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Increasing the demand for water and energy resources as well as the clean energy policies and the need for pollution emission reduction has developed the research on the renewable energy resources. This paper reviews the recent advances in multi-generation units for heat, electricity, and desalinated water production. It tries to investigate the new papers in this arena which were not mentioned in the previous reviews. The main parts of this work introduce the co-generation systems for the production of "water and electricity", "heat and water", and "electricity, water, and heat". The results show that among the solar energy receiving devices, Photovoltaic/Thermal panels (PV/Ts) and parabolic trough solar collectors are the most used in co-generation systems. On the other hand, a combination of solar collectors and power cycles makes them a suitable choice for the establishment of a large-scale co-generation plant. Therefore, solar collector-based electricity and water co-generation systems have at least 10 times more output capacity than PV- or PV/T-based systems. On the other hand, most heat and water co-generation systems can achieve an output temperature of 60 °C or more and produce around 10000 kg/year of freshwater based on system size. Examining the co-generation systems of water, electricity and heat also shows that these systems have a high production capacity compared to other co-generation systems. Systems based on heliostat solar tower can produce maximum powers of more than 18 MW and fresh water of 90 kg/s. • Recent advances in solar multi-generation units are reviewed. • PV/Ts and parabolic trough solar collectors are most used collectors in cogeneration systems. • RO and MED have more applications in solar-based co-generation units. • Using waste heat can increase technical and economic performance of system. [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]

In recent years, the government has attached great importance to promoting innovation in new energy technologies, particularly in the area of solar power generation. Photovoltaic (PV) power generation has become an essential factor in the promotion of renewable energy due to its inherent safety, environmental sustainability and abundant solar energy resources. The efficiency and stability of PV generation systems are closely related to the accurate prediction of real-time global horizontal irradiance (GHI). However, some existing research methods have made some progress in hourly resolution, but the multi-step prediction accuracy and model generalization ability in high time resolution are still unsatisfactory. Therefore, this paper proposes a novel minutely multi-step irradiance forecasting based on all-sky images using LSTM-InformerStack hybrid model with dual feature enhancement scheme. In this scheme, the advantages of both LSTM and InformerStack models are combined to further enhance the global and local features, which can better capture the short-term time series patterns and long-term dependencies and improve the accuracy of the time series. The experimental validation results show that the performance of this prediction scheme is improved by 11.2%, and 26.0% for nRMSE and R2, respectively, and the 24-step prediction can be improved up to 26.1% vs. the persistent model. Meanwhile, the prediction scheme of this paper is also expected to provide a reference in future multi-step time series accurate prediction with higher resolution. [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]

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

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

This paper utilizes data from 26 countries to document the relationship between renewable energy consumption, shadow banking, traditional banking, and economic growth covering 2010–2021. Employing panel quantile approaches, the results indicate a negative and significant impact of renewable energy consumption on economic growth, highlighting the initial high costs inherent in transitioning to renewable energy sources. Moreover, the findings reveal that shadow banking, along with traditional banking, tends to weaken the negative impacts of renewable energy consumption on economic growth, particularly in countries with medium and high levels of economic development. Finally, panel causality tests confirm bidirectional causality between economic growth and renewable energy consumption, economic growth and shadow banking, and renewable energy consumption and shadow banking. These insights underscore the need for policymakers to consider the role of alternative financing mechanisms in the transition to renewable energy. [ABSTRACT FROM AUTHOR]

This paper presents a tool that allows to perform an ex-ante estimation of the profitability to participate to the electric energy and service markets for a microgrid or a polygeneration power plant. The tool is the evolution of an Energy Management System (EMS) previously developed by the University of Genoa and Renantis Solutions Srl. Constraints are added to the original EMS to model the Ancillary Service Market (ASM) and Day Ahead Market (DAM) mechanisms in the framework of a planning tool that allows to get estimates of the microgrid/plant costs and revenues over a long-time horizon. As the market outcome strongly depends on the Transmission System Operator (TSO) acceptance/rejection of bids and offers, a statistical approach is proposed. The optimization process is split in two steps: the first one optimally dispatches the energy units and proposes DAM offers and ASM bid and offers over a specified time horizon. Then, a Monte Carlo method is launched; at each extraction the TSO acceptance/rejection of each ASM bid and offer is simulated and there is a second step of optimization in which the energy sources are optimally re-dispatched to meet the bidding program and so to avoid imbalance power exchange that would increase the energy cost. The cost/revenue is thus computed at each extraction. The result is the probability density function of the costs/revenues that allows to estimate the profitability of the participation to the electricity markets. One test performed on a real microgrid shows that the computational time needed to complete one iteration related to a time horizon of one month is approximately 20 s. Moreover, the comparison of the outcomes with two independent launches to define the set of bids and offers to be accepted in ASM demonstrates that the statistical analysis allows to avoid misleading conclusions on the costs/revenues. Consequently, the fact that the developed EMS allows to perform a statistical analysis in a reasonable time enables the possibility of comparing multiple scenarios and gives an indication related to the range of cost/revenues in which the microgrid/plant may incur. Finally, the computational efficiency of the proposed approach allows to conclude that the developed EMS is appropriate to perform a long-time horizon (of the order of magnitude of years) analysis and, at the same time, allows to model all the involved components with a high level of details. [ABSTRACT FROM AUTHOR]

The depletion of conventional fuel reserves and the high carbon emissions have alarmed the energy sector drivers. The integration of renewable energy resources (RERs) towards power system transformation is an obvious approach for a new-zero future. However, this integration also brings challenges in terms of voltage stability and power losses due to the variable output of RERs. To address these challenges, this work proposes a novel approach utilizing photovoltaic (PV) inverters and static var compensators (SVCs) for reactive power control in power distribution networks (PDNs). It enhances voltage stability and minimizes power losses. The proposed study deploys a proximal policy optimization (PPO) algorithm for real-time communication and control between reactive power devices. Performance evaluation was made on an IEEE-33 Bus system to demonstrate the effectiveness of the proposed scheme in integrating RER-based distributed generators (DGs). The proposed system achieved 68 % voltage control, keeping the voltage within a certain range of ±5 % while minimizing power losses. The proposed system also reduced the voltage out-of-control ratio to 0.044, which indicates minimum voltage deviation from the standard value. The proposed study provides a promising solution for controlling the voltage of DGs integrated PDN, which can potentially enhance the efficiency and reliability of the power system. • This paper introduces PV inverters and SVC-based reactive power voltage regulation for renewable DG-integrated systems. • Reactive power control in the DG-integrated system is adopted using Reinforcement Learning-Driven PPO. • The validation of the proposed method uses the IEEE-33 bus system to integrate RERs-based DGs and achieve PPO-based control. • The proposed method achieved 68% voltage control, reduced power loss, and reduced the voltage out-of-control ratio to 0.044. • This research compares the suggested approach to different methodologies to demonstrate its validity and applicability. [ABSTRACT FROM AUTHOR]

The intermittent nature of renewable energy resources is a major obstacle in utilizing such resources for power generation despite their abundance. In this paper, a dynamic analysis of an integrated system based on photovoltaic (PV) panels and wind turbines coupled with pumped hydro energy storage is presented. When the power generation exceeds the power demand, surplus electricity is used to pump the water to the upper reservoir. The natural inflow of the river obviates the necessity for oversized reservoirs and saves costs. A recently introduced multi-objective equilibrium optimizer algorithm is developed to find the optimal size of the integrated hybrid energy system. The objectives are to minimize the levelized cost of electricity and maximize the reliability of the proposed system. The results show that the correlation between solar and wind power generation in the selected location is mildly strong with a value of 0.3. The total energy generation from hydro, solar, and wind resources are 77,508.7 kWh, 103,323 kWh, and 47,375.4 kWh, respectively. Comparing the results with other algorithms indicates that the equilibrium optimizer has a better performance in finding the optimal design. [ABSTRACT FROM AUTHOR]

Energy complementarity is a promising approach in the realm of renewable energy systems, enabling the integration of multiple energy sources to achieve a stable and sustainable power supply. This paper presents a comprehensive solar-wind complementarity study encompassing all regions of Morocco. A novel method for assessing complementarity is introduced, utilizing a fluctuation approach that classifies the power output of each source based on the median and the standard deviation. The classifications of both solar and wind sources are then combined to establish a basis for complementary classification, categorizing each day as complementary, non-complementary, low-complementary, or concurrent for both sources. An innovative complementarity index is proposed, ranging from 0 to 1, with values closer to 1 indicating high complementarity. This index is applicable to any location and is used to generate maps illustrating solar-wind complementarity. Additionally, dominance metric is derived from this approach that indicates which source is most likely to complement the other source. The results reveal that temporal complementarity is high in the internal Northern regions reaching indices above 0.7, followed by the Northern coastal regions with indices that range between 0.6 and 0.7. The lowest complementarity occurs in the South with indices below 0.6. Additionally, the complementarity plots that displays the monthly indices reveled, for most regions, that complementarity maintains certain level except in the summer where it peaks or drops. The solar dominance is higher in the South reaching above 60 %, even though the wind potential is important in those regions. For spatial complementarity, two locations were considered and the method is applied for four scenarios: solar-wind, wind-solar, solar-solar and wind-wind. Results favor all the combinations except for the solar-solar scenario. The suggested index yields similar results to Kendall's tau correlation. The negative correlation index indicates high complementarity when positive and a low complementarity when close to −1. For this approach, it is not limited to the degree complementarity but can describe other states of complementarity and its frequency of occurrence compared to other states. This provides additional insights compared to the commonly used correlation methods. The proposed method contributes to a more profound understanding of complementarity between two energy sources. [ABSTRACT FROM AUTHOR]

The uncertainty of renewable energy makes the optimal scheduling of integrated energy systems (IES) challenging and complex. The paper suggests a novel two-stage optimized scheduling model based on distributionally robust adaptive model predictive control (DRAMPC), which effectively improves scheduling accuracy and efficiency while taking robustness and economy into account. In the day-ahead stage, the multi-objective distributionally robust optimization (MODRO) model is composed based on the imprecise dirichlet model (IDM), which incorporates robustness and operating cost metrics into the optimization objective to achieve synergistic optimization of robustness and economy. The adaptive step double-loop model predictive control (ASDL-MPC) utilizes the dual closed-loop feedback of renewable energy output prediction errors and operating cost prediction errors to adaptively adjust the rolling time step, correcting the day-ahead scheduling bias while improving scheduling efficiency. The model is resolved using the cross-entropy-radar scanning differential evolution (CE-RSDE) algorithm. The results show that the DRAMPC model can balance economy and robustness, improving the economy of IES by 2.27% while ensuring robustness. The ASDL-MPC intra-day rolling optimization further improves scheduling accuracy and also increases computational efficiency by 6.58%. [ABSTRACT FROM AUTHOR]

The issue of large-scale retirements of renewable energy modules has attracted considerable attention from governments and the public worldwide. Using the Chinese wind and photovoltaic industries as examples, this paper, for the first time, employs a real options approach to explore the optimal time for energy firms to retire their power plants and to predict the future trend of renewable energy wastage from a bottom-up perspective. The results show that the optimal retirement time for the four subdivided categories ranges from 16 to 23 years. The later the investments are made, the earlier the decommissioning time comes and the lower the project returns. Furthermore, wind projects have been found to generate more revenue than PV projects for the same size, and the gap continues to widen. Offshore wind technology always has the longest optimal service life (18.62 years in 2022) and the greatest project returns (10.84 million CNY in 2022). Annual utilization hours, the renewable electricity price and the waste price can accelerate the retirement of plants and generate more revenue for energy companies, while the initial investment cost has a completely opposite effect. The first wave of PV retirements will occur in 2035 at 305.82 GW, while onshore wind will be in 2036 at 530.77 GW and offshore wind will not explode until 2040 at 65.84 GW. An assessment of potential policies suggests that for every 100 CNY increase in incentive strength, the retirement wave arrives 1 year earlier. In contrast, for every 100 CNY increase in the strength of penalties, the retirement wave is delayed by 1 year. In addition, we verify that the size of the plants does not affect the forecast of the retirement trend, indicating that the assumptions about installed capacity do not cause bias. [ABSTRACT FROM AUTHOR]

This paper addresses the critical challenge of sizing hybrid renewable energy systems over their lifespan while accounting for uncertainties in energy sources and load demands. By leveraging stochastic programming, we introduce a novel modeling technique that ensures robust optimization without compromising numerical tractability. Unlike conventional methods, which determine system parameters for the entire project duration upfront, our approach enables annual adjustments to the renewable components' sizing. This dynamic strategy alleviates early-stage under-utilization, aligning system capacity with evolving demand patterns. Central to our approach is the reformulation of the two-stage stochastic program as a quasi-optimal control. This reduces the number of optimization variables significantly, simplifying the solution process. Moreover, thousands of constraints are replaced with a system of differential equations, enhancing computational efficiency. By minimizing capital costs and dynamic operating expenses, we achieve an optimal system size. Through a real-world application in a rural area of South Africa, we demonstrate the effectiveness of our approach. The results showed a remarkable improvement in problem-solving efficiency, reduced under-utilization, and heightened adaptability to unpredictable weather conditions. This innovative framework represents a substantial leap forward in hybrid renewable energy systems design, offering a more agile and efficient solution for sustainable energy generation. [ABSTRACT FROM AUTHOR]

Accurate wind power prediction is a vital factor in day-ahead dispatch and increasing the high penetration of renewable energy integration. Some combined prediction models based on weight are proposed to provide uncertain information about wind power. However, the optimal weight issues to be addressed are still a tough problem. This paper proposes an adaptively combined prediction model based on the induced ordered weighted averaging operator. First, four classical prediction models are used as a predictor to forecast wind power. Second, the optimal weight is expressed as an optimization problem to minimize the prediction errors with the nonnegative constraints and summation constraint of weights, and the optimal optimization problem is transformed into a quadratic programming model, which can be easily solved to determine the optimal weights for multiple prediction models. Third, an interval prediction mode based on errors distribution produced by the proposed combined model with different confidence intervals is constructed, which can perform deterministic and probabilistic prediction of wind power simultaneously. Furthermore, the model's effectiveness is comprehensively evaluated using both deterministic and probabilistic approaches. This analysis is further enriched with a comprehensive examination of a year-round dataset obtained from a wind farm cluster in Ningxia, China. The data, recorded at 15-min intervals, offer robust evidence of the combined model's superior performance. The results show that the proposed approach can achieve higher accuracy than other benchmark prediction models, further verifying its reliability for wind power dispatch. [ABSTRACT FROM AUTHOR]

Stand-alone renewable energy systems, which harness and use electricity without connection to a distribution grid, can benefit from using resource forecasts when making real-time decisions on how to manage power. An example of this is an ocean observation system that uses a wave energy converter (WEC) and battery bank to produce and store electricity in order to meet electrical load requirements for oceanographic sensors and instrumentation. By using wave forecasts to optimize the power consumption of this system, it is possible to reduce the size of the WEC and battery bank needed, thereby reducing the overall system's cost and complexity. In this paper, we assess the benefits of forecasting for a generic wave-powered ocean observation system. We model an observation platform powered by a WEC and battery-storage system that can switch between four power states, full power: 600 W, medium power: 450 W, low power: 45 W, and survival mode: 1 W. To determine which power state to enter over time, we present a stochastic optimization method that interprets wave forecasts and system information to select a power state on an hourly basis and simulate over two months of consecutive decisions. Using this simulation framework, we compare 8 power management strategies across a range of WEC sizes (3–5 m) and battery capacities (2.5–35 kWh). We find that it is possible to maintain full (600 W) power consumption over the entire simulation window with a 5 m diameter WEC and 25 kWh battery bank, which is on the upper end of the ranges evaluated. To employ a smaller WEC or battery, load flexibility is required. We find that forecast-based methods for this power management handle tradeoffs in performance (e.g., cumulative power consumption, reducing intermittencies and meeting scheduled load targets) more effectively than power management strategies that do not use forecasting. Overall, our results indicate that forecasting for our stand-alone renewable energy system is most impactful when some loads must be met on or within a defined schedule. • A forecast-based method for managing a load met by wave energy is presented. • This method handles performance tradeoffs better than non-forecasting strategies. • These benefits are most impactful when loads must be met on or within a schedule. • Forecast uncertainty has notable influence, suggesting opportunities for improvement. [ABSTRACT FROM AUTHOR]

With the increasing proportion of variable renewable energy characterized by fluctuation and the promotion of the "Clean Heating" policy, the problem of seasonal energy imbalance of the system has become increasingly challenging. There is a lack of effective means to mitigate this challenge under the background of gradual compression of the construction space for traditional thermal units. Concentrated solar power (CSP) is a promising technology to replace thermal units by integrating emergency boilers to cope with extreme weather, and can meet long-time energy balance as a seasonal peak regulation source. In this paper, we propose a long-term high-resolution expansion planning model of energy systems integrating CSP to address seasonal energy imbalances. The model takes into account both investment and operation considerations in full hourly resolution for the whole year based on a fast cluster optimization method. With the projection to 2050, we take the energy system in Xinjiang province which is a typical area of the "Clean Heating" project with rich irradiance as a case study. It shows that the optimal deployment of CSP and electric boiler (EB) can result in a 8.73 % reduction in costs, a 19.72 % decrease in the peak-valley difference of net load, and a substantial 58.24 % reduction in renewable curtailment at 65 % renewable penetration compared to the base scenario. [ABSTRACT FROM AUTHOR]

Ocean current energy is a stable, reliable, and highly predictable renewable energy source. The effective development of ocean current energy conversion technology is beneficial in addressing the issue of power shortage. However, the majority of current velocities worldwide are below 1.5 m/s, limiting the feasibility of using conventional current energy capture devices. This paper presents a vortex induced vibration based deep sea microfluidic eel energy capture device (VIV-EEL) designed to efficiently harness energy in low-speed current environments. The system employs Computational Fluid Dynamics (CFD) to develop a series of computational models that couple underwater multibody fluid-solid interactions. These models are subsequently validated through experiments. The study analyzed the flow field of VIV-EEL under different working conditions and discussed the impact of several dimensional parameters of the elastically supported cylinders, structural parameters of raft plates, and flow velocity on the hydrodynamic performance. The results demonstrate that the energy capture efficiency of VIV-EEL is enhanced by the vorticity vibration effect. It is evident that there exists an optimal radius size for the elastic support column to achieve the most favorable resonance effect in the flow field of VIV-EEL. The energy capture characteristics of the raft plate show a linear relationship with its structural parameters, enabling quantitative design according to the requirements of the power take-off system (PTO). Moreover, VIV-EEL exhibits a lower start-up flow velocity compared to traditional current energy capture devices, enabling it to initiate and capture energy at a flow rate of 0.3 m/s. This innovative solution offers technical support for efficient low-speed current energy capture. [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 global water scarcity crisis has led to increased demand for reverse osmosis (RO) desalination technology. In response to the rising global temperatures, there is a growing focus on renewable energy sources such as solar photovoltaic (PV) and wind energy to power small-scale RO plants. This paper presents a comprehensive techno-economic analysis of stand-alone photovoltaic reverse osmosis (PV-RO) systems in Pakistan. It focuses on the design and optimization of PV-RO systems for Karachi and Lahore, evaluating the systems with and without energy recovery devices. A parametric study was conducted to optimize system performance, considering various configurations. The analysis includes a comparison of costs when operating with PV, diesel, and grid power. It was found that for Karachi, a four-stage PV-RO system without an energy recovery device provides water at $1.359 per cubic meter, while in Lahore, a three-stage system with an energy recovery device achieves $1.336 per cubic meter. These systems offer significant annual savings compared to diesel-powered systems, with Karachi and Lahore saving up to $10,138 and $17,664 respectively over ten years. The findings of this study show that PV-RO systems, especially those with multiple stages and energy recovery devices, offer a cost-effective and sustainable solution for water desalination in these regions. [ABSTRACT FROM AUTHOR]

The implementation of hydropower to stabilize electrical grids dictates more frequent off-design operations of these renewable energy resources. Flow instabilities under such conditions reduce the efficiency of hydro turbines. Part-load operation is particularly detrimental since the development of a rotating vortical structure termed rotating vortex rope (RVR) in the draft tube leads to periodic pressure pulsations that jeopardize turbine performance. This paper experimentally explores a novel solution involving the protrusion of flat plates into the turbine draft tube. Three flat plates equally separated by 120° were vertically installed on the draft tube wall. The plates were protruded up to 83% of the draft tube local radius under four different part-load conditions. Their impact was observed through time-resolved pressure measurements in the draft tube and vaneless space, as well as efficiency measurements. The results demonstrated successful RVR mitigation, achieving a maximum 85% reduction in pressure oscillation amplitudes. Protruding flat plates disrupted RVR periodicity and coherence, confining its orbit to the draft tube center. This approach proved particularly effective at lower part-load conditions, enhancing turbine hydraulic efficiency by increasing torque extraction. Reducing the adverse effects under part load, the proposed method appears promising in extending the operational range of hydraulic turbines. [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]

This paper proposes a robust optimization siting and sizing strategy for the unified power quality conditioner (UPQC) in the active distribution network (ADN) integrated with renewable energy generators. The optimization objectives including investment cost, line loss, and voltage deviation are integrated into an overall cost for both the planning and operating stages. A novel UPQC equivalent power injection model is proposed based on the auxiliary branch method. A set of linear and second-order conic (SOC) constraints are employed to accurately describe the steady-state operation of UPQC. An improved branch current flow model is developed to incorporate radial distribution networks with UPQCs. The UPQC siting and sizing planning is formulated as a mixed integer second-order conic programming (MISOCP) problem. Accounting for the uncertainty in loads and distributed generators, a two-stage robust optimization model is established to address worst-case operational scenarios. A solving strategy is developed based on the strong duality theory and the column-and-constraint generation (C&CG) algorithm, and then commercial solvers can solve the problem accurately and efficiently. Case studies on an IEEE 33-node distribution network demonstrate this strategy's robustness in dealing with loads and renewable energy uncertainties, making it applicable across different UPQC topologies and operation modes. The proposed method has superior performance over the representative meta-heuristic approaches in terms of global optimality as well as an over 7.5 times faster computation speed. This work has significance in enhancing the quality and efficiency of modern power systems highly integrated with renewable energy. [ABSTRACT FROM AUTHOR]

This paper aims to broaden the concept of Net Zero Energy Building (NZEB), assessing its suitability to the emerging grid-based energy sharing models. The energy exchange between two buildings with shifted load schedules, namely, a new residential NZEB (prosumer) and an existing school building (consumer), is analysed to investigate energy sharing dynamics. The buildings are in Southern Italy, a region with high solar radiation. The model of the existing school building is calibrated on measured data and the study is performed by dynamic energy simulation. Three energy sharing scenarios based on the design of a photovoltaic system installed on the roof of the NZEB have been investigated, i.e., PV system peak power designed: to satisfy the NZEB target on annual basis; to satisfy the NZEB target on monthly basis; to fully exploit the available roof area of the NZEB. Considering annual and monthly net energy balances, oversizing the NZEB PV system leads to the compensation of a high share of school building non-renewable energy consumption. However, self-consumption analysis performed on hourly basis shows that increasing the PV system size negatively affects the self-consumption rate of the renewable energy community, and the presence of the school building only mitigates this effect. [ABSTRACT FROM AUTHOR]

In this era of global warming the importance of climate risk cannot be understated, and finding ways to reduce climate risk is a topic worthy of study. In this paper, we innovatively examine the role of renewable energy technology innovation on climate risk in China. In addition to their linear relationship, we also investigate the asymmetric and heterogeneous impact, detect the impact channels, and find that (1) increased renewable energy technology innovation can effectively alleviate the climate risk situation. Accordingly, the development of renewable energy technology innovation can make a significant contribution to CR abatement. (2) Pronounced impacts of renewable energy technology innovation on climate risk can be observed in the higher quantiles (75th and 90th) of climate risk. (3) Renewable energy technology innovation is a powerful weapon for eradicating climate risk in provinces with a higher level of government fiscal expenditure. And (4) renewable energy technology innovation has an impact on climate risk by facilitating energy consumption efficiency, which is a necessary mediator. These conclusions inspire us to put forward several corresponding policy implications for better climate risk governance and renewable energy technology innovation development. [ABSTRACT FROM AUTHOR]

To achieve the goal of "carbon peaking and carbon neutrality", China is vigorously developing renewable energy. This paper aims to examine the major contributors to regional inequality in renewable energy consumption by analyzing the 30 provinces in China. We have analyzed the inequality of China's renewable energy consumption with Dagum Gini coefficient, and then use the Generalized Divisia Index Model (GDIM) to decompose the influencing factors contributing to the inequality of renewable energy consumption. The main conclusions are summarized as follows: First, renewable energy consumption was higher in the western region than in the central and eastern regions, and the regional inequality in renewable energy consumption have increased over time. Second, the study reveals that inter-group disparity is the primary source of regional inequality in renewable energy consumption in China. Third, at the national level, the results of the GDIM decomposition show that economic development continues to rely on fossil energy consumption, which negatively impacts the low-carbon transition of energy. Given China's complex geographical conditions, it is crucial for different provinces to develop their own strategies for renewable energy development, taking into account unique resources and challenges. [ABSTRACT FROM AUTHOR]

The major obstacle in designing of the wind turbine/photovoltaic/battery storage system lies in the task of choosing the most optimum solution while simultaneously considering techno-economic objectives. Consequently, this study provides a distinctive amalgamation of multi-objective multi-perspective and group multi-criteria decision-making methodologies to ascertain collections of Pareto optimum configurations and to weight, rank, and pick up the most desirable optimum solutions that represent best trade-off between conflicting objectives. In this paper, the multi-objective Dragon fly algorithm is developed based on mutation scheme and crossover tactic algorithm is advanced to handle a lack of diversity and poor convergence, while non-dominated sorting and crowding distance strategy is addressed to store the best found optimum solutions in the achieve. Loss of load probability, excess energy, and life cycle cost are conflicting techno-economic objectives solved by multi-objective method. The optimum Pareto front solutions are ranked based on hybrid multi-criteria decision-making method for determining the most favorable solution for the WT/PV/BS system. The experimental outcomes indicated that the developed approach has a distinct performance in constructing a collection of Pareto front solutions in terms of diversity, converge, and convergence. Moreover, it demonstrates outstanding results when compared to well-organized multi-objective optimization methods. The future direction can be accomplished by applying the proposed hybrid sizing methodology for solving gird-connected system along with electric vehicle based on techno-economic scenarios. [ABSTRACT FROM AUTHOR]

The correlation between renewable energy technology innovation (RENTI) and carbon neutrality has been studied extensively. Meanwhile, an efficient digital financial system and a favorable innovation environment are essential to promote RENTI. This paper examines the effects of digital finance and the regional innovation environment on RENTI using panel data from 31 Chinese provinces spanning 2011 to 2020. The findings demonstrate that digital finance directly promotes RENTI. The marginal effect of digital finance is more significant for eastern regions and regions with developed finance. The threshold effect analysis shows that the positive impact of digital finance on RENTI reaches a higher level when government financial support and ICT development exceed a certain threshold. In addition, appropriate levels of human capital are conducive to the positive impact of digital finance. Finally, we find that digital finance promotes local RENTI and benefits RENTI in neighboring regions. Our findings offer insights for policymakers to guide digital finance to support RENTI and optimize the innovation environment. [ABSTRACT FROM AUTHOR]

Data analysis is one of the most important tools that can contribute to unveiling critical relationships. Econometric approaches are evolving continuously, while older methods although are not completely abandoned, they may be used vis-à-vis the new ones for comparison purposed. This special issue has called for papers that have used novel research methods in a panel data framework for the studying and modelling renewable energy use and management. This special issue is useful for readers because it provides them an overview of the those novel methodologies employed in the field of renewable energy economics and econometrics. [ABSTRACT FROM AUTHOR]

Geothermal energy is one of the largest renewable energy sources available in Mexico and is the most important non-conventional renewable energy source utilised in this country. It is estimated that Mexico has geothermal reserves of approximately 8000 MWe, which can be implemented for the generation of green hydrogen. The paper focuses on the geothermal energy potential of high-enthalpy resources dominantly found South and Southeast Mexico, through investigations of 12 hot springs, with temperature depths as high as 400 °C in some areas. The utilization of suitable power production equations is further refined using the Monte Carlo Method to optimize the numerical findings of the power potential. Therefore, the Visual Basic Application (VBA) in Microsoft Excel is used through the Visual Basic Editor (VBE) function to code the Volumetric Method Equations. Furthermore, 20,000 iterations are then simulated to produce the minimum and maximum power potential/capacity (MWe), from which 10 % and 90 % confidence intervals are then determined. The study calculates that, considering a production lifetime range of 25 years, a total of 147.57 MWe of geothermal power capacity could be generated across all 12 hot springs in Mexico. [ABSTRACT FROM AUTHOR]

Sustainable development has many dimensions, such as clean energy, green growth, and sustainable industry. Green finance has a key role in realizing these targets. The core focus of the existing paper is to demystify the influences of green finance, green energy on environmental dynamics in Malaysia covering the timeframe from 1990 to 2018. To this end, we employ the QARDL technique to peruse the effect of green finance, renewable energy, and economic growth on CO 2 emissions and ecological footprint separately. By doing so, we crosscheck the empirical outcomes and inspect the consistency of the analysis. Firstly, empirical results show that the ecological footprint model yields similar results to the CO 2 emissions model in terms of statistically significant quantile ranges and the magnitude of the coefficients. According to the overall results, GDP increases environmental degradation while green finance and renewable energy have a mitigating role. Finally, we confirm a significant EKC hypothesis for Malaysia. Based on these findings, we recommend Malaysian policymakers focus on energy efficiency, realize financial reforms and promote green energy through the financial system to achieve the 2050 net-zero emission target. [Display omitted] [ABSTRACT FROM AUTHOR]

The generation of electricity from free-flowing water is an important renewable energy source. In this paper, we introduce a new turbine concept based on a vertical axis turbine with two extraction planes and a rectangular harvest area suitable for rivers or ocean currents. Using numerical simulations, we show that the extension of the classic vertical axis turbine by linear sections can significantly improve efficiency, with gains of up to 12% in the case of the Reference Model 2 turbine, a benchmark prototype used for comparison. Based on the findings, a prototype is designed and built with a focus on optimizing the blade pitch using computational fluid dynamics simulations. The prototype is subsequently evaluated through performance measurements on a test boat. Our results show that the Linear Turbine has the potential to significantly improve the efficiency of kinetic energy converters for free-flowing water, with an efficiency of around 42% achieved in our small prototype. Key features of the turbine include its ability to generate electricity from rivers and ocean currents with minimal impact on the natural environment, its high potential efficiency, vertical construction, and linear working range. The design of the turbine is complex, but its potential applications include use in small-scale systems and as a environmental alternative to traditional hydroelectric dams. Further investigations are needed to focus on economic design and to validate improved designs with 3D fluid simulations. • New turbine concept with a vertical axis and two extraction planes. • High potential efficiency comparable or beyond traditional horizontal axis turbines. • Prototype design with tip speed ratio of 2 and 42% efficiency. • Design allows for easy extension and increased harvesting area. • Dynamic pitch adjustment for improved power generation. [ABSTRACT FROM AUTHOR]

Large-scale distributed renewable energy sources as well as emerging controllable loads such as electric vehicles connected to the distribution grids have posed challenges to the efficient management of these distributed energy resources. Promoting energy-sharing of distributed energy resources through economic incentives is a cost-effective and equitable solution to the challenge. This paper proposes a two-stage transactive energy control mechanism by the virtual power plant (VPP) for energy-sharing of multiple prosumers. Firstly, a VPP internal price mechanism based on the supply-demand ratio of prosumers in the energy-sharing alliance is proposed. Then, a two-stage economic optimization model is developed. Trading prices between VPP and prosumers are established in the first stage, and prosumers make operating plans based on those prices in the second stage. Then, considering that prosumers and VPP are independent stakeholders, a decentralized optimization algorithm based on the analytical target cascading method is developed. The upper-level VPP coordinates the energy-sharing alliance and sets the transaction price following the overall supply and demand ratio. Lower-level prosumers independently make decisions and feedback on the trading power of the VPP. Then, iterative calculations provide a distributed and independent solution. Finally, the case study verifies that the proposed energy-sharing model can reduce the operating cost of prosumers by 14.29 %. Moreover, prosumer privacy protection is achieved by the analytical target cascading-based distributed model at a cost of less than 1.5 % accuracy. [ABSTRACT FROM AUTHOR]

PHPS is a mature energy storage technology, leading to more efficient energy management and further integration of Renewable Energy Sources (RES). This paper investigates the implementation of such a system at Kastraki, Greece, combined with a second upstream reservoir with a total capacity of 10 hm3 and evaluates its efficiency. The position and the geometric characteristics of the second reservoir are determined with ArcGIS. When excess of energy exists, water is pumped from Kastraki reservoir and stored in the upper one. Otherwise, the stored water is used for energy production. The simulation is performed for 40 years with a monthly time step. Furthermore, another simulation for a typical day in July is conducted, when water and energy needs are increased. The system is evaluated in terms of covering the water supply and energy needs, as well as in terms of efficient energy management. Several results can be obtained, as follows: i) the reliability of the PHPS system for the coverage of monthly water supply and irrigation needs is 82.84 %. ii) The coverage of water supply needs for a typical day in July is 99.99 %. iii) The average monthly coverage of energy needs amounts to 83.86 %. [ABSTRACT FROM AUTHOR]

The China Certified Emission Reduction scheme (CCER) and the Cap-and-Trade (CaT) mechanisms jointly constitute the Emission Trading Scheme (ETS), which is expected to have a profound impact on China's social and economic decarbonization. However, previous studies have primarily focused on the impact of the CaT mechanism, neglecting the fact that CCERs serve as a carbon offset trading mechanism. This paper focuses on the impact of the CCER mechanism on China's energy economy and environment by constructing a recursive dynamic computable general equilibrium model that couples CaT and CCER mechanisms. Introducing the CCER mechanism based on the CAT mechanism leads to increased output and reduced prices in renewable energy sectors, cascading positive effects across various energy-utilizing sectors to counter the decline in Gross Domestic Product (GDP) and residential welfare. The finding suggests that China's unique CCER mechanism is poised to play a more important role in promoting renewable energy to mitigate global warming, given its potential to transform the structure of energy production and consumption. Finally, targeted policy recommendations are proposed for implementing the CCER mechanism. [ABSTRACT FROM AUTHOR]

Offshore wind turbines (WTs) are crucial in offshore wind energy development. However, the dynamic responses of WTs are subject to significant uncertainties which are usually not properly considered. To the end, this paper proposes an efficient method for quantifying the uncertainties in WTs' dynamic responses based on cumulative distribution function (CDF)-manifold learning. First, a probabilistic model is developed to represent the environmental parameters and sampling for aerodynamic-hydraulic-servo-elastic simulations. Then, the CDF is obtained by statistically analyzing the simulated data. To tackle the higher dimensionality resulting from discretizing the CDF, a manifold learning-based approach is subsequently proposed to reduce its dimensionality and obtain a manifold space. Furthermore, a mapping relation is established between the environmental parameters and the low-dimensional data to efficiently obtain the response CDF under different environmental parameters, leading to the construction of a probability box (P-box) model. To demonstrate the effectiveness of the proposed method, the National Renewable Energy Laboratory (NREL) 5 MW offshore WT on an Offshore Code Comparison Collaboration (OC3) monopile is selected as a case study and analyzed accordingly. The results show P-box models of seven WT responses and validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Large-scale distributed renewable energy sources as well as emerging controllable loads such as electric vehicles connected to the distribution grids have posed challenges to the efficient management of these distributed energy resources. Promoting energy-sharing of distributed energy resources through economic incentives is a cost-effective and equitable solution to the challenge. This paper proposes a two-stage transactive energy control mechanism by the virtual power plant (VPP) for energy-sharing of multiple prosumers. Firstly, a VPP internal price mechanism based on the supply-demand ratio of prosumers in the energy-sharing alliance is proposed. Then, a two-stage economic optimization model is developed. Trading prices between VPP and prosumers are established in the first stage, and prosumers make operating plans based on those prices in the second stage. Then, considering that prosumers and VPP are independent stakeholders, a decentralized optimization algorithm based on the analytical target cascading method is developed. The upper-level VPP coordinates the energy-sharing alliance and sets the transaction price following the overall supply and demand ratio. Lower-level prosumers independently make decisions and feedback on the trading power of the VPP. Then, iterative calculations provide a distributed and independent solution. Finally, the case study verifies that the proposed energy-sharing model can reduce the operating cost of prosumers by 14.29 %. Moreover, prosumer privacy protection is achieved by the analytical target cascading-based distributed model at a cost of less than 1.5 % accuracy. [ABSTRACT FROM AUTHOR]

PHPS is a mature energy storage technology, leading to more efficient energy management and further integration of Renewable Energy Sources (RES). This paper investigates the implementation of such a system at Kastraki, Greece, combined with a second upstream reservoir with a total capacity of 10 hm3 and evaluates its efficiency. The position and the geometric characteristics of the second reservoir are determined with ArcGIS. When excess of energy exists, water is pumped from Kastraki reservoir and stored in the upper one. Otherwise, the stored water is used for energy production. The simulation is performed for 40 years with a monthly time step. Furthermore, another simulation for a typical day in July is conducted, when water and energy needs are increased. The system is evaluated in terms of covering the water supply and energy needs, as well as in terms of efficient energy management. Several results can be obtained, as follows: i) the reliability of the PHPS system for the coverage of monthly water supply and irrigation needs is 82.84 %. ii) The coverage of water supply needs for a typical day in July is 99.99 %. iii) The average monthly coverage of energy needs amounts to 83.86 %. [ABSTRACT FROM AUTHOR]

The China Certified Emission Reduction scheme (CCER) and the Cap-and-Trade (CaT) mechanisms jointly constitute the Emission Trading Scheme (ETS), which is expected to have a profound impact on China's social and economic decarbonization. However, previous studies have primarily focused on the impact of the CaT mechanism, neglecting the fact that CCERs serve as a carbon offset trading mechanism. This paper focuses on the impact of the CCER mechanism on China's energy economy and environment by constructing a recursive dynamic computable general equilibrium model that couples CaT and CCER mechanisms. Introducing the CCER mechanism based on the CAT mechanism leads to increased output and reduced prices in renewable energy sectors, cascading positive effects across various energy-utilizing sectors to counter the decline in Gross Domestic Product (GDP) and residential welfare. The finding suggests that China's unique CCER mechanism is poised to play a more important role in promoting renewable energy to mitigate global warming, given its potential to transform the structure of energy production and consumption. Finally, targeted policy recommendations are proposed for implementing the CCER mechanism. [ABSTRACT FROM AUTHOR]

Offshore wind turbines (WTs) are crucial in offshore wind energy development. However, the dynamic responses of WTs are subject to significant uncertainties which are usually not properly considered. To the end, this paper proposes an efficient method for quantifying the uncertainties in WTs' dynamic responses based on cumulative distribution function (CDF)-manifold learning. First, a probabilistic model is developed to represent the environmental parameters and sampling for aerodynamic-hydraulic-servo-elastic simulations. Then, the CDF is obtained by statistically analyzing the simulated data. To tackle the higher dimensionality resulting from discretizing the CDF, a manifold learning-based approach is subsequently proposed to reduce its dimensionality and obtain a manifold space. Furthermore, a mapping relation is established between the environmental parameters and the low-dimensional data to efficiently obtain the response CDF under different environmental parameters, leading to the construction of a probability box (P-box) model. To demonstrate the effectiveness of the proposed method, the National Renewable Energy Laboratory (NREL) 5 MW offshore WT on an Offshore Code Comparison Collaboration (OC3) monopile is selected as a case study and analyzed accordingly. The results show P-box models of seven WT responses and validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Ocean renewable energy (ORE) is one of the most important clean sources of energy and a major player towards the EU ambitions of being net zero emission by 2050. However, at present, there are few examples of commercially viable ORE technologies and no large-scale projects currently under implementation. Together with social and environmental analyses, the assessment of economic impacts is one of the key elements to help policy makers build a compelling case to gain local community acceptance and implement ORE projects. This paper presents a systematic methodology to assess local economic impacts of renewable energy projects, including jobs creation and impacts on gross value added and income. By combining the use of Location Quotients – which are indexes informing on local industrial specialisation – with the Input-Output multipliers the method can be used to map the supply-chain potential of a local economy and estimate local impacts compared to global ones. The method has been applied to a tidal project carried out in Orkney, Scotland. The research demonstrates the merit of early economic assessments for understanding the economic benefit of ORE projects, particularly for the local communities in which they are located, and it provides a methodological framework to be tested in other case studies. [Display omitted] [ABSTRACT FROM AUTHOR]

Green financing is essential because it guarantees that initiatives that seek to reduce the world's reliance on fossil fuels receive the necessary funding to be successful. Green financial metrics can be simultaneously made possible by specialized technological innovation and natural resource rent, thereby encouraging the adoption of sustainable energy. In this paper, we investigate the mediating mechanisms underlying the relationship between green financial indicators and renewable energy consumption RNEC and we contribute to the ongoing discussion surrounding this relationship. Our observational example is derived from data collected for 66 countries between 2004 and 2019 using a panel threshold regression model defined with the difference generalized method of moments (GMM). We find that green funding initiatives have accelerated the adoption of renewable energy sources by encouraging increases in the proportion of renewable energy in the fuel supply. Multiple analyses of reliability corroborate the existence of this link. Green funding has been shown to have an encouraging effect on the transition to renewable energy, and our findings indicate that technological advancement and natural resource leasing serve to mitigate this effect. To assist policymakers in reducing the world's reliance on polluting energy, our study demonstrates how green financial markets can be established through improved technological innovation and natural resource management. [ABSTRACT FROM AUTHOR]

The increasing generation of renewable energy and its concurrent technological innovation are driving the transformations of the energy sector, but little effort has been made at the regional level to specify the heterogeneous impacts of different types of renewable energy development on multi-dimensional energy security. Therefore, this paper establishes a four-dimensional energy security assessment system based on the current energy market situation and economic context of China. Renewable energy generation and technological innovation were simultaneously used as the core proxies for renewable energy development. Then, the panel quantile regression method was used to analyze the heterogeneity of the impact of solar, hydro, and wind energy on energy security, covering 30 provinces in China from 2006 to 2019. The empirical results show that renewable energy power generation generally promotes energy security, but it cannot be effectively applicable to all levels of energy security. Renewable energy technology innovation has surprisingly hindered energy security. These results can serve as a policy and practical guide, suggesting that provincial governments in China should implement energy security and renewable energy policies in line with local characteristics. • The redefined provincial energy security presents obvious spatial heterogeneity. • Panel quantile regression was applied to balanced provincial panel data. • Hydropower contributes only to medium and low levels of energy security. • Wind and solar power contribute differently to all levels of energy security. • Renewable energy technological innovation impedes energy security. [ABSTRACT FROM AUTHOR]

Open Cycle - Ocean Thermal Energy Conversion (OC-OTEC) is one of the most important renewable energy sources that generate electricity and fresh water from seawater utilizing the temperature gradient between the warm surface seawater and the cold deep seawater. This paper aims to develop non-linear data-driven model-based adaptive Feedback Control (FBC) schemes for the OC-OTEC process to track the output power and a dynamic Feed-Forward Control (FFC) scheme to reject the effects of temperature disturbance on power caused by climate variations in OC-OTEC. The experiments are conducted on a laboratory-scale OC-OTEC experimental setup at the National Institute of Ocean Technology, Chennai. Firstly, linear data-driven models are developed using system identification techniques. Based on the developed models, gain scheduling-based adaptive control schemes are developed: Proportional Integral (PI) control and Model Predictive Control (MPC). Secondly, the closed-loop performances of the developed FBC schemes are analysed under servo and regulatory operations. Furthermore, it is observed from the sensitivity analysis results that the temperature disturbance highly influences output over the manipulated variable. Hence, a dynamic FFC scheme is implemented to reject known disturbance of 1 °C variation in temperature gradient from 18 °C to 19 °C due to Sea Surface Temperature (SST) changes in temperature gradient. The results show that the proposed MPC-based FBC-FF scheme effectively enhances the tracking and disturbance rejection performance compared to the PI-based FBC-FF control scheme with a minimum Integral Square Error (ISE) of 3.055 and Control Effort (CE) of 7.505. This experimental data-based, data-driven modelling and adaptive control research provides a new direction for automating OC-OTEC. Further, these studies will help to develop a rugged and automated upscaling OC-OTEC plant control system with proposed feasible control schemes. [ABSTRACT FROM AUTHOR]

The global energy sector is experiencing a transition towards renewable energy primarily driven by issues related to climate change and energy security. In this paper, we investigate the impact of uncertainties and potential drivers connected to energy security on the volatilities and returns of renewable energy stocks. Further, we examine how uncertainty and potential drivers connected to energy security affect the volatilities and returns of renewable energy stocks. By applying the MS-GARCH (1,1) and MS-GJR-GARCH (1,1) approach we calculate the Value at Risk (VaR) and Conditional Value-at Risk (CVaR). In addition, we estimate a fixed effects model to determine the impact of the uncertainty variables on the estimated conditional volatility and returns. Our findings indicate that economic policy uncertainty (EPU) positively impacts the returns of renewable stocks, attributing to an increased engagement towards a renewable transition. However, the prices of crucial green metals were found to have a negative impact on renewable stocks suggesting that the transition to renewable energy might impose implications regarding energy security if not managed correctly. The findings of this study have important implications for policymakers, market participants, and governmental agencies in devising a roadmap to promote the transition process to renewable energy sources. [ABSTRACT FROM AUTHOR]