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Numerical weather prediction (NWP) models have been widely used for wind resource assessment. Model runs with higher spatial resolution are generally more accurate, yet extremely computational expensive. An alternative approach is to use data generated by a low resolution NWP model, in conjunction with statistical methods. In order to analyze the accuracy and computational efficiency of different types of NWP-based wind resource assessment methods, this paper performs a comparison of three deterministic and probabilistic NWP-based wind resource assessment methodologies: (i) a coarse resolution (0.5° × 0.67°) global reanalysis data set, the Modern-Era Retrospective Analysis for Research and Applications (MERRA); (ii) an analog ensemble methodology based on the MERRA, which provides both deterministic and probabilistic predictions; and (iii) a fine resolution (2-km) NWP data set, the Wind Integration National Dataset (WIND) Toolkit, based on the Weather Research and Forecasting model. Results show that: (i) as expected, the analog ensemble and WIND Toolkit perform significantly better than MERRA confirming their ability to downscale coarse estimates; (ii) the analog ensemble provides the best estimate of the multi-year wind distribution at seven of the nine sites, while the WIND Toolkit is the best at one site; (iii) the WIND Toolkit is more accurate in estimating the distribution of hourly wind speed differences, which characterizes the wind variability, at five of the available sites, with the analog ensemble being best at the remaining four locations; and (iv) the analog ensemble computational cost is negligible, whereas the WIND Toolkit requires large computational resources. Future efforts could focus on the combination of the analog ensemble with intermediate resolution (e.g., 10–15 km) NWP estimates, to considerably reduce the computational burden, while providing accurate deterministic estimates and reliable probabilistic assessments. [ABSTRACT FROM AUTHOR]

Climate change present a range of equity issues for human being, but operating the equity principle within emission allocation scheme to various parties has raised multiple challenges. Although more than 40 schemes have been suggested in the literatures, there is little consistency in the way that baselines, scope, coverage and other key parameters have been compared or discussed. In this paper, we reviewed 41 schemes used in the literature so far and compared the cumulative allowances of main countries under various schemes in a consistent framework. Two cases are considered, i.e. only energy and industry related CO 2 and all Kyoto GHGs. Besides the allowances, carbon Gini coefficient in the global perspective and the reduction tradeoffs between country groups are further quantitatively discussed. It is demonstrated that the full ranges of countries’ allowances are quite large resulting from different equity considerations and implement methods behind different effort-sharing schemes. However, absolute mitigations are always required in developed countries in order to realize the 2 °C target, regardless of schemes. [ABSTRACT FROM AUTHOR]

Many meteorological parameters present a natural diurnal cycle because they are directly or indirectly dependent on sunshine exposure. The solar radiation diurnal pattern is important to energy production, agriculture, prognostic models, health and general climatology. This article aims at introducing a new type of radial frequency diagram – hereafter called sunflower – for the analysis of solar radiation data in connection with energy production and also for climatological studies. The diagram is based on two-dimensional data sorting. Firstly data are sorted into classes representing hours in a day. Then the data in each hourly class is sorted into classes of the observed variable values. The relative frequencies of the value classes are shown as sections on each hour’s segment in a radial diagram. The radial diagram forms a unique pattern for each analysed dataset. Therefore it enables the quick detection of features and the comparison of several such patterns belonging to the different datasets being analysed. The sunflower diagram enables a quick and comprehensive understanding of the information about diurnal cycle of the solar radiation data. It enables in a graphical form, quick screening and long-term statistics of huge data quantities when searching for their diurnal features and finding the differences between the data for several locations. The results of the data analysis using the sunflower diagram show how daily or monthly-based patterns are identified within small or huge data sets. The paper demonstrates the sunflower diagram usefulness over a wide range of applications from green energy production to weather analysis. [ABSTRACT FROM AUTHOR]

Many energy systems models have sought to develop pathways for deep decarbonization of the global energy system. Most often, these pathways minimize system costs or greenhouse gas emissions; with few exceptions, they ignore the constraints imposed by political, social, and economic factors that slow transition processes, making them prone to producing implausible decarbonization pathways. This paper integrates a key socio-technical factor—social acceptance of low-carbon nuclear power—into an energy systems model to illustrate how it alters the optimal energy generation mix. The United States was chosen as the example, but the approach itself is designed to be general and applicable to any region of interest. An empirically grounded risk tolerance model is developed to characterize acceptance of nuclear power and estimate an upper-bound deployment limit for the technology. Illustrative scenarios are presented to improve our understanding of how the socio-technical constraints that exist in the real world can alter deep decarbonization pathways. The cost-optimal generation portfolio to achieve net zero CO 2 emissions by 2050 primarily relies on nuclear power. If risk tolerance concerns constrain nuclear deployment to socially acceptable levels, deep decarbonization scenarios are up to 11% more expensive than the reference scenario and require low-carbon options to be available and replace the reduced nuclear share. Results from this novel framework improve our representation of the effect of social acceptance on the adoption and diffusion of energy technologies. They also contribute to a growing literature that seeks to firmly embed the social sciences in climate and energy policy. • Plausible decarbonization pathways must also account for social constraints. • We combine a social risk tolerance model with an energy system optimization model. • We assess the role of social acceptance in constraining nuclear power's deployment. • Social acceptance limits nuclear's share by as much as 71% in CO 2 reduction pathways. • Decision-making can be strengthened with integration of socio-technical analyses. [ABSTRACT FROM AUTHOR]

• Rooftop PV key driver in the PV market, thus modelled in IMAGE IAM. • Global estimated potential 8.3 PWh y−1: 1.5 times residential electricity demand. • Scenarios show key role for rooftop PV but regional characteristics crucial. • Income levels and grid electricity prices dominate regional deployment. • Low-irradiation western Europe better than high-irradiation Middle East. The use of solar photovoltaic has strongly increased in the last decade. A significant part of this growth comes from home owners installing rooftop photovoltaic. Despite this key role, most long-term model-based scenarios do not consider decentralized supply of rooftop photovoltaic but concentrate on utility-scale photovoltaic instead. In this paper, we implement rooftop photovoltaic in the Integrated Assessment Model IMAGE to study its possible role in energy and climate scenarios. We first calculated the global technical and economic potential to derive regional cost-supply curves for rooftop photovoltaic. Next, we have added a new decision in the IMAGE model allowing household investment in rooftop photovoltaic based on the comparison of the whole-sale electricity price with the price of rooftop photovoltaic. The global suitable roof surface area was assessed at 36 billion m2, or 4.7 m2 capita−1, leading to a potential for rooftop photovoltaic of 8.3 PWh y−1, roughly 1.5 times the 2015 global residential electricity demand. In the baseline scenario, adding rooftop photovoltaic could lead to a 80–280% increased share of photovoltaic electricity production in 2050 (i.e. from 6% to 17% in total power production). This increase depends on regional characteristics that are essential to the deployment of rooftop photovoltaic: differences in social-economic and policy factors (capital costs, household income, and electricity prices) are considerably more important than physical factors, such as solar irradiance. [ABSTRACT FROM AUTHOR]

• A daylighting model and an energy simulation model of the vacuum PV glazing were developed. • The applicability and limitation of the vacuum PV glazing in different climates were investigated. • The vacuum PV glazing improves the energy performance of typical PV glazing. • The vacuum PV glazing can balance the daylighting availability and visual comfort. • The reversed and the reversible vacuum PV glazing can enhance the potential of the application. Amorphous silicon-based semi-transparent photovoltaic windows can produce renewable electricity and offer a certain amount of natural daylight for occupants. However, it has a deficiency as the absorbed solar energy would be partially transferred into additional cooling demand in summer. In this respect, a novel semi-transparent photovoltaic vacuum glazing is proposed to improve energy performance. The selection of appropriate glazing of an energy-efficient building should take into consideration the specific climate conditions. The daylighting behaviour of the glazing will also affect the daylighting performance as well as the lighting consumption. In this paper, the thermal performance, daylighting performance and overall energy performance of the proposed vacuum PV glazing in different climate regions have been investigated. A daylighting model was conducted by DAYSIM to evaluate the annual daylighting performance. It was found that the vacuum PV glazing can balance daylighting availability and visual comfort by providing sufficient daylight in the anterior half of the room and reducing daylight glare to the minimum level. The energy simulation by EnergyPlus demonstrated that the vacuum PV glazing has the energy-saving potential up to 43.4%, 66.0%, 48.8%, and 35.0% in Harbin, Beijing, Wuhan and Hong Kong, respectively. However, the applications of the vacuum glazing lead to additional cooling consumption in the moderate climate zone, such as Kunming. The results advanced the understanding on the applicability and limitation of the vacuum PV glazing in different climate backgrounds. Furthermore, the reversed and the reversible vacuum PV glazing were proposed to enhance the adaptability. The results suggest that the reversible vacuum PV glazing can act energy response in a more efficient way and fully utilize the energy-saving potential of the integration of the PV glazing and the vacuum glazing. [ABSTRACT FROM AUTHOR]

• Ideal U -values for 2050 climate projection in the Mediterranean region are determined. • Current well insulated buildings are not prone to overheating in the future. • In the future, ideal U -values are similar or even lower than current ones. • Casablanca is the only location presenting an overheating risk. • The increase of cooling demand will overcome the heating demand decrease. One of the effects of climate change is global warming, which will increase cooling demand in buildings. However, scientific literature does not show consensus on the risk of highly insulated buildings being prone to overheating. This paper presents a statistical comparison of two synthetic datasets for current and future climates in sixteen Mediterranean locations. The weather data for the 2050 climate projection was generated by 'morphing' current weather data. The buildings were created using a generative design method to produce random geometries and random U -values for the envelope elements. Energy performance was evaluated using dynamic simulation. In addition to the expected general increase in cooling demand (up to 137 %) and a smaller reduction in heating demand (up to 63 %), the results demonstrate that the ideal U -values used in the current climate in almost all of the locations will not cause overheating. In several cases, the decrease of the U -values is even recommended for Podgorica, Valencia, Tunis, Malaga, Larnaca, and Alexandria, as the reduction of heating demand compensates the increase of cooling demand. Casablanca was the only location showing an increase in the ideal U -values, thus presenting risk of overheating if using current ideal U -values. [ABSTRACT FROM AUTHOR]