OPTIMAL DESIGN METHOD FOR DISTRIBUTED ENERGY SYSTEM UTILIZING WASTE HEAT BY MEANS OF GENETIC ALGORITHMS

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1 Imperial College London The University of Tokyo 2nd Joint Symposium on Innovation in Energy Systems 24 th.sep.29 OPTIMAL DESIGN METHOD FOR DISTRIBUTED ENERGY SYSTEM UTILIZING WASTE HEAT BY MEANS OF GENETIC ALGORITHMS Ryozo Ooka Institute of Industrial Science, The University of Tokyo

2 Contents of This Presentation Development of Optimal Design Method for Building Energy System Using Genetic Algorithms Optimal Design for Distributed Energy System utilizing Waste Heat from CGS by Genetic Algorithms. Optimization Energy System of Building Complex (composed of office and apartment) Using Genetic Algorithms

3 Contents of This Presentation Development of Optimal Design Method for Building Energy System Using Genetic Algorithms Optimal Design for Distributed Energy System utilizing Waste Heat from CGS by Genetic Algorithms. Optimization of Energy System of Building Complex Using Genetic Algorithm

4 Background Energy Saving is very important problem in the building section. In the resent years, efficiency of each building equipment has been improved very much. On the other hand, energy systems of buildings have been becoming very complex and appropriate design or operation planning are not conducted yet. (Designed or operated still empirically)

5 Problems in Designing Energy Systems 1Difficulty of predicting Energy Demand. *Energy Loss caused by too excess capacity. demand 2Design Complexity *enormous combinations. time *different operations in same System.? Design Method for Optimal Energy Systems must be established!

6 Previous Researches COP'HHV( *Optimization by using linear program. ex) Ito, G.Sundberg, etc However, 1.66 inverter control techniques non-linear functions. y = x x 負荷率 Difficulty in accurate System Design using LP. Objectives of this Research )Optimal Design Method for Building and Urban Energy System by using GA, which can optimize capacity of each equipment and its operation planning simultaneously.

7 Optimal Design Method Outline 1Selection of Base Systems (Combi. of equipment.) Engineers, Designers objective function, Weight Constraints Demand Data [1] Selection of Base Systems System-1 System-i System-N Capacity Optimal Design [2] Selection of Equipment Capacity (GA) 2Selection of Equipment Capacity 'GA( Seasons [3] Output Change (if needed) Operation Planning (GA) Cool /Hot 3Operation Planning 'GA( Objective calculation 4the Best System System-1 Optimal solution System-i Optimal solution System-N Optimal solution [4] Capacity & Operation The best System

8 [in genetics] Optimization by using genetic Algorithms - ex. Capacity Optimization - The 1 st generation Gene information The 2 nd generation The N th generation Fit: crossover mutations Superior gene sets individual environment environment [in engineering] Capacity DATA? individual Objective evaluation Selection, Crossover, Mutation Combination optimization n-step Objective evaluation Highevaluated combination Optimal result

9 Description of Chromosome

10 Description of Chromosome For Selection of Equipment Capacity ER CGS HP1 HPw1 HPw Chromosome 染色体 (binary system) ER CGS HP1 HPw1 HPw2 4. (2) 機器容量 Capacity 機器容量 Capacity 機器容量 Capacity 機器容量 Capacity 16 機器容量 Capacity [ kw ] [ kw ] (12) [ kw ] (1) [ kw ] (16) (16) [ kw ] 但し Here Turbo Refrigerator ER : ターボ冷凍機 Cogeneration System CGS : コジェネシステム Heat Pump HP : ヒートポンプ HPw: 給湯用ヒートポンプ Heat Pump for Hot Water 各々に数種類の機器容量の Choice of Capacity is according to line-up of 選択肢を設定しておく equipments

11 Description of Chromosome For Selection of Operation Planning Hours 時刻 α 染色体 Chromosome 但し α: 稼動係数 Operating (.~1.) rate (.~1.)

12 1 基本システムの選定 System- 1 System -i System- N 2-layers Optimization Capacity & Operation 機器容量変更日付変更 System- 1 最適解 2 冷 / 暖切替機器容量選定 'GA( 3 24h 運転出力選定 'GA( 評価項目計算 System- i 最適解 System - N 最適解 [upper GA] Capacity Optimization n step Good! 4 System & 運用計画最適解 α Operation Planning Evaluation Results ' 時 ( '%( α Evaluation Results Operation Planning ' 時 ( '%( α Evaluation Results Operation Planning ' 時 ( '%( [lower GA] [lower GA] [lower GA]

13 The Fundamental Flow of the GA The initial population is produced by random The fitness of each individual in the population is calculated The Genetic Operations are performed such as selection, crossover, and mutations to the chromosome of each individual The population of the next generation is produced. Genetic Operations Selection The individual who has higher rank of fitness is selected The next generation is produced a b c d e 1 2 c d e a b a b c d e crossover mutation a b C d e It is considered that the excellent parents have high possibility of producing the excellent children. If the process is repeated many generations, it is possible that we can find the optimum individual.

14 Experimental Analysis Purpose: Validation of the proposed method by comparison with the exact solution.

15 Target and Conditions : Hospital 4, m2 Building Date : a representative day, in Aug.(24hours) Demand Data: the default data for HOSPITAL in Computer Aided Simulation for Cogeneration Assessment & Design III (CASCADE III) Objective System (released by 空気調和衛生工学会 ) ER HP CDer(α ) CDhp HDhp ED CD objective functions > an amount of CO 2 emisson and Energy consumption HPw1 HPw2 WDhp1(β ) WDhp2 HD WD ED: Elec. Dem. CD :Cooling Dem. HD: Heating Dem. WD: Hot Water Dem.

16 Target and Conditions Demand Data: the default data for HOSPITAL in Computer Aided Simulation for Cogeneration Assessment & Design III (CASCADE III)

17 Objective Function Energy Consumption Q: Heat Load [kw] Turbo Refrigerator (12kW) CO2 Emission Electric Heat Pump (2kW) Life Cycle Cost etc. Operating Rate COP of Equipment under for Operating Rate In this study, energy consumption and CO2 emission will be minimized.

18 Variables Turbo Refrigerator(ER) Cooling/Heating HP (HP) Hot Water HP1 (HPw1) Hot Water HP2(HPw2) Operation of ER Operation of HPw1 Capacity ex. Chromosome shape (Operation Planning) 228 [kw] (Fixed) 16 [kw] (Fixed) 55, 6 [kw] 15, 2 [kw] Operation Planning ' Load Factor ( α i (i=_23): (,.4,.6,.8, 1.) β i (i=_23): (,.4,.6,.8, 1.) Operation of ER Operation of HPw1 α β

19 GA Parameters Parameter Selection of equipment Selection of operation planning Size of Sub Population 5 5 Number of Island 2 3 Population size 1 15 Number of Generations 2 6 Rate of Migration.5.5 Interval of Migration 5 5 Rate of Crossover 1 1 Rate of Mutation.3.1 CO 2 [ton]/day This point is selected

20 Analysis and Limitation Conditions )Automatic Change for Cooling/Heating If Heating Demand is for 24 hours, HP is changed for Cooling Operation. )Avoiding Radical Change of Output Penalty on Over 6% hourly Change ER HP Output α 冷暖 )Avoiding Frequent ON/OFF Operation Penalty on Operation Less than 2 Hours Output Time Time

21 Results Selected Capacity Selected Operation Program Turbo Refrigerator (ER) Cooling/Heating HP (HP) 228 (kw) 16 (kw) α time α Hot Water HP1 (HPw1) Hot Water HP2 (HPw2) 55 (kw) 15 (kw) β time β

22 冷房出力 [MJ] 機器稼働率 [-] 給湯出力 [MJ] COP[-] 冷房出力 [MJ] COP[-] 暖房出力 [MJ] COP[-] The Method Proposed Here Analysis Results 時刻時刻 Results of Entire Inquiry: Exact Solution 時刻 HP 出力 ER 出力 [Cooling Operation] ER-COP HP-COP 時刻 hpw2 出力 hpw1 出力 hpw2-cop hpw1-cop [Hot Water Supply Operation]

23 Contents of This Presentation Development of Optimal Design Method for Building Energy System Using Genetic Algorithms Optimal Design for Distributed Energy System utilizing Waste Heat from CGS by Genetic Algorithms. Optimization Energy System of Building Complex Using Genetic Algorithm

Distributed Energy System based on CGS Cogeneration System (also combined heat and power, CHP) is the use of a heat engine or a power station to simultaneously generate

24 Distributed Energy System based on CGS Cogeneration System (also combined heat and power, CHP) is the use of a heat engine or a power station to simultaneously generate both electricity and useful heat. It is one of the most common forms of energy recycling. However, the energy efficiencies of the actual CGS are often lower than expected.

25 Purpose The reasons why the energy efficiency of CGS is so low, are considered as. 1. Failure in prediction of heat demand. 2. Failure in optimum combination of equipment. 3. Failure in optimum operation planning 4. Low efficiency of power generation to Solve these Problems Using Genetic Algorithm

26 CASE STUDY Building Type Hospital Building Size 2,m 2 (Tokyo) Optimize Method Multi Island Genetic Algorithm (MIGA) Objectives Minimization of Primary Energy Consumption Demand Data Default Data of CASCADEIII Comparison Waste heat Usage (Case1 and Case2) Case1:Waste Heat Available Case2:Waste Heat NOT Available Gas Input TR HP AR HP Output Output Cooling Demand CD Heating Demand HD Gas Input TR HP AR HP Output Output Cooling Demand CD Heating Demand HD Electric Input Waste Heat GB HEX HP Output Hot Water Demand WD Electric Input GB HEX HP Output Hot Water Demand WD Solar PV CGS GB Output Electricity Demand ED Solar PV CGS GB Output Electricity Demand ED

27 Type of Equipment COP Input Output C H GAS ELE CD HD WD ED AR Absorption Refrigeration Machine [ RT ] TR Turbo Refrigeration Machine [ RT ] EHP Electrical Heat Pump System [ HP ] GHP Gas Heat Pump System [ HP ] GB Gas Boiler [ kw ] CGS Co-Generation System [ kw ] *1) Commercial Electricity:9.97MJ/kWh Commercial Gas:3.6MJ/kWh *1) Small CGS'~3kw(:.33, Large CGS'35kw~(:.38 28

28 Fuel Consumption Rate [ - ] Fuel Consumption Rate Machinery Performance HP.4 CGS TR AR.2 B Machine Load Rate [ - ] the value of the machinery database of the CEC/AC calculation program "BECS/CEC/AC for Windows" published by Institute for Building Environment and 29 Energy Conservation (IBEC)

29 Coding of chromosome Gas Electric Input Input Waste Heat TR HP AR HP GB HEX HP Output Output Output Cooling Demand CD Heating Demand HD Hot Water Demand WD Packaging System combination as gene information Solar PV CGS GB Output Electricity Demand ED Chromosome coding Cool Heat Supplier Hot Heat Supplier AR1 AR2 TR1 TR2 HPc1 HPc2 enehp GBh1 GBh2 HPh1 HPh2 enehp USRT USRT USRT USRT HP HP G/E kw kw HP HP G/E 7,12,17th:Fuel of HP Machinery division Hot Water Supplier Electricity Supplier GBw1 GBw2 HPw1 HPw2 enehp CGS1 CGS2 PV kw kw HP HP G/E kw kw m 2 3

30 GA Parameters Size of Sub-Population 5 Number of Islands 5 Population Size 25 Number of Generations 1 Total Individual Size 25, Rate of Crossover 1. Rate of Mutation.1 Rate of Migration 1

31 Design Variables AR Absorption Refrigeration Machine [ RT ] TR Turbo Refrigeration Machine [ RT ] EHP Electrical Heat Pump System [ HP ] GHP Gas Heat Pump System [ HP ] GB Gas Boiler [ kw ] CGS Co-Generation System [ kw ] PV Photovoltaic Power System [ m 2 ] , 32

32 Result of Machinery Combination (Case 1) Waste Heat Available Cool Heat Supplier Cool and Hot Hot Heat Supplier Hot Water Supplier Electricity Supplier TR1 TR2 HP1 HP2 AR1 AR2 GB1 GB2 HP1 HP2 GB1 GB2 HP1 HP2 CGS1 CGS2 PV1 [ USRT ] [ USRT ] [ HP ] [ HP ] [ USRT ] [ USRT ] [ kw ] [ kw ] [ HP ] [ HP ] [ kw ] [ kw ] [ HP ] [ HP ] [ kw ] [ kw ] [ m2 ] , 33

33 Result of Machinery Combination (Case 2) Waste Heat NOT Available Cool Heat Supplier Cool and Hot Hot Heat Supplier Hot Water Supplier Electricity Supplier TR1 TR2 HP1 HP2 AR1 AR2 GB1 GB2 HP1 HP2 GB1 GB2 HP1 HP2 CGS1 CGS2 PV1 [ USRT ] [ USRT ] [ HP ] [ HP ] [ USRT ] [ USRT ] [ kw ] [ kw ] [ HP ] [ HP ] [ kw ] [ kw ] [ HP ] [ HP ] [ kw ] [ kw ] [ m2 ] , 34

34 Supply [ kw ] Supply [ kw ] Operation Pattern Comparison Demand [ kw ] Demand [ kw ] Supply [ kw ] Supply [ kw ] Demand [ kw ] Demand [ kw ] Case1: Waste Heat Available Case2: Waste Heat NOT Available 1,2 1, CD Demand 1,2 1, 1,2 1, CD Demand 1,2 1, 8 6 TR TR TR2 2 2 TR1 2 AR (Waste Heat from CGS) Hour Hour 1,2 1,2 1,2 1,2 1, HD 1, 1, HD 1, 8 Demand 8 8 Demand AR1 GB2 GB1 Waste Heat from CGS AR2 AR1 GB AR2 HP2 HP Hour 2 2 GB2 HP2 HP Hour 2 35

35 Primary Energy Consumption [ MJ/days ] Primary Energy Consumption Case1: Waste Heat Available Case2: Waste Heat NOT Available 15, 1, Cool Heat Supply Hot Heat Supply Hot Water Supply Electricity Supply 115, ,785 19,324 14,797 62,582 71,594 5, Case1 Case2 Case1 Case2 Case1 Case2 Waste Heat Available Winter Day Middle Season Day Summer Day Waste Heat NOT Available Waste Heat Available Waste Heat NOT Available Waste Heat Available Waste Heat NOT Available 36

36 Contents of This Presentation Development of Optimal Design Method for Building Energy System Using Genetic Algorithms Optimal Design for Distributed Energy System utilizing Waste Heat from CGS by Genetic Algorithms. Optimization Energy System of Building Complex (composed of office and apartment) Using Genetic Algorithms

Case Study OFC APT 事務所 OFFICE 集合住宅 APARTMENT 延床面積 [ m 2 ] 15,8 33,6 最大需要 ( 電力 ) [ kw ] 499 34 最大需要 ( 冷熱 ) [ kw ] 1134

37 Case Study OFC APT 事務所 OFFICE 集合住宅 APARTMENT 延床面積 [ m 2 ] 15,8 33,6 最大需要 ( 電力 ) [ kw ] 最大需要 ( 冷熱 ) [ kw ] 1134 最大需要 ( 温熱 ) [ kw ] 最大需要 ( 給湯 ) [ kw ] 33 OFFICE 事務所集合住宅 APARTMENT 電力区画道路構内通路都市ガス中圧導管系統電力網電力一括受電施設エネルギーシステム機器

38 Cases Case Combination Operation Building REMARK Case *- Manual Manual Individual Basecase of Individual Building Case *-1 Manual Optimization Individual Optimized only Operation Case *-2 GA Optimization Optimization Individual Optimized including CGSs Case *-3 GA Optimization Optimization Individual Optimized NOT including CGSs Case 3- Manual Manual Connected Basecase of Energy Connection Case 3-2 GA Optimization Optimization NOT Connected Sum up the results of Case *-2 Case 3-3 GA Optimization Optimization NOT Connected Sum up the results of Case *-3 Case 3-4 GA Optimization Optimization Connected Optimized Energy Connection * : Case 1 or 2

39 Energy Supply Flow Case 1, 2 Individual Case 3 Energy Connection OFC APT OFC APT ES ES ES electricity gas Gas Input TR HP AR HP Output Output Cooling Demand CD Heating Demand HD Transferring Heat Loss is 5% Each building doesn t have its own machinery in Energy Center Case Electric Input Waste Heat GB HEX HP Output Hot Water Demand WD Solar PV CGS GB Output Electricity Demand ED Energy System

40 Waste heat supply Electricity Demand Hot Water Demand Heating Demand Cooling Demand ED WD HD CD HEX HEX AR Absorption Refrigerator CGS Waste heat 1 2 3

41 Result: Combination and Objective Cool heat supply ool and H Hot heat supply Hot water supply Electricity supply OBJECTIVE HP1 HP2 TR1 TR2 AR1 AR2 GB1 GB2 HP1 HP2 GB1 GB2 HP1 HP2 CGS1 CGS2 PV1 Winter Day Middle Day Summer Day Total [ HP ] [ HP ] [ USRT ] [ USRT ] [ USRT ] [ USRT ] [ kw ] [ kw ] [ HP ] [ HP ] [ kw ] [ kw ] [ HP ] [ HP ] [ kw ] [ kw ] [ m2 ] [MJ/day] [MJ/day] [MJ/day] [MJ/3days] Case ,679 83, ,83 3,429 Office Case ,36 8,967 12,46 287,319 Case , 76,43 69,49 85,56 231,399 Case , 78,172 67,67 87, ,5 Case ,489 63,64 54, ,545 Apartment Case ,448 61,113 52, ,692 Case , 72, 55,653 5, ,884 Case , 71,418 56,294 51, ,211 Case , , ,64 424,172 Energy Connection Case ,43 125,61 135,792 49,283 Case ,59 123,91 138, ,216 Case , 139,648 19, , ,34

42 OFFICE Cases (Individual) Case1-2: GA Optimization Cool heat supply Cool and Hot Hot heat supply Hot water supply Electricity supply TR1 TR2 HP1 HP2 AR1 AR2 GB1 GB2 HP1 HP2 GB1 GB2 HP1 HP2 CGS1 CGS2 PV1 [ USRT ] [ USRT ] [ HP ] [ HP ] [ USRT ] [ USRT ] [ kw ] [ kw ] [ HP ] [ HP ] [ kw ] [ kw ] [ HP ] [ HP ] [ kw ] [ kw ] [ m2 ] , Case1-, 1-1(Combination by Manual)

43 Supply [ kw ] Operation; OFFICE(Case1-2: GA optimization) Demand [ kw ] Supply [ kw ] Supply [ kw ] Demand [ kw ] Demand [ kw ] 1, Heating demand in Winter day 1, 1, Cooling demand in Middle day 1, GB GB2 demand Hour Base load is supplied by waste heat in each season Waste heat from CGS All of Cool heat demand is supplied by waste heat in Middle season TRs supply cool heat for daytime peak 2 1,6 1,4 1,2 1, demand Waste heat from CGS Hour Cooling demand in Summer day TR1 HP2 demand 2 1,6 1,4 1,2 1, TR2 HP1 Waste heat from CGS

44 OFFICE (no CGS Case) Case1-3: GA optimization Cool heat supply Cool and Hot Hot heat supply Hot water supply Electricity supply TR1 TR2 HP1 HP2 AR1 AR2 GB1 GB2 HP1 HP2 GB1 GB2 HP1 HP2 CGS1 CGS2 PV1 [ USRT ] [ USRT ] [ HP ] [ HP ] [ USRT ] [ USRT ] [ kw ] [ kw ] [ HP ] [ HP ] [ kw ] [ kw ] [ HP ] [ HP ] [ kw ] [ kw ] [ m2 ] , Case1-2

45 APARTMENT Case (Individual) Case2-2: GA optimization Cool heat supply Cool and Hot Hot heat supply Hot water supply Electricity supply TR1 TR2 HP1 HP2 AR1 AR2 GB1 GB2 HP1 HP2 GB1 GB2 HP1 HP2 CGS1 CGS2 PV1 [ USRT ] [ USRT ] [ HP ] [ HP ] [ USRT ] [ USRT ] [ kw ] [ kw ] [ HP ] [ HP ] [ kw ] [ kw ] [ HP ] [ HP ] [ kw ] [ kw ] [ m2 ] , Case2-, 2-1(Combination by Manual) Because of small demand, CGS is not selected. (Efficiency of Small CGS is lower than Large CGS)

46 Supply [ kw ] Demand [ kw ] Supply [ kw ] Demand [ kw ] Supply [ kw ] Demand [ kw ] Supply [ kw ] Demand [ kw ] Operation; APARTMENT(Case2-2 : GA optimization ) 1 8 Hot Water Demand Winter Day Hot Water Demand Middle Season Day 需要 GB1 GB2 HP1 HP Hour GB1 HP2 HP1 GB2 需要 Hour 4 2 1, 8 Heating Demand Winter Day 1, 8 1, 8 Heating Demand Middle Season Day 1, 需要 HP2 2 2 HP1 HP2 需要 2 HP Hour Hour Base load is supplied by HPs in Hot water demand

47 APARTMENT (no CGS Case) Case2-3: GA optimization Cool heat supply Cool and Hot Hot heat supply Hot water supply Electricity supply TR1 TR2 HP1 HP2 AR1 AR2 GB1 GB2 HP1 HP2 GB1 GB2 HP1 HP2 CGS1 CGS2 PV1 [ USRT ] [ USRT ] [ HP ] [ HP ] [ USRT ] [ USRT ] [ kw ] [ kw ] [ HP ] [ HP ] [ kw ] [ kw ] [ HP ] [ HP ] [ kw ] [ kw ] [ m2 ] , Case2-2

48 Energy Connection Case Case3-4 Cool heat supply Cool and Hot Hot heat supply Hot water supply Electricity supply TR1 TR2 HP1 HP2 AR1 AR2 GB1 GB2 HP1 HP2 GB1 GB2 HP1 HP2 CGS1 CGS2 PV1 [ USRT ] [ USRT ] [ HP ] [ HP ] [ USRT ] [ USRT ] [ kw ] [ kw ] [ HP ] [ HP ] [ kw ] [ kw ] [ HP ] [ HP ] [ kw ] [ kw ] [ m2 ] , Case3-

49 Supply [ kw ] Supply [ kw ] Operation in Middle Season Day Demand [ kw ] Demand [ kw ] Supply [ kw ] Supply [ kw ] Demand [ kw ] Demand [ kw ] 1 8 Hot Water Demand 1 8 1, 8 Cooling Demand 1, 需要需要 2 CGS 排熱利用 Hour 2 2 CGS 排熱利用 HP Hour 2 1, 8 Heating Demand 1, 8 1, 8 Electricity Demand 需要 1, Commercial 4 2 HP2 需要 2 2 CGS Hour Hour

50 Supply [ kw ] Operation in Summer Day Demand [ kw ] Supply [ kw ] Supply [ kw ] Demand [ kw ] Demand [ kw ] 1 8 Hot Water Demand 1 1,6 1,4 8 1,2 Cooling Demand HP2 HP1 1,6 1,4 1, , 1, TR2 需要需要 CGS 排熱利用 Hour AR2( 排熱利用 ) Hour 4 2 All of Hot Water Demand is supplied with Waste heat from CGS Base load of Cool Heat Demand is supplied by Waste heat demand About 4% of peak load is supplied by CGS in Electricity Demand 1, Electricity Demand CGS 需要 Commercial 1, Hour

51 Primary Energy Consumption in 3 days [ MJ/3days ] Primary Energy Consumption 5, 4, OFFICE APARTMENT Energy Connection Summer Day Middle Day Winter Day 一次エネルギー消費量 [MJ/3days] 3, 2, 1, 133,83 12,46 83,667 8,967 85,56 87,226 54,992 52,131 69,49 67,67 63,64 61,113 5,231 51,499 55,653 56, ,64 135, , , ,61 123,91 129,598 19, , ,43 149,59 139,648 83,679 86,36 76,43 78,172 8,489 79,448 72, 71,418 Case1- Case1-1 Case1-2 Case1-3 Case2- Case2-1 Case2-2 Case2-3 Case3- Case3-2 Case3-3 Case3-4 Base case Opt only Operation GA Opt GA Opt No CGS Base case Opt only Operation GA Opt GA Opt No CGS Base case Case1- +Case2- GA Opt (2BLDS) Case1-2 +Case 2-2 GA Opt No CGS (2BLDS) Case 2-3 +Case 3-3 GA Opt Energy Connect

52 CONCLUSIONS (1) New Optimal Design Method for Building Energy System which optimizes system type, its capacity and its operation planning simultaneously, has been proposed. (2) Using the above method, the optimum system and operation planning for CGS are selected and the energy efficiency of this system is confirmed. (3) Toward adapting the design method for practical use, evaluation of optimal combination considering economical factor is needed, for example, using Multi-Objective Genetic Algorithm (MOGA).

53 Thank you for your attention! Acknowledgement: These researches are cooperated by Mr. Komamura and Mr. Kayo.

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