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Hydrogen is a promising energy carrier that can be obtained from various feedstocks using renewable energy sources. Direct solar thermochemical hydrogen (STCH) production by water splitting can utilize the full spectrum of solar radiation and has the potential to achieve high solar energy conversion efficiencies. Currently STCH research areas focus on material discovery. This paper evaluates the performance of various STCH materials in the context of a system platform to assess techno-economic benefits and gaps in the path to STCH scale-up. To analyze the hydrogen production cost, a concentrating solar thermal (CST) system is introduced as a platform for integrating STCH materials and accommodating generalized thermochemical processes. The thermochemical process is based on a two-step STCH cycle using metal oxide that consists of a high temperature step for metal oxide reduction, followed by an oxidation step for water splitting at a lower temperature. A preferred configuration is to have the high temperature step occurring in a directly irradiated solar receiver reactor. To this end, we conceptualized a receiver design and associated solar field layout and investigated STCH operational boundaries, component costs and sensitivity parameters on the $2/kgH 2 goal of hydrogen production. The study explored system-related variables and factors associated with scaling up. The CST platform allows more comprehensive studies that encompass aspects of STCH materials and systems such as cost, hydrogen productivity and replacement frequency, alongside other system components like heliostat field, tower, and potential receiver costs. [Display omitted] [ABSTRACT FROM AUTHOR]

The Oscillating Water Column (OWC) is leading the Wave Energy Converters in terms of efforts and research devoted. However, the efficiency achieved by these devices is still low enough to become a real renewable energy source. The main objective of this paper is to study the influence of the turbine characteristics in the thermodynamic process inside the OWC chamber, which is defined by the polytropic exponent associated with the polytropic process. Wind tunnel tests have been carried out to obtain experimental data under stationary flow, applying a real gas model where moisture plays a fundamental role. The results show the importance of the turbine characteristics and its effects on the thermodynamic compression process, affecting the power output of the OWC device. In conclusion, this study provides a better understanding of the OWC performance to improve its efficiency. [ABSTRACT FROM AUTHOR]

Electric power shortfalls driven by inadequate generation, electrification, and lack of clarity on the planning side have worsened Pakistan's power crises that deformed economic growth. This study focuses on the estimation of the electricity supply and demand in the China-Pakistan Economic Corridor (CPEC) perspective and on the main reasons that motivate a transition to green resources. This paper estimated the demand-supply gap (1990–2015) and forecasted it (2016–2035), including CPEC projects using the System Dynamics model. The results show that the power demand will be met by 2021, fact highlighted also by a battery of robustness tests. We find that the CPEC efforts will increase energy growth from 4.5% (1990–2015) to 14.6% (2016–2035). The capacity growth will increase from 4.9% to 12.3% during 2016–2035. Moreover, the current renewable energy production and investment (Wind & Solar Power) will not help achieve policy targets. A total investment of $4806.69 million is required to meet policy targets, while a value of $57,683 million is needed for a complete conversion to green energy. The study highlights the challenges on the green energy path and discusses policies needed to overcome these problems. [ABSTRACT FROM AUTHOR]

An important methodological point that has been over-looked by most of the literature is that renewable energy consumption (REC) is not only correlated in time but also in space. The omission of relevant spatial interaction terms in econometric analysis could be a methodological issue of major importance as it could lead to biased and inefficient estimates. This study accounts for spatial interaction effects to investigate the determinants of REC using a balanced panel of 41 countries in sub-Saharan Africa (SSA) over the period 2002 to 2015. The paper computes the Spatial Moran's I value to explore the spatial agglomeration phenomenon. Then, the Spatial Durbin Model (SDM) is applied to investigate the determinants of REC. The result from the Moran's I value indicates positive spatial correlation between REC and the spatial spill-over effect, representing spatial agglomeration. The decomposition of the SDM Model reveals that the direct effect of Gross Domestic Product per capita is negative while the direct effect of quality of governance level is positive. [ABSTRACT FROM AUTHOR]

This paper studies and proposes a composite indicator for evaluating the employment of renewable sources of energy in some strategic sectors. Nowadays, the relevance of such an issue is not only theoretical but has also important practical implications. Actually, this is a crucial topic addressed by the European Commission in order to assign funds and to raise fares to the European countries that, respectively, complies or not with the task of transforming the traditional energy consumption into a one based on renewable energies. However, the currently used indicators are not based on a comparison among countries, assume the compensability among simple indicators and are not robust to outliers and abnormal values, therefore, do not reflect appropriately the effort done in pursuing the targets required. Here, an alternative approach, based on productive efficiency methods, is proposed in order to rank countries appropriately and at the same time to promote incentive strategies on how to combine in an optimal way the consumption of renewables according to the priorities that countries have in the involved sectors. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between renewable energy consumption and ecological footprint in Saudi Arabia by considering the important role of economic growth in the environmental function during 1980–2017 periods. Capital and trade openness are determinants of environmental degradation. To determine the cointegration between the variables, we relied on the bootstrap autoregressive distributed lag (ARDL) bound test. We also used the Granger causality based on the bootstrap ARDL approach to identify causal relationships between the factors of environmental degradation in the presence of structural break. The empirical results show the presence of cointegration between variables with a structural break. Furthermore, the results of the ARDL model state that an increase in human capital and renewable energy consumption improves environmental quality (decreasing the ecological footprint), while an increase in trade openness and GDP deteriorates environmental quality (increasing the ecological footprint). In the short run, the results of the VECM model show the existence of a bidirectional causal relationship between human capital and trade openness. GDP causes the ecological footprint, while human capital causes renewable energy consumption. Moreover, there is a causal relationship from GDP and renewable energy consumption to trade openness. In the long run, all variables cause renewable energy consumption on the one hand, and trade openness on the other. In view of these results, policy makers have an interest in implementing favorable measures that reduce the ecological footprint and improve the quality of the environment by using renewable energy consumption as an economic tool. [ABSTRACT FROM AUTHOR]

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

This paper presents a new model to maximize the utilization of existing transmission system infrastructure by optimally sizing and siting the future developments of variable renewable energy sources (VRES). The model tries to maximize the integration of VRES in power systems with minimum expected energy curtailment without relying on new investments in the transmission systems. The proposed model is formulated as a linear stochastic programming optimization problem where VRES output scenarios are generated such that their spatio-temporal correlations are maintained. The Progressive Hedging Algorithm (PHA) with bundled scenarios is utilized to solve the proposed model for large-scale cases. The proposed model is tested on the modified Garver 6-bus and IEEE 118-bus test systems, and its results are compared with the results of the conventional VRES integration model. These results and comparisons illustrate the effectiveness of the proposed approach in terms of maximizing VRES integration and enhancing computational performance. [ABSTRACT FROM AUTHOR]

Numerous studies have assessed a variety of factors affecting renewable energy consumption, but the role of information and communication technologies (ICT) in affecting renewable energy consumption is neglected. The aim of this paper is thus to investigate the impact of ICT on renewable energy consumption based on panel data of 90 countries over the period 2000–2014. Moreover, we use the spatial econometric approach to determine whether they have spatial spillover effects. The empirical results show that the development of ICT will increase the consumption of renewable energy. Meanwhile, spatial spillover effects have also been confirmed, specifically, in a particular country, a 1% increase in individuals using the Internet, exports of ICT goods, and imports of ICT goods separately lead to 0.137%, 0.087%, and −0.251% changes in renewable energy consumptions in that country, and 0.380%, −0.155% and 0.485% changes in its neighboring countries. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper presents an alternative perspective on the renewable energy-economic growth nexus from the lens of installed capacity. Twenty OECD countries in Europe are assessed using panel data methodologies with data period spanning from 1993 to 2018. Electricity generation share-weighted coal and natural gas price indexes are incorporated in explaining country differences in fossil fuel reliance. Compared to results obtained from unweighted price indexes, weighted energy price indexes seem vital in obtaining a more plausible description of the relationship between energy prices and renewable capacity growth. Our findings confirm the crucial role of economic growth for renewable energy capacity development and long-term causality is apparent. Results indicate coal price increases trigger a greater positive impact on renewable capacity than natural gas. Finally, climate policies that raise fossil fuels costs could potentially stimulate the addition of renewable capacity and expedite the transition to renewable energy through price effect. [ABSTRACT FROM AUTHOR]

One of the strategies for achieving sustainable development goals is to close the gap between environmental vulnerability and economic progress (SDGs). The significance of renewable energy in harmonizing environmental and economic conditions is becoming a touchy subject in current debates. Against this backdrop, the major objective of this paper is to assess whether economic growth and environmental sustainability can promote renewable energy consumption in the Indian economy by using ARDL and Novel Dynamic ARDL estimation techniques. The results obtained from both the methodology shows that rising efficiency in the economic growth coupled with environmental sustainability leads to an increase in renewable energy consumption. We also find, from the frequency domain causality test, that there is a unidirectional causality between renewable energy consumption and economic growth & renewable energy consumption and environmental sustainability in both the short- and long run. The former causation validating the conservation hypothesis for the Indian economy. [ABSTRACT FROM AUTHOR]

This paper analyses the effects of ethnic and religious diversity on renewable and non-renewable energy consumption for 187 countries worldwide (categorised into high-, middle-, and low-income countries) from 1990 to 2020. This study determines the long-run relationship between the variables using panel fixed effects and generalized method of moments (GMM) models. The results show that ethnic and religious diversity have significant positive impacts on renewable energy consumption and vice versa on non-renewable energy consumption. Furthermore, the results are more significant for high- and middle-income countries than for low-income countries. This study suggests that diversity is a natural phenomenon; however, its disastrous effects may be curtailed by providing equal opportunities and promoting a peaceful society, as done in high-income countries, to ensure the well-being of the people through cohesiveness. Policymakers need to promote collective action and communication among different groups while acknowledging that investment for public benefits often requires broad social consensus and solidarity. [Display omitted] [ABSTRACT FROM AUTHOR]

Poverty alleviation is an important work of the Chinese government in recent years. According to the international poverty standards of the world bank, China's poverty reduction population accounts for more than 70% of the world. Photovoltaic poverty alleviation is a significant way for regions rich in solar energy resources to transform the advantages of renewable energy resources into the driving force of social and economic development. It is also an effective means for China to implement power poverty alleviation. While promoting emission reduction in these regions, it accelerates economic development and implements targeted poverty alleviation. This paper analyzes the comprehensive benefits of typical market entities of photovoltaic poverty alleviation projects, and establishes the environment, economic and social benefit evaluation models for poor households, the state and government, photovoltaic enterprises and grid corporation. In the model, we creatively put forward an index system to characterize the poverty alleviation effect, and consider the additional industrial benefits brought by photovoltaic poverty alleviation projects. Finally, a typical photovoltaic poverty alleviation project in Haiyuan County, Northwest China is selected to verify the feasibility of this model. [ABSTRACT FROM AUTHOR]

As the world's largest energy consuming country, it is important to study the mechanisms of improving renewable energy and energy efficiency to achieve green economic growth in China and globally. Therefore, this study investigates the impact of environmental regulations on green economic growth and renewable energy development in China, respectively. First, this study measures the level of green economic growth in Chinese cities by the Metafrontier-Global-SBM super-efficient data envelopment analysis (DEA) model from the perspective of energy efficiency. And the environmental regulation intensity was measured using an improved fuzzy comprehensive evaluation method. Second, a spatial econometric model based on longitudinal data was constructed to examine the non-linear effects of environmental regulations on green economic growth and its spatial spillover and feedback effects. The results indicated a U-shaped non-linear effect of energy efficiency-related environmental regulation on green economic growth as well as a spatial spillover effect and spatial feedback effect. Finally, the impact of environmental regulation on renewable energy development was investigated. The results show that renewable energy development, an important component of green economic growth, is equally dependent on the implementation of environmental regulation policies and that environmental regulation makes a significant contribution to renewable energy development. The findings of this paper provide a basis for the realization of green economic growth and the formulation of renewable energy policies in China. • Impact of environmental regulation on renewable energy development was investigated. • A spatial econometric model of 286 Chinese cities from 2003 to 2018 was constructed. • Environmental regulation has a significant U-shaped effect on green economic growth. • Non-linear effects include spatial spillover and spatial feedback effects. • Environmental regulation promotes green growth earlier in resource-based cities. [ABSTRACT FROM AUTHOR]

The aim of this paper is to understand consumer's perceptions towards new energy services. A comprehensive literature review was conducted prior to implementing an online questionnaire with 212 respondents. Consequently, a qualitative in-depth interview was conducted with six representative early adopters of new energy services. The main identified trends in the market were that more than half of the respondents would accept third-party device control but have serious security concerns. Consumers are also willing to invest in renewable energy systems if the payback time is less than 5 years. Factors such as financial rewards and reductions in bills are the main drivers for consumer's acceptance of new services. While, data security and privacy, ignorance of the nature of control and low benefits versus high initial cost were the main barriers. Results of this study would enable the actors in the Egyptian energy domain to identify the consumers' perspectives towards future scenarios and their readiness to be active players in the market. This will in return enable energy actors to adapt their plans based on the questions that current consumers consider relevant as well as decide on how to encourage consumers to become active players in the energy market. [ABSTRACT FROM AUTHOR]

This paper examines the nexus between fiscal decentralization and renewable energy demand by including income inequality, economic growth, urbanization, and economic globalization as the main determinants in the energy demand function for the case of China. Quarterly frequency data for the period 1980–2018 are used for empirical analysis. Our empirical results show fiscal decentralization positively impacts the demand for renewable energy; in other words, fiscal decentralization can help increase renewable energy demand. In addition, income inequality is negatively linked to renewable energy consumption, while economic growth is positively associated with renewable energy demand. Urbanization and economic globalization add to renewable energy consumption. This study presents Chinese authorities with new guidelines for using fiscal decentralization as an economic tool to formulate effective energy policies for long-term development of the use of renewable energy sources. [ABSTRACT FROM AUTHOR]

Concerning the growth rate in Renewable Energy share, especially in the EU, it is fact that Rooftop PV systems have a significant role to play. Nevertheless, it is also accepted that with current policies, schemes and scientific methods, today's Renewable Energy growth rate is still far from the desired one. Based on that, this study proposes a novel integration of a Mixed-Integer Linear Programming based model in a Public Tender Procedure, which enables the bulk installation and thus, the rapid expansion of Rooftop PV systems, in a least-cost manner. The main objective and contribution of this paper is to provide an economic solution to interested Public Bodies for utilizing the rapid growth of rooftop PV systems through novel mechanisms and hence, to better achieve the desired Renewable Energy targets. To further highlight the capabilities of the proposed method, the results from a real-life project, showed that the bulk installation of PV systems, in 404 public schools with a total PV capacity of 4.8 MWp, can be realized at an optimum cost. The main achievements are: (i) high-level competition, due to the ability of the proposed model to select multiple PV Installers, (ii) a 35% lower project cost compared to the initial estimated budget and (iii) a significantly lower unit cost of about 0.7 million €/MWp, compared to about 0.91 and 1.54 million €/MWp obtained from other similar projects. Finally, the outcomes of the proposed MILP model are validated by those obtained from an exhaustive optimal solution search procedure. [ABSTRACT FROM AUTHOR]

Wind power is becoming a main alternative energy source to meet the growing electricity needs. Forecasting wind speed is important to mitigate generation uncertainty and optimize asset utilization. This paper proposes a hybrid wind speed prediction model with multivariate input and multi-step output capability. The model synthesizes linear time series regression with nonlinear machine learning algorithm. The input neurons of the hybrid model are determined by the number of lag observations in autoregressive integrated moving average (ARIMA), and also by correlated meteorological features, such as wind direction, air pressure, humidity, dew point, and temperature. The output neurons are further derived based on the forecasting horizon. The hybrid model is trained, validated, and tested by using 1.73 million hourly meteorological records from three cities with diverse wind profiles. The performance of the model is compared with several existing methods based on root mean square error and mean absolute error. Though the hybrid model does not show obvious advantage in 1-h ahead prediction, it outperforms persistence model, ARIMA, and univariate neural network models in 3-to-24 h ahead prediction. The hybrid model is able to reduce the prediction error by 20% in comparison with univariate neural networks. [ABSTRACT FROM AUTHOR]

The increasing greenhouse gas (GHG) emission and environmental degradation (ED) is a serious concern for the various economies, and a similar issue is observed in Asia. This paper investigated the role of clean energy from renewable sources, urbanization, and economic growth in determining the level of GHG emissions from 1995 to 2018 for ten Asian States through a cross-sectional autoregressive distributed lagged (CS-ARDL) model. Meanwhile, the current research also examined the cross-sectional dependence, unit root properties, and co-integration between the study variables. The study findings confirmed that clean energy and GDP2 played their constructive role in reducing GHG emissions in the natural environment or targeted economies. In contrast, urbanization and economic growth caused more GHG emissions both in the long and short run. Furthermore, the robust check through augmented mean group (AMG) and common correlated effect means group (CCEMG) also confirmed that clean energy and GDP2 have a good sign for lowering ED compared to GDP and urbanization. The study findings could support policymakers, specifically in the field of energy economics and environmental sustainability. Therefore, it is highly recommended that some strong policy implications are needed to reduce environmental issues through controlling the negative impact of economic growth and urbanization. This study contributes in the literature of GHG emission with respect to economic growth, urbanization and clean energy and guided the regulators while formulating policies related to control the GHG emission. • The increasing GHG emission is a serious concern for the various economies. • We examined the clean energy, urbanization and economic growth impact on GHG emissions. • We examined cross-sectional dependence, unit root and cointegration among variables. • Clean energy plays constructive role in reducing GHG emissions in natural environment. • Urbanization and economic growth caused more GHG emissions in long and short run. [ABSTRACT FROM AUTHOR]

This paper proposes a new stochastic optimization framework for day-ahead (DA) energy management of a renewable-based isolated rural microgrid. Non-dispatchable distributed generations (DGs) including wind and photovoltaic (PV) power plants have been operated by the microgrid in order to meet consumers' demand. The main challenge in this condition is to keep balance between generation and demand at all times. In order to cope with this challenge, a pumped-storage unit and an incentive-based demand response program have been used in this work. Moreover, the accurate model of pumped-storage unit, responsive demand and distribution network and also the inherent uncertainty of wind and PV power generation have been considered in this paper. Finally, the effectiveness of the proposed optimization framework has been evaluated through extensive numerical studies. The results show that the pumped-storage unit optimal scheduling and demand response implementation, significantly improve economic and technical performance indexes. [ABSTRACT FROM AUTHOR]

Wave energy is one of the most concentrated ocean renewable energy resources. Although wave energy has been studied extensively for more than four decades, there is no large commercial installation for wave energy production or a consensus framework on how to exploit this resource. Wave energy is a complex resource that directly depends on two meteorological parameters, which produced significant fluctuations of wave energy in both temporal and spatial criteria. This paper presents a new concept called Energy Event, to analyze meteorological data generated by WaveWatch III over 36 years in the U.S. to characterize and assess wave energy behavior using the peak-over-threshold methodology. This methodology used extreme statistics, segmented the wave energy with different thresholds, and assessed wave energy production on a temporal and spatial framework. Three areas were studied in this paper, including the Gulf of Mexico, the East and West U.S. Coasts. The results indicated that wave energy behaved as a two-state energy system with each state having independent characteristics. The main difference among the three studied areas was the constant baseline of wave power density, with the West Coast having the highest constant baseline and the Gulf of Mexico having the lowest baseline. [ABSTRACT FROM AUTHOR]

This paper presents geographic maps of the profitability of PV and CPV solar projects over 1,960,000 km 2 . Such maps supplement the irradiance maps used in solar project planning and are relevant to government policy makers to assess the impact of incentives on profitability. Also, they are useful to solar project developers to identify geographical areas in which a required rate of financial return can be expected. Eight such maps of central Canada are presented in this paper and show the impact on profitability of different levels of government incentives, the difference in profitability between PV and CPV and the impact on profitability of delaying a project start date from 2016 to 2020. Government incentives of 10%–30% of the capital cost improve the internal rate of return (IRR) by between 0.7 and 4.67. For PV, the orientation of the modules is optimized to achieve maximum IRR resulting in an IRR increase of between 1.3% and 8.2%. Since CPV is a more recent technology than PV, its capital costs are projected to decline faster and we quantify the impact on IRR by showing that CPV has a higher IRR than PV of between 0.55 in 2016 and 3.6 in 2020. [ABSTRACT FROM AUTHOR]

In this paper a new hybrid method combining variational mode decomposition (VMD) and single or Multi-kernel regularized pseudo inverse neural network (MKRPINN) is presented for effective and efficient wind power forecasting. The original non-linear and non-stationary time series data is decomposed using VMD approach to prevent the mutual effects among the different modes. The proposed VMD-KRPINN (VMD based kernel regularized pseudo inverse neural network) and VMD-MKRPINN methods are then used to predict wind power generation of a wind farm in the state of Wyoming, USA for different time intervals of 10 min, 30 min, 1 h and 3 h ahead. Comparison with empirical mode decomposition (EMD) based kernel regularized pseudo inverse neural networks is also presented in the paper to validate the superiority of the VMD based wind power prediction models. Also to improve the performance of the proposed EMD-MKPRINN and VMD-MKRPINN models, their parameters are optimized using vaporization and precipitation based water cycle algorithm (VAPWCA). Further a fast reduced version of the VMD-KRPINN is presented in the paper to reduce the execution time substantially using randomly selected support vectors from the data set while resulting in a reasonably accurate forecast. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to analyze and propose the United Arab Emirates (UAE) plan of Renewable Energy mix in 2030 to achieve the government target of reducing the greenhouse gas emissions of CO 2 while maintaining the electricity price to a minimum. The study includes many proposals of the energy mix for electricity to achieve the target, and a comparison study to IRENA's proposal to select the most suitable energy mix plan in terms of CO 2 reduction while maintaining the electricity price. The analysis has been conducted in two different cases: the first case analyzed where the government of UAE does not implement CO 2 taxation of hydrocarbon fuels used to generate electricity; however, the second case analyzed if the government apply CO 2 taxations what would the results be. Further on, calculations of the surplus energy were implemented for future studies on utilization of the excess energy or reducing the power capacity. This paper provides a background analysis of UAE's electricity consumptions and future government plans. [ABSTRACT FROM AUTHOR]

The paper experimentally investigates the performance and exhaust emissions of a variable compression ratio engine fueled with 100%, 90%, 70% and 50% Jatropha bio-diesel and turpentine oil. In this paper, highly viscous Jatropha biodiesel is blended with relatively low viscosity turpentine oil in various proportions resulting in effective viscosity comparable with that of standard diesel with a view to developing a new alternative fuel which is comparable equally cost effective and eliminates dependency on conventional diesel fuel. Experimental analysis is carried out on ATE Make VCRE_MF single cylinder, naturally aspired, four stroke, and variable compression ratio diesel engine to study combustion performance and emission properties via conventional diesel fuel and different blends of Jatropha biodiesel with turpentine oil. Dual biofuel blend is found to be the greatest substitute to fossil (diesel) fuel in all aspects such as emissions and performance. Experiments have been accompanied at CR (compression ratio) of 15.5:1, 17:1, 18.5:1 and 20:1. Further, dual biofuel blend (JBT 50) resulted at full load and compression ratio 20, there was 2.17% increase in brake thermal efficiency and 13.04%, 17.5%, 4.21% and 30.8% decrease in CO, HC and NOx and smoke opacity respectively while CO 2 was observed to increase by 11.04% respectively. [ABSTRACT FROM AUTHOR]

This paper introduces a simple methodology to aid in the decision of how to distribute financial incentive funding for residential solar electric systems in order to maximize demand. Incentive funding can be used more effectively if adjusted according to the decrease in price and the increase in demand of solar electric systems. The decision of how to reduce these incentives due to changing annual funds, while increasing or even maintaining the growth of solar electric system demand, is of great interest to policymakers and public benefit funded program administrators. In order to aid in this process, a three step methodology is described in this paper. The first step uses the concept of learning-by-doing to determine the relationship between historic pricing and demand. The second step employs discrete choice modeling with spatial analysis to determine the relationship between market, financial, and social factors with historic demand. The final step uses nonlinear programming to forecast incentive structuring for maximizing demand. Finally in order to validate the model the study uses solar market data from Portland, Oregon. The results show a decrease in incentives over the period of study from 2014 until 2020, except for 2017 and 2018 when federal and state tax credits expire respectively. [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]

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]

Thailand had summited its Intended Nationally Determined Contributions (INDCs) in 2015 and ratified the Paris Agreement in September 2016. Its INDCs stated that by 2030 GHG emissions will be reduced by 20–25% when compared to the business- as -usual (BAU) scenario by using mainly domestic renewable energy resources and energy efficiency improvement. Therefore, this paper assesses the potential of greenhouse gas (GHG) emission reduction by the use of renewable energy in Thailand's INDCs and the economic impacts from GHG emission reduction. This paper employed the Asia-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE). Besides the BAU scenario, four mitigation scenarios are assessed at given GHG emission levels and renewable power generation targets. Results show that Thailand's INDC can be achieved under the current renewable energy target in Thailand's Power Development Plan 2015. As a result, macroeconomic loss will be small under the light GHG reduction target; however, it will be large under the stringent GHG emission reduction target. The GDP loss ranges from 0.2% in the case of a 20% reduction target to 3.1% in the case of a 40% reduction target in 2030. Thus, the availability of land for deploying the renewable energy technologies such as solar, wind and biomass needs to be assessed. [ABSTRACT FROM AUTHOR]

Renewable Energy Sources (RES) have prompted an additional burden on power system planners due to their stochastic nature. Hence it increases the need for Ancillary Services (AS) in power system. In deregulated electricity markets, AS has become an important issue because they are necessary for reliable and secure operation of a power system. Operating Reserve (OR), considered in this paper, is a measure of generators ability to increase their output under contingencies. ISO uses this service either for balancing purpose or for replacing the energy that had been scheduled to be provided by the unit that malfunctions. Thus establishing an efficient market for reserve services has become crucial. The premises of this paper is the development of penalty based Short-Term Market (STM) for the procurement of energy and OR. The effects of stochastic behavior of WPP on Social Benefit (SB) and Procurement Cost (PC) are investigated for the development of efficient STM. The proposed approach has been analyzed on IEEE-30 bus test system by implementing a sequential dispatch approach on various market structures. The results obtained under different market scenarios shows that there is a mandated requirement of effective penalty mechanism in order to discourage the imbalance behavior of RPPs. [ABSTRACT FROM AUTHOR]

To find an efficient and economic way to convert wave energy, a one-base multi-buoy offshore floating wave energy converter Sharp Eagle Wanshan is designed, consisting of four absorbing buoys, one semi-submersible barge, and other components. The working principle of the device is described in this paper. An experiment of a 1:13.78 model machine was carried out to test the hydrodynamic performance of the device and make an initial evaluation for the design The influence of wave period, wave height, work load and wave direction was tested. After construction, two-stage open sea tests have been finished in the waters near Wanshan Islands from November 2015 to June 2016. The device showed great power generation capacity with total generated output of 30530.57 kWh, and largest daily generation of 1847.09 kWh. During the open sea tests, the energy conversion efficiency was measured, and results show that capture width ratio of Wanshan remains higher than 20% in the wave period between 4 and 6.5 s and wave height range of 0.6–1.8 m. After operating in a wide range of conditions, including a tropical storm on May 27, amounts of tests data, experiences and lessons have been obtained and will be summed up and presented in the paper. [ABSTRACT FROM AUTHOR]

Composition of renewable power system for stratospheric airships is introduced, curved paving model of solar array is established, the high accuracy calculation method for output power of solar array is proposed, and operation process of renewable power system is simulated. On basis of this, endurance of the stratospheric airship is evaluated, and the influence of some important factors on endurance is analyzed, including wind resistance strategy, photoelectric transformation efficiency of solar array and the specific energy of lithium battery. Simulation results show that, 1) endurance of the stratospheric airship can be improved by adjusting power sequential during day and night with maneuverable wind resistance strategy, for the case in this paper, endurance increases 24.2 h compared with fixed point wind resistance strategy, 2) endurance can be greatly improved in a certain range through increasing photoelectric transformation efficiency of solar array and the specific energy of lithium battery, for cases in this paper, endurance increases 73.5 h and 47.5 h respectively, 3) to realize closed cycle of renewable power system during day and night, attenuation of radiation flux with the change of date must be considered in design of renewable power system, and sufficient redundancy should be designed for solar array. [ABSTRACT FROM AUTHOR]