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Distributed energy systems are becoming increasingly popular worldwide. The 100% renewable energy system can be an important energy supply alternative to reduce the carbon emissions and address energy shortage, especially for remote areas. However, the performance of 100% renewable energy system (RES) at community level needs further detailed analysis, which is affected by the climates, availability of renewable energy and local energy markets. This paper proposes a design optimization framework of 100% renewable energy systems for low-density communities and investigates the system performance. To investigate the integration and performance of 100% RES, thirty typical cities located in different regions of China were chosen considering different climates, geographical features, and renewable energy distributions. By taking the economic performance as the optimization objective, the optimal design for the 100% RES is obtained, and the energy and economic performance is analyzed. Results show that, under the current energy market conditions, the 100% RES is feasible for low-density communities in most regions of China. The payback periods of the systems in most cities are less than six years. When the cost of PV is reduced by half considering future technological developments, the payback period of a 100% RES can be reduced by 30%–60%. This paper would provide design suggestions and application recommendations in regard to promoting 100% RES in China. • Application of 100% renewable energy system at community level is studied. • 30 zones are selected considering climates, renewable energy sources and markets. • Solar energy and biomass can contribute to over 90% of the total energy supply. • Investment of 100% RES can be paid back in 6 years for most regions. • Payback periods can be reduced by 30%–60% when PV technology is further developed. [ABSTRACT FROM AUTHOR]

To improve the renewable energy sources (RES) utilization of isolated microgrids (IMG) in frequency regulation process, an adaptive frequency regulation strategy with high renewable energy participating level is proposed in this paper: 1) an adaptive droop control method is conceived for primary frequency regulation, in which the designed droop coefficients are adjustable with the real-time wind velocity and state of charge (SoC) of energy storage units (ESUs); as the most important contribution, 2) a general flexible model predictive control (FMPC) framework with adaptive cost function and constraints is developed for secondary frequency regulation. For 2), i) the weight coefficients of the cost function can be adaptively adjusted according to frequency deviation through a pre-designed intelligent function, in which the specification of determining the key parameters is involved; ii) the designed constraints' boundaries can dynamically shift with wind velocity and ESU's SoC; iii) the negative impact of model's uncertainties can also be mitigated by implementing the proposed FMPC. Finally, the effectiveness of the proposal is verified by the comprehensive simulation and experimental study, and the results show that the proposal can significantly improve RES's utilization level while guaranteeing frequency regulation performance and operation safety in IMG. [ABSTRACT FROM AUTHOR]

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

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

Over the last two decades, there has been a significant interest in developing solutions to improve the efficiency of power systems, such as optimal energy consumption and management. The demand response programs (DRP) have long been one of the most effective strategies to encourage consumers to change their consumption behavior. In this paper, a novel hybrid DRP has been implemented by combining the time of use (TOU) and incentive-based (IB) DRP methods. To meet the load demand, a PV-WT-BESS hybrid renewable energy system (HRES) has been proposed in this work. Further, to analyze the impact of hybrid DRP on the capacity allocation of different system components, optimal sizing has been performed in two scenarios, one with the actual load and the second with the load after considering the hybrid DRP. The optimization has been carried out by using an improved search space reduction (ISSR) algorithm. To demonstrate the effectiveness of the ISSR algorithm, the obtained results are compared with other algorithms such as particle swarm optimization (PSO), grey wolf optimization (GWO), teaching-learning based algorithm (TLBO), seagull optimization algorithm (SOA), and sine cosine algorithm (SCA). Finally, a novel energy management strategy (EMS) is also proposed in this work on the supply side of the HRES, which intelligently uses the variations in the grid tariff to charge the BESS when the tariff is low. To demonstrate the proposed DRP and EMS-based sizing, a case study has been performed for a grid-connected PV-WT-BESS for the location of Kanyakumari, India. From the results obtained, it has been concluded that the hybrid DRP reduces the peak demand up to 5%, which reduces the levelized cost of energy (LCE) by 22%. Furthermore, the proposed novel EMS reduces the LCE further by 5%. The overall cost reduction of up to 25% has been achieved by using both the proposed hybrid DRP and EMS, while satisfying all necessary constraints. [Display omitted] [ABSTRACT FROM AUTHOR]

Using daily data from the beginning of 2018 to the end of 2022 and the Seemingly Unrelated Regression method, this paper sought to determine the relationship between green and non-green cryptocurrency indices as representative of blockchain technologies on three green bond indices in two periods before the outbreak of COVID-19 and the corona period. The preliminary results confirmed that by spreading the coronavirus, the sensitivity of the green bond market has increased to the gold index. In addition, the green cryptocurrency index (Cardano) significantly impacts green bonds more during the Corona era. The negative effect of the Bitcoin index on the index of green bonds in the Corona era is less than its adverse effect in the pre-corona period. Developing green cryptocurrencies and linking cryptocurrencies and digital green financing are two significant recommended policy implications for scholars and policymakers. • Green cryptocurrency has a greater impact on green bonds during the Corona era. • During coronavirus, the sensitivity of the green bond to gold price has increased. • Bitcoin has negative impact on green bond indices before the COVID-19. [ABSTRACT FROM AUTHOR]

This paper investigates the potential of 15 RCEP (The Regional Comprehensive Economic Partnership) member countries to advance green investments from 2000 to 2021. Based on the results from the Panel Pooled Mean Group (PMG) technique, the investment potential has positive short- and long-term coefficients. The EKC (Environmental Kuznets Curve) hypothesis exists, indicating that gross domestic product per capita in the short term is the factor that inhibits the growth of renewable energy consumption. The financial development index has a non-significant coefficient in the short term. However, it has a positive coefficient in the long term. The climate policy uncertainty negatively impacts renewable energy deployment in the short- and long-term. The conclusions provide some policy implications, such as the support of green SMEs, the establishment of a digital green financing market, and the creation of green multilateralism through the establishment of a green investment network. • Environmental Kuznets Curve hypothesis exists in RCEP economies. • Financial development index has a positive and significant effect coefficient. • The climate policy uncertainty negatively impacts renewable energy deployment. [ABSTRACT FROM AUTHOR]

It is becoming increasingly important for human societies to use renewable energy. More effective energy storage solutions are crucial to increase the share of renewable energy in the domestic and industrial sectors, which consumes a large amount of thermal energy for heating and cooling purposes. There has been little quantitative analysis of thermal energy storage in solar-based cold production units. An efficient method of designing heat and/or cold storage systems and smart operation planning can help a cooling/heating system to be smaller for the same capacity of supply and thereby reducing the costs of initial investment and costs of operation. This paper attempts to show how the size of a heat and cold storage system affects its thermal performance and how it can lower costs and emissions. For this, a solar-assisted thermally driven cooling supply system with various designs and integrations of their cold storage unit is simulated dynamically through a whole year in Qassim region as the case study. The results show that a system design with double solar heat storage and double cold storage in parallel arrangement results in the best techno-economic-environmental performance. For this case, the levelized cost of cooling will be 383 USD/MWhr, which will be at least 3% lower than the typical hot/cold buffer tank design scenario. [ABSTRACT FROM AUTHOR]

Hydropower plants play an integral role in energy supply and are one of the main renewable energy sources. The efficiency of hydropower reservoir systems is controlled by operating rules that need to be optimized to maximize energy production. Here, a novel optimization algorithm, influential flower pollination algorithm (IFPA), is developed in this paper. IFPA has three enhancements: an efficient exploration mechanism based on an adaptive coefficient, a new exploitation mechanism around the best solution, and a rank-based strategy to smoothly transition from exploration to exploitation. IFPA is tested in two benchmark multi-reservoir systems and a real-world five-reservoir hydropower system in Iran. The results show IFPA outperforms other advanced methods in terms of convergence speed and preciseness to achieve the global solution. The tested algorithms include adaptive guided differential evolution (AGDE), composite DE (CODE), hybridizing sum-local search optimizer (HSLSO), improved teaching learning-based optimization (ITLBO), self-adapting control parameters in DE (jDE), self-adaptive TLBO (SATLBO), and a multi-strategy hybrid of DE and particle swarm optimization (MS-DEPSO). IFPA also achieved better total power production in the real-world case study compared to other algorithms. Overall, IFPA can provide better solutions with less power deficit and shows as a promising method for optimizing hydropower reservoir problems. [ABSTRACT FROM AUTHOR]

Accurate prediction of renewable energy generation acts as a critical role which not only provides short-term power generation in the future, but also facilitates scheduling and pre-configuration of energy storage systems. More importantly, the power generation prediction is of great significance to the demand response management (DRM) of renewable energy to participate in the electricity spot market. Therefore, DRM helps improve the stability and reliability of renewable energy systems. This paper presents a novel prediction-smoothing based methodology to reduce and eliminate the influence caused by the uncertainty of renewable energy output. Firstly, the Whale Optimization Algorithm (WOA) is combined with Long Short-Term Memory (LSTM) to predict short-term wind power output. Then, the Hampel-Butterworth-SG filtering strategy with outlier regression and specific risk band elimination is introduced. After that, according to the short-term output forecast results, the scheduling and pre-configuration scheme of peak-filling type energy storage is developed. Finally, based on the predicted renewable energy output and electricity price, the dynamic changes of demand response (DR) are calculated based on Logistics function, optimistic response and pessimistic response factors. Through extensive case studies, it is demonstrated that the assessment deviations in different scenarios is less than 5%, which are 0.64% for Scenario 1 and 3.64% for Scenario 2, and no additional penalty is required at this time. In addition, the proposed Demand Response Load Adjustment Rate (DRLAR) help compare the differences between predicted and actual DR, which are DRLAR = 0.03% in Scenario 1 and DRLAR = 0.01% in Scenario 2. Users are able to adjust the DR dynamically according to the electricity price to realize the optimal scheduling of their renewable resources. The proposed methodology creates a special connection between DRM and renewable energy prediction, which provides a reliable reference for future work. [ABSTRACT FROM AUTHOR]

World electricity consumption has grown permanently since 1990, dramatically increasing the emission of greenhouse gases. The worldwide efforts to reduce them and address climate change are leading to the massive electrification of different end-users, increasing the power demand. Therefore, achieving Sustainable Development Goals regarding affordable and clean energy requires the extensive availability of capital investments in electricity generation from renewable sources. Project Finance (PF) is a suitable financing alternative for large projects. This paper presents the advantages of PF contracts in allocating risks to appropriate counterparts and reducing the likelihood of default. Additionally, this study analyzes the characteristics that favor PF financing for renewable energy projects. The results reveal that country's governance levels, well-developed financial systems, and project costs affect the financing probability of a PF initiative, particularly for low and middle-income countries. Therefore, public policies aimed at increasing the availability of resources to renewable projects must tackle these factors. • The world requires massive renewable energy investments to meet Sustainable Goals. • Project Finance is a widely financing structure for large investment projects. • We study the determinants of renewable energy Project Finance. • Low governance and financial development hinder renewable energy PF initiatives. • Policies to develop capital markets and PF will help to reach the net zero target. [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]

The number and importance of renewable energy communities (RECs) are increasing in all European countries due to the support of EU and national policies in order to foster the energy transition through participatory strategies for distributed energy systems. However, their development is rather slow in some countries, including Italy. The paper addressed the issue of the emergence of these socio-technical configurations in Italy, after the related law which entered into force in 2020 allowing the possibility of creating them. Our study focused on the alliances among different actors (professionals, institutions, NGOs, citizens) highlighting possible trends or models for the future that need to be confirmed in further research. The approach proposed to analyse this current process is the Actor-Network Theory, aiming at drawing the assemblages of human and non-human actants at a general level. Accordingly, we selected three case studies in order to show their different ways of organising, the relevance of trust in establishing each REC and the influence of local context on the composition and features of the actor-networks. [ABSTRACT FROM AUTHOR]

Developing new technologies to utilize natural renewable energy resources and fluxes is critical to mitigate climate change and energy crisis. As a renewable energy resource, wave energy is more predictable and has a higher power density compared to solar and wind. However, the technology for wave energy harvesting is still relatively nascent. One of the major challenges as the sector developers is the lack of spatial–temporal representation of wave resources, or wave power generation, from a generic WEC. This paper proposes a novel Wave Net Power Assessment (WNPA) method by utilizing the theoretical Wave Energy Converter (WEC) power limits (including the radiation power limits, Budal's limit, and gross wave power), and applies it to a decade of wave conditions across the USA East Coast, West Coast and Hawaiian Islands. The WNPA method assesses the wave resource from device point of view (unlike traditional gross wave resource assessment) and is generally applicable to WECs regardless of dimension, size, and degrees of freedom (DoFs). The geospatial application of the WNPA method allowed for the analysis of wave power production potential along U.S. coastlines and provides WEC developers meaningful data to make decisions about the best spatial region to deploy certain WECs. Numerical simulations are conducted for WECs with 5 different sizes, 3 different operating modes across the West Coast, East Coast, and Hawaii over 10 years. In addition, productivity, efficiency and gross power quality comparisons are made in terms of the size and degree-of-freedom for WECs deployed at certain ocean sites (e.g., PacWave). A few of the most important findings: (1) The net wave power is dramatically smaller than the gross wave power, which indicates the new WNPA method has a more conservative assessment of the wave power potential; (2) A heaving 10 m WEC is found to have the highest (compared to other sizes and degrees of freedom) absorption efficiency on the West Coast and Hawaii; (3) For smaller 2 m surging/pitching WECs, ocean sites on the East Coast (e.g., Diamond Shoals, NC) maximize power production. (4) There are distinct trade-offs between maximizing power or minimizing power quality and dependencies exists between the location of interest, the WEC size, and the priority degree of freedom. The proposed methodology can be utilized by WEC technology and project developers to conduct apriori preliminary analysis of WEC deployment locations and device characteristics. • Introduction of novel Wave Net Power Assessment (WNPA) methodology for assessing the wave energy resource by providing an upper practical limit of WEC power generation. • Derivation of generic methodology to predict the performance of a WEC; depending only on WEC characteristic dimension and mode of motion. • Geospatial analysis of WEC generation across the US West Coast, East Coast and Hawaiian Islands with five differing WEC sizes, with three different degrees of motion, over a 10-yr period. • Quantification of the mean power, conversion efficiency and co-efficient of variation over a decade. • Ability to quickly understand the spatial and temporal monthly variability of various WEC archetypes in specific locations within US waters. [ABSTRACT FROM AUTHOR]

Wave energy is a huge renewable energy resource with great potential for development. It can be used to power monitoring sensors on offshore work platforms and bridges. In this paper, a novel piezoelectric wave energy harvester based on cylindrical-conical buoy structure and magnetic coupling (C-PWEH) is proposed. As the wave oscillates up and down, it drives the buoy up and down and forces the piezoelectric patches to deform under magnetic force, thereby collecting the energy from the wave. The efficiency of the wave energy conversion can be improved by optimising the design of the buoy. With magnetic coupling, the low-frequency wave motion can be converted into high-frequency vibrations of the piezoelectric patches thus enabling the device to achieve a better output performance. Experiment findings reveal that the C-PWEH may deliver effective energy production when subjected to wave excitation. When the best matching resistance is 5000Ω, the maximum output power of 41.5 mW can be obtained. Under the optimal experimental parameters, 82 LEDs can be illuminated and power storage tests are performed using capacitors, further demonstrating the feasibility of the C-PWEH powering some low-power sensors. [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 increase of Renewable Energy (RE) production to fight the climate crisis is posing new technological and financial challenges, due to the availability and variability of RE Sources (RES). These challenges can be addressed by selecting the most suitable mix of RES to optimise power production, to assure grid resilience and to promote local energy use. To facilitate the selection of such combination, this paper presents an original methodology that allows to compare mixing scenarios with different RES, also in presence of batteries and backup system. It simultaneously optimises the energy surplus with respect to the eventual external electrical load and the missing energy with respect to the same electrical load. This method, which can cope with isolated or plugged-to-grid systems, is here applied to a novel case study, an oil&gas platform under decommissioning, located in the Adriatic Sea (Italy). The RE production from wind, wave and solar panels is supposed to support other activities for the platform reuse, such as aquaculture, monitoring and mineral deposition. In this case, solar energy is providing the greatest contribution to the optimal mix in terms of production, while wave energy assures the most relevant contribution in terms of continuity. [ABSTRACT FROM AUTHOR]

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

The paper proposes an innovative power-sharing model, i.e., a power-system architecture for aggregation of users able to share the power produced by common generators and energy services. The model is suitable for both multi-tenant buildings and groups of multiple buildings and it is applicable for both existing and new buildings. It is scalable for larger systems and suitable for an easier integration with storage systems. The novel principle of the model is that the energy produced by common generators can be shared among the end-users in a unidirectional way, so that each user remains passive towards the distributor, except a single active user that assumes the role of balance node. This key feature allows for easily implementing the model in all the residential and tertiary multi-units buildings in full compliance with national regulations, with the adoption of power sharing contracts as well. This paper discusses the feasibility of the model through a dynamic Matlab/Simulink model, which is used to show its effectiveness in several case studies. The significance of this work consists of approaching the energy sharing in buildings with a completely new strategy, based on an innovative system architecture that can be effectively implemented. • Innovative democratic power-sharing model for building and energy communities. • It allows for aggregation of users. • It allows to share the power produced by common generators. • Each user remains passive towards the distributor. • The model can be implemented in all multi-units buildings. [ABSTRACT FROM AUTHOR]

Tremendous development and integration of intermittent renewable energy resources have forced the hydraulic turbines to experience transient processes more frequently and thus endangered the stability of the machine. The principal objective of this paper is to present the numerical investigation into load rejection towards a high-head model Francis turbine based on the structured overset mesh, which provides greater flexibility over the standard mesh techniques and adequate mesh quality during the movement of the guide vanes. The numerical results show good agreement with experimental measurements. The formation and evolution process of the vortex rope in the draft tube are elaborated by the velocity invariant Q and the swirl number S reaching its maximum value due to the formation of precessing vortex rope. Evidence is presented which shows that significant influence of the rotor-stator interaction is captured, and the pressure fluctuation amplitudes caused by the changing of angular position of guide vane are also powerful. Furthermore, the velocity profiles in axial and radial imply that an increasing recirculating flow region is developed at the center of the draft tube during the transient process. The findings presented in this paper enhance the insight into transient features of load rejection process. - Load rejection process was simulated through the structured overset mesh technology. - Dynamic characteristics during load rejection was analyzed. - Formation of processing vortex rope was presented. [ABSTRACT FROM AUTHOR]

The present study of the district heating (DH) system in the city of Kisa, Sweden, shows how, through energy cooperation with a nearby sawmill and paper mill, a local energy company contributes to energy-efficient DH and cost-effective utilization of a new biofuel combined heat and power (CHP) plant. Cases of stand-alone and integrated energy systems are optimized with the linear program MODEST. The European power market is assumed to be fully deregulated. The results show clear advantages for the energy company to cooperate with these industries to produce heat for DH and process steam for industry. The cooperating industries gain advantages from heat and/or biofuel by-product supply as well. The opening to use a biofuel CHP plant for combined heat supply results in cogenerated electricity of almost 29 GWh/a with an increased biofuel use of 13 GWh/a, zero fuel oil use and CO 2 emission reductions of 25,800 tons CO 2 /a with coal-condensing power plant on the margin and biofuel as limited resource. The total system cost decreases by −2.18 MEUR/a through extended cooperation and renewable electricity sales. The sensitivity analysis shows that the profitability of investing in a biofuel CHP plant increases with higher electricity and electricity certificate prices. [ABSTRACT FROM AUTHOR]

At present, energy represents a key element in the social and economic development of a territory. This is particularly true for fragile territories that are not interconnected, such as small island states. As is true for many of the Small Island Developing States (SIDS) worldwide, the energy situation of the Comoros is substantially based on fossil fuel imports. The socioeconomic development of this archipelago is overwhelmingly dependent on energy security from sustainability, availability and affordability perspectives. As a forward-looking response to energy vulnerability, the deployment of renewables to diversify the generation of electricity appears to be an essential prerequisite for guaranteeing a sustainable future. Nevertheless, despite a high potential for renewable energy, only 3.8% of the electricity supply in the Comoros is provided by hydropower. This paper provides a comprehensive overview of the energy situation throughout the Comoros and focuses on renewable energy opportunities to facilitate the supply of green power. This study ultimately shows that renewable energies are rarely exploited despite the powerful potential of different resources. Finally, this paper attempts to provide recommendations for the Comoros to pursue a more sustainable future. • Analysis of the current energy situation of Comoros. • Defining renewable potential and ongoing actions. • Analysis of the specific barriers and policy to the development of RES in the Comoros. [ABSTRACT FROM AUTHOR]

In the pursuit of sustainable energy solutions, the integration of renewable energy sources and hydrogen technologies has emerged as a promising avenue. This paper introduces the Integrated Renewable Energy-Driven Hydrogen System as a holistic approach to achieve energy independence and self-sufficiency. Seamlessly integrating renewable energy sources, hydrogen production, storage, and utilization, this system enables diverse applications across various sectors. By harnessing solar and/or wind energy, the Integrated Renewable Energy-Driven Hydrogen System optimizes energy generation, distribution, and storage. Employing a systematic methodology, the paper thoroughly examines the advantages of this integrated system over other alternatives, emphasizing its zero greenhouse gas emissions, versatility, energy resilience, and potential for large-scale hydrogen production. Thus, the proposed system sets our study apart, offering a distinct and efficient alternative compared to conventional approaches. Recent advancements and challenges in hydrogen energy are also discussed, highlighting increasing public awareness and technological progress. Findings reveal a payback period ranging from 2.8 to 6.7 years, depending on the renewable energy configuration, emphasizing the economic attractiveness and potential return on investment. This research significantly contributes to the ongoing discourse on renewable energy integration and underscores the viability of the Integrated Renewable Energy-Driven Hydrogen System as a transformative solution for achieving energy independence. The employed model is innovative and transferable to other contexts. [Display omitted] • Integrating renewable energy and hydrogen for energy independence. • Advantages of Integrated Renewable Energy-Driven Hydrogen System: Zero emissions, resilient energy. • Economic viability: Payback period of 2.8–6.7 years. • Technological progress in hydrogen energy. • A novel and transferable system dynamics model for sustainable solutions is proposed. [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]

In the pursuit of sustainable energy solutions, the integration of renewable energy sources and hydrogen technologies has emerged as a promising avenue. This paper introduces the Integrated Renewable Energy-Driven Hydrogen System as a holistic approach to achieve energy independence and self-sufficiency. Seamlessly integrating renewable energy sources, hydrogen production, storage, and utilization, this system enables diverse applications across various sectors. By harnessing solar and/or wind energy, the Integrated Renewable Energy-Driven Hydrogen System optimizes energy generation, distribution, and storage. Employing a systematic methodology, the paper thoroughly examines the advantages of this integrated system over other alternatives, emphasizing its zero greenhouse gas emissions, versatility, energy resilience, and potential for large-scale hydrogen production. Thus, the proposed system sets our study apart, offering a distinct and efficient alternative compared to conventional approaches. Recent advancements and challenges in hydrogen energy are also discussed, highlighting increasing public awareness and technological progress. Findings reveal a payback period ranging from 2.8 to 6.7 years, depending on the renewable energy configuration, emphasizing the economic attractiveness and potential return on investment. This research significantly contributes to the ongoing discourse on renewable energy integration and underscores the viability of the Integrated Renewable Energy-Driven Hydrogen System as a transformative solution for achieving energy independence. The employed model is innovative and transferable to other contexts. [Display omitted] • Integrating renewable energy and hydrogen for energy independence. • Advantages of Integrated Renewable Energy-Driven Hydrogen System: Zero emissions, resilient energy. • Economic viability: Payback period of 2.8–6.7 years. • Technological progress in hydrogen energy. • A novel and transferable system dynamics model for sustainable solutions is proposed. [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]

Due to the inherent uncertainty and the mismatch between renewable energy output and load demand, renewable energy-based system optimization is increasingly important in China. Therefore, electricity demand response (EDR) is critical to the stability and efficiency of an integrated renewable energy system (IRES). The customers' demand response under the time-of-use (TOU) mechanism is the key to the entire process. However, how to optimize integrated renewable energy system based on accurately identifying the electricity demand response participation of customers is one of the main issues at this stage. To solve the problem, this paper presents a novel approach for integrated renewable energy system optimization considering electricity demand response management and multistage energy storage systems from the perspective of Fujian Province, China. To enable large-scale renewable energy integration, the original output is processed using the Hampel identifier (HI)-Savitzky-Golay filter (SGF) technology. A machine learning (ML)-based identification model is developed for EDR to determine whether the customers are participating or not. Driven by the time-of-use mechanism, customers can make dynamic adjustments based on the Logistics function. After that, an integrated renewable energy system model optimization strategy is proposed based on satisfying customers' electricity demand response. Throughout the optimization process, the multistage energy storage system plays a vital role in the residual fluctuation absorption for renewable energy filtering, the dynamic adjustment process of the demand response, and the integrated renewable energy system optimization. Through extensive case studies and discussions, it is demonstrated that random forest (RF) and support vector machine (SVM) reach 80 % identification accuracy with a 20 % testing ratio. According to the optimization approach proposed in this paper, the minimum operation cost can be calculated to be 9.336*106 CNY. The methodology presented in this paper establishes a special relationship between electricity demand response identification and dynamic adjustments, and provides an optimization model for Fujian Province, China, which is a reliable reference for future work. [ABSTRACT FROM AUTHOR]

Innovation and adaptation of renewable energy technologies significantly reduce carbon dioxide (CO2) emissions and increase energy efficiency. Understanding the convergence patterns in renewable energy innovation will enable policymakers to design policies to increase energy efficiency and renewable energy consumption and reduce CO2 emissions. This paper applies the convergence algorithm proposed by Philips and Sul to assess the convergence in renewable energy innovation for 90 countries covering the period between 1993 and 2018. This paper also examines the determinants/factors driving the convergence clubs of the countries using Probit and Logit regression. The convergence analyses suggest that there is no global convergence in renewable energy innovation. Findings highlight two convergence clubs: i) a club comprising more innovative countries and ii) another club that includes less innovative countries. We find that countries with higher income per capita, CO2 emissions per capita, research and development (R&D) investment, better environmental regulations and stronger institutional settings are more likely to be part of the innovative club. Countries should increase their R&D investment and environmental regulations and improve their institutional quality to increase their likelihood of belonging to a more innovative club. Furthermore, less innovative countries could promote policies to transfer renewable energy technologies from innovative countries. [ABSTRACT FROM AUTHOR]

Pathways to decarbonisation are commonly explored by government and industry through the use of energy system models. However, such models rarely consider where new energy infrastructure might be located. This is problematic as the spatial context of new renewable energy infrastructure will determine, in part, the environmental, social, and technical impacts of the energy transition. This paper presents the ADVENT-NEV model which brings together innovations in energy and natural capital modelling to identify the optimal locations of multiple renewable energy technologies at a national scale and high spatial resolution. Using Great Britain as a case study, the results show how the spatial distribution of renewable energy technologies changes when a natural capital approach is taken. In particular, the least-cost locations for onshore wind farms and bioenergy crops are highly influenced by the value of carbon sequestration, or emissions associated with their land use change. Siting using a natural capital approach produced appreciable ecosystem service benefits, such that the overall welfare gain to society was estimated at nearly £25 B. Overall, this paper demonstrates that understanding the geospatial context of the energy transition is essential to identifying which renewable energy pathways are consistent with decarbonisation and environmental objectives. [Display omitted] [ABSTRACT FROM AUTHOR]

The increasing importance of innovation-driven development requires empowering enterprises as the main drivers of innovation in emerging energy technologies. The existing literature primarily emphasizes government support for renewable innovation, with limited research on external collaborations. This paper searches and collects announcements of listed renewable energy companies regarding strategic alliances during 2009–2021 to explore whether renewable energy alliances promote innovation and their mechanism to motivate innovation efforts, using the Two-way Fixed Effects Model. The main results suggest that renewable energy alliances increase innovation output significantly. The mechanism analysis reveals that establishing renewable energy alliances indirectly enhances innovation output via transaction cost savings and alleviates financing constraints. The heterogeneity analysis indicates that factors such as firm ownership, size, knowledge absorption capacity, green investor concerns, and enterprise life cycle significantly impact the innovation incentives from renewable energy alliances. This paper enriches the research on the drivers of renewable energy innovation and provides policy insights for encouraging autonomous innovation in renewable energy enterprises. [ABSTRACT FROM AUTHOR]

With the size and adaptability of commercial wind turbines are both drastically rising, the aeroelastic performance analysis is crucial for the design and optimization of large-scale megawatt wind turbines. However, most existing aeroelastic methods based on assumption of small deflections are linear models and not suitable for flexible blades often undergoing large deflection. The present study aimed at developing a novel time-variant two-way aeroelastic coupling numerical model for calculating the unsteady load, operational modal analysis, aerodynamic damping and nonlinear large deformation is proposed in this paper. Compared to software GH bladed, this study takes aerodynamic damping analysis and aeroelastic coupling of flexible blades between unsteady load and large structural deformation into consideration. The subspace iteration method widely used in software ANSYS, hybrid beam theory, and modified Newmark-β displacement iteration method are first combined with the blade element momentum theory (BEM). A validation is carried out by comparing the present results with commercial software GH Bladed as well as the NREL (National Renewable Energy Laboratory) public data with a maximum difference of 3.13%, it is validated that the aeroelastic model in this paper is accurate and reliable. Furthermore, the unsteady aeroelastic performance such as aerodynamic force, aerodynamic damping, and dynamic response of flexible blade is further analyzed instantaneously during time-domain simulation in detail. The results indicate that the reduction of aerodynamic load and dynamic response caused by aeroelastic coupling is mainly the out-of-plane structural deformation, yet the blade vibration-induced velocity can be ignored. [ABSTRACT FROM AUTHOR]

Green innovation is a feasible and potential option that governments can adopt to reduce reliance on fossil fuels and speed the transition to renewable energy sources. This study analyzes the relationship between green finance development and green innovation using listed Chinese companies to investigate the role of green finance. In the preliminary analysis, it employs Poisson and negative binomial distributions based on the sample characteristics. The additional research considers enterprises' internal and external characteristics and adds a zero-inflated factor with enterprise-scale and bank competition. Our main findings suggest that green finance positively improves green technological innovation, and zero-inflated factors can significantly lower the possibility of enterprises refusing to engage in green innovation. By using a representative green finance policy in 2012 as a quasi-natural experiment, this paper also demonstrates the promoting effect of green finance development. The above results are still valid by the robustness test. Furthermore, this paper applies the panel threshold model and finds that the enterprise scale and bank competition not only affect the choice of the enterprise but also stimulate the promotion intensity in different development stages. This paper contributes to targeted policy implications for the renewable energy transition. [ABSTRACT FROM AUTHOR]

The development of information and communication technology (ICT), a subset of many functions such as visual communication, can positively affect renewable energy development in different countries. In this paper, focusing on China as the world's largest emitter of greenhouse gases, the relationship between visual communication and the deployment of renewable energy is investigated using the dynamic ARDL (autoregressive distributed lag) method and annual data from 1990 to 2021. Regarding the empirical estimations, a 1% increase in green finance market size leads to an improvement of sustainable energy deployment by 0.01%. In addition, the ICT development index has a significant and positive effect only in the long term. Furthermore, there is evidence of uni-directional causality from ICT to renewable energy deployment in the medium and long term. The recommended practical policies by the research are developing green financing tools, green economic growth policies, and creating virtual portals for financial interactions. • There is uni-directional causality from visual communication to renewable deployment. • ICT development index has a positive effect in the long term. • Effect of GDP on the development of renewable energy consumption is negative. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy, the traditional energy storage operation based on individual framework —users own and operate independently mode may become impracticable due to conflicting interests, insufficient utilization, and poor interoperability. Riding on the wave of the proliferation of sharing economy, storage energy sharing expands the existing storage energy without requiring costly and time-consuming infrastructure investments. However, the development path of shared energy storage (SES) mode is not clear due to the asymmetric decision-making of the owners of energy storage systems under bounded rationality. In this paper, the diffusion of the business model of SES among multiple renewable energy stations (the owners, RES) and its key factors are analyzed based on the evolutionary game. The goal is to maximize social welfare and ensure the continuous growth of the sharing market. The numerical simulation is carried out based on the data from northwest China to interpret the development path. The result shows that, in renewable energy cluster the stations with intermittent output or with the higher prediction accuracy are more willing to participate in sharing. The energy storage sharing mode fails when the energy storage capacity ratio of RES is less than 10%. While the high-level ratio (more than 30%) is not conducive to the diffusion of the sharing model in RESs with low power generation prediction accuracy. [Display omitted] [ABSTRACT FROM AUTHOR]

Many subarctic communities rely entirely on fossil fuels for their energy needs. Solar-assisted ground-coupled heat pumps (SAGCHP) can be a solution to integrate renewable energy sources into their energy portfolios. However, it is currently unknown how such systems could operate in the context of the high North (cold ground, extreme mismatch between insolation and heating need, electricity from diesel). The objective of the paper is to develop a detailed model of a SAGCHP heating a house in Nunavik (Quebec, Canada) in order to gain a better understanding of its potential and limitations. The solar assistance is provided by PVs. Simulations with and without electric storage (batteries) were run. A complex tradeoff between four different modes of operation was obtained depending on the conditions of the system at each time step. For the test case, results show that the ground experiences a weak long-term thermal depletion partly compensated solar energy, but that a significant portion of the PV power production is preferably used by the compressor or stored in batteries rather than stored in the ground as heat. Over ten years, the SAGCHP system reduced fuel consumption respectively by 38.2% (without a battery) and 59.1% (with a battery). [ABSTRACT FROM AUTHOR]

By considering the significant role economic growth has played in environmental function between 1990 and 2019, this paper examines the relationships between renewable energy development, trade openness, and institutional quality in the development of green finance in South Asian countries. The effectiveness of institutions and trade transparency are factors in green finance. We use a panel data model to ascertain the cointegration among the variables. The study employs the OLS fixed pool method along with a quantile regression to assess the model and reveal that, although growth in renewable energy development and trade openness worsen the ecological environment, institutional quality and the use of renewable energy promotes green finance development. The findings indicate that trade openness and institutional quality have a positive link with green financing. Institutional quality drives the use of renewable energy, whereas gross domestic product (GDP) drives the environmental impact. In light of these findings, policymakers can put in place beneficial policies for renewable energy development as an economic instrument to minimize ecological footprints and enhance green finance development. • Trade openness. • Renewable energy development. • Green finance. • Institutional quality. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energies in power systems demands new services from renewable generation plants. System operators are concerned about system stability since renewable generators behave as constant power sources. Therefore, new requirements have been imposed to grid-following generators to improve their contribution to system stability acting as grid-supporting generators. Nevertheless, grid-supporting control can still compromise system stability for high penetration of renewables, and grid-forming control has arised to ensure proper operation. This paper proposes a novel grid-forming control scheme for doubly-fed induction generators, so they behave as real voltage sources. The proposed grid-forming control is based on the rotor flux orientation to a reference axis obtained from the emulation of the synchronous generator swing equation. The rotor flux is oriented to the reference axis by means of a flux controller that also controls the flux magnitude. The flux orientation in turn allows to control the doubly-fed induction generator torque, while the flux magnitude control allows to regulate the generator reactive power or terminal voltage. The proposed control system has been validated through a comprehensive real-time simulation with hardware in the loop, assessing its grid-forming capability. Moreover, small signal analysis has also been performed to assess system stability. [ABSTRACT FROM AUTHOR]

The primary purpose of this paper is to evaluate the impacts of green finance on green energy deployment in China's tourism industry. To this end, the annual data over 1992–2021 through the ARDL (Autoregressive Distributed Lag) approach for the case of China are analyzed. The findings confirm that the issued green bonds positively impact the green efficiency of China's tourism industry in the long term. In contrast, the coefficient of issued green bonds is insignificant for the short-term approach. In addition, in the short and long term, the consumer price index also has a significant adverse effect on the green efficiency of China's tourism industry. The recommended practical policies are improving executive regulations and transparent market information, implementing green fiscal policies, green construction in hotels and tourist accommodations, green globalization, operational phases of innovation, adaptation, and stabilization (IAS), and eco-tourism regionalism. • Green bonds have a positive impact on the green efficiency of China's tourism. • Consumer price index has a negative effect on green efficiency of China's tourism. • Economic globalization index has a significant coefficient on green efficiency. [ABSTRACT FROM AUTHOR]

The lack of partnerships between biomass generating producers is one of the main barriers to efficient biogas production. Partnerships can help producers properly dispose of waste, assist in the supply of biodigesters, allow the union of properties with different potential biomass generators into clusters, and optimize operating costs. In this sense, new measures must be adopted with regard to the establishment of strategic partnerships between producers. Therefore, in this paper, we propose a new clustering algorithm, BiogasCluster , to decide the optimal number of clusters for generating a renewable energy community. To test the proposed algorithm's applicability, we implemented the algorithm in R language. Experiments with real databases showed that the BiogasCluster proved to be efficient in generating stable clusters for all considered scenarios. It was also found that the filters and criteria used in the proposed algorithm have not been used in previous studies. This study provides important guidance to creating strategic partnerships within bioenergy generation in addition to obtaining a preliminary assessment of the possibility of establishing clusters of rural properties with the aim of achieving energy security and adding value to their businesses. [ABSTRACT FROM AUTHOR]

This paper investigates the validity of the environmental Kuznets curve (EKC) hypothesis for the G7 group of countries through the ecological footprint and its components (namely built-up land, carbon, cropland, fishing grounds, forest land, and grazing land). Most previous contributions rely on CO 2 emissions as a measure of environmental damage, whereas using disaggregated ecological footprint allows us to consider resource consumption and waste generation compared to nature's resource generation and waste absorption. The empirical application is based on a panel data analysis for G7 countries from 1980 to 2018. The results indicate that the EKC hypothesis is confirmed for the ecological footprint overall, but the impact of GDP growth is unequal across its components. Long-term elasticities point to renewable energy generation significantly improving environmental sustainability. Globalization also decreases pollution indicators, while economic complexity seems to contribute to environmental degradation. [ABSTRACT FROM AUTHOR]

Renewable energy attracts increasing attention from both industry and academia under the context of carbon neutrality. For wind and solar energy, the strong dependence on natural processes results in the imbalance between energy production and real demands. Energy storage technologies, e.g., Compressed Air Energy Storage (CAES), are promising solutions to increase the renewable energy penetration. However, the CAES system is a multi-component structure with multiple energy forms involved in the process subject to high temperature and high-pressure working conditions. The CAES system is a complex process flowsheet consisting of charging and discharging process. The process should be optimized to achieve the best thermodynamic and economic performance. Under the optimal design conditions, it might lead to severe consequences once a failure occurs, e.g., harm to humans, the environment, and assets. Limited attention and scarce available information have been paid to the CAES system risk management yet. Hence, this paper applies the System-Theoretic Process Analysis (STPA), which is a top-down method based on system theory, to identify the CAES system safety hazards. The results are expected to provide a preliminary guideline for practitioners regarding the safety and reliability of the CAES system. As a result, a more reliable CAES system can contribute to a more flexible energy system with more efficient and economic utilization of fluctuating renewable energy. [ABSTRACT FROM AUTHOR]

Over the past decades, the penetration of renewable energy sources is growing at a significant pace on a global scale. In parallel, the utilization of renewables could possibly act as the basis for the transition to a Circular Economy, which aims to reduce the use of finite resources. Compared to other European countries, Greece has achieved a significant share of renewables in its' energy mix and in parallel promotes various policies aiming to accomplish zero-carbon emissions by 2050. An important element of this procedure is the broader acceptance of renewables by the public. This paper investigates this concept by capturing citizens' view on renewable energy sources along with the overall concept of Circular Economy in Greece. By implementing a questionnaire analysis, empirical findings capture the intention of citizens to further develop and invest on renewable energy. Besides, our analysis identifies that as education level increases, the willingness to pay more for renewables and their acceptance from the public increases, as well. Finally, empirical findings indicate that Greek citizens are aware of the concept and the benefits arising from Circular Economy, yet additional efforts need to be addressed for the growth of this concept in Greece. • The study captures citizens' view on RES and Circular Economy in Greece. • Participants are aware and sensitive on environmental and climate change issues. • Participants accepted the existence of RES and are willing to pay additional cost. • As the level of education increased, willingness to pay for RES also increased. • Additional efforts need to be addressed for the growth of Circular Economy in Greece. [ABSTRACT FROM AUTHOR]

The emergence of new technologies, such as photovoltaic (PV) solar systems, household Battery Energy Storage Systems (BESS) and electric vehicles (EVs), has introduced challenges for the Distribution Network Operators (DNOs) in the operation of power distribution networks. Uncontrolled renewable energy generation and storage, and EV charging can cause over/under voltage problems. Recently, there has been much effort, from control and scheduling of BESSs and EVs to PV curtailment, to address these issues. However, the knowledge about how much penetration of these technologies can be well-tolerated by the grid when all charging/discharging are uncontrolled, would still be of interest to DNOs. There are many depending factors such as customer consumption profiles, grid conditions and network constraints. In this paper, we propose a framework to determine the critical balance between PV, BESS and EV in a distribution grid. Using real data from 4000 customers in Victoria, Australia, we find that in the absence of advanced control and scheduling algorithms, the best approach to prevent voltage violation in the grid is to keep the installation of PV and BESS balanced with the EV penetration. We also show that power export to the grid by EVs using the vehicle-to-home (V2H) technology may contribute to keeping the grid voltage in the standard range if 40%–80% of EVs are in the discharge mode in critical peak times. While complete coordination of PV, EV and BESS has not yet been fully realized in many networks, our methodology provides a framework for DNOs to perform optimised planning. [ABSTRACT FROM AUTHOR]

The combination of biomass and solar energy in the combined cooling heating and power (CCHP) system is conducive to reducing carbon dioxide emissions of the system. To improve the system's economic performance under the punishment of carbon tax, this paper optimizes the system from system design and operation strategies. A hybrid renewable energy CCHP system with shaft power distribution structure of an internal combustion engine (ICE) is proposed. By optimizing the shaft power distribution of ICE, the direct drive heat pump is realized, and the efficiency of the shaft power of ICE is improved. A double-layer optimization method considering the long and short-term benefits is proposed based on the system, fully improving the system economy in design and operation. The case study shows that the CCHP system reduces the annual cost by 40.37% and the CO 2 emission by 88.93% compared with the separate production system. Compared with the non-shaft power distribution system, the maximum daily operation cost is saved by 557.11CNY. Compared with the hourly optimization method, the proposed optimization method based on total daily benefit reduces the annual cost by 7.73% and CO 2 emission by 35.87%, and the utilization rate of the energy storage device is higher. [ABSTRACT FROM AUTHOR]

Wind energy is an important renewable clean energy resource. However, the stochastic and volatile nature of wind power brings significant challenges to the power system's reliable and secure operation. Accurate and reliable wind power prediction is critical for the integration of wind power into the grid. The existing wind power forecasting (WPF) methods lack an assessment of the reliability of the predicted results, which may result in a financial penalty for the wind energy producers. An accurate prediction with reliability measurement is urgently needed to encounter the intricate nature of the problem. In this paper, a Bayesian framework-based bidirectional gated logic unit (BiGRU) method was proposed for ultra-short-term wind power forecasting. First, an encoder-decoder (ED) architecture was combined with a BiGRU time series modeling and feature–temporal attention (FT-Attention) to improve the accuracy of wind power prediction. Then, two uncertainty losses were applied to improve the model's performance further. The proposed method obtains the uncertainty of forecast results, which effectively eliminates the untrusted results. Our proposed method demonstrated promising results for ultra-short-term wind power forecasting due to its competitive performance compared with traditional forecasting methods. • A novel ultra-short-term WPF method is proposed with a deep Bayesian model. • Quantifying the uncertainty of WPF is a unique ability of our proposed framework. • Aleatoric uncertainty loss and epistemic uncertainty loss are introduced. • Temporal attention and feature attention are designed to enhance the accuracy of WPF. [Display omitted] [ABSTRACT FROM AUTHOR]

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

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

Due to the stochastic grid loads and intermittent renewable energy sources in today's power grid, hydro-generator and grid system stability is increasingly dependent on the hydro-turbine governing system (HTGS). For the HTGS with external disturbances and system uncertainties, an intelligent nonlinear robust controller (INRC) based on state-dynamic-measurement feedback linearization (SDMFL) is proposed to enhance the HTGS's control performance in this paper. This strategy establishes a dynamic output function including actuator displacement, generator rotor angle, and turbine parameters. The relationship between dynamic output and control input is derived based on this function to establish the corresponding linear state-space model. Then, the mixed H 2 / H ∞ robust controller based on the linear model is designed to suppress the effects of external disturbances and system uncertainties on the system performance. In addition, the controller performance is optimized by levy flight and chaos theory-based gravitational search algorithm (LCGSA), resulting in the INRC. Simulations are performed in MATLAB, and the results are compared with the nonlinear robust controller (NRC) and PID controller. The results show that the proposed controller INRC improves the control performance of nonlinear HTGS with smaller overshoot and shorter adjustment time in all cases. • Design of nonlinear robust controller based on SDMFL method for HTGS. • Application of LCGSA algorithm for optimal tuning of NRC parameters. • Damping the speed oscillations against large fluctuations in grid power. • Excellent damping efficiency, especially, low overshoot, steady-state error, and settling time. • Study of robustness of HTGS against variations of system parameters. [ABSTRACT FROM AUTHOR]

This paper investigates the connectedness among the climate change index, green financial assets, renewable energy markets, and geopolitical risk index from June 1, 2012 to June 13, 2022, using Quantile Vector Autoregressive (QVAR) and wavelet coherence (WC). The Total connectedness index (TCI) varies as long as the highest TCI originates in the upper quantile. We also note that the higher TCI decreases after the second wave of COVID-19 and increases during the first 100 days of the Russia-Ukraine conflict. Moreover, the results show that Geopolitical risk (GPR) is a net transmitter of the climate change index during the Russian invasion of Ukraine. The green bond and clean energy markets are negatively connected to the GPR at extreme 10 th and 90 th quantiles. The wavelet coherence confirms the QVAR results that the climate change market can be a safe haven against GPR during the Russian invasion. The climate change index, green financial assets, and clean energy are strong influencers in the financial markets and are vital to international peace, reducing geopolitical risk. The study reports a few novel conclusions and implications from a sustainable development perspective. • Connectedness among climate change index, green financial assets, renewable energy markets. • Daily data was used from June 1, 2012 to June 13, 2022. • Role of geopolitical risk for green financial assets and renewable energy market. • Climate change index, green financial assets, and clean energy are strong influencers in the financial markets. [ABSTRACT FROM AUTHOR]