A coupled energy & air quality model for lowest cost energy …€¦ · 2014/5/6 · A coupled...

A coupled energy & air quality model for lowest

cost energy solutions, respecting air quality constraints:

development and initial results

1

Dan Zachary

May 6, 2014

NOAA SEMINAR - NCWCP Conference Center College Park

Introduction to energy – air quality modeling

• The model

• An illustrative example

• Results

Daniel Zachary (Applied Physicist, Mathematician)

Ulrich Leopold (Geographer, finishing PhD)

Olivier Baume (Computer Scientist)

• Luis Alexandre Duque Moreira De Sousa (GIS, Computer Scientist, PhD student)

• Chris Eykamp (GIS expert, programer)

• Chris Braun(GIS expert, geographer)

• Lara Reis (Atmospheric scientist) - now in Milan, Italy

• Laurent Drouet (Computational scientist) – now in Milan, Italy

TUDOR Modeling Group – Research snapshot

Integrated assessment: Energy-AQ

Geospatial

analysis

Energy

projections

Bayesian risk

(atmospheric

forecasting)

AQ

Integrated assessment: Impact

Ref. Lara Reis, Ph.D. Defense

Air pollution – a major concern

A need for integrated solutions

Some models explore solutions via simulation – e.g.

(EC4MACS)

Introduction / Motivation

• The model (Energy-emissions / Air Quality)


• Results

14

Energy transition

Emissions/AQ

Geocomputation

Approaches – e.g.

optimization & coupling

Energy Technology

Environmental Model -

ETEM

GIS / geospatial analysis

tools

The integrated assessment model – the LEAQ model

http://crteweb.tudor.lu/leaq/

crteweb.tudor.lu/leaq

The ETEM model

ETEM (Energy Technology Environmental Model)

The ETEM model

###############

# #

# ETEM model #

# #

###############

# Date : 11/2008

# Version : 2.0

# Authors : L. Drouet et J. Thenie

# Language : GMPL

# command : glpsol -m model_file.mod -d data_file.dat -y display_file.txt -o

output_file.txt

# example : glpsol -m etem.mod -d geneva.dat

param nb_periods >=1;

param period_length >=1; # expressed in year.

set T := 1..nb_periods; # time periods.

set L; # localization

set S; # slice periods

set P; # processes

set C; # commodities

set DEM within C; # useful demands

set IMP within C; # imported commodities

set EXP within C; # exported commodities

set FLOW; # commodities groups labels

set FLOW_IN{P} within FLOW; # incoming flows

set FLOW_OUT{P} within FLOW; # outcoming flows

set C_ITEMS{FLOW} within C; # set of commodities

set P_MAP{L} within P; # localization of processes

Plus about 350 more lines

The ETEM data data;

# set of time slices

set S:=

WD # Winter Day

WN # Winter Night

SD # Summer Day

SN # Summer Night

ID # Intermediate Day

IN; # Intermediate Night

# set of localisations (cities)

set L:=

LUXEMBOURG

HAUTSURE

CADIOM;

# set of processes

set P:=

# Electricity Industrial

I11 # Electrical appliances # existing technology

I13 # El. savings industrial 2 # new technology



I1A # El. savings heat pump # new technology

I1B # El. savings clim/air # new technology

I1C # El. savings cold # new technology

I1I # El. savings compressor # new technology

I1J # El. savings pumps # new technology

I1K # El. savings fax/photocopy # new technology

# LTH Industrial Area

IA1 # Electric # new technology

Plus about 2500 more lines

The ETEM interface

Emissions

The air quality model

The air quality model – example of dynamics in a

Lagrangian model

Brownian motion

Other dynamics included - Brownian diffusion - Brownian turbulence

The air quality model – dynamics

…

• ..

33

The air quality model

O3

VOC NOX

34

Energy transition

Approaches – e.g.

optimization & coupling

Energy Technology

Environmental Model

GIS / geospatial analysis

tools

The integrated assessment model

http://crteweb.tudor.lu/leaq/

crteweb.tudor.lu/leaq

The coupled model – combining ETEM and AQ with

OBOE

Cost minimization problem: Min { (e) : AOT(e,p) AOTmax}

= min {c’x |Ax = b , x 0} p = pollution (O3 concentration in ppb) level decision variable e = emissions (tonnes per year) decision variable AOT = Accumulated Ozone exposure over a Threshold

ETEM

Introduction / Motivation

• The model (Energy-emissions / Air Quality)


• Results

Example – a very simple economy

Car of type 1 –

inexpensive, heavily

polluting, maximum

N1

Car of type 2 –

expensive, lightly

polluting, maximum

N2

Supply & demand – for 2 types of cars

Car – type 1 Car – type 2

A short overview of convex optimization

En

ergy C

ost

s

number of cars (of type 1)

cost

demand

o

Optimal cost without

AQ constraints

N1

Convex Bounds due

to non-linear AQ

constraints


En

ergy C

ost

s


cost

demand

Emission constraints

o Optimal cost with

AQ constraints

Optimal cost without

AQ constraints

Bounds (min, max

cars of type i)


En

ergy C

ost

s


convex cost curve

Bounds to cost

Select a starting

point


En

ergy C

ost

s

f(x) = cost curve


Bounds (min, max

cars of type i)

Bounds to cost

Select a starting

point

Simulated cost


En

ergy C

ost

s

f(x) = cost curve


Bounds (min, max

cars of type i)


En

ergy C

ost

s

f(x) = cost curve


Sub-gradient

(support plane)‏

Simulated cost


En

ergy C

ost

s


Sub-gradient

(support plane)‏ Acceptable region

(where solution must

be found)‏


En

ergy C

ost

s


Sub-gradient



be found)‏

Geometric center of

acceptable region


En

ergy C

ost

s


Sub-gradient



be found)‏

Geometric center of

acceptable region

2nd suggested point


En

ergy C

ost

s


Sub-gradient

(support plane)‏

Acceptable region


be found)‏

Geometric center of

acceptable region

2nd suggested point

2nd Sub-gradient


En

ergy C

ost

s


Sub-gradient

(support plane)‏

Acceptable region


be found)‏

Geometric center of

acceptable region

2nd suggested point

2nd Sub-gradient

New lower bound


En

ergy C

ost

s



En

ergy C

ost

s


Solution point

(in 1D)‏


En

ergy C

ost

s


Results

62

Three results

1) Emissions & Air quality management 2) Public health & planning 3) Energy policy

Results - e.g. Luxembourg (Scenario 1)

Results - e.g. Luxembourg (Scenario 2)

Results - e.g. Luxembourg region

Thank you Zachary D.S. Land cover change using an energy transition paradigm in a statistical mechanics approach, Physica A: Statistical Mechanics and its Applications, 2013. Zachary D.S. and Dobsen, S. Does urban space evolve deterministically or entropically? An exploration of urban development models for Sheffield, UK in relation to decentralized energy policies, Energy Policy (In review), 2013 Reis, L.A., Drouet L., Zachary D.S., Peters B., Melas D., Leopold U., Implementation of a full air quality model in an integrated assessment, Environmental Modeling and Software, in press, 2013 Reis L.A., Melas D., Peters D., Zachary D.S., Developing a fast photochemical Calculator for an integrated assessment model, International Journal of Environmental Pollution, Vol. 50, Nos. 1/2/3/4, 2012 Zachary D.S., Drouet L., Leopold U., Reis L.A. Trade-offs Between Energy Cost and Health Impact in a Regional Coupled Energy-Air Quality Model, Environmental Research Letters, 6(201), doi:10.1088/1748-9326/6/2/024021, 2011.

67

2) Public health and planning

Without Impact

considerations

With Impact

considerations

Zachary D.S., Drouet L., Leopold U., Reis L.A. , Environmental Research Letters, 6(201), 2011.

Min{()I( ): p() - } 0 = emission (tons per year) (decision variable) = total discounted energy costs I = impact function (ozone - health) p = ozone concentration (ppb) = average ozone concentration (ppb) (decision variable

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