**By Abhk Kumar Das,**

**Del2infinity Energy Consulting**

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 and wind 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.

**Grid instability in aggregated forecast / Geographical Integration**

The grid is not a 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 the 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 socioeconomic consequences.

Without going complex structure of grid network let define a simple structure where we have three variable generation grid nodes say *G*_{1}, *G*_{2} and *G*_{3} for simplicity, let define three Load dispatch points *L*_{1}, *L*_{2 }and *L*_{3} such that *L*_{1} lies between *G*_{1} and *G*_{2}, *L*_{2} lies between *G*_{2} and *G*_{3}, and *L*_{3} lies between *G*_{3} and *G*_{1}. 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 *G*_{1}, *L*_{3} and *G*_{2}, *L*_{1},

min{G_1 (t),L_3 (t)}≥X(t)≥0

min{G_2 (t),L_1 (t)}≥Y(t)≥0

Considering the transmission capacity between *G*_{1} and *L*_{3}, and the same between *G*_{1} and *L*_{3}

T_13≥X(t)

T_11≥G_1 (t)-X(t)

The last two equations transforms into

T_13≥X(t)≥G_1 (t)- T_11

Similarly, considering the transmission capacity between *G*_{3} and *L*_{2}, and the same between *G*_{3} and *L*_{2},

T_32≥G_3 (t)-L_3 (t)+X(t)

T_33≥L_3 (t)-X(t)

The last two equations transforms into

T_32-(G_3 (t)-L_3 (t))≥X(t)≥L_3 (t)-T_33

Hence,

min{G_1 (t),L_3 (t),T_13,T_32-(G_3 (t)-L_3 (t))}≥X(t)≥max{0,G_1 (t)- T_11,L_3 (t)-T_33 }

Similarly, considering the transmission capacity between *G*_{2} and *L*_{1}, and the same between *G*_{2} and *L*_{2},

T_21≥Y(t)

T_22≥G_2 (t)-Y(t)

The last two equations transforms into,

T_21≥Y(t)≥G_2 (t)-T_22

Hence,

min{G_2 (t),L_1 (t),T_21 }≥Y(t)≥max{0,G_2 (t)-T_22 }

Hence we can write,

X_MAX (t)≥X(t)≥X_MIN (t)

Y_MAX (t)≥X(t)≥Y_MIN (t)

Where

X_MAX (t)=min{G_1 (t),L_3 (t),T_13,T_32-(G_3 (t)-L_3 (t))}

X_MIN (t)=max{0,G_1 (t)- T_11,L_3 (t)-T_33 }

Y_MAX (t)=min{G_2 (t),L_1 (t),T_21 }

Y_MIN (t)= max{0,G_2 (t)-T_22 }

At load node *L*_{1} and *L*_{2} we can state that

G_1 (t)-X(t)+Y(t)≥L_1 (t)

G_3 (t)-L_3 (t)+X(t)+G_2 (t)-Y(t)≥L_2 (t)

The last equations shows that

{G_3 (t)-L_3 (t)}+{G_2 (t)-L_2 (t)}≥Y(t)-X(t)≥ -{G_1 (t)-L_1 (t)}

Hence for network stability we have three major working inequalities:

{G_3 (t)-L_3 (t)}+{G_2 (t)-L_2 (t)}≥Y(t)-X(t)≥ -{G_1 (t)-L_1 (t)}

X_MAX (t)≥X(t)≥X_MIN (t)

Y_MAX (t)≥X(t)≥Y_MIN (t)

Using simple Linear Programming, one can state that these three inequalities define a region as follows,

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 *G*_{1}, *G*_{2} and *G*_{3 }separately but not on the sum of its values i.e. *G*_{1} + *G*_{2} + *G*_{3}. 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 *G*_{1}, *G*_{2} and *G*_{3} 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 *G*_{1}, *G*_{2} and *G*_{3} are not known separately, then the following situation may arise:

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.

**Forecast at Pooling Station: Who is Paying Whose Penalty?**

A forecast model of solar and wind 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/wind plants with high variability with minimum number of intraday revisions. This article concentrates on the theoretical structure of the aggregated forecast.

**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 or wind plants of capacity *C*_{1} and *C*_{2}. 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)=ω_1 e_1 (i)+ω_2 e_2 (i)

Where ω_1 and ω_2 are the scaling factor such that ω_1+ω_2=1 and ω_1/ω_2 =C_1/C_2. Hence, in the average case (or the expected value in error according to statistical theory) we can consider

μ_e=ω_1 μ_e1+ω_1 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 ω_1 and ω_2 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.

**B. Measure of Dispersion**

The generation of solar and 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 power generation is deterministic. Hence,

- Solar and wind power generation are not random. Moreover the ramping occurrences have specific distribution depending on plant characteristics according to Wold’s theorem.
- The power generation characteristics (or statistical distribution) is not same for each PV panel / turbine or each plant of the group of aggregation

Considering the variation in error forecasting the variance of the aggregated error can be represented as,

σ_e^2=ω_1^2 σ_e1^2+ ω_2^2 σ_e2^2+2ω_1 ω_2 ρσ_e1 σ_e2

Where σ_e^2, σ_e1^2 and σ_e2^2 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 ρσ_e2>σ_e1, which is a natural phenomena unless the characteristics and power generation patterns in both plants are same,

σ_e1<σ_e<σ_e2

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.

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 panels/Turbine

ii. All plants under same pooling substation can not have same solar insolation/wind speed distribution at the same time

iii. All plants under same pooling substation can not have a same transmission loss

iv. All plants under same pooling substation can not 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

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 want 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.

**Techno-Legal perspective – a logical and rational point of view:**

In case of any dispute between the SLDC & any IPP(s) with regards to its forecasting and scheduling related DSM, the issues will be as follows in case to case basis, which bring the dark reality: **what is the point having a QCA as a Third Party**?

a) If any IPP want 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 customers for example a 100 MW plant/IPP, then QCA shall not support said the 100 MW IPP, as that may jeopardize the F&S for the 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.

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.

c) Had it been a case that IPP would have it registered as QCA, then IPP would have approached the appropriate forum or court to adjudicate the matter and resolve the issue in the judicial process. But in the current scenario, QCA may not file any case, as it 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 the QCA stand against the SLDC then SLDC shall increase the strictness of its scrutiny which QCA would not like to face for a single customer.

d) Had it been a case that IPP would have it registered as QCA, then IPP would have approached the appropriate forum or court to adjudicate the matter and resolve the issue in the judicial process as it directly has a PPA with state or directly have a PPA with the 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 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 of 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 with 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 victimized.

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 encased, 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.

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 require a 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 honored, 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 invoke BG for an amount of Rs 43 Lacs. Which might be submitted by some other 100 MW wind plant and this 100 MW wind 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 victims of an act of a QCA.

h) Most of the QCA is a small entity 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 a rational or wise decision. QCAs may fool IPPs upto a level, but the basic fact, the QCA is a very weak-small-entity to fight against SLDCs, whereas an IPP shall even fight more seriously as ultimately, it’s IPP or its investor’s money which is at stake. QCA has nothing at stake at the end of the day, QCA is merely just a __Forecasting and scheduling consultant__.

i) 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.

j) 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 appointed 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, smaller developers will be reluctant to set up the system.

k) Last but not the least, considering the 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 scrutinized, interpreted and analyzed 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 misinterpretation of financial benefit, then all such IPPs, who will be working with such QCA or appointed such QCA innocently will be worth his. The 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 unnecessarily harass and humiliate and force all such IPP officials to face court cases and inquiries before different forum, without any of its failure or offense, being totally innocent.

**Then what is the point having a QCA? **

**IPP is the best QCA, there may be short term relief by appointing so called QCA as a third party, but in long term IPP will suffer and victimized; and the Grid will suffer the most with its high penetration of wind and solar energy. **