Transcript 分散型電源MGTの導入に 関する最適化モデル分析

Optimization model analyses for measuring
the effects of introducing MGTs
Tatsuo OYAMA, Miki TSUTSUI
National Graduate Institute for Policy Studies
Tomonori SATO
Chubu Electric Power Co.
Research Background
• General trend from “large scale” central
power generating plants to “small-sized
diversified” power generating plants
(DPGP)
• Energy conservation, global environment
conservation and technical innovation as
background factors
• Diversified power generating plants :
Diesel Engine (DE), Gas Engine (GE),
Gas Turbine (GT)
Diversified Power Generation
Plants
• Reducing transmission facilities,
power supply cost,
and improving
reliability of power
source
• Locating power
plants close to
demand areas.
Diversified Power Generation Plants
Type
Solar power
Natural Wind power
energy Micro hydraulic
power
Cogeneration
Fossil （rotator, fuel
fuel cell）,DE,GE,GT
Electrical power storage
Characteristics
Clean and expensive
Influenced by weather
Energy cost reduction
Located in densely
populated areas
High cost
• http://www1.infoc.nedo.go.jp/nedoinfo/caddet/infostore/JP-2003-018.html
• NEDO HP
• Copyright © CADDET Energy Efficiency, 2003 . All
rights reserved.
• Daihatsu Diesel Co.
• http://www.jfe-holdings.co.jp/dme/03-yoto.html
• Sapporo Brewery Co.
• http://www.eccj.or.jp/succase/02/b/c_01.html
Structure of MGT
• Simple structure
(turbine, presser,
generator)
• Recycling
structure
(exhaustion gas)
• Durable and
convenient
Properties of MGT
• Small-size, light, low
noise, low oscillation
• No cooling, no
lubricant
• Low pollution
• Fuel (gas, liquid)
• Easy maintenance
Future Problems for MGT
 Technical, Engineering
–
–
–
Steam recycling MGT with high use of gas exhaust
High efficiency (high turbine temperature)
Combined system with fuel cell
 Regulatory
–
–
–
Electric Utility Law（Technical standards, regulatory
maintenance, engineer’s responsibility）
Air Pollution Reduction Law
Guideline for system connection technology
Building an Optimization Model for the
Optimal Introduction of ＤＰＧＰ’s
• Objective
– Determine an optimal set of facilities and
an optimal operating pattern of MGT such
that introducing MGT would bring
“maximum economies of scale”
• Assumption
– Second generation MGT
Chubu Electric Power Co. （2005）
Definition of set 1
• I=｛ i=1 (Hotel)、i=2 (Hospital)、 i=3 (Store)、
i=4 (Office building)、 i=5 (Sport facility)｝
その他
11%
ホテル
19%
地域冷暖房 13%
20%
9% 1%
3%
研究・電算C5%
内側：件数割合
4%
ガソリンスタンド
1654件
6%
外側：発電割合
1%
研修・保養所
13% 店舗
1
88万kW
4%
浴場 6%
18%
2%
スポーツ施設
10%
12%
9%
11%
事務所
病院
12%
11%
ホテル
店舗
事務所
病院
スポーツ施設
浴場
研修・保養所
ガソリンスタンド
研究・電算C
地域冷暖房
その他
1999年3月現在
蒸気タービン型、燃料電池は含まず
電力用発電設備（各電力会社と自家発）の発電容量の約1.9％
Definition of set 1
• I=｛ i=1 (Hotel) 、 i=2 (Hospital) 、 i=3 (Store) 、
i=4 (Office building)、 i=5 (Sport facility)｝
11%
9%
4%
1%
4%
13%
1%
3%
5%
6%
Hotel
19%
Hotel
20%
内側：件数割合
1654件
外側：発電割合
88万kW
6%
2%
Sports
Sports
Facilities
Facilities
Hospital
10%
9%
11%
Hospital
11%
Hotel
Store
Office
Hospital
Sports Facilities
Store
13% Store
18%
Office
12%
Office
12%
1999年3月現在
蒸気タービン型、燃料電池は含まず
電力用発電設備（各電力会社と自家発）の発電容量の約1.9％
Electricity Demand Curve of Each Facility
Hotel
Hospital
Store
Office
Sports Facilities
kW
200
150
100
50
0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
hour
for facilities of 3000m2
Heat Demand Curve of Each Facility
Hotel
Hospital
Store
Office
Sports Facilities
Mcal
300
250
200
150
100
50
0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
hour
for facilities of 3000m2
Operating capacity by CGS: Hotel
kW
Hotel 3000m2
200
DE150
DE300
GE100
GE250
GT50
GT150
Electricity
Heat: DE
Heat: GE
Heat: GT
180
160
140
120
100
80
60
40
20
0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
hour
Operating capacity by CGS: Hospital
kW
250
Hospital 3000m2
DE150
DE300
GE100
GE250
GT50
GT150
Electricity
Heat: DE
Heat: GE
Heat: GT
200
150
100
50
0
0 1
2 3
4 5
6 7
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
hour
Operating capacity by CGS: Store
kW
250
Store 3000m2
DE150
DE300
200
GE100
GE250
GT50
150
GT150
Electricity
100
Heat: DE
Heat: GE
Heat: GT
50
0
0 1
2 3 4
5 6
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
hour
Operating capacity by CGS: Office
kW
Office 3000m2
140
DE150
120
DE300
GE100
100
GE250
GT50
80
60
40
GT150
Electricity
Heat: DE
Heat: GE
Heat: GT
20
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
hour
Operating capacity by CGS: Sports Facilities
kW
Sports Facilities 3000m2
400
DE150
350
DE300
GE100
300
GE250
250
GT50
GT150
200
Electricity
Heat: DE
150
Heat: GE
100
Heat: GT
50
0
0 1
2 3
4 5
6 7
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
hour
Definition of set 2
• J=｛ j=1 (30kW)、j=2 (30kW×2)、j=3
(100kW)、 j=4 (100kW×2)｝
店舗
500
450
400
事務所
300
100kW
夏期
冬季
100kW×2
夏期
冬季
中間期
250
350
200
kWh
kWh
300
250
200
150
100
150
100
50
50
0
0
1
2
3
4
5
6 7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
時刻
1
2
3
4 5
6
7
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
時刻
Definition of set 3
• K=｛ｋ=1 (Peak)、ｋ=2 (Middle)、ｋ=3
(Base)｝
Peak：Avail. 0~30%、Duration 2628 Hrs.
Max.
Demand
Middle：Avail. 30~60%、Duration 5256 Hrs.
Base：Avail. 60~100%、Duration 8760 Hrs.
Oper. Hrs.
Actual LDC
Oper. Hrs.
Approximate LDC
Decision variables
●
xij i  I , j  J
integer variable showing the number
of installed MGT sets with type i and
operation type j
Constraints 1
(i) Upper bounding constraints on the
installed MGT sets by unit capacity
 x
ij
 A1
: 30kW
iI jJ
 x
ij
 A2
: 100kW
iI jJ 2
J1  1,2,
J 2  3,4,
J  J1  J 2
Constraints 2
(ii) Upper bounding constraints on the number
of installed sets of MGTs by facility type
x
ij
 BTi
iI
jJ
xij  BMi
4
x
4j
j 3
i  I , j  Max.(except
 BM 4
j  4)
Constraints ３
(iii) Upper bounding constraint on the total
exhausted heat from MGT by facility type
q
xij  Qi
ij
iI
jJ
(iv) Upper bounding constraint on the total
power generation by MGT by facility type
e
ij
jJ
xij  Ei
iI
Constraints 4
(v) Bounding constraints on the share of MGT
for each region of approximate LDC
Dk   d k eij xij  Dk
iI jJ
(vi) MGT output constraint
x14  x24  0
k K
Objective function
• Maximizing “the economies of scale”
(amount of saving) obtained from
introducing MGT
Maximize z   cij eij xij
iI jJ
Standard optimal solution：(# of
Units Installed)
30kW×2
30kW
100kW
100kW×2
計
hotels
0
287
143
0
430(4)
hospitals
0
1,261
1,352
0
2,613(25)
stores
0
0
210
462
672(6)
office
buildings
0
5,904
0
845
6,749(64)
sport
facilities
0
0
0
70
70(1)
total
0
7,452(71)
1,705(16)
1,377(13)
10,534
Standard optimal solution：(Amount
of Saving, M yen)
30kW×2
30kW
100kW
100kW×2
計
hotels
0
156
132
0
288(4)
hospitals
0
711
1,312
0
2,023(27)
stores
0
0
175
733
908(12)
office
buildings
0
2,678
0
1427
4,105(55)
sport
facilities
0
0
0
112
112(2)
total
0
3,545(48)
1,619(22)
2,272(31)
7,436
Standard optimal solution：(Amount
of Saving, M yen)
7,000
100kW×2
100kW
6,000
30kW×2
普及台数[台]、節約額[百万円／年]
30kW
5,000
4,000
3,000
2,000
1,000
0
ホテル
病院
店舗
事務所ビ ル
スポーツ 施設
MGT Share to Total Demand
Type
Hotel
Hospital
Store
Office Building
Sport Facility
Electr. Demand MGT Power Gen. MGT Share Heat Demand MGT Hest exhaust Heat Avail. Price Gap
kWh/Yr.
kWh/Yr.
%
kcal/Yr.
kcal/Yr.
% Yen/kWh
1,400,000
1,190,000
1,581,525
1,092,000
1,749,825
396,000
396,000
684,000
684,000
684,000
28
33
43
63
39
1,820,000
1,638,000
1,281,000
722,400
1,134,875
275,854
275,854
476,474
476,474
476,474
100
100
100
80
100
2.33
2.45
2.32
2.47
2.33
Optimal standard solution analysis
• Power generation by MGT : 3,629×106 kWh
occupying 2.6％ of the total （118,200×106
kWh ）
• Share of DPGP will amount to around 10％ in
the future
• Cost decrease : 7,436 M yen
Office building effect : 55％; MGT share (# of
units installed): 64％
• Load factor will increase from 58.93％ to 59.38%
by 0.45％
Simulation of the optimization
model
• Assumption
Price data : 2001
System connection cost not considered
• Simulation on gas price change,
installment cost, and number of installed
sets of MGT
Simulation on gas price (1)
• The ±10% change of city gas fare
– Office buildings’ change is the largest such
as ±18% at the maximum while hospitals’ is
±15%. 1% decrease of gas price leads to an
8.4%(62,653 thousand yen) saving.
9,000,000,000
8,000,000,000
節約額[円]
7,000,000,000
ホテル
病院
店舗
事務所ビル
スポーツ施設
計
6,000,000,000
5,000,000,000
4,000,000,000
3,000,000,000
2,000,000,000
1,000,000,000
0
-10%
-5%
基準
ガス料金変化幅
5%
10%
Simulation on gas price (2)
• Simple investment recovery period of sport
facilities is within 0.93 years at the earliest and
office buildings within 1.76 years at the latest.
Even if the gas price increases by 10%, the
recovery period would be 2.03 years.
• “Less than 5 years” is generally acceptable.
2.1
単純投資回収年数[年]
1.9
1.7
-10%
-5%
基準
5%
10%
1.5
1.3
1.1
0.9
0.7
ホテル
病院
店舗
事務所ビル
スポーツ施設
Summary
• All 5 facilities have economic merits from MGT
especially effective with gas price decreases
• Share to the total power generation : 2.6％; load
factor improvement : 0.45％
• Investment recovery period : less than 5 years
• Economic effects (descending order) : Sport
facility, Store, Hospital, Hotel, Office building
Future problems (1)
• MGT as a cogeneration system needs to
be evaluated quantitatively : energy
conservation, low NOX・CO2, load on the
environment, reliability, and so on
• Desirable future electric power supply
system including MGT as DPGP (Diesel
engine, Gas engine, Gas turbine)
Future problems (2)
• Improvement effects on the load factor of
the total power supply system
• Data availability, reliability, and uncertainty
• Future technological innovation
• Future of electric power storage and fuel
cells
Thank you very much