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Working Papers | 2017

Managing the Power Grid Ramping challenges critical to success of India's Renewable Energy Targets

Rajeev Annaluru and Amit Garg

Power grids operators around the world have been experiencing challenges in operating the grid with increasing penetration of Variable Generation (VG) sources like Solar PV and Wind. Variability in one form of generation must always be compensated with other forms of generation at all times to ensure grid stability. This paper focuses on the diurnal variability introduced into the Indian power grid and the consequent increase in ramping requirements due to the 175GW by 2022 renewable energy target enunciated by the Government of India. Ramping requirements were quantified for 3 potential renewable energy penetration levels on the grid by the year 2027. Nine separate solution alternatives are created using Coal, Natural Gas and Renewable & Emerging technologies as solution options to meet the identified ramping needs. Energy-mix and carbon prices are calculated for each of the solution scenarios and compared with the baseline scenario computed from the Intended Nationally determined Contribution (INDC) adopted by India in COP22. The paper concludes that the most energy cost and carbon price efficient paths for India to integrate ambitious RE capacity into India's power grid would be to convert and operate existing coal plants as peaker plants instead of as base load plants.

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Working Papers | 2017

Assessment of Density Forecast for Energy Commodities in Post-Financialization Era

Bisht Deepak and A. K. Laha

Probability density for the future price of an asset can be estimated from historical asset prices or exchange-traded derivatives. In this paper, prices of futures and options contracts that embed the forward-looking information are used to obtain the density forecast of the underlying asset under Q- measure. Along with Probability Integral Transform (PIT), various statistical testes are conducted to determine whether the option-implied density forecast is unbiased under the real world measure, P. We have worked with the settlement prices of NYMEX traded futures and options contracts for WTI crude oil and Henry Hub natural gas during the post-financialization period of 2006 to 2013. Statistical analysis of the PIT values indicate that the option-implied density forecast is unbiased under the real world measure, P.

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Working Papers | 2017

Pricing Option on Commodity Futures under String Shock

Bisht Deepak and A. K. Laha

Forward curve movements, particularly of industrial and energy commodities, suggests that futures price do not move in tandem with the spot price, and not all futures contracts move in the same direction. We incorporate these subtleties into our model with parsimony. This article offers a new approach to value commodity derivatives by using string shock. We use it to perturb the term structure of future convenience yield as if every futures contract has its source of risk. The no-arbitrage condition on the drift of future convenience yield and closed-form formula for the European call option written on a futures contract is derived. Our model has separate volatility and correlation functions that ensure easier parameterization and calibration to market data. We compare absolute and relative option pricing errors of our model with the two factor Schwartz (1997) model for 440 trading days. It is found that the new string shock based model has better performance than the Schwarz's model regarding having lesser pricing errors.

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Working Papers | 2017

The Horror of Urban Development in India – Identifying The Real Issues

Sebastian Morris

The horror of India's urban spaces cannot be understood without recognizing the core errors in planning and in the approach of infrastructural development. These stem mainly from the low FSI's that are used, the lack of any recognition of central place needs of different economic activities, the lack of even a modicum of integration of transport planning with layout planning (the so called "Master Plans"), an" architects' approach" to urban design, and most importantly to the organization mess-up that the urban local bodies are. Unless these change the large sums of money that would be spent would actually result in much subtraction of social and public value.

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Working Papers | 2017

A Study of the Evolution of Nature and Narration of Brands in an Emerging Market

Abraham Koshy and Priya Narayanan

Brands evolve over time as a result of factors that facilitate or force. Such changes could, firstly, be the result of changes in the operating environment, such as changes in either consumer-related aspects like consumer needs, tastes, preferences, and buying behavior; or changes in external aspects such as technological developments, regulations, competitive landscape and competitive behavior. The paper studies how local (Indian) brands have evolved over the past in terms of their nature and narration, through an analysis of the elements of brand identity and image, brand communication, and brand portfolio. The paper then identifies the parallels that this evolution might have with evolution of an emerging market, where technology, competitive scenario, consumer expectations and consumer demographics have evolved rapidly.
A study of ten local brands through case studies and consumer perceptions shows that brand identity is perceived to lie on the spectrum of completely changed to completely unchanged. This evolution can be the result of strategy or situation, or a combination of both. Also, brand evolution can be classified along a two-dimensional matrix of gradation of transformation (drastic vs. gradual) and driver of transformation (internal vs. external). Components of brand identity might change and yet, identity might be perceived as remaining unchanged. Based on the pattern of evolution, brands can be categorized into four groups: stable brands (no change), contemporized brands (marginal change), evolved brands (major change), and transitional brands (sequential change). Unlike prior literature, the current study provides a useful framework to analyze the evolution of brands in an emerging market.

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Working Papers | 2017

Transitions in currency denomination structure as supply disruption and demand distortion: Efficiency, Effectiveness and Bullwhip

Joshi Harit and Saral Mukherjee

Transition from one currency denomination structure to another is infrequent but not rare.
Central Banks may adopt such transition for various reasons like prevention of counterfeiting or
combating hyperinflation and may include demonetisation of specific denominations or introduction of new denominations. We study transitions in currency denomination from a supply chain perspective. Currency as a product flows through a three-stage supply chain in which currency denominations are substitutable products. We show that demand for a specific denomination depends on the denomination structure and distribution of transactions in the economy. During a transition from one denomination structure to another, the demand for a specific denomination is affected due to change in step size. In addition, the demand may be distorted due to hoarding resulting from supply shortages. Such transaction related hoarding behaviour may occur for lower denominations, in contrast to wealth accumulation related hoarding of higher denominations known in the literature, and can lead to a Bullwhip Effect. We propose efficiency and effectiveness related measures for the remonetisation process and study the impact
of prioritisation of supply of one denomination over another on demand distortion. In doing so, we extend the literature on efficient transactions by introducing an aggregate transaction efficiency measure considering the transaction distribution and show how this measure is sensitive to transaction slabs, denomination structures and transaction distributions. Such analysis may inform Central Banks about relative vulnerabilities of different denominations to a supply disruption which distorts currency demand.

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Working Papers | 2017

Vehicle Routing at a Food Service Marketplace

Didugu Kavitha Chetana and Chetan Soman

In this paper, we explore the case of an aggregator-cum-restaurant that also offers pickup and delivery services to third party restaurants registered with it. The aggregator must decide on its fleet size and the optimal routes to assign to each vehicle deployed. We propose a heterogeneous, compartmentalised vehicle routing model with pickup and delivery for the aggregator involving time windows and source selection, to minimise the route duration (or the total cost) of its fleet. The model accounts for traffic conditions (captured by speed data) over the route, maximum service radius of the fleet and time windows for customers as well as restaurants. This paper, to the best of our knowledge, is probably the first one that deals with vehicle routing problem for an online hyperlocal food service marketplace (also referred to as aggregator) that functions as a quick service restaurant (QSR) as well.

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Working Papers | 2017

Turning Over a Golden Leaf?
Global Liquidity and Emerging Market Central Banks'
Demand for Gold after the Financial Crisis

Balagopal Gopalakrishnan and Sanket Mohapatra

The quantity of gold reserves held by central banks in emerging markets and developing economies (EMDEs) has risen sharply following the global financial crisis in 2008. This paper examines factors driving holding of gold by central banks in 50 EMDEs using a dynamic panel generalized method of moments model. We find that monetary expansion in advanced economies is robustly related to the post-crisis increase in EMDE gold reserves, after controlling for domestic factors and changes in the global risk environment. This effect holds across different measures of global liquidity, and is robust to alternate model specifications, inclusion of additional covariates, and alternate estimation methods. We argue that the unprecedented monetary expansion in advanced economies has resulted in a shift in EMDE reserve asset holding strategy, resulting in continued accumulation of gold reserves even after the peak of the financial crisis.

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Working Papers | 2017

Intra-Industry Trade and Labour Market Adjustment: Indian Manufacturing Sector

Poornima Varma and Issar Akash

The study investigates the role of trade, labor market regulations and institutions on labour adjustment costs. The study develops a linear dynamic panel model using quasi-maximum likelihood fixed effects estimator. Using a panel data of 40 Indian manufacturing sectors we find that the better labour market regulations and institutions reduce the labour market adjustment costs. This result using both the set of proxies for labour adjustment costs -job re-allocation rates as well as absolute employment change- supported this view. We find the same to be true when examining the male and female labour adjustment costs individually. Nonetheless, the study did not find any evidence to support the impact of trade expansion as well as the structure of trade expansion on labour market adjustment costs. The results are robust to static and dynamic panel methods.

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Working Papers | 2017

Travel Time Prediction for Taxi-GPS Data Streams

A. K. Laha and Sayan Putatunda

The analysis of data streams offers a great opportunity for development of new methodologies and applications in the area of Intelligent Transportation Systems. In this paper, we propose a new incremental learning approach for the travel time prediction problem for taxi GPS data streams in different scenarios and compare the same with four other existing methods. An extensive performance evaluation using
four real life datasets indicate that when the drop-off location is known and the training data sizes are small to moderate the Support Vector Regression method is the best choice considering both prediction accuracy and total computation time. However when the training data size becomes large the Randomized K-Nearest Neighbor Regression with Spherical Distance becomes the method of choice. Even when
the drop-off location is unknown then the Support Vector Regression method is the best choice when the training data size is small to moderate while for large training data size the Linear Regression method is a good choice. Finally, when continuous prediction of remaining travel time and continuous updating of total travel time along the trajectory of a trip are considered we find that the Support Vector
Regression method has the best predictive accuracy. We also propose a new hybrid method which improves the prediction accuracy of the
SVR method in the later part of a trip.

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