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Abstract: We present results of a major survey of Chinese opinion leaders conducted from March to April 2009, supported by EU–UK–China near zero emissions coal (NZEC) initiative. Respondents were drawn from 27 provinces and regions using an online survey with follow-up face-to-face interviews. A total of 131 experts and decision-makers from 68 key institutions were consulted through online survey. This survey is the first to focus on demonstration projects in particular and is the most geographically diverse. We aim to understand perceptions of applying CCS technologies in the first large-scale CCS demonstration project in China. Though enhanced oil recovery (EOR) and enhanced coal bed methane recovery (ECBM) may not be long-term solutions for CO2 storage, they were viewed as the most attractive storage technologies for the first CCS demonstration project. With regard to CO2 capture technology, on the whole, post-combustion (which would be most applicable to the vast majority of existing power plants which are pulverised-coal) received slightly higher support than pre-combustion. More surprising, respondents from both the power and oil industries favoured pre-combustion. There was no consensus regarding the appropriate scale for the first demonstration. A large number of respondents were concerned about the energy penalty associated with CCS and its impact on the long-term sustainability of coal supply in China, although such concerns were much reduced compared with surveys in 2006 and 2008. [Copyright &y& Elsevier]

Wind speed forecasting technology is important in the field of wind power. However, the wind speed signals are always nonlinear and non-stationary so that it is difficult to predict them accurately. Aims at this challenge, a new hybrid approach has been proposed for the wind speed high-accuracy predictions based on the Secondary Decomposition Algorithm (SDA) and the Elman neural networks. The proposed SDA combines the Wavelet Packet Decomposition (WPD) and the Fast Ensemble Empirical Mode Decomposition (FEEMD), which includes twice decomposing processes as: (a) the WPD decomposes the original wind speed into the appropriate components and the detailed components; and (b) the FEEMD further decomposes the WPD generating detailed components into a number of wind speed Intrinsic Mode Functions (IMFs). The experimental results in five real forecasting cases show that: (a) the proposed hybrid WPD-FEEMD-Elman model has satisfactory performance in the multi-step wind speed predictions; and (b) the hybrid WPD-FEEMD-Elman model has improved the forecasting performance of the hybrid WPD-Elman model and the standard Elman neural networks considerably. [ABSTRACT FROM AUTHOR]

Highlights • A batch-test method to all potential calendar effects in different dimensions of time frequencies. • A big data test in an electricity market with 12,000 transmission lines and hourly updated prices. • Calendar effects exist in hourly, daily, monthly and seasonal dimensions in electricity markets. • Electricity markets have commonalities with financial markets on price movement. • Calendar effects have strong explanatory power to the electricity market. Abstract How to balance supply and demand has become a long-term question in the electricity market, and anomalies related to calendar issues are critical factors to affect the resource allocation. This paper introduces a test method to assess the significance of all possible calendar effects in different time frequencies. We implement our test method to the largest electricity trading platform in the United States. Using the high-frequency intraday trading data, we assess the calendar effects in different time frequencies (Day-of-the-week, Hour-of-the-day, Month-of-the-year, Day-of-the-month and season). Our results confirm that calendar effects exist in every dimension of time frequency, and specify those calendar effects with statistical significance. Moreover, this study discovers commonalities between electricity markets and financial markets, which makes it feasible to apply the management of financial markets to electricity markets. Besides, the detected calendar effects depict periodic patterns of market inequilibrium and facilitate the implementation of corresponding technical solutions in electricity markets. [ABSTRACT FROM AUTHOR]