تجزیه و تحلیل حساسیت مدل ریاضی انرژی های تجدید پذیر بهینه بر روی تغییرات تقاضا
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|25514||2000||13 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Energy Conversion and Management, Volume 41, Issue 2, January 2000, Pages 199–211
Energy demand is increasing rapidly because of developments in the industrial, agricultural, commercial and transportation sectors. The population rise and improved life style are other reasons for the increase in energy demand. Since commercial energy sources are non-renewable and depleting in nature, it is essential to seek renewable energy sources. After a few decades, renewables have to play a major role to meet the growing energy demand. A Delphi study has been conducted to find the level of social acceptance in the utilisation of renewable energy sources for the year 2020–2021. The experts’ opinion revealed that 25% of total energy demand could be met by renewable energy sources. An Optimal Renewable Energy Mathematical (OREM) model is developed to allocate the predicted renewable energy demand for different end uses. The model is analysed to meet 20, 25 and 30% of the total energy demand in the year 2020–2021. In the case of the 25% energy demand, the renewable energy contribution would be 8.127 × 1015 kJ. A sensitivity analysis has been done to validate the OREM model. The analysis reveals that the energy distribution pattern changes, even with an increase of 1% energy demand, for which the coefficient of sensitivity is 1.84%. This study will help in the formation of strategies for effective utilisation of renewable energy sources in India.
Energy is a vital input for the economic and social development of any country. India has made rapid strides in the development of electricity generation, with capacity increasing from nearly 1700 MW in 1950 to nearly 85,000 MW in 1998. Yet, the demand is continuously increasing. The utilisation of commercial energy resources is increasing enormously which will inevitably lead to a tremendous amount of atmospheric pollution and global warming due to the green house effect. It is, therefore, essential to search for non-polluting renewable sources of energy. Solar, wind and biomass are accepted as dependable and widely available sources of renewable energy. Generation and utilisation of energy from these renewables are non-polluting in nature and are acceptable as environmentally clean sources. Renewable energy sources are being used in the industrial, transportation, domestic, agricultural and commercial sectors. Considering the health impairment effects and depleting nature of commercial energy sources, it can be said that renewable energy can be obtained at a reasonable social cost. Though the present cost of renewable sources is on the high side, there is a possibility for further reduction by achieving higher efficiencies of energy conversion devices by using advanced technology, large scale demands, by adopting more efficient manufacturing methods and by discovering suitable sites where renewables could be applied effectively .
نتیجه گیری انگلیسی
The Optimal Renewable Energy Mathematical (OREM) model has been developed for renewable energy allocation for the year 2020–2021. The model was formulated with the objective of minimising the cost/efficiency ratio subject to the constraints of social acceptance, the reliability factor of renewable energy system, potentials of renewable energy and energy demand for different end uses. • The renewable energy distribution pattern for different end uses is obtained for the year 2020–2021. The extents of renewable energy contribution would be 5.931 × 1015, 8.127 × 1015 and 10.169 × 1015 kJ in the cases of 20, 25 and 30% of total demand, respectively. • The sensitivity analysis proved that the OREM model is highly sensitive, even with 1% variation in the energy demand. If 1% is increased in the renewable energy demand, the coefficient of sensitivity is found to be 1.84%. • The OREM model would be very helpful to policy makers for future energy planning in India.