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Renewable energy is no longer 'alternative energy'

By the rule of the thumb, India will require about 100 gigawatts (Gw)—100,000 megawatts—of capacity addition in the next five years. Encouraging trends on energy efficiency and sustained efforts by some parts of the government—the Bureau of Energy Efficiency in particular needs to be complimented for this—have led to substantially lesser energy intensity of economic growth. However, even the tempered demand numbers are unlikely to be below 80Gw. As against this need the coal supply from domestic sources is unlikely to support more than 25 Gw equivalent capacity. Imported coal can add some more, but at a much higher cost. Gas-based electricity generation is unlikely to contribute anything substantial in view of the unprecedented gas supply challenges. Nuclear will be marginal in the foreseeable future. Between imported coal, gas, large hydro and nuclear, no more than 15–20Gw equivalent can be expected to be added in the five-year time block.As against this, capacity addition in the renewable energy based power generation has touched about 3Gw a year. In the coming five years, the overall capacity addition in the electricity grid through renewable energy is likely to range between 20Gw and 25Gw. Additionally, over and above the grid-based capacity, off-grid electricity applications are reaching remote places and touching lives where grid-based electricity supply has miserably failed. Newer applications in lighting, heating and household applications are now reaching a point of “unconditional competitiveness”. All of this is at a substantially lower carbon footprint. Renewable is thus no longer “alternative energy”, it is pretty much as mainstream as it gets.

The bottlenecks to renewable energy development are more practical. The nature of “infirm” electricity from renewable sources is somewhat different from conventional coal, gas or nuclear power. Large-scale ingress of such infirm electricity in the grid causes grid management problems. A utility control room becomes a much more complex and busier place as the proportion of renewable electricity increases. There is a natural reluctance on part of utilities to take in renewable electricity beyond a point.

Key to solving this conundrum is recognizing that renewable power is indeed infirm, but is predictable. Predictions made with reasonable accuracy on how much electricity will be generated at any time will lead to solutions being developed by pressing in quick response complementary sources of electricity to complement the renewable energy generation. Thus, pairing solar with gas, and wind with storage hydro can address the key issue of variability. This is more a management issue, and with the right policies and regulations on these aspects renewable electricity generation can receive a huge fillip.

That would still leave the issue of local grid management where large scale variations in generation can cause huge disturbances and lead to grid outages. This will need to be addressed through network strengthening and introduction of newer technologies such as Flexible AC Transmission Systems. Eventually, large scale renewable resources such as wind and solar would need to connect with the national grid and the power flows would become a part of the inter-state power flows.

Fortunately, in the area of renewable energy the country has been seeing quite a bit of progressive action led by the Central Electricity Regulatory Commission (Cerc). The regulator has acted extremely proactively to ensure that a Renewable Purchase Obligation is implemented at the state level. This has been backed by innovative commercial mechanisms such as tradable Renewable Energy Certificates that help states, which are deficient in renewable energy resources, fulfil their purchase obligations. Cerc has also made it mandatory to bring about “scheduling” of grid connected renewable energy from January 2012. This will bring about much desired predictability and reduce the impact of infirmity of the main renewable energy resources—wind and solar.

On its part the government of India has been working on filling some of the loopholes in the policy framework and the implementation wherewithal.

A credit enhancement mechanism for the solar projects under the first phase of the National Solar Mission has been put in place. The government has also approved the use of the proceeds of the coal cess accruing to the National Clean Energy Fund for investments in the transmission grid and improving reliability. A number of market innovations are also being proposed by the government to promote competition and bring down cost of renewable energy production and delivery.

All of these are positive pointers in the otherwise troubled energy scenario that the country is facing. However, it comes with an important caveat. Renewable energy resources are very local. For grid-based electricity production the resources are concentrated in a few states. Development is mired in land related issues, availability of permits, consents and clearances—all the typical issues that bog down infrastructure project development in India.

If the promise of renewable energy has to play out in practice, the government would need to demonstrate sustained leadership to overcome the issues, address state level parochial attitudes through the right mix of incentives, and remove bureaucratic sloth in the state machineries. Investor interest in renewable energy is high at this time, but can be very fickle. One has always suspected that the National Action Plan for Climate Change and its ambitious targets were born out of propaganda rather than belief. However, a combination of circumstances may just make achievement of some of the important targets possible to a significant degree. It is crucial that the country and its leadership do not let the opportunity pass.

Anish De is chief executive, Mercados EMI Asia.




Building Your Own Start-up Technology Company, Part 1
Building Your Own Start-up Technology Company, Part 2
Building Your Own Start-up Technology Company, Part 3
Building Your Own Start-up Technology Company, Part 4

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  • Home
  • Applied Analytics
    • Analytics for Decision Making >
      • What is Cluster Analysis
      • Data Reduction and Unsupervised Learning
      • Preparing Data and Measuring Dissimilarities
      • Hierarchical and k-Means Clustering
      • Defining Output Variables and Analyzing the Results
      • Using Historical Data to Model Uncertainty
      • Models with Correlated Uncertain Variables
      • Creating and Interpreting Charts
      • Using Average Values versus Simulation
      • Optimization and Decision Making
      • Formulating an Optimization Problem
      • Developing a Spreadsheet Model
      • Adding Optimization to a Spreadsheet Model
      • What-if Analysis and the Sensitivity Report
      • Evaluating Scenarios and Visualizing Results to Gain Practical Insights
      • Digital Marketing Application of Optimization
      • Advanced Models for Better Decisions
      • Business Problems with Yes/No Decisions
      • Formulation and Solution of Binary Optimization Problems
      • Metaheuristic Optimization
      • Chance Constraints and Value At Risk
      • Simulation Optimization
    • Analytics for Marketing >
      • Marketing Analytics and Customer Satisfaction
      • Customer Satisfaction
      • Measurements and Scaling Techniques – Introduction
      • Primary Scales of Measurement
      • Comparative Scaling
      • Non-Comparative Scaling
      • Experiment Design: Controlling for Experimental Errors
      • A/B Testing: Introduction
      • A/B Testing: Types of Tests
      • ANOVA – Introduction
      • Example -Inspect Spray and Tooth Growth
      • Logit Model - Binary Outome and Forecastign linear regression
      • Text Summarization
      • Social media Microscope
      • N-Gram - Frequcy Count and phase mining
      • LDA Topic Modeling
      • Machine-Learned Classification and Semantic Topic Tagging
    • Data Engine >
      • Understanding The Growth Of Data
      • Evaluating Methods Of Data Access
      • Communication journey
      • Data Journey
      • Planning for data visualisation
      • Visualisation Component
      • Content Connection and Chart Legitibility
    • Customer Insights >
      • Introduction
      • What is Descriptive Analytics?
      • Survey Overview
      • Net Promoter Score and Self-Reports
      • Survey Design
      • Passive Data Collection
      • Media Planning
      • Data Visualization
      • Causal Data Collection and Summary
      • Asking Predictive Questions
      • Regression Analysis
      • Data Set Predictions
      • Probability Models
      • Results and Predictions
      • Perspective Analytics (Maximize Revenue and Market Structure Competitions)
    • Analytics for Advance Marketing >
      • Visualisation and statistics (Political Advertising,Movie Theater and Data Assembly)
      • Excel Analysis of Motion Picture Industry Data
      • Displaying Conditional Distributions
      • Analyzing Qualitative Variables
      • Steps in Constructing Histograms
      • Common Descriptive Statistics for Quantitative Data
      • Regression-Based Modeling
      • Customer Analytics
      • Illustrating Customer Analytics in Excel
      • Customer Valuation Excel Demonstration
  • Soft Skills
    • Adaptability
    • Confidence
    • Change Management
    • Unlearning and Learning
    • Collaboration and Teamwork
    • Cultural Sensitivity
  • Marketing
  • Finance
  • Economics
    • Introduction to Managerial Economics >
      • Basic Techniques
      • The firm: Stakeholders, Objectives and Decision Issues
      • Demand and Revenue Analysis >
        • Demand Estimation and Forecasting
        • Demand Elasticity
        • Demand Concepts and Analysis >
          • Formulation and Solution of Binary Optimization Problems
      • Scope of Managerial Economics
    • Prodution and Cost Analysis >
      • Production Function
      • Estimation of Production and Cost Functions
      • Cost Concepts and Analysis I
      • Cost Concepts and Analysis II
    • Pricing Decisions >
      • Pricing strategies >
        • Adding Optimization to a Spreadsheet Model
      • Market structure and microbes barriers to entry
      • Pricing under pure competition and pure monopoly
      • Pricing under monopolistic and oligopolistic competition
    • Narendra Modi Development Model of Gujarat
  • JBDON Golf
    • Digital Marketing Application of Optimization
  • Let's Talk
  • MBA Project Sharing
  • About Us
    • Good Read >
      • IIMC says PepsiCo CEO Indra Nooyi was an average student
      • India’s middle class figures in Fortune’s Top Ten list of those who matter
      • The Start-Up of you.
      • BUYING AND MERCHANDISING
      • HUMAN RESOURCE MANAGEMENT
      • Do You Suffer From Decision Fatigue?
      • New Page
      • About social media and web 2.0
      • Building Your Own Start-up Technology Company, Part 1
      • Building Your Own Start-up Technology Company, Part 2
      • Building Your Own Start-up Technology Company, Part 3
      • Building Your Own Start-up Technology Company, Part 4
      • Renewable energy is no longer alternative energy
      • What Makes an Exceptional Social Media Manager?
      • The Forgotten Book that Helped Shape the Modern Economy
      • Home
      • How to Think Creatively
      • A Lighthearted Looks at Project Management and Sports Analogies
      • Why Trust Matters More Than Ever for Brands
  • CET Knowledge Zone
    • Tips From JBIMS Students >
      • Prasad Sawant
      • Chandan Roy
      • Ram
      • Ashmant Tiwari
      • Rajesh Rikame
      • Ami Kothari
      • Ankeet Adani
      • Sonam Jain
      • Marketing Analytics and Customer Satisfaction
      • Mitesh Thakker
      • Tresa Sankoorikal
    • Speed Techniques
    • CET Workshops
  • Untitled
  • New Page
    • Cluster analysis using excel and excel miner
    • Chance Constraints and Value At Risk
    • Adding Uncertainty to a Spreadsheet Model
  • Adidas