Volume : 5 Issue : 3 March 2017 ` 10/-

Chairman IWPA, Prof. Dr. K Kasthurirangaian delivering a talk on Renewable Energy at The Institution of Engineers (India), Chennai.

powerING a GreeNer ToMorrow

IndIan WInd poWer assocIatIonnatIonaL coUncILoffice Bearers :

chairmanProf. Dr. K Kasthurirangaian, CMD, RSM Autokast, Coimbatore

Honorary Vice President, WWEA, Bonn, Germany

Vice chairmen

Shri S V Arumugam, CMD, Bannari Amman Spinning Mills, Coimbatore

Shri Rajiv B Samant, Head - Asset Management & Technical

The Tata Power Company Ltd., Mumbai

Honorary secretary

Shri Chetan Mehra, MD, Weizmann, Mumbai

Honorary treasurer

Shri A Raja Sukumar, President, Indo Wind Energy, Chennai

council Members :

Shri S Babu, Director, RS Yarns & Power, Tirupur

Shri T Balachandran, MD, Arvind Green Infra, Karur

Shri Balram Mehta, Sr. VP Techno Commercial,

Renew Power Ventures, Gurgaon

Dr. V Bapeshwar Rao, VP - Business Developmemt,

Suzlon Energy, Chennai

Shri Chandra Shekhar Khunteta, Director, Indocot, Jaipur

Shri M K Deb, MD, Consolidated Energy Consultants, Bhopal

Shri V Dev Anand, Director, Violet Green Energy, Rajasthan

Shri T S Jayachandran, VP, F & A, Premier Mills, Coimbatore

Shri R Kannan, Sr. VP, Beta Wind Farm, Chennai

Dr. N Karuna Moorthy, ED, AWT Energy, Mumbai.

Shri V K Krishnan, ED, Leitner Shriram Manufacturing, Chennai

Shri K R Nair, GM, (Liaison), Wind World (India), Mumbai

Shri Rajeev Karthikeyan, MD, Leap Green Energy, Coimbatore

Shri Rajsekhar Budhavarapu, Head, Wind Business & CTO -

Renewable Investments, IL&FS

Shri K Ravi Kumar Reddy, MD, Axis Energy Ventures India, Hyderabad

Shri U B Reddy, MD, Enerfra Projects (India), Bengaluru

Shri S Senguttuvan, Proprietor, Saravana Chem Dyes, Tirupur

Shri Siva Girish Arepalli, AGM (Comm. & Regulatory),

Bindu Vayu Urja, Hyderabad

Shri K V S Subrahmanyam, VP - Power, MSPL, Bengaluru

Dr. R Venkatesh, President, Power Quality Solutions,

EPCOS India, Nashik

IndIan WInd poWer assocIatIonDoor No. E, 6th Floor, Tower-1, Shakti Towers

No. 766, Anna Salai, Chennai 600 002 Phone : 044 4550 4036 | Fax : 044 4550 4281

E-mail : iwpahq@windpro.org | Website : www.windpro.org

(For Internal Circulation Only)

Contents... Page No.

From the Chairman’s Desk 4

IWPA’s comments on Floor Price and Forbearance Price on REC 6

Paper on Statistical behaviour of error in wind and solar forecasting 11

IWPA’s letter to MNRE regarding allotment of forest land in Karnataka for WEGs 18

IWPA’s comments on TANGEDCO's ARR 22

IWPA’s request to CERC to reduce technical minimum of thermal plants to accommodate RE 32

IWPA’s comments on ARR filed by Karnataka ESCOMs for revision of Retail tariff 33

GERC directs Discoms to procure electricity from WEGs through competitive bidding 36

AdvertisementsCape Electric Private Ltd. 1

Wind World (India) Ltd. 2

KAY ARR Engineering Agency 5

Suzlon Energy Ltd. 20-21

Pioneer Wincon Pvt. Ltd. 31

RRB Energy Ltd. 39

For Suggestions / Comments : secretary.general@windpro.org

e-copy of this magazine can be downloaded from www.windpro.org

March 2017 11

Paper on Statistical behaviour of error in wind and solar forecasting

Applicability of Error Limit in Forecasting & Scheduling of Wind and Solar Power in India

Abhik Kumar Das del2infinity Energy Consulting, India

abhik@del2infinity.xyz

Abstract— Forecasting of power generation is an essential

requirement for high penetration of variable renewable energy in existing grid system as the major purpose of forecasting is to reduce the uncertainty of renewable generation, so that its variability can be more precisely accommodated. This paper focuses on the statistical behaviour of error in solar and wind power forecasting considering Indian regulations and analyse the applicability of the error limit in calculating the energy accuracy of forecasting and the stability of the grid.

Keywords—Forecast error, Variability, Penalty due to deviation

I. INTRODUCTION One of the major challenges in renewable enabled smart grid is to ensure the demand-supply balance for electricity. Wind and solar energy are two major components of renewable energy generation in India [1-4] but the variability and unpredictability inherent to wind and solar create a threat to grid reliability due to balancing challenge in load and generation. The unscheduled fluctuations of wind and solar generation produce ramping events and hence the integration of significant wind and solar energy into existing supply system is a challenge for large scale renewable energy penetration [5-11]. To accommodate the variability, the day-ahead and short-term renewable energy forecasting is needed to effectively integrate renewable energy to the smart grid and hence the forecasting and scheduling of wind and solar energy generation has become a widely pursued area of research at Indian context [12, 13]. The concept of forecasting and scheduling (F&S) of renewable energy generators and the commercial settlement was introduced in Indian context by CERC through Indian Electricity Grid Code (IEGC), 2010 [14] and the Renewable Regulatory Fund mechanism [15]. Due to several implementation issues, the mechanism was never made operational. To formulate an implementable framework, CERC also issued 2nd amendment to regulation for Deviation settlement mechanism and other related matters [16]. After CERC regulation, Forum of Regulators (FOR) [17] and other state regulators issued or drafted regulation related to the forecasting and scheduling of Wind and Solar power generation Since the system operators in India have to do curtailment on variable renewable energy due to intermittency and variability of the wind and solar power generation, the forecasting takes an important role in creating a sustainable solution for maximum utilization of renewable energy.

The most usable and conventional strategies in F&S of wind and solar power generation is predicting the weather parameters using of NWP (Numerical Weather Prediction) models and converting the values of weather parameters into power generation using the turbine or PV models considering the CFD based analysis in local regions. 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 forecasting methodology is to regard it fundamentally as a statistical rather than deterministic solutions as the stability of the grid needs not just the prediction of power generation but also the uncertainty associated with it. Thus from a mathematical viewpoint, forecasting is best considered as the study of the temporal evolution of probability distributions associated with variables in the power generation. Forecasting with proper uncertainty analysis is required since the initial value vector and the neural architecture of DNN can never be precisely defined and there are observational limitations in different variables required to predict the power generation. Without proper forecasting models considering non-linear dynamics approaches, a small but appreciable fraction of error can create a large error due to temporal evolution. In this paper, a simple functional relation is established considering the variability of power generation and error limit in forecasting for different regulations. The average penalty due to deviation is calculated using the exponential model of forecast error distribution. The remaining of the paper is organized as follows: The section II briefly describes the forecast error and the functional relationship of variability of actual power generation is derived in section III. Section IV shows the computational model of penalty due to deviations. The results and brief analysis is discussed in Section V and conclusion is presented in section VI.

II. FORECAST ERROR The most usable mathematical techniques in defining the

forecast error are MAE (Mean Absolute Error), RMSE (Root Mean Square Error). But considering the system stability, the error at each Time Block (1 Time Block = 15 minute) has more practical consequences and the error at i-th time block is defined as [15, 17]

March 201712

( ) | ( ) ( )| (1)

Where AvC is the available capacity; ( ) and ( ) are actual generation and scheduled generation respectively. Considering the Indian regulation, the penalty due to deviation can be generalized as shown in Table I [13].

Table I: Generalized Structure of Deviation charge Error Band Deviation Charge (Regulation specific)

per Unit (INR)

PPA Based Fixed

No penalty 0

10% of PPA 0.35 / 0.50 / 0.60

20% of PPA 0.70 / 1.00 / 1.20

30% of PPA 1.05 / 1.50 / 1.80

The value of m for different regulations are as follows Table II: Value of m fir different regulations

Value of m Regulation Wind

(Old) Wind (New)

Solar (Old)

Solar (New)

FOR 0.15 0.10 0.15 0.10 Gujrat 0.12 0.08 0.07 0.07

Jharkhand 0.15 0.10 0.15 0.10 Karnataka 0.15 0.15 0.15 0.15

Madhya Pradesh 0.15 0.15 0.15 0.15 Odisha 0.15 0.15 0.15 0.15

Rajasthan 0.15 0.15 0.15 0.15 Tamil Nadu 0.10 0.10 0.05 0.05

III. VARIABILITY IN POWER GENERATION Variability of power represents the change of generation

output due to unscheduled fluctuations of wind velocity or sun radiation patterns while uncertainty describes the inability to predict in advance the changes in generation output. Large unscheduled changes in wind or solar power generation are called ramp events which hamper the penetration of variable power in the existing grid [13]. The variability can be quantified as a measure of dispersion in variable renewable power generation and be easily quantified using the concept of Lorenz curve [18]. Though there are different ways to quantify variability, the simplest way to measure variability is using the normalized squared deviation about mean of the actual generation for some time block n as follows,

∑ ( ( ) )

(2.A)

Similarly the dispersion in the schedule generation for the same time span can be represented as

∑ ( ( ) )

(2.B)

Here and are the mean of the power generation in the time span of n. For a good forecast in which and , we can show that (A.1)

√ ( ) (3)

Here r is the correlation coefficient of the schedule and actual power generation. The relationship (3) shows how much variability a forecast model can effectively accommodates considering the no-penalty is 100m%, the value of which is defined by the regulation. As shown in fig. 1, for some value of r, the maximum permissible variability of the forecast model can be calculated for different regulations i.e. no penalty error limit as 5% (m=0.05), 10% (m=0.10) and 15% (m=0.15). The value of r depends on the choices of models and methodologies used in forecasting.

Fig. 1: shows the relation of correlation coefficient of actual and

schedule generation and the variability of the actual power generation which the forecast model effectively accommodates.

IV. PENALTY DUE TO DEVIATION The penalty per available capacity can be represented as

∫ ( ) ( ) (4)

Where c(e) is the penalty due to the error and h(e) can be represented as probability of error in the forecast models. Here considering the Indian regulations, for simple statistical calculation we can assume that

( ) ( ) if (5)

= 0 , otherwise

Considering the phenomenological models of forecast error, the probability distribution can be viewed as an exponential distribution of parameter λ as follows,

( ) ( ) (6)

As shown in Fig. 2, the frequency plot of forecast error in solar and wind forecasting for different days. The frequency plot closely match the exponential distribution define in (6) for different values of λ.

March 2017 13

(A)

(B)

Fig. 2 shows the normalized frequency distribution of forecast error for different days of (A) solar forecasting and (B) wind forecasting

Using mathematical manipulation, we can state that (A.2)

[( ) ]( ( )) (7)

Here P(m) is the probability the error is under no penalty error band i.e.

( ) ( ) ∫ ( ) (8)

And is defined as the standard deviation of the error distribution. Though the average penalty due to deviation is always positive (fig. 3.B and 4.A) and can be zero when P(m) = 1 i.e. prediction of power generation with 100% accuracy (As shown in fig. 3.A and 4.B).

V. RESULTS AND ANALYSIS Equation (3) represents an interesting relationship

connecting physical parameter of the power generation ( ), regulatory parameter (m which is defined in Table I as no-penalty error limit) and accuracy parameter (r which is defined as correlation coefficient) of power generation forecast. This

relationship shows the ability to forecast model to accommodate the maximum variability in the actual power generation considering different regulations (m) as the accuracy of the forecast model depends on the variability of the actual power generation.

(A)

(B)

Fig. 3 (A) and (B) shows the relationship of m-> P(m) ->C

As shown in Fig 3.(A), the probability of the error under no penalty due to forecast is shown for two different variability, it is easy to interpret that, if the variability of power generation is high the probability in forecasting the power generation becomes low. Fig. 3(B) explains the relationship between the probability and the penalty calue for different regulation value m.

As an example, the fig. 4 shows the daily-forecast of a 160 MW plant in India, though there are variations in the power generation, the del2infinity‟s AI based algorithm is useful to forecast the pattern of fluctuations. Fig. 4 and 5 shows the actual and schedule power generation of wind and solar plant. It is interesting to see that though there are some penalties due to deviation in some days, but achieving high predictability of the forecast model, the penalty due to deviation in some days can become zero.

March 201714

(A)

(B)

Fig. 4. Shows the actual and forecast power generation of wind and its penalty due to deviation per actual generation

March 2017 15

March 201716

Fig 6. The relationshio of coefficient of variation and correlation coeffient in detrmining the phase change in forecast process.

VI. CONCLUSION Forecasting and Scheduling (F&S) of variable renewable

power generation like wind and solar is an essential requirement for a sustainable energy future as the proper forecast methodology reduces the uncertainty and helps to accommodate the variable energy in existing grid. The temporal evolution of predicted variables is non-deterministic and the statistical prediction of the distribution of power generation plays an important role in forecasting with acceptable uncertainties. The relationship of variability of actual power generation and the correlation coefficient in forecast for different regulations can be useful in forecasting and the statistical formulation of average penalty due to deviation can be used in the analysis of different forecast strategies. The simple relationship proposed in this paper can be useful to determine the number of revisions in the forecasting process as a good forecast methodology is a process which not only reduces the penalty due to deviation but does the same in the minimum number of intra-day revisions without any unnecessary revisions in every one or two hours.

Acknowledgements

This paper is presented at Indian Smart Grid Week 2017, Indian Smart Grid Forum, New Delhi, India as technical paper and major part of the paper is available at their compendium. The author wishes to thank the unknown reviewers for their comments in the same.

REFERENCES [1] National Action Plan on Climate Change, GOI

(http://www.moef.nic.in/downloads/home/Pg01-52.pdf last visited on 30 Sept. 2016)

[2] INDIA‟S INTENDED NATIONALLY DETERMINED CONTRIBUTION: WORKING TOWARDS CLIMATE JUSTICE (http://www4.unfccc.int/submissions/INDC/Published%20Documents/India/1/INDIA%20INDC%20TO%20UNFCCC.pdf last visited on 30 Sept. 2016)

[3] Report of the Expert Group on 175 GW RE by 2022, NITI Aayog, GOI

(http://niti.gov.in/writereaddata/files/writereaddata/files/document_publication/report-175-GW-RE.pdf last visited on 30 Sept. 2016)

[4] Strategic Plan for new and renewable Energy Sector for the period 2011-17, Ministry of New and Renewable Energy, Government of India, 2011

[5] Das Abhik Kumar, “An analytical model for ratio based analysis of wind power ramp events”, Sustainable Energy Technology and Assessments, Elsevier vol. 9, pp.49-54, March 2015

[6] Das Abhik Kumar et al., “An Empirical Model for Ramp Analysis of Utility-Scale Solar PV Power”, , Solar Energy, Elsevier, vol. 107, pp. 44-49, September 2014

[7] Kamath, C. 2010. “Understanding Wind Ramp Events through Analysis of Historical Data.” Transmission and Distribution Conference and Exposition, 2010 IEEE PES in New Orleans, LA, United States., April 2010

[8] Das Abhik Kumar & Majumder Bishal Madhab, “Statistical Model for Wind Power based on Ramp Analysis”, International Journal of Green Energy, 2013

[9] Gallego C., Costa A., Cuerva A., Landberg L., Greaves B., Collins J., “A wavelet-based approach for large wind power ramp characterisation”, Wind Energy, vol. 16(2), pp. 257-278, Mar. 2013

[10] Bosavy A., Girad R., Kariniotakis G., “Forecasting ramps of wind power production with numerical weather prediction ensembles”, Wind Energy, vol. 16(1), pp. 51-63, Jan. 2013

[11] Kirby B., Milligan M., “An exemption of capacity and ramping impacts of wind energy on power systems”, The Electricity Journal, vol.2(7), Sept. 2008, pp.30-42

[12] Steffel, S.J., 2010. Distribution grid considerations for large scale solar and wind installations. IEEE, 1–3, Transmission and Distribution Conference and Exposition, 2010 IEEE PES

[13] Das Abhik Kumar, 'Forecasting and Scheduling of Wind and Solar Power generation in India', NTPC's Third International technology Summit „Global Energy Technology Summit‟ 2016

[14] Indian Electricity Grid Code, Central Electricity Regulatory Commission, 2010 (http://cercind.gov.in/2010/ORDER/February2010/IEGC_Review_Proposal.pdf last visited on 30 Sept. 2016)

[15] Procedure for implementation of the mechanism of Renewable Regulatory Fund , Central Electricity Regulatory Commission, 2011 ( http://www.cercind.gov.in/Regulations/Detailed_Procedure_IEGC.pdf last visited on 30 Sept. 2016)

[16] Framework on Forecasting, Scheduling and Imbalance Handling for Variable Renewable Energy Sources (Wind and Solar), Central Electricity Regulatory Commission, (http://www.cercind.gov.in/2015/regulation/SOR7.pdf last visited on 30 Sept. 2016)

[17] Model Regulations on Forecasting, Scheduling and Deviation Settlement of Wind and Solar Generating Stations at the State level (http://www.forumofregulators.gov.in/Data/study/MR.pdf last visited on 30 Sept. 2016 )

[18] Das Abhik Kumar, “Quantifying photovoltaic power variability using Lorenz curve”, Journal of Renewable and Sustainable Energy, AIP, vol.6 (3), June 2014

APPENDIX A1. ( )

| ( ) ( )|

|( ( ) ) ( ( ) ) ( )|

March 2017 17

For a good forecasting system we can consider, and . Hence using the no penalty band in Table I, for some n we can state that

∑ ( )

(

) ∑(( ( ) ) ( ( ) ))

( )

∑[( ( ) ) ( ( ) ) ( ( )

)( ( ) )]

( ) , which implies (3). A.2. Using (4) and (6),

∫

( )

Integrating,

[( )

( )

] ( )

Using (8), ( ) ( ) and for exponential distribution . Hence replacing ( ) and λ, we get (7). Author: Abhik Kumar Das Abhik holds a Dual Degree (B.Tech in Electronics & Electrical

Communication Engineering & M.Tech in Automation and Computer Vision) from Indian Institute of Technology, Kharagpur, India. He has a vast experience in computational modelling of complex systems. He contributed in different verticals of analytical modelling related to renewable energy and techno-economics and published several well-cited research

articles in internationally acknowledged journals and peer-reviewed conferences. He is founding member of del2infinity, an accurate Wind Energy & Solar Energy Forecasting and Scheduling Solutions Company. (e-mail: contact@del2infinity.xyz).

Meeting of representatives of IWPA, TASMA and SIMA held at IWPA National Office on March 24, 2017