Application of weather forecasting model WRF for

Theoretical and Applied Climatology https://doi.org/10.1007/s00704-018-2639-6

ORIGINAL PAPER

Application of weather forecasting model WRF for operational electric power network management—a case study for Phailin cyclone Bishnupriya Sahoo 1 & Prasad K. Bhaskaran 1 & Ashok K. Pradhan 2 Received: 20 March 2018 / Accepted: 18 September 2018 # Springer-Verlag GmbH Austria, part of Springer Nature 2018

Abstract Extreme weather events like tropical cyclone result in colossal catastrophe during landfall causing widespread inland flooding due to storm surge and also the post-landfall event result in extensive damage to infrastructural facilities and property hinterland. The state of Odisha located in east coast of India experienced a Very Severe Cyclonic Storm (VSCS) named Phailin during the post-monsoon season of October 2013. Timely warnings and alertness on storm surge coordinated with a massive evacuation effort by National Disaster Management Authorities (NDMA) were quite effective in minimizing the loss of human life. However, there was a trial of destruction due to extremely high winds and rainfall that followed during post-landfall causing extensive damage to property and major infrastructure facilities in the Odisha State. This study critically investigated the Phailin post-landfall phase focusing on the impact of high winds and rainfall on the power distribution network using the Weather Research and Forecasting (WRF) model. The study evaluated the spatial and temporal variability of wind speed and rainfall distribution from the WRF model configured for three different spatial domains and selecting the best available microphysics and land surface parameterization schemes. The proposed outer, intermediate, and inner domains had spatial resolutions of 27, 9, and 3 km respectively and that provided the best estimate for onshore wind speed, track forecast, and rainfall distribution highly relevant for the management of power distribution and transmission network. In context to weather model application for the Indian region, this effort is novel and probably for the first time that linked a suitable customized weather model output to evaluate its impact on observed tripping in transmission network of electric power grids. The dynamic model outputs from WRF were compared with data from synchrophasors used in electrical technology that monitored the transient and dynamic behavior of power systems in real-time operations. A close examination of the results signifies that the atmospheric model performed exceptionally well in capturing the tripping time of power lines, and the overall knowledge obtained from this study has a broader scope to develop a framework for efficient planning operations of the power network, resource allocation, and emergency preparedness.

1 Introduction Weather plays a vital role in day-to-day operational activities. Power distribution regulators should be aware of weather conditions that characterize the operating service area to evaluate cost effects arising from unfavorable weather conditions. A

* Prasad K. Bhaskaran [email protected]; [email protected] 1

Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India

2

Department of Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India

proper analysis of weather-related variables can effectively reduce the risk of failures in the system and thereby improve the distribution system reliability. Overhead lines, underground cables, transformers, and switching stations comprise the key components in transmission and distribution networks more susceptible to extreme weather. Amongst these four components, the overhead lines suffer the severe threat from extreme weather conditions. A study conducted on electricity distribution system in the USA (Burke and Lawrence 1983) advocates that around 40% of permanent faults and about 5% of faults in the underground distribution occur during adverse weather conditions. There are many factors attributing to weather events that can affect the normal operation of overhead lines such as atmospheric lightning, strong winds, extreme temperature, excessive rainfall, and humidity. Lightning strikes can damage

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transformers, switching devices, and transmission lines. It is responsible for about 30% of all faults on high voltage distribution system during extreme weather condition. Therefore, it is imperative that weather forecast plays an essential role in the day-to-day operational activity of power transmission and its distribution. With recent advances in computational power, NWP (Numerical Weather Prediction) models have become quite popular in operational weather forecasting, decisionmaking, and information dissemination to stakeholders. The present study utilizes the state-of-art Weather Research and Forecast (WRF) model to forecast the wind scenario hinterland associated with the 2013 Phailin cyclone that resulted in a trial of destruction after its landfall in the state of Odisha located in the east coast of India. The Earth System Science Organization –India Meteorological Department (ESSO-IMD) under the Ministry of Earth Sciences, Government of India, has the mandate to provide operational weather bulletins during extreme weather events such as tropical cyclones for the North Indian Ocean region. The ESSO-Indian National Centre for Ocean Information Services (ESSO-INCOIS) under the same ministry uses the ESSO-IMD weather bulletins and further disseminates information on probable storm surges and associated coastal flooding scenarios. There are several routine modeling studies carried out using cyclone track information to compute storm surges and coastal inundation for the east coast of India that is highly prone to strike from tropical cyclones. Some of the recent studies are as follows: Naresh et al. (2012, 2013a, b, c, d); Nayak et al. (2012, 2013), Nayak and Bhaskaran (2014); Murty et al. (2014); Bhaskaran et al. (2013, 2014); Sahoo and Bhaskaran (2015a, b). Nevertheless, the application of realtime weather forecasts using WRF model to effectively plan and propose the overall power grid operation, transmission and distribution of electric supply during extreme weather events such as tropical cyclones for the Indian region is not attempted so far. As mentioned above, the application and use of weather data from the WRF model for real-time operations of the power distribution system for the Indian region are still in the nascent stage. It was only very recent in May 2015 as reported by The Economic Times; an initiative was proposed to increase the efficiency of the weather-proof power sector. In this context, the ESSO-IMD signed a memorandum of understanding (MoU) with Power System Operation Corporation Limited (POSOCO), a subsidiary unit of the Power Grid Corporation for optimum use of weather information in the power sector. As an example, the demand for power during the winter season dwindles, whereas under poor rainfall conditions, the power demand in the agriculture sector increases. Besides, on a daily basis, weather-related information is essential for energy management, production, and distribution infrastructure. The pact signed between ESSO-IMD and POSOCO will facilitate the use of current weather information

for identified stations and forecasts at different time scales ranging from nowcasts to medium range forecasts of environmental parameters such as temperature, humidity, wind speed and direction, rainfall, and other associated variables. Blackouts are a major issue faced in the coastal regions and hinterland cities during cyclones. During the 1999 Super Cyclone in the Odisha State, the power failure occurred up to 2-week time in major cities, whereas during the recent Phailin cyclone, the blackout remained for approximately 1 week, and it prevailed for more than a month along the countryside. The WRF model forecast can provide valuable information on the atmospheric state that includes rainfall distribution during and after the time of landfall. Post-landfall information on the atmospheric state will be required for power grid management and emergency preparedness. Information obtained from the model can be used to optimize management plans in the event of a potential power failure. In a broader perspective, a proper study can lead to the development of a power grid map and optimized transmission lines to minimize blackouts in the event of a strong landfalling cyclone. Therefore, considering the importance of this problem, it would be worthwhile to leverage the capability of WRF forecast and develop a framework to provide information to power distribution authorities and switching operators to safeguard hazards resulting from strong winds on transmission lines during the cyclonic activity that has wide socioeconomic impacts. The present study is an attempt and motivation to provide real-time wind field and rainfall estimates from the WRF model for Phailin event that left an enormous trail of destruction in the state of Odisha. The authors believe that reliable environmental information on wind and rainfall distribution would be a value-added product that can provide proper maintenance plan to power grid authorities during extreme weather conditions. In a global context, the WRF model is widely used for the routine weather forecast of atmospheric state since its development (Skamarock et al. 2008) in 2000. Also, there are several case studies that successfully demonstrated the robustness and applicability of the WRF model over global ocean basins for extreme weather events such as tropical cyclones. In context to Indian Ocean, some of the recent studies include predictive skill improvement of WRF model using 4DVar initialization for North Indian Ocean tropical cyclones (Gopalakrishnan and Chandrasekar 2018); track and intensity forecast of tropical cyclones over North Indian Ocean region using multilayer feed forward neural networks (Chaudhuri et al. 2015); impact of radiance data assimilation on performance prediction of SIDR cyclone using WRF-3DVAR modeling system (Singh et al. 2017); impact of lateral boundary and initial conditions in prediction of Bay of Bengal cyclones using WRF model (Mohanty et al. 2010) and 3D-VAR data assimilation system (Singh and Bhaskaran 2018); impact of local data assimilation on prediction of tropical cyclones

Application of weather forecasting model WRF for operational electric power network management—a case study...

over Bay of Bengal region using ARW model (Greeshma et al. 2015); sensitivity study of intensity and track for GONU cyclone to variety of parameterizations using advanced hurricane WRF model (Alimohammadi and Malakooti 2018); and many others. Almost all the studies using WRF and advanced data assimilation techniques for the Indian Ocean region focused on either improving the skill level of intensity, track, and landfall prediction for numerous severe and very severe cyclonic cases. The primary objective of previous studies focused on the predictive skill levels using the WRF model. However, there are no available studies in the literature that used WRF for the post-landfall event especially in context to understand the influence of extreme winds and rainfall distribution on power transmission and distribution network. In a global perspective, the recent studies used local specific observations of meteorological parameters to investigate wind loading on power line distribution over limited coverage. Most of the studies used Monte Carlo techniques as well static analysis of WRF simulated output on a single time frame. The transient response of dynamic wind loading on power transmission networks and how they influence the distribution system are not been considered in these studies. The authors believe that the real-time application of a dynamic weather prediction model especially emphasizing on the spatial and temporal variability of wind speed and rainfall distribution and analyzing its impact on power transmission and distribution system for a wide geographical coverage area is still lacking. Accurate weather-related information is essential for designing of power transmission lines in terms of material standards and location. Use of weather-related products for the energy sector is an active area of research undertaken in the USA and China. The Chinese Electric Power Research Institute (CEPRI) under the State Grid Corporation of China (SGCC) collaborates with the National Center of Atmospheric Research-Research Applications Laboratory, USA (NCARRAL) to improve the NCAR WRF-based Real-Time FourDimensional Data Assimilation and Forecasting (RTFDDA) technology to support development of weather modeling capabilities at Numerical Weather Prediction Center at CEPRI, China, in context to electric power production. The technology can provide real-time RTFDDA forecast for icing rates on transmission lines over 200 kV in China. Technological advancements using the WRF model undertaken at NCARRAL, USA, is quite advanced as compared to other countries worldwide. The products, tools, and technologies from NCAR-RAL uses weather information to cover a broad spectrum for societal needs such as agriculture and food, aviation sector, high impact weather, human health, national security, renewable energy, surface transportation, testing and evaluation, and water resources. In other words, the technological advancements made in USA and China using weather-related information for societal needs is much advanced as compared

to developments made in India. Therefore, this paper is an effort to develop a framework and bring together more concentrated efforts from researchers in the usage of weather product for the energy sector. In a broader perspective, the novelty of the present study is an attempt to use the WRF prediction effectively and attribute the impact of extreme winds and precipitation resulting on the power outage in the complex network distribution system. The authors believe this work is the first of its kind reported for the Indian subcontinent that is aimed to build synergy between electric power distribution network engineers and atmospheric scientists to develop a future framework for operational use during extreme weather events. There is a future scope to improve this study, and the limitations of the present study are also addressed in the last section. The subsequent section provides more details on relevant studies carried out in a global perspective pertaining to extreme weather modeling for risk assessment of power systems, the WRF modeling system, methodology used in the computation of the wind fields, followed by results and discussion, and finally the summary and conclusions.

2 Extreme weather modeling studies for risk assessment of power systems Ciapessoni et al. (2018) reported on a probabilistic risk-based methodology and tool combining threat analysis and power system security assessment for operational planning. Their study integrated a risk-based security assessment tool with a weather forecasting system providing a 3-day advance prediction of weather forecast characterizing the threat. The weather variables obtained from two different high-resolution models, the WRF-ARW and RAMS, constituted necessary input for wet snow hazard model. Simulations were executed on two nested grid domains having horizontal spatial resolutions of about 15 km (coarse domain) and about 5 km (finest domain). Further, the wet snow threat model was used to demonstrate the capability of the proposed tool applied for a real scenario that occurred during February 2015 in the northern province of Italy. Panteli and Mancarella (2015) investigated the effect of extreme weather and climate change on the reliability and operation of power system components. Their study also provided a comprehensive review of the existing methodologies to assess the impact of weather on power systems. Impact of human resilience on the weather was highlighted in their work leading to a generic framework for weather-related resilience studies. Ward (2013) provided another review on the effects of weather events on grid systems and reliability aspects of electricity supply for Europe and North America. Based on recent climate projections, Schaeffer et al. (2012) advocates that global climate change is expected to have considerable impacts on both natural and human systems. Their work

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summarized on the impact of climate change on energy systems. Liu and Singh (2011) investigated the effect of weather on power system reliability emphasizing on how the reliability parameters of system components are affected. A fuzzy inference system using fuzzy clustering approach combined with a regional weather model was used to map the relationship between hurricane parameters and increment multipliers of failure rates of transmission lines. Their study investigated short-term reliability indices over a hurricane lifecycle estimated using minimal cut-set method proven to be effective. Winkler et al. (2010) proposed a new methodology combining hurricane damage prediction and topological assessment to characterize the impact of hurricane on power system reliability for the USA. The failure probability for individual transmission and distribution power network elements are predicted using the component fragility models. Campbell (2018) investigated outputs from a numerical weather prediction model for electrical transmission lines. They examined weather conditions such as wind speed and air temperature surrounding a power line highlighting that by knowing the weather condition around a power line and maximum allowable temperature, the maximum electrical current limit can be estimated. Barben (2010) analyzed the vulnerability of electricity power supply under extreme weather conditions. They considered major power plants and main load centers to identify critical common mode contingencies utilizing space-time correlation of extreme weather conditions linked with the state of a power network. Deng et al. (2016) provided an overview of the risk assessment of power system under extreme rainfall weather and subsequent geological disasters. Their study proposed a transmission line outage model for coupled meteorological and geological disasters. Nonsequential Monte Carlo simulation and optimal load shedding method were used to examine the system risk indices under the combined environmental disaster. Yang et al. (2018) analyzed the outage and weather data of a 110 kV overhead transmission line in China during 2011– 2014. Their study revealed uneven distribution of outage events due to spatio-temporal variations of severe weather. The split and aggregation method was used to smooth the outage and weather data, and a Poisson model was used to examine the statistical characteristics of transmission line outage events. Li et al. (2014) examined the risk of power distribution systems in the northeast province of the USA under windy storm conditions. A probabilistic wind storm model was constructed using 160 years of storm events recorded by NOAA Atlantic Ocean basin hurricane database. The wind storms under different category were evaluated using sequential Monte Carlo method enhanced by a temporal wind storm sampling strategy. Gao et al. (2015) examined the normal and extreme wind conditions for power at 12 different coastal locations of China using daily meteorological data measured at 10-m height above ground level for a long-term

period of 40–62 years analyzed statistically. The extreme wind conditions were examined and the destructive effects of typhoons on coastal wind farms were discussed. Guikema et al. (2010) used regression and data mining techniques to estimate the replacement number of utility poles using damage data from past storms. Their study indicates that pole-based assessment provides a stronger basis for pre-storm planning by utilities. Quiring et al. (2014) utilized the National Hurricane Centre track and intensity forecast to build a decision support system useful for power outage modeling. Their study highlights that error in track and intensity forecast prior to landfall can influence the skill level of the decision support system. The uncertainty involved in model forecast and its influence on decision support application was emphasized in their study. An ensemble of about 1000 forecast realizations was generated using Monte Carlo wind speed probability model for hurricanes Dennis, Ivan, and Katrina. Further, the power outage model was executed for each realization to predict the spatial distribution of power outages.

3 Details of Phailin cyclone The very severe cyclonic storm Phailin derived from the Thai language meaning Bsapphire^ was one of the strongest cyclones in 2013 experienced over the North Indian Ocean basin. It made landfall at Odisha coast (Fig. 1) and considered as the second strongest cyclone over this region as compared to the 1999 Odisha Super Cyclone. It originated as a tropical depression over the Gulf of Thailand on 4th October, crossing the Malay Peninsula and finally entering on 6th October into Andaman Sea in the Bay of Bengal. The system intensified into an equivalent Category 1 hurricane starting from 10th October onwards, and later rapidly intensified into a super cyclone that is equivalent to a Category 5 hurricane in the Saffir-Simpson scale. The intensification was so rapid, and in a time scale of 24 h, the wind speed has increased from 83 to 213 km h−1. Further, the system weakened as it approached towards Odisha coast and finally made landfall at Gopalpur (Fig. 1) around 17 UTC on 12th October, 2013. During the time of landfall, the maximum sustained wind speed was about 215 km h−1 with a core central pressure of 940 mb. The IMD report (2013) provides more details on the intensification process and forward motion of the Phailin cyclone. Computed intensity was around ten times the decadal average power of a cyclone, estimated using Power Dissipation Index (PDI) proposed by Emanuel (2005). The average power of a cyclone per decade is in the order of 107, whereas the estimated PDI for Phailin was 4.25 × 108 (m3 s2). The estimated figure clearly shows its destructive potential and devastation over the Odisha State. Reported loss was about 42.4 billion Indian rupees (US$ 668 million). Damages were quite substantial resulting in collapse of about 240,000 dwellings and damages

Application of weather forecasting model WRF for operational electric power network management—a case study...

Fig. 1 Track details of Phailin cyclone

worth 30 billion Indian rupees occurred in Ganjam District during the landfall. Power consumption during Phailin declined from 2800 to 1300 MW due to disruptions in the distribution system. Also, several economic sectors such as production houses and storage units took several weeks to recover back in a fully functional form.

4 Power distribution system for the Odisha State Figure 2a shows the location of the Odisha State in the Indian sub-continent, and Fig. 2b shows the locations (marked in star) of Odisha hydropower stations. In general, the Energy Department has various public sector undertakings under its administrative control such as the Grid Corporation of Orissa Limited (GRIDCO), Orissa Power Transmission Corporation Limited (OPTCL), Orissa Hydro Power Corporation Limited (OHPC), and Orissa Power Generation Corporation Limited (OPGC). The independent body, Orissa Electricity Regulatory Commission (OERC) ensures transparent regulatory of power

sector within the state. In terms of utilization, the maximum power is consumed by the industrial sector (45.65%), followed by domestic (37.09%), commercial (5.52%), irrigation (2.86%), and others (8.88%). The CESU (Central Electricity Supply Utility of Odisha) has the maximum number of transformers (17944 nos.), followed by NESCO (North Eastern Electricity Supply Company of Odisha Limited) having 17472 nos., WESCO (Western Electricity Supply Company of Odisha) having 16101 nos., and SOUTHCO (Southern Electricity Supply Company of Odisha) having 10906 nos. The anticipated peak demand (in MW) for 2016–2017 was around 6330 MW, and for the year 2021–2022 the demand would be 10,074 MW. The power map for the Odisha State can be divided into four parts viz, the east, west, north, and south zones. A total of about 6 hydroelectric and 2 thermal power stations cater to the need of power demand in this State. Total generation from these eight stations is about 2776 MW. Two thermal power stations are located at Jharsuguda (IB Thermal under OPGC), northwest of the Odisha State, and the other at Dhenkanal (Talcher Thermal under NTPC). The eight hydroelectric power stations are Hirakud I and II, Rengali, Upper Indravati,

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Fig. 2 a Location of the Odisha State in the Indian sub-continent. b Location of OHPC (Odisha Hydro Power Corporation Ltd.) stations in the state of Odisha. c Flowchart of WRF modeling system

Upper Kolab, Balimela, Machkund, and Potteru (Fig. 2b). The total power generation from two thermal power stations is about 880 MW (460 MW from Talcher and 420 MW from IB Thermal). The hydroelectric stations generate a total power of 1896 MW (331.5 MW from Hirakud, 360 MW from Balimela, 250 MW from Rengali, 320 MW from Upper Kolab, 600 MW from Upper Indravati, 34.5 MW from Machkund respectively). A total of about 13 load transmission systems of 440 V capacity and 13 major 220 V transmission systems works in the Odisha State. Amongst the existing power plants, the Nalco, Hirakud, and Jharsuguda are the major ones situated in central, west, and south Odisha respectively. The major fault in any of the transmission lines increases the chance for power failure in any two-power zones of the state. The east and north zones situated along the coastal belts are more vulnerable to high winds and rainfall caused by cyclones. As a result, the north and west zones that are highly active in socio-economic grounds suffers due to blackouts. A forecasting system well in place is very much required to maintain the stability of the system and

network operations. Table 1 shows the existing captive generation plants in the state of Odisha. From a total of 33 number plants, 27 numbers thermal, three numbers diesel, one number thermal with diesel, one number steam with diesel, and one number naptha gas power generation plants are available. The maximum number of plants are located in the central zone (13 numbers), followed by western zone (12 numbers), northern zone (06 numbers), and the southern zone (02 numbers). The devastation that resulted from Phailin was quite enormous affecting over 12 million people, besides the biggest evacuation measure that happened in the past 23 years moving around 800,000 residents inland. Around 20 districts comprising of more than 18,000 villages and agricultural farms of more than 600,000 ha were affected from this cyclone.

5 WRF modeling system The Weather Research and Forecasting model also widely known as WRF is a three-dimensional mesoscale forecast

Application of weather forecasting model WRF for operational electric power network management—a case study... Table 1

Existing captive generating plants in the Odisha State

S. Name of captive No. generating plants (CGP)

Zone

1

NALCO (Angul)

Central

960.00

2

RSP (Rourkela)

Western

248.00

3

ICCL (Choudwar)

Central

108.00

Thermal

20

4

HINDALCO (Hirakud)

Western

367.50

Thermal

21

5 6

NALCO (Damanjodi) FACOR (Bhadrak)

Southern 55.50 Northern 12.00

Thermal Diesel

22 23

7

ACC (Bargarh)

Western

Diesel

24

8

Kalinga Iron Works (Barbil) Orient Paper Mills (Brajarajnagar) Orissa Textiles Mill (Choudwar)

Northern 12.00

Thermal

Western

19.70

Central

5.188

Paradeep Phosphates Ltd. (Paradeep) Balasore Alloys

Central

38.00

9 10

11 12 13 14 15 16 17

FCI (Talcher) NINL (Duburi) Nava Bharat Ferro Alloys (Meramundali) Bhusan Steel (Jharsuguda) IFFCO (Paradeep)

Installed Type capacity (MW)

30.00

S. Name of captive No. generating plants (CGP)

Zone

Installed Type capacity (MW)

Thermal

18

Arati Steels

Western

40.0

Thermal

Thermal

19

Bhushan Steel & Strips (Meramundali) Shyam DRI

Western

110.00

Thermal

Western

Tata Sponge Iron Ltd., Joda Vedanta (Lanjigarh) Jindal Stainless Ltd.

30.00

Thermal

Northern 26.00

Thermal

Southern 90.00 Central 250.00

Thermal Thermal

Western

83.00

Thermal

25

SMC Power Generation Ltd. Scaw Industries Ltd.

Central

8.00

Thermal

Thermal

26

Pattanaik Steels Ltd.

Central

15.00

Thermal

27

VISA Steels Ltd.

Northern 75.00

Thermal

28

Aryan Ispat Ltd.

Western

45.00

Thermal

Northern 40.45

Thermal and diesel Steam and diesel Diesel

29

Vedanta (Jharsuguda)

Western

540.00

Thermal

Central 28.35 Northern 62.50 Central 95.00

Naptha Gas 30 Thermal 31 Thermal 32

Rathi Steel & Power Western Mahavir Ferro Alloys Ltd. Central Bindal Sponge Ltd. Central

20.00 12.00 12.50

Thermal Thermal Thermal

Western Central

Thermal Thermal

Orissa Sponge Ltd. Total

12.00

Thermal 3648.688

100.00 110.00

model and assimilation system to advance validation and prediction of mesoscale features including storm-scale research, air quality modeling, regional climate, operational weather prediction, and tropical storm prediction. Figure 2c depicts flowchart highlighting the various steps involved in WRF modeling covering the pre- and post-processing modules. It uses the fully compressible non-hydrostatic equations with hydrostatic options (Janjic 2003a, b). The inherent model physics in WRF comprises of cloud microphysics, surface layer physics, land surface model, boundary layer, and shortand long-wave radiation components. The model performs both forecast and analysis of atmospheric parameters precisely using available global atmospheric parameters as the initial and boundary conditions. The Eulerian equation along with the Bernoulli’s equation calculates the flux in conservative form. Besides, the conservative form of potential temperature and density are used in this model, and pressure being a nonconservative cannot be used together with potential temperature, which is one of the disadvantages in this model. Time integration uses the second- and third-order Runge-Kutta method, whereas the spatial discretization uses the secondand sixth-order advection options. The lateral boundary condition has the flexibility for periodic, open, symmetric with specified boundary options. The model assigns the free slip

33

Central

condition as the bottom boundary condition and can be executed on a global scale, as well limited area regional scales with various spatial resolutions. The forecast quality depends on the prescribed initial and boundary conditions and the choice of model physics.

5.1 Choice of domain size and spatial resolution WRF model replicates the flux as a periodic function dependent on the model size and spatial resolution specified. It is imperative to choose the right domain size for better representing the atmospheric phenomena being investigated. For tropical cyclones simulation, the prescribed domain size should essentially capture the radius of influence associated with the cyclonic system. Besides, prescribing a larger spatial domain has the chance to duplicate the effects, as the flux function is periodic in nature. Choice of grid resolution is flexible, wherein the user can select any range varying from 30 to 1 km depending on the complexity of the analyzed region. WRF model has an option for nesting, which facilitates the user to choose varying domain size with higher grid resolutions. In that case, the time varying boundary information from a coarse simulation serves as a boundary condition for the subsequent finer run. Improvement in accuracy and decreased computational time is the advantage

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of using nesting option in the WRF model. Changing the horizontal grid spacing can lead to significant changes concerning maximum intensity which in turn is a measure of the surface wind speed and minimum sea level pressure. Differences can be expected in terms of kinematics as well the microphysics that in turn can affect the overall rainfall distribution. Coarser grid spacing can result in larger cyclone eye-walls having larger mass flux and updraft volume that has implications on condensation and ice particles and vice-versa for finer grid spacing. Stern and Nolan (2009) advocated an increase in the radius of maximum winds and slope of eye-wall with increased horizontal grid spacing. Fierro et al. (2009) examined the structural details of convection to forecast coastal and inland flooding using WRF-ARW model, investigating the impact of horizontal grid spacing on microphysical and kinematic structures of strong tropical cyclones. Their study (Fierro et al. 2009) signifies that rain bands were better resolved at a horizontal grid size of 3 km resolution and recommended this grid spacing for operational needs. Choice behind the selection of domain grid size plays an important role as it is directly related to exchange coefficients of latent heat, wind speed, pressure, and various gradients along the prescribed domain boundary. The fixation of lateral boundaries used in this study is the best choice and based on findings from several numerical experiments, and more details are elaborated in the subsequent section. Considering the Phailin as a closed system, the continuity equation is valid for the outer domain 1 (Fig. 3). Mathematically, the continuity equation is expressed in the form:

∂ui ui −ui−1 ¼ þ OðΔxÞ ∂x Δx
ui ¼ ui−1 þ ∂ui þ O Δx2

ð2Þ

Assuming that the domain has n grids along the x-direction, the discretized form of gradient for u along this direction results in the set of equations:
u1 ¼ u0 þ ∂u0 þ O Δx2
u2 ¼ u1 þ ∂u1 þ O Δx2 ð3Þ :::::::::::::::::::::::::::::::::::::
un ¼ un−1 þ ∂un−1 þ O Δx2 Adding the set of equations we obtain

i¼1

i¼1

ð4Þ

i¼1

The approximate error is (Δx2). Since the model is dependent on grid size and prescribed time step, the increment in wind speed δu can be estimated using the Newton’s law ∂u = Δx/Δt and in the limiting condition: n
∑ ∂ui þ Δx2 ¼ 0 i¼1

Δx ≈Δx2 Δt Δx n≈ Δt n:

ð5Þ

where Δx is the grid spacing in meters, Δt is the time step in seconds. In the present study, the outer domain has a grid resolution of 27 km and time step of 3 min. Using these prescribed values, i.e., Δx = 27,000 m, Δt = 180 s, the estimated number of grid points would be n = 150. The domain diameter for this case would be n × Δx = 4050 km, which has an equivalent radius of approximately 2000 km and which satisfies the experimental theory of Stanley (1971).

ð1Þ

where u, v, and w represents the wind components along the x, y, and z directions. In WRF model, the discretization of equations use the upwind method, where

n

n n

un −u0 ¼ 0 ¼ ∑ ∂ui þ O Δx2 ¼ ∑ ∂ui þ Δx2

5.2 Choice of parameters

∂u ∂v ∂w þ þ ¼0 ∂x ∂y ∂z

un −u0 ¼ ∑ ∂ui þ O Δx2

Considering the cyclonic system being nearly symmetric in nature, the x component of wind speed is nearly equal in magnitude and opposite in direction at the opposite points of the quadrant with eye of cyclone as center point. The discretization of u considering the magnitudes can be expressed in the form:




As mentioned above, the computation of WRF model solely depends on the selection of domain size, grid resolution, and physical parameterization schemes prescribed by the user. It is therefore very important to choose the right combination of variables to obtain a better quantitative estimate of atmospheric parameters being investigated from the model computation. The microphysics options contain the commonly used warm rain scheme (Kessler scheme) to the sophisticated scheme of Lin et al. that essentially captures ice, snow, and graupel processes with a suitable high resolution. The surface layer physics option can handle the soil temperature along with moisture variability. The planetary boundary layer physics accounts for turbulence and mixing process, besides the cumulus parameterization option controls the cloud parameters and determination of rainfall. Chawla et al. (2018) performed an extensive study to assess and evaluate the performance of WRF model in simulating extreme rainfall events for the Upper Ganga Basin using ensembles from different options of microphysics, cumulus parameterizations, planetary boundary layers, and land surface physics at different grid resolutions. In their study, the WRF simulated rainfall was evaluated using observations from 18

Application of weather forecasting model WRF for operational electric power network management—a case study... Fig. 3 Three nested domains (coarse: domain 1; intermediate: domain 2; and fine: domain 3) used in WRF model for Phailin simulation along with the comparison of WRF model predicted track against real Phailin track

rain gauges and Tropical Rainfall Measuring Mission MultiSatellite Precipitation Analysis (TMPA) datasets. Influence of model resolutions were also examined with two sets of downscaling ratios viz, 1:3 for global to regional scale (G2R) and 1:9 for global to convection (G2C) permitting scale. Their study indicates that G2R performed better as compared to G2C and therefore adopted as the best choice to simulate heavy rainfall event. The nested grid resolution used in their study (Chawla et al. 2018) was 29, 9, and 3 km and further experimented with two downscaling ratios as mentioned above. A recent study by Singh and Bhaskaran (2017, 2018) examined the impact of PBL, convection parameterization schemes, lateral boundary, and initial conditions to predict six severe cyclones in the Bay of Bengal region. Their modeling study used two interactive nested domains with horizontal grid resolutions of 27 and 9 km respectively for the outer and inner domains which performed best when compared against observations. Singh et al. (2017) investigated the impact of radiance data assimilation on performance evaluation of SIDR cyclone using the WRF-3DVAR modeling system. Their study (Singh et al. 2017) used a two-way interactive nested domain size of 27 and 9 km, and that resulted in best performance simulating the track of SIDR cyclone as well the rainfall distribution validated against observations. Another numerical modeling study for JAL cyclone by Reddy et al. (2014) investigated the effect of cumulus and microphysical parameterizations using three nested domains of 27, 9, and 3 km. The characteristic features of JAL cyclone were captured reasonably well using this domain size.

All these studies converge to the fact that domain size of 27, 9, and 3 km is the best option for tropical cyclone simulation for the Indian Ocean region, and the same is used in the present study.

6 Methodology The present study is an application of WRF weather model for Phailin event in the Bay of the Bengal region. Model simulation was executed for a period of 4 days starting from 9 October 2013 until post-landfall on 13 October 2013. The choice of domain size and resolution was configured to cover the entire Bay of Bengal region bounded between the geographical coordinates 70°–110° E along the meriodional direction and 25°–equator along the zonal direction. Choice of this domain size would essentially capture the land effects on the eastern side of Bay of Bengal as well the Indian subcontinent on the western side. This is referred to as the outer domain having a spatial grid resolution of 27 km. The study used a two-way nesting, wherein the intermediate nested domain has a resolution of 9 km, and the innermost domain has 3 km resolution with 30 vertical levels. Figure 3 shows the three nested domains used for Phailin simulation along with the model simulated track compared against the real cyclone track. The domain with 9 km resolution covers the entire cyclone track thereby simulating the high-resolution outputs for the Phailin episode; however, the domain with 3 km resolution concentrated on the landfall location and post-landfall to

B. Sahoo et al.

comprehend the impact of winds and precipitation over land leading to its application and impact assessment for the power grid distribution network in the Odisha State. For this case study, the model output parameters are computed at every 3 min interval and the integrated quantity of output parameters at three hourly intervals used for analysis. Based on several numerical experiments that employed various combinations of physical parameterizations, the best optimum combination of selected parameters used in this study are as follows. A sophisticated scheme that has ice, snow, and graupel processes and suitable for high-resolution simulations (referred as Lin et al. scheme) is found to best suit for the microphysics option. Similarly, for the planetary boundary layer physics, the BouLac scheme designed for the urban model is the best option. As this study also deals with the rainfall distribution during Phailin event, the clouds play an important role in the atmospheric forecast. The best possible cumulus parameterization is the Bette-Miller-Janjic (BMJ) scheme and used in this study. The cumulus parameter named new Tiedtke scheme that includes the mass flux scheme with CAPE removal time scale, shallow components, and momentum transport resulted in predicting Phailin track with minimum RMSE; however, unlike in case of wind speed prediction compared to BMJ scheme. In addition, a five layered thermal diffusion scheme by NoAH is chosen as the best possible land surface physical parameterization. The initial input condition pertains to six hourly forcing of boundary condition available at a resolution of 0.5° downloaded from the NCEP-FNL operational model. The intermediate domain provides the output at a spatial resolution of 9 km at every 1-h interval. As this case study pertains to application of the WRF model to assess and evaluate the power management and distribution system for the Odisha State, critical investigations are made on essential parameters such as wind speed, probable landfall location, and the rainfall distribution. The optimal choice of parameters is based on utmost model performance with respect to the parameters such as wind speed, precipitation, and cyclone track. Simulations with the above setup of model domains, resolution, and physics clearly reveal that the model computed landfall location matched exactly with the observed landfall point, and the overall error was about 1% in the estimated wind speed and rainfall validated against observed data. The track has a maximum displacement error of 0.3° and that is quite reasonable. Figure 3 shows the actual Phailin track provided by the Joint Typhoon Warning Center (JTWC) validated against the WRF predicted track.

7 Results and discussion 7.1 WRF simulations for Phailin cyclone The best possible microphysics combination used in this study simulated very well the track (Fig. 3), associated winds and

rainfall during and after the landfall of Phailin. As this study confines to the application of WRF for power grid management during extreme weather events like tropical cyclones, the model performed simulation 18 h past the time of landfall, as the core cyclonic winds advanced far hinterland away from the coast crossing the Odisha State and further advancing to the neighboring Chhattisgarh State. Phailin sustained as a very severe cyclonic storm (VSCS) during its landfall time at the Odisha State and thereafter reduced its intensity. It continued as a severe cyclonic storm (SCS) and thereafter as a cyclonic storm (CS) in the hinterland of the Odisha State. In this study, 12 specific locations (some locations are nearshore and the remaining far onshore) were considered to assess and evaluate the WRF computed wind speed and rainfall. The simulations covered the period from 9 to 13 October 2013, more importantly to assess the wind magnitudes and rainfall scenarios hinterland where the power stations, transmission lines, and distribution systems are located. Figure 4 and Table 2 show the respective locations (Fig. 4a) and their corresponding minimum distance from the cyclone track (distance from track shown in Fig. 4b). As seen from this table, the area of analysis covers a maximum radial distance of 300 km from the Phailin track, where one can expect the maximum impact from wind loading and rainfall. Figure 5 shows the time series of WRF wind field simulated for the 12 locations shown in Table 2 starting from 9 October 2013 (1800 hours) when Phailin was in the midBay of Bengal approximately 250 km from Port Blair, Andaman Islands. An increasing trend in wind field was evident at all the 12 specified locations at 30 h lead-time starting from the reference time of 9 October 2013 (1800 hours), when the cyclone center was located in the central Bay of Bengal (16.0° N; 88.5° E). The cyclone category was VSCS, and during this point of time, the radial distance from cyclone eye to the southern coastal point located at Ichchapuram, the border between the states of Andhra Pradesh and Odisha was about 531.6 km. Besides, the radial distance to the Odisha State capital, Bhubaneswar and at Balasore (northward limit of Odisha coast) were 601.8 km and 672.6 km respectively. The coastal belt of Odisha experienced wind speed in the order of 20 km h−1, and that was noticed rising steadily as the cyclone approached towards the coast. Thereafter, the forward motion of Phailin was almost in the northwest direction until the time of landfall. On 11 October 2013 (1000 hours), i.e., 40 h lead-time from the reference date (9 October 2013, 1800 hours), the cyclone eye was located at 16.8° N; 87.7° E (category VSCS) and the estimated radial distance was 401.2 km from Ganjam location. During this time, the onshore locations along the right side of cyclone track experienced higher winds as compared to the left side. For instance, as seen in Fig. 5 (representative locations shown in Table 2), the locations such as Chatrapur, Ganjam, Narendrapur, Puri, and Bhubaneswar experienced higher winds (order of 40 km h−1),

Application of weather forecasting model WRF for operational electric power network management—a case study... Fig. 4 a Twelve selected locations in the Odisha State and their respective distance from Phailin track. b Same as a represented in geographical coordinate system

whereas those along the left side of track experienced relatively lower winds (order of 30 km h−1). During 12 October 2013 (1200 hours), the cyclone was centered at 18.7° N; 85.2° E and the estimated radial distance was about 77.61 km from Ganjam location. Also, the wind speed experienced at all these 12 Table 2

locations was slightly lower than the core winds (considering the radius of maximum winds as 45 km). Wind magnitude scenarios for locations along the right side of cyclone track viz, Chatrapur was about 130 km h−1, Ganjam was about 120 km h−1, Narendrapur was about 100 km h−1, and at Puri

Location of stations w.r.t. Phailin track

S. No.

Location

Latitude (in ° north)

Longitude (in ° east)

Minimum distance from cyclone track (in km)

Distance from the state capital Bhubaneswar (in km)

01 02 03 04

Chandaka Nayagarh Chatrapur (Ganjam) Asika

20.3667 20.1160 19.3500 19.6000

85.7667 85.0100 84.9800 84.6500

130 65 55 40

26 86 151 168

05 06 07 08 09 10 11 12

NALCO Indrawati Ganjam Bhanjanagar Narendrapur Puri Bhubaneswar Balasore

20.8490 19.2024 19.3830 19.9300 20.4293 19.8106 20.2700 21.5000

85.1540 82.5882 85.0500 84.5800 86.2552 85.8314 85.8400 86.9000

95 230 10 50 30 115 140 298

123 579 143 211 165 59 0 199

B. Sahoo et al.

Fig. 5 Time series distribution of WRF simulated wind speed (in km h−1) at the 12 selected locations. The X-axis in all figure panels represents the time (in hours) after 9th October, 2013 (1800 IST) and the blue dot in all

figures indicate the landfall time. The Y-axis in all figures represents the wind speed (in km h−1)

it was about 100 km h−1. The remaining stations centered along the left side of the track also experienced high winds in the order of 75 km h−1, and Balasore located 358.4 km during this time experienced winds in the order of 45 km h−1. A sudden dip in wind field was noticed (Fig. 5) just prior to the landfall, clearly seen at all the stations, notably at Asika, Ganjam, Bhanjanagar, Narendrapur, and Chatrapur. It is expected that cyclone speed tends to slow down over land due to high surface roughness. The dual peaks in the time series of wind speed prior to landfall attributes due to bigger cyclone size. The results also clearly show that the peaks are centered at the outer periphery corresponding to the radius of maximum winds that conforms and provides a measure on the size of Phailin cyclone, which in turn proves the confidence in model accuracy and the methodology of computation used in this study. During the time of landfall near Gopalpur (19.2° N; 84.9° E) the cyclonic system remained as VSCS category and continued to remain in that category for another 7 more hours until 13 October 2013 (0000 UTC). Higher wind speed continued to remain at all these 12 specified locations. As the cyclonic system advanced onshore, a reduction in wind intensity occurred all along the coastal stations such as Chatrapur (reducing from 130 to 100 km h−1), Ganjam (reducing from 120 to 100 km h−1), Puri (reducing from 100 to 90 km h−1). Stations located interior and onshore experienced higher wind intensity such as NALCO (increased from 58 to 80 km h−1), Nayagargh (increased from 70 to 90 km h−1), and Indravati (increased from 35 to 48 km h −1 ). The other interior stations such as

Bhanjanagar and Narendrapur (Fig. 4a) experienced higher winds in the order of 80 and 100 km h−1 respectively. In addition, remaining stations located onshore and far off from Phailin track had more or less similar wind strengths viz, Chandaka, Asika, Bhubaneswar, and Balasore, as the cyclonic system continued to maintain the category of VSCS. As the system progressed far inland (Fig. 5) until crossing the Odisha border with Chhattisgarh, the wind magnitudes have reduced reaching an order of about 35 to 40 km h−1. However, from model simulation, it is evident that stronger winds persisted for a longer time after the time of landfall in the state of Odisha. Reports mention that in places like Balasore that was quite far and at a distance of approximately 300 km from cyclone track also suffered disruption of power supply, due to uprooting of electric poles experiencing wind gust of 60 km h−1. Under this scenario, considering 60 km h−1 as the threshold wind speed limit to destroy the power networks, it is evident that almost 70% area in the state of Odisha will suffer from power supply during a tropical cyclone with a similar intensity as Phailin. Figure 6 shows the time series of maximum rainfall (in mm h1) associated with Phailin event for the 12 specified locations (Fig. 4a). As seen, the maximum accumulated rainfall was about 80 mm h−1. WRF simulated maximum rainfall (80 mm h−1) occurred at the stations Nayagarh, Asika, and Narendrapur interior from the coastline. The two stations Asika and Narendrapur are located on the left side of the track and lie within the core of maximum winds. The station Nayagarh is located on the right side of track that experienced

Application of weather forecasting model WRF for operational electric power network management—a case study...

strong winds after the landfall event. Chandaka, Bhubaneswar, and NALCO also fall within the right side of the track, however, located quite far away. The maximum rainfall computed at these stations is 30, 35, and 40 mm h−1 respectively. The two nearest stations close to cyclone track was Chatrapur and Ganjam located close to the coast, and that experienced rainfall of about 70 mm h−1. The remaining two stations Indravati (left of track) and Balasore (right of track) where quite far off from the cyclone track and the computed rainfall at these stations were 6 and 8 mm h−1 respectively. The station Puri experienced rainfall of about 15 mm h−1 about 8 h prior to the landfall. In addition, as seen from Fig. 6, there were sporadic events of rainfall at Puri much before the cyclone made landfall at Gopalpur. The rainfall distribution for stations such as Chandaka, Nayagarh, Asika, and Bhubaneswar was about 10 mm h−1 just 8 h prior to landfall. NALCO located far off the coast experienced heavy rains 12 h after the occurrence of landfall. As noticed from Fig. 6, the rainfall distribution increased as Phalin approached its landfall point, and the coastal station Chatrapur that was nearest to cyclone track showed maximum rainfall during the time of landfall. Simulations very well show that those stations located far off from the coast, interior on-land, experienced the maximum rainfall distribution much later compared to those stations located near the coast.

7.2 Impact on Odisha power system Figure 7a clearly illustrates that the National power demand had steeply gone down from an average of 107,000 MW (peak

Fig. 6 Time series distribution of WRF simulated rainfall distribution (in mm h−1) at the 12 selected locations. The X-axis in all figure panels represents the time (in hours) after 9th October, 2013 (1800 IST) and

value of 120,000 MW) to 85,000 MW during the Phailin event. In a national perspective reduction factor of 20.56% is quite substantial. Average power demand for the Odisha State was only 2.6% of the national average. The demand reduction for Odisha went down from a level of around 2800 MW (3300 MW during peak evening hours) to about 500 MW during the night hours (Fig. 7b). It indicates that the catastrophe was quite visible, with a substantial reduction in the national power demand reducing the national average by 20.56%. It had taken about 2.5 days for the national power demand threshold to reach the mean value (Fig. 7a), whereas for the Odisha State, it had taken about 4.5 days to reach the normalcy. For the neighboring state of Andhra Pradesh, it went down from 9500 to about 9000 MW. From a national perspective, the estimated demand reduction was in the order of 15,000 MW to 17,000 MW. The neighboring states of Jharkhand, Chhattisgarh, and Bihar also experienced the effects from Phailin. Overall, during the off-peak period during night, the national load demand tumbled from 104,000 to about 84,000 MW. Five districts were the worst affected areas in Odisha viz, Ganjam, Khurda, Puri, Nayagarh, and Jagatsinghpura. During the cyclone period there were several actions taken, that includes changing the HVDC power order on TalcherKolar, Gajuwaka, and Bhadrawati sectors. The 400 kV and 200 kV lines switched off to control high voltages. During this event, nine numbers of 400 kV lines, 27 numbers of 220 kV lines, and 37 number of 132 kV lines in Odisha tripped. Precautionary steps taken were adequate in

the red dot in all figures indicate the landfall time. The Y-axis in all figures represents the rainfall distribution (in mm h−1)

B. Sahoo et al.

Fig. 7 a National power demand during the period 8–19 October 2013 (expressed in 1000’s MW). b Power demand of the Odisha State during this period. c Hourly distribution of positive sequence voltage, frequency

plots, and time variation of frequency plots from Phasor Measurement Unit based at Talcer (source: Mukhopadhyay et al. 2014)

maintaining the overall connectivity of Odisha from rest of the grids, as well the inter-regional connectivity between the eastern and southern regions. The Phasor Measurement Unit (PMU) at Talcher had helped and provided valuable data to observe tripping in the eastern and southern regions that enabled the operators to have an insight on the power situation. Figure 7c shows the hourly distribution on the positive sequence voltage, frequency plots, and df/dt plots recorded by Talcher PMU during the period 12–13 October 2013 (source: Power System Operation Corporation Limited (POSOCO), http://posoco.in/2013-03-12-10-34-42/synchrophasors). The sporadic event associated with tripping during this episode is evident, and based on the hourly record (1700–1800 hours) during 12 October 2013 (Fig. 7c and Table 3), the 400-kV Rourkela-Talcher line had tripped due to overloading. The positive sequence voltage, frequency plots, as well the df/dt plots show clearly the tripping signature resulted from overloading. The Pole-I at Talcher-Kolar sector also tripped

(Fig. 7c and Table 3) in the early hours of 13 October 2013 (0000–0100 hours). As noticed from Table 3, there is a strong correlation between the reported date and time of transmission line faults during Phailin event by the Phasor Measurement Unit (Fig. 7c) and the estimates of extreme wind speed obtained from WRF simulation. Several line faults occurred over various sectors in the Odisha State on 12th and 13th October 2013, and the associated tripping is due to malfunctions such as overloading, over-voltage, and over-fluxing. Extreme wind speeds exceeding 80 km h−1 caused line faults along the various sectors. Table 3 provides a comprehensive overview on the observations seen at PMU and its correlation with the probable damage to the near-field connections associated with the extreme winds computed using WRF model. The overloading in the Rourkela-Talcher 400 kV line on October12, 2013 is associated with the damage due to extreme winds (≈ 140 km h−1). Far away from the storm track near

Application of weather forecasting model WRF for operational electric power network management—a case study... Table 3 Problems with transmission line faults (as reported by Phasor Measurement Unit) and its relation with Extreme wind speed estimated from WRF model for Phailin cyclone Line fault (sector)

Date and Reason of time of tripping tripping (in IST)

Probable damage of connected line(s)

Estimated wind speed Remarks at the connecting line (from WRF model)

Rourkela-Talcher 400 kV

1700–1800 Overloading

Kolar-Kaniha 500 kV transmission line passes through Asika

140 km h−1 at Asika

Angul-Meramundali 400 kV Meramundali-Jindal 400 kV

1800–1900 Over-voltage Meeramunduli-Bhanjanagar 110 km h−1 at Bhanjanagar 400 kV 1800–1900 Over-voltage Connected to Meramundali 80 km h−1 at Meramundali Junction

12 October, 2013

Talcher-Meramundali 1900–2000 Over-voltage Connected to Meramundali 400 kV Junction

80 km h−1 at Meramundali

Angu l-Bolangir 400 kV

80 km h−1 at Angul

1900–2000 Over-voltage Anugur-Talcher line due to load on Meramundali junction at the same time

Therubali-Indravati 1900–2000 Unknown Bhanjanagar-Therubali 220 kV 400 kV Talcher-Kolar HVDC 2300–2400 DC line fault High-capacity line Pole-II Talcher-Kolar Pole I/II 13 October, 2013 Talcher-Kolar Pole-I

0000–0100 Unknown

HVDC Talcher-Kolar 0100–0200 Unknown Pole-I 315 MVA Baripada ICT-II

85 km h−1 90 km h−1 at Talcher

Damage due to high wind gust, as the line situated along the right of Phailin track Line situated very near to the storm, along the left side of the storm Over-voltage at Meramundali station might have affected the connecting lines, and tripped due to overloading Talcher has power control system, the lines tripped after 1 h due to excess flow of current from Meramundali junction Tripping of Talcher-Meramundali 400 kV line might have caused excess current flow along this line Rapid fluctuation of wind speed and gust at Bhanjanagar Line situated along the right side of the storm might have damaged late because of high tensile strength

High capacity Talcher Kolar Pole I/II

85 km h−1

High-capacity transmission line owned by PGCIL

80 km h−1 at the north Advance of Phailin towards central of Talcher Odisha—transmission line affected due to wind gust 50 km h−1 at Balasore The low voltage line might have damaged late due to wind gust and low tensile strength though situated far from the storm

0100–0200 Over-fluxing Duburi-Balasore 400 kV and frequency rise

Sustained 1 h more than the high capacity line Talcher-Kolar HVDC Pole-II because of the high tensile strength

Balasore, the low voltage line has damaged due to wind gust damaging the Duburi-Balasore 400 kV line resulting in overfluxing and frequency (Fig. 7c) rise affecting the 315 MVA Baripada ICT-II.

earlier reported in the works by Vickery (1978), Kareem (1999), and Kareem and Kijewski (2001). Maximum wind force on a transmission structure considering it as a rigid body at a height z above the ground level can be expressed in the form:

7.3 Wind loading and risk assessment from cyclone

F ¼ Ps ðzÞC d A

The above discussions clearly demonstrate the variations of wind speed and rainfall distribution during the passage of Phailin cyclone in the Odisha State. Wind loading is an important problem associated with tropical cyclones, as the trial of destruction left to buildings, power distribution systems, and coastal infrastructure is enormous. The pioneering efforts to represent complex randomly varying phenomena of wind loading and its application on tall slender structures were

ð6Þ

where Ps(z) is the stagnation pressure, also termed as dynamic wind pressure. The stagnation pressure is equal to: 0:5  ρa  V g ðzÞ2

ð7Þ

where ρa is the mass density of air; Vg(z) is the gust velocity at a height z above the ground surface; Cd is the drag coefficient of the body; and A is the area exposed perpendicular to the wind direction. Aerodynamic admittance also referred as Bsize

B. Sahoo et al.

effect^ is a matter of concern associated with the gusts during extreme weather events such as tropical cyclones. There are instances when gusts can envelope the entire transmission structure for a very short span of time. The span factor (SF) can therefore be introduced into the maximum wind force equation conveniently. Mathematically, in terms of SF, the maximum force equation is expressed in the form: F ¼ Ps ðzÞ⋅SF⋅C d A

ð8Þ

The other way to approach the aerodynamic admittance considering the resonant effect is the BGust Response Factor^ (GRF). Approach using GRF specifies a force F if applied statically causes the system to reach its expected peak response. In this case, the above equation that uses SF can be expressed using GRF, wherein the stagnation pressure Ps(z) will be equal to: 0:5  ρa  V m ðzÞ2

ð9Þ

Here, Vm(z) is the 10-min average of wind velocity. The resultant maximum force equation considering GRF can be written in the form: F ¼ Ps ðzÞ⋅GRF⋅C d A

ð10Þ

Wind directionality is another important factor that needs to be used in the design equation to determine the wind force on a span. The above equations consider the wind direction perpendicular to the area under influence. However, if the wind direction were at a certain incidence angle from normal to the span, the force would decrease by a factor equivalent to the square of the cosine of incidence angle. It essentially means if the wind direction is at 45° then the force would go down by a factor of 2, and for incidence angle greater than 18°, the force would depreciate more than 10%. In a probabilistic approach, the wind direction is one of the major uncertainties in the estimation of wire loads. The ASCE Manual number 74 edited by Wong and Miller (2009) provides published information on drag coefficients for cables, structural members, and other assemblies. The recommended value of Cd for poles is close to one. The electric power delivery system involves three groups such as generation, transmission, and distribution. For financial planning operations, the formulation for damage with and without the blackout situations is important. During the approach of a cyclone towards the landfall point, infrastructure in the regions located at the high wind speed envelope experience partial or permanent damages. In a mathematical sense: n

pðwÞ ¼ ∑ I i ðwÞ:

ð11Þ

i¼1

where p(w) represents the sum of all power equipment damaged due to the cyclone, and Ii(w) is the damage caused to

individual equipment (Hou et al. 2014). In the event of higher damages to equipment, the power grid can lead to blackout accidents. There is no well-defined relation between damage caused to equipment and blackout, which is quite complicated. Therefore, it would be worthwhile to set a threshold value to distinguish whether there would be blackout after damage to the equipment. Mathematically, the appropriate relation would be: pðwÞ ≥ 50%pðwÞall

ð12Þ

where p(w) exceeds the total value of all equipment that probably results in the blackout. This would suffice for damage lost without a blackout accident. In the event of a blackout, the loss should also consider the supply of no energy. In this context, the index that is universally accepted termed BLOLP^ (loss of load probability) and BEENS^ (expected energy not supplied) can be used to estimate the loss caused by the blackout accident (Hou et al. 2014). In a mathematical sense, the variable LOLP is expressed as (Hou et al. 2014):   NL Ti ð13Þ LOLP ¼ ∑ ∑ PðsÞ T i¼1 s∈ F i And NL

EENS ¼ ∑

i¼1



 ∑ PðsÞC ðsÞ T i

s∈ F i

ð14Þ

where P(s) is the probability of status s. The parameter C(s) is the load shedding (expressed in MW) under the status s; Fi represents the mathematical set of all system failures under load level i; NL is the load classification number; Ti is the time duration under load level i; and T is the total time duration of the load curve. To estimate the probability calculation, P(s) the Monte Carlo simulation is the most commonly used method in risk assessment for power systems (Wang et al. 2010). The total loss is the sum of Ii and EENS expressed as: n

pðwÞ ¼ ∑ I i ðwÞ þ EENS

ð15Þ

i¼1

Model simulations from WRF therefore find its application and considered as an essential input in the final determination of damage assessment. Figure 8 provides an overview on the WRF simulated spatial distribution of wind field (in m s−1) at three different domains and for three time instances on Phailin track during 9 October 2013 (1800 hours), 11 October 2013 (1800 hours), and 13 October 2013 (0000 hours). The left panel (Fig. 8) on 9 October 2013 shows the simulated wind field for domain 1 and domain 2 that have grid resolutions of 27 and 9 km respectively. As seen, during this time (9 October), the Phailin winds did not influence the coastal region of Odisha. The spatial distribution using the grid

Application of weather forecasting model WRF for operational electric power network management—a case study...

Fig. 8 Spatial distribution of WRF simulated wind speeds (in m s−1) for Phailin cyclone represented at three different domains (the respective domain resolutions are D1 (27 km), D2 (9 km), and D3 (3 km))

resolutions of 27 and 9 km looks almost similar. The central panel (Fig. 8) shows the distribution for 11 October 2013 (1800 hours). The coarser domain (27 km) exhibits maximum wind speed of 45 m s−1, and significant improvements were seen for WRF executed in the domain resolution of 9 (centralmiddle panel) and 3 km (central-lower panel). During the time of landfall (13 October), the spatial distribution of wind speed along the Odisha State is seen (right panel of Fig. 8). Maximum wind speed for the domain 3 (having 3 km resolution) is about 45 m s−1. These figures only present a snap-shot of spatial wind field distribution at various time instances. In an operational scenario, the model time resolution of output variables such as surface winds, and rainfall distribution can be refined (about 15-min interval) and that provides a clear-cut picture on the spatial coverage of threshold wind speed and rainfall distribution. It can help planning operations for power generation, transmission, and distribution scenario. Extreme wind can pose damage to the electric power subsystems that includes generation, transmission, and local

distribution. The vulnerabilities associated with each of these sub-systems are different in context to a landfalling cyclone. The design of power generation plants sustains very high winds. The design of transmission towers and lines should sustain high wind loads. These are elevated structures such that trees do not fall on the transmission lines and towers. Therefore, the consequences of damage from cyclones to generation facility and overhead transmission lines are relatively rare. On the other hand, the distribution lines have a higher risk exposed to high winds as compared to the transmission lines. Their reliability in the eventuality of extreme winds is questionable. In general, the faults associated in electric power distribution during cyclonic events can be either transient or permanent. Rothstein and Halbig (2010) reported that dynamic pressure increases due to surface area under contact, and rope swing over time causes malfunctioning of the grid. According to the National Hurricane Center, USA, hurricanes are categorized based on the Saffir-Simpson hurricane wind scale. The sustained wind

B. Sahoo et al. Fig. 9 Strategy flowchart and steps to reduce blackout during a tropical cyclone landfall

speed for Category 1 hurricanes can vary in the range 119– 153 km h−1 (source: http://www.nhc.noaa.gov/aboutsshws. php). The type of damage associated with such winds is quite extensive to power lines and poles that can result in severe power outages. As per the classification by India Meteorological Department (IMD), the category of deep depression (T 2.0) and cyclonic storms (T2.5–T3.0) where the wind speed range between 52–61 and 62–87 km h−1 respectively, the damages are minor to the communication systems. For very severe cyclonic storms (T4.0–T4.5 and above) with winds ranging from 118 to 167 km h−1 and higher can have significant impact on the overhead power

lines. It can also result in bending and uprooting of power and communication poles (source: http://www.imd.gov.in/ services/cyclone/impact.htm). The environment in which the power transmission and distribution network facilities usually reside has significant bearing on their reliability and in particular their failure rates. Also long transmission lines and their criss-crossed network usually encounter several weather regions that can experience different weather conditions during extreme events like tropical cyclones. The WRF modeling system used in the present study can be further improved using the advanced 4D VAR assimilation system. It can provide better estimates of

Application of weather forecasting model WRF for operational electric power network management—a case study...

intensity, track, and landfall locations for real-time operations including location-specific microclimatic analytics. In addition to extreme winds and rainfall, there are thunderstorms and lightning strikes that can affect the overhead conductors causing short-circuit faults sometimes causing disconnection of lines. These faults are usually transient in nature; however, there are voltage surge caused by the thunderstorm strike that can cause damage to equipment and transformers. A highresolution WRF can be configured for thunderstorm studies coupled to the weather prediction and that can provide additional information on rain, strong winds and thunderstorm events for emergency preparedness. The current practice of pre-disaster analysis primarily considers outputs from weather forecast models and lacks a critical look on the microclimate analytics. A strategy flowchart mentioning the steps along with hard and soft measures that would reduce blackout during tropical cyclone landfall is shown in Fig. 9. There are the limitations in the present study and it is anticipated that in the future, a decision support system enabling the above-listed components will serve as valuable tool for power grid management during extreme weather events.

8 Summary and conclusions Tropical cyclones are characterized by a low pressure center, spiral rain bands with extremely strong winds. The destructive potential of this natural phenomenon is quite high, and their impacts can extend over wide areas in the nearshore and onshore regions accompanied by extremely strong winds and torrential rainfall. Tropical cyclone induced storm surge is one dangerous phenomenon in coastal regions associated with the piling up of water arising due to combined effect resulting from reduced central pressure, astronomical tide during landfall time, and wave-induced setup. Vulnerability near coastal belts results from potential flooding due to coastal inundation and rainfall linked with cyclone strength. The impact and destructive potential from tropical cyclones as well extend beyond coastal regions as the cyclone track progresses onshore after its landfall. There are many past instances especially in the Indian Ocean region where the tracks of tropical cyclones have progressed over considerable distance onshore before they finally dissipate. Post-landfall events normally encounter strong gusty winds and heavy rainfall, and both contribute to the damages of infrastructural facility and property hinterland. There are damages to buildings, power distribution lines, agriculture and food, aviation sector, etc. The present work is a case study on the application of weather forecasting model WRF to evaluate model sensitivity and impact on the power distribution network for the state of Odisha. While WRF is a widely acclaimed atmospheric forecast model used extensively for routine weather forecast and emergency preparedness during tropical cyclones, its application in context to power

distribution network for the Indian sub-continent is relatively new. The state of Odisha experienced a very severe cyclonic storm Phailin during October 2013 and considered as the second strongest cyclone in history. Timely warnings and bulletins periodically issued by the operational weather agencies could save huge loss of human life. However, the damages that were caused to major infrastructural facilities such as power distribution lines during post-landfall event was quite significant, and there was no mechanism or decision support system in place by operational weather agency in India involved alerting possible damages in the energy sector and power regulatory authorities. In other countries like the USA and China, the technological advancements using weatherrelated information for societal needs is much advanced as compared to technological developments made in India. The Chinese Electric Power Research Institute, China, collaborates with the Research Applications Laboratory at National Center of Atmospheric Research, USA, to improve the forecasting capabilities of real-time WRF model using 4D-VAR technology to support industries dealing with power production. Significant advancements using weather-related information for industry such as agriculture and food, human health, aviation sector, and renewable energy is achieved in the USA. There are need and necessity to bring in more awareness amongst the scientific community in optimum utilization of weather-related information for societal needs. The present study is novel in this direction that critically examined the weather model outputs pertaining to wind speed and rainfall distribution for the Odisha State during post-landfall of Phailin cyclone. Based on several numerical experiments, a suitable configuration of WRF model in terms of model domain, resolution, and physics was implemented that provided the best estimates of wind speed, cyclone track, landfall location, and rainfall distribution and that was used to study the impact assessment on power grid lines. A suitable optimum configuration of a three-way nested model with domain resolutions of 27, 9, and 3 km and 30 vertical levels essentially captured well the required atmospheric parameters in order to carry out a detailed study. The choice of domain resolution used in the present study is also supported by studies performed elsewhere on tropical cyclones. The best possible microphysics combination was also used in the present study that simulated very well the track, associated wind speed, and rainfall during the post-landfall event of Phailin cyclone. Model simulations of the above mentioned parameters were analyzed 18 h post-landfall to evaluate the impact on power distribution lines. To verify the impact of wind speed and rainfall from model simulations with actual measurements the real-time data obtained from synchrophasors at Phasor Measurement Unit, Talcher were compared, and that signifies an excellent agreement matching with power line tripping across the power distribution network system. This study identified 12 hinterland stations, and the minimum distance

B. Sahoo et al.

of these locations varied between 10 and 298 km from the actual Phailin track. These stations located at strategic points provided the necessary base to evaluate and understand the dynamic impact of wind speed and rainfall from the WRF model on power trip events during the passage of Phailin onshore. The study signifies that on 12 October, 2013 (1700–1800 hours) the 400 kV Rourkela-Talcher line tripped due to overloading, and this is clearly evident in the extremely high wind speed simulated by the WRF model. On the same day, the hourly record (1900–2000 hours) data of tripping scenarios from synchrophasors reported that the 400 kV Angul-Bolangir and Talcher-Meramundali sectors, as well the 220 kV Therubali-Indravati line were affected. These tripping events strongly correlate with the WRF model simulations providing us necessary confidence in its applicability for real-time operations. Further, the study also discussed on the effects of wind loading on transmission structures and risk assessment from a financial perspective associated with tropical cyclones. The scope of this work can extend to generate synthetic or the most probabilistic cyclone track based on archived historical storm tracks, followed by several numerical experiments assuming landfall along various locations of the coastline. It will provide valuable information on vulnerable zones, facilitating the identification of a smart grid system for future power distribution sectors and storage systems regulated accordingly. Acknowledgements The authors sincerely thank the Department of Science and Technology (DST), Government of India, for the financial support. This study was organized as a part of the Center of Excellence (CoE) in Climate Change studies activity established at IIT Kharagpur and funded by DST, Government of India. This study forms a part of the sub-project BWind-Waves and Extreme Water Level Climate Projections for East Coast of India^ conducted under the CoE in Climate Change at IIT Kharagpur. The authors sincerely thank the data providers such as Power Grid Corporation of Odisha, India for providing information on failure of power grid data during Phailin cyclone for calibration and analysis. The authors sincerely thank the developers of the WRF model, an efficient and open source atmospheric model, and also the researchers who periodically upgraded the model physics that helped to improve model skill and accuracy in the model forecast.

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