تجزیه و تحلیل حساسیت پارامتری برای پارامتر های اقتصادی در بخش برق هند

Sensitivity analysis is a technique that evaluates the model response to changes in input assumptions. Due to uncertain prices of primary fuels in the world market, Government regulations for sustainability and various other technical parameters there is a need to analyze the techno-economic parameters which play an important role in policy formulations. This paper examines the variations in technical as well as economic parameters that can mostly affect the energy policy of India. MARKAL energy simulation model has been used to analyze the uncertainty in all techno-economic parameters. Various ranges of input parameters are adopted from previous studies. The results show that at lower discount rate coal is the least preferred technology and correspondingly carbon emission reduction. With increased gas and nuclear fuel prices they disappear from the allocations of energy mix.

The MARKAL Business As Usual (BAU) case presented in [1] provides a projection of the evolution of the Indian energy system from the year 2005 to 2045. The BAU case was generated using best estimates for the values of model inputs, such as the characteristics of current and future technologies, energy service demands, and regulations on criteria pollutant emissions. Since the true values for many of these inputs are unknown, the BAU case represents only one of many possible outcomes. Further, it does not itself convey information regarding the sensitivity of the energy system to input variations and assumptions. Kannan [2] has performed an analysis for uncertainties in key low carbon power generation technologies in UK. Chen et al. [3] represents a two-stage inexact-stochastic programming model for planning carbon dioxide emission trading under uncertainty. Various studies have been conducted in India using MARKAL energy model. In an integrated energy policy report by Planning Commission of India [4] fossil fuel and renewable energy technologies have been considered for future supply options. In a national energy map for India, i.e., Technology Vision 2030 [5], supply scenarios have been developed for new and renewable energy sources. In a dissertation, Mathur [6] has developed a modified dynamic energy and green house gas reduction planning approach for Indian power sector. Shukla et al. [7] prepared a report entitled Development and Climate: an Assessment for India by using MARKAL modeling. Sensitivity analysis has not been adequately discussed in any of these studies so far. This paper describes the application of formal sensitivity analysis techniques to evaluate the model’s response to changes in input assumptions. The results aid in characterizing and communicating the drivers that leads to such outcomes as: the penetration of particular technologies, the increase in fuel prices, efficiency improvement in new power generation technology, or an increase in availability factor. Sensitivity analysis also allows one to view the BAU case in the context of the range of possible future energy scenarios that may occur.

The parametric analysis of the BAU case provides considerable insight into the inner-workings of the MARKAL model and the response of the model to alternative input assumptions. In most cases, the results of these analyses confirmed expected behavior in the model. Additionally, the results provided insight into the complicated response of the system to criteria pollutant emission limits indirectly. In this section, we summarize many of the key observations from the sensitivity analysis. The lower discount rates as compared to BAU case prefers large hydro as the main component in energy mix while coal experience stagnation in the planning period. In higher discount rates the coal is the major source of energy production technology. Increasing efficiencies of PFBC prefers the other renewable energy technologies while in case of increasing efficiencies of IGCC prefers coal as energy technology. Availability factor increase of new technologies does not change the proportion in energy mix of various technologies. Coal remains almost constant in each case. Due to market fluctuations, if the gas prices increases the gas proportion becomes zero in the energy mix and the large hydro also decreases. The electricity generation hurdle rate has an additional impact on future-year energy sector technologies. Increasing this rate effectively makes it more difficult for new, more efficient technologies to penetrate the electricity generation market. The uncertainty in investment cost of nuclear affect the nuclear penetration in the power sector. Very high cost gives no allocation in energy mix. The large hydro becoming most competitive and covers about 50% market of total installed capacity. Sensitivity analysis such as is presented here has been a useful component of the MARKAL model database development and quality assurance. By identifying key drivers and interactions, sensitivity analysis facilitates an understanding of how the model responds to alternative input assumptions which, in turn, aids model refinements.