Comments / Suggestions on the Draft Procedure formulated towards implementation of MERC (Forecasting, Scheduling and Deviation Settlement for Solar and Wind Generation) Regulation 2018 in the State

del2infinity Energy Consulting Pvt. Ltd.
(An accurate solution for Wind Power & Solar Power Forecasting & Scheduling)
Email: contact@del2infinity.xyz || Website: www.del2infinity.xyz;
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18/3/9 Kumud Ghosal Road, Phalguni Kolkata – 700057, West Bengal
Page 1 of 39
5th
November 2018
To
Maharashtra State Electricity Transmission Co. Ltd.
Prakashganga, Plot No.C-19, E-Block,
Bandra-Kurla Complex, Bandra (E),
Mumbai - 400051
Sub: Comments / Suggestions on the Draft Procedure formulated towards
implementation of MERC (Forecasting, Scheduling and Deviation Settlement for
Solar and Wind Generation) Regulation 2018 in the State
Dear Sir,
We have been doing forecasting for various wind and solar plants situated in various states
of India. We have faced the some of the issue in relation to the compliance and
implementation of the MERC (Forecasting, Scheduling and Deviation Settlement for
Solar and Wind Generation) Regulation 2018 (‘Regulation’) read with state other
regulation for better stability, management and grid discipline. We personally feel that
there are various issues which shall be raised by various stakeholders in various judicial
and quasi-judicial forum for proper clarification if not provided by SLDC. We have following
comments/ issues in this regard, which we would like to raise at this point of time for your
consideration and clarification:
1. Grid Stability should be maintained
The legislative intention for these Regulations is intended to facilitate Grid
integration of Wind and Solar energy generated in State while maintaining Grid
stability and security as envisaged under the State Grid Code and the Act, through
forecasting, scheduling and a mechanism for the settlement of deviations by such
Generators. DSM has also been released by the SLDC at this point of time, but
SLDC has not come with clean hands. We feel that this is high time that SLDC
should submit before public that how much grid stability has actually happened due
to implementation of the Regulation. We understand that the Regulation has not
been implemented or introduced only for collecting penalties from the IPPs but also
to ensure that grid stability and grid discipline is maintained. SLDC has a statutory
liability to ensure that grid stability is maintained. We feel that SLDC has concentred
more on collecting the penalty less on the stability on grid or grid discipline. Only
collecting penalty will just an event of unjust enrichment. Unjust enrichment is not
at all expected at the cost of grid discipline. An accurate forecasting shall reduce
the probability of backing down and curtailment.1
1 http://www.saurenergy.com/solar-energy-articles/forecasting-mitigate-back

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2.
2.2
The QCA shall have the capabilities of
Modeling wind energy generation potential
on seasonal time scales with impact
surfaces, a tool to visualize the wind energy
generation potential in “Climate Space”.
Why the capabilities of Modeling
solar energy will not be
considered? How the capabilitied
will be defined? Who will define the
capabilities?
2.3
The QCA shall have the experience in the
field of Wind/Solar Power forecasting and
scheduling in different terrain and regions
for minimum period of one (1) year including
pilot project work with appropriate accuracy
levels in forecasting.
Who, how will check the approprite
accuracy? As per the DSM penalty
in Rajasthan, AP which QCA is as
per with the satisfactory accuracy
level?
2.4
The financial strength of the QCA must be
such that it should be in a position to handle
the risk of penalties due to deviation charges
applicable to generator. Considering this,
the Average Net Worth of the QCA for
forecasting & scheduling services must be in
positive amounting to at least Rs.1.50
Crores (Net worth = Share Capital +
Reserve – Revaluation Reserve – Intangible
Asset – Misc. Expenditure to the extent not
written off – Carried Forward Losses –
Liabilities) in the current financial year which
should reflect from its audited balance sheet
or CA’s certificate.
Any QCA exist in India? They are
already got huge penalty in other
states. If the BG are evoked…..
2.5
QCA should have established team of: a.
Renewable resource analyst, b. Modeling
statisticians, c. Energy model, d. Software
developers e. 24 x 7 operation and
monitoring team,
Regulation must not say how QCA
should work. Moreover basic
finance accounting is missing here

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2.5
The QCA shall possess/provide authorization
as per Annexure - I from at least 51 % of
the Generators connected in the Pooling
Station in terms of their combined installed
capacity for appointment as QCA. (Not
applicable if Generator is connected through
dedicated inter-connection facility with the
Grid) at the time of Registration.
If there is no mutual consensus of
other 49% Generators for going
with the samee QCA, then SLDC
should responsibility of their
penalty. How SLDC can force
mutual consensus if one QCA is
not performing well but connected
with 51% generators.
3.3
The QCA shall establish a Control Center
round the clock and shall have complete
control over Wind/Solar injection feeders
connected to pooling stations. The Control
Centre shall have facilities of voice
communication with MSLDC and Wind/Solar
Generators with voice recording facilities,
Fax machine and internet connection
available for all the 24 hours. The QCA shall
comply the instructions of the System
Operator in normal condition as well as
during emergencies,appropriate decisions
taken by the System Operators in view of
Grid security and safety.
The role of SLDC must be included
here. As per our understanding the
SLDCs are not well equipped with
required facilities. If some problem
arises at SLDCs end, how to check
that possibilities?
3.7
Energy accounting and Deviation monitoring
for each pooling Station of wind and/or solar
power generation shall be undertaken
separately.
It means that geographic
agrregation or agrregation of
pooling stations is not possible.
3.18 Reduction in authorization from generators
in a pooling Station below 51 % of the total
installed Capacity of the Pooling Station.
If reduction is done, what will be
the status of other 49%
generators. It also means that
49% generators are not using
same QCA.

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3.19
Keep MSLDC indemnified at all times and
shall undertake to indemnify, defend and
save the MSLDC harmless from any and all
damages, losses including commercial
losses due to forecasting error, claims and
actions including those relating to injury to
or death of any person or damage to
property, demands, suits, recoveries, costs
and expenses, court costs, attorney fees,
and all other obligations by or to third
parties, arising out of or resulting from the
transactions undertaken by the Generators.
The QCA shall submit the indemnity bond
(Format – 3) on Non-Judicial Stamp Paper of
value notified from time to time by the State
Government at the time of registration.
How it is possible if it is the fault of
SLDC?
4.4
Once the QCA is registered, the generator/s
shall not re-appoint another QCA, at least
within three (3) years from the date of
successful registration of the QCA at MSLDC.
Provided that in case of defaults by the QCA,
the generator/s can reappoint another QCA
by giving prior notice of three (3) month to
MSLDC and the process of registration of
new QCA shall be carried in accordance with
these regulations and procedures.
How it is possible if QCA is not
working well in all seasons?
Suppose due to QCAa fault IPPs
are getting huge penalty. Why
IPPs have to go with the same QCA
for next three years? Why three
months notice? Penalty in three
months is really huge. Just
consider the DSM of other states.
If there is issues in de-pooling
mechanism, why QCA can not be
changed?

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5.2
The MSLDC shall be responsible for
scheduling, communication, coordination
with QCAs’. Forecasting of the renewable
energy generation shall be done by the
MSLDC and the forecast will be available on
the website. The generation forecast shall be
done on the basis of the weather data
provided by IMD or on the basis of other
methods used by the Forecasting Agency
whose service may be availed. However, the
forecast by the MSLDC shall be with the
objective of ensuring secure grid operation.
MSLDC should open public
document in each month in how
they are ensuring the secure grid
operation using the forecast data.
How GRID is stable using the
forecast data.
5.3
The MSLDC shall maintain records and
accounts of the time blockwise Schedules,
the actual generation injected and the
deviations, for the Pooling Station and the
individual Generators separately.
Grid is created in public money
and these data must be available
in public space. SLDC should
maintain absolute transperancy.
9.3
In the event of QCA adopting forecast
provided by MSLDC, charges amounting to
Rs. 3,000/- per Pooling Station per day, shall
be paid by the QCA to MSLDC. The
consequences of any error in such forecast
provided by MSLDC which results in a
deviation from scheduling shall be borne by
the concerned Generators through their QCA
and QCA shall indemnify MSLDC on account
of the commercial impact.
F&S cost is very high and MSLDC
is not taking any gurantee on their
own forecast. How they are
assuming that the IPPs will have
forecasting & scheduling
procedure? The accuracy of
MSLDC forecast must be available
and must be open. SLDC must
open their experience in
forecasting at least for last one
years.
9.4
The MSLDC shall consolidate and forecast,
based on various parameters and weather
data obtained from IMD or from any other
forecast service provider (which could be
different from that provided by QCA)
Can you please specify the use of
it.
9.6 The Pooling Station wise day ahead forecast
submitted by QCA shall be on 15 min time
block basis in MWh up three decimal places.
How the penalty will be calculated
if there is error after three decimal
place? IPPs have to pay the
penalty or not?
9.8
Process for submission of a day ahead
Forecast for Intra-State Transactions is as
follows:
If SLDC misses the timing, what
will be consequences and who will
pay for it?

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13.8
In case of insufficient/exhausted corpus,
QCA shall make up corpus amount within
seven (7) days from receipt of such
information from MSLDC. Failure to make up
corpus amount within prescribed time limit,
the Wind/Solar generation which QCA is
representing shall not be scheduled.
All the exsisting QCA has huge
financial liablity as per the DSM
published till date. Considering the
fact that the DSM amount is much
higher for their networth and
financial condition, Then who will
schedule?? NEW QCA??
13.9
If the QCA fails to pay deviation charges
within Ninety (90) days from the issue of the
accounts and billing, MSLDC shall utilize the
corpus deposited by the QCA during
registration process for payment of deviation
charges.
What is corpus amount as
percentage of Penalty? Is it a
usefull settlement? We think the
penalty amount is much higher
than the corpus amount
considering the other states.
3. QCA: a curse in disguise
a) In our opinion this section related to appointment of QCA, needs to be amended
and clarified, as there may be a situation when there is no consensus in between
all the wind and solar generators for a single QCA. If there is no mutual
consensus between different Solar or Wind power generators single QCA
cannot work. A provision must be there to accommodate Power generator who
can go alone without opting for a QCA or would like to change once suffered by any
QCA’s inaccurate performance. Monopoly by any QCA for any pooling station,
should not be encouraged by statute is illegal in limine. The way SLDC is
encouraging the QCA, it apparently seems that QCA has more stake at risk than
the IPPs. QCA being a supportive function should not be encouraged at the risk and
cost of grid stability and discipline.
b) Moreover, IPP should have the liberty to submit its own forecasting, it will
be unjust, irrational, unreasonable if IPPs are compelled to choose with a single
QCA even if the QCA fails to perform with accurate forecasting and IPP suffers for
the activities of the QCA in DSM. In current situation, we have received requires
from various IPPs for providing F&S services, but as because their no consensus
for all the IPPs in a particular sub-station so they are not able to appoint different
QCA or even not able to provide the forecasting and scheduling without the existing
the QCA.
c) If only one QCA is appoint for a particular sub-station and SLDC disagree to appoint
second QCA or alternative QCA, in such case if the IPPs raise an dispute/issue with
the accuracy of the QCA’s forecasting accuracy, then SLDC should compensate
such IPPs for inaccurate forecasting by such QCA, as the IPPs does have a
choice to appoint its own QCA or forecasting service provider, so IPPs cannot be
compelled to pay unnecessary DSM charges for SLDCs actions.

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d) Lastly how, wind and solar generators/IPPs can appoint a single/same QCA when
wind and solar energy forecasting is totally different and have different time frame.
So, wind & solar forecasting & schedules should be separated, otherwise it will be
gaming and such QCA shall always be inclined to aggregated forecasting
unstabilising & disturbing the grid.
e) It can be shown that the plant with lower capacity but having good ‘capacity
factor’ will suffer most due to the presence of single QCA. The detailed
mathematical explanation is shown in Appendix I. The lower capacity plant in
same sub-station must get the fair chance in submitting their forecast without
opting for QCA.
Appendix-I
Measure using Central tendency
The major assumption in aggregated forecast is that the positive and
negative error can cancel each other in the long run and hence the
average error in aggregated forecast is very small or under
acceptable limit.
To formulate the theoretical structure in a simplified manner lets
consider two solar/wind plants of capacity C1 and C2. Without
representing the detail algebraic construction of the error
distribution, the aggregated forecast error at i-th time-block can be
represented as
𝑒 𝑎𝑔𝑔(𝑖) = 𝜔1 𝑒1(𝑖) + 𝜔2 𝑒2(𝑖)
Where 𝜔1 and 𝜔2 are the scaling factor such that 𝜔1 + 𝜔2 = 1 and
𝜔1
𝜔2
=
𝐶1/𝐶2. Hence, in the average case (or the expected value in error
according to statistical theory) we can consider
𝜇 𝑒 = 𝜔1 𝜇 𝑒1 + 𝜔1 𝜇 𝑒2
Where 𝜇 𝑒, 𝜇 𝑒1and 𝜇 𝑒2 are the average error in aggregated forecast,
forecast of plant 1 and forecast of plant 2 respectively. Since 𝜔1 and
𝜔2 are in the ratio of their plant capacity, the existent de-pooling
mechanism considers to divide the penalty due to deviation of two
plants according to their plant capacity or depending on the ratio of
the energy generation at a particular time-block in which the penalty
exists.
This assumption in aggregated forecast is correct in some cases, but
not sufficient, as it does not consider the variability analysis of the
power generation and only plays with the measure of central
tendency of the error distribution. This incomplete theory in the
agrregated forecast is an issue and hence no valid logical framework
is available in calculating the ‘de-pooling’ mechanism in calculating
the penalty payable for each plant in case of aggregated forecast.

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Measure of Dispersion
The generation of solar/wind power is best described using the
Wold’s representation theorem according of which solar/wind
generation can be represented as the summation of deterministic
and stochastic time series. The error in forecasting comes from the
stochastic time series in Wold’s decomposition while the maximum
portion of the solar/wind power generation is deterministic. Hence,
1. Solar/Wind power generation is not random. Moreover, the
ramping occurrences have specific distribution depending on
plant characteristics according to Wold’s theorem.
2. The power generation characteristics (or statistical
distribution) is not same for each PV panel or each plant of
the group of aggregation
Considering the variation in error forecasting the variance of the
aggregated error can be represented as,
𝜎𝑒
2
= 𝜔1
2
𝜎𝑒1
2
+ 𝜔2
2
𝜎𝑒2
2
+ 2𝜔1 𝜔2 𝜌𝜎𝑒1 𝜎𝑒2
Where 𝜎𝑒
2
, 𝜎𝑒1
2
and 𝜎𝑒2
2
are the variance in the error distribution for
aggregated forecast, forecast of plant 1 and forecast of plant 2
respectively. Here 𝜌 is the correlation coefficient. Considering the two
plants are almost in the same location, this value tends to 1, i.e. 𝜌 →
1. With some simple algebraic manipulation, it can be shown that, if
𝜌𝜎𝑒2 > 𝜎𝑒1, which is a natural phenomena unless the characteristics
and power generation patterns in both plants are same,
𝜎𝑒1 < 𝜎𝑒 < 𝜎𝑒2
Hence, in the long run, the variance of the error distribution lies
between the variance of each plant. Without much loss of generality,
we can consider the error distribution in forecasting of solar /Wind
follows a Gaussian distribution with mean 0 but with different
standard deviations as shown in the figure.

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For the error distribution, the area under the curve in -15% - +15%
can represent the accuracy of the forecast as this represents the
probability that the plant does not have to give any deviation penalty
as the deviation error is under +/- 15%.
As shown in the figure, since the standard deviations are different
for two plants, the accuracy of plant 1 is reduced due to the
aggregated forecast. Hence, in the long run, the plant 1 is actually
paying the penalty due to deviation of plant 2 due to aggregated
forecast.
Hence, considering the multiple plants we can state that, with
aggregation, the occurrence of high variability in the
generation of one plant affects the error of other plants
having stable generation even in the long run. Interestingly,
plant specific forecasting does not have this type of anomaly
as it solely depends on its own performance not affected by
the performance of other plants.
4. Solar/Wind Plant specific Forecast should be encouraged rather than
formation of QCA
a) Team Del2inifnity believes that system-operators needs the plant specific forecast
and schedule to maintain the grid stability. Ms. A Axilium Jayamary, Director
(Operation), TANTRANSCO at Panel discussion on ‘Current practices in Wind/Solar
Power forecasting’ at ‘International Workshop on “Current Practice in Wind and
Solar forecasting” organised by NIWE & IWPA,2
had correctly expressed her opinion
2 January 22, 2018, Hotel Trident, Chennai;

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that from system-operators point of view, system-operators needs the plant
specific forecast and schedule to maintain the grid stability and discipline.
b) Performance of one plant affects the performance of other plant. The question
arises who is paying whose penalty.
c) There exists no standard de-pooling mechanism3
due to the following reasons:
i. All plants under same pooling substation may not have similar nature
PV/Turbine
ii. All plants under same pooling substation cannot have same solar
insolation/wind speed distribution at same time
iii. All plants under same pooling substation cannot have same transmission
loss
iv. All plants under same pooling substation cannot have same inverter
efficiency/characteristics
v. Plants under same pooling substation differs in PPA
vi. Plants under same pooling substation differs in their capacity factors
d) Since proper de-pooling mechanism is not a technically feasible option, the F&S at
Pooling level can be seen as an option but not only option of F&S. All the QCA has
not clue for acceptable de-pooling mechanism. Most of the cases QCA is forecasting
even if they do not have any data or received any from the IPPs, which is actually
a gaming. All the QCA should come with clean hands. We are aware of cases where
the IPPs has disputes with the OEMs for last few years, and OEM has not provided
any data, but still QCA has been forecasting for the sub-station without taking up
the matter with SLDC. This is kind of activity by QCA should not be encouraged.
e) If a Solar/Wind plant/IPP wants to submit their own forecast separately, the
possibility must be entertained and encouraged and the plant must get a fair chance
to present their case properly.
f) A provision needs to incorporated in the Regulation to submit F&S separately by a
plant without opting for QCA.
5. Aggregation of Multiple Pooling Station must not be allowed at the risk
and cost of grid stability and discipline
a. The forecast of aggregation of variable energy generation at multiple pooling
substations must not be allowed in F&S as it brings the instability of the grid
network.
b. F&S with geographical integration of different pooling station/sub-station
increases the non-reliability of maintaining demand-supply ratio in the
transmission-distribution network.
The detail mathematical framework is shown in appendix-II for better
understanding.
Appendix-II
3 http://www.forumofregulators.gov.in/Data/Meetings/Minutes/TC/24.pdf

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Without introducing the complex transmission-distribution network structure, a
simple computational framework describes that the aggregated forecast not
only violates the regulations in the name of relaxation or compromise, but this
type of forecast plays with the stability of the grid making the grid an unstable
one which has socio-economic consequences.
Without going complex structure of grid network let define a simple structure
where we have three variable generation grid nodes say G1, G2 and G3 for
simplicity let define three Load dispatch points L1, L2 and L3 such that L1 lies
between G1 and G2, L2 lies between G2 and G3, and L3 lies between G3 and G1.
The structure is made as simple as possible for the energy flow such that a
generation station can distribute its generation in its two nearest Load dispatch
points. For simplification, this analysis considers only energy flow in the network
to find the stability of the network. Any complex grid network can be simplified
into this basic working model.
At any time-instant t, the rectangular box in each network path shows three
variables: the amount of energy (or Power) transferred from Generating node
to Load node, the transmission capacity (in terms of energy or Power) of the
network path and cost of transmission.
X(t) and Y(t) is two basic variables in such a way that considering the generation
of G1, L3 and G2, L1,

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min{𝐺1(𝑡), 𝐿3(𝑡)} ≥ 𝑋(𝑡) ≥ 0
min{𝐺2(𝑡), 𝐿1(𝑡)} ≥ 𝑌(𝑡) ≥ 0
Considering the network capacity between G1 and L3, and the same between G1
and L3
𝑇13 ≥ 𝑋(𝑡)
𝑇11 ≥ 𝐺1(𝑡) − 𝑋(𝑡)
The last two equations transforms into
𝑇13 ≥ 𝑋(𝑡) ≥ 𝐺1(𝑡) − 𝑇11
Similarly, considering the network capacity between G3 and L2, and the same
between G3 and L2
𝑇32 ≥ 𝐺3(𝑡) − 𝐿3(𝑡) + 𝑋(𝑡)
𝑇33 ≥ 𝐿3(𝑡) − 𝑋(𝑡)
𝑇32 − (𝐺3(𝑡) − 𝐿3(𝑡)) ≥ 𝑋(𝑡) ≥ 𝐿3(𝑡) − 𝑇33
Hence
min{𝐺1(𝑡), 𝐿3(𝑡), 𝑇13, 𝑇32 − (𝐺3(𝑡) − 𝐿3(𝑡))} ≥ 𝑋(𝑡) ≥ max{0, 𝐺1(𝑡) − 𝑇11, 𝐿3(𝑡) − 𝑇33}
Similarly, considering the network capacity between G2 and L1, and the same
between G2 and L2
𝑇21 ≥ 𝑌(𝑡)
𝑇22 ≥ 𝐺2(𝑡) − 𝑌(𝑡)
The last two equations transforms into,
𝑇21 ≥ 𝑌(𝑡) ≥ 𝐺2(𝑡) − 𝑇22
Hence,
min{𝐺2(𝑡), 𝐿1(𝑡), 𝑇21} ≥ 𝑌(𝑡) ≥ max{0, 𝐺2(𝑡) − 𝑇22}
Hence we can write,
𝑋 𝑀𝐴𝑋(𝑡) ≥ 𝑋(𝑡) ≥ 𝑋 𝑀𝐼𝑁(𝑡)
𝑌𝑀𝐴𝑋(𝑡) ≥ 𝑋(𝑡) ≥ 𝑌𝑀𝐼𝑁(𝑡)
Where

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𝑋 𝑀𝐴𝑋(𝑡) = min{𝐺1(𝑡), 𝐿3(𝑡), 𝑇13, 𝑇32 − (𝐺3(𝑡) − 𝐿3(𝑡))}
𝑋 𝑀𝐼𝑁(𝑡) = max{0, 𝐺1(𝑡) − 𝑇11, 𝐿3(𝑡) − 𝑇33}
𝑌𝑀𝐴𝑋(𝑡) = min{𝐺2(𝑡), 𝐿1(𝑡), 𝑇21}
𝑌𝑀𝐼𝑁(𝑡) = max{0, 𝐺2(𝑡) − 𝑇22}
At load node L1 and L2 we can state that
𝐺1(𝑡) − 𝑋(𝑡) + 𝑌(𝑡) ≥ 𝐿1(𝑡)
𝐺3(𝑡) − 𝐿3(𝑡) + 𝑋(𝑡) + 𝐺2(𝑡) − 𝑌(𝑡) ≥ 𝐿2(𝑡)
The last equations shows that
{𝐺3(𝑡) − 𝐿3(𝑡)} + {𝐺2(𝑡) − 𝐿2(𝑡)} ≥ 𝑌(𝑡) − 𝑋(𝑡) ≥ −{𝐺1(𝑡) − 𝐿1(𝑡)}
Hence for network stability we have three major working inequalities:
These three inequalities define a region as follows,
If the region A(t) exists for each t then the network is stable i.e. maintaing the
grid is nothing but to maintain the area A(t) positive.

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By simple calculation one can show that A(t) depends on the each value of G1,
G2 and G3 but not on the sum of its values i.e. G1 + G2 + G3. Interestingly since
it is a variable generation and A(t) is not constant but to get the +ve value of
A(t) we need a prediction of G1, G2 and G3 separately but not as a sum or
aggregation of those values. Due the variability the region A(t) becomes as
follows:
Here the red area is actual requirement and the area of A(t) decreases due to
the uncertainty of the generation. Suppose schedule generation of G1, G2 and
G3 are not known separately, then the following situation may arise:
Hence the aggregated forecast at different pooling station creates instability
when A(t) is not positive.

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6. Penalty section again applicable only for Deviation Settlement Mechanism (DSM)
for wind and solar generators, so permissible tolerance limits and deviation band
as stated in the Regulation, i.e. within the limits of >85% &< 115% is also available
at wind & solar individual generators level only, not aggregated / QCA level. So,
it can also be interpreted that for Aggregator or QCA level if there is an aggregated
forecasting and scheduling i.e. clubbing of all the plants as a single plant, then
there is no tolerance available under the Regulation. So, in such case
aggregation of the forecasting and scheduling cannot claim any kind of deviation
relaxation. So, relevant section needs to be deleted, to avoid any kind of ambiguity.
In case of state level aggregated forecasting is allowed then the band and penalty
should the same as is applicable like ±150 MW or 12% which ever is lesser.
7. Even the table states that it is applicable for Wind & Solar generators having PPA.
8. In view of the act, team del2infinity feels that there is no scope for aggregation of
forecasting and scheduling under Regulation, but that should not be mis-
interpreted with aggregated forecasting or scheduling of all the plants as a whole
single, so the heavy penalty of wind generator is shared with the solar power
generators. So, aggregation of forecasting and scheduling should be interpreted in
consonance with reading with all the sections of the Act as a whole, not in peace-
meal. Aggregation of forecasting cannot be jumbled or misinterpreted or misread
with aggregated forecasting without getting into the actual wind turbine or solar
inverter level performance.
9. Moreover, there is no DSM charges or penalty calculation mechanism in the current
act for aggregator/QCA level forecasting or aggregated forecasting as per the
reasons stated above. Penalty should be as per IPPs level and QCA shall collect the
same and deposit with the SLDC.
10. The BG amount submitted by the QCA should be increased to ensure
that SLDC can recover the DSM penalty amount in case SLDC wish to
recover the same. Regulation states that, the financial strength of the QCA must
be such that it should be in a position to handle the risk of penalties due to deviation
charges applicable to generators. Considering this the net worth of the QCA for
forecasting & scheduling services must be in positive amounting to at least Rs. 2.75
Crores in the current financial year which should reflect from its audited balance
sheet or CA‟s certificate.
In case if the net-worth INR 2.75 Crores is already utilised or represented as a
financial strength of QCA in another state for example if any QCA is already
providing forecasting for 2500 MW of Solar & Wind IPPs in Karnataka where the BG
for DSM are Rs. 10,000/MW for Solar & Rs. 43,000/MW for Wind then the QCA’s
financial strength is already exposed for more than its net worth, then what is the
point to have such insolvency criteria, as the QCA will not be able to make any
payment as it is not a solvent company at all:
Case - 1 - QCA is already guaranteed for DSM
Capacity Penalty

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Solar 500 MW @Rs10,000/MW 50,00,000
Wind 2000 MW @Rs43,000/MW 8,60,00,000
2500 9,10,00,000
Case - 2 QCA is already guaranteed for DSM
Capacity (MW) Penalty
Solar 1200 @Rs10,000/MW 1,20,00,000
Wind 1300 @Rs43,000/MW 5,59,00,000
2500 6,79,00,000
In current scenario all the QCA is exposed to more than their net worth or financial
capabilities. The BG submitted is not sufficient for the SLDC to adjust the total
penalty for last 2-3 months DSM. So, the sanctity that the BG will be sufficient for
ensuring the collection of the penalty is not possible. If the BG is invoked in current
situation, SLDC can maximum recover the penalty amount for one month only. It
will be advisable that the QCA should increase the BG amount so that the
Regulation can be implemented with its spirit and essence. In the current scenario
an apparent analysis as per information available following is the condition of all
the QCAs
QCA Name Month State Total DSM Amount (Rs.)
1 RE Connect Jun-18 Rajasthan SLDC
15,06,84,475
2 Manikaran Jun-18 Rajasthan SLDC
3 Statkraft Jun-18 Rajasthan SLDC
4 Manikaran Jun-18 Rajasthan SLDC 9,05,07,772
5 RE Connect Jun-18 Rajasthan SLDC 4,84,37,139
6 Manikaran Jul-18 Rajasthan SLDC 1,15,06,705
7 RE Connect Jul-18 Rajasthan SLDC 10,83,52,655
8 Manikaran Aug-18 MP-SLDC 1,72,41,679
9 RE Connect Aug-18 MP-SLDC 9,08,62,257
51,75,92,682
From this analysis it is clear that all the QCA is going to face a huge financial crisis.
Moreover, none of the SLDC has Bank-guarantees for the whole amount due at the
moment, BG amount submitted is maximum equivalent to one month’s DSM. The
DSM penalty for the months of August 2018, September 2018, October 2018 is yet
to be published for Rajasthan SLDC and September 2018, October 2018 for MP
SLDC. Moreover, AP-SLDC has also published its DSM but has kept it very
confidential for some reasons better known to them. There may be situation that
any taxpayer law-abiding citizen can approach to concerned High Court for bringing
more transparency in SLDC’s activities. SLDC should come clean hands and disclose
what is the penalty imposed and details calculation, block-wise details for reaching
to such decisions of DSM.

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It seems that financial wealth criteria of net worth have been incorporated to
eliminate new entrants in the field or domain in violation of competition law. Please
clarify. In such case does QCA needs to declare what all guarantees it has already
submitted with various SLDCs or what capacity it is forecasting in other states. All
the QCA has huge DSM liability to be met by them which is beyond their
financial capability. This needs to be addressed by SLDC immediately.
Either these QCA should be stopped to provide F&S services or their BG
should be increased proportionately or all the IPPs should be allowed to
provide forecasting and scheduling on their own.
11. Other States has already implemented similar regulation, wherein they have
already stated that DSM charges or penalty at individual plants level. So, a different
interpretation, may create a disbalance in the rules and regulation as well as grid
stability.
12. There is an enormous need for transperancy to be maintained by SLDC,
tomorrow all the DSM related matter shall be reviewed by judicial and quasi-judicial
body, so they shall also expect that SLDC should be able to provide all the details
like block wise penalty as well as other relevant details, to prove their calculation
to be accurate and transparent.
13. We have found that in few cases, the Developers has no deviation. Their
Schedule Power and Forecast power is same or accurate. Which is not practically
possible, neither technically possible. What is the reason behind it, and how the
penalty will be calculated in this case under the regulation?
14. In the DSM we have found that in some cases, the available capacity (AvC)
and the schedule are zero. But there exists some penalty amount. In this case,
which formula is applicable? How SLDC is calculating the penalty in such cases, as
Regulation is silent on such matters?
15. Throughout the months the Available capacity remain fixed in all plants, is
the available capacity (AVC) considered as per the submitted value by the QCA or
IPPs; or SLDC is checking or cross verifying the 'actual' available capacity since it
is the major factor in calculating the error. If there is a misrepresentation in the
AVC then the same shall be considered as gaming which is again an offence. As per
our understanding and data-interpretation of all the available plants, probably the
'available capacity' is not constant in each time-block of the whole month. We are
aware of plants which were not working due to maintenance issue and for other
issues, but we found that the AvC is being considered for the whole capacity? Please
let us know how this issue is being addressed by SLDC?
We humbly submit following proposal for your consideration:
A. A white paper may be submitted by the SLDC to provide the balance of
convenience in between the total DSM recovery vis-à-vis total financial impact
due to the such imbalance should be made to public at large. From DSM penalty
SLDC should not make unjust enrichment. A detailed study may be conducted
to identify the actual ground level situation.

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B. Number of Revision should be reduced and encouraged only if Revision is
necessary. Penal provision needs to be incorporated so that unnecessary
revision should attract penalty.
C. Forecasting and Scheduling data should be actually, intelligently utilised and
should not be just a dump of data like all other data. An intelligent utilisation of
the F&S data will increase the grid stability and discipline and shall reduce the
curtailment and backing down issues.
D. QCA should be pushed to allowed new QCA to step in so that the DSM penalty
may be reduced by all those who have better solutions.
E. QCAs who are forecasting even without collecting the data intentionally or due
to some problem in between the OEM and IPPs should be penalised heavily for
gaming and misusing the regulation. Should also be penalised for unjust
enrichment.
F. New QCA should get chance so that better accuracy can be achieved and both
SLDC and IPPs should be benefitted as the penalty shall be reduced and grid
discipline will be increased. All the QCA who has huge DSM penalty in other
states should also be asked to submit further BG or letter of assurance that
their financial strength is sufficient to meet the penalty. If any of the QCA
becomes insolvent then SLDC shall not be able to recover any penalty as the
Penalty is to be paid by the QCA only. Total exposure of these QCA needs to be
declared and disclosed to SLDC along with sufficient proof.
G. Please let us know how to download the detailed calculation time-block wise
generation, schedule and time-block wise penalty so that we can analyse our
penalty.
H. We are unable to understand why SLDC is getting scared of disclosing the whole
time-block wise generation, schedule and time-block wise penalty. If SLDC fails
to provide transparency, then tomorrow the question may raise that whether
SLDC has given some special benefits to some of the QCA or IPPs? Anyone can
approach to CERC/APERC or vigilance with such claims that SLDC is not
maintain transparency and giving special favour to some of the IPPs and QCAs,
by manipulating their accuracy and reducing their penalty or imposing their
penalty to other IPPs account illegally. All other SLDC like MP SLDC, CG SLDC
has also published all the data in reference to DSM for all the IPPs on their
website. If there would have been any restriction, then MPSLDC/ CGSLDC also
would have not published the same on their website.
I. In view of the above facts and circumstance we humbly submit before you that
all calculation for all IPP may be provided as soon as possible so that anyone
can analyse and compare the same and identify if there are any anomalies in
SLDC’s calculation or in our analysis as the case may be. Else we shall be at
constrain to approach the appropriate forum for necessary instruction and
orders for transparency and clarity.
J. All IPPs should be allowed to provide forecasting and scheduling
Hope good conscience will prevail over all and SLDC shall maintain its transparency and
share all the necessary data asap.
This is humbly submitted before you for your consideration and reference.

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Thanking you.
Yours Faithfully.
For del2infinity Energy Consulting Private Limited
Abhik Kumar Das
B.Tech& M.Tech(Dual)(IIT-Kgh)
Director
A. Annexure – 1 - Game Of Forecast Or Gaming In Forecast4
B. Annexure – 2 - Gaming Or Forecasting?5
C. Annexure – 3 - Forecasting Solar Power Generation In India: A Path Of Unnecessary
Revisions6
D. Annexure – 4 The Dark Truth of QCA: Who is Paying Whose Penalty?7
E. Annexure – 5 Forecasting to mitigate back-down8
4 http://www.saurenergy.com/solar-energy-articles/game-of-forecast
5 https://view.publitas.com/climatesamurai-com/climate-samurai-january-2018-issue/page/1
6 http://www.saurenergy.com/solar-energy-articles/forecasting-solar-power-generation-india-path-unnecessary-revisions
7 http://www.saurenergy.com/solar-energy-articles/the-dark-truth-of-qca-who-is-paying-whose-penalty

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Annexure – 1
GAME OF FORECAST OR GAMING IN FORECAST9
As per F&S regulations, it is clear that the wind and solar power generators
connected to the State grid shall, either by themselves or through a QCA or
through an Aggregator,
Considering National Action Plan on Climate Change, Nationally Determined Contributions
(NDCs), India’s national policies and other initiatives encouraging renewable, India has a
massive target of 175GW of installed capacity from renewable energy by 2022, of which
100GW is to come from solar and 60GW from wind. Considering India as an evolving place
to put up solar businesses with international and domestic investors, the 100 GW target
in Solar cannot be considered as unrealistic or over-ambitious. But the large-scale
penetration of solar power in the existing grid can create the grid instability due to the
variability and intermittency of solar and wind power generation; a proper energy mix of
the conventional energy and RE is required due to which ‘forecasting and scheduling’ of
the solar power generation is an essential requirement for grid management to optimize
the effect of unscheduled fluctuations. Hence CERC, FOR and other state regulations
propose the mandatory requirement of forecasting the variable power (solar and wind)
generation. The forecasting requirement is one of several key aspects of making RE grid
integration cheaper and more scalable, as RE penetration grows, its challenges for the rest
of the grid will increase. Hence, according to F&S regulations, power producers themselves
or third party or QCA or an Aggregator can produce the F&S for the plant.
Game of Forecast
From a mathematical viewpoint, the game of forecasting is best considered as the study
of the temporal evolution of probability distributions associated with variables in the power
generation. Considering the present computation power, the plant specific forecasting is a
doable job. The formal strategy in F&S of wind and solar power generation is predicting
the weather parameters using of NWP (Numerical Weather Prediction) models and
changing the values of weather parameters into power generation using the turbine or PV
models considering the CFD (Computational Fluid Dynamics) based analysis in local areas.
The recent development of deep learning algorithms in ANN (Artificial Neural Network)
based methodologies have created a huge scope in forecasting the power generations.
Considering the uncertainty in the initial value vector in NWP and learning vectors in DNN
(Deep Neural Network), a powerful perspective regarding the forecasting methodology is
to regard it fundamentally as a statistical rather than deterministic solutions as the
stability of the grid needs not just the production of power generation but also the
uncertainty associated with it.
Defining AvC
As per F&S regulations, it is clear that the wind and solar power generators connected to
the State grid shall, either by themselves or through a QCA or through an Aggregator,
furnish week-ahead, day-ahead and Intraday generation schedules for each polling-station
or each generating-station as the case may be by using respective forecasting tools at
their wind-farm / solar facility centric to the SLDC. The insertion of ‘each pooling-station
or each generating-station’ shows the legislative intention that the forecasting has to be
provided for each pooling-station or each generating-station as the case may be. It also
specifies that forecasting should be wind-farm/solar facility centric. Hence, as per
9 http://www.saurenergy.com/solar-energy-articles/game-of-forecast

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different regulations, the error is defined as, %Error = (Actual Generation – Schedule
Generation) x 100 / AvC. Where AvC means for wind or solar generators cumulative
capacity rating of the wind turbines or solar inverters that are capable of generating power
in a given time-block; and only applicable to a solar or wind generator. The act is very
clear and specific, AvC of the wind or solar generator shall be considered for % error
calculation; not the whole portfolio of an aggregator or QCA as a whole, but all the plants’
capacity shall be considered individually and independently. Moreover, the insertion of
‘wind turbines or solar inverters’ also specifies that it is required for plant level individually
or independently not aggregated level as a whole. For interpreting AvC of QCA or
Aggregator as a whole, the word should have been wind plants or solar plants in the
definition. By inserting the word turbine & inverter, the intention of the act is very clear,
that it is required for turbine or inverter level for each plant independently and individually
and not on an aggregated level.
Defining ‘Deviation’
‘Deviation’ means in a time block, for a seller his total actual injection of energy minus his
total scheduled generation and for a buyer means his total actual drawal of energy minus
his total scheduled drawal
The definition of ‘Deviation’ is also not applicable to an Aggregator or QCA, as a Seller /
Buyer is decided depending upon various PPAs as the case may be. Aggregator or QCA
does not have any PPA, so a QCA or Aggregator’s ‘Deviation’ cannot be calculated under
the current Act or methodology, as it is today. For calculating Deviation under this Act only
AvC at plant level, i.e. generator’s level AvC needs to be considered. Otherwise Act is silent
about Deviation calculation of QCA or Aggregator’s level or Deviation by Aggregator or
QCA. So, if we consider the whole portfolio of an Aggregator or QCA to decide his AvC,
then it will be a wrong interpretation of the Act. This definition also clearly states that this
Act is applicable only for wind & solar generators not Aggregator or QCA. So, Aggregator
or QCA can aggregate and coordinate, but all deviation has to be calculated at the
generators level or pooling station level. There is nothing called Aggregator or QCA
Deviation under the Act.
Gaming in the forecast
A forceful adoption of ‘aggregation of forecast’ as ‘aggregated’ forecast creates the
instability in the generation transmission system as it clearly explained in. To nullify the
‘gaming’ in ‘aggregated’ process regulations clearly defines the deviation as
solar/generation centric. It also clearly mentioned in all existing regulations,
the misdeclaration of AvC will be considered as ‘gaming’ which has strict consequences.
Power producers knowingly or unknowingly can take part of the gaming of forecast but for
this activity who will face the consequences scaring the foreign direct or private equity
investment. One has a choice to play games with AvC for an example in a name of
aggregation one can produce following output.
Time Time-Block AvC Schedule
09:30-09:45 39 1800 281.18
09:45-10:00 40 1800 314.05
10:00-10:15 41 1800 355.05
10:15-10:30 42 1800 391.16
10:30-10:45 43 1800 366.13
10:45-11:00 44 1800 374.71
11:00-11:15 45 1800 381.33
11:15-11:30 46 1800 385.65

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11:30-11:45 47 1800 389.06
11:45-12:00 48 1800 389.00
12:00-12:15 49 1800 384.26
12:15-12:30 50 1800 383.37
12:30-12:45 51 1800 385.43
12:45-13:00 52 1800 388.96
13:00-13:15 53 1800 381.89
13:15-13:30 54 1800 380.60
13:30-13:45 55 1800 358.30
This type of scheduling is not only illogical, but violates the different clauses of F&S
regulations. This type of F&S has a no use to stabilize the grid for a sustainable future and
the no-penalty deviation i.e, +/- 15% is applicable to this type of forecasting. Even it
brings the primary question of capacity factor of the overall RE generations considering
the scheduled power and declaration of AvC.
This type of pure ‘gaming’ should be stopped to stabilize the grid otherwise one should
face the strict consequences in the name of ‘gaming in Forecast’ as per the regulation
where it is clearly mentioned that ‘Gaming’ in relation to the Regulation means, an
intentional wrong/mis-declaration of available capacity or schedule by any seller in order
to make an undue commercial gain through charge for deviations. Interestingly, as per
the definition of ‘Gaming’ the QCA or aggregator are not responsible, but through
their act the power producers become responsible for the consequences.
It is already explained in that how the power producers are affected in name
of aggregated forecast. Even according to APSLDC regulations it is already mentioned that
if there is no consensus among the Generators connected in a pooling station or connected
to Grid SS through a common feeder for the formation of QCA, then the generators who
are not willing and consensus with other generators to form QCA can take separate
connectivity from STU/Discoms and have a separate SEM at interconnection point and
furnish the Schedule. Most (transmission) grid operators need to predict the supply and
demand at each grid node, i.e., the pooling station (sub-station) level due to the reason
that transmission lines can often be congested, and are at the level of system control (in
some ways they are the bridge between supply and demand). Unfortunately, the forceful
and wrong adoption of ‘aggregation of forecast’ as ‘aggregated forecast’ can have a huge
financial impact on power producers, transmission company and overall the grid in very
near future considering the massive target of 175 gigawatt (GW) of installed capacity from
renewable energy by 2022.

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Annexure – 2
“GAMING OR FORECASTING?”10
The RE (renewable energy) can be seen as a transformative solution to meet steadily rising
energy demand as well as economic challenges and to mitigate the climate change and to
reduce the carbon emission from traditional energy sources. At present, India has a target
of 175 Giga Watt (GW) of installed capacity from renewable energy by 2022, of which
100GW is to come from solar, 60GW from wind. In addition, India’s NDC goal is to achieve
40% of total installed power generation capacity from renewable energy by 2030.
With growth of RE (renewable energy) in India, the variability and the intermittency
became the two major parameters in the generation-transmission network of the power
supply. Hence the large-scale deployment of renewable energy technology involves a
combination of interventions involving policy and regulatory mechanisms, technological
solutions and institutional structures.
Due to natural phenomena no one can control the generation patterns of the wind and
solar energy unlike to the other energy sources. The major issue is to forecast and to
schedule the variable generation for a proper grid management. Without proper forecast,
the grid become unstable. Hence CERC, FOR and other state regulations propose the
mandatory requirement of forecasting the variable power (solar and wind) generation as
the forecasting requirement is one of several key aspects of making RE grid integration
cheaper and more scalable.
Sometime state-level prediction of variable power generation can be useful to see the
overall generation, but for the smooth operation of the transmission network, the grid
management requires the forecast at each node of the generation. Hence, for proper grid
management, the temporal and spatial granularity of the forecast methodology are the
crucial issues in managing the grid.
According to CERC and FOR model regulations, the granularity of the temporal space is
considered as 15 minutes defining it as 1 time-block. To define the spatial granularity the
concept of ‘Available Capacity’ is introduced while defining the %error in deviation where
the error is defined as, %Error = (Actual Generation – Schedule Generation) x 100 / AvC.
Where AvC means for wind or solar generators cumulative capacity rating of the wind
turbines or solar inverters that are capable of generating power in a given time-block.
Here the AvC considers the minimum spatial granularity of forecasting as it is generation-
centric and remains same in all regulations. The act is very clear and specific, AvC of the
wind or solar generator shall be considered for % error calculation; not the whole portfolio
of an aggregator or QCA as a whole, but all the plants’ capacity shall be considered
individually and independently.
Since 'Deviation' means in a time block, for a seller his total actual injection of energy
minus his total scheduled generation and for a buyer means his total actual drawal of
energy minus his total scheduled drawal; and Available Capacity is solar or wind
generation-centric as per the Act, so a QCA or Aggregator’s ‘Deviation’ cannot be
calculated under the current Act or methodology as Aggregator or QCA does not have any
PPA. For calculating Deviation under the Act only AvC at plant level or pooling station level,
10 https://view.publitas.com/climatesamurai-com/climate-samurai-january-2018-issue/page/1

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i.e. generator’s level AvC needs to be considered. Otherwise Act is silent about Deviation
calculation of QCA or Aggregator’s level or Deviation by Aggregator or QCA. So, if we
consider the whole portfolio of an Aggregator or QCA to decide his AvC, then it will be a
wrong interpretation of the Act. This definition also clearly states that this Act is applicable
only for wind & solar generators not Aggregator or QCA. So, Aggregator or QCA can
aggregate and coordinate, but all deviation has to be calculated at the generators level or
pooling station level. There is nothing called Aggregator or QCA Deviation under the Act.
Though the existence of QCA and the aggregator is as per regulation, the definition of AvC
and ‘deviation’ remains unchanged and logical as the forecast is not only to predict the
overall generation but to predict the generation at each grid-node to facilitate the
transmission. For proper grid management the system operators depend not only the
overall generation, but on the predicted generation of the grid-node as the transmission
capacity is not infinite and the grid cannot be considered as infinite source or sink of
energy.
But knowingly or unknowingly encouraging the forceful adoption of ‘aggregation of
forecast’ as ‘aggregated’ forecast creates the instability in the generation-transmission
system, violates different clauses of the regulations and starts the ‘Gaming’ in forecast
methodology. Defining the ‘AvC’ as the summation of different pooling stations or
cumulative sum of portfolio not only violates the regulations, it also paralyses the grid
system and can bring a huge financial impact on power producers, transmission company
and overall the grid in very near future. And interestingly, it is not a forecast at all, dividing
the wrongly defined ‘deviation’ with high value of wrongly defined ‘AvC’ one can minimize
the error in the overall generation (which is actually not required for the grid) but is it as
per regulations or is it facilitate the transmission and overall the stability of the grid?
Because the ‘Gaming’ in relation to the Regulation means, an intentional wrong/mis-
declaration of available capacity or schedule by any seller in order to make an undue
commercial gain through charge for deviations. Interestingly, as per the definition of
‘Gaming’ the QCA or aggregator are not responsible, but through their act the power
producers become responsible for the consequences and this ‘Gaming’ policy can bring a
massive transmission failure due to the spatial dispersion of power generation of different
grid nodes.

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Annexure – 3
FORECASTING SOLAR POWER GENERATION IN INDIA: A PATH OF
UNNECESSARY REVISIONS11
To achieve 40% of total installed capacity (power generation) from renewable energy by
2030 as per India’s NDC goal, India has a target of 175 gigawatt (GW) of installed capacity
from renewable energy by 2022, of which 100GW is to come from solar, 60GW from wind.
Taking India as an evolving place to put up solar businesses with international and
domestic investors, the 100 GW target in Solar cannot be considered as unrealistic or
over-ambitious.
The major issue in large scale penetration of solar power is the variability and intermittency
of power generation which creates the grid instability and the system relies on a proper
energy mix of the conventional energy sources. To optimize the effect of unscheduled
fluctuations, ‘forecasting and scheduling’ of the solar power generation is an essential
requirement for grid management. Hence CERC, FOR and other state regulations propose
the mandatory requirement of forecasting the solar power generation. If the error in
scheduling the power is more than a certain limit, then there exists a penalty due to
deviation, but for solar power forecasting 8 Intraday revisions (one revision in every 1
hour 30 minutes) are allowed. This revision is effective from 4th
time-block (1-time block
= 15 minutes) onwards. Here the revisions are not mandatory, but revision is useful to
tune the forecast accuracy.
Daily solar power generation shows a periodical trend due to which forecasting is
comparatively easy using the proper pattern search algorithms which detects the
possibilities of unscheduled fluctuations considering the weather models. Revision in
forecasting is necessary, but the recent developments of proper pattern recognition
techniques using DNN (Deep Neural Network) in forecasting methodology reduce the
number of intraday revision as well as optimize the penalty due to deviation for the power
generators. If the similar optimized forecasting solution is possible in lesser number of
revisions, providing unnecessary intraday revision in every one or two hours can increase
the indirect cost of grid stability.
To prevent the unnecessary intraday revision in the solar power forecast, the cost per
revision in scheduling is already imposed at the SLDC level for some variable power
generators depending on their power purchase agreements and at present this charge is
same as the cost of revision of scheduling other energy sources. Here only R0 is free and
other revisions are allowed but chargeable. This issue creates an extra financial burden
for the power generators.
Though the present draft regulations in forecasting and scheduling of solar power
generation propose the allowed revisions to be free, the unnecessary utilization of intraday
revision strategy increases the complexity of demand-supply stability of the grid due to
which the cost per revision is already imposed in some cases. Hence a proper forecasting
in solar is an essential requirement which not only shows good forecast accuracy and
reduces the penalty due to deviation, but it must optimize the penalty in the minimum
number of intraday revisions for power generators and must have an acceptable level of
accuracy to maintain the grid stability.
11 http://www.saurenergy.com/solar-energy-articles/forecasting-solar-power-generation-india-path-unnecessary-revisions

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Annexure – 4
The Dark Truth of QCA: Who is Paying Whose Penalty?12
The solar and wind power generation shows a promising future in India. But due to the
variability and intermittency, large scale renewable energy penetration in existing grid is
a challenge and the proper policy and regulatory mechanisms, technological solutions and
institutional structures are key issues in solar energy penetration. The ‘Forecasting and
Scheduling’ (F&S) of variable renewable energy (Solar and Wind) generation is an essential
requirement of the stable grid system due to the balancing challenge in load and
generation. The concept of forecasting and scheduling of renewable energy generators and
the commercial settlement was introduced in Indian context by CERC through Indian
Electricity Grid Code (IEGC), 2010. Considering the recent development of different state
regulations, the DSM charges are to be computed on a monthly basis. Hence IPPs have to
submit their day-ahead power generation forecast and schedule (F&S) to SLDC to manage
the grid stability.
1. Grid instability in aggregated forecast / Geographical Integration
The grid is not of source/sink of infinite capacity and transmission capacity is not infinite
hence as per regulations the F&S is generation centric. But interestingly, in name of
forecast few stakeholders are encouraging the aggregation of different pooling station
having different spatial position violating the regulations and this aggregation breaks the
basic structure of grid-network with a massive penetration of RE energy in the grid.
Without introducing the complex transmission-distribution network structure, a simple
computational framework in this article describes that the aggregated forecast not only
violates the regulations in the name of relaxation or compromise, but this type of forecast
plays with the stability of the grid making the grid unstable which has socio-economic
consequences.
Without going complex structure of grid network let define a simple structure where we
have three variable generation grid nodes say G1, G2 and G3 for simplicity let define three
Load dispatch points L1, L2 and L3 such that L1 lies between G1 and G2, L2 lies between G2
and G3, and L3 lies between G3 and G1. The structure is made as simple as possible for the
energy flow such that a generation station can distribute its generation in its two nearest
Load dispatch points. For simplification, this analysis considers only energy flow in the
network to find the stability of the network. Any complex grid network can be simplified
into this basic working model.
At any time-instant t, the rectangular box in each network path shows three variables: the
amount of energy (or Power) transferred from Generating node to Load node, the
transmission capacity (in terms of energy or Power) of the network path and cost of
transmission.
12 http://www.saurenergy.com/solar-energy-articles/the-dark-truth-of-qca-who-is-paying-whose-penalty

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X(t) and Y(t) is two basic variables in such a way that considering the generation of G1,
L3 and G2, L1,
min{𝐺1(𝑡), 𝐿3(𝑡)} ≥ 𝑋(𝑡) ≥ 0
min{𝐺2(𝑡), 𝐿1(𝑡)} ≥ 𝑌(𝑡) ≥ 0
Considering the transmission capacity between G1 and L3, and the same between G1 and
L3
𝑇13 ≥ 𝑋(𝑡)
𝑇11 ≥ 𝐺1(𝑡) − 𝑋(𝑡)
𝑇13 ≥ 𝑋(𝑡) ≥ 𝐺1(𝑡) − 𝑇11
Similarly, considering the transmission capacity between G3 and L2, and the same between
G3 and L2,
𝑇32 ≥ 𝐺3(𝑡) − 𝐿3(𝑡) + 𝑋(𝑡)
𝑇33 ≥ 𝐿3(𝑡) − 𝑋(𝑡)
𝑇32 − (𝐺3(𝑡) − 𝐿3(𝑡)) ≥ 𝑋(𝑡) ≥ 𝐿3(𝑡) − 𝑇33
Hence,
min{𝐺1(𝑡), 𝐿3(𝑡), 𝑇13, 𝑇32 − (𝐺3(𝑡) − 𝐿3(𝑡))} ≥ 𝑋(𝑡) ≥ max{0, 𝐺1(𝑡) − 𝑇11, 𝐿3(𝑡) − 𝑇33}

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Similarly, considering the transmission capacity between G2 and L1, and the same between
G2 and L2,
𝑇21 ≥ 𝑌(𝑡)
𝑇22 ≥ 𝐺2(𝑡) − 𝑌(𝑡)
The last two equations transforms into,
𝑇21 ≥ 𝑌(𝑡) ≥ 𝐺2(𝑡) − 𝑇22
Hence,
min{𝐺2(𝑡), 𝐿1(𝑡), 𝑇21} ≥ 𝑌(𝑡) ≥ max{0, 𝐺2(𝑡) − 𝑇22}
Hence we can write,
Where
𝑋 𝑀𝐴𝑋(𝑡) = min{𝐺1(𝑡), 𝐿3(𝑡), 𝑇13, 𝑇32 − (𝐺3(𝑡) − 𝐿3(𝑡))}
𝑋 𝑀𝐼𝑁(𝑡) = max{0, 𝐺1(𝑡) − 𝑇11, 𝐿3(𝑡) − 𝑇33}
𝑌𝑀𝐴𝑋(𝑡) = min{𝐺2(𝑡), 𝐿1(𝑡), 𝑇21}
𝑌𝑀𝐼𝑁(𝑡) = max{0, 𝐺2(𝑡) − 𝑇22}
At load node L1 and L2 we can state that
𝐺1(𝑡) − 𝑋(𝑡) + 𝑌(𝑡) ≥ 𝐿1(𝑡)
𝐺3(𝑡) − 𝐿3(𝑡) + 𝑋(𝑡) + 𝐺2(𝑡) − 𝑌(𝑡) ≥ 𝐿2(𝑡)
The last equations shows that
Hence for network stability we have three major working inequalities:
Using simple Linear Programming, one can state that these three inequalities define a
region as follows,

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If the region A(t) exists for each t then the network is stable i.e. maintaining the grid is
nothing but to maintain the area A(t) positive. By simple calculation one can show that
A(t) depends on the each value of G1, G2 and G3 separately but not on the sum of its values
i.e. G1 + G2 + G3. Interestingly since it is a variable generation and A(t) is not constant
but to get the +ve value of A(t) we need a prediction of G1, G2 and G3 separately but not
as a sum or aggregation of those values. Due the variability the region A(t) becomes as
follows:
Here the red area is actual requirement and the area of A(t) decreases due to the
uncertainty of the generation. Suppose schedule generation of G1, G2 and G3 are not known
separately, then the following situation may arise:

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Here A(t) does not exist and can be considered to 0. Hence the aggregated forecast creates
instability when A(t) is not positive.
Even in a simple network, the aggregated forecast creates the instability in the grid
system. Considering the state level complex network structure of generation-transmission-
distribution of power, it is very simple to state that the grid will fail in case of aggregated
forecasting of wind and solar power generation.
2. Forecast at Pooling Station: Who is Paying Whose Penalty?
A forecast model of solar power generation can be viewed as probabilistic evolution to
generate different plausible patterns considering the unscheduled fluctuations. A good
forecasting system is a process which gives proper accuracy with minimum penalty due to
deviation even in a small capacity of solar plants with high variability with minimum
number of intraday revisions. This article concentrates on the theoretical structure of the
aggregated forecast.
2. A. Measure using Central tendency
The major assumption in aggregated forecast is that the positive and negative error can
cancel each other in the long run and hence the average error in aggregated forecast is
very small or under acceptable limit. But it only works when the error is measure using
MAE or RMSE while the error measurement in Indian regulation is different.
To formulate the theoretical structure in a simplified manner lets consider two solar plants
of capacity C1 and C2. Without representing the detail algebraic construction of the error
distribution, the aggregated forecast error at i-th time-block can be represented as
𝑒 𝑎𝑔𝑔(𝑖) = 𝜔1 𝑒1(𝑖) + 𝜔2 𝑒2(𝑖)
Where 𝜔1 and 𝜔2 are the scaling factor such that 𝜔1 + 𝜔2 = 1 and
𝜔1
𝜔2
= 𝐶1/𝐶2. Hence, in the
average case (or the expected value in error according to statistical theory) we can
consider
𝜇 𝑒 = 𝜔1 𝜇 𝑒1 + 𝜔1 𝜇 𝑒2

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Where 𝜇 𝑒, 𝜇 𝑒1and 𝜇 𝑒2 are the average error in aggregated forecast, forecast of plant 1 and
forecast of plant 2 respectively. Since 𝜔1 and 𝜔2 are in the ratio of their plant capacity, the
existent de-pooling mechanism considers to divide the penalty due to deviation of two
plants according to their plant capacity or depending on the ratio of the energy generation
at a particular time-block in which the penalty exists.
This assumption in aggregated forecast is correct in some cases, but not sufficient, as it
does not consider the variability analysis of the power generation and only plays with the
measure of central tendency of the error distribution. This incomplete theory in the
aggregated forecast is an issue and hence no valid logical framework is available in
calculating the ‘de-pooling’ mechanism in calculating the penalty payable for each plant in
case of aggregated forecast.
2.B. Measure of Dispersion
The generation of solar power is best described using the Wold’s representation theorem
according of which solar generation can be represented as the summation of deterministic
and stochastic time series. The error in forecasting comes from the stochastic time series
in Wold’s decomposition while the maximum portion of the solar power generation is
deterministic. Hence,
3. Solar power generation is not random. Moreover the ramping occurrences have
specific distribution depending on plant characteristics according to Wold’s
theorem.
4. The power generation characteristics (or statistical distribution) is not same for
each PV panel or each plant of the group of aggregation
Considering the variation in error forecasting the variance of the aggregated error can be
represented as,
𝜎𝑒
2
= 𝜔1
2
𝜎𝑒1
2
+ 𝜔2
2
𝜎𝑒2
2
+ 2𝜔1 𝜔2 𝜌𝜎𝑒1 𝜎𝑒2
Where 𝜎𝑒
2
, 𝜎𝑒1
2
and 𝜎𝑒2
2
are the variance in the error distribution for aggregated forecast,
forecast of plant 1 and forecast of plant 2 respectively. Here 𝜌 is the correlation coefficient.
Considering the two plants are almost in the same location, this value tends to 1, i.e. 𝜌 →
1. With some simple algebraic manipulation, it can be shown that, if 𝜌𝜎𝑒2 > 𝜎𝑒1, which is a
natural phenomena unless the characteristics and power generation patterns in both plants
are same,
𝜎𝑒1 < 𝜎𝑒 < 𝜎𝑒2
Hence, in the long run, the variance of the error distribution lies between the variance of
each plant. Without much loss of generality, we can consider the error distribution in
forecasting of solar follows a Gaussian distribution with mean 0 but with different standard
deviations as shown in the figure.

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For the error distribution, the area under the curve in -15% - +15% can represent the
accuracy of the forecast as this represents the probability that the plant does not have to
give any deviation penalty as the deviation error is under +/- 15%.
As shown in the figure, since the standard deviations are different for two plants, the
accuracy of plant 1 is reduced due to the aggregated forecast. Hence, in the long run, the
plant 1 is actually paying the penalty due to deviation of plant 2 due to aggregated
forecast.
Hence, considering the multiple plants we can state that, with aggregation, the occurrence
of high variability in the generation of one plant affects the error of other plants having
stable generation even in the long run. Interestingly, plant specific forecasting does not
have this type of anomaly as it solely depends on its own performance not affected by the
performance of other plants. Moreover the commercial settlement in penalty due to
deviation is comparatively simple in case of plant specific forecast. Hence, Solar/Wind
Plant specific Forecast should be encouraged rather than formation of QCA due to the
following reasons:
a) Performance of one plant affects the performance of other plant. The question
arises who is paying whose penalty.
b) There exist no standard de-pooling mechanism (hence no concrete guidelines are
available in any regulations) due to the following reasons:
i. All plants under same pooling substation may not have similar
nature PV/Turbine
ii. All plants under same pooling substation can not have same solar
insolation/wind speed distribution at same time
iii. All plants under same pooling substation can not have same
transmission loss

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iv. All plants under same pooling substation can not have same inverter
efficiency/charecteristics
v. Plants under same pooling substation differs in PPA
vi. Plants under same pooling substation differs in their capacity
factors
c) Since proper de-pooling mechanism is not a technically feasible option, the F&S at
Pooling level can be seen as an option but not only option of F&S.
d) If a Solar/Wind plant/IPP wants to submit their own forecast separately, the
possibility must be entertained and encouraged and the plant must get a fair chance
to present their case properly.
e) A provision must be there to submit F&S separately by a plant without opting for
QCA like the provision made by AP regulations.
3 Techno-Legal perspective – a logical and rational point of view:
In case there is any dispute in between the SLDC & any IPPs with regards to its forecasting
and scheduling related DSM, may observation will be as following in case to case basis:
(a) If any IPP wants to dispute a DSM issued by SLDC, QCA shall not support such
IPP, as that will damage QCA’s relation with SLDC. If a QCA is forecasting for
2000+ MW in a state, if there is any issue/dispute with DSM with any of its
customer for example a 100 MW plant/IPP, then QCA shall not support said 100
MW IPP, as that may jeopardize the F&S for remaining 1900 MW. So, whenever
there will be any dispute with SLDC, QCA is going to leave such IPP alone to
fight and resolve and litigate with SLDC. Then what is the point having a
QCA?
(b) Once the BG invocation letter is issued by SLDC, in case of failure to deposit
penalty in time, if QCA fails to take appropriate legal steps within time, and fails
to obtain a stay order against such Bank Guarantee (‘BG’) invocation letter
issued by SLDC, then it may become very tough for IPPs to save it’s BG provided
if IPP would have submitted its own BG directly to SLDC. Then what is the
point having a QCA?
(c) Had it been a case that IPP would have itself registered as QCA, then IPP would
have approached appropriate forum or court to adjudicate the matter and
resolve the issue in judicial process. But in current scenario, QCA may not file
any case, as the same shall put QCA in SLDC’s bad-book and may deteriorate
its relationship with SLDC, so all its other customer whom QCA is providing
services may suffer or QCA may be scared that if QCA stand against the SLDC
then SLDC shall increase strictness of its scrutiny which QCA would not like to
face for a single customer. Then what is the point having a QCA?
(d) Had it been a case that IPP would have itself registered as QCA, then IPP would
have approached appropriate forum or court to adjudicate the matter and
resolve the issue in judicial process as it directly has a PPA with state or directly
have a PSA with state Discom through SECI or NTPC. In both cases, it will be
easy for IPPs to invoke the SERC or CERC or court jurisdiction and get a stay
order against such invocation of BG. But in case QCA comes in between it will

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be no more be a simple bye-party dispute in between the generator and State.
Moreover, there is no underlying agreement with SLDC directly with IPP so,
ideally no application for temporary relief under arbitration act, as the case may
be shall not be available for protecting the BG. Court may not entertain, as it
will be become a back to back BG invocation dispute. Then what is the point
having a QCA? Having a QCA in between would actually weaken IPPs
case to get a relief against the State or SLDC directly. Moreover, if there
is a QCA in between, then IPP will not have an argument that there is
no point encashing the BG as substantial payment is receivable from
the State itself so DSM/F&S penalty may be adjusted from such
payments to be received from States. This shall help IPP to above
immediate cash crunch and liquidity issue and save the BG.
(e) In another case, when the IPP has not submitted separate BGs with SLDC, and
submitted a BG to QCA and QCA would have submitted a back to back BG to
SLDC. In such case, if there is any BG invocation notice, IPP would like to go
against such invocation as incorrect, but in that case also as IPP is not directly
linked with SLDC for F&S / DSM settlement so IPP may not be able to file a case
against SLDC without making QCA a party. So, in one-way IPPs BG is lying with
QCA and back to back BG is with SLDC. In such case if IPP loses the case
because of negligence of QCA, IPP will be victimise.
(f) In the above-mentioned scenario, the back-back BG arrangement will fail,
because IPP may get an injunction from its court that will protect the BG given
by IPP to QCA but shall not protect the BG issued by QCA to SLDC. So,
ultimately, some other IPPs’ BG would be invoked and released to SLDC, in this
process. So, back to back BG issuance may turn out to be very costly for some
other IPP who would have nothing to do this transaction or not even a party for
the same state even. But his BG will be encashed, QCA will not be able
replenished the same as it is not at all that cash rich, ultimately one IPP shall
suffer at the end of it only because of the fact that it had a common QCA. Then
what is the point having a QCA?
(g) In the above referred case, if a back to back BG is issued to SLDC but there is
no mechanism to identify which one is whose BG. So, in a case when BG for
100 MW wind plant is invoked toward DSM demand it will required total
(43,000*100= 43,00,000/-) to pay such demand of F&S DSM settlement. In
case if such wind plant is not in a position to pay at that point of time and fail
to make payment in due time and its BG with QCA is not honoured, then at the
same time SLDC may have invoked the BG for the same amount submitted by
QCA-BGs. The same QCA would have submitted BG for another 1900 MW plants
with SLDC, then SLDC will invoked BG for an amount of Rs 43 Lacs. Which might
be submitted by some other 100 MW wind plant not in the same state but a
plant from a different state altogether or 430 MW solar plant would be sufferer
only because of the fact that QCA is common. So, having a QCA in between as
a common feature may become a reason for being affected financially. In that
case, QCA is not all that rich or does not even have that net worth/ liquidity to
replenish such BG with SLDC. So, this innocent IPP, who does not even have a
DSM settlement at all, shall be victimise of an act of a QCA. Then what is
the point having a QCA?

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(h) Most of the QCA is a small entity (paid up capital Rs. 1 lac – 2.4 lac – 10 Lacs)
in comparison with a wind plant (INR 7.5–8 Cr./MW) & Solar plant (INR 5-7.5
Cr./MW). So, expecting that QCA shall fight properly diligently against the SLDC
if there is any dispute with SLDC is not rational or wise decision. QCAs may fool
IPPs upto a level, but the basic fact it QCA is a very weak-small-entity to fight
against SLDCs, whereas a IPP shall even fight more seriously as ultimately, it’s
IPP or its investor’s money which is at stake. QCA has noting at stake at the
end of the day, QCA is merely just a Forecasting and scheduling consultant.
Then what is the point having a QCA?
(i) While fighting against SLDC in case of any disputes, QCA will not able to handle
and fight as it’s a very small entity (paid up capital Rs. 1 lac – 2.4 lac – 10 Lacs)
in comparison with a wind plant (INR 7.5–8 Cr./MW) & Solar plant (INR 5-7.5
Cr./MW). Financially also QCA will not be able to fight as good as IPP would
fight these cases. Moreover, fighting a pseudo war on behalf of QCA, for
protecting the BG by any IPP, may not be liked by other IPPs. Then what is
the point having a QCA?
(j) If there is no mutual consensus between different Solar or Wind generators,
single QCA cannot work. A provision should be incorporated, to accommodate
Power generator who can go alone without opting for a QCA or would like to
change once suffered by any QCA’s inaccurate performance like the provisions
made by AP regulations.
(k) Monopoly by any QCA for any pooling station or ‘Forcing Consensus’, should
not be encouraged by statute is illegal in limine. Moreover, IPP should have the
liberty to submit its own forecasting, it will be unjust, irrational, unreasonable
if IPPs are compelled to choose to work with a single QCA even if the QCA fails
to perform with accurate forecasting and IPP suffers for the activities of the
QCA in DSM. If only one QCA is appoint for a particular sub-station and SLDC
disagree to appoint an alternative option or QCA or IPP itself, in such case then
the IPPs should have right to raise a dispute/issue with the accuracy of the
QCA’s forecasting accuracy or performance, then SLDC should compensate
such IPPs for inaccurate forecasting by such QCA. The IPPs should have
a choice to appoint its own QCA or Forecasting service provider or allowed to
submit its own F&S itself, so IPPs cannot be compelled to pay unnecessary DSM
for SLDCs actions. Otherwise the plant with low capacity installation but having
good ‘Capacity Factor’ will suffer the most. Since there is no risk mitigation of
penalty by the low capacity installation, the small developers will be reluctant
to set up the system.
(l) Last but not the least, I am apprehensive that in a hypothetical situation,
whenever there will be a major grid failure attracting attention of all general
public that such failure may have caused due to some aggregated forecasting,
a concept proposed and implemented by few QCA, then all these Forecasting &
scheduling acts/ regulations will be scrutinised, interpreted and analysed
judiciously in view of the fact that all needs to protect the Grid, which is national
property. If in that case court finds that QCA is involved in any kind of gaming
and has been involved in any kind of mis-interpretation for financial benefit,

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then all such IPPs, who will be working with such QCA or appointed such QCA
innocently will be wort his. Investigating authority shall take all the IPPs in loop
believing that IPP would have appointed such QCA for getting such financial
benefits as the cost of grid stability. This will unnecessary harass and humiliate
and force all such IPP officials to face court cases and inquiries before different
forum, without any of its failure or offence, being totally innocent.
Then what is the point having a QCA?
IPP is the best QCA, there may be short term relief by appointing a so called QCA
as a third party, but in long term IPP may suffer and victimised.

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Annexure – 5
Forecasting to mitigate back-down13
Unlike other renewable energy rich countries where grid management is for the purpose
of energy management, the grid management in India actually targets to solve the
capacity management problem. The power market structure of India is comparatively
complex unlike other countries and with the penetration of massive variable renewable
energy like wind and solar, the grid management becomes the crucial issue. Though the
policies in RE target a massive inclusion of wind and solar for a sustainable future and to
achieve 40% of total installed power generation capacity from renewable energy by 2030,
the back down issue in wind and solar in different RE rich states becomes a common
problem and the IPPs/generators become reluctant about the wind and solar production
even in the RE rich states due to heavy financial loss created through back-down.
It is interesting that Wind and Solar got the status of ‘Must Run’, but due to the variability
and intermittency inherited in these varied sources, system operators like SLDCs require
curtailing the generation of Wind and Solar to manage the grid. Without going in the deep
analysis of frequency, reactive power and voltage regulation, let us stick to the problem
of capacity management as it is the basic problem required to be solved first to mitigate
the issues back-down. Other parameters like frequency, reactive power or voltage control
can be interrelated using some extra constraints in the capacity problem.
Back-down problem can be viewed as the balancing of demand and supply, it is quite
natural that if the demand of energy is high and generators are not able to supply that
required amount, there is a possibility of shortage and the same time if the supply is so
high that the demand is not able to consume the total supply, the curtailment must be
there. One can go for the wheeling in and out analysis in this respect with complex analysis
considering different utilities and control areas, but considering the market structure and
complex grid network of India, the extra supply cannot be managed so smoothly, hence
back-down becomes the easiest approach for system operators to maintain the balance in
the network by reducing the overflow.
To mitigate the power shortage and back-down, the forecasting and scheduling of the
Wind and Solar power generation is an effective tool. As per the F&S regulations, each
wind and solar generators have to submit the day-ahead schedule of their power
generation as it gives a rough idea to the system operators about the availability of the
variable energy and the capacity requirements of other conventional sources. The day-
ahead schedule is the most crucial for any grid management as it decides the capacity
allocation of different energy sources. Hence the day-ahead schedule of solar and wind
generation must be as much accurate as possible to reduce the possibility of back-down.
Since SLDCs will have the schedule one-day ahead for each wind and solar generators,
there should not be any capacity allocation problem if they implement the balancing
properly.
But wind and solar are variable in nature and it is obvious that due to some natural
phenomena the day-ahead schedule is not as much accurate as predicted all time. Hence,
as per regulations, wind and solar generators can update their schedule maximum 16
times and 8 times, respectively, but the unnecessary revisions create problems in capacity

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management. It is easy to see that the variability of wind and solar and the variation in
conventional energy sources are different, moreover the generation of conventional
energy sources cannot alter instantaneously due to the technical infeasibility. Hence
forecast with minimum revision is an effective tool to mitigate the back-down issue.
It is quite natural that the aggregation of forecast or aggregated forecast as the case may
be, cannot be an effective tool for balancing the demand supply. For example, let us
consider two plants A and B are at East and West Rajasthan respectively, such that the
geographically Plant A and Plant B are geographically separated. Plant A and Plant B have
the schedule generation of 200MW and 400 MW respectively, but their actual generation
is 350 MW and 250 MW respectively. Hence the Plant A has (350-200) MW = 150 MW
over-production at East Rajasthan and Plant B has (400 – 250) MW = 150 MW under
production at West Rajasthan. If aggregation is allowed, the aggregated schedule of plant
A and plant B is (200+400)MW = 600 MW and the aggregated generation is (350 + 250)
MW = 600 MW. In aggregation there is no deviation. But in reality, there is 150 MW over-
production in East Rajasthan and 150 MW under production at West Rajasthan. Since the
transmission capacity is not infinite, that 150 MW over production cannot be transferred
to West Rajasthan instantly and freely. Hence there can be a back-down at East Rajasthan
and power shortage in West Rajasthan in reality. Hence, the plant specific level forecast
creates an effective solution for planning and balancing the grid without proposing
erroneous deviation like aggregation.
As SLDC is a statutory functionary under section 32 of the Electricity Act and in the
discharge of its functions, SLDC is entitled to act in the larger public interest while dealing
with the scheduling and dispatch. But, unfortunately, probably SLDC is not doing its load
forecasting properly so that it can achieve to solve its capacity management problem
perfectly. In case any IPP/Generator is providing accurate day ahead forecasting, then
SLDC should also plan accordingly, so that it can instruct the conventional power
accordingly to generator as per SLDC’s requirement, so that back-down on the renewable
energy can be minimized and IPP/ Generator should not suffer financially. In case SLDC
would have down proper load forecasting arrangement, then all the IPP/Generators who
have submitted accurate day ahead forecasting, should not be back-down. So, in the
regulation, even if the day-ahead accurate forecasting is provided, then there is no
responsibility on the SLDC to plan properly. It is still vague that even if any generator has
already given a day ahead forecasting then SLDC has sufficient time so that it can plan its
energy management as well as requirement. Such, enabling provisions have not been
incorporated, which is the need of the day.
IPP/Generators are providing their forecasting, but SLDC has not planned properly, so
IPP/Generators are suffering enormously. So, in that case SLDC is failing to perform its
statutory duty. Back-down provision was incorporated in the Act, believing that such back-
down will be properly planned and scheduled. Till date, as conventional energy was used
as the main source of supply for energy, so SLDC had no other option than to back-down.
When there is green energy available, SLDC should understand its load forecasting and
plan accordingly, so that green energy should be accommodated. It should not be a case
that SLDC is doing charity by accommodating green energy, it is highly required and need
of the hour. So, the Commission should also direct SLDC, that SLDC should also start
utilizing the forecasting data so that back-down can be reduced significantly. When, there
is a day ahead forecasted data available with SLDC, they should also guarantee that if the
same is accurate, then SLDC should also plan accordingly and such plant generation shall

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Page 39 of 39
not be backed down at all. Effective utilization of the forecasted data is not evident from
the act of SLDC. Regulation is silent on these issues and has put only conditions to the
IPPs/Generators, but no responsibility is imposed on SLDCs to utilize the same and reduce
the back-down issues.
IPP/generators must access the data of load variations, conventional energy generation
and wind/solar actual and schedule generation to analyse the back-down issue to check
the back-down was inevitable or not. If by analysis it is found that the backdown was not
inevitable then who will take care of that high financial loss of the IPPs due to forced back-
down?

Comments / Suggestions on the Draft Procedure formulated towards implementation of MERC (Forecasting, Scheduling and Deviation Settlement for Solar and Wind Generation) Regulation 2018 in the State

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Comments / Suggestions on the Draft Procedure formulated towards implementation of MERC (Forecasting, Scheduling and Deviation Settlement for Solar and Wind Generation) Regulation 2018 in the State