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Current situation and future perspectives of European natural gas sector
Vincenzo BIANCO, Federico SCARPA, Luca A. TAGLIAFICO
Frontiers in Energy. 2015, 9 (1): 1-6.
https://doi.org/10.1007/s11708-014-0340-8
Gas market in Europe is experiencing a radical change for different reasons, partially determined and accelerated by economic downturn of the last period. In the past few years, many European countries adopted energy policies largely based on the utilization of natural gas. In fact, a sharp increase of the demand was observed and, at the same time, a lot of infrastructures were developed to assure the necessary supply. In the last few years, due to the economic downturn, natural gas demand decreased, causing a consistent oversupply on the market, which altered the consolidated dynamics of the sector. Understanding the changes currently under development in the European gas market is of paramount importance in order to design future strategies for the sector; in particular, it is necessary to understand if the present situation will cause a reshaping of the sector.
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A modified neural learning algorithm for online rotor resistance estimation in vector controlled induction motor drives
A. CHITRA,S. HIMAVATHI
Frontiers in Energy. 2015, 9 (1): 22-30.
https://doi.org/10.1007/s11708-014-0339-1
Online estimation of rotor resistance is essential for high performance vector controlled drives. In this paper, a novel modified neural algorithm has been identified for the online estimation of rotor resistance. Neural based estimators are now receiving active consideration as they have a number of advantages over conventional techniques. The training algorithm of the neural network determines its learning speed, stability, weight convergence, accuracy of estimation, speed of tracking and ease of implementation. In this paper, the neural estimator has been studied with conventional and proposed learning algorithms. The sensitivity of the rotor resistance change has been tested for a wide range of variation from -50% to+50% on the stability of the drive system with and without estimator. It is quiet appealing to settle with optimal estimation time and error for the viable realization. The study is conducted extensively for estimation and tracking. The proposed learning algorithm is found to exhibit good estimation and tracking capabilities. Besides, it reduces computational complexity and, hence, more feasible for practical digital implementation.
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Intelligent optimization of renewable resource mixes incorporating the effect of fuel risk, fuel cost and CO2 emission
Deepak KUMAR, D. K. MOHANTA, M. Jaya Bharata REDDY
Frontiers in Energy. 2015, 9 (1): 91-105.
https://doi.org/10.1007/s11708-015-0345-y
Power system planning is a capital intensive investment-decision problem. The majority of the conventional planning conducted since the last half a century has been based on the least cost approach, keeping in view the optimization of cost and reliability of power supply. Recently, renewable energy sources have found a niche in power system planning owing to concerns arising from fast depletion of fossil fuels, fuel price volatility as well as global climatic changes. Thus, power system planning is under-going a paradigm shift to incorporate such recent technologies. This paper assesses the impact of renewable sources using the portfolio theory to incorporate the effects of fuel price volatility as well as CO2 emissions. An optimization framework using a robust multi-objective evolutionary algorithm, namely NSGA-II, is developed to obtain Pareto optimal solutions. The performance of the proposed approach is assessed and illustrated using the Indian power system considering real-time design practices. The case study for Indian power system validates the efficacy of the proposed methodology as developing countries are also increasing the investment in green energy to increase awareness about clean energy technologies.
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