Thursday, December 27, 2012

Putting your best foot forward - Best Practices in Consumer Indexing

As the operator tries rolling out the services, the foremost task at-hand is creating the base-line for the utility. This, typically, is collated using a consumer survey called consumer indexing (CI). The operator main aim is to collect the information required for consumer demography and demand estimation.

The figure below shows the variety of data the consumer indexing, if designed properly, can yield:

Consumer Indexing - Why is it important?

Here's the catch though.

Since the consumer indexing process is an extensive full-city activity, our observation has been that it becomes extremely important for the operator to monitor the survey process continuously and ensure data accuracy.

Some of the Critical Factors for Success (CFS) for successful consumer indexing survey are listed below:

  1. S.M.A.R.T. Goals: It is very important for the operator to upfront decide the data he wants to collect as a part of the consumer indexing process. The goals need to be identified across the teams which are going to be the users of the data collected. At the same time, the goals need to be clearly defined to the survey vendor so as to apprise him of the data usage. During our engagement with our clients, we have noticed that not much is done in documenting all the teams' requirements right in the beginning. We strongly recommend writing up a  well defined Data Requirement Document (DRD). It not only becomes one of the most important pillars of the CI Process but also avoids endless discussions between teams on what is required, by whom and by when.
  2. Data collection methodology and medium: The operator should identify all the options available to select the best suited methodology to collect the data identified in the goals of the CI process. In the same step, the operator should also develop and design the medium, usually a survey form, for the CI process. The "wow" effect of the survey form design changes and improved efficiency of the consumer interview was observed when we re-designed the survey form from a functional flow question to a consumer interaction flow.
  3. Well Defined Technical Specifications: The geo-spatial information being collected in the consumer indexing requires high end technical inputs to be ready for the survey before the start of the survey. These specifications include the likes of a high resolution satellite image of the area under survey, clear definitions of boundaries of zones and sub-zones (if any), etc.
  4. Appropriate Data Validation Rules (DVR): Once the DRD and Technical spcs are frozen, the operator should identify for itself the validation rules he needs to apply to check the data being collected by the CI vendor. Inappropriate or too rigid validation rules lead either to mass rejection or too poor quality of data being collected.
  5. Vendor Selection and Engagement: Identifying a vendor capable of delivering the DRD as per the pre-set data standards becomes the core of the CI project execution. As much important it is to identify the correct vendor, it is also the operator's responsibility to engage the vendor in a manner where the CI process can be handled in a smooth and timely manner.
  6. Design and Deployment of Project Management Techniques: The CI activity usually spans a substantial period of time. This necessiates deployment of a Project Management team which is well versed with the DRD and Data Collection methodology. A well formed PM team with participation across stakeholder teams forms one of the important CFS for CI Process. The KRAs of the PM team should include Unitization of Work, Timeline planning, Progress monitoring, etc.
  7. Business Process Management: The operator should be engaging in monitoring of not just the data being collected, but also the vendor processes. This, in the long term, helps the operator to achieve more accurate and timely data. Independent audits of internal as well as vendor processes always helps.
The data collected through CI process forms the base-line for the operator to put its plans (CapEx, OpEx, Revenue Planning) in place. Therefore, we believe, the onus of successful execution of the CI process is as much on the operator as it is for the survey vendor.

Saturday, December 8, 2012

Price bid curves and analytics from Bihar Distribution Franchisee projects

Our earlier blog 'Key amendments in Bihar Distribution Franchisee after pre-bid meeting' covered key points raised in pre-bid meeting and its responses in revised amendments.

Bihar State Power (Holding) Company Limited (BSPHCL) has given minimum benchmark input price curve for all the regions, mandating bidders to bid higher than given price curve for all 15 years. The bidders in pre-bid meeting requested to lower or removed the rates, as current rates to allow them  innovating on financial structuring.

See below table with useful bid analytics (click on the image to see enlarged view):

Some top level observations are:
  • Average growth rate of around 20% in initial first 2 years, with max. in first year; followed by receding growth rate to average 10% by 5 years; almost stable and slow receding of around 0.5% until 15 years
  • Gaya has lowest start point at Rs. 1.684/kWh, with PESU highest at Rs. 3.330/kWh
  • Bhagalpur has lowest end point at Rs. 4.124/kWh, with Muzaffarpur highest at Rs. 4.742/kWh  
  • The ratio of max. to min. rates for each curve is close to 0.4 and above  
    • The highest ratio is for PESU, resulting into lowest delta of Rs. 1.085 between the max and min rates of price curve
    •  The lowest ratio is for Gaya, resulting into highest delta of Rs. 2.563 between the max and min rates of price
  • The Levelised Input Price (LIP) calculated at discount rate of 11.08% is highest for PESU at Rs. 4.154/kWh, followed by Muzaffarpur, with lowest for Bhagalpur at Rs. 3.280/kWh
  • Average Billing Rate (ABR) of Gaya is lowest at Rs. 5.310/kWh, revealing greater improvement opportunity for that DF. On the other hand, PESU has highest ABR of Rs. 5.770/kWh
  • Ratio of ABR to LIP is highest 1.726 for Bhagalpur, 1.591 for Gaya and lowest 1.380 for Muzaffarpur. (This ratio is static indication of profit margin for bidders, with close to 1.00 value is indication of lower margins and higher risks for DF operator)
Posted by: Kunjan Bagdia @ pManifold

Thursday, December 6, 2012

Key amendments in Bihar Distribution Franchisee after pre-bid meeting

BSEB's (hereafter referred as Bihar State Power (Holding) Company Limited (BSPHCL)) organized pre-bid meeting on revised Bihar Distribution Franchisee on 16th Nov. 2012 saw good participation from around 10+ companies. The companies that participated are Reliance Infrastructure, Tata Power, Direct Media Distribution Pvt Ltd, Supreme Infrastructures, Spanco Ltd, Delhi, SPML, CESC, Pan India Network Ltd., Shyam Indus Pvt Ltd, etc. 

This is the third attempt by BSEB to privatize the power distribution in its areas of Patna, Muzaffarpur, Bhagalpur & Gaya. Earlier in 2009, Glodyne Power Limited and CESC were the highest bidder and second highest bidders respectively. Later, CESC became highest bidder as Glodyne was held ineligible for participating in the bid after its partner stepped out of the consortium. In June 2011, BSEB cancelled CESC's bid, saying that it would lose heavily if it awards bid to CESC as the tender was floated in 2009 on the basis of tariff rate of 2007-08. In second attempt, only Essar Power submitted and others refrained from submitting bids due to certain issues on Minimum Benchmarking Price, but as Essar has not submitted formal acceptance within timescale, LOA was cancelled

Wednesday, November 28, 2012

Ease of doing Distribution Franchisee business - Muzaffarpur Local Intelligence

Muzaffarpur located at  26°07′N 85°24′E. The district occupies an area of 3173 km. Muzaffarpur lies between the Burhi Gandak River and Furdoo nallah. Muzaffarpur is one of the many gateways to Nepal.

Municipal Corporation
Muzaffarpur Municipal Corporation
No. of Household
Male population
Female population
Total population
Slum population
77456 (2011)

Tuesday, November 27, 2012

Ease of doing Distribution Franchisee business - Gaya Local Intelligence

Gaya is located at 24.78°N 85.0°E. Gaya is the second largest city of Bihar. Gaya is 100 kilometers south of Patna, the capital city of Bihar. Situated on the banks of Phalgu. It is surrounded by small rocky hills by three sides and the river flowing on the fourth (eastern) side.

Municipal Corporation
Gaya Municipal Corporation
No. of Household
Male population
Female population
Total population
Slum population

Monday, November 26, 2012

Ease of doing Distribution Franchisee business - PESU Local Intelligence

Apart from technical & financial parameters it is important for the bidders to evaluate the local intelligence for its operation & ease of setting up a business. pManifold has developed a process to research on the local intelligence through its network & secondary research to provide bidders with glimpse of the region on Demographics - Social & Economical, Political stability & activities & enable them making right decisions. Below is the quick snapshot from the local intelligence report from pManifold on Ease of Operationalizing Distribution Franchisee.

Patna is the capital city of Bihar state. It is located on the south bank of the Ganges River. The city is approximately 35 km long and 16 km to 18 km wide. The table below shows the quick facts about the region
Municipal Corporation
Patna Municipal Corporation (PMC)
No. of Household
726,364 (Census 2001)
Male population
Female population
Total population
Slum households
Slum population
63.5% (Census 2001)
Literacy Rate

Saturday, November 24, 2012

Bihar calls again: Revised 4 Power Distribution Franchisees for Patna, Muzaffarpur, Bhahalpur and Gaya

Bihar State Electricity Board (BSEB), after a long gap of nearly 2 years has come back, issuing revised Request for Proposal (RFP) for appointment of Distribution Franchisee for following areas/regions:
  • PESU Area
  • Muzaffarpur town and adjoining areas 
  • Gaya town and adjoining areas
  • Bhagalpur town and adjoining areas
At last, BSEB got a final go ahead from the High Court to re-initiate its privatization initiative for Power supply, which went into long litigation detour with earlier entrusted Essar Power group. Having not received formal acceptance from Essar to its issued LOI in-time, BSEB has cancelled the bid, and issued fresh tender on Oct 22, 2012.

The model is 15 years Input Based DF, with below compared heterogeneity in Network, Consumers and Revenue. Key parameters like Consumer Base, Connected Load (MW), Unit Sales (MUs), Revenue Billed (Rs. Cr.) and Revenue Collected (Rs. Cr.) for all the areas/regions are compared across consumer categories (broadly Residential, Commercial, Industrial (LT & HT), Irrigation).

    Other important parameters like Losses, Average Billing, Collection Efficiency are shown below. Few excerpts from the same are as follows:

    • Input Units is highest for PESU, followed by Muzaffarpur, Gaya and Bhagalpur
    • Transmission & Distribution (T&D) losses is highest for Gaya, followed by Bhagalpur, Muzaffarpur and PESU
    • Collection Efficiency is least for Bhagalpur and highest for PESU
    • Calculated AT&C losses is highest for Gaya, followed by Bhagalpur, PESU and Muzaffarpur.
    Important Dates:
    • A pre-bid meeting is scheduled on 16th Nov, 2012.
    • Last date of submission of Technical bid - 17th Dec, 2012.
    We wish good turnout and rationale bidding for these new coming DF opportunities. pManifold services include for DF turnkey bid advisory including Partner Identification, Technical Due-Diligence, Financial Bid Modeling and Bid Preparation. For more details, contact at or
    RFP can be downloaded from the following link:

    Posted by: Kunjan Bagdia @ pManifold

    Friday, November 2, 2012

    'Financial Institutions with outstanding debt to Discoms should take Equity position under planned restructuring to bring effective Performance Management' says Amulya Charan

    Mr. Amulya Charan, Chief Mentor, Power Trading and Advocacy at Tata Power has 22+ years of experience in the Indian Power Sector, spanning Generation, Transmission, Distribution and Trading. With senior Mgmt. roles at NTPC, Power Grid, Tata Power Ltd., he was ex- MD at Tata Power Trading Company Ltd for four years. Mr. Charan shared following views in recent meet with pManifold.

    Question 1: What are the Key Issues with our Discoms?
    • SEB Financial Health – another sub-prime crisis in the making
      • The cash losses of SEBs have increased 40x FY05-09 to a colossal Rs 284 bn and AT&C losses continue to scare at 28% (All India)
      • These losses along with theft of electricity and insufficient increase in tariffs have been the reason for staggering financial losses and curtailing their ability to service their customers
      • The investment by discoms in upgrading the distribution infra is much lower than required due to unavailability or limited availability of cash
    • High Debt exposure of lenders to the Power Sector
      • Outstanding debt of state power utilities have grown to a staggering Rs 6 lakh crore or 6% of the GDP. Roughly a third of these are loans taken to fund past losses which cannot be serviced through tariff hikes and, hence, are being considered for a benign restructuring by the Centre. Unless big reforms are undertaken to stem losses and spur revenue streams, these liabilities would grow further to Rs 7.3 lakh crore by March 2013. This looks like a reasonable estimate, given that annual losses (after receipt of subsidy) of discoms in the country were Rs 42,415 crore in 2009-10, up 18% over the previous year.

    Wednesday, October 31, 2012

    Contingent Valuation Methodology - a means to the capture and analyze willingness to pay

    If people merely ‘want’ something, it may not be backed with a willingness to pay for that service. Hence one needs to ascertain people’s maximum willingness to pay for service options under consideration. Willingness-To-Pay (WTP) is the maximum amount an individual is ready to pay for a particular goods/service. Consumer surveys are carried out to estimate the WTP for goods or service under consideration.

    The WTP can be estimated using three different ways:
    1. By observing prices that people pay for similar goods in various other markets
    2. By observing individual expenditures on money, time and labor, etc. to obtain goods, OR to avoid their loss. This method might involve an assessment of coping strategies and involve observations, focus group discussions and even house-hold surveys.
    3. By directly asking people what they are willing to pay for goods and services in the future
    The first two approaches are based on observations of behavior and are called Revealed Preference Techniques. The third technique is based upon Stated Preferences and includes Contingent Valuation Method.

    Contingent Valuation Methodology (CVM) creates a hypothetical market scenario and tries to obtain the value for particular goods, contingent to the scenario. The economic concept that the CV Surveys are trying to capture is the maximum amount that the individual would be willing to pay for certain goods/services.

    The most important part of the CV Survey is to simulate a realistic contingent valuation scenario, which has accurately priced options that reflect the levels of prices the goods/service provider will have to charge.

    The advantages and disadvantages of the CVM are presented below:


    CVM captures a fuller range of benefits of service improvements by investigating people’s maximum willingness to pay for different levels of service that are currently not available
    The cost of CVM analysis for a smaller project in terms of time and money are significant. However, incremental costs are relatively modest
    Consumers can bid on a range of different service options, thereby defining project designs and technology choices
    The results of CVM are often not transferable between locations. This creates a special focus requirement on sample planning
    The CVM generates information on household ability and willingness to pay for on-going services, thereby guiding tariff and cost-recovery policy
    Sample size needs to be substantial to avoid problems involved with aggregation of responses
    If stated clearly, the results of CVM Survey are conceptually easy for non-specialists
    Individual biases can cause misleading results

    The CVM has proven to be the most popular of available methods for monetary valuation of environment.[1]

    The application of Willingness-to-Pay Survey for a utility roll-out is depicted in the following figure. 

    How Willingness-to-Pay Study fits into the operational roll-out for utilities?
    The chart shows how the study integrates with the other functions of the utility and interacts with them to create an eco-system based on optimum approximations.

    When a respondent does not answer a survey question truthfully, it is said to introduce a bias into the survey that undermines the validity of the survey. Therefore, the basic design consideration of the CV Survey is to avoid the many biases that might occur.

    The different types of biases and the ways to avoid the biases in your CVM study are shown in the table below. These biases also need to be controlled during the interview conducted for the CVM Study.

    Main CVM Biases and Errors
    How to avoid
    Low Strategic Bid
    Respondent lowers their bid assuming that the state, or others, will pay more
    Emphasize on policy of state that if the community is not willing to pay sufficiently, the project might not take off
    High Strategic Bid
    Respondent raises their bid above the real WTP to ensure that the project goes ahead
    Make it clear that there will be no subsidies, this is the real amount. Choose correct bidding model
    Hypothetical Bias
    Respondent does not understand or believe in the options
    Explain options clearly.
    Poor Sampling
    Non-random sample selected which might result in poor quality of data collected.
    Ensure accurate mapping of survey area and an appropriate random sampling methodology
    Starting Point Bias
    Starting price for bidding games influences the final WTP
    Vary the starting prices within the sampling frame
    Interview and Compliance Bias
    Enumerator influenced biases
    Analyze responses by enumerator and discard biased responses
    Payment Method Bias
    Payment method does somehow affect the responses
    This might be a realistic bias revealing preferences to certain payment method. This can be safely ignored.

    Best practices for Designing WTP Survey
    1.     ·  Survey Design should involve easily understood and pre-tested language, taking feedback from all focus group ·   Data Collection should be planned with appropriate attention to sample size, collection methods, sample representation of general population, and randomized selection ·     Correct statistical tests need to be applied for accurate interpretation of the results ·  Regression results for CVM bids should be conducted for validating the data ·  Question Design and interview process should be developed to reduce the bias introduced in the survey

    For a case study on WTP for Water, visit our blog here.

    [1] “The Contingent Valuation Method: Retrospect and Prospect” (2008), Clive L. Spash

    Tuesday, October 30, 2012

    DF Attractiveness Matrix: Revised 4 RFPs for Bihar Distribution Franchisees

    Recently, BSEB has released revised RFPs for Power Distribution Franchisee for 4 districts in Bihar - PESU, Muzaffarpur, Gaya and Bhagalpur.

    pManifold's DF Attractiveness Matrix, provides a quick comparative study on key decision parameters for Bihar 4 DFs with reference to other states DFs like for Nagpur, Agra, Gwalior and Jamshedpur. (click on the image to see enlarged view)

    Some of the key excerpts are highlighted below:
    • The current DF scope is at District Level for all the areas. Adjoining areas (approx within 15-20 km) are also in scope of DF.   
    • Geographical Area (Sq.Km.) is not mentioned in the RFP. However, a rough map (not to scale) is provided in RFP for indicative purpose.
    • Patna city and its adjoining areas comes under PESU, which has the highest consumer base compared to other areas/regions. Others are in the range of consumer base of 1.15 lakhs.
    • Connected Load (in KWs) is highest in PESU area, followed by Agra, Jamshedpur and Nagpur in that order. Connected Load for Muzaffarpur and Bhagalpur is provided only at urban (i.e. town) level and not at district level.
    • Electricity Sales (LUs) is highest in PESU area, followed by Jamshedpur and Agra in that order
    • Collection Efficiency is second highest for PESU area, after Nagpur, while it is comparatively low for other towns (i.e Gaya, Muzaffarpur and Bhagalpur in that order) creating good opportunity there for improving commercial losses from effective DF operations.
    • Distribution losses is highest for Gaya, followed by Bhagalpur, Gwalior in that order. PESU area has least Distribution losses in Bihar regions.
    • AT&C losses is highest for Gaya, followed by Gwalior. In Bihar, AT&C losses from high to low rank from Gaya, Bhagalpur, PESU and Muzaffarpur. 
    • Average Billing Rate (ABR) is among the highest for all the areas in Bihar (Avg. Rs. 5.58/kWh), which is highest when compared to others like Gwalior, Nagpur, etc. (This raises some concerns on the reliability of the shared ABR rates)
    • BSEB has specified minimum Benchmark Input Price's for bidders. The minimum benchmark price is for Gaya, followed by Bhagalpur, and highest for PESU area. 
    Clearly, across all regions, PESU area will likely attract more bidders because of its volume. The other 3 regions will attract small and new players to develop their base in fast emerging DF landscape in India. The baseline shared in RFP is still not very strong, and will attract many questions during the pre-bid meeting, that is planned for 16th Nov, 2012. Emerging Bihar calls again and this time for success of its first DF model, which will hopefully see increased participation.

    Reference: BSEB RFP

    Posted by: Kunjan Bagdia @ pManifold

    Thursday, October 25, 2012

    Part 2 of 2: Interview at Reuters on future of Power Distribution Franchisee model in India

    This is sequel to "Part 1 of 2: Interview at Reuters on future of Power Distribution Franchisee model in India". 

    pManifold: Some suggestions on the same are as follows:

    Needed Intervention
    Inadequate and mis-represented
    Baseline information for bidding
    ·      Irrational bidding
    ·      Delayed bid decision, because of revisions, and litigation's
    ·      Financial and Non performance risk from DF operator
    ·      DISCOM to invest in right Technical and Commercial due-diligence for forming the RFP baseline and have it audited by an independent agency
    ·      DISCOM taking responsibility of  wrong baseline
    Poor stakeholder engagement during the bid process
    ·      Poor final bid participation
    ·      Risk of re-tendering to mitigate poorer competition
    ·      Non-optimal DF terms and conditions, leading to later contractual conflicts, and non performance
    ·      Engage State Govt., DISCOM, Regulator, Bidders and Utility employees well into the DF conceptualization
    ·      Increase transparency of processes and decisions
    Constrictive, open       
    ended and non-optimal contract design
    ·      Minimum benchmark bid prices, disallow financing creativity from Bidders
    ·      Constrictive elements like improper Escrow design, unclear asset ownership etc. creates difficulty for financing
    ·      Constrictive qualification criterion (like asking for end-to-end distribution experience, not allowing consortium bidding) brings poorer participation and hence poorer bid price discovery
    ·      No clearer SLAs led to poorer performance monitoring 
    ·      There is need for clearer Exit options, to make the model attractive for private Developers and PE investors
    ·      Better design of Escrow mechanism to be favourable for bank financing
    ·      Strong SLAs commitment from both DISCOM & DF:
    o  Discom: Committed power supply and quality
    o  DF: Meeting AT&C loss reduction targets; making power purchase payments regularly; meeting customer satisfaction and other SLAs.       

    Week Governance of the Bid process & final bid evaluation     
    ·      Multiple revisions of RFP and DFA
    ·      Delayed bid closing
    ·      Poorer bid participation
    ·      Multiple extensions
    ·      Litigations
    ·      Delayed Start
    ·      Increasing transparency of bid process and evaluation
    ·      Discom taking responsibility of wrong baseline, and delayed decision making
    ·      Invest in proper stakeholder         engagement and online bid room/portal                                
    Poorer access to finance to Operationalizing DF (both high Working Capital requirements and Capex for first 3 years)
    ·      Delayed start of the project
    ·      Hiccups and non-performance in first year, which further exaggerates opposition against DF model
    ·      Making Bankers and broader Finance community understand DF model better, and distinguish it from debt burden utility
    ·      Forming DF initial viability funding from nodal agencies like PFC, REC etc.
    ·      Allowing right consortium partnership with competent partners on Technical, Operational, Management and Financial side.
    ·      Improving constrictive clauses in contract design, to allow DF to procure easy bank financing
    Weak SLAs to     Monitor Performance of DF     
    ·      Increase opposition to DF model, without any quantified performance assessment
    ·      Have clearer milestone, with right incentive/penalty mechanism to encourage performance
    ·      Have independent customer satisfaction assessment, to establish true metrics for end quality impact from DF model
    ·      Have transparent reporting mandatory for DF to Regulators
    Weak Regulatory      purview of DF model
    ·       Weak Performance monitoring            
    ·      DF to stand alone report performance from baseline to ERC (together with Utility)
    ·      If DF is able to meet its target AT&C reductions, then its customers should benefit with tangible returns (like              either reduced tariff rates as compared to state level Tariff, or reduced load shedding, etc.)  
    Weak integration     between different reform schemes (like R-APDRP,    
    National Electricity Fund, DF, RGGVY,etc)
    ·      Poorer end performance i.e. not much AT&C loss reduction, poorer PQR, and dissatisfied customers                           
    ·      Ensure well integration of R-APDRP and DF projects, to guarantee DF operators with full amount and timely completion of R-APDRP project
    ·      Ensure interest subsidy from National Electricity Fund
    ·      No Service tax liability on DF model
    ·      Allow Electricity duty collected from customers to be used towards Working capital loan

    • At pManifold, we tend to believe the future of DF model is very strong, and infact only way to really bring more accountability and decentralization to the power distribution business, which otherwise is the weakling in the overall power value chain. The model of course needs to be strengthened, and some key improvements are shared above.
    • All our decentralized & alternative Generation efforts will not scale, until we have a strong distribution mechanism, and DF has that potential. So we are looking forward to right regulatory changes to bring integration of these models for better ‘Open Market’ with stronger Private participation:
      • Distributed Generation
      • Distribution Franchisee
      • Open Access (with net metering provisions for smaller generators as well)
      • Energy Efficiency and DSM
    • A much bigger rural market for DF is still awaiting innovation. (See our blog Rural Franchisees - Could they become pilot ground to raise next level of Distribution services?
    • India’s Telecom sector has proved how open market mechanism with supporting Regulations and increased private participation has helped increase mobile penetration, reduce tariff rates, and increase customer services. Same is now looked upon in the Power sector, and I feel we are closer now.
    • The future is bright, if all stakeholders can really work together. CUSTOMERS are the most important link for success of a DF model. The operator that can give more choices to Customers, and meet their PERCEPTION, while manage its Business PERFORMANCE, will succeed. That is the whole reason, that pManifold has developed EUCOPS (Electric Utility Customer Opinion Preferences and Satisfaction) to capture customers voice, and help DF and utility operators track their end performance, as seen by customers. We are glad that more and more DF operators have started using our customer engagement services, and we have worked at Nagpur, Gwalior, Ujjain, Sagar and Dhenkanal, interacting with 7000+ customers from urban and rural. 
    • Customers cooperation is must for DF success, and there is not much in current models to incentivize customer support, as Tariff rates are set at State level. So DF customers will continue to pay higher for inefficiencies in other circles. A tangible and good incentive model within regulatory purview to DF customers (like reduced tariff rates, higher power availability, no reliability charges, reduced Electricity duty or Demand charges etc.), can further expedite DF operationalization. This will also create pressure on local civic bodies to compete and support DF models in their regions, and faster penetration of DF model including in rural areas as envisioned by Planning Commission.


    Posted by: Kunjan Bagdia @ pManifold