International Journal of Energy and Statistics

Microscopic structures of porous media are mainly reconstructed using physical experimental methods and numerical reconstruction methods. However, their accuracy is closely related to the resolution of real rock core images and polishing techniques of core chips amongst others, which significantly limit the development of micro-network flow simulation. In this paper, we proposed a new method for digital reconstruction of rock cores on the basis of the random fractal theory. First, we quantitatively describe and identify the multiple fractal dimension and self-similar interval of CT scanning data of 10 sets of rock cores based on the fractal characteristics of microscopic pore structures. Then, we derive a set of fractal expressions of the random probability density function, mean, and variance based on the random distribution theory, and apply the direct sampling method of continuously distributed random variables to obtain the porous media pore data satisfying multi-fractal characteristics. The results show that the rock core data constructed using our method are in a good agreement with the original experimental data, and better than those obtained from single fractal dimension and other distribution characteristics. Furthermore, we analyzed the size distribution of microscopic pores of 3 real rock cores obtained using mercury intrusion porosimetry based on the multi-fractal theory, and determined the multi-fractal interval and dimension of their microscopic pore distribution. We applied our method to design the pore structure and the throat structure of porous media and established a three-dimensional PNM. At last, the network model simulator, which simulates the oil-water flow through the interconnected pore model, proves to be a powerful investigative tool to study the nature of fluid flow at a pore scale level.

Transition towards low carbon development is an inevitable course of bringing forth a sustainable global society, and the core of energy developing strategy, peculiarly urgent for rapidly growing developing countries. This paper aims to identify the character index system and develop an integrated approach of entropy and COPRAS method for low carbon development evaluation of developing countries. In the numerical case study, 20 representative developing countries worldwide were selected as alternatives. We demonstrate the proposed approach can be successfully used in the study of assessment of low carbon development, and the findings of our study can provide policy recommendations and key research topics in the future.

In fuzzy time series forecasting, the weight of fuzzy logical relationships and the midpoint of interval values are extensively used in the product of defuzzified matrix and the transpose of the weight matrix into the final forecast model. An improved forecast can be achieved through this model. Additionally, because of its excellent performance, successful applications can be widely achieved by exploiting real life data such as enrollment, stock indices and exchange rates for example. However, the improvement of mean square error is still not significant when the midpoint and the weight values are strictly increased in both of the matrixes. In order to reduce the mean square error, a reversal model in which the weight elements are reversed in the transpose matrix is proposed in this paper. Moreover, a theorem is also proposed to justify this reversal model and the annual data of electricity load by regions of Taiwan from 1981 to 2000 is examined as further evidence. The result indicate a consistently significant advantage in the proposed reversal model, i.e. the mean square error is smaller than the non-reversal model for each region.

Restructuring energy industries in developing countries has started with dividing the vertically integrated national systems into regional companies. Analysis of multi-dimensional interactions of these companies especially in long-term is a complex task that necessitates development of analytical tools. In the present paper, we have focused on the problem of GEP in the restructured energy supply system. A new energy supply model based on Cournot equilibrium is presented which could help to analyze long-term expansion planning after restructuring. The formulation of the model in a competitive environment is changed from traditional cost-minimization program to several profit-maximization programs with regional and inter-regional constraints, which leads to a LCP formulation and it can be solved by the well-known Lemke's algorithm. The electricity-supply system of Iran is then used as a case study to analyze the perspective of generation technologies in a 20-year time horizon. Analysis of the results in comparison with the traditional models shows that changes in the structure of electricity supply system of Iran provide more opportunities for innovative and renewable technologies in the electricity market.

The NIE time series have great importance for the Brazilian energy long term planning, as the decisions about the optimal dispatch of hydroelectric and thermoelectric plants are highly dependent on them. Therefore, reliable and accurate long term forecasts and simulations for the NIE series are crucial. To develop such forecasts, the proposed approach relies on the decomposition of NIE series from the South subsystem into signal and noise using the MSSA technique. After that, synthetic series are generated from the noise using the Bootstrap procedure, forecasts of 60 months ahead for each series using the traditional Holt Winters are calculated to arrive at the final predictions obtained by taking the average of the individual series forecasts. The results indicate that the proposed methodology significantly improved the results when compared with forecasts achieved by directly applying the Holt Winters model to the original series, and prove to be more effective for lead time of more than 36 months than others techniques applied in series without simulation.