Chapter 1 Introduction
5.1 Scenarios
Chapter 5
Long-Term Scenario Analysis with NIMRT Model
This chapter outlines the results from NIMRT model having intra-regional RE variability information, corresponding to various parametric scenarios. Definitions of scenarios are described first, afterwards, the numerical results are presented. Result related to variation of RE penetration levels, nature of power dispatch, technology capacity, role of inter-regional energy exchange and storage, coal supply, and CO2 emission are discussed. Due to the difference of resource potential, technological suitability, and demand, each region’s (i.e state’s) generation mix is unique. Hence, regional interpretations of results are also drawn.
Due to a large number of cases considered in this study, only those which present interesting observations, are outlined.
scenario variationsi.e., Reference (R), low (L), and high (H), are taken. Combinations of these three scenarios results in 243 model cases and considered for this study. Reference scenario of every parameter indicates the value used to build the base case of the model. ‘H’
and ‘L’ cases are constructed using available literature and assumptions (for coal, base, high, and low scenarios are termed as LL, LH, and LV respectively as discussed in the following paragraphs). Table 5.2 outlines various model cases and assumptions associated with it.
Table 5.1Parametric scenarios considered for long-term scenario analysis using NIMRT model
Parameter Description Scenarios
CO2price Additional price on producing CO2
Reference scenario: No CO2 price. Low and high scenario: Two additional CO2 price projections.
Coal price Coal production rate increase High scenario: Present coal production trend. Two production rate increase cases:
one is taken as reference and other as low price scenario.
Solar Cost Solar investment and operating cost reduction.
High and low scenarios: Flat and steep cost reduction trends respectively com- pared to reference cost reduction trend.
Wind Cost Wind investment and operating cost reduction.
High and low scenarios: Flat and steep cost reduction trends respectively com- pared to reference cost reduction trend.
Storage Cost
Storage investment and operating cost reduction.
Low and high scenarios: Certain percent- age change (lower and higher) compared to the reference costs reduction trend.
The parameters considered for this study have the potential to impact overall future energy system portfolio. One of the major challenges for large-scale RE capacity expansion is their higher cost compared to other options like coal-fired power plant. Costs of these technologies are decreasing steadily due to technological advancements. Energy storage device is a key enabler for integrating variable RE power e.g. solar. Though the present cost of utility-scale storage is not favorable for large-scale deployment, available projections indicate drastic cost reduction in coming years. India is still largely dependent of coal for power generation. Current initiatives to improve coal production rates can ensure better utilization of existing coal-based power generation capacity and encourage new installations.
Finally, CO2prices act as a driver for RE capacity expansion. Though detailed modeling of learning curves related to cost reduction of technologies, or CO2price implementation is not attempted, data from available literature has been compiled. In case of unavailability of India specific data, either international sources are used or assumptions are taken.
5.1 Scenarios 77 Previous studies related to long-term scenario analysis of Indian power sector using similar kind of models focus primarily on CO2tax implementation, specific CO2emission reduction, or RE integration targets, as discussed in Chapter 2. Earlier works have also not analyzed future energy system evolution using a large number of cases, similar to the present study. In the present exercise, the chosen parameters are treated as key drivers of RE expansion, irrespective of any external policy inputs. Therefore, results from scenario analysis could help in policy formulation considering the effects of these drivers. Due to uncertainty related to values in the future, three scenarios are considered for each of these parameters. The number of scenarios chosen is according to the scope and focus of the present research work. Any future exercise can focus on larger number of scenarios and to analyze the impact of a specific parameter on system development.
0 2 4 6
2020 2030 2040 2050
Year
MINR/ Kt
CH CR CL
A) CO2 Price Scenario
2500 5000 7500 10000 12500
2020 2030 2040 2050
Year
Peta Joule
LH LL LV
B) Coal Price Scenario
30000 40000 50000
2020 2030 2040 2050
Year
MINR/ GW
SH SR SL
C) Solar Cost Scenario
45000 50000 55000 60000
2020 2030 2040 2050
Year
MINR/ GW
WH WR WL
D) Wind Cost Scenario
Figure 5.1CO2price, coal price, solar cost, and wind cost scenarios
Coal is a major energy resource in the Indian power generation portfolio. The recent government decision to rapidly increase domestic coal production rate and restrict foreign import will decrease the price of non-coking coal and encourage new installation of coal power plants. Therefore, a variation of coal supply is an interesting parameter to look for in
Indian case, considering the RE penetration targets. The coal price scenarios in this article reflect domestic coal production rates. State-wise historical coal production (available to North-Indian states) rates of 2006-2017 are used to calculate future production rates using simple trend line analysis [185]. Calculation shows that present production rate will increase from 1743 PJ in 2012 to 4230 PJ in 2050. This scenario is termed as high coal price scenario (LH). Two increasing cases are considered for future coal production increase. In low coal and very low price scenarios (LL, LV), coal production rates are assumed to increase by two and three times respectively in 2050, as compared to the LH case (plot B in Figure 5.1). LL scenario is taken as the reference scenario for coal price.
Table 5.2Scenario Matrix Sl.
No.
Case CO2Price Coal Price Solar Cost Wind Cost Storage Cost
1. CR.LH.SR.WR.TR Ref Hi Ref Ref Ref
2. CL.LH.SR.WR.TR Lo Hi Ref Ref Ref
3. CH.LH.SR.WR.TR Hi Hi Ref Ref Ref
4. CR.LL.SR.WR.TR Ref Lo Ref Ref Ref
5. CL.LL.SR.WR.TR Lo Lo Ref Ref Ref
.. ... .. .. ... ... ...
.. ... .. .. ... ... ...
242. CL.LV.SH.WH.TH Lo Vlo Hi Hi Hi
243. CH.LV.SH.WH.TH Hi Vlo Hi Hi Hi
Imposing exogenous CO2price encourages model to choose cleaner generation options.
Here, two cases are explored. In the Ref (CR) case, there is no imposition of CO2price. In CH case, price of CO2is expected to increase from 1.6 MINR/ Kt in 2022 to 7.2 MINR/ Kt in 2050 [232]. In CL case, the values are 1 and 2.3 respectively (plot A in Figure 5.1). Level of CO2price and its imposition in India is quite uncertain. In the present study, consideration of CO2price scenarios are primarily to simulate high RE penetrated system portfolio.
Due to technological progress, capital costs of PV, wind, and storage technologies are gradually reducing. For PV and storage, this reduction will be drastic in the coming years.
Therefore, the impact of different cost reduction trends of these technologies compared to Ref case and their impact on generation portfolio is interesting to explore. As data for the alternate cost scenarios are not readily available in literature, own assumptions are taken.
Motive was to build three scenario which portrays three plausible values for future years.
While constructing the cost scenarios, technological learning is kept in mind so that cost decline in the recent years are much steeper than later. Cost reduction trends of solar and wind are depicted in plots C) and D) of Figure 5.1 respectively. Cost of solar and wind for the years