Search

There is some new challenges about the greenhouse gases, climate changing, and weather warming. The applying of the various Energy Policies (EPs) on production sections, is the prevalent solution that considers promotion of the renewable energies. The result is enhancement of penetration rate of the Renewable Energy Sources (RESs) in the power systems. However, till now, there are little papers that assessed the effects of EPs on the social welfare. The social welfare consists of the financial profits of the RES production company as well as the surplus welfare of subscribers. In this paper, the results of the implementation of a well–known EP as the Renewable Energy Certificate (REC) and its effect on the surplus welfare are assessed, based on the Generation Expansion Planning (GEP). In order to obtain the paper aims, the desired EP and the GEP are mixed, and a comprehensive model is presented from the perspective of an RES production company. The model is solved in the form of a Mixed Integer Non–Linear Problem (MINLP) in the GAMS optimization software using the BARON optimizer. Also, in order to reach complete solution, two scenarios as without and with the proposed EP are considered, and the results are compared with other EPs. For evaluation of the social welfare, a criteria as the Virtual Price Index (VPI) is introduced. With analysis of the results, it could be seen that the surplus welfare of the subscribers increases in the long term horizon, while it was lower than profit of the RES production companies at short terms. • Using of REC-EP to study the GEP, in presence of RES units. • Analysis the SW, social welfare, and the company profits under REC-EP. • Using of robustness method, MINLP, to solve the problem, considering real limits. • Comparing the first scenario results with other methods secured proposed method. • Comparing REC-EP results with others in the second scenario shows its high worth. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources is increasing worldwide to achieve energy production sustainability. To reach this goal and mitigate against climate changes all energy resources must be optimally managed. In this paper, an innovative multi agent-based heuristic optimisation system is developed to address renewable energy systems' challenges associated with the management of renewables including storage devices, planning of energy distribution, and consumption flexibility. A new heuristic algorithm is developed to introduce an efficient and sustainable storage strategy of surplus energy generated by different renewable resource options. This leads to archiving optimised storage levels of energy across different located storage devices in order to face risk of production shortages due to weather conditions. In order to test the behaviour of the developed system and address its benefits, a case study in the Republic of IRAQ is developed. Twelve major cities (in order of population size) distributed in 5 main regions including North, South, East, West and Middle parts of the country are selected, and hybrid renewable power generation sources such as Wind, PV-solar and Hydro are implemented based on weather changes. The results highlight the effect of high suppliers' rate on electricity exchange and production planning from differently used renewable technologies. A sensitivity analysis is conducted to verify the behaviour of the developed model on different demand behaviour types. • This paper considers a complex renewable energy production-distribution problem. • This study proposes an agent-based model to satisfy consumers' demand of energy. • A new heuristics algorithm to provide a sustainable storage strategy of energy. • Simulation result presents optimal energy production and minimum level of shortage. [ABSTRACT FROM AUTHOR]

Non-dispatchable Renewable Energy Sources (RES) changed energy production from being centralised and fully dispatchable, to be more decentralised and less predictable. Despite the substantial growth, RES must be increased to fulfil the power production decarbonization targets set by several countries. In several countries, Italy included, RES development must be based on solar PV. Thus, relevant energy quantities will be shifted from day hours to night hours. Such "Load Shifting" is done with energy storage technologies. A few technologies suited for this task are already available, whereas several others have been proposed, but not tested in the practice. In this paper, such storage technologies are reviewed focusing on the performance and costs. Based on the review, current and future storage economic outlooks are assessed by focusing on the Italian scenario. In the paper, the storage operation is optimized at the hourly level to calculate the maximum achievable annual revenue. The optimisation is performed with a linear programming (LP) approach. Since none of the reviewed storage is economically feasible, the energy price modification required to achieve feasibility are estimated. Based on such results, the distance between the current situation and the one favourable to storage is assessed. In this way, the future outlook of each storage technology is discussed. • A review of storage technologies suited for grid-scale applications is presented. • The data from the review are used for an economic feasibility analysis. • The revenue is maximised over a year through a linear programming problem. • The cost over revenue ratio quantifies the required incentive from support schemes. • The energy price modification required to make any storage feasible is discussed. [ABSTRACT FROM AUTHOR]

The growing energy demand places its focus on renewable energy resources as a sustainable energy resource. Wind Power is one of the important sources of renewable energy. However, it exhibits challenges in the form of stability and control when integrating with the conventional grid. Though the connection of wind power plants does not require any major redesign, it is, however, necessary to ensure that issues are taken care of through control and compensative devices, to enable the grid to recover from grid faults and system disturbances. One such proposal is discussed here. This paper analyses the design and implementation of Proportional-Integral (PI) controlled Static Synchronous Compensator (STATCOM) controllers and is compared with Fuzzy Logic Controlled(FLC) STATCOM controllers. These controllers are primarily used to stabilize the voltage in wind power plants by preventing tripping of Wind Generators (WG) in the event of a grid side disturbance or fault and can recover from the fault by injecting reactive power in the grid. This paper applies STATCOM for restoring the voltage levels at the terminals of the wind power plants and simulated using MATLAB Simulink. It is observed from the simulation results, that STATCOM with fuzzy logic controls tends to reduce the voltage fluctuation and ensure the stability of the system as compared to STATCOM with PI. • STATCOM with PI controller improves voltage regulation. • Enhanced performance with the usage of STATCOM with Fuzzy Logic controller. • Enhanced gain stability in the event of network disturbances. • Improves transient performance. [ABSTRACT FROM AUTHOR]

Short-term forecasting of electricity generation from variable renewable energy sources is not an end in itself but should provide some net benefit to its user. In the case of electricity trading, which is in the focus of this paper, the benefit can be quantified in terms of an improved economic outcome. Although some effort has been made to evaluate and to improve the profitability of electricity forecasts, the understanding of the underlying effects has remained incomplete so far. In this paper, we develop a more comprehensive theoretical framework of the connection between the statistical and the economic properties of day-ahead electricity forecasts. We find that, apart from the accuracy and the bias, which have already been extensively researched, the correlation between the forecast errors and the market price spread determines the economic implications - a phenomenon which we refer to as 'correlation effect'. Our analysis is completed by a case study on solar electricity forecasting in Germany which illustrates the relevance and the limits of both our theoretical framework and the correlation effect. • The economics of short-term electricity forecasts are studied analytically and empirically. • The value and the risk of forecast-based electricity trading are evaluated. • We find that the economics depend on the correlation of forecast errors with prices. • Hence, the most accurate forecast is not necessarily the most accurate. [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 renewable energy sources such as wind farms become dominant, new challenges emerge for operating and controlling them. Traditionally, wind farm control aims to dispatch power set-points to individual turbines to maximize energy extraction and, thus, their usage as assets. Yet, grid balance and frequency support are fundamental in presence of high renewable penetration and volatility of energy prices and demand. This requires a paradigm change, moving from power maximization to revenue maximization. In this paper, three active power control strategies pushing this shift of paradigm are investigated, namely: wake-loss compensation, thrust balancing, and load-limiting control. The findings of large eddy simulations of a reference wind farm show that wake-loss compensation indeed improves the power generation on waked wind farms, but at the price of increased structural loads on certain turbines. The addition of a thrust balancing can equalize the stresses of individual turbines and their wear in the long term, while still attaining the required power output at the farm level. Furthermore, load-limiting controllers could potentially aid by allowing maintenance to be scheduled in a single time window, thus reducing operation and maintenance costs. • Wind farms can provide ancillary services while considering instantaneous loads. • A three-pronged approach enhances power reliability and improves structure safety. • Less is more: derating allows extended service life by reducing structure damage. • The profitability of wind farms can be increased via power tracking strategies. [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]

The modern lifestyle and economic development of the European Union countries are closely connected to high energy consumption and environmental pollution. Renewable energy has arisen as one solution to this problem, even though the obstacles and challenges regarding the deployment of renewable energy lie in high costs, technology and legislation. The investigation of the determinants of renewable energy has become very attractive and popular because of the Sustainable Development Goals and COP26 targets. Therefore, this paper aims to explore the role of energy taxes and environmental taxes in addition to that of economic and environmental indicators in renewable energy development for the panel of EU countries by applying reliable and robust econometric techniques to the annual data from 1995 to 2019. The empirical results suggest that an increase in economic growth and oil prices supports renewable energy while environmental taxes and energy taxes have a negative impact on renewable energy deployment in EU countries. Thus, it is crucial to reform the structure of taxes to support the use of renewable energy. In addition, increasing environmental taxes to transform EU countries into energy-efficient economies will require additional EU policy adjustments. [Display omitted] • The nexus between energy and environmental taxes, and renewable energy is analyzed. • Several reliable and robust econometric techniques are employed for estimations. • Environmental taxes negatively impact on renewable energy deployment. • Energy taxes decrease renewable energy. • Economic growth and oil prices support renewable energy. [ABSTRACT FROM AUTHOR]

Understanding the interactions among climate change, carbon emission allowance trading, crude oil and renewable energy stock markets, especially the role of climate change in this system is of great significance for policy makers, energy producers/consumers and relevant investors. The present paper aims to quantify the time-varying connectedness effects among the four factors by using the TVP-VAR based extensions of both time- and frequency-domain connectedness index measurements proposed by Antonakakis et al. (2020) and Ellington and Barunik (2021) [8,48]. The empirical results suggest that, firstly, the average total connectedness among climate change, carbon emission allowance trading, crude oil and renewable energy stock markets is not so strong for the heterogenous fundamentals underlying them. Nevertheless, the time-varying total connectedness fluctuates fiercely through May 2005 to September 2021, varying from about 8% to 30% and rocket to very high levels during the global subprime mortgage crisis and the COVID-19 pandemic. Furthermore, the total connectedness mainly centers on the short-term frequency, i.e., 1–3 months. Secondly, climate change is generally the leading information contributor among the four factors, although not particularly strong, and its leading role also performs mainly on the short-term frequency (1–3 months). Thirdly, renewable energy stock market and crude oil market show tight interactions between them and they are the two major bridges of information exchanges across various time frequencies (horizons) in this system. Finally, we confirm the evidence that the primary net connectedness contributor and receiver switch frequently across different time frequencies, implying that it is extremely essential for policy makers, energy producers/consumers and investors to make time-horizon-specific regulatory, production/purchasing or investment decisions when facing the uncertain effects of climate change on the interactions among carbon emission allowance, crude oil and renewable energy stock markets. [ABSTRACT FROM AUTHOR]

Against the backdrop of rising environmental concerns and an increase in awareness for renewable energy, the current paper explores the factors influencing renewable energy consumption by drawing a sample for BRICS nations from the period 1990 through 2015. To do so, we use dynamic common correlated effects, panel quantile regression methods and other robustness checks and find that environmental policy stringency index and human development index have significant and positive impact on renewable energy consumption. Other variables including carbon dioxide emission and gross fixed capital formation show statistically significant and negative link with renewable energy use. Our results emphasize upon stringency in existing environment policies and promoting renewable energy consumption. [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]

The environmental impact of foreign direct investment (FDI) is inconclusive, and FDI's different effects are generally explained through various factors. Meanwhile, this paper explores the effect of FDI on renewable energy consumption. Environmental regulation is considered as a moderating variable and a threshold variable simultaneously to capture the nonlinear association between FDI and renewable energy consumption. The panel threshold method was employed for empirical estimations using the dataset of the BRICS countries from 1990 to 2015. In addition, the generalized method of moments (GMM) models—both system GMM and difference GMM models—were applied to verify the robustness of this work. When formal environmental regulation was used as a moderating variable, the empirical results indicate that FDI reduces renewable energy consumption when the degree of regulatory stringency is lower than a threshold level. In addition, it turns to foster renewable energy consumption once the stringency is higher than the threshold. A similar finding was obtained when an informal environmental regulation was considered. Furthermore, the findings suggest that stricter environmental regulation can substantially promote renewable energy consumption emanating from FDI. [ABSTRACT FROM AUTHOR]

Well-managed demand-side flexibility can effectively alleviate the stress of power systems caused by the increasing renewable energy penetration. The development of smart grid technologies provides win-win opportunities for system operators and demand-side users. Buildings have great flexibility potential through smart control of flexible loads, passive thermal mass storage and distributed active storage. However, a few fundamental problems on flexibility quantification, optimal coordination and profits sharing are faced when aggregating buildings to reach sufficient size for biding multiple flexibility services in electricity markets. Therefore, this paper proposes a multi-level optimal dispatch strategy and profit-sharing mechanism for non-residential building clusters to maximize flexibility profits and reduce the complexity of the optimization. At building level, a model-based quantification method is developed to evaluate flexibility potential of multiple resources. At cluster level, the coordinated bidding strategy of the aggregator is proposed, and flexibility profits are distributed based on the cooperative game theory at the post-market stage. Test results show that the proposed dispatch strategy can reduce up to 20.4% of daily electricity cost at cluster level and ensure profit increase of individual buildings with different flexibility characteristics. The active participation of building clusters in electricity markets could reduce the use of costly backup generators. [ABSTRACT FROM AUTHOR]

This paper focuses on hybrid renewable energy systems (HRES) as an alternative for conventional energy systems, especially fossil fuels. The problem is to find the optimal number of components of a standalone hybrid system, based on minimizing the value of the annualized cost of the system (ACS), in the presence of the constraints. To reduce the CO2 particles as one of the most threatening particles for the planet, an additional constraint is added to the objective function. For solving the problem of system sizing, Harmony Search and Ring Theory-based Evolutionary Algorithm have drawn attention to be preferred to numerous techniques. In addition,a combination of both, which has never been used before, is proposed. The proposed hybrid algorithm shows its ability to mitigate both the emissions and costs of the system promisingly in comparison with the others. Clearly, with Carbond = 15 and different values of LPSPMax as well as using RTHS, RTEA and HS algorithms for PV/WT/BSS/DG configuration, the best performance in terms of ACS minimization belongs to RTHS algorithm with the minimum cost of $1528.2. Also, considering LPSP = 0 and changing carbon from 0 to 20 tons/year, the ACS of the PV/WT/BSS/DG configuration is reduced from $38586.3 to $21153.5 (45% reduction). • Using a hybrid algorithm called RTHS to minimize the annual costs of the system. • Considering the LPSP constraint to supply the load demand at an acceptant level. • Reducing CO 2 emissions to increase the renewables penetration. • Considering uncertainty on both the generation and demand sides. • Validating of the proposed method based on real data for a case study in Iran.. [ABSTRACT FROM AUTHOR]

The electric vehicle to grid (V2G) interaction technology can improve the utilization of renewable energy and stabilize its grid connection. At the same time, renewable energy can be used for a microgrid nearby, or incorporated into a large grid, to effectively address the volatility of renewable energy sources. Motivated by the increasing number of electric vehicles (EVs) and the randomness of renewable energy output, this paper proposes an effective strategy to improve the security and economy of the microgrid system. The uncertainty of wind power and EV's state of charge (SOC) is modeled as uncertainty prediction sets. And considering the worst-case scenario, this proposed strategy can increase the absorption ratio of renewable energy while orderly guiding the charging and discharging of EVs in peak-load reduction and valley filling and thus, lower operating costs under various practical constraints. To solve the problem of over-conservatism of the robust optimization, this paper introduces a dispatch interval coefficient to adjust the degree of conservatism, while improving the economy of microgrids system. The robustness and feasibility of the proposed dispatch strategy are demonstrated by numerical case studies. • Electric vehicle is introduced as both load and source (V2G mode) to help the consumption of wind power. • Operation stability and economic performance of microgrid is improved with the adjustable robust optimization (RO). • The robustness and economy of the system has been quantified. • Sensitivity analysis of uncertain variables to system total cost is carried out. [ABSTRACT FROM AUTHOR]

The electric vehicle to grid (V2G) interaction technology can improve the utilization of renewable energy and stabilize its grid connection. At the same time, renewable energy can be used for a microgrid nearby, or incorporated into a large grid, to effectively address the volatility of renewable energy sources. Motivated by the increasing number of electric vehicles (EVs) and the randomness of renewable energy output, this paper proposes an effective strategy to improve the security and economy of the microgrid system. The uncertainty of wind power and EV's state of charge (SOC) is modeled as uncertainty prediction sets. And considering the worst-case scenario, this proposed strategy can increase the absorption ratio of renewable energy while orderly guiding the charging and discharging of EVs in peak-load reduction and valley filling and thus, lower operating costs under various practical constraints. To solve the problem of over-conservatism of the robust optimization, this paper introduces a dispatch interval coefficient to adjust the degree of conservatism, while improving the economy of microgrids system. The robustness and feasibility of the proposed dispatch strategy are demonstrated by numerical case studies. • Electric vehicle is introduced as both load and source (V2G mode) to help the consumption of wind power. • Operation stability and economic performance of microgrid is improved with the adjustable robust optimization (RO). • The robustness and economy of the system has been quantified. • Sensitivity analysis of uncertain variables to system total cost is carried out. [ABSTRACT FROM AUTHOR]

A power system with 80% renewable energy sources (RES) requires significant provision of flexibility to balance the deviations of fluctuating solar and wind power. This paper focuses on how a smart mix of renewable generation technologies can reduce the demand for flexibility and therefore the overall system costs. To measure the demand for flexibility in systems with high RES generation, the term flexibility is defined and described using predictable indicators such as the difference between the highest and the lowest residual demand or the number of hours with negative residual load during a year. This definition is required to determine the optimal mix of RES installations. Optimization is performed for Germany based on the grid development plan of the transmission system operators. In contrast to most studies of the future energy system that foresee a further expansion of onshore wind, this paper shows that higher shares of offshore wind and, to some extent, photovoltaic are better suited to reducing the demand for flexibility and thus the cost of integrating fluctuating RES into the system. • Extended definition of flexibility demand for power systems based on renewables. • Offshore wind and photovoltaic requires less flexibility than onshore wind. • Uncertainty about the future renewable mix increases system integration costs. • Challenge: trade-off between levelized cost of generation and system integration. [ABSTRACT FROM AUTHOR]

The development and utilization of renewable energy sources can not only effectively reduce carbon dioxide emissions, but also provide access to electricity to more parts of the world. A standalone hybrid system based on renewable sources is a promising way to supply reliable and continuous power in remote areas to which the grid has not extended. This paper designs and investigates a photovoltaics (PV)-wind-hydropower station with pumped-storage installation (HSPSI) hybrid energy system in Xiaojin, Sichuan, China as case of study. HSPSI can use the available flow of the river and store surplus energy generated from wind and PV by pumping water from the lower reservoir to the upper one. From the perspective of the investors, the techno-economic index is usually used to design the PV-wind-HSPSI hybrid energy system which aims to find the optimal configure with maximum power supply reliability and minimum investment cost. The trade-off analysis between the two objectives is based on Pareto optimality theory by means of Multi-Objective Particle Swarm Optimization (MOPSO). Besides, this paper takes the curtailment rate (CR) of the wind and PV power into consideration due to policy requirements. The relationship between the two objectives under various CR are analyzed and compared. Several results can be obtained as follows: 1) Comparing with the PV-HSPSI and wind-HSPSI hybrid energy system, the levelized cost of energy (LCOE) of the PV-wind-HSPSI hybrid energy system can reduce by 32.8% and 45.0% respectively. 2) For the PV-wind-HSPSI hybrid energy system, the LCOE can be as low as 0.091 $/kWh when 5% LPSP can be acceptable. 3) The policy of CR is unfavorable for the investors which leads to a higher investment cost. 4) Particle Swarm Optimization (PSO) performs better than genetic algorithm (GA) and Simulated Annealing method (SA) with a least LCOE. 5) both MOPSO and weighted sum approach (WSA) have a good performance to find the Pareto fronts and its hypervolume indicator (HV) is calculated. • A real PV-wind-HSPSI hybrid system is designed in Xiaojin, Sichuan, China. • The curtailment rate is introduced into the techno-economic index. • The LCOE can be as low as 0.091 $/kWh when 5% LPSP can be acceptable. [ABSTRACT FROM AUTHOR]

Biogas is a locally accessible and renewable energy source that can be generated from organic feedstocks under anaerobic conditions. This paper aims to assess the future biogas potential in India from varied sources (crops residues, animal manure, municipal solid wastes, municipal and industrial wastewater). The study uses a bottom-up, resource focused approach for the assessment of biogas potential. The paper assess agriculture and animal residues as well as generation, collection and availability of municipal and industrial waste for biogas production. The results show considerable potential of biogas in the next two decades due to rising waste generation and deployment of infrastructure for improving waste collection efficiency. The analysis estimates that the biogas potential ranges from 310 to 655 billion m3/year in the year 2040 depending upon availability of different resources. The estimated biogas potential in the year 2040 is around 36% of India's current (2015) total primary energy supply in the high availability scenario. Finally, the paper offers policy recommendations aimed at realizing the biogas potential. • Future Biogas potential from animal and agricultural based waste resource in India is estimated. • Bottom-up resource based approach is used for assessment. • Estimated future biogas potential is around 12.8 EJ in 2040. [ABSTRACT FROM AUTHOR]

The alarming rate at which the deposit of non-renewable or fossil energy resources are depleting around the world has been nudging us in the direction of green energy for quite a long time. Considering the current and upcoming global energy crisis, we have no choice but to pay our serious attention to renewable resources to meet the energy demand of an ever-growing global population and industry. Rice husk has about 20% mass percentage of rice, with low energy and bulk density. Due to these properties, it is not viable to directly combust rice husk as a fuel. Different thermochemical pretreatment technologies can be effectively implemented to improve the fuel characteristics. However, there have been no comprehensive review article on different pretreatments of rice husk. This paper reviews most common rice husk pretreatments technologies namely, gasification, torrefaction, pyrolysis, and hydrothermal carbonization including multiple sub-categories of each technology. All of these processes are successful in improving the fuel characteristics of rice husk such as higher heating value or calorific value, moisture content, fixed carbon, etc. Each of them serves the purpose of converting a low energy-density biomass into an energy-rich material like coal differently. Location, resources, available technology, etc. should also be considered while deciding which pretreatment would be optimum for a given particular case. A comparison has been presented on how much energy can be extracted from rice husk by implementing different technologies given they can be upscaled to an industrial scale. For 527,534 kg of rice husk produced from a sample location in Bangladesh per annum, wet torrefaction before pyrolysis of rice husk yields 85 MW of power which is the highest among the 4 pretreatments discussed in this paper. • Review of different thermochemical processes conducted on rice husk. • Study on gasification, pyrolysis, torrefaction, hydrothermal carbonization process. • Comparison in terms of moisture content, fixed carbon, and heating value gain. • A case study of power generation potential of rice husk produced in Bangladesh. [ABSTRACT FROM AUTHOR]

The curtailment of renewable energy worsens with increasing penetration in power systems, so it is necessary to explore the upper limit value of the penetration of renewable energy (PRE). This paper uses artificial neural networks (ANN) to study the historical data of California independent system operator (CAISO), analyze the fluctuation balance strategy of wind and solar power, and predict the upper limit value of the PRE, which will peak at 40.5% in 2025. In addition, this paper also simulates the inclusion of energy storage unit (ESU) with an installed capacity of 3 GWh in the grid to reduce curtailments, and analyzes the grid operating conditions and the upper limit value of the PRE, showing that the storage units with an installed capacity of only 3% of the average daily power output of solar energy recover 58% of the annual curtailments (2021), and the maximum PRE is 41.2%. [ABSTRACT FROM AUTHOR]

Renewable energy technology innovation (RETI) has become a key factor in mitigating climate change and promoting sustainable development of the world economy, but there is no literature on the effect of information and communication technology (ICT) on RETI and its impact mechanism. This paper measures RETI using panel data for 70 countries from 2005 to 2019 based on the combination of diffusion and depreciation effects of technological innovation and systematically examines the direct and mediating mechanisms of ICT's effect on RETI. The results show that ICT significantly contributes to RETI and even holds after addressing endogeneity, replacing the explanatory or explanatory variables, excluding extreme values, and excluding the effects of the financial crisis. The results of the transmission mechanism analysis indicate that ICT affects RETI through human capital accumulation, financial development, and information disclosure, meaning that human capital, financial development, and information disclosure are important mediating transmission mechanisms. The results of heterogeneity analysis note significant economic organizational heterogeneity and innovation environment heterogeneity of ICT to RETI. Specifically, in IEA and OECD member nations as well as in the high renewable energy consumption and high R&D investment groups, the contribution effect of ICT on RETI is more significant. • Examine the influence of ICT on RETI and its impact mechanism. • The findings show that ICT can greatly increase RETI. • Human capital, financial development, and information disclosure are the impact mechanisms. • In IEA and OECD member nations, the contribution effect of ICT to RETI is more significant. • In high R&D and high RE nations, the contribution effect of ICT to RETI is more significant. [ABSTRACT FROM AUTHOR]

Nowadays, with the development of international policies and agreements to promote the integration of renewable energy sources, mainly solar and wind, modeling the solar resource by including the spatio-temporal variability is crucial to determine future PV power plant locations and estimate potential power generation performances. However, contributions involving long-term periods and different time windows to explore such potential solar resource variability are generally scarce. Under this framework, the present paper proposes a methodology focused on characterizing and clustering the spatio-temporal solar resource variability through the global horizontal irradiance analysis. Hierarchical clustering technique is firstly used to classify the spatial data. Different time windows — from short-term to long-term data — can be subsequently evaluated by using various sources of information. The Spanish territory is selected as case study, considering 22-year period data (1999–2020) and 1,936,917 observations from online satellite database. Spatial variability and geographical clustering differences are discussed and compared depending on the selected time windows, identifying relevant spatial variations for some specific months. Additionally, some years present more variability as well, in line with the sunspot peak of the solar cycles. The proposed approach gives an alternative comprehensive spatio-temporal clustering and characterization of GHI evolution, providing a suitable methodology to help the current European sustainable energy transition. • Solar irradiance resource assessment is proposed through spatio-temporal modeling. • Dynamic clustering model of global horizontal irradiance is proposed and evaluated. • Different time windows are compared from short-term to long-term data. • The Spanish area is considered as case study including two solar cycles. • 761,059 on-ground meteorological data and 1,936,917 online data are analyzed. [ABSTRACT FROM AUTHOR]

This paper studies the production decisions of a power plant with two different risk attitudes, in which the power plant faces the risks of uncertainty in renewable energy production, and two types of risk attitudes are considered, namely, risk neutrality and risk aversion. First, we construct the optimization models of a power plant with two different risk attitudes under the Feed-in-Tariffs (FIT) policy, and obtain the optimal outcomes by solving the models, including the optimal output and the expected profit. Then, by comparing the optimal outcomes under the two risk attitudes, we reveal the impacts of the different risk attitudes of power plants on their power production decisions. In addition, we also give an extended study to analyze the change from the FIT policy to the market-oriented Tradable Green Certificates (TGC) policy. The results show that the expected profit of the power plant decreases if it switches from risk-neutral to risk-averse, and the fossil energy output of the risk-neutral power plant is higher than that of the risk-averse power plant if the difference between the levelized costs of renewable energy and fossil energy exceeds a certain threshold. [ABSTRACT FROM AUTHOR]

The accuracy of models to capture the uncertainty of renewables significantly affects the planning and operation of renewable energy-based stand-alone (REB-SA) microgrids. This paper aims to first study different stochastic and deterministic models for renewables, then evaluate the performance of an REB-SA microgrid planning problem and provide qualitative and quantitative comparisons. A modified Metropolis-coupled Markov chain Monte Carlo simulation is considered for the first time in the planning of an REB-SA microgrid to predict the behavior of renewables with minimum iterations. The modified model is benchmarked against two prevalent models including the retrospective model with worst-case scenarios and the Monte Carlo simulation. The operations of three designed microgrids (by these three methods) are evaluated using the last three-year historical data of a city in northern Sweden including solar radiation, wind speed, the water flow of a river, and load consumption. The impacts of the considered methods on using PV panels and hydrogen systems are investigated. The results verify that the modified model decreases the risk of planning and operation of an REB-SA microgrid from the energy and power shortage viewpoints. Moreover, the designed microgrid with the modified model can cope with all possible scenarios from economic, technical, and environmental viewpoints. • Uncertainties of renewable energy are major concerns for replacing fossil-fuel energy. • A modified Metropolis-coupled MCMCS is applied to the microgrid planning problem. • Uncertainty modeling methods on microgrid planning and operation are benchmarked. • Microgrid planning is highly affected by different uncertainty modeling methods. • The planning results besides the benchmarks show economic and reliability improvement. [ABSTRACT FROM AUTHOR]

Nowadays, the local distribution grids have been facing technical, economic, and regulatory challenges, because of the increased integration of renewable energy sources (RESs) and electrification of vehicles. The traditional solutions to the grid expansion, e.g., to build an additional power line, are utility-centered solutions, i.e., the distribution grid operators (DSOs) are the only party involved to tackle grid issues. The DSOs have to engage grid users with technology providers to develop innovative solutions that tackle one problem and overcome several cost-effectively. This paper presents a holistic solution to optimally control cross-sectoral energy flow between interconnected microgrids (MGs) consisting of different RESs, hydroelectric power plant (HPP) and wind turbines (WTs) to meet electric vehicles (EVs), residential, commercial and industrial demands with the main grid contribution. This issue will provide the advantages of community-based MGs for local energy trading which causes for an active and engaged system, however, an adequate control strategy for proper operation is required. The proposed solution is based on a new interconnection line between two MGs through a multiport converter (MPC) with the techno-economic consideration of newly installed components such as MPC, cables and the required battery energy storage system (BESS). The proposed case study is evaluated under three different conditions e.g., load increment, demand response (DR) and N-1 criterion in separate, interconnect and island modes. The CPLEX solver of GAMS software is employed to solve the mixed-integer linear programming model. The results show that the applied interconnection line for MGs compared to the separated operation mode can decrease the system's total costs, reduce the applied peak to the upstream grid, and enhance the system's reliability under different conditions. Furthermore, the applied solution provides the ability for MGs operation even in island mode under different conditions for a full day (24 h). • Minimizing OPEX and CAPEX costs under load variations, N-1 criterion, and DR. • Optimal BESS sizing for separate, interconnect and island conditions in MGs. • Optimal MV cables cross-section size for new interconnection line between MGs. • MPC Sizing for more functionality to Host EVs and BESS in interconnected MGs. [ABSTRACT FROM AUTHOR]

Hybrid platforms composed of a floating offshore wind turbine (FOWT) and wave energy converters (WECs) may possibly reduce the levelised cost of energy of both energy harvesting devices and improve the platform dynamics. This paper investigates the dynamics of a hybrid platform composed of a semi-submersible FOWT combined with three spherical heaving point absorbers (PAs), either floating or submerged, under turbulent wind and irregular waves conditions. Since the WECs are attached to the floating structure, complex hydrodynamic couplings occur between the floating bodies. In this regard, a parametric investigation of the power-take-off system is performed to understand the wave power absorption and motion characteristics, which are compared against their respective stand-alone configurations. At sea states with short wave peak period, both hybrid platforms (with floating or submerged PAs) were able to operate under maximum wave power absorption condition and extract nearly the same amount of wave energy as the PAs attached to the ground (stand-alone), while providing the additional benefit of reducing the horizontal nacelle accelerations of the FOWT. On the other hand, when operating at sea states with long wave periods, the hybrid platforms may need to restrict the wave power absorption capability to prevent additional nacelle horizontal acceleration. • Analyses the dynamics of two hybrid platforms under random wind and wave loads. • Provides insights on the performance and nacelle acceleration of hybrid platforms. • Presents a critical comparison of power and motion compared to standalone platforms. • Presents a sensitive analysis of the power take-off coefficients of hybrid platforms. [ABSTRACT FROM AUTHOR]

Hydroelectric power plants have been losing competitiveness to renewable resources in Brazil, specifically wind and solar power resources, given the current water crisis in the country, which has left main reservoirs in the Brazilian hydroelectric power system depleted. However, other renewable energy resources show disadvantages in terms of intermittence and energy storage. Pumped Hydropower Storage (PHS) can help mitigate this problem, supplying energy with fewer variations. This paper studied using PHS concepts together with solar and/or wind renewable resources to recover water levels at the Furnas reservoir in terms of energy and economic performance. The energy studies were developed by sizing the PHS and identifying solar and wind resource potential for supplying energy for pumps in the hydropower plant. The economic analyses considered three different scenarios (I; II and III), and used the NPV (Net Present Value), and LCOE (Levelized Cost of Electricity) methods for a period of 30 years. Energy calculations showed that 1692 MWh/day of energy could be generated using PHS. However, 2729 MWh/day of energy would be needed to store generated energy, and a 1037 MWh negative energy balance will be compensated for by using daily wind and/or solar photovoltaic power production. This would allow the Furnas reservoir to regain 0.45 m of its water level per year. The economic studies showed economic viability for PHS scenarios I and III, with estimated NPV at 151.5 million dollars, and 64.5 million dollars for the last year of the project, respectively. Scenario II was unviable, giving rise to several strong discussions on this application within Brazil. • Wind and solar sources were considered for the pump energy supply. • The proposed system can recover 0.45 m/year of the level in the Furnas reservoir. • The energy production of the reversible power plant was estimated as 1,692MWh/day. • Economic feasibility was evaluated in many scenarios using NPV and LCOEmethods. • Market regulation is required for the diffusion of this technology in Brazil. [ABSTRACT FROM AUTHOR]

The energy communities based on the integration of microgrids make it possible to gain economic, environmental, technical, and social benefits. The paper aims to propose a unified multi-criteria approach covering both the planning stage and the stage of managing the energy community, in the context of various interests of its participants. Planning stage should take into account the long-term goals of the community and possible changes in external conditions. Therefore, we suggest an approach relying on the multi-attribute value theory considering the uncertainty of decision makers' preferences. Interval estimators used to express preferences enable a choice of community configuration with robust performance under changing conditions within some limits. In the operation stage, the new multi-criteria model of an intelligent "energy community operator" is proposed. It is based on bi-level programming and reinforcement learning, implementing the structure of a fair local market for sustainable development of the community. To optimize the operation of individual microgrids within the community, the multi-objective Monte-Carlo Tree Search (MCTS) algorithm is used, which helps to improve the convergence in the Stackelberg game. The multi-criteria version of the MCTS algorithm allows implementing an adaptive local automation model to solve a multi-objective lower-level problem: minimize operating costs, risk of power shortage, and CO2 emissions; smooth load peaks, and optimize power exchange between microgrids. At the top level, a management strategy that will be beneficial to all members of the community is chosen to guarantee their long-term aggregation. The effectiveness of the proposed approach is demonstrated by the example of an energy community created for three remote villages located on the coast of the Sea of Japan. The natural and climatic conditions of the area allow the efficient use of wind, solar, and biomass resources. Building the community involves the consideration of three scenarios, in which priority is given to economic efficiency, environmental efficiency, or balanced development. • The practical multi-criteria approach for development a sustainable energy community. • Energy community sizing under uncertainty of decision makers' preferences. • Bi-level programming for a "fair" community operator model. • Monte-Carlo Tree Search algorithm for multi-criteria community management. [ABSTRACT FROM AUTHOR]

The deployment of intermittent renewables in distributed energy systems has to be managed so as to maximise local energy self-consumption. This paper deals with the application of sector coupling strategies to increase energy self-consumption and decarbonise urban energy districts. The aim of this work is to investigate the combined implementation of Power-to-Gas and Power-to-Heat strategies in Renewable Energy Communities. Power-to-Power, Power-to-Heat and Power-to-Gas systems, along with their combined adoption, have been implemented in a residential community under different renewable excess conditions. For each strategy, the storage system's size has been optimised and the configurations have been compared in energy, environmental and economic terms. The Power-to-Heat strategy is the most cost-effective solution, although it presents intrinsic limitations. The Power-to-Gas configuration involves the hydrogen injection into the gas grid, thus exploiting the local network as a free storage system. Higher self-consumption can be achieved; nevertheless, energy and emissions savings are lower due to the electrolyser poor efficiency. The combined application of the two sector coupling strategies allows the strategies individual advantages to be exploited and the limitations of both to be overcome. Furthermore, this solution leads to higher self-consumption and lower annual costs than conventional electric batteries. • Sector coupling strategies for improving self-consumption in energy communities. • Combined implementation of Power-to-Heat and Power-to-Gas systems. • Analysis of energy storage systems in two renewable excess conditions. • Assessment of energy, environmental and economic impacts. • Self-consumption based tariffs in accordance with the Italian incentive scheme. [ABSTRACT FROM AUTHOR]

The exploitation and utilisation of solar energy is challenging because of both diurnal and seasonal variation. Seasonal thermal energy storage is a prominent solution to solve the problem of seasonal variation of solar production. This paper investigates both the optimal design and energy management of a renewable energy plant with seasonal thermal energy storage. As a case study, the thermal, cooling and electrical energy demands of a university campus during one year are considered. Three scenarios, characterized by a different available area (20,555 m2, area covered at present; 50,000 m2, potentially available area by exploiting the entire rooftop and parking area; unlimited area) for solar thermal collectors and photovoltaic panels are investigated. In the first two scenarios, the exploitation of solar energy allows a primary energy saving of 31% and 57%, respectively compared to the use of a conventional plant. The seasonal storage reaches an overall efficiency (thus considering both charging and discharging) up to 84%. Finally, the analysis of the third scenario shows that the feasibility of meeting the energy demands by only exploiting solar energy is technically challenging because a large area equal to about 100,293 m2 is needed for solar thermal collectors and photovoltaic panels. [ABSTRACT FROM AUTHOR]

In order to mitigate the influence of global warming and greenhouse gasses emission, different proceedings are suggested such as utilizing renewable energies and demand response programs. This paper investigates the expansion of renewable resources and electrical energy storage units in distribution systems towards reducing investment costs and environmental pollution. Since the development of components is not possible in a single-stage due to the limitation of staff and funds, a multi-stage programming is applied to consider various restrictions. According to the penetration of crypto-currency miners in recent years, their impact is evaluated on the problem as the high-rate energy consumers. Moreover, the demand side management strategy and risk-averse scenario-based approach are implemented to analyze the role of responsive loads and model the renewable resources uncertainty, respectively. The results approve that simultaneous expansion of wind turbines, photovoltaics and electrical storage systems decreases the total pollution and cost by 100% and 99.98% after the third year, respectively. The simulations also validate that crypto-currency miners reduce the total revenue by 4.16%, whereas the responsive loads increase it by 9.89%. As well, the fluctuations of wind and solar power decrease the total revenue by 13.27%, in return, the robustness notably improves. [ABSTRACT FROM AUTHOR]

The sustainability of economic growth has recently become a major objective for the global economy. Innovation is a newly emerged key factor for an efficient energy market and sustainable development. It can reduce pollution by increasing production efficiency (the first effect of CO 2 e reduction) and direct involvement in renewable energy development (the second effect of CO 2 e reduction) by replacing fossil fuel instruments with their counterparts that rely on renewable energy. The main question of this research is: Will the impact of innovation on per capita CO2e be the same on a threshold with different levels of renewable energy consumption? This paper explores the impact of innovation on CO 2 emissions in a panel of 29 selected EU countries from 2000 to 2019 using the fixed-effect panel threshold model. The results indicated that innovation had a significantly negative effect on per capita CO 2 e. In the early levels of the use of renewable energy, innovations have been directed more towards clean energy (the second effect on CO 2 e). Innovations were directed more towards clean energy in using renewable energy at early levels. It can be concluded that this policy has led to more innovation in clean energy production and more use of renewable energy, thus reducing CO2e considerably (the second effect). [ABSTRACT FROM AUTHOR]

Globally, renewable energy technologies, such as solar photovoltaic (PV) contribute significantly to sustainable development. However, adoption of solar PV is limited in many developing countries. Governments have rolled out policy strategies like financial inclusion to stimulate deployment of modern energy technologies. Notwithstanding, little is known about financial inclusion and household solar PV adoption. Using two waves of Uganda National Household Survey data covering 28,913 households, this paper examines the effect of financial inclusion on adoption of solar PV. The study employed logit model and pooled ordinary least squares for estimation. Taking endogeneity into account, we find that financial inclusion has a strong positive effect on solar PV adoption. The estimates show that unit increase in financial inclusion increases the likelihood of residential solar PV adoption by 3.6% (p < 0.01). This finding is consistent across all robustness checks. The findings further suggest that use of mobile money is more important for solar PV adoption. The study identified household income and education as possible pathways through which financial inclusion influences solar PV adoption. Thus, policy makers and renewable energy practitioners should continue prioritizing financial inclusion particularly use of mobile money and financial remittances so as stimulate adoption of solar PV. • The link between financial inclusion and adoption of solar PV in Uganda is examined. • The National Household Survey data for Uganda and logit regression model were used. • We found that financial inclusion increases adoption of solar PV in households. • Income and education are pathways by which financial inclusion affects PV adoption. • Policy on financial inclusion should prioritize use of mobile money and remittances. [ABSTRACT FROM AUTHOR]

— Seasonal and location dependence of renewable energy resources have limited their applications in power generation. Energy storage systems are promising solutions to the intermittence of renewable energy resources. Rural electricity grids are faced with economic sustainability challenges due to low power demand and poverty. A rural grid design around economic drivers like agriculture and micro industries can mitigate poverty and improve economic sustainability of rural grids. This paper presents an integrated design for photovoltaic power generation with a pumped hydro storage system for irrigation and community utilization. The design explored the natural availability of water body in an elevated settlement area that offers a natural storage height for hydro energy storage. A photovoltaic generation plant was designed to power a pump as a turbine system for water storage and generation. HOMER® energy simulation software was deployed in the simulation. The result shows a satisfactory net present cost for the possible integration of a pumped hydro storage system in a photovoltaic generation plant as the most viable option to provide power at a power supply probability of 99.9% and water for irrigation. The benefits from the irrigation and water supply will enhance the community's socio-economic development and sustain the investment. [Display omitted] • We proposed a strategy to help an African community to have access to electricity. • The scheme will provide irrigation facilities and water supply to the community. • Photovoltaic Generation System was deployed as the primary energy source. • Intermittence of renewable energy was addressed through pumped hydro storage. • The result shows a satisfactory net present cost and 99.9% power supply probability. [ABSTRACT FROM AUTHOR]

This paper introduces a flexibility concept in development of a novel multigeneration energy system driven by a renewable energy heat source. Nevertheless, the energy system can be upgraded to a highly productive system by incorporating other subsystems for achieving more production such as domestic hot water and freshwater. A novel design is proposed for a system comprising organic Rankine cycle, absorption compression heat pump, liquid natural gas to natural gas converting system, domestic water heater, and humidifier-dehumidifier desalination to exploit the Sabalan geothermal source for multigeneration purposes. Due to its high-efficient and environmentally friendly characteristics, n-pentane is utilized in the organic Rankine cycle. Also, the Ammonia-water is employed in the absorption compression heat pump subsystem. Energy, exergy, and thermoeconomic analysis is conducted on the plant along with multi-objective optimization based on the parametric study of key variants. According to the results, the novel step-by-step flexibility analysis can give a wide viewpoint to the designers to make the best multiple plant. The results of multi-objective optimization indicate that the freshwater production increases from 0.803 kg/s to 0.834 kg/s, the capacity of the domestic water heater rises from 1254 kW to 1265 kW, and the net output power surges up by 137.96 kW. However, the heating production falls from 1276 to 1088 kW. Furthermore, the total energy and exergy efficiencies increase by 6.43% and 4.47% with a 2.08 $/GJ reduction in the unit cost of the product. [ABSTRACT FROM AUTHOR]

In the context of sustainable development, the renewable energy technology innovation (RETI) may have a potential impact upon the industrial structure upgrading, while green finance will play a moderating part. This paper studies the role of green finance and renewable energy technological innovation on industrial structure in China. We found that RETI has contributed significantly to the adjustment of industrial structure. The research results of PVAR (Panel Vector Autoregressive) model further prove this dynamic and continuous positive promotion. The threshold regression results indicate that with the improvement of green finance, there is a non-linear relationship between RETI and industrial structure. In areas where green finance is relatively low, RETI can inhibit the upgrading of industrial structure. When the degree of green finance development is relatively high, the influence of RETI on industrial structure is remarkably positive. Heterogeneity analysis shows that in eastern China, renewable energy technological innovation has significantly promoted the industrial structure upgrading. Therefore, policy makers should take various measures to expedite the green finance development and strengthen the green finance support to renewable energy technology innovations. Chinese government has the need to continue to implement related green credit policies and expand the source of green funds. • Study the impact of renewable energy technology innovation on industrial structure. • Green finance is a moderating variable. • Threshold regression model is used to study nonlinear relationship. • Renewable energy technology innovation positively impacts industrial structure. • Green finance has a positive moderating effect. [ABSTRACT FROM AUTHOR]

To fully exploit the use of water/nanofluids in the photovoltaic thermal (PVT) unit condenser and further increase renewable energy resources in energy mix, the current paper numerically studies the effect of operating water/PVT unit, Al 3 O 2 -water/PVT unit and Ag-water/PVT unit on the overall performance. In the ANSYS Fluent 18, the mathematical equations related to these models were discretized by the second order upwind scheme and solved at steady state, considering the fluid inside pipe as laminar flow. The results were obtained and discussed in terms of coefficient of performance (COP), Nusselt number (Nu), specific exergy (E in,out) and outlet flow temperature (T out), at different nanoparticle fractions and operating conditions of both flow and weather. A higher inlet mass flow rate of nanofluids (Ag-water and Al 3 O 2 -water) was recommended both for better COP and handling nanoparticles deposition problem. In addition, due to thermal conductivity difference, the maximum COP of about 3 was achieved from PVT unit condenser with Ag-water as fluid medium. The optimum conditions of flow and weather were proposed for the operation of three considered PVT units condenser. [ABSTRACT FROM AUTHOR]

In 2020 the European Commission approved the GND (Green New Deal), a strategic plan aimed at the decarbonization of the EU by 2050. In this scenario, the diffusion of alternative renewable energy sources (RESs) play a key role, particularly electric energy production from wind and photovoltaic power plants. Regardless, the nonprogrammable nature of these power sources led recent studies to focus the interest on the power-to-gas solution (PtG), consisting of the electrolytic transformation of excesses of unused electric energy into hydrogen. The complexity of this technology requires the development of strategic optimization models aimed at finding the best locations for new PtG plants in specific territorial scenarios. This paper presents the development of a GIS-based optimization model that combines an operational research approach with territorial services based on geographic information system (GIS) data. The study is located in Sicily (Italy), considering the large renewable energy penetration in the larger Mediterranean island. The developed model allows us to find the best location for new PtG installations in the considered area by combining GIS automated processing with cost function resolution. The results of this work could be useful in the years to come in light of the rapid spread of hydrogen energy production in Italy. [Display omitted] [ABSTRACT FROM AUTHOR]

Although the socio-economic determinants of terrorism are conducted by many researchers using different samples and time spans, the impact of renewable energy consumption on terrorism has been ignored for a long time. To bridge the gap, this paper aims to investigate the impact of renewable energy consumption on terrorism regarding economic growth, non-renewable energy consumption, and income inequality over the period straddling 1990 and 2019 for a panel of the USA, China, India, Iran, Germany, and Russia. We employ the CUP-FM and CUP-BC estimators to reveal the long-run relationship between variables. The empirical findings suggest that renewable energy consumption has a negative impact on terrorism. A 1% increase in renewable energy consumption decreases terrorism by 0.315% and 0.631%, according to the CUP-FM and CUP-BC estimators, respectively. Economic growth also has a negative impact on terrorism, whereas non-renewable energy consumption and income inequality positively affect terrorism. Our empirical findings highlighted that renewable energy consumption and economic growth are crucial for combating terrorism in these countries. Therefore, these countries need to improve comprehensive policies in terms of renewable energy consumption and simultaneously decrease non-renewable energy consumption and income inequality. • Renewable energy consumption helps to lower the terrorism index. • Inequality of income expedites the rise of terrorism. • Terrorism is exacerbated as a result of fossil fuel consumption. [ABSTRACT FROM AUTHOR]

Super-hot rock geothermal is an emerging source of renewable and carbon-free energy. This paper is the first attempt to explore fluid and heat flow dynamics in the reservoir-wellbore coupled system, to assess the power generation performance of a super-hot (>450 °C) enhanced geothermal system (EGS). We developed a high-performance code and built a 3-D wellbore-reservoir coupled model based on data from a recently completed deep-drilling project at Larderello, Italy. The general pattern of the super-hot EGS is characterized by a significant temperature plummet (>60 °C), after which the production fluid evolves from steam to a two-phase mixture till the end of the operation period. Reservoir pressure emerges as a key parameter to determine the temperature of the two-phase mixture. By realistically capturing phase transitions driven by coupled thermo-hydraulic processes during operations, our numerical model predicts a lower power generation efficiency compared to previous attempts based on ultra-simplified models. Although finalized at assessing the thermodynamic viability of a specific system, this modeling approach provides general information on fundamental thermo-hydraulic processes in the Earth crust that might be applied for the design of similar EGS projects elsewhere. [ABSTRACT FROM AUTHOR]

Green economic growth and sustainable development are inextricably linked, and encouraging green economic growth has practical implications in today's world. In this context, this study examines the impact of environmental regulation on renewable energy development and China's green economy from 1990 to 2018. We look at how environmental regulations affect green economic growth and renewable energy technologies. Using the Meta frontier SBM model's super-efficient data envelopment analysis, it is possible to determine the relative level of green economic growth in China's provinces based on their energy efficiency. Environmental regulations were also assessed using an improved fuzzy inclusive appraisal method. Finally, we investigate the possible mechanism and discuss our findings. The study discovered an unreliable indicator of green growth during the research period, attributed to the non-serious nature of government strategies. The results show that the DEA method consistently confirms how the piece is put together. On the other hand, the sub-results models invariably affect countries with high GDP per capita. Finally, the impact of environmental regulations on the growth of renewable energy sources was studied. Green economic growth depends on environmental regulation policies, which contribute significantly to renewable energy development. Population size, economic development level, technological innovation, industrial structure, urbanization, environmental regulation, and FDI influence green economic growth, but the spatial effect of each influencing factor varies. The findings of this paper form the basis for China's pursuit of green economic growth and the development of renewable energy policies. • Proposed a novel framework on R & D expenditures, low carbon energy transition and green economic growth. • Study analyzes the effects of the multiple R & D expenditures in the context of Belt & Road Initiative economies. • Dynamic linking is considered when the connections of players' change. • Established vibrant empirical model and suggested implications for green economic recovery. [ABSTRACT FROM AUTHOR]

The energy-growth nexus has been explored extensively by researchers and academia; however, the role of cleaner energy, especially in the context of emerging economies, i.e., Nex-11 countries, have been ignored. Therefore, to fill the gap in the literature, this paper considers a non-parametric panel method, to test directly for the importance of renewable energy in economic performance for Next eleven (N-11) countries. The econometric estimations—which focused on a panel of N-11 countries—determined that: (i) renewable energy consumption boosts economic growth in short run only; and (ii) industry value added, human development, and consumption expenditures accelerate economic, whereas globalization decrease it. Hence, all countries need to reduce the risk associated with renewable energy so that to make them effective for economic growth. Moreover, there is a need to provide different types of invested capital for ongoing activities of renewable energy production. In general, this study provides relevant policy implications in the context of renewable energy, globalisation and economic growth for the Nex-11 economies. • Renewable Energy led Economic Growth Hypothesis is tested in the Next-11 countries. • The novel Method of Moments Panel Quantile Regression is employed. • Renewable energy led economic growth is valid in the selected panel economies. • Consumption expenditures, Human Development, Industry Value Added enhances growth. • Globalisation is found to limit economic growth in case of Next-11 countries. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for site selection of Offshore Renewable Energy (ORE) systems, addressing the growing global energy demand and the need for sustainable solutions to climate change. Focusing on the wave energy, a relatively untapped renewable energy source, this research focuses on optimizing the Wave Energy Converter (WEC) deployment amidst the uncertainties of the offshore environment. The study involves a comprehensive evaluation of potential sites, considering key factors like power generation capacity, mooring system fatigue life, and tether response to extreme loads. Initial wave data analysis for various locations is followed by numerical simulations of a point absorber WEC under different environmental conditions. A Bayesian Network (BN) model is then employed to integrate uncertainties into a multi-criteria decision-making (MCDM), enhancing the robustness of site selection. This approach facilitates the calculation of utility values for various sites, leading to the identification of the optimal decision alternative based on maximum expected utility. This work provides a detailed framework for renewable energy stakeholders, helping in the assessment of both profitability and survivability of WECs in chosen locations. It significantly contributes to minimizing economic and performance risks associated with ORE system installations, promoting efficient and sustainable energy production from ocean resources. [ABSTRACT FROM AUTHOR]

This research paper focuses on techno-economic modeling and optimal sizing of autonomous hybrid microgrid systems. The optimal configuration of the suggested stand-alone system was performed by developing a size optimization model based on the metaheuristic novel Grasshopper Optimization algorithm (GOA) method to minimize the Total Net Present Cost (TNPC), unmet load, and Cost of Energy (COE) in the Nsukka Community which comprises 88 villages. The GOA and HOMER Pro Software are employed to compare results across four possible configurations of hybrid renewable power systems (HRES). The comparative analysis between GOA and HOMER shows that configuration-4 (biogas/Diesel), emerges as the optimal solution, with a 0 % unmet load at the COE of $0.01783 per kWh. The findings indicated that the GOA-based HRES, with a higher saturation of Biogas and photovoltaics (PV), proves to be more affordable in comparison to the HOMER-based solutions. The reduction in the COE and NPC of renewable energy for peak demands highlights the growing importance of biogas generators as an affordable local power supply to meet energy demands. This underscores the potential of the GOA in optimizing hybrid renewable energy systems for remote communities, producing an economically viable and sustainable energy solution. [ABSTRACT FROM AUTHOR]

Energy extraction from ocean or wind currents through flow-induced vibration (FIV) of cylindrical structures is an important means of generating clean and renewable energy in nature. In this paper, we focus on investigating the response and energy harvesting performance of a semi-circular cylinder subjected to low- and high- Re (Reynolds number) cross-flow. Irrespective of Re , we observe six different types of FIV responses within the angle of attack (α) range of 0°–180°. Based on the characteristics of the vibration amplitudes and frequencies, these responses are categorized as typical VIV, extended VIV, narrowed VIV, galloping, combined response, and transition response. To assess the energy harvesting performance of these responses, we analyze the harvested power (P h) and efficiency (η h). Our findings indicate that in the VIV regimes, P h and η h are strongly dependent on the amplitude. However, an exception is noticed in the third branch of the extended VIV, where the amplitude is significantly large but P h and η h are small. In the galloping regime, the dependence of P h and η h on the amplitude becomes weak. Amongst all the response regimes, the galloping at α = 180° exhibits the best energy harvesting performance, with the highest η h reaching 46.5%. In the combined response, P h and η h are significantly small, regardless of whether it occurs in the VIV or galloping (or large-amplitude vibration) region. Further, we confirm the significant influences of the damping coefficient and mass ratio on the energy harvesting performance. By selecting the optimal damping coefficient and mass ratio, both P h and η h are significantly improved. [ABSTRACT FROM AUTHOR]

The application of green energy technologies to supply berthed ships in ports with the necessary power instead of using their diesel generators is considered an initiative towards sustainability and climate change mitigation. Therefore, the current paper investigates the implementation of solar panels, offshore wind turbines, and hydrogen fuel cell systems as green technologies to supply the berthed ships with power in three Egyptian ports. The power requirements are determined by analyzing the annual ship traffic data and estimating the energy demand for each ship type. As a relevant result, the annual energy demand for ships berthed in Alexandria, Damietta, and Safaga ports is about 44.2 × 103 MWh, 22 × 103 MWh, and 5.6 × 103 MWh, respectively. The analysis results reveal a potential annual carbon dioxide reduction of 31.7 k-tons, 15.7 k-tons, and 4.2 k-tons in Alexandria, Damietta, and Safaga ports, respectively. From an economic perspective, the levelized cost of energy (LCOE) of solar panels is estimated to be 80.7–90.1 $/MWh, while hydrogen fuel cells have a cost of 166.8–172 $/MWh. On the other hand, LCOE in the case of wind turbines is estimated to be 140.8 $/MWh for Alexandria ports, 178.3 $/MWh for Damietta port, and 103.9 $/MWh for Safaga port. [ABSTRACT FROM AUTHOR]

The present paper describes the development of a novel framework for the creation of specialised Micro-Credentials supporting the training and re-training of students and professionals on the integration of renewable energy and sustainable fuels in urban settings. The framework is built upon a set of stackable micro-credentials, co-designed with the stakeholders input and it uses advanced natural language processing (NLP) and machine learning algorithms to assess the impact on the job market at regional level. The use of Micro-Credentials are considered to be the optimal methodology for enhancing the knowledge of specific topics especially for life-long learning for professionals. A co-design approach of such an innovative educational framework with relevant stakeholders fosters a tailored educational path for green upskilling. Finally, the framework is validated processing and analysing a large dataset of job descriptions, extracting the skills required by the market, organising the skills in categories and clusters. All this process lead to the creation of a basket of 74 Micro-Credentials oriented to students and professional with both a technical and non-technical backgrounds. • Renewable job-market skill needs identification through NLP and machine learning. • Educational co-creation approach based on stakeholders interaction. • Microcredential (MC) framework for life-long learning in the renewable sector. [ABSTRACT FROM AUTHOR]