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Using household level Energy Performance Index to determine energy distribution strategies during power-scarce situations in Indian cities.



The electricity sector in India suffers from three major constraints: First, the installed capacity cannot withstand the peak demand, which leads to unscheduled power cuts. Second, gap between supply and demand stemming from a rising GDP and a increasingly energy-intense GDP growth has led to a severe energy shortfall. The consequence of these two constrains is that India faces a energy shortage of 9.8% and a peak shortage of 16.6%[1]. Thirdly, the electricity production is very carbon intensive.

One way to tackle this problem is to put consumers in a position to save electricity by incentivizing behavioural change as well adoption of technological alternatives through the economic influencers of appropriate energy and peak-demand pricing and social influencers of being socially elevated into the class of conscious consuming citizens.

The research and pilot implementation effort seeks to investigate the impact of the economic and social scenarios through the offer of a choice: continual adherence to existing power and energy consumption patterns at high cost of back-up power during power-cut periods or behavioural alteration or energy efficiency / smart metering technology adoption to either significantly diminish exposure to power cuts or receipt of back-up power at lower rates compared to high energy / peak power users through cross-subsidization.  This can be enabled through the introduction of a market, which determines supply and demand, Hence the equilibrium price will be the price one buys units for. Depending on the demand and supply curves, the price will be determined.

This smart-grid project will entail not just the more conventional ‘energy trading’ aspect of prior efforts at understanding behavioural responses of consumers in an energy market,  but also include a pioneering peak-power or capacity trading market (in kW) amongst participants.

[1] Source: Ministry of Power, 2009-2010 statistics

[2] Source: Coal Ministry & Bureau of Energy Efficiency

What actions do you propose?

The first step would be to benchmark Energy Performance Index values (kWh/m2/year) for various building use-cases and climatic conditions. 3 residential societies will be selected for the project from 3 different strata of society i.e.  upper middle class, middle class and lower income group. The benchmark EPI values are planned to be used as means for determining household energy budgets that are monitored and regulated through Smart Meters. Additionally, based on empirical data or discussions with technical personnel from the builder-developers (engaged as research partners in this project, described platter), each household will be sanctioned a certain amount of kW, based on its household size or house size. This amount however, varies at least hourly. In night times, where the installed capacity is sufficient, the sanctioned capacity is high, mostly sufficient. In the afternoon, the capacity per household is very low. If this household wants to use more capacity than sanctioned, it would have to buy it from someone who will use less capacity e.g. by shifting some electrical usage to the night (e.g. washing machine). This whole process should not really affect the consumer, if he is not interested in it. Hence an indicator light would be sufficient to show that one is above the sanctioned load[1]. The process of billing will go automatically and when you are above your limit you could still do everything you want yet you have to pay more.

In order to extend this with regard to renewable energies, one could offer the option to buy additional kW by investing into a renewable energy project. Hence, the resident could by e.g. 1kW of solar power and his cap will increase by 1kW. Alternatively, depending on the Marginal Cost for providing an additional kW of connected capacity, the builder-developer could choose to install larger backup-generators to serve this capacity and the premium charges for this (to be paid by high-power users) would be consolidated into an institution level ‘green-fund’ which could be used to invest in socio-economically sensitive, pro-poor renewable energy projects such as decentralized solar-energy powered appliances in neighbouring villages (essentially ‘buddy villages’ that are in effect ‘adopted’ by the buildings involved in the project). This would serve as a surrogate for reducing overall grid load and perhaps prove more effective than renewable power purchase agreements as it would lead to off-gridding of a measurable component of the regional electric grid.

The Social influencers that are sought to be investigated through this project will be integrated through reinforcement of the above mentioned ‘green fund’ and ‘buddy village’ feature. Besides the peak-power penalty related funds, the ‘green-fund’ can also receive contributions from residents who have earned revenue from sale of ‘energy certificates’ or ‘power certificates’ to their residential counterparts. These contributions can be in the form of soft-loans (low interest loans) which could be paid back to the residents through the energy savings realized by the hosts of the renewable energy projects i.e. an ESCO model could be put into place where the residents serve as social investors. These socially beneficial actions will be publicly recognized with the aim of creating a positive image of the energy efficient households / individuals in order to understand its role in driving behaviour vis-a-vis the economic incentives described earlier.

Accurate electricity savings per annum can be tabulated by the smart meter from each category and integrated to calculating national level savings along with future predictions of energy usage.

Most importantly the Smart Meter allows consumers to monitor their energy usage more efficiently and provides incentives to customers to change their consumption behavior.

After the pilot is successful the project can be implemented at a larger scale i.e. 100,000 households. Shortcomings in the pilot can be improved upon and key learnings can be noted for improving efficiency on increasing the scale. On increasing the scale, the reduced energy usage will be pivotal in closing the gap between inadequate flow between electricity supply and demand.

[1] A choice Experiment and a lifestyle analysis can be applied to design the optimal product.

Who will take these actions?

  • cBalance  Solutions along with its partners
  • Residential societies selected for the pilot
  • State Electricity Board

Where will these actions be taken?

The whole project will focus on a finite set of builder-developers in India or any other developing country, catering to the lower, middle and upper-middle class economic strata in urban regions and interested in experimenting with smart meters in their apartment building stock. Hence, the project will develop a model only for a closed set of households. The trading will take place only in these apartment buildings.

The project will aim to understand the relative efficacy of the economic drivers versus the social drivers in the context of creating a positive change in energy consumption behavior. This is possible through the availability of a large stock of economically and socially homogenous households groups within the wide network of apartment buildings belonging to the carefully selected builder-developer groups that will be chosen for the study. Control groups will be established wherein only social drivers and only economic drivers will be propagated, as well as groups wherein both will strategies will be employed, with the goal of influencing energy consumption behavior to learn of their relative importance, their differences, as well as the interplay between these two forces.

How much will emissions be reduced or sequestered vs. business as usual levels?

The amount of energy conserved by shifting to energy efficient appliances adopting the smart metering technology will significantly reduce CO2 emissions. Using the appropriate Emission Factors the amount of emissions reduced can be calculated.

What are other key benefits?

  • Reducing power shortages by using smart metering.
  • Reducing CO2 emissions by incentives to reduce consumption and to shift to energy efficient appliances.
  • Inducing behavioral shifts to more energy awareness and energy efficiency.
  • Establishing a market for capacity (in watts) to increase efficiency in the electricity sector.
  • Designing adequate smart grid products which can fulfill the requirements for the aforementioned aims.
  • Creating knowledge on preferences of smart metering, dynamic tariffs and load management.
  • Identifying different consumer groups who react different to load management technologies.

What are the proposal’s costs?

Project Cost Components

Cost in US Dollar ($)

Research Support: 30,000

Preliminary Survey & Analysis: 60,000

Project Plan Development – Technical & Management Consulting: 112,500

Project Plan Development – Social Consulting: 67,500

Project Plan Development – Financial Consulting: 45,000

Social Workshop/Seminar/conference: 15,000

Concept Testing: 45,000

Documentation: 25,000

Technical and Social support activities after implementation: 45,000

Finance and Admin staff: 315,000

Total: 760,000

  • Note: Office infrastructure, travelling, resources for workshop/seminar organisation (external consultants/experts, venue, equipments etc.) and other admin operations costs are not included in the above estimated cost.

Time line

Project Steps:                                   Duration (no. of month)

Research Support                                                    2

Preliminary Survey & Analysis                                 3

Project Development                                                2

Concept Testing                                                        3

Project Implementation                                             7

Social Workshop/Seminar/conference                   1.5

Documentation                                                        2.5

Technical and Social support activities-                    3

after implementation

Total Duration (no. of month)                               24

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