1. Role of policies in deployment of wind energy – evidence across States of India Riddhi Panse (and Vinish Kathuria) (Indian Institute of Technology Bombay) Prepared for COSMAR 2014 The 14 th Consortium of Students in Management Research November 21-22, 2014

2. Outline Introduction – Motivation – Research Question Policies to promote wind energy in India Methodology Data and Variables – Principal Component Analysis Econometric Modeling Results Conclusions Limitations and Future Work 21/11/2014Indian Institute of Technology Bombay2

3. Introduction – Motivation Renewable Energy (RNE) - reducing local air pollution, increasing energy access and improving energy security. Indian RNE Program - resource assessment, demonstration, awareness creation, and providing useful operating experience to industry and Utilities. Wind Energy in India – short gestation periods, increasing reliability and performance of turbines. – CAGR 34 per cent: From 52 MW is 1991 to 19000 MW in 2012. – MNRE guidelines for clearance of wind power projects: mandatory for States to make guidelines, facilitate infrastructure, provide financial incentives to support wind projects since 1996. – Notable Acts and Policies at national level: Accelerated Depreciation, Electricity Act 2003, National Electricity Policy 2005, Tariff Policy 2006, Generation Based Incentive, REC Mechanism. – Policies at State level: Capital subsidy, Feed in tariff (FIT), Renewable Purchase Obligation (RPO), banking, wheeling charges, Green energy funds. 21/11/2014Indian Institute of Technology Bombay3

4. Motivation (Contd..) Cumulative wind installed capacity as of 2012: 21/11/2014Indian Institute of Technology Bombay4 Sl. No. StatePotential @ 50m Potential @ 80m Installed Capacity in MW (per cent of potential @ 50m) 1Andhra Pradesh (AP)539414497 447.65 (8.3) 2Gujarat (GJ)1060935071 3,174.66 (29.92) 3Karnataka (KN)859113593 2,135.30 (24.86) 5Madhya Pradesh (MP)9202931 385.99 (41.96) 6Maharashtra (MH)54395961 3,021.85 (55.56) 7Rajasthan (RJ)50055050 2,684.25 (53.63) 8Tamil Nadu (TN)537414152 7,162.27 (>100) 9Others68669280 3.2 Total4913010277819,050.37 - TN exploited > 100% - GJ, KN, and MP exploited < 50%. MH and RJ just above 50%. - AP < 10%.

5. Motivation (Contd..) ItemsAP (1997)TN(1995)KN(1993)MH(1998)GJ(1993)*MP(1998) Wheeling charges** (% of energy) 225222 Banking facility (months) 12 6--- FIT** (Rs./kWh) 2.25 (5% escalation, 97-98) 2.25 (5% escalation, 95-96) 2.25 (5% escalation, 94-95) 1.75 (no escalation)2.25 (no escalation) Captive use/ Third Party Sale AllowedNot AllowedAllowed Not AllowedAllowed Capital Subsidy 20% (max 25 lakhs) - Same as for other industries 30% (max 20 lakhs) Same as for other industries Other IncentivesIndustry statusNo generation tax No generation tax for 5 years Sales tax exemption (up to 100%) Sales tax exemption (up to 50%) Sales tax exemption (up to 100%) Notes : *- Policy expired in 1998, **- FIT and wheeling has been revised several times by respective SERCs. 21/11/2014Indian Institute of Technology Bombay5 Summary of selected policies for wind power in 1990s and 2012 ItemsAPTNKNMHGJMPRJ Wheeling Charges (%) At par with conventional 5% of energy 5% of energy + Rs.1.15/kWh as cross subsidy for 3rd party sale. 2% of Energy as wheeling + 5% as T&D loss. 7% of energy for investor having one Turbine & 10% for others 2% of energy + transmission charges as per ERC 1% of energy @ 33kV and 4% of energy @ 132/220 kV system Banking facility 5% (12months, FY) Allowed @ 2% energy input 12 monthsNot allowed 6 months RPO 5% since 2005 (with no escalation), reduced to 4.75% in 2012 10% since 2006 (with no escalation), reduced to 8.95% in 2012 2% since 2007 (with no escalation), increased to 12% in 2012 3% from 2006 (with annual 1% escalation till 2009), 5.75% from 2010 (with annual 1% escalation till 2012) 1% from 2006, 10% in 2009, 4.5% in 2010 (with annual 0.5% escalation till 2012) 10% from 2008 (with annual 1% escalation till 2009), reduced to 0.8% in 2010, 2.5% in 2011, 4% in 2012. 2% from 2006, 4% from 2007 with annual 1% annual escalation till 2009, 6.75% from 2010, reduced to 5.5 in 2012. FIT(Rs./kWh) Min.MaxAvg.Min.MaxAvg.Min.MaxAvg.Min.Max.Avg.Min.Max.Avg.Min.Max.Avg.Min.Max.Avg. 2.254.72.812.253.572.652.255.012.792.254.652.932.254.232.682.254.352.32.754.783.47

6. Motivation (Contd..) Growth of wind power across various States 21/11/2014Indian Institute of Technology Bombay6 - Essential to know how policies are performing vis-à-vis expectations. - Evaluation can help identify potential adaptations and allocate scarce financial resources as efficiently as possible.

7. Research Question Do State level policies impact on their relative attractiveness for deployment of wind energy across States of India? 21/11/2014Indian Institute of Technology Bombay7

8. Literature Review for RNE/wind power For India – Schmid (2011) – impact of two national level policies, Electricity Act 2003 and Tariff Policy on grid connected RNE sources in India. – Rao and Kishore (2009) – Bass model + composite index for AP, GJ, TN, and MH. Index strong correlates with States’ ranking as per diffusion parameter. – Benecke (2008) – Case study on TN and Ke for wind energy. Issues in design and execution of policies. – Jagadessh (2000) – Determinants of high growth and subsequent stall in TN and AP for wind energy. For US/Europe – Bird et al. (2005) – Policy and market factors for wind energy in US – Menz and Vachon (2006) – OLS for 39 States in US from 1998 to 2003. RPS and mandatory green power option significant. – Marque and Fuinhas (2012) – Public policy in selected EU countries to contribute to wider use of RNE. 21/11/2014Indian Institute of Technology Bombay8

9. Methodology Use heterogeneity in various policy parameters to compute an index of attractiveness Using index and control variables, model is regressed. – IC s,t = Installed capacity of wind power in a State – PC i = i th Principal component of State’s wind power policies – X s,t = Vector of State’s other characteristics affecting IC s,t – β i is the estimated parameter i th principal component of State’s policies – γ’s are the coefficients of control variables. Estimation models – Simple pooled Ordinary Least Squares (OLS) – Fixed Effect (FE) – Random Effect (FE) 21/11/2014Indian Institute of Technology Bombay9

10. Methodology – computing an index Each policy variable is individually normalized and then aggregated across time t for each State Final scores across time t is further normalized to obtain final index values Bi,s = Value of parameter i for State s, and Ai,s= Score received by each parameter i for State s Bi,s considered for FIT, RPO, Banking, and Wheeling Charges. 21/11/2014Indian Institute of Technology Bombay10

11. Methodology – Principal Component Analysis Correlation of indices (* - 90% confidence level) Different policies to exert influence at different stages – assigning weights Multivariate statistical weighing approach – principal component analysis - extract a small number of sub-indices – Linear combination of original indices – maximum variance – Components orthogonal to each other – Use components with eigen value greater than 1. 21/11/2014Indian Institute of Technology Bombay11 FITRPOWheelingBanking FIT1.0000 RPO -0.2105*1.0000 (0.0125) Wheeling 0.5175*0.00401.0000 (0.0000)(0.9630) Banking 0.5006*0.2014*0.5387*1.0000 (0.0000)(0.0170)(0.0000)

12. Data and Variables 21/11/2014Indian Institute of Technology Bombay12 Dependent variable – Installed capacity (MW) from 1993 to 2012 Policy Variables – FIT, RPO, Banking, Wheeling charges Computation of principal component using above policies First two components were selected (with eigen value >1 & ≈ 80% variance) – First component is loaded by FIT, Wheeling charges, and Banking – Second component is loaded by RPO VariablesDescriptionData SourceRemarks FITPer unit tariff for energy fed to the Grid MNRE, 1993; Tariff Orders by State Regulatory Commissions Increasing trend of adopting levelized per unit cost is observed RPOMandating distribution utilities to purchase certain quantum of power generated using RE Tariff Orders by State Regulatory Commissions Obligation has been increased on annual basis regularly Wheeling Charges Charges imposed on generator for transfer of energy across grid Same as for FITWheeling charges have declined with discriminatory charges imposed on high, medium, and low transmission Lines Banking Charges Allows future withdrawal of energy for earlier fed energy Same as for FITAllowed in all States. Few impose restriction on the months. ComponentsEigen valueProportionSE_PropCumulativeSE_cumBias comp12.040.510.0360.510.0360.026 comp21.130.280.0300.790.020-0.009 comp30.460.120.0150.910.0120.024 comp40.360.090.0121.000.000-0.019

13. Data and Variables (Contd..) Policy indices versus installed capacity – High index in MH and GJ inverse relation with capacity installed. – AP – neutral relationship between index and capacity. – Other factors influencing deployment? Control variables – per capita net State domestic product, power deficit, geographic pot ential. Expected relation with dependent variable 21/11/2014Indian Institute of Technology Bombay13 VariablesDescriptionData SourceExpected Sign Aggregate IndicatorsTwo components selected by applying PCA to policy variables Policy variables as shown in Table 6+ Control Variables PCNDPReserve bank of India+ Power DeficitAnnual average Peak power deficit faced by States CEA Annual Reports; Socioeconomic Review reports for each State + Geographic PotentialRatio of geographic potential to installed capacity for States MNRE, 1993; MNRE, 2005-

14. Data and Variables (contd..) Control variables – per capita net State domestic product, power deficit, geographic potential. Expected relation with dependent variable 21/11/2014Indian Institute of Technology Bombay14 VariablesDescriptionData SourceExpected Sign Aggregate Indicators Two components selected by applying PCA to policy variables Policy variables as shown in Table 6 + Control Variables PCNDPReserve bank of India+ Power DeficitAnnual average Peak power deficit faced by States CEA Annual Reports; Socioeconomic Review reports for each State + Geographic Potential Ratio of geographic potential to installed capacity for States MNRE, 1993; MNRE, 2005 -

15. Econometric Modeling Correlation between control variables State having high PCNDP = high deficit = high ratio of capacity to potential. Can’t use all the variables together. Estimation – Pooled OLS- if time invariant omitted variables ->biased results – Need to use panel data techniques 21/11/2014Indian Institute of Technology Bombay15 PC1 t-1 PC2 t-1 ln PCNDPdeficitRpot PC1 t-1 1.0000 PC2 t-1 -0.00001.0000 (1.0000) ln PCNDP 0.3109*0.5299*1.0000 (0.0029)(0.0000) deficit -0.0212-0.1482*-0.1654*1.0000 (0.6320)(0.0110)(0.0508) Rpot 0.09130.5071*0.4092*-0.13151.0000 (0.2832)(0.0000) (0.1214)

16. Results Does policy influence wind deployment? 21/11/2014Indian Institute of Technology Bombay16 Variable Pooled OLS (1) FE (2) RE (3) FGLS (4) PC1 t-1 0.273*0.3920.3740.149** (1.81)(1.21)(1.17)(2.06) PC2 t-1 -0.1030.000-0.0180.0276 (0.84)(0.00)(0.17)(0.29) ln PCNDP 2.512***2.267**2.300***1.183*** (8.74)(3.42)(3.60)(5.85) Rpot 3.218***3.0073.087**2.81*** (5.38)(1.23)(1.98)(4.56) Constant -20.499***-18.045**-18.387***-7.14*** (6.91)(2.80) (-3.59) R2R2 0.600.55 N133 F test/ wald chi square12.78 (0.004) 9.49 (0.00)90.87(0.00) Hausman test0.49 (0.9743) Note: * p<0.1; ** p<0.05; *** p<0.01 F value = 12.78 > tabulated value. Reject null hypothesis that model is pooled OLS. Fixed Effect (FE) and Random Effect (RE) models – Hausman test for selection. Hausman – 0.49 < critical value of chi-squared (1 df, 5 per cent = 3.84), null of RE being more efficient. Handling autocorrelation and heteroskedasticity – Feasible Generalized Least Squares (FGLS) procedure

17. Results – Robustness test Combinations of different control variables 21/11/2014Indian Institute of Technology Bombay17 VariableModel 1Model 2Model 3Model 4Model 5 PC1 t-1 0.1730.1740.1680.1460.121 (2.02)**(2.22)**(2.15)**(1.87)*(1.70)* PC2 t-1 0.1950.0880.1010.1320.122 (1.99)**(0.86)(0.98)(1.45)(1.43) lnPCNDP 1.4901.453 (7.09)***(6.87)*** deficit -0.014 -0.006 (1.15)(1.19)(0.53) Rpot 2.68610.291 (4.65)***(6.94)*** rpot 2 -6.445 (5.40)*** constant5.181-9.656-9.0824.7543.945 (19.04)***(4.62)***(4.26)***(14.03)***(11.63)*** N133 Note: * p<0.1; ** p<0.05; *** p<0.01 Model 1- only policy component used Model 2 – ln PCNDP is introduced Model 3 – ln PCNDP and deficit used Model 4 – deficit and Rpot used Model 5 – deficit, Rpot, and Rpot^2 used. Sign and significance of policy variable is always at or above 90 per cent confidence level.

18. Contributions and Conclusions Computation of aggregate indicator for 7 States of India – four policy variables (FIT, RPO, banking, wheeling charges) Panel data techniques – impact of policy differences on deployment using control variables (PCNDP, deficit, potential) Policy indicator significant – regardless of control variables High per capita income -> encouraging installed capacity Power deficit – no impact 21/11/2014Indian Institute of Technology Bombay18

19. Limitations and Future Work Following aspects not considered – Social: opposition from land owners – Economic: Land prices, business priorities – Grid strength: Utility’s willingness to absorb wind – Infrastructure: Accessibility to potential site – Power Generation: Plant Load Factor at aggregate level – Political willingness Weights to individual policies – through discussions with investors Implementation of model for forecast – Uttar Pradesh, Jammu & Kashmir 21/11/2014Indian Institute of Technology Bombay19

20. Work done in my research till now… Diffusion of wind power across countries (1980-2012) – Five countries: China, US, India, Germany, and Spain – Standard diffusion models: Bass, Logistic, and Gompertz – Explaining model parameters with respective parameters. Role of Policy in wind diffusion in the States of India location choices of investors in State of Maharashtra – Interviews with developers, investors – Site visits – Discussion with authorities from MERC, MEDA, SLDC, MSEDCL. – Discussion with farmers, land owners. 21/11/2014Indian Institute of Technology Bombay20

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22. Thank you 21/11/2014Indian Institute of Technology Bombay22