coal, Hydro, and IP are employed. • Reaches the negative impact of RSolar on CO 2 in the USA for the period 1989:1–2020:1 at 1-2 year-cycle period. • Through potential energy policies, residential solar energy consumption efficiency should be increased. [ABSTRACT FROM AUTHOR]

The dispatch-down of excess wind energy is a growing concern, especially for countries integrating high levels of variable renewable energy. Demand flexibility presents an opportunity to move consumers loads to periods of excess wind energy, which could provide numerous values to the system. While previous research has focused on managing wind energy curtailment (a system-wide issue), much wind energy is rejected due to constraint (a local issue) and hence can only be resolved by local load-on-demand. This paper provides a framework to assess the value of demand flexibility for managing wind energy constraint and curtailment. A methodology to determine the optimal number of subscribers to yield sufficient reduction in excess wind energy while ensuring reasonable cost savings for the subscribers is developed. Analysis shows that this optimal number of subscribers could provide a 67% reduction in constraint and a 74% reduction in curtailment. Consumers can save up to £220 per year, depending on their priority in the dispatch process. A 10-MW wind farm could earn £19,400 annually from avoided curtailments. System operators could save up to 78% on constraint payments. The paper also assesses the network impact of flexible loads and provides a methodology for calculating the heat-pump hosting capacity of the grid. • A framework for utilizing wasted wind energy for heating homes is investigated. • Currently wasted wind energy could save fuel poor households up to £220 per year. • A 10 MW wind farm could earn about £20,000 annually from avoided curtailment. • System Operators could save up to 78% on constraint payments. • Mathematical model to determine heat pump hosting capacity of a grid is developed. [ABSTRACT FROM AUTHOR]

Solid fuel use is harmful to the health of human beings and hinders the sustainable development of society. Although a considerable literature has sufficiently researched the negative consequences of solid fuel use, fewer studies have explored the determinants of residential polluted energy use in the framework of culture and socioeconomics by focusing on ethnic diversity groups. This paper fills this gap by investigating the impact of religion on the decision to use solid fuel for rural households in ethnic minority regions of China. In particular, we argue that to a large extent, religion provides spiritual support in ethnic minority regions, and it is positively associated with solid fuel use. We examine the issue by employing a large-scale dataset, namely, the China Household Ethnic Survey. Furthermore, this paper identifies two channels of the above finding. First, a pension is regarded as a living support that has a moderate effect in shifting towards clean energy adoption. Second, households located in less remote regions are able to counteract the sole spiritual support of religion, which also mitigates solid fuel use. The results are robust after addressing endogeneity and applying recent developments of alternative models. This paper further confirms that households with various religious activities turn out fewer working hours and have larger related expenditures, which results in less disposable income for energy use. In terms of policy implications, this article suggests the importance of recognizing the role of substantial living support and social inclusion in mitigating the negative impact of religion on clean energy use among rural households of ethnic minority regions. [ABSTRACT FROM AUTHOR]

Given the role that renewable energy consumption plays in transitioning to a low-carbon economy, it is critical for policymakers to understand its determinants. Although uncertainty has increased significantly since the early 2000s, little is known about its effect. To fill this gap, the paper provides novel evidence of the effect of policy-induced uncertainty on renewable energy consumption after controlling for income, institutions and innovation in G7 countries over the period 1997–2019. Using the nonlinear autoregressive distributed lag model, the paper reveals its asymmetric effects in the long run, implying that both a negative and a positive shock in lagged uncertainty reduce renewable energy consumption. The effect of uncertainty proved to be significant in the same direction, but not asymmetric in the short run. However, the effect of all other variables remained insignificant in the short run, which is not the case in the long run. Indeed, the results confirm that income and investment in research and development drive renewables, while it is hindered by more economic freedom in the long run. Therefore, consistent, clear and well-communicated energy and economic policies are called for, along with technological advances, as they can help minimize policy uncertainty and promote renewable energy use. [ABSTRACT FROM AUTHOR]

The continuous increase in global energy consumption as a result of rapid population growth, combined with the detrimental impact of fossil fuels on the atmosphere, has made it critical to extract renewable energy through various technologies and turn it into electricity. A hybrid photovoltaic-thermal collector can be an innovative alternative to harness renewable energy. The utilization of hybrid photovoltaic-thermal solar collectors is increasing day by day due to its advantages. This type of collector can generate both heat and electricity at the same time and convert absorbed solar radiation more efficiently than a solar thermal collector or a typical photovoltaic module. In order to improve the performance of the hybrid photovoltaic-thermal (PV/T) solar collector, nanofluid can be an effective solution. It has been found that serpentine, rectangular, microchannel, sheet and tube, etc., are some categories of geometries utilized in the PV/T system. Both experimental and computational studies were conducted related to nanofluids application in PV/T collectors in recent years. Most of the studies were done utilizing some common nanofluids, e.g., Al 2 O 3 , CuO, ZnO, and SiO 2. It has been noticed that utilizing nanofluids significantly enhances overall performance, thermal and electrical efficiency, heat transfer characteristics, and exergy. This paper comprehensively reviewed various aspects of using nanofluids, including energy efficiency, exergy efficiency, overall efficiency, heat transfer coefficient, daily yield, power generation, entropy production, exergy loss, and temperature drop in hybrid PV/T collectors in a systematic way. Besides, the paper incorporated all recent findings and included experimental, numerical, and combined studies in the review. • MWCNT nanoparticle provides more efficiency compared to oxide-based nanoparticles. • Ag–H 2 O can enhance thermal output up to 13% than the water-cooled system. • SWCNT nanoparticles improve daily yield and have a relatively cheap production cost. • Energetic and exegetic performance can be increased by utilizing nanoparticles. • Using nanoparticles notably enhance power generation rate, and heat transfer ability. [ABSTRACT FROM AUTHOR]

The transition to renewable energy is key for sustainable development. Therefore, one of the sustainable development goals (SDG) of United Nations (UN) is to shift towards clean energy. However, in many resource-rich countries several factors can stand as barriers towards the transition process. In this regard, this paper examines the influence of higher oil price, CO 2 emissions, and income on renewable energy consumption in Iran from 1980 to 2019, employing General to Specific (Gets) modeling approach. Estimation results indicated that oil price and CO 2 emissions both have statistically significant and negative impact on renewable energy consumption. The negative influence of higher oil prices on renewable energy consumption may be interpreted as sign of satisfaction coming from higher oil prices, which postpones the transition from traditional energy sources to renewable for Iran. Furthermore, we concluded that income does not have a statistically significant influence on renewable energy use. The paper discusses alternative policies to promote renewable energy consumption. • The effect of higher oil price on renewable energy consumption was studied. • A novel version of General to Specific (Gets) modeling approach was employed. • We found significant, negative impact of oil price on renewable energy use in Iran. • The paper discusses alternative policies for Iran to promote renewable energy use. [ABSTRACT FROM AUTHOR]

The most important challenge for both developed and developing countries is to ensure sustainability while struggling with environmental degradation. CO 2 emissions as a proxy for environmental degradation can be considered an obstacle to sustainability. There exist several significant works in the literature on the effects of solar energy use on environmental degradation/sustainability. In this study, the effects of the use of solar energy within different time and frequency dimensions on CO 2 emissions were examined with the methodology of the continuous wavelet transform. The paper investigated the association between solar energy consumption and total energy-related CO 2 emissions in the USA through Morlet wavelet analysis, which is one of the most advanced time-frequency analysis methods for the period 1990:1–2022:6. In the wavelet coherency computations, geothermal energy consumption, hydroelectric energy consumption, industrial production, and manufacturing industry production variables were also included as control variables. Empirical findings demonstrate that solar energy consumption can have reducing effects on CO 2 emissions at lower frequencies (longer-term cycles) and sub-time periods (2014:1–2022:1) in the USA. The findings can guide the energy and environmental policies of developed and developing countries that aim to struggle with global warming and/or climate change through the increase in solar energy usage. Source: The graphical abstract is a slightly modified version of Bilgili et al. (2022), Fig. 2. [Display omitted] • Investigates the impacts of solar energy use on CO2 emissions in the USA. • Follows wavelet/partial wavelet transform models. • Employs the control variables of industrial production, geothermal and hydro energy, as well. • The reducing effect of solar energy on CO 2 emissions appears at a lower frequency. • Solar energy is not effective in the reduction of CO 2 emissions at higher frequencies. [ABSTRACT FROM AUTHOR]

In this paper, a high-efficient and three-dimensional compact heat pump system integrated with the concentrated photovoltaic plus fins (CPV/fin-SAHP) is constructed and built. Smaller space occupation and higher energy utilization are realized. The mathematical model is completed and validated by experimental data. By changing the throttling device and adjusting the mass flow rate, the system performance is improved. From the results, the exergy efficiency, heating capacity, total output, COP PVT , and COP th of the CPV/fin-SAHP system are increased by 7.92%, 13.2%, 11.01%, 4.73%, and 7.12%. Moreover, compared with traditional single-source heat pumps, the CPV/fin-SAHP achieved the highest total output and COP PVT , showing great competitivity and potential. Then two-dimensional environmental variable coordinates are established to explore the compound impact of different ambient factors, and the capacities of the three systems with adjusted opening degrees are also comparatively analyzed. In general, wind speed and ambient temperature have greater impacts on the finned structure system, while the irradiance is dramatically affecting the PV-SAHP system. The compact CPV/fin-SAHP system is less dependent on a single ambient element, showing satisfied performance under various operating ambient and presenting the most potential in comprehensive ability. [ABSTRACT FROM AUTHOR]

This study analyzes the impact of green finance and renewable energy on energy efficiency in E−7 economies. We analyzed the nonlinear and asymmetric effects of green finance and renewable energy on energy efficiency. We collected data from 1985 to 2017 and applied the NARDL and 2SLS methods. The results reveal that the differences in green finance development in the E−7 region are as follows: China (0.61), Brazil (0.55), India (0.53), Indonesia (0.49), Mexico (0.37), and Russia (0.39). In 2019, the Gini coefficient was the largest in Russia (0.57), followed by Turkey. Based on empirical findings, the largest barrier to green energy efficiency is insufficient private and governmental investment in the energy sector to improve access to power, to increase energy security and promote economic growth in an environmentally sustainable manner. The findings point to a promising but vulnerable future for renewable energy and energy efficiency deployment in E−7 countries. This paper emphasizes that green financing and renewable energy policy limitations must be addressed to realize the funding potential of energy efficiency in E−7 countries. • We have analyzed the barrieres for renewable energy and energy efficiency. • Non-linear and asymmetric effects is observed on energy efficiency. • We have applied NARDL model to examine the relationship among variables. [ABSTRACT FROM AUTHOR]

This paper presents and compares three approaches to deal with the uncertain nature of energy supply and demand in the problem of optimal sizing of a stand-alone, reliability-constrained hybrid renewable energy system. The system is composed of wind turbines, photovoltaic solar panels, a battery bank, and a diesel generator. The proposed approaches are: (a) adaptive robust optimization with unmet demand as the (un)-reliability metric, (b) scenario-based robust satisficing, and (c) stochastic-free robust satisficing. A column-and-constraint generation algorithm is developed to solve the adaptive robust optimization problem. Furthermore, the theoretical connection between the proposed approaches is drawn, and their numerical performances are compared, in terms of optimal solution cost and reliability, through computational experimentation on a realistic case study. Numerical results show that both the computational times and the out-of-sample performances of the optimal solutions obtained from the two proposed robust satisficing approaches outperform that of the adaptive robust optimization approach, providing a strong justification for their application • Robust optimal design of a stand-alone hybrid renewable energy system. • Adaptive robust optimization versus robust satisficing optimization. • Optimization under the uncertainty of demand and supply. [ABSTRACT FROM AUTHOR]

This paper seeks to identify promising sites and technologies, in Portugal, for co-located wave energy conversion and offshore aquaculture, whilst providing benchmark implementation references and guidelines to researchers. Accordingly, two case study sites are considered for deployment of five wave energy devices and up to six aquaculture species. A thorough analysis in terms of power ratios, efficiency, redundancy, species viability, device survivability and costs is performed, seeking to find viable co-located solutions. It is found that the Wave Dragon device yields the most promising energy demand coverage and energy output (5 226 to 6 817 MWh/year). Nevertheless, it may require rescaling towards optimal operation, while the OCECO 4 excels in terms of capacity factor (0.24–0.29) and default adaptation to the deployment sites. The WaveRoller® has the lowest single-unit cost (125 €/MWh) but requires up to nine units to cover all the energy demand targets. Larger wave farms are required for the BBDB and AquaBuOY, albeit with potentially greater economies of scale and single-unit redundancy. These sites also enable cultivation of most of the considered species, even under ideal conditions. Lastly, it is recommended that the devices enter survivability mode at a significant wave height threshold of 5.5 m. • Co-located potential of wave energy harvesting and offshore aquaculture in Portugal. • 5 WECs co-located with 3 + 3 aquaculture species in two deployment sites. • Estimation of energy/power ratios, efficiency, redundancy and species viability. • Wave Dragon stands-out on energy coverage, WaveRoller® on costs and OCECO on capacity factor. • 6/6 and 4/6 viable species for Figueira da Foz (2 ideal) and Sines (3 ideal) sites. [ABSTRACT FROM AUTHOR]

Improving the efficiency of wind turbine state prediction is an important goal of wind energy utilization. But much of abnormal data existing in supervisory control and data acquisition (SCADA) seriously affects the health state prediction of wind turbine. In this paper, a new composed method is proposed to clean SACAD data according to abnormal data type of wind turbine. In proposed composed method, a preprocessing method is first presented to get rid of outliers of power curve based on operational mechanism, and a new data cleaning method called TTLOF (Thompson tau-local outlier factor) is proposed to quantify particularly data points and eliminate outliers by setting correlation parameter thresholds. In TTLOF cleaning data, Empirical copula-based mutual information (ECMI) is used to select correlation parameters for anomaly characteristic assessments, and each parameter interval is divided for performing segmentation fine cleaning which can reduce the model complexity of identifying anomaly characteristics. Finally, a deep learning network which is long short-term memory (LSTM) is used to verify the effectiveness of the proposed data cleaning method. By analyzing the state monitoring results, it is shown the proposed composed method is more effective for cleaning anomy data than other methods. [ABSTRACT FROM AUTHOR]

Pressure retarded osmosis (PRO) is an evolving form of a renewable energy process which utilizes the salinity gradient energy from two solutions of different concentrations. One of the main problems limiting the application of PRO is the low performance of the commercially available spiral-wound modules which have poor flow distribution and high pressure drop. The present paper proposes a new spiral-wound module design for PRO application. The performance of the new module design was investigated numerically and compared with the available spiral-wound module. Compared to the available module, the power density of the new design was higher by 25% and 15% at draw concentrations of 35 g/kg and 60 g/kg respectively, while there was a 35% decrease in the pressure drop. The new spiral-wound module design presents an enhanced overall performance due to the better flow distribution and lower pressure drop, yielding a higher water flux and power density. [ABSTRACT FROM AUTHOR]

Hydropower renewable energy is known to be pro-environment: flexible and affordable. Above all, it is compatible with other energy frames and also with the policies that otherwise prove to be inimical towards its future. Where environmental stability is concerned it needs an assiduous study coupled with research by the policymakers to get around knowing the different perspectives of hydropower renewable energy. The study has taken nine different and diverse perspectives of hydropower renewable energy into account to stop the process of environmental degradation. EKC framework has been put to use in a case study of South and East Asia economies. Time period has been selected from 1975 to 2020 to measure the long-term impact. GMM model has been used for the panel estimation besides getting future dynamics. The results which have come about confirmed EKC in the selected countries. Non-renewable energy (coal and gas) had an adverse impact on environmental stability. However, switching towards hydropower renewable energy can mitigate these environmental losses. Empirical findings have proved that more use of clean energy production can bring about a considerable amount of environmental stability. Insofar as East Asia economies are considered they have reached the second stage of EKC with a turning point of $13844 (per capita income). This paper, therefore, suggests that the future depends upon renewable hydropower energies in South and East Asia economies. That being said, there appears to be a dire need to undertake the role of various socioeconomic and geographic factors that influence hydropower renewable energies to check the long-term effect. [Display omitted] [ABSTRACT FROM AUTHOR]

As the penetration of distributed renewable energy gradually increases, prosumer groups in high-altitude areas are being expanded. However, the operation of these prosumers in high-altitude areas faces numerous challenges. To address the issue, this paper proposes a double layer energy cooperation framework for high-altitude prosumer groups, considers peer aggregation and shared composite energy storage, aims to adapt to the trend of diversified energy interactions and improve the economic efficiency, maximize social welfare and ensure the continuous growth of the sharing market of prosumers operations in high altitude areas. To meet the diversified energy interaction needs of prosumers, a composite energy storage provider, an energy aggregator, and a two-stage profit clearing scheme are proposed. Furthermore, the overall benefits of the framework and the impact of profit clearing schemes on fairness under various scenarios are studied, and the comprehensive effects of the double layer energy framework are evaluated. The study results show that compared to independent operation by prosumers, under the guidance of shared electricity prices and composite energy storage providers, the economic benefits, evaluation indicators and energy storage utilization efficiency have all been improved. In addition, compared to other profit distribution modes, the two-stage profit clearing scheme proposed in this paper has higher fairness, which greatly encourages the participation of interested entities in energy cooperation. This study provides a feasible framework for energy cooperation among prosumers, and the framework's effectiveness is well-validated. [Display omitted] [ABSTRACT FROM AUTHOR]

The increasing demand for renewable energy sources has led to the development of HRES that combine multiple sources of renewable energy to provide a reliable and efficient energy supply. However, renewable energy sources typically come with higher costs than conventional energy production methods. Hence, the optimal sizing of HRES is essential to prevent the oversizing of the system while ensuring a reliable supply of load demand, accommodating variations in meteorological conditions and changes in power demand. Numerous research papers addressing the optimal sizing problem are available in the literature. However, none of the mentioned studies take into account the power degradation of all renewable sources. The degradation of renewable sources, such as PV and WT, can have a significant impact on the performance of HRES. The degradation of PV panels can result in reduced efficiency and output, leading to a decrease in the overall energy production of the HRES. Similarly, WT may experience wear and tear over time, leading to reduced performance and output. The reduction in the output of renewables in the standalone system causes an increase in the loss of power supply probability (LPSP). This paper analyzes the optimal sizing of the HRES, considering the degradation of the sources. Further, analysis has also been carried out based on the number of BESS to be added to HRES to meet the derived constraints and their impact on the cost of energy as well. [Display omitted] [ABSTRACT FROM AUTHOR]

The increasing demand for renewable energy sources has led to the development of HRES that combine multiple sources of renewable energy to provide a reliable and efficient energy supply. However, renewable energy sources typically come with higher costs than conventional energy production methods. Hence, the optimal sizing of HRES is essential to prevent the oversizing of the system while ensuring a reliable supply of load demand, accommodating variations in meteorological conditions and changes in power demand. Numerous research papers addressing the optimal sizing problem are available in the literature. However, none of the mentioned studies take into account the power degradation of all renewable sources. The degradation of renewable sources, such as PV and WT, can have a significant impact on the performance of HRES. The degradation of PV panels can result in reduced efficiency and output, leading to a decrease in the overall energy production of the HRES. Similarly, WT may experience wear and tear over time, leading to reduced performance and output. The reduction in the output of renewables in the standalone system causes an increase in the loss of power supply probability (LPSP). This paper analyzes the optimal sizing of the HRES, considering the degradation of the sources. Further, analysis has also been carried out based on the number of BESS to be added to HRES to meet the derived constraints and their impact on the cost of energy as well. [Display omitted] [ABSTRACT FROM AUTHOR]

With the manufacturing reshoring to the US, increasing attention are focus on its energy consumption and environmental effects and accurate prediction of carbon emissions is vital to controlling growth from the source. Considering the slowing growth in carbon emissions with the Gompertz's law, this paper establishes a Gompertz differential equation. According to the differential information principle and fractional accumulation operator, this differential equation is transformed into a fractional accumulation grey Gompertz model. Furthermore, the chaotic whale optimization algorithm is used to optimize the order of accumulation generation and the grey background value in the proposed model. Then the Gompertz's datasets and six validation cases about carbon emissions are used to show that the proposed model demonstrates better accuracy in all cases and efficiency in the carbon emissions forecasting with several existing models. Three case studies indicate that the proposed model can fit the trend of American industrial carbon emissions better. The model results also reveal the recent policy changes have promoted the uptrend of the industrial and the total carbon emissions in the U.S. The future forecasting suggests that U.S. carbon emission is estimated to be 17.01% (in total emissions) or 17.89% (in industrial emission) percent below 2005 levels by 2025 under current policies, falling short of its commitment submitted to the United Nations Framework Convention on Climate Change. [Display omitted] • This paper discussed the Gompertz's law in carbon emissions. • A FAGGM(1,1) model is created for predicting carbon emissions. • The case study reveals the rising of peak in the industrial carbon emission in U.S. • The reduction percentage of carbon emission on 2005 levels by 2025 will be 17.01% (in total), 17.89% (in industry) and 26.23% (in commerce) in U.S. [ABSTRACT FROM AUTHOR]

There has been an increase in the use of renewable energy sources over recent years, which has led to a strand of literature examining the determinants of renewable energy consumption. However, most of the research used linear estimation models while reviewing the determinants of renewable energy consumption and ignored the indirect effect of democratic institutions on renewable energy consumption. With the use of a panel threshold model, this paper demonstrates that democratic institutions play a significant role in renewable energy consumption. In countries in which the democratic rights of people are preserved better, higher economic growth leads to increased use of renewable energy consumption; however, there is a negative association between economic growth and renewable energy consumption in less democratic countries. Increased trade openness leads to lower growth rates of renewable energy consumption in less democratic countries, while increases in real oil prices lead to increased renewable energy consumption in less democratic countries but play no significant role in more democratic countries. The findings of this paper suggest that democratic institutions are vital in channelling economic resources (economic growth) to renewable energy, and increased trade openness is associated with lower rates of renewable energy deployment in less democratic countries. [ABSTRACT FROM AUTHOR]

This paper considers off-grid microgrids (MGs) from the Philippine archipelago and analyses their energy generation in differents aspects. Seven different energy clusters are used, representing realistic configurations and renewable energy shares. A Robust Model Predictive Control (MPC) framework is used for the energy management and coordination task of these island MGs. The MPC is based on a min./max. optimization procedure, which takes into account the whole uncertainty set. The reliability of the MG operations are analysed with respect to the different clusters; this evaluation is conducted using μ -analysis, performed with respect to the baseline model and the uncertainty set. The demand-side compliance of the MG is also investigated, with respect to stochastic behaviours of the demands and of the renewable sources (wind and solar). Numerical simulation results are presented in order to demonstrate that reliable power outlets are produced despite variation in renewables and of the demands. This paper offers a thorough analysis of simple energy system coordinated via MPC, showing how this method can indeed be used for renewable MG management, offering robustness and ensuring reliability. • A generalized model is presented for Off-Grid Microgrids with multiple carriers. • The Philippine Off-grid islands are considered, with diesel, solar, and wind energy. • A Robust Model Predictive Control scheme is proposed to manage these Microgrids. • Demand compliance and operation reliability are assessed through robustness analysis. • Numerical simulation illustrate reliability under multiple scenarios. [ABSTRACT FROM AUTHOR]

The global economic costs of environmental pollution increase the importance of renewable energy sources. This paper analyzes the impact of financial development on renewable energy consumption in 34 upper middle income developing countries from 1994 to 2015. The long-term relationship between variables is estimated by applying Panel Pedroni cointegration and Kao cointegration tests. The long run effect of financial development on renewable energy consumption is investigated by using Fully-Modified OLS (FMOLS) approach. The empirical results indicate the presence of long run relationship between renewable energy consumption and financial development. Moreover, financial development increases the demand for renewable energy. Economic growth has a negative effect on renewable energy consumption, but consumer prices have a statistically insignificant impact on renewable energy consumption. The empirical evidence reveals that financial development triggers in increasing demand for environmentally friendly energy sources, i.e., renewable energy. Renewable energy consumption reduces the amount of greenhouse gases in nature as opposed to fossil energy consumption. Therefore, to achieve sustainable development goals, governments should implement incentives and tax policies that increase the demand of enterprises for renewable energy resources. In addition, investment opportunities in renewable energy resources to be created by public-private cooperation via financial arrangements should be increased. - This paper examines how financial development affects renewable energy consumption? - The panel Pedroni cointegration and Kao cointegration approaches are applied. - The Fully-Modified OLS (FMOLS) approach is employed for long run association. - Financial development promotes renewable energy demand. - Economic growth declines renewable energy consumption. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the association between hydroelectric energy consumption and CO 2 emissions in the USA from 1980:1 to 2019:8 by using the wavelet transform model. This research revealed that (a) in the short runs (at higher frequencies), hydro energy uses intensified CO 2 emissions for the periods 1990:01–1992:12, 1994:01–1994:12, and 2002:07–2007:11, and (b) during the longer periods (at lower frequencies), however, hydro energy consumption diminished CO 2 emissions for the periods 1983:01–2001:12 and 2011:01–2017:03. The paper explained as well why hydro energy can yield adverse and affirmative contributions to Greenhouse gas emissions by emphasizing the role of energy generation from hydro plants in shorter runs and longer runs in the USA considering all sub-samples of the sample period 1980:1–2019:8. This research eventually suggests some energy policies to enhance the positive environmental influences of hydropower energy production in the USA. [ABSTRACT FROM AUTHOR]

This paper investigates the sustainability of Tabriz City focusing on its decarbonization. Statistical, quantitative, and comparative research methods are used to analyze the total energy consumed, conversion of primary energy, final exergy, and losses analysis according to the renewable and carbon-based energies. Then, the renewable resource potentials of the city are simulated using Grasshopper plug-in-Rhino simulator, THERM, PVSyst photovoltaic software, and ArcMap. In terms of the socio-economic assessment, non-oil Per Capita GDP, emissions, and energy intensities are analyzed concerning the B2DS scenario by 2050. The per capita GDP of the city is 71% of the country's average value. Tabriz's real GDP growth averaged −1.11% per year, primary energy demand increased by an average of 6.1%, CO 2 emissions from energy use grew by an average of 6% annually and the emission intensity of the economy increased by a total of 52.02%, in the period from 2011 to 2019. Finally, it is shown that the city consumes 58050 GWh/year of different carbon-based resources that only produced 20110 GWh/year of useful work with 5.63 MT CO 2 emissions, while the same work could locally be supplied by 30000 GWh/year RE with a minimum rate of 0.18 MT CO 2 emissions. The city Landfill receives 1000 tons/day of MSW, but two-thirds of this amount can be recycled. Regarding different sectors, buildings produce half of the CO 2. The cement industry with dual pollutants (17%) is the most polluting factory. Carbon-based electricity (66%) is the most polluting form of energy. In terms of carbon sequestration requirements with trees, shifting from fossil fuels toward green energies, EE, and recycling MSW can plunge required trees from 81.9 to 1.16 million trees. This paper highlights the importance of electrification of transportation based on RE, energy performance of buildings, and all sectors. • Large city decarbonization strategies • Application of RE according to geographic and climatic factors. • Electrification of sectors based on RE locally. • MSW recycling and sequestration by trees for decarbonization regarding the LULC and NDVI analysis. • Conversion and storage efficiencies of carbon-based fuels vs RE. [ABSTRACT FROM AUTHOR]

This paper constructs four integrated power generation systems using liquefied natural gas (LNG) cold energy and geothermal energy. These systems contain a geothermal single flash cycle, LNG expansion process and organic Rankine cycle (ORC). Different mathematical models are established to simulate the systems under steady-state conditions respectively. The influences of key working parameters on energy efficiency, exergy efficiency and levelized cost of energy (LCOE) are analyzed, and the comprehensive performances are estimated. The results show that increasing separator pressure, LNG turbine and Rankine turbine inlet pressures could increase the energy and exergy efficiencies. Also, a higher LNG turbine outlet pressure leads to a higher LCOE. To achieve the best system performance, single- and multi-objective genetic algorithm (GA) based optimizations are performed respectively. By single-objective optimization, GSF-TSORC and GSF-TPORC can reach the highest energy and exergy efficiencies of 34.80% and 52.61% respectively, and GSFC has the lowest total cost rate of 0.326 M$·year−1. After using multi-objective optimization, the energy and exergy efficiencies of GSF-TPORC are 29.56% and 51.29% respectively, which are superior to the other three systems, from the thermodynamic point of view. From an economic perspective, GSF-ORC has the lowest LCOE of 0.028 $·kWh−1, implying that it is the best system. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy (RE) generation in power systems, RE companies are gradually accepted as regular players in electricity markets. Compared with the conventional way, RE generation promotes economic and environmental protection, but also brings uncertainty to power systems. Hydrogen storage stations (HSSs) can alleviate RE's fluctuating generation. Moreover, the integration of hydrogen and electricity has superiorities to mitigate the spatial imbalance of the RE distribution and achieve higher energy efficiency. To this end, the framework of a local integrated electricity - hydrogen market is designed in this paper. Trading mechanisms among RE generation companies (GenCos), HSSs, power distribution company and other hydrogen-related participants are first introduced, based on which the mathematical models are presented. A two-stage market clearing model is then proposed to determine the optimal trading amount and allot profits based on Shapley value. A simple case shows that by participating in proposed market, the power deviation between offering and available capacity of GenCos decreases by 69.5% and 86.0%, respectively, while the profit of HSSs and GenCos is $ 1.60 × 103 within a week. Therefore, cooperating with the hydrogen market can be a potential way for power systems to integrate high proportion of RE. [ABSTRACT FROM AUTHOR]

Due to the consumption of renewable natural resources caused by population growth, industrialization, and urbanization, renewable resources' effectiveness has become controversial in recent decades. This paper examines the effect of issued green bonds on the effectiveness of renewable natural resource utilization in 15 selected Asia-Pacific countries during 2012–2021. Conducting the Fully Modified Ordinary Least Squares (FMOLS) estimation, the preliminary results confirmed that the issued green bonds have positive and statistically significant impacts on renewable natural resource utilization efficiency in the short-term and long-term. However, the magnitude of the impacts is more extensive in the long-term context. Urbanization has a negative short-term coefficient, while it positively impacts the natural resource utilization efficiency in the long term. Inflation rate negatively affects renewable natural resource utilization efficiency. As practical policies, implementing operational plans to develop the green bonds market, promoting green foreign direct investment, efficient monetary policy to lower inflation rate, and enhancing digital green bonds are recommended. • The green bonds positively impact on green resource consumption efficiency. • Inflation rate is harmful for green resource utilization efficiency. • FDI has only long-term positive impact on green resource utilization efficiency. [ABSTRACT FROM AUTHOR]

Parabolic trough solar collector (PTC) system with a flat receiver is widely used in concentrating photovoltaic/thermal (CPV/T) systems, where the flat receiver and PTC are the key components to exchange the solar radiation into heat or electricity. The optimal design and geometric structure of PTC using a flat receiver are essential to achieve high efficiency and energy yield for CPV/T systems. In this paper, a mathematical model of PTC with the flat receiver was developed to investigate and evaluate optical properties based on Monte Carlo Ray Tracing method, whose feasibility and reasonability was verified by the comparison between the model and published literature's results. An innovative method was proposed to calculate the minimum width by vectoring. The optimal geometric design for the same geometrical focusing ratio was derived through simulations based on MATLAB. The influence of geometric parameters and errors were discussed, which showed that the optical performance has large variations in local concentration ratio (LCR) at the center of the flat receiver and is superior at rim angles of 75°–90° under ideal conditions with the same geometric focusing ratio. The errors were found to have a significant effect on the optical performance, especially on the peak LCR. [ABSTRACT FROM AUTHOR]

Electricity demand in Benin has continuously increased due to rapid population growth, and demand is expected to increase further in coming years. The Benin government wants to increase its renewable energy production capacity by 2030 via its Action Program (PAG), to reduce energy deficits, and guarantee electricity access for its entire population by 2035. This article analyzed the energy status of the country with a focus on energy production, consumption, and imports, from 2010 to 2018, based on reports from national research agencies and international academic papers. We found that, although Benin has good potential for using renewable energy, potential generation would only supply a small portion of the country's energy needs resulting in increased imports from neighboring countries, which were already at about 1320 GWh in 2018. We observed that solar power electricity production increased considerably, from 495 MWh in 2014 to 5391 MWh in 2018. Meanwhile, only 4.8 million of inhabitants had access to electricity in 2017. Analyses of the West African Economic and Monetary Union (WAEMU), show that Benin is the largest consumer of oil products (26.6%), and an electricity importer (25.6%), despite being home to only 9% of the total WAEMU population (more than 123 million inhabitants) in 2018. [ABSTRACT FROM AUTHOR]

The present paper explored the effect of renewable energy, financial development, and technological innovation on the ecological footprint in Brazil using a yearly dataset stretching between 1990 and 2018. The research leverage dynamic autoregressive distributed lag, Bayern and Hank cointegration, and frequency domain causality to explore these interrelationships. The ARDL bounds test outcome confirms a long-run connection between ecological footprint and its drivers. Besides, we employed the Bayern and Hank cointegration, and the outcome resonated with the ARDL bounds test result. Moreover, the dynamic ARDL outcomes disclosed that economic progress worsens the ecosystem whilst renewable energy, trade globalization, and technological innovation lessen the ecological footprint. In addition, no significant interrelationship was found between financial development and ecological footprint. Moreover, with the exemption of financial development, all the exogenous variables can forecast the ecological footprint in Brazil. The study proposes that policymakers in Brazil should undertake investments in research and development in order to achieve technological innovation. Furthermore, since technological innovation has been shown to contribute significantly to lowering the ecological footprint, it is possible to assume that enhancing current technology is the ultimate remedy to Brazil's ecological problems. • Dynamic ARDL applied to Brazil's time series data. • Renewable energy and Technology innovation improve environmental quality. • Trade and globalization also reduce Ecological Footprints (EF). • Economic Growth contributes to higher EF. [ABSTRACT FROM AUTHOR]

In the current paper, a novel portable solar-based poly-generation system is proposed and it is experimentally investigated. The system is consisted of photovoltaic panels, evacuated solar collector, vapor compression refrigeration, and desalination section. Total power consumption of cooling cycle, heating cycle, HDH desalination cycle and RO desalination cycle is measured equal to 330 W, 70 W, 36 W and, 36W, respectively. Photovoltaic panels could supply 471 W electricity and the compressor uses the most produced power. 16.5 L distillated water per hour could be supplied by the system, also. Excess electricity and heat are stored during the day to guarantee continues operation of the system. Unique electronic boards are applied in the facility and several sensors are used to evaluate the operation of different sub-systems. Wireless sensor data acquisition and control monitoring are used according to the energy demand. [ABSTRACT FROM AUTHOR]

For the physical model testing of wave energy converters (WECs) in the wave basin, it is necessary to test the models in a small number of sea states. Previously, the H – T binning method was widely used to determine the sea states that are representative of an ocean area. However, it omitted much useful information such as the wave directionality. In this paper, a novel method, the K -means clustering technique is used in combination with High Frequency (HF) radar measured data from Wave Hub, UK. The results show that K -means clustering method better preserves the characteristics of the ocean area than the binning method. Furthermore, the impact of different regrouping methods on assessing the annual energy output of the model is investigated, by applying the K -means clustering method to a 1:25 two-body hinged raft WEC. It is found that although non-linear performance can be clearly observed in the model both physically and numerically. Due to the fact that most sea states from Wave Hub are out of the non-linearity range of the model, the non-linear effect on the overall performance of the WEC model in this ocean area is limited. It allows the annual energy output to be accurately predicted by using only a small number of representative sea states (defined as K) ≤15, based on K -means clustering method. • K -means method selected representative sea states tested on a physical WEC model. • K -means method is effective in selecting the sea states for WEC model testing. • Representative sea states can obtain accurate annual energy output estimation. • Non-linearity of WEC tested had limited influence on annual energy output estimation. [ABSTRACT FROM AUTHOR]