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 trans- mission facilities, power supply cost, and improving reliability of power source

• Locating power plants close to demand areas.

Diversified Power Generation Plants

Type Characteristics

Natural energy

Solar power

Wind power 　　　 Micro hydraulic power

Clean and expensive

Influenced by weather

Fossil fuel

Cogeneration（ rotator, fuel cell ） ,DE,GE,GT

Energy cost reduction

Located in densely populated areas

Electrical power storageHigh cost

• http://www1.infoc.nedo.go.jp/nedo-info/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) ｝

20%

13%

12%

11%

10%

6%

6%

5%3%1%

13%

ホテル19%

店舗18%

事務所12%

病院11%

スポーツ施設9%

浴場2%

研修・保養所4%

ガソリンスタンド1%

C研究・電算4%

地域冷暖房9%

その他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

C研究・電算地域冷暖房その他

1

内側：件数割合　　　 1654 件外側：発電割合　 　 　 88 万kW

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) ｝

Hotel20%

Store13%

Office12%Hospital

11%

SportsFacilities10%

6%

6%

5%3%1%

13%

Hotel19%

Store18%

Office12%

Hospital11%

SportsFacilities9%

2%

4%1%4%

9%

11%

HotelStoreOfficeHospitalSports　Facilities内側：件数割合

　　　 1654 件外側：発電割合　 　 　 88 万kW

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

0

50

100

150

200

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23hour

kW

Hotel Hospital Store Office Sports Facilities

for facilities of 3000m2

Heat Demand Curve of Each Facility

0

50

100

150

200

250

300

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23hour

Mcal

Hotel Hospital Store Office Sports Facilities

for facilities of 3000m2

Operating capacity by CGS: Hotel 　Hotel 3000m2

0

20

40

60

80

100

120

140

160

180

200

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

DE150DE300GE100GE250GT50GT150ElectricityHeat: DEHeat: GEHeat: GT

hour

kW

Operating capacity by CGS: HospitalHospital 3000m2

0

50

100

150

200

250

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

DE150DE300

GE100GE250GT50GT150

ElectricityHeat: DEHeat: GE

Heat: GT

hour

kW

Operating capacity by CGS: Store

Store 3000m2

0

50

100

150

200

250

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

DE150

DE300

GE100

GE250

GT50

GT150

Electricity

Heat: DE

Heat: GE

Heat: GT

hour

kW

Operating capacity by CGS: Office 　Office 3000m2

0

20

40

60

80

100

120

140

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

DE150

DE300

GE100

GE250

GT50

GT150

Electricity

Heat: DE

Heat: GE

Heat: GT

hour

kW

Operating capacity by CGS: Sports Facilities

Sports Facilities 3000m2

0

50

100

150

200

250

300

350

400

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

DE150

DE300

GE100

GE250

GT50

GT150

Electricity

Heat: DE

Heat: GE

Heat: GT

hour

kW

Definition of set 　 2

• J= ｛ j=1 (30kW) 、 j=2 (30kW×2) 、 j=3 (100kW) 、 j=4 (100kW×2) ｝　

店舗

0

50

100

150

200

250

300

350

400

450

500

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

kWh

100kW夏期冬季

事務所

0

50

100

150

200

250

300

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

kWh

100kW×2夏期冬季中間期

Definition of set 　 3

• K= ｛ｋ =1 (Peak) 、ｋ =2 (Middle) 、ｋ =3 (Base) ｝

Peak： Avail.　0~30%、 Duration　2628　Hrs.

Middle： Avail.　30~60%、 Duration　5256　Hrs.

Base： Avail.　60~100%、 Duration　8760　Hrs.

Actual　LDC

Oper.　Hrs.

Oper.　

Output

Max.　Demand

Approximate　LDC

Oper.　Hrs.

Oper.　

Hrs.

Decision variables

integer variable showing the number of installed MGT sets with type i and operation type j

　 　 　 　 　 　 　 　 　 　 　 　 　 　 　 　 　 　 　 　 　 　 　 　 　

　

　ijx ,Ii Jj ●

Constraints 　 1

(i) Upper bounding constraints on the installed MGT sets by unit capacity 　　　　　 　　　　　　

kWAxIi Jj

ij 30:1

kWAxIi Jj

ij 100:2

2

,2,11 J ,4,32 J 21 JJJ

Constraints 　 2(ii) Upper bounding constraints on the number

of installed sets of MGTs by facility type IiBTx i

Jjij

)4except.(Max, jjIiBMx iij

4

4

34 BMx

jj

Constraints 　３(iii) Upper bounding constraint on the total

exhausted heat from MGT by facility type

(iv) Upper bounding constraint on the total power generation by MGT by facility type

IiQxq iJj

ijij

IiExe iijJj

ij

Constraints 　 4

(v) Bounding constraints on the share of MGT for each region of approximate LDC

(vi) 　 MGT output constraint

02414 xx

KkDxedD kIi

ijijJj

kk

Objective function

• Maximizing “the economies of scale” (amount of saving) obtained from introducing MGT

Ii Jj

ijijij xeczMaximize

Standard optimal solution ： (# of Units Installed)

  30kW 30kW×2 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 30kW×2 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)

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

ホテル 病院 店舗 事務所ビル スポーツ施設

[]

[]

普及

台数

台、

節約

額百

万円

／年

100kW× 2

100kW

30kW× 2

30kW

MGT Share to Total Demand

Type El ectr. Demand MGT Power Gen. MGT Share Heat Demand MGT Hest exhaust Heat Avai l . Pri ce GapkW h/Yr. kW h/Yr. % kcal/Yr. kcal/Yr. % Yen/kW h

Hotel 1,400,000 396,000 28 1,820,000 275,854 100 2.33

Hospi tal 1,190,000 396,000 33 1,638,000 275,854 100 2.45

Store 1,581,525 684,000 43 1,281,000 476,474 100 2.32

Off i ce Bui l di ng 1,092,000 684,000 63 722,400 476,474 80 2.47

Sport Faci l i ty 1,749,825 684,000 39 1,134,875 476,474 100 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.

0

1,000,000,000

2,000,000,000

3,000,000,000

4,000,000,000

5,000,000,000

6,000,000,000

7,000,000,000

8,000,000,000

9,000,000,000

- 10% - 5% 基準 5% 10%ガス料金変化幅

[]

節約

額円

ホテル病院店舗事務所ビルスポーツ施設計

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.

0.7

0.9

1.1

1.3

1.5

1.7

1.9

2.1

ホテル 病院 店舗 事務所ビル スポーツ施設

[]

単純

投資

回収

年数

年

- 10%- 5%基準5%10%

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