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 TypeCharacteristics Natural energy Solar power Wind power Micro hydraulic power Clean and expensive Influenced by weather Fossil fuel Cogeneration （ rotator, fuel cell ）,DE,GE,G T Energy cost reduction Located in densely populated areas Electrical power storage High cost

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Daihatsu Diesel Co.

Sapporo Brewery Co.

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) ｝ 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) ｝ 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 for facilities of 3000m2

Heat Demand Curve of Each Facility for facilities of 3000m2

Operating capacity by CGS: Hotel

Operating capacity by CGS: Hospital

Operating capacity by CGS: Store

Operating capacity by CGS: Office

Operating capacity by CGS: Sports Facilities

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

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.

Decision variables 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

Constraints 2 (ii) Upper bounding constraints on the number of installed sets of MGTs by facility type

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

Constraints 4 (v) Bounding constraints on the share of MGT for each region of approximate LDC (vi) MGT output constraint

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

Standard optimal solution ： (# of Units Installed) 30kW 30kW×2 100kW 100kW×2 計 hotels (4) hospitals01,2611,35202,613(25) stores (6) office buildings 05, ,749(64) sport facilities (1) total07,452(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 (4) hospitals07111,31202,023(27) stores (12) office buildings 02, ,105(55) sport facilities (2) total03,545(48)1,619(22)2,272(31)7,436

Standard optimal solution ： (Amount of Saving, M yen)

MGT Share to Total Demand

Optimal standard solution analysis Power generation by MGT : 3,629×10 6 kWh occupying 2.6 ％ of the total （ 118,200×10 6 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 ％ 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.

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.

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 NO X ・ CO 2, 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