Role of policy in deployment of wind energy: evidence across states of India

Panse, Riddhi; Kathuria, Vinish

doi:10.1016/j.rser.2015.08.056

Cited by 21 publications

(8 citation statements)

References 11 publications

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“…The question that comes to mind is whether the policies enacted in the form of feed in tariffs (FiTs) and renewable purchase obligation(RPO) have failed to live up to the expectation of achieving the 10% target. The following studies have argued that FiTs and RPO are effective in scaling up wind energy and RES in general and should be pursued (Mohsin et al 2018b , Panse and Kathuria 2016 , and Mohsin et al 2019b ). In the wake of rising population growth, can the old pathway continue to meet the energy demand of the country, giving the projected high case scenario of the population of 45 million people in 2040?…”

Section: Introductionmentioning

confidence: 99%

Energy insecurity, pollution mitigation, and renewable energy integration: prospective of wind energy in Ghana

Sun

Khan

Bashir

et al. 2020

Environ Sci Pollut Res

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Wind energy is seen as an important energy to sustainably meet the energy needs of Ghana. However, the industry in Ghana is yet to take off due to policy uncertainty and regulatory costs. The paper analyzed the key determinants and how they interact to impact the scaling up of wind energy in Ghana, using time series data, the vector auto regression (VAR) model from 2013 to 2019.There were four endogenous variables, grouped under policy, population growth, wind capacity, and electrification rate. The findings revealed the dynamic behavior of the variables from the VAR to a strongly significant positive correlation to deploying wind energy in Ghana. The impulse response functions (IRFs) equally exhibited a positive impact long-run trajectory growth of the variables after a shock to the system. The response of the first lags had differences of log policy and that of the log of GDP produced a curious result from the shock by taking a steady positive growth path in the short run and nosedived to a negative pathway in the long run. On the other hand, the interaction of the first differences of the lags of log wind capacity and log policy is quite instructive, as the headwind produced a negative relationship in the short run and to a positive growth path in the long run. This was anticipated, as the wind capacity installation of Ghana is expected to increase in the long run, when pipeline projects materialize.

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Section: Introductionmentioning

confidence: 99%

Energy insecurity, pollution mitigation, and renewable energy integration: prospective of wind energy in Ghana

Sun

Khan

Bashir

et al. 2020

Environ Sci Pollut Res

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“…____________________________________________________________________________ 2020 / 24 193 In order to evaluate the comparative scenario and the likelihood of wind power deployment considering all the three variables (FIT rate, PPA duration and RPO target), multivariate approach has been applied. Among the different multivariate statistical analysis methods, PCA has been identified as reliable and effective [14] to calculate aggregate policy indices representing the combined effect of considered policy variable of seven states (refer to Section 3.1). Methodology for the multivariate statistical analysis performed on the state-wise wind energy policy data is elaborated in Section 2.1.…”

Section: Methodsmentioning

confidence: 99%

“…A recent study compared the state-specific policy scenarios for seven windiest states of India [33] by applying a principal component analysis (PCA) under multivariate statistical approach. The influential parameters (FIT, PPA duration, RPO target and wheeling charges) are used as an input [14]. Here, the study has incorporated the combined input of FIT and wheeling charges policy.…”

Section: Introductionmentioning

confidence: 99%

“…For multiple interrelated variables, multivariate approach is essential. There are different types of methods available for multivariate statistical analysis , like principal component analysis (PCA) [14], factor analysis, fixed-random effect analysis [32] and Bass model (mixed influence model) [29]. In Bass model, the weights of differ ent ____________________________________________________________________________ 2020 / 24 191 parameters have to be assigned as input which requires additional data and knowledge.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Factors Affecting Onshore Wind Power Deployment in India

Das

Jani

Kachhwaha

et al. 2020

Environmental and Climate Technologies

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AbstractThe Present study focuses on the impact of various factors on the growth of wind power generation in seven most wind energy prone states of India, that contain 97 % of India’s total wind power potential. The impact of state-wise policy parameters Feed-in Tariff (FIT) rate, Renewable Purchase Obligations (RPO) and Power Purchase Agreement (PPA) are evaluated in terms of aggregate policy indices that indicate the likelihood of wind power deployment in that state, through multivariate statistical analysis. The wind energy technology with reference to wind turbine specific policies, scaling of the project, the impact of hybrid policy, grid-related technological advancements and the improvement of capacity utilization factor (CUF) are discussed. Further, the impact of per capita net domestic product (PCNDP) and power demand-supply scenario are assessed. It was found that these two factors are non-influential on wind power growth. The outcome of the present study is that aggregate policy indices, captive/third party use of feasibility, presence of repowering policy, actual CUF obtained at the location, delay in cash flow and total available power potential are the factors that significantly influence the growth of cumulative installed capacity. This study provides an insight for policymakers for a quantitative assessment of the existing policies along with other factors and assists the project developers to compare and identify suitable locations for wind power projects in the near future. The exchange rate of 1 USD ($) = 69.38 INR (₹) has been taken throughout the manuscript.

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“…Scholars have commented on the effective use of climate policy instruments in India, leading to its steady growth (Carolin & Fernandez, ; Khare, Savita, & Prashant, ; Rohankar, Jain, Prakash, & Prasoon, ; Schmid, ; Sharma, Jaya, Sanjay, & Anil, ; Thapar, Seema, & Verma, ). Though the wind capacity doubled from 16 GW to 33 GW during 2012–2017, the growth has been uneven both spatially (across states) and temporally (boom and bust cycles; Kathuria, Pradeep, & Rekha, ; Panse & Kathuria, ; refer to Figure ).…”

Section: Country‐wise Energy Usage Assessmentmentioning

confidence: 99%

Analysis of factors impacting wind and solar sectors—Challenges to sustainable development (Four Country Study)

Thapar

Sharma

Verma

2019

Sustainable Development

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Deployment of renewable energy has progressed remarkably, with 1,000 GW installations (end of 2017), comprising 540 GW wind and 400 GW solar photovoltaic assets. However, the growth has been nonhomogenous, with spatial and temporal variations witnessed even within countries. Existing literature is focused on policyrelated factors, ignoring nonpolicy variables. We conducted a panel data regression to identify nonpolicy variables impacting wind and solar power sectors in four countries-Germany, India, Japan, and the United States; these rank among top five globally in terms of renewable deployment. Impact assessment of nine variables on wind (period 2004-2016) and 10 variables on solar (period 2006-2016) was undertaken for this study. Long-term energy data trends highlighted continuous use of oil, amid a growing share of gas and renewable power, along with a diminishing role of nuclear and coal power. Regression analysis reflected significance of commercial factors such as module cost and renewable country index, impacting the growth of these two sectors. Energy imports came out as a strong driver for wind as the sector was initially promoted for enhancing energy security. Contribution of fossil fuels in energy supply also displayed a high significance. Greenhouse gases emissions did not come strongly due to continued dominance of fossil fuels, carbon emissions from 'nonelectricity' use, and reduction in use of nuclear power. Suggestions for a steady growth include facilitating business transformation of utilities, enhancing ease of doing business, and developing new markets such as electric mobility and heating.

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Role of policy in deployment of wind energy: evidence across states of India

Cited by 21 publications

References 11 publications

Energy insecurity, pollution mitigation, and renewable energy integration: prospective of wind energy in Ghana

Energy insecurity, pollution mitigation, and renewable energy integration: prospective of wind energy in Ghana

Assessment of Factors Affecting Onshore Wind Power Deployment in India

Analysis of factors impacting wind and solar sectors—Challenges to sustainable development (Four Country Study)

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