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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 development of clean energy has made the green finance market attractive to investors, but existing studies lack an analysis of the impact of uncertain environmental shocks on the volatility of green finance stocks. This paper focuses on the impact of major events on clean energy stock returns by using a mixed-frequency model. Specifically, to identify asymmetric effects and extreme information spillovers, we construct extreme positive returns/positive returns and extreme negative returns/negative returns, respectively. The in-sample findings suggest that asymmetric effects and extreme shocks contain valid information about future clean energy stock price volatility. The out-of-sample analysis shows that the extended model can successfully predict the volatility of clean energy stock markets. More importantly, the extended model can generate predictive gains from both statistical and economic aspects. This study can provide a reference for implementing and adjusting energy policies, which is important for optimizing the energy structure and developing the green energy industry. • This paper investigates the performance of the extended GARCH-MIDAS model. • Extreme shocks have different effects on the clean energy stock markets volatility. • The model with long and short-term extreme shocks has better predictive power. • The predictability of extreme shocks is robust by several robustness checks. [ABSTRACT FROM AUTHOR]

The global energy sector is currently undergoing a fundamental transformation as it shifts away from fossil fuels towards renewable energy sources. Green hydrogen is one of the most promising energy carriers of the future in the worldwide effort to reduce the carbon footprint of energy systems. The advancement and uptake of green hydrogen technologies depend on various technological, environmental, and economic factors. In this paper, a comprehensive review of recent studies in green hydrogen energy systems (GHES) is conducted, highlighting technoeconomic aspects from the component to the technology and broader system level. Firstly, the technological principles and recent progress of different green hydrogen generation, storage, and utilization technologies are summarized. Subsequently, an in-depth analysis of various technoeconomic modeling and experimental activities focused on integrated GHES is undertaken. By examining applications in the stationary and transport sectors, this review paper offers an assessment of the current state-of-the-art in terms of development and integration options. The technical challenges, key factors, and future research directions of green hydrogen technologies and systems are outlined, demonstrating that an effective, intelligent, and innovative integration of GHES with other technologies will encourage a more efficient use of resources and enhanced energy system flexibility, resilience, and energy security. • Characteristics, components and configurations of green hydrogen systems are presented. • Technoeconomic studies are collected in tabulated form and exhaustively analyzed. • State-of-the-art applications in stationary and transport sectors are discussed. • A critical review is provided to assess green hydrogen economic viability potential. • Challenges and prospects are evaluated, and future research directions are suggested. [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]

Renewable Energy Systems are considered to be a powerful component of local sustainability innovation strategies. This paper uses a policy mix approach to study innovation policy criteria, which include municipalities' location factors, stakeholder collaboration activities, and local knowledge about renewable energy sources and projects. The aim is to study the impact of these factors on decision-making and developing renewable energy projects and their local sources. Moreover, this paper aims to understand the impact of the mix of different policies on the energy sector and technological innovation systems, through the innovation policy criteria that has been identified. These impacts are studied as they constitute the policy mix, and affect the process of developing renewable energy sources in the Palestinian municipalities and local councils. In this study a sample of 216 Palestinian municipalities and local councils is analyzed. The results indicate the need for policymakers to promote cooperation between the public and private sectors, and the importance of focusing on location factors such as energy import reduction, incentives, and emissions that directly affect renewable energy sources. Whereas, local knowledge does not have a direct impact on the process of developing renewable energy systems, as it can constitute indirect support at a later stage for the implementation of other policy criteria discussed in this study. The results confirm that increasing the potential for developing renewable energy sources takes place when policymakers adopt the role of facilitator and establish a local innovation network in which different actors cooperate so that public and private stakeholders can provide support in order to develop local renewable energy systems. • Different policies impact on RE sector by innovation policy criteria are measured. • The policies and practices of 216 Palestinian local government units are analyzed. • Positioning factors, collaborative activities, & local knowledge standards are used.≥. [ABSTRACT FROM AUTHOR]

Integrating biomass, photovoltaic, and other renewable energy sources for hydrogen production can form a biomass-photovoltaic-hydrogen integrated energy system (BPH-IES). The system features multi-energy storage and joint supply, as well as cascaded utilization, is a promising co-generation way to meet the system's electricity, heat and hydrogen needs, and has significant energy-exergy-economy-environment (4E) performance. Therefore, scientific and effective evaluation of the BPH-IES is an important prerequisite for promoting its development. Firstly, by analyzing the structure of the BPH-IES system, this paper constructs a comprehensive performance evaluation index system of the system from the 4E dimensions. Secondly, a hybrid multi-criterion decision making (MCDM) framework for the 4E comprehensive performance evaluation of the BPH-IES is constructed, in which the anti-entropy weighting method is used to determine the objective weights, the subjective weights are determined by the decision making trial and evaluation laboratory method improved by intuitionistic fuzzy sets, and the final combined weighting results are obtained based on a game-theoretic method that the objective and subjective weights' heterogeneity can be fully minimized. The grey-relation-analysis-based measurement alternatives and ranking according to the compromise solution method is proposed to determine the ranking of alternatives. This paper selected 8 typical BPH-IES cases for 4E evaluation analysis. The results showed that energy performance is the core dimension that reflects the 4E performance of the system, and system's economy indicators such as energy supply income and energy supply cost will also significantly affect the system's 4E performance. Therefore, the 4E performance of BPH-IES can be effectively improved by expanding the system's demand and supply scale and increasing the system's installed proportion of clean energy and hydrogen penetration. Finally, the ranking consistency test, Leave-One-Out analysis and sample separation test demonstrate that the developed hybrid MCDM can ensure robustness meanwhile improve the decision efficiency. [ABSTRACT FROM AUTHOR]

With the increasing demand of users for distributed energy storage (ES) resources and the emerging development of peer to peer (P2P) transaction technology, shared energy storage (SES) has great potential to contribute into new business models of demand-side ES. In order to compromise essential elements like safety, stability and efficiency of P2P trading, as well as to improve the utilization rate of demand-side ES, this paper devotes to construct a P2P transaction framework based on a partially decentralized topology and proposes a two-stage trading optimization strategy of SES in a P2P market, considering the equilibrium state of supply and demand flow. In the first stage, this paper simultaneously balances the interests of buyers and sellers and brings the carbon trading mechanism into the transaction process. The interaction of interests of bilateral parties with consideration of carbon trading mechanism has been investigated, and a SES capacity sharing model is, then, established based on the bargaining game theory. In the second stage, a unique pricing mechanism for SES leasing fee is designed based on a multi-strategy evolutionary game model, considering bounded rational decision-making for SES operators and communities. Finally, numerical simulation verified the feasibility and superiority of the proposed P2P trading strategy of SES. [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]

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]

In this paper, the authors present a two-step model to obtain quasi-optimal solutions for the storage and transportation of renewable energy as its installation becomes more widespread in remote areas. The model is formulated as a total cost minimization linear programming problem and considers both renewable energy facilities and the energy supply and demand of the entire country. The first step of the model optimizes the entire country, while the second step determines the marginal capacity of energy storage and transportation facilities when the excess production capacity of remote renewable energy is added. This approximation for reducing computational complexity is shown to be sufficiently accurate under realistic conditions. The authors apply the model to analyze energy storage and hydrogen production at renewable energy power plants in Japan in 2050. The study shows that photovoltaic power generation uses storage batteries attached to the power plant for electricity transmission. In contrast, wind power generation transmits most electricity directly without storage batteries and converts some into hydrogen for consumers. The proposed lightweight model allows for numeral sensitivity analyses, which provide valuable insight into optimizing energy storage and transport of renewable energy. • We developed a 2-step model for quasi-optimization of equipment at VRE site. • This model optimizes energy storage and transport with VRE deployment. • If additional VRE is sufficiently small, the model division retains sufficient accuracy. • Energy supply costs minimized by combining power transmission and H 2 transportation. • PV has large LIB, while WT has direct transmission and H 2 production. [ABSTRACT FROM AUTHOR]

This paper aims at optimizing the energy performance of an innovative integrated collector-storage solar water heater (ICSSWH) prototype. The system incorporates a high-vacuum gap, which significantly reduces convective thermal losses, and a serpentine absorber system coated with a low-E layer to minimize radiative thermal losses. The methodology is based on a detailed dynamic simulation model, implemented in the MatLab environment, suitable conceived to accurately predict the energy performance and economic profitability of the ICSSWH under various weather conditions and energy market prices. This approach aims to initially identify the most influential geometrical, thermophysical and optical parameters that impact the energy performance of the system through the assessment of different objective functions. After identifying the most influential parameters, the range of existence with the interval step for each variable is defined, and the optimization procedure is conducted to determine the optimal set of variables that maximize/minimize the selected objective functions. To demonstrate the effectiveness of the proposed methodology, a proof of concept was conducted to optimize the energy performance of novel ICSSWH by considering nineteen parameters with the aim of maximizing/minimizing three different objective functions. The results of this analysis yielded various sets of optimised parameters for the ICSSWH. Therefore, by implementing the set of optimised parameters into the dynamic simulation model, the energy performance of the optimised and non-optimised ICSSWH collectors was evaluated in 42 different weather zones and compared to a reference case scenario. [ABSTRACT FROM AUTHOR]

The fundamental way for China to achieve carbon peak and carbon neutrality is to continuously improve the penetration of renewable energy (RE) in the power system and establish an electricity mix dominated by RE power. According to China's 14th Five-Year Plan, the proportion of non-fossil energy power generation should reach about 39% by 2025. However, the impact of the increase in share of RE power on carbon emissions of the electricity sector remains to be estimated. To this end, the purpose of this paper is to capture the nonlinear characteristics of RE on electricity-related carbon emissions by using panel smooth transition regression (PSTR) model and provincial panel data covering the period 1991–2020 under the background of provincial RE quota system. The estimation results indicate that, (i) in the four transition variables of PSTR model such as the installed capacity of power generation, substation equipment capacity, total electricity consumption and total power generation, the nexus between RE deployment and electricity-related carbon emissions was detected as nonlinear. (ii) The heterogeneous RE carbon abatement effect is reflected in its increasing penetration in provincial power systems. (iii) Carbon abatement performance of RE generation decreases in the negative range with the increase of transition variables. (iv) The estimation coefficient of RE is positive at the higher value of the transition variable in the PSTR model of carbon emissions per capita. As a result, the RE quota with the best emission abatement performance should be designed based on the provincial power resource endowment and the medium and long-term expected electricity consumption. [ABSTRACT FROM AUTHOR]

The linkage of renewable, non-renewable energy and carbon markets is increasing, and there is a complex network structure for the risk transmission among multiple markets. Based on the methods of network topology analysis and DY spillover index, this paper analyzes the time-varying spillover effect and network structure of risk transmission among renewable, non-renewable energy and carbon markets. The results show that: according to the static spillover index, there are significant spillover effects among renewable, non-renewable energy and carbon markets, and they are asymmetric. Moreover, the total spillover index further shows that the spillover effect between energy and carbon markets is time-varying, especially during the extreme events. Specifically, the net spillover index shows that the spillover effects among renewable, non-renewable energy and carbon markets are bidirectional, asymmetric and time-varying. Additionally, under the influence of various extreme events, the spillover effect and network structure of risk transmission among renewable, non-renewable energy and carbon markets are heterogeneous. Compared with the shale oil revolution and the Sino-US trade dispute, the influence of COVID-19 is more significant and complex, and it is long-term and comprehensive. Finally, some policy implications for preventing risk transmission and optimizing the energy structure to promote emission reduction are put forward. [ABSTRACT FROM AUTHOR]

This paper aims to examine the relationship between oil prices and the indices of renewable energy and technology companies using Quantile Regression (QR.), Quantile-on-Quantile Regression (QQR), and Granger-Causality in Quantiles (GCQ) techniques. The results show strong evidence of an asymmetric effect at the sum of oil prices and technology companies' index on clean energy, indicating that the relationship is vital when these variables decrease than increase. The main finding of the QQR method demonstrates that the impact of oil prices and the technology stock index on clean energy is higher at the upper quantile than the lower quantile. We also establish bi-directional GCQ indicating the considerable change in oil price led to a change in renewable energy; besides, the outcome of the change in renewable energy does not support the causal link to the variation in oil prices across lower extreme quantile at 1%. Finally, our findings support the bi-directional causality from the tech companies market to the conventional and unconventional energy markets. Results identify the substantial role of renewable energy, with implications for global policy, decision-makers, and fund managers. [ABSTRACT FROM AUTHOR]

In the growing wind energy sector, as in other high investment sectors, the need to make assets profitable has put the spotlight on maintenance. Efficient solutions which leverage from condition or performance based maintenance policies have been proposed during the last decades, but the proposed methods generally focus on individual components or stand for specific application areas. This paper aims to contribute to the development of performance based maintenance strategies within the wind energy sector by providing a condition monitoring based generic methodology for wind turbine performance assessment at system level. The proposed methodology is based on the detection of critical periods in which low performance is detected repeatedly. Multiple machine learning methods and models are applied to assess the wind turbine performance. This methodology has been applied in a case study with SCADA data of eight wind turbines. An analyst could benefit from the implementation of the methodology and the easy-to-interpret results shown in the proposed control chart, especially in cases in which there is less know-how about which variables have higher impact on systems performance. • A methodology for performance-based maintenance of wind turbines is proposed. • The methodology has been applied in a case study with eight wind turbines. • Machine learning methods are applied to assess the wind turbine performance. [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]

Heat waves, combined with an increasing share of weather-dependent renewable energy sources, lead to disruptions in the operation of energy systems. This paper estimates how energy consumption in Poland changed during four heat waves (2010–2022). It also analyzes how often wind conditions may occur in the summer, that interrupt energy production by land-based wind farms. Moreover, authors are looking at future changes of climate indices responsible for increased energy consumption and possible reduction of energy production in wind farms. During the 2010 heatwave, the increase in energy consumption during the hottest days did not exceed 4 %. Later summer demand increased; in 2019, it was 8 % higher than during the 2015 heatwave. In 2016–2022, energy consumption at the average daily air temperature above 26 °C was over 8 % higher on working days than at 18 °C. Recently, even in regions with favorable wind conditions, the number of summer days with low wind speed (2 ms−1) exceeds 14. For Poland, the number of days with low wind speeds will increase by about ten. Therefore, the increasing demand for energy on hot days, combined with their more frequent occurrence, may have a negative impact on the stability of the energy system in the future. [Display omitted] [ABSTRACT FROM AUTHOR]

DSR is crucial for leveraging user flexibility to integrate renewable energy efficiently, also for smaller users like households, and for utilizing surplus energy from prosumers, highlighting its potential. Public perceptions, habits and responsiveness are crucial to its success. A method to determine the initial state of interest in DSR is a survey. This paper presents the results, obtained on the basis of statistical analysis of responses and averaging of scores according to the Likert scale. The survey was conducted among 400 Polish small electricity users. Poland is an exemplary country where external and internal signals can potentially accelerate building a new customer-supported energy sector. The research found low awareness of DSR among individual users, but a strong interest in the concept. It suggested that incentive schemes are preferred over dynamic tariff systems. In general, energy users show rational behaviour regarding specific DSR options, with financial benefits indicated as the primary motivation and social pressure and fulfilling energy policy objectives were deemed least important. This public perception, however, allows for a wider implementation of DSR. Transparent participation rules are crucial. The results research results can inspire policymakers, energy service providers, and aggregators to tailor DSR solutions for diverse end-users. • Public perception of DSR is crucial for the success of their implementation. • Most small energy users are interested in DSR programmes. • Small users prefer a bonus-based approach to billing for DSR response. • The majority of users reject the extreme options for DSR parameters. • Financial benefits are the main motivation for participating in DSR programmes. [ABSTRACT FROM AUTHOR]

Examining coupled renewable-battery power plants ("hybrids") in congested areas provides insights into a future of increased wind and solar penetration. Our study focuses on two types of congested regions, Variable Renewable Energy (VRE)-rich Areas and Load Centers, and explores likely plant configuration choices for developers and transmission network planners. This paper examines how hybrid value, comprising energy and capacity value, varies by plant configuration and congested region type considering factors such as storage duration, battery degradation, and ability to charge from the grid. We select plant locations from across the seven main U.S. independent system operators (ISOs). Hybrid value for each configuration is computed based on profit-maximizing plant operation given perfect foresight, according to observed wholesale power market real time prices from 2018 to 2021. In VRE-rich Areas, the median increase in energy value from extending storage duration from one to 4 h is 29.4 % for solar and 26.8 % for wind, assuming low battery degradation costs and storage sized to 100 % of the plant's nameplate generation capacity. Increasing storage duration beyond 4 h does not substantially increase its value from energy markets, even in VRE-rich Areas. We find that solar hybrids reach a 90 % capacity credit with 4 h of storage, while wind hybrids require 8 h of storage, based on the capacity factor of each hybrid during the top 100 net load hours. [ABSTRACT FROM AUTHOR]

In contemporary times, where most academic research mainly focuses on the factors of economic and environmental sustainability and emissions reduction. Yet, very little attention has been paid to the identification of the factors of renewable energy, which requires appropriate policy-level attention. Consequently, this research investigated two developed economies, i.e., Germany and the USA, from 1991 to 2021 where, the objective of the study includes using novel and robust empirical methods to test the causal relationship between renewable energy and CO2 emissions, economic growth, technological innovations and oil prices. Using the normality and unit root estimators, this study observed that non-normal data distribution, yet all the variables are stationary. Using time-series and panel cointegration tests, the results validate the cointegration between economic growth, oil prices, carbon emission, technological innovation, and renewable energy adoption in the United States whereas Germany does not show cointegration between the variables. This study employ 's the Morlet-Wavelet approaches and key findings show that all these variables have a significant role in improving renewable energy adoption in both the region. Furthermore, results show a unidirectional and bidirectional causal association between the variables via the panel-stacked Granger causality test. This study recommends effective policy ramifications concerning improved investment in technological innovation, improved low-carbon production, and diverting economic growth to renewable energy transition. Use of improved new time-series method of wavelet coherence show the key contribution in this paper with new evidence of time frequency analysis on how external variables affect renewable energy consumption in developed countries of US and Germany. The objective includes understanding the effects of CO2 emissions, economic growth, technological innovations and oil prices on renewable energy which would give evidence to policy makers and environmentalists on how developed countries should improve clean energy adoption. [ABSTRACT FROM AUTHOR]

Integrated Energy Systems (IES) represent an effective strategy for increasing the use of renewable energy sources. However, the inherent uncertainty in forecasting both renewable energy generation and loads significantly impacts the accurate dispatching of IES. This paper proposes a two-stage dispatching strategy for Park-Level Integrated Energy Systems (PIES) based on a master-slave-cooperative hybrid game model. This approach aims to address the aforementioned challenges and achieve cooperative optimization in dispatching across Multiple Park Integrated Energy Systems (MPIES). The strategy comprises two key components: a day-ahead master-slave game model incorporating demand response and energy storage, and a real-time cooperative game model based on energy sharing. In the day-ahead stage framework, price signals are used to optimize the energy supplier's output and devise the customer's energy consumption plan, thereby achieving economic dispatch. In the real-time stage, the optimization target is shifted to the load aggregator, which employs cooperative game theory to refine certain plans established during the day-ahead stage. Validation using an MPIES consisting of three PIESs showed that the model can accelerate real-time response time by 7 s, achieve 100 % dispatch accuracy, and reduce energy procurement costs by 3.46 % for the PIESs compared to other dispatch models. [ABSTRACT FROM AUTHOR]

Solar-driven ejector-compression combined refrigeration holds significant importance in reducing building energy costs and carbon emissions. However, existing systems encounter challenges including low thermal efficiency, large thermal storage device, and thermal storage heat loss, reducing energy-saving potential. Aiming at this, a high-solar-efficiency ejector-compression partially-coupled refrigeration system with cooling storage at sub-low temperature is proposed in this paper. The new system can efficiently utilize solar energy to partially produce cooling energy at sub-low temperature. And this cooling energy is much less than driven heat and close to ambient temperature, reducing thermal storage device size and heat loss. A theoretical model was established with the ejector model experimentally validated. Results show that the maximum cooling storage mode has better performance, with maximum COP oe and COP og of 10.72 and 3.25 respectively, which are 16.9 % and 13.3 % higher than no cooling storage mode, respectively. Compared to the traditional ejector-compression system and its auxiliary system, the new system shows superior performance, with COP oe improvement of 19.7 % and 14.5 %, and COP og improving by 21.5 % and 17.0 %, for maximum and partial cooling storage modes, respectively. These results show good energy efficiency of the system and provide a more energy-efficient solution for solar driven ejector-compression refrigeration systems. [ABSTRACT FROM AUTHOR]

In this paper, we study optimal market operations and grid effects of office building from an energy community perspective. Commercial buildings such as offices can include many stakeholders and new flexible assets that can offer suitable use cases to form energy communities or enter energy and reserve markets. The office building we focus in this study has a real-world counterpart with historical measurement data and involves three stakeholders and their assets. The stakeholders are the owner, the tenant, and the operator of flexible assets. These assets are the battery energy storage system (BESS) and the electric vehicles (EV) charging system (EVCS) that both can operate in response to energy price fluctuations and in a reserve market. Different cases are studied where either the owner or the tenant is paying the EV charging, and where the flexibility of the EV charging is either active or not. We show that, when the owner pays for the energy of the EVCS and the tenant is using it, there is a beneficial case to form a coalition between them and share the benefits. Operative cost-benefit calculations are conducted where the community benefits are shared according to the Shapley value and the power flows at the building's connection point are simulated based on the market operations. The sensitivity of the flexibility and the maximum power of EVCS to the results are studied. Harnessing benefits from the markets shows significant effects on the power flows at the connection point. • With multiple stakeholders, flexible assets and varying energy price can initiate a community use case. • Having only fixed energy price is not giving coalitional benefits. • Having excess energy to share or sell is not necessary to form an energy community. • More flexibility and electric vehicle charging power can increase cost savings and coalitional benefits. • Spot price and reserve market operations can have a high impact at the connection point. [ABSTRACT FROM AUTHOR]

The world is turning towards renewable energies to sustainably meet its increasing demand for energy. Naturally, this is being accompanied by a strong momentum in trading within the renewable energy market. Today, behavioral finance acknowledges the major role of wider psychological and social factors in shaping the stock market, through influencing investors' sentiment. Therefore, this paper explores the understudied question of whether environmental television newscasts can be used as a proxy for measuring investors' sentiment and in helping to improve the forecast accuracy of renewable energy stock prices. First, we compute the sentiment scores of the environmental newscasts of CNN, BBC News, MSNBC, and Fox News. We then use machine learning to implement a baseline forecast model, as well as an augmented one which takes the newscasts' sentiment scores as input. Using four different accuracy metrics, we find that environmental TV newscasts can improve the forecast accuracy of renewable energy stock prices in 78 % of the experiments, and decrease the Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error in 83.3 % of the experiments. We also find that the sentiments of conservative news outlets, such as Fox News, can improve the forecast accuracy of renewable energy stock prices more than liberal ones. Finally, we provide some insights into potential psychological dynamics that can help us make sense of the results, such as the negativity bias theory. [ABSTRACT FROM AUTHOR]

Effective municipal solid waste management (MSWM) posed a significant challenge for numerous developing nations, including Algeria. The imperative to convert household waste into value-added products for both environmental and economic benefits was crucial. However, limited knowledge and resources often hindered the exploitation of waste for energy generation. In Algeria, as in many African countries, landfilling remained the predominant method for disposing of household waste. This paper presented findings from a study conducted in Tlemcen Province, Algeria, shedding light on the severe environmental and health consequences resulting from mismanaged waste in the region. The investigation revealed that all waste sites in Algeria were not harnessed for energy production. A detailed analysis was conducted on a specific site, Jabal Al-Hadid, uncovering a waste composition of 62 % organic, 33 % recyclable, and 5 % non-recyclable materials. Anaerobic digestion was estimated to have produced approximately 84.23 × 106 m3/year of methane, with a potential electricity generation of about 192.37 GWh/year at this site. Highlighting the untapped potential, the utilization of landfill sites for energy production in Algeria could have reduced national electricity consumption in the construction sector by approximately 11.3 %. Despite the focus on a specific province, these findings had broader implications for addressing waste management challenges on a national scale. • Municipal Mismanagement Stalls Algeria's Zero Waste Goal. • Landfills in all Algerian cities can generate approximately 9485 GWh/year. • Algeria's waste-to-energy potential can cut 11.3 % of construction sector energy use. • Altering sites landfill into economic hubs offers novel waste management solution. [ABSTRACT FROM AUTHOR]

The increasingly volatile and complex geopolitical landscape has impacted the global energy market more frequently, posing serious challenges to the global energy low-carbon transition strategy. However, the heterogeneity of the impact of geopolitical risks (GPR) on the dynamic correlation between oil and clean energy across various market conditions and sub-industries of clean energy remains distinct. To address this gap, this paper combines a dynamic conditional correlation with the mixed data sampling (DCC-MIDAS) model and the Markov regime-switching model to investigate the influence of GPR on the correlation between oil and clean energy, particularly in diverse market conditions. Empirical findings reveal that the impacts of GPR on the long-run correlations of oil-clean energy vary across different market regimes, both in terms of the overall market and industry sectors. Specifically, GPR negatively affects the oil-clean energy correlation with stronger effects in the low-correlation regime. In particular, GPR plays a positive effect on the oil-biofuel nexus, however, the impact of GPR on the oil-fuel cells nexus exhibits opposite directions in high- and low-correlation regimes. Moreover, the transmission of geopolitical risks to the energy markets is predominantly driven by threats (GPT) during periods of low volatility and correlation, while acts (GPA) play a more significant role during periods of high volatility and correlation. These results emphasize the significance for both investors and policymakers in considering the time-varying impact of geopolitical risks across various market conditions and individual clean energy sub-sectors. [ABSTRACT FROM AUTHOR]

Flat plate solar collector (FPSC) produces only thermal energy without electricity, whereas the photovoltaic thermal (PV/T) module produces electricity and low-temperature thermal energy. This paper presents a low-cost method to strengthen the functional uniqueness of individual PV/T and FPSC components through connecting a PV/T module in series with the other component. Under various water volume conditions, an experimental test on high mass flow rate was carried out. Results showed that the FPSC produced a better heating contribution and that reverse heat transfer of the PV/T module might be reduced by changing the operation strategy. The water volume is a crucial factor that influences the system's available heating time, a system that operates in the optimal water volume range will produce greater energy savings. Furthermore, the maximum values of the thermal, electrical, exergy, and primary energy-saving efficiency were 51.91 %, 13.52 %, 19.13 %, and 87.20 %, respectively. The optimal water volume distribution range of the system is 160–180 L. With an average annual decrease of 2.96 t in CO 2 emissions, the system provides a 4.28-year payback period and saves 5285.71 USD in electricity costs throughout its life cycle. The novel system performs extremely well in terms of energy and cost efficiency. [ABSTRACT FROM AUTHOR]

In previous work, a single-tether submerged spherical point absorber with asymmetric mass distribution (SPAMD) was proposed to enable harvesting wave energy induced by the surge and heave motion of the buoy. By taking advantage of the motion coupling arising from the asymmetric mass distribution of the buoy, the SPAMD was found to be up to 3 times more efficient than a generic single-tether point absorber (with uniform mass distribution buoy) under regular waves. For motion-coupled systems like the SPAMD, the mass distribution is a significant factor that governs the kinematics and the efficiency of the device. Therefore, in this paper, a 3DOF (surge, heave and pitch) spectral-domain model considering viscous drag was developed, to investigate the sensitivity of the mass distribution on the power output of the SPAMD in irregular waves. The aim of the sensitivity study is to provide a guideline for the wave energy industry when designing such devices. At the end of this paper, the yearly mean power output of the SPAMD with optimal mass distribution was assessed at three test sites near Yeu Island (France), Perth (Australia) and Sydney (Australia). It was found that in such sea sites, the SPAMD was at least 1.5 times more efficient than the generic single-tether point absorber, indicating that the SPAMD might have a significant commercial potential. Furthermore, for the three sites investigated, the performance of the SPAMD was found to be relatively tolerant to non-optimal PTO configuration, implying that sea-state specific tuning is unnecessary. • A SPAMD with low weight-to-buoyancy ratio is more productive at small wave periods. • The mass ratio of the SPAMD has a limited impact on the power output. • Mass-offset position has the greatest impact on the power production of the SPAMD. • PTO loading is relatively insensitive to the mass-offset position. • SPAMD can produce up to 53% more annual mean power than the generic single-tether PA. [ABSTRACT FROM AUTHOR]

Abstract This paper reviews the energy production potential of historic hydro sites (watermills, weirs and other) located in 21 EU countries which so far have not been used for power generation. It is based on the data collected by the RESTOR Hydro project, consisting of some 65,000 hydro site data. The lion's share of this data is attributed to micro-hydro sites, mini and small hydro sites being left in a marginal position. The main aim of this paper is to explore the collected project data of micro-hydro sites in terms of scientific analysis, to present them in a consolidated form, to identify the countries with the largest hydro potential and most prominently to indicate those of them that fall into environmentally sensitive areas. It provides only representative data, evaluates the potential capacity and hydropower generation at sites of the individual countries, gives an insight on their restorability, exhibits the distribution of micro-hydro sites within the EU network of protected areas (Natura 2000) and visualizes micro-hydro data all over the EU in a concise way. An approximate share of the generating energy resulting from the historic hydro sites in the remaining small hydropower potential of the investigated countries is identified (6.8 TWh/yr). Highlights • Over 350,000 micro-mini hydro sites may have existed in Europe at one time or another. • Some 65,000 hydro site entries, along with additional data reviewed in detail. • A concise information of the EU's historical micro-hydro potential in 21 EU countries. • Some 6.8 TWh/yr can realistically be generated at these sites, if re-powered. [ABSTRACT FROM AUTHOR]

Abstract The mass of ice on wind turbines blades is one of the main problems that energy companies have in cold climates. This paper presents a novel approach to detect and classify ice thickness based on pattern recognition through guided ultrasonic waves and Machine Learning. To successfully achieve a supervised classification, it is necessary to employ a method that allows the correct extraction and selection of features of the ultrasonic signal. The main novelty in this work is that the approach considers four feature extraction methods to validate the results, grouped by linear (AutoRegressive (AR) and Principal Component Analysis) and nonlinear (nonlinear-AR eXogenous and Hierarchical Non-Linear Principal Component Analysis), and feature selection is done by Neighbourhood Component Analysis. A supervised classification was performed through Machine Learning with twenty classifiers such as Decision tree, Discriminant Analysis, Support Vector Machines, K-Nearest Neighbours, and Ensemble Classifiers. Finally, an evaluation of the classifiers was done in single frequency and multi-frequency modes, obtaining accurate results. Highlights • Ice on the surface of the blade. This paper presents a. • Novel approach to detect and classify thickness ice in wind turbine blades. • Patterns recognition through guided ultrasonic waves and Machine Learning. • Features extraction methods: linear AR and PCA and nonlinear NARX and h-NLPCA. • Neighbourhood Component Analysis to select the features in AR and NARX by Machine Learning. [ABSTRACT FROM AUTHOR]

Financial sector performance is increasingly linked with the transition to renewable energy in the sustainability discourse of developing economies. This paper examines the nexus and implication(s) of financial development on renewable energy consumption in Nigeria (the largest and most populous economy in Africa). Specifically, this study utilized the broad-based financial development index data to effectively address the multidimensional nature of financial development and the portion of renewable energy in total energy consumption as key variables, while other relevant pieces of information (growth rate of per capita GDP, foreign direct investment and consumer price index) were incorporated. The study employed a blend of the ADF test and Zivot-Andrew test to ascertain stationarity properties as well as the likelihood of structural breaks, while the ARDL was utilized to determine the long-run relationship(s) using data from 1981 to 2019. The study estimation finds, among other things, that financial development is critical for renewable energy consumption in Nigeria and recommends policies to promote better outcomes for the financial and energy sectors, respectively. [ABSTRACT FROM AUTHOR]

Global Solar Radiation (GSR) prediction models are critical to improve the dispatch, control, and stabilization of solar renewable power, and to integrate the solar energy into the electrical grid system. GSR, especially on a short-term scale, can have important fluctuations, which may affect the total energy expected to be supplied to the grid. To overcome this issue, prediction models with a high forecasting performance are needed. In this paper a novel framework based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Deep Residual Network with Bidirectional long short-term memory, i.e., DRESNET model, is proposed for obtaining accurate multi-step ahead GSR predictions. To train the proposed ICEEMDAN-DRESNET hybrid model, minute-level daylight data from Energy Sector Management Assistance Program in Nepalgunj (mid-western Nepal) are used. The results demonstrate ICEEMDAN-DRESNET model is an excellent tool for short-term solar energy monitoring, yielding excellent predictions, in all metrics such as MAE 9.769 W/m2, MAPE 5.657 % , TIC 1.143, CPI 4.739, and TIC 0.023 for 5-min time-horizon predictions, improving the results from the benchmark models. As the forecasting time-horizon is increased, the ICEEMDAN-DRESNET model accuracy drops, with MAE 33.672 W/m2; MAPE 31.749 % for 1-hr, MAE 22.625 W/m2; MAPE 18.312 % (30-min) and MAE 14.897 W/m2; MAPE 10.358 % (15-min), also better than the benchmark models. The results confirm the competitive merit of ICEEMDAN and DRESNET integration to improve deep learning and the potential of proposed model for the monitoring of solar or other renewable (e.g., wind or solar) energies. [Display omitted] • A deep learning algorithm for short-term solar prediction is proposed. • The system considers deep residual and bidirectional long short-term memory network models. • Nepal (a solar-rich region) is selected to evaluate the deep learning algorithm. • Tests are carried out at multi-step ahead horizons of 5-min, 10-min, 30-min and 1-h. • Excellent performance of the proposed deep learning system is reported. [ABSTRACT FROM AUTHOR]

As the price of solar modules has decreased, oversizing PV system becomes a general practice. Without proper energy management, the oversized systems could lead to over-generation waste which cause a loss in revenue. Battery energy storage system (BESS) can be integrated to the PV system for utilizing the over-consumption energy and increasing the system's financial benefits. This paper highlights the influence of technical and financial factors on the photovoltaic (PV) and PV with BESS design. To analyze the impacts of the factors on PV systems, thirty-six system configurations with a variety of geographic locations, tariff structures, billing methods, and PV system technology are simulated using System Advisor Model (SAM) as a tool with result validation from measurements on a real oversized residential grid-tied bifacial PV+BESS system. To study the financial criteria that affect the optimal system design, we conducted the sensitivity analysis of the DC/AC ratio, battery replacement threshold, and battery size to observe the optimal system design based on three financial outputs: levelized cost of energy (LCOE), net present value (NPV), and payback period. The findings show that the optimal DC/AC ratio for residential-scale PV systems lies between 1.1 - 1.3 and can go up to 1.6 with BESS. PV+BESS systems can reduce the payback period by 20% and boost NPV by 80% compared to the PV-only systems. The sensitivity study of the battery replacement shows that the battery can be degraded until 50% of its maximum capacity for the lowest LCOE. To achieve the optimal grid-tied PV system design, each economic parameter should be evaluated together. [ABSTRACT FROM AUTHOR]

Many nations want to use only renewable energy by 2050. Given the recent rapid expansion in RE use in the global energy mix and its progressive impact on the global energy sector, the evaluation and analysis of Renewable Energy's impact on achieving sustainable development goals is insufficient. Wind energy could be renewable. For smart grid supply-and-demand issues, wind power forecasting is critical. One of the biggest challenges of wind energy is its significant fluctuation and intermittent nature, which makes forecasting difficult. This study's goal is to develop data-driven models to predict wind speed and power. This paper uses Machine Learning (ML) and Deep Learning (DL) to improve a wind speed prediction and recommendation system for wind turbine power production using site climatological data. This system optimizes turbine use by selecting the right number of turbines to operate solely based on the required energy, which is related to wind power and speed, and recommends the best power station location to determine the best turbine run time. The Proposed Enhanced Recommendation System (PERS) includes the Wind Speed Prediction Module (WSPM), Wind Speed vs. Power Consumption Calculation (WSPC), and Recommendation Module (RM). Test results showed the suggested method works in run time. The XGBoost or Random Forest regressor predicted 15-day power output with 94 % accuracy and 6 % mean average percentage error. [ABSTRACT FROM AUTHOR]

With the emergence of distributed resources (DRs), developing an effective, profitable, and green management method is of great importance. Virtual power plant (VPP) can aggregate various DRs, optimize their schedules, and participate in electricity markets uniformly to earn profit. The optimal scheduling problem in spot market has been widely studied, however, few studies have extended the services of VPP to the long-term scale. This paper proposes a coordinated scheduling method for a multi-energy virtual power plant (MEVPP), considering the integration of hydrogen facilities. A holistic market framework, including capacity market, energy market (EM), and ancillary service market (ASM) is developed jointly for the coordination among the annual capacity schedule, daily energy schedule, and daily reserve schedule of MEVPP. A hybrid hydrogen storage system including daily hydrogen storage and seasonal hydrogen storage is modeled to achieve both intraday and cross-seasonal peak shaving. Stochastic programming is used to tackle the uncertainties and risk management is considered by conditional value at risk. With the help of numerical analysis in case studies, it can be concluded that the proposed coordinated scheduling method can improve the economy of MEVPP. The profit from joint market decreases the total cost by about 52 %. The capacity adequacy and flexibility of MEVPP can be improved by the coordination of capacity and reserve services. The hybrid hydrogen storage system can realize the efficient integration of hydrogen and increase hydrogen production and consumption by about 9 %. [ABSTRACT FROM AUTHOR]

Investment in distributed energy resources (DERs) is crucial for the expansion of distribution networks. However, traditional methods often lack comprehensive project management throughout their entire lifecycle and precise matching between distribution network operator (DNO) and energy consumer (EC) at the investment level. To address these issues, this paper proposes a novel investment model based on energy performance contracts (EPCs). Three EPC mechanisms are introduced: energy shared savings model (ESSM), energy guaranteed saving model (EGSM), and energy cost trust model (ECTM). Additionally, DERs investment strategies under EPC mechanisms are proposed to satisfy full-process management of project and precise matching between EC and DNO. The investment and operation problem is solved by an evolutionary game theory that ensures bounded rational agent to achieve satisfactory expected returns through repeated dynamic games. Results from the case study of an actual urban 37-bus system demonstrate that different EPC mechanisms are suitable for various scenarios depending on the EC's profit demand and project scale. The proposed model provides a comprehensive framework for DERs investment, considering the interests of both DNO and EC, and promotes the optimal expansion of distribution networks in a more practical and effective manner. [ABSTRACT FROM AUTHOR]

The world is moving toward renewable rich electricity generation systems. Pakistan being the sixth most populous country in the world and having median age of 21 years requires extensive resources for the next couple of decades to fulfill its energy requirements. Punjab is the largest province of Pakistan with 60% of total population and consumes about 80% of the electricity. The province does not have a lot of hydro and wind potential, but it has tremendous opportunity to utilize solar energy to fulfill its energy needs. In this paper, we explore the solar energy production possibilities in the province. Our objective in this paper is to find the best locations to install utility-scale solar power plants to fulfill 30% of energy needs by 2030. In addition to the technical feasibility of solar PV power plants, we also look at the economic aspects of such plants. To this end, we look at the complete life cycle of a solar PV power plant. Using data from an existing solar PV power plant we derive Profitability Index (PI) of various possible locations. According to our results, the transmission line losses range between 0.7% and 12.2% depending on the load and length of the transmission lines. Using data from transmission line losses the PI of potential utility-scale solar PV sites ranges between −9.11% and 69.65% based on various economic factors. These results provides detailed information on the best places to establish utility-scale power plants from technical as well as economic perspective. [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]

The uncertainty of the renewable energy output and load demand poses significant challenges to the efficiency and stability of energy community operation. To reduce the adverse effects of uncertainty factors, this paper proposes a dual time-scale energy management method for a distributed energy community. In the day-ahead scheduling, an adaptive robust optimization model is proposed. This model uses the Dirichlet process mixture model (DPMM) to establish the fuzzy set of uncertainty for solar power generation and load demand. The column-and-constraint generation algorithm (C&CG) to solve the optimization model. In the intra-day scheduling, a rolling optimization method based on model predictive control (MPC) is combined with short-term source-load forecasting, which ensures consistency between the intra-day and day-ahead scheduling plans and real-time supply-demand balance. Experiments show that the robustness of energy management in the proposed method is enhanced significantly in various scenarios. At the same time, the operating costs of energy community are also reduced with different degrees of conservatism. [ABSTRACT FROM AUTHOR]

Energy is one of the essential components for the social and economic growth of urban and rural communities worldwide. However, the lack of energy supply is one of the most significant challenges facing remote or local communities in Nigeria. Distributed generation systems based on renewable energy, conventional sources, or hybrid resources are possible energy production solutions for these communities. This paper, therefore, focused on providing an energy generation system based on photovoltaics (PV) and diesel generators (DG) for stand-alone applications. A comparative analysis was carried out to assess the impact of control strategies, namely load following (LF), cycle charging (CC), and combined dispatch (CD). The designed energy systems: 60 kW PV, 78.69 kW PV, PV-DG-LF, PV-DG-CC, and PV-DG-CD were simulated using HOMER Pro while technical, economic, and environmental indices were used as performance metrics. Simulation results showed that 60 kW PV generated 84,927 and 75,859 kWh, without and with temperature effect, respectively, with the corresponding unmet electric load (UEL) of 13.8 and 14.2%. The result further demonstrated that PV-DG-LF is the optimal design with the highest renewable penetration of 80.7% and the least annual total fuel consumption (TFC) of 6,594 l. The economic results further confirm the suitability of PV-DG-LF with the lowest fuel cost (FC) and moderate cost of energy (COE) of $219,934 and 0.302 $/kWh, respectively. The PV-DG-LF is found to be more environmentally friendly, with the least annual CO 2 emission of 16,629.652 kg. The study considers veritable strategies to achieve affordable, clean, and reliable energy in line with research efforts on realizing the sustainable development goal (SDG) 7. [ABSTRACT FROM AUTHOR]

With the growing share of the climate-sensitive renewable energy market supply in China, estimating future energy drought risk (ED) is essential. In this paper, based on CMIP6, energy demand and power generation are estimated from 2023 to 2100 under SSP2-4.5 and SSP5-8.5 climate scenarios, and ED is defined from high demand (HD), low wind-solar generation (LWS), and residual load (RL). The results show that the ED exacerbation is relatively high under the SSP5-8.5. Between 2023 and 2100, the duration and severity of HD and RL notably increase, while LWS changes are relatively weak. We find that HD and RL exhibit high summer risks in the south and high winter risk in the north. The return periods vary significantly and extends over time in most regions. Moreover, there are few ED when the installed power generation capacity (IPGC) is increased. When the IPGC increases by 2%–5%, the LWS will decrease at least 20%–72%. With a 10% increase, the LWS will be mitigated by at least 80%, whereas the mitigation for RL is limited, approximately 3–17% at different rates. This work performs the first evaluation of future ED and provides the useful references for risk mitigation in China based on CMIP6. [ABSTRACT FROM AUTHOR]

The proliferation of charging stations entails multiple challenges for power systems. In this regard, the installation of photovoltaic-battery systems may help to mitigate the negative effects of charging points. However, such assets should be carefully planned, paying attention to economic aspects, principally. Most of existing works optimize the photovoltaic-battery system in charging infrastructures taking a representative-space of the involved variables (e.g. photovoltaic potential, charging demand or energy prices). However, this approach tends to ignore low-probable scenarios. Thus, the best-case scenario for charging demand (i.e. that for which the highest charging profit is accessible) may not be included in the analysis and therefore such demand could be not attended properly, thus losing this monetary opportunity. This paper focuses on this issue and questions if considering the best-case scenario into planning photovoltaic-battery systems for charging stations is worthwhile or not. To this end, a novel best-case-aware planning tool is developed, including the best-case scenario through a novel chance-constrained formulation. The overall problem is then decomposed into a master-slave structure by which the economy of the system is optimized together with the number of scenarios for which the best-case profile can be attended. A case study serves to validate the developed tool and shed light on the questions arisen in this work. In particular, it is checked that considering the best-case scenario into planning tools is questionable from a monetary point of view. Nevertheless, its inclusion unlocks some collateral advantages such as incrementing the users' satisfaction or reducing the grid-dependency. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated ocean thermal energy conversion (OTEC) system that couples power generation, refrigeration, and desalination. Based on the mathematical model, the thermodynamic, economic, and exergoeconomic performances are explored by evaluating several crucial parameters such as the ammonia-water concentration of the power subsystem (X gen), the turbine inlet pressure (P turb), etc. The results indicate that integrated OTEC system significantly improve the exergoeconomic performance since the efficient utilization of the exhausted cold seawater. The integrated system can achieve maximum net output power of 16.79 kW, refrigeration capacity of 268.87 kW, freshwater production of 53.22 t/d, as well as exergetic efficiency of 31.69% when the power subsystem's flow rate is 1.0 kg/s. Moreover, there is a maximum net output power when P turb is under 800 kPa, and increasing P turb leads to higher optimal X gen. The maximum net output power, for instance, is 2.18 kW, 4.62 kW, and 9.79 kW when P turb value is 600 kPa, 700 kPa, and 800 kPa, respectively, with corresponding optimal ammonia concentration values of 0.73, 0.83, 0.93. Furthermore, the maximum exergetic efficiency increases as P turb enlarges. The maximum exergetic efficiency rises from 20.40% to 31.69% when P turb increases from 600 kPa to 950 kPa. Besides, compared to the single OTEC plant's levelized cost of energy at 3.56 $/(kWh), the integrated system's can be as low as 0.098 $/(kWh). [ABSTRACT FROM AUTHOR]

Although renewable energy has been emphasized to mitigate carbon emissions, energy policies may require rearrangement. This paper aims to suggest the possibility of needs in energy policy rearrangement by revealing if the mitigation impact of the renewable energy supply has been diminished with a case study on Organization for Economic Cooperation and Development (OECD) countries. Adopting the idea of the environmental Kuznets curve hypothesis, the study investigates the nonlinear relationship between the renewable energy supply and carbon emissions. We collected the longest panel data possible for 34 OECD countries from 1971 to 2019. While the cointegrating equation results suggest that renewable energy has contributed to carbon mitigation, nonlinear estimation and quantile regression propose that the marginal mitigation impact from renewable energy might be weakening. 1% increase in renewable energy supply and the share of renewable in energy mix has mitigated carbon emissions by 0.134% and 1.260%, respectively. The empirical results insist that policymakers discover alternatives to decarbonize the energy sector, other than renewable energy expansion. Options to achieve carbon neutrality include heat expansion via unused heat, that is, national energy system innovation towards wasted energy utilization. [ABSTRACT FROM AUTHOR]

Corporate environmental information disclosure systems are important for controlling pollution caused by corporate activities. However, different motives associated with environmental information disclosure have different influences on the financing of companies. This paper adopts renewable energy companies instead of high pollution industries as a research object. The study analyzes the resource-dependent disclosure motivation of renewable energy companies, and the impact of environmental information disclosure on the scale and cost of direct and indirect financing. The results show the following. First, renewable energy companies in China have a resource-dependent motivation to disclose environmental information, with a gap between the level of environmental information disclosure and their actual environmental performance. Second, renewable energy companies may obtain more direct financing amount and lower indirect financing costs, but cannot obtain lower direct financing costs and more indirect financing amount by improving the level of environmental information disclosure with the resource-dependent motivation. Third, there are differences in the specific incentives for environmental information disclosure among the Eastern, Middle, and Western regions of China. The government needs to standardize the detailed rules and standards of the corporate environmental information disclosure system, and improve the environmental protection awareness and the implementation of green credit in the Western region. [ABSTRACT FROM AUTHOR]

Preventing an increase in emission intensity requires a transition to a renewable-based electricity system. Wind and solar power are often the major contributors to this transition. The inherently variable nature of the generation pattern of these renewable sources impels the inclusion of storage technologies. But how to combine renewable and diverse storage technologies in a cost-competitive way? This paper addresses this question by constructing the first open-sourced, spatially, and hourly resolved model of the Indian power system and analysing the system for cost-optimal storage combinations. Increasing the renewable share targets from 30% to 75% of electricity supply, the levelized costs and storage technology options for various energy scenarios are assessed. Results show that the system cost of electricity can vary between 3.65 R/kWh and 4.37 R/kWh with the help of transmission expansion and inter-regional coordination. 50% of RES penetration is found to be a critical target beyond which the total annual system cost increments rise sharply. The optimal solution at 50% RES share includes a maximum of 11 GWh of battery and 29.8 GWh of hydrogen storage capacity. Sensitivity analysis shows that cost reduction in the storage technologies results in the lowest system cost even with limited transmission expansion. • Hourly-resolved multi-region model of the Indian power sector. • Cost optimal systems are evaluated in the range of 30–75% renewable penetration. • Up to 50% of renewable integration is feasible with minimum cost escalation. • From 60% renewable share, the requirement of storage capacities increases steeply. • Optimal combination of batteries, hydrogen storage and pumped hydro results in least-cost decarbonization. [ABSTRACT FROM AUTHOR]

Wind energy has become one of the most cost-effective renewable sources nowadays. However, the stochastic nature associated with wind-energy production represents a great challenge for power-system operations. Therefore, probabilistic techniques are necessary to evaluate the performance of power systems with substantial amounts of wind generation. This paper presents a probabilistic based bi-level optimization approach for evaluating the impact of wind farm location and control strategy on the penetration level of wind farms and electricity market prices. The bi-level optimization model is formulated as mathematical program with equilibrium constraints (MPEC) and solved by means of the NLPEC solver in the General Algebraic Modeling System (GAMS) environment. Several cases studies are presented in this paper to determine to the optimal wind generation penetration and market prices with different locations and control strategies for wind farms. Moreover, some scenarios are discussed in regards to the practical allocation of wind farms. [ABSTRACT FROM AUTHOR]

In recent decades global warming has turned into an international concern. Anthropogenic Greenhouse gases (GHG) are produced through burning fossil fuel leading to an increase in the global warming effect; thus, GHG reduction contributes to mitigating climate change. Utilizing renewable energies, increasing efficiency of and capacity factor (CF) of existing power plants, and recycling waste energy in various industries are convenient solutions to improve the energy systems performance to reduce fossil fuel consumption, leading to GHG emission. So, there is a substantial need for planning and evaluating the energy systems in which various technologies are used, technically and economically. Theis study's main objectives are utilizing the capacity of renewable energies such as biomass, wind, solar, hydropower, and enhancement of CF and efficiency of the power plants in the Mazandaran region in the south of the Caspian Sea. The energy system considered for the region is modeled and analyzed by six scenarios in this paper. The results indicated that the share of renewable energies in electricity production could be increased from 5.4% to 47.7%. The results of cost analysis show that by applying the sixth scenario, which is integrated policies of increasing RER share and improvement of efficiency and capacity factor. The trends of total annual cost, CO 2 emission, and fossil fuel consumption are reduced compared to the business as usual (BAU) scenario. The average cost saved by this scenario is about 220 MUSD/year. The average amount of fossil fuels and CO 2 emission save are 11 TWh/year and 2.438 Mt/year between 2020 and 2030, respectively. [ABSTRACT FROM AUTHOR]

The benefits analysis of the store-on grid (SoG) scheme, an energy business model for fostering rooftop solar photovoltaic technology in developing countries, is proposed in this paper. An expression for evaluating the feed-in grid rate for the solar energy fed to the grid under the SoG scheme is presented. The results of the SoG scheme were compared with those of the time of use (ToU) scheme for the case of the industrial sector in Uganda. Under the SoG model, the prosumer would feed-in the solar energy to the grid at 16.28¢/kWh after tax, while the utility would sellback energy to the prosumer at 17.03¢/kWh. Of the solar energy fed to the grid, the prosumer would annually profit $ 389.46, while the utility would annually profit $ 304.27 under the SoG scheme model and $ 170.39 under the ToU scheme. These results show that both the prosumer and utility record more profits by adopting the SoG scheme than the ToU scheme. Also, the government would collect about 4.21 ¢/kWh in tax from the solar PV energy fed to the grid under the SoG scheme. Thus, the adoption of the SoG scheme would increase the government's revenue from the energy sector. • A feed-in grid rate methodology of the store-on grid scheme model was proposed. • A sensitivity analysis of the feed-in grid rate was conducted. • Government tax revenue collection under the store-on grid model was evaluated. • Prosumer's benefits under the store-on grid scheme were evaluated. • Utility's benefits under the store-on grid and the Time of Use schemes were evaluated. [ABSTRACT FROM AUTHOR]