Transcript AIM-LOCAL Model

Managing Local Energy and
Environment Interfaces:
AIM-Local Model Applications
P.R. Shukla
Introduction
• Modeling the dilemma of providing energy services
and protecting the environment in a local region
SO2, NOX, SPM, CO2
Acid precipitation
Air pollutants
Energy Technology
Energy Service Demand
AIM Local Model: Methodology
Definitions
Parameters
Variables
Constraints
Service demand
Environmental target
Available energy supply
Linear programming
Operating capacity
Maximum shares of technologies
Maximum exchange of stock
Objective Function
Total cost  minimum
Solver
Model Formulation
(1) Service demand constraint
D j ,i 

( l , p )W j
l , j ,i
 Al , j ,i  X l , p ,i
Al,j,i : Service supply quantity by technologies and
regions
Ψl,j,i : Social service efficiency by technologies and
regions
Xl,p,i : Operating quantity by technology combinations
(l,p) and
regions
(2) Operating capacity constraint
X l , p,i  l ,i  Sl , p,i
Λl,i : Operating efficiency by technologies and regions
Sl,p,i : Stock quantity by technology combinations and
regions
(3) Energy supply constraint

 ˆ
   k ,l ,i  Ek ,l , p ,i  X l , p   Ek



i  (l , p )

Ek ,l , p,i : Energy consumption by fuels, technologies and
regions
ξk,l,i : Energy efficiency improvement by operation style
and maintenance
(4) Emission constraint
m
ˆm
Q

Q
 i
z
iR z
Emissions:
Emission factors:
Qim 
 X
l , p ,i
elm, p ,i

(l , p )


elm, p ,i   f 0m,l   f km,l   k ,l ,i  Ek ,l , p ,i   dlm, p ,i
k


dl,p,im : Pollutant release ratio by technology combinations and regions
(5) Cost functions
Initial cost:
Operating cost:
 


x
C  r


l,p
l , p ,i   C l , p1  p  M l , p1  p ,i


(l , p)
p1




0
(
g
 l , p,i   gk ,i  k ,l ,i  Ek ,l , p,i )  X l , p,i
(l , p )
Environmental cost:
k

m
m
 Qim
(6) Objective function


 






TC     C l , p  rl , p ,i   C x l , p1  p  ml , p1  p ,i  ( gl0, p ,i   g k ,l , p ,i   k ,l ,i  Ek ,l , p ,i )  X l , p ,i    m  Qim   min
i
p1
k
 m
 (l , p ) 




Model Formulation in GAMS
An Example of Transport Problem
SETS
PARAMETERS
VARIABLES
EQUATIONS
AIM-Local Database System
Sum of Valu e En ergy_De vice
Remo val
C K1
C K2
COL BLR
Year
N ON
N ON
N ON
SFGD
_b as e
4 ,700 ,00 0
1 ,300 ,00 0
24 ,528 ,00 0
1 ,752 ,00 0
1 99 5
4 ,700 ,00 0
1 ,300 ,00 0
24 ,528 ,00 0
1 ,752 ,00 0
1 99 6
6 ,000 ,00 0
26 ,134 ,53 3
1 ,664 ,40 0
1 99 7
6 ,180 ,00 0
27 ,624 ,83 4
1 ,584 ,46 6
1 99 8
6 ,365 ,40 0
29 ,010 ,70 7
1 ,510 ,87 6
1 99 9
6 ,556 ,36 2
30 ,301 ,83 6
1 ,442 ,66 2
2 00 0
6 ,753 ,05 3
31 ,506 ,41 8
1 ,379 ,09 1
2 00 1
6 ,955 ,64 5
32 ,631 ,54 3
1 ,319 ,58 7
2 00 2
7 ,164 ,31 4
33 ,683 ,44 7
1 ,263 ,69 4
2 00 3
7 ,379 ,24 4
34 ,667 ,68 0
1 ,211 ,03 5
2 00 4
7 ,600 ,62 1
35 ,589 ,23 2
1 ,161 ,30 0
2 00 5
7 ,828 ,64 0
36 ,452 ,61 8
1 ,114 ,22 6
Input File
for GAMS
AIM-Local
Database System
AIM-Local model
GAMS version
Output file
from GAMS
GAMS
Input file
from IDRISI
0.00
12.50
25.00
37.50
50.00
62.50
75.00
87.50
100.00
112.50
125.00
137.50
150.00
162.50
175.00
187.50
> =200.00
0.00
12.50
25.00
37.50
50.00
62.50
75.00
87.50
100.00
112.50
125.00
137.50
150.00
162.50
175.00
187.50
>=200.00
0.00
6.25
12.50
18.75
25.00
31.25
37.50
43.75
50.00
56.25
62.50
68.75
75.00
81.25
87.50
93.75
>=100.00
IDRISI32
AIM workshop 2001
Large Point Source and Area Source
3.Sector
Emission by point
Large Point Source
Emission by City
Emission by City
and Sector
Emission by Region
and Sector
Area Source
2.Region
Structure of AIM-Local Database
Energy service tech. (LT)
& Air Pollution Control (P)
5. Service
Service Demand
(IJ)
Technology Selection
Module
(AIM-Local GAMS ver.)
- Energy Cons.(K L P T),
- Service Supply (L J T)
- Fixed Cost (L P T)
- Removal Rate (L P T) etc.
Stock Quantity
( L T0 )
Share Potential
(ILJT)
Energy Service Tech.(L T)
Air Pollution Control(P)
7.Stock
8.Share
6.Technology
Subsidy
(ILT)
Regulation
( ME M T)
10.Countermeasure
Maintenance etc.
( I LT )
Operating Rate
(ILT)
Energy Consumption
CO2 Emission
SO2/NO2 Emission
(I LT)
Energy Price
Emission Factor
(KT)
9.Performance
Tax
( ME M T)
4.Energy
I : LPS or Area
L : Energy Device
P : Removal Porcess
J : Service
K : Energy
M : Gas (CO2, SO2, NO2)
T : Time
ME : Group on measure
Input and Output
Input:
(1) Energy
 Fuel type, Fuel price,
 Emission factors by fuels and technologies
 Energy resource constraints
(2) Technology
 Initial cost, Operating cost
 Life-span, Capacity, Share
 Energy consumption by fuels for a unit production
 Pollutants removal technologies and combinations
(3) Service demand by regions and sectors
 Historical service data
 Future service demand forecast


(4)
Economic development plans from the local
government
Development plans from the local industries
Air pollutant emission constraints
 Current air pollutant emissions
 Local environmental protection policies
Output:
(1) Aggregated results
 Total energy consumption by years
 Total costs by years
 Total CO2 emissions by years
 Total air pollutant emissions by years
(2) Technology options
 CO2 emissions by technologies and years
 Air pollutant emissions by technologies and years
 Energy consumption by technologies and years
(3) Service output
 Service output by regions, sectors, technologies
and years
(4) Energy balance table
 Energy balance table for the local region by years
(with energy information for sectors, technologies
and fuel types)
Model Features






Simplified Structure
Modeling local environmental constraints
Direct benefit and co-benefit of counter
measures
Flexible model structure to cope with various
practical situation in different regions
GAMS programming
GIS Interface
Geographical Information System
(GIS)
Why GIS?
Capture location sensitivity
 Provide layered information
 Analyze time slices
 Integrate location and time information
in a consistent framework

Spatial Data Characteristics




Spatial data are characterized by information about
position, connections with other features and
details of non-spatial characteristics
latitude and longitude as a geographical reference
connection details such as which service roads,
lifts and ski trails would allow the meteorologist
access to the weather station
non-spatial (or attribute) data, for instance details
of the amount of snowfall, temperature, wind
speed and direction
Data Models
Raster data
Model
(sometimes referred
to as grid)
Vector Data
Model
(an entity is a
component or
building block used
to help data
organization)
GIS Database
Integrated
GIS
Database
Examples of GIS Application to
AIM-Local
Beijing City

Economic Features
 Per capita GDP: 3 times of the national level
 Industry > 60% of GDP
 Heavy Industry > 80% of industrial GDP
Beijing City: Regional Details
Center
Source: Beijing Statistical Yearbook 2000.
522
339
708
529
444
Huairou 119
Miyun 193
Yanqing 125
Beijing City 721
Pinggu
Daxing
Shunyi
Tongzhou
Fangshan
Changping
176
373
4813
3610
Shijingshan
Haidian
Mentougou
3452
3976
Chaoyang
Fengtai
Xuanwu
Xicheng
Chongwen
Dongcheng
37232
28964
28389
28642
40000
35000
30000
25000
20000
15000
10000
5000
0
Population Dens ity (pers on/km )
2
Outs ide
0
Center
Source: Beijing Statistical Yearbook 2000.
Beijing City
Yanqing
Miyun
Huairou
Pinggu
Daxing
Tongzhou
Shunyi
Changping
Fangshan
Mentougou
Haidian
Shijingshan
Fengtai
Chaoyang
Xuanwu
Chongwen
Xicheng
Dongcheng
40000
per capita GDP (Yuan)
35000
30000
25000
20000
15000
10000
5000
Outside
Permanent
2020
Temporary
( Million)
2010
2000
1990
0.00
5.00
10.00
Sources:
(1)
Beijing Municipal Statistics Bureau (1999).
(2)
Beijing Municipal Government (1992).
(3)
Beijing Municipal Planning Commission (2000b).
15.00
20.00
50.00
45.00
40.00
35.00
30.00
25.00
20.00
15.00
10.00
5.00
0.00
1980
2000 – 2005: 9.5%
2006 – 2010: 9.0%
2011 – 2020: 8.5%
1990
2000
2010
2020
Sources:
(1)
Beijing Municipal Statistics Bureau (2000).
(2)
Beijing Municipal Planning Commission (2000).
(3)
Beijing Municipal Government (1992).

Large Point Sources data
Beijing Cement Plant
Technology: dry kiln with pre-decomposition process
Capacity : 2000 t/d
Production: 525# Portland cement, 0.74 Mt /a
Location: Changping District, Beijing City
Beijing Yanshan Petroleum and Chemical Group Corporations
Refinery
Technology: heavy oil based refinery process
Capacity : 6.0 Mt /a
Location: Fangshan District, Beijing City
Beijing Yanshan Petroleum and Chemical Group Corporations
Ethylene
Technology: Diesel oil based process
Capacity : 0.45 Mt /a
Location: Fangshan District, Beijing City
Beijing Shijingshan Thermal Power Plant
Technology: coal boiler
Capacity : 4000 MW
Location: Shijingshan District, Beijing City
Beijing Capital Steel Corporations
Capacity : 8.00 Mt
Location: Shijingshan District, Beijing City
Area source data
 Residential sector
 Commercial sector
 Transportation sector
 Other sectors
Results
Beijing Capital Steel Corporations
Beijing Shijingshan General Power Plant
1995
SO2 intensity (t-SO2/km2)
2020 (case 1)
2020 (case 8)
2020 (case 6)
1995
CO2 intensity (t-C/km2)
2020 (case 1)
2020 (case 6)
Ahmedabad City, India

High economic and demographic growth

Industrial base

Growing transport demand
India
Gujarat
Ahmedabad
Ahmedabad District
Ahmedabad
Urban
Area
Ahmedaba
d
District
Area Population Households
Categories
Villages Taluka Sq. Km. Thousands Thousands
Gujarat
18509
184 196024 (3) 41310 (34) 7493 (36)
Ahmedabad Dist. 648
7
8707 (6) 4802 (75) 920 (75)
Note: Figures in brackets show % Urban share
Ahmedabad Municipal Area
North Zone
West Zone
East Zone
River
Sabarmati
Central Zone
South Zone
Ahmedabad Municipal Area (South Zone)
Pirana Landfill
Chandola Lake/Landfill
Pirana MSP
Vatva GIDC
Narol GIDC
*GIDCs have many
Textile processing and
Chemical units
AEC Gas
Power plant
Ahmedabad – Area CO2 Emissions – 2030
(MT)
Ahmedabad – Area SO2 Emissions – 2030 (MT)
Ahmedabad – LPS CO2 Emissions – 2030 (kT)
Ahmedabad – LPS SO2 Emissions – 2030 (kT)