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RENEWABLE ENERGY 69 MR. RAVI SEETHAPATHY P; INDIA SMART GRID FORUM Is India Ready for Energy Storage? This question is often asked whether countries with unreliable (or less reliable) grids should invest in energy storage applications. The popular consensus is to fix the grid first, then take up energy storage. This is correct, sort of. I was invited to speak (and moderate two sessions) at the India Energy Storage Alliance (IESA) Annual Conference in New Delhi in December 2015 (I am on its Advisory Board). All the big global players in the energy storage industry were present (almost all representing advanced lead-acid and LiON technology). Earlier this year in Bengaluru, at the India Smart Grid Forum 2015 (I am on its BOG), I was questioned after my talk; whether Energy Storage was appropriate for a “frail grid” like India (i.e. less reliable grids). One even went to say that India should not be concerned with Energy Storage till it fixes its grid problems. This questioned emerged again at IESA 2015. My views have not energetica INDIA · JAN | FEB16 changed (over the years) that India is indeed ready for Energy Storage. It all depends on the application. Here’s why The use of Energy Storage within a developed nation’s context is that of a fast-acting flexible grid resource; a “peak shaver”; a “load-shifter”; a T&D deferral asset, etc. All these business cases are priced under the electricity market norms. Today, it is primarily the first two categories that have seen positive business cases (10 MW or larger) in the grid ancillary services market. In India’s case there are other needs over and above fast-ramping and peak-shaving. These are (1) managing high penetration of renewables; (2) Remote off-Grid development and (3) Short-term islanding to prevent brownouts (India has the world’s largest number of residential battery backups and commercial diesel gensets. India’s high penetration wind generation in its southern coastal states at near-zero operating cost (together with accelerated depreciation over the years) are now essentially “sunk” investments in the grid. Yet, the regional grid often sheds wind-generation due to its inability to manage it (limited T&D capacity). So, if suitable tariff for energy storage is designed (with accelerated depreciation), then such Wind-ESS combo (avoiding duplicate balance of plant) would be far more valuable on the utilization side that sees load shedding regularly. This price of energy storage ($/Kwhr) should be set as a comparator to load-shedding economic cost (not fossil plant marginal cost). The same would go for India’s accelerated solar program which will outstrip the wind program in just a year or so. Second, India is a country where thousands of its villages and smalltown outskirt hamlets have no access to grid or extremely poor access (less than a few hours per day). Again in this “no grid/ frail grid” scenario, energy storage (duly charged by solar/wind/ biomass local generation) would be an economic “adder” to such communities in the short/medium term till T&D wires are eventually brought. With a good design of energy efficiency/conservation technologies (beyond just the LED light bulb), the average storage need (and hence costs) comes down dramatically. Here the capex adder would be the avoided cost of grid T&D lines for 10-15 years or so. Thirdly, Indian younger generation consumers are getting tired of living “very frugally” during load shedding (often two fans and a TV). Again investing in energy efficiency appliances, they can live a near-normal lifestyle during load shedding using advanced energy storage devices (less diesel backups). So, Energy Storage does make sense in a “frail T&D grid, it depends on how one deploys it. We often tend to use our “deterministic” western solutions to solve “probabilistic sub-par” eastern scenarios. The price is not grid-price but applicable economic price. shear, turbulence intensity conditions which can impart some additional uncertainty in the calculation. Multiple measurement test couple with operational performance can reduce this uncertainty to a certain extent. Long-term wind variability – Perhaps the most important and the most discussed one from an investor perspective; uncertainty associated with the long-term wind resource prediction at the monitoring site. To predict the long-term wind speeds at the monitoring location, correlations between measured short-term site data and long-term reference site data are carried out as part of the MCP method. Usually the reference site is a near meteorological station which has been recording wind speeds and directions for several years. In the absence of a quality/consistent ground measurement, reanalysis data sets (NCEP/NCAR, MERRA etc.) are widely being used across the globe (though there are claims and counter claims on the consistency and accuracy) now. An uncertainty value is derived from the amount of data available and the correlation strength between the reference and site datasets. Poor reference data quality can affect this correlation. Typical problems encountered include missing, equipment degradation etc. Quality long term data sets and good correlation with site measurements will reduce the uncertainty. Other Uncertainties – Wake loss model and loss factor assumptions are other two standard sources of uncertainties. While an intelligent design of the wind park coupled with a proven wake model will help to reduce the uncertainty due to the first, a thorough analysis of the wind farm losses (transmission loss, contractual availabilities, grid availability based on historic information if the station exists) will reduce the loss factor assumption related uncertainties. Calculation and Presentation of uncertainties The wind speed related uncertainties are quantified, con


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