The Monte Carlo Method
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The Monte Carlo Method A Case Study for 1-Hour NO 2 Modeled Impacts Greg Quina SC DHEC Bureau of Air Quality [email protected] 803-898-3405. Outline. SC 1-hr NO 2 /SO 2 Workgroup Peak Shaving AERMOD Modeling Analysis Monte Carlo (MC) Approach General Concepts

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The Monte Carlo MethodA Case Study for 1-Hour NO2 Modeled ImpactsGreg QuinaSC DHECBureau of Air [email protected]


Outline

  • SC 1-hr NO2/SO2 Workgroup

  • Peak Shaving AERMOD Modeling Analysis

  • Monte Carlo (MC) Approach

    • General Concepts

    • State of Washington Department of Ecology MC Code

    • EPRI MC Code

    • Peak Shaving Monte Carlo Results


SC 1-hour NO2/SO2 Workgroup

Background and Goals

  • The goal of the workgroup is to pinpoint potential compliance problems and investigate ways to produce more realistic models and results in pursuit of demonstrating compliance for the 1-hour standards.

  • Modeling Approaches:

    • Develop and analyze various scenarios for different locations (coastal, inland, complex terrain)

    • Test various emissions and stack parameters (stacks heights).

    • Test NO2/NOx in-stack ratios using PVMRM, OLM

    • Test different NO2 and SO2 background data


SC 1-hour NO2/SO2 Workgroup

Types of Facilities

  • Peak Shaving Generators

    • 1461 HP / 1.0MW

  • Smaller (<50 MW) Biomass boilers

    • 275 MMBTU/hr electric utility stoker boiler fueled by clean wood waste

  • Specialty Chemical facilities

    • Two 200 MMBTU/hr Combustion Engineering stoker coal-fired boilers fueled by 2.1% Sulfur coal

    • Two 315 MMBTU/hr Riley Stoker coal-fired boilers fueled by 2.1% Sulfur coal

    • Four 43 MMBTU/hr Dowtherm Vaporizors

  • Wood Product facilities

    • Drying Kilns

    • 200 MMBTU/hr Wood-fired Boiler

    • RTO controls plywood dryers


Peak Shaving Generator

Definition

  • Peak Shaving – the process of using local electricity generation for the express purpose of “shaving off” a facility’s peak power demand to lower the source’s overall electrical costs.

    • Banks

    • Hotels

    • Office Buildings

    • Stores

  • Most units operate only during the winter or summer months when energy demand is high.


Peak Shaving Generator

Scenerio Design

  • Units operate no more than 8 hours per day.

  • Units operate up to 18 days per year for a flexible limit.

  • Units run at 100% capacity when in operation.

  • Sample AERMOD test run design

    • Columbia Meteorological Data (2002-2006)

    • Assume Maximum 8 Hour Per Day Operation (6am-2pm)

    • 522 receptors

    • NOx Emission Rate: 16.8 lb/hr (Model run only for NOx)

    • Stack Height: 15 ft

    • Stack Temperature: 900 F

    • Stack Exit Velocity: 126 ft/sec

    • Stack Diameter: 1.17ft

    • 2 Buildings (10ft and 40ft building heights)


Peak Shaving Generator

AERMOD Design


Peak Shaving Generator

AERMOD Graphical Results – Total NOx

Max Concentration = 813.6 (µg/m3)


Peak Shaving Generator

AERMOD Graphical Results – PVMRM

Max Concentration = 444.7 (µg/m3)


Peak Shaving Generator

AERMOD Graphical Results – OLM

Max Concentration = 351.5 (µg/m3)


Peak Shaving Generator

Concentration Distribution by PercentileUnit Operated Year-Round

Standard = 188


Peak Shaving Generator

Concentration Distribution by PercentileUnit Operated Year-Round

PVMRM > OLM

OLM > PVMRM

Standard = 188


Peak Shaving Generator

Concentration Distribution by RankUnit Operated Year-Round

Standard = 188


Peak Shaving Generator

Cumulative Distribution by Rank from One Random Sampling Unit Operated 18 Days Per Year

Standard = 188


Monte Carlo

General Concepts

  • Highly intermittent sources (such as Peak Shaving Generators) present modeling challenges.

  • A Monte Carlo method is a probabilistic technique that involves using random numbers to draw samples from deterministic model outputs (such as from AERMOD).

  • A random number generator is used to extract data from model outputs to better reflect actual operation, considering intermittent operation and variable emission rates.

  • How many samples or iterations is enough to yield a stable result? Hundreds, Thousands, Millions? Determine the effect on computed 98th percentile of varying number of samples drawn.


Monte Carlo – Peak Shaving (Total NOx)

Average Concentration for Varying Number of Iterations

1 iteration = 481.8 µg/m3

Average of 10 iterations = 454.5 µg/m3

Average of 50 iterations = 448.5 µg/m3

Average of 100 iterations = 450.0 µg/m3

Average of 500 iterations = 451.0 µg/m3

Average of 1000 iterations = 448.8 µg/m3


Monte Carlo

General Procedure

  • Develop emission bins, and determine the appropriate number of operating days (or hours) per year for each emission bin for the modeled unit.

  • Run AERMOD.

  • Loop through output POSTFILE concentrations.

    • Sample the distribution according to the defined modes, and randomly select concentrations for each emissions bin.

    • Calculate daily maximum concentrations.

    • Compute H8H or 98th percentile for each sample.

    • Repeat many times.

  • Extract 50th-percentile, average, or median from all samples. This correlates with how standard is calculated – “3-year average of the 98th-percentile of the annual distribution of daily maximum 1-hour concentrations.”


Monte Carlo

State of Washington Department of Ecology Code

Procedure

  • Define all distinct modes of operation.

  • Configure and run AERMOD for all types of simulations.

    • Input power levels and appropriate emission rates.

    • Choose appropriate daily operating schedule (ok to specify emissions by hour-of-day in AERMOD).

    • Choose Total NOx, OLM, PVMRM, In-Stack Ratios, Ozone and NOx background data, etc.

    • Select hourly output to POSTFILE (ASCII PLOT format).

  • Run Perl Script for each year of AERMOD run

    • The Perl script calculates the daily maxima concentration and outputs this information (in .csv file format) for each receptor.


Monte Carlo

State of Washington Department of Ecology Code

Procedure - Continued

  • Configure and run AERMOD for all types of simulations.

    • Enter filename and day/year operation (in format at bottom of R script.)

    • The R script selects random days and distributes modeled values based on the day per year operation entered. The day is not removed in the dataset, but zeroes fill days that are NOT selected as an operational day.

    • The R script computes 98th percentile value for each iteration at each receptor.

    • The R script will repeat the previous 2 bullets 1000 times (as designed).

    • Output of the R script returns Median 98th percentile and the percent chance of exceeding the standard for each receptor.

    • Each year is run independently through the Monte Carlo code.


Monte Carlo

EPRI EMVAP/EMPOST Code

Procedure

  • Define all distinct modes of operation.

  • Configure and run AERMOD for all types of simulations.

    • Input power levels and appropriate emissions rates. Unity emissions can be used.

    • It may not be appropriate to use EMVAP and specify hour-of-day emissions in AERMOD.

    • Choose Total NOx, OLM, PVMRM, In-Stack Ratios, Ozone and NOx background data, etc.

    • Select Hourly output to POSTFILE (binary UNFORM format).


Monte Carlo

EPRI EMVAP/EMPOST Code

Procedure - Continued

  • Setup an input file and run EMVAP.

    • Input up to 5 years of AERMOD POSTFILEs within the EMVAP input file.

    • The FORTRAN code cycles through the selected number of iterations to draw random samples considering the percentage of time each emissions bin is to be used. Percentages from all emissions bins must add up to 100%.

    • EMVAP calculates daily maxima concentrations at each receptor AFTER randomly sampling the data.

    • EMVAP calculates the highest 4th or highest 8th highs corresponding to 98th or 99th percentile.

    • EMVAP writes out receptor-specific data to a unique file for each iteration.


Monte Carlo

EPRI EMVAP/EMPOST Code

Procedure - Continued

  • Setup an input file and run EMPOST.

    • EMPOST calculates and returns the average, median, and various percentile values of the models highest 4th or highest 8th highs (consistent with the model’s 98th or 99th percentile-value concentration distribution) as a result of running the selected number of iterations.


Monte Carlo

Configure Emission Bins

Example of Hourly Emissions Sequence


Monte Carlo

Configure Emission Bins

Example Emissions Profile


Monte Carlo

Configure Emission Bins

Example Emission Bins for Monte Carlo


Monte Carlo

Peak Shaving - Emission BinsState of Washington Department of Ecology Code


Monte Carlo

Peak Shaving - Emission BinsEPRI’s EMVAP Code


Monte Carlo Peak Shaving Results

(Including Design Value Background)


Monte Carlo Peak Shaving Results

(Number of Receptor Exceedances)


Conclusions

  • Use of peak emission rates for intermittent or variable emissions sources can result in unrealistic peak predictions for a probabilistic NAAQS.

  • A Monte Carlo technique can result in a more reasonable estimate of the distribution of 98th and 99th percentile values over a given simulation period (e.g., 1000 years).

  • Clint Bowman’s Monte Carlo Code produced lower concentrations than EMVAP.

  • EMVAP is slightly easier to run, but refinements to EMVAP are recommended to account for a maximum day per year operation.

  • For sources with variable emissions, Monte Carlo techniques can be very beneficial in demonstrating compliance with the 1-hr Standards.


Thank You!

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