Waste cooking oil as an energy resource: review of Chinese policies

Converting waste cooking oil into biofuel represents a three-win solution, dealing simultaneously with food security, pollution, and energy security. In this paper, we encode the policy documents of waste cooking oil refining biofuel in China based on content analysis, and explore the related policies from the two dimensions as basic policy tools and enterprises supply chain. Research indicates the weak institution coordination of policy issuing entities. Also, the findings show that tools of regulatory control and goal planning are overused. Policies of government procurement, outsourcing and biofuel consumption are relatively scarce. Generally, government focuses more on formulating policies from the strategic, administrative and regulatory aspects, while less on market-oriented initiatives as funding input and financial support.

Potential of making - Over to sustainable buildings in the Kingdom of Bahrain

Two unique large buildings in the Kingdom of Bahrain were selected for make-over to sustainable buildings. These are the Almoayyed Tower (the first sky scraper) and the Bahrain International Circuit, BIC (The best world Formula 1 Circuit). The amount of electricity extracted from using renewable energy resource (solar and wind), integrated to the buildings-has been studied thoroughly. For the first building, the total solar electricity from the PV installed at the roof and the 4 vertical facades was found 3 017 500 kWh annually (3 million kWh), i.e. daily energy of 8219 kWh (enough to Supply electricity for 171 houses, each is rated as 2 kW house-in Europe the standard is 1.2 kW). This means that the annual solar electricity produced will be nearly 3 million kWh. This correspond to annual CO, reduction of 3000 t (assuming each kWh of energy from natural gas lead to emission of 1 kg of CO2). For the second building (BIC) the solar electricity from PV panels installed at the roof top, fixed at tilt angle of 26 degrees facing south, will provide annual solar electricity of is 2.8 x 10(6) kWh. The solar electricity from PV panels installed on the windows (12,000 m(2)) will be 45.3 x 10(6) kWh. This means that the total annual electrical power from PV panels (windows and roofs) will be nearly 12 MW (32 kW per day). The CO2 reduction will be 48,000 t. Under the carbon trading or CDM scheme the revenue (or the reward) would be (sic)480,000 million annually (the reward is (sic)10 per tonnes of CO2). The BIC circuit can have diversified electricity supply, i.e. from solar radiation (PV), from solar heat (CSP) and from wind (wind turbines), assuring its sustainability as well as reducing the CO2 emission.

Energy-efficient resource allocation in OFDM systems with distributed antennas

In this paper, we develop an energy-efficient resource-allocation scheme with proportional fairness for downlink multiuser orthogonal frequency-division multiplexing (OFDM) systems with distributed antennas. Our aim is to maximize energy efficiency (EE) under the constraints of the overall transmit power of each remote access unit (RAU), proportional fairness data rates, and bit error rates (BERs). Because of the nonconvex nature of the optimization problem, obtaining the optimal solution is extremely computationally complex. Therefore, we develop a low-complexity suboptimal algorithm, which separates subcarrier allocation and power allocation. For the low-complexity algorithm, we first allocate subcarriers by assuming equal power distribution. Then, by exploiting the properties of fractional programming, we transform the nonconvex optimization problem in fractional form into an equivalent optimization problem in subtractive form, which includes a tractable solution. Next, an optimal energy-efficient power-allocation algorithm is developed to maximize EE while maintaining proportional fairness. Through computer simulation, we demonstrate the effectiveness of the proposed low-complexity algorithm and illustrate the fundamental trade off between energy and spectral-efficient transmission designs.

Nectar resource diversity organises flower-visitor community structure

Communities of nectar-producing plants show high spatio-temporal variation in the patterns of volume and concentration presentation. We illustrate a novel approach for quantifying nectar reward structures in complex communities, demonstrating that nectar resource diversity (defined as the variety of nectar volume-concentration combinations available) may be a fundamental factor organising nectarivore communities. In a series of diverse bee and entomophilous flower communities in Israel, our measure of nectar resource diversity alone explains the majority of variation in bee species richness, while other nectar variables (volume, concentration, energy value, and water content) have little predictive value per se. The new measure of nectar resource diversity is highly correlated with floral species richness and particularly with the species richness of annuals, yet it is additive in its effect on bee diversity. We conclude that relying solely upon measurements of mean nectar volume and mean nectar concentration overlooks a key characteristic of community-level reward structure, nectar resource diversity, so that previous studies may have failed to identify an important determinant of flower-visitor community structure.

Validation of three new measure-correlate-predict models for the long-term prospection of the wind resource

The estimation of the long-term wind resource at a prospective site based on a relatively short on-site measurement campaign is an indispensable task in the development of a commercial wind farm. The typical industry approach is based on the measure-correlate-predict �MCP� method where a relational model between the site wind velocity data and the data obtained from a suitable reference site is built from concurrent records. In a subsequent step, a long-term prediction for the prospective site is obtained from a combination of the relational model and the historic reference data. In the present paper, a systematic study is presented where three new MCP models, together with two published reference models �a simple linear regression and the variance ratio method�, have been evaluated based on concurrent synthetic wind speed time series for two sites, simulating the prospective and the reference site. The synthetic method has the advantage of generating time series with the desired statistical properties, including Weibull scale and shape factors, required to evaluate the five methods under all plausible conditions. In this work, first a systematic discussion of the statistical fundamentals behind MCP methods is provided and three new models, one based on a nonlinear regression and two �termed kernel methods� derived from the use of conditional probability density functions, are proposed. All models are evaluated by using five metrics under a wide range of values of the correlation coefficient, the Weibull scale, and the Weibull shape factor. Only one of all models, a kernel method based on bivariate Weibull probability functions, is capable of accurately predicting all performance metrics studied.

The importance of accurate wind resource assessment for evaluating the economic viability of small wind turbines

The techno-economic performance of a small wind turbine is very sensitive to the available wind resource. However, due to financial and practical constraints installers rely on low resolution wind speed databases to assess a potential site. This study investigates whether the two site assessment tools currently used in the UK, NOABL or the Energy Saving Trust wind speed estimator, are accurate enough to estimate the techno-economic performance of a small wind turbine. Both the tools tend to overestimate the wind speed, with a mean error of 23% and 18% for the NOABL and Energy Saving Trust tool respectively. A techno-economic assessment of 33 small wind turbines at each site has shown that these errors can have a significant impact on the estimated load factor of an installation. Consequently, site/turbine combinations which are not economically viable can be predicted to be viable. Furthermore, both models tend to underestimate the wind resource at relatively high wind speed sites, this can lead to missed opportunities as economically viable turbine/site combinations are predicted to be non-viable. These results show that a better understanding of the local wind resource is a required to make small wind turbines a viable technology in the UK.