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A Planning Model of Integrated Energy Service Systems for a Specific Area
Copyright ~ IFAC Large Scale Systems: Theory and Applications, Bucharest, Romania, 200 I
A PLANNING MODEL OF INTEGRATED ENERGY SERVICE SYSTEMS FOR A SPECIFIC AREA Hideharu Sugihara*, Ryo Takao*, Kiichiro Tsuji*, Ryohei Yokoyama** , Koichi Ito** and Koichi Nara*** * Graduate School of Engineering, Osaka University, 2-1 Yamada-oka, Suita, Osaka 565-0871 JAPAN (E-mail: [email protected]) ** Graduate School of Engineering, University of Osaka Prefecture, 1-1 Gakuen, Sakai, Osaka 599-8531 JAPAN *** Department of Engineering, Ibaraki University, 1-1 Nakanarisawa, Hitachi, Ibaraki 316-8511 JAPAN
Abstract: The integrated energy service system for an specific area is supposed to deliver electric and thermal energy in an integrated manner for purpose of reducing cost, primary energy consumption and CO2 emission. This paper proposes a planning model for the integrated energy service, consisting of three sub-models. First problem is to determine the optimal share of energy system options including the District Heating and Cooling (DHC) system. Next, for the area with the DHC system, the piping network for the transportation of thermal energy is determined to transport thermal energy. Finally, electric energy delivery network is determined. Particularly, this paper focuses on a coordination procedure of the energy system planning sub-model and the piping network planning sub-model. Copyright @20011FAC Keywords: Large-scale systems, Multi-objective optimization, Trade off, District heating, Pipelines, Coordination
1. INTRODUCTION
dispersed-type generators is thermal energy as well as electricity, the facility expansion planning for both electric utilities and city-gas utilities may have mutual influence on the other planning. Therefore, the coordinated planning and operation of mUltiple energy suppliers will be necessary to achieve the best performance from economic and environmental viewpoints. Also, from legal point of view, the deregulation of energy industry has been performed in many countries. Through M&A (Merger and Acquisition) among energy supply companies in different type of business, it is possible to implement an integrated energy service that supplies electricity, city-gas and heat in an integrated manner to a specific area.
Energy demand in the business and commercial sector and the residential sector of Japan is still increasing, although the global warming issue is highly concerned. To attain the target of green house gases reduction including CO2, it is very important that the efficient energy supply systems are designed in an urban area where these sectors are concentrated. Also, the technical development of dispersed-type generators such as Gas Turbine, Fuel Cell and so on, have significantly advanced in recent years. As a result, the dispersed-type generators have been a powerful option of energy systems in urban areas. Because the output of
187
The planning and operation of the integrated energy service system is a very large scale system problem. Therefore, in this paper, the problem is divided into three sub-models and a mutual coordinated solution among these sub-models is adopted. First of all, one sub-model determines what is the proper ratio of energy systems to improve the economic performance, the primary energy consumption and the CO2 emission in the area The next sub-model determines the piping network infrastructures to deliver thermal energy in the partial area selecting Distinct Heating and Cooling (DHC) system as an energy system option. Finally, the optimal network configuration for delivering electric energy is determined in another sub-mode!. The authors have already developed the individual sub-models in R.Takao et.al. (2000), K.lto et.a!. (2000) and K.Nara et.a!. (2000). This paper describes the each sub-model briefly and how to coordinate among the sub-models to optimize the multi-objective function in an urban area. However, due to the lack of space, this paper mainly focuses a thermal energy part of overall planning mode!. Also, in the numerical simulations, the authors report the results that the planning model is applied to a certain city in Japan.
energy system option, this paper assumes the DHC (Distinct Heating and Cooling) system that Gas-Turbine cogeneration system at a certain block supplies thermal energy to the surrounding blocks by piping network. In general, the piping network is laid along the streets. Therefore, to evaluate the three indices of the DHC system accurately, transportation cost of thermal energy must be evaluated based on realistic piping route. However, as the computational effort is too large, the sub-model 1 uses a simplified model about the transportation of thermal energy. Next, the sub-model 2 focuses only on the area selecting the DHC system and determines the optimal route of piping network to transport the thermal energy from central cogeneration plant to all blocks in the area. Consequently, the sub-model 2 is able to calculate the transportation cost strictly by using the cost of and electric energy for pump. Returning the calculated transportation cost to sub-model I, the sub-model 1 determines the optimal share of the energy system including DHC system again. In other words, the area selecting the DHC system is modified based on the strict transportation cost. In consequence of the above calculation process, electric energy demand and dispersed-type generators for each block of the area are determined based on the optimal share of energy systems. Many dispersed-type generators may be allocated In various blocks of the area. Therefore, the electric power flow in the area can become quite complex; a reverse power flow from the customer to the supply network can occur. The configuration of the conventional distribution network is not necessarily suitable in order to maintain the supply reliability to the customers. The concept of Flexible, Reliable, Intelligent Electric Energy Delivery System (called as FRIENDS) has been proposed by K. Nara & J.Hasegawa (1997). Here, the FRIENDS is assumed as an desirable electric delivery system and the network configuration of the FRIENDS is determined in sub-model 3 (K.Nara, & lHasegawa, 1999).
2. PLANNING FRAMEWORK OF INTEGRATED ENERGY SERVICE SYSTEM Under the assumption of providing the integrated energy service to customers in a specific area, this chapter describes the overall framework for planning of the service system. The proposed flowchart of planning models is shown in Fig. l . As input data of this planning, daily end-use demand curve per floor area of each customer and, spatial distribution of customers, are used in the energy system planning sub-model (sub-model I in Fig. I ). In the sub-model I , the optimal share of energy system options is determined at each customer based on mUlti-objective optimization. As energy system options, this paper considers the gas engine system, fuel cell system, etc. in Business and Commercial sector, and Photovoltaic solar generation system etc. in Residential sector. As each energy system has different performance of three evaluation indices to other systems, there is an optimal energy system share to minimize the weighting sum of the three indices. Also, as an
Particularly, this paper focuses on a thennal energy part of this overall planning mode!. The sub-model I &2 are described in the following chapters.
188
Table 1 Energy system options in residential houses. Sub-model 1 Energy system planning for the target area ·Specification of ORe area "Energy demand in the DHC area Sub
Components Air-conditioner + Stove + Gas-boiler
SJ!!!!..bols CNV SLR
-CNV -"+ --S~iar --g~~er~ti~~ -syste~- -+-
ELE
-Air-co~di~ -.+.- El~-~tri~ -h~t- ~~t~;: -stipply-
Solar-type hot water supply system
· Transportation cost of tbermal cner&y
system + Electric cookil!.[ ~atus DHC (Distinct Heating & Cooling)
______~______~________- ,
DR
Piping network planning for thermal energy "Electric load curve at each block "allocation and
0
eration of DGs
Table 2 Energy system options in business &commercial buildings
Szmbols ARH
Fig. I
ER HP
Flowchart of overall planning model
GEI*,GE2*, FCI *, FC2* 3.
ENERGY SYSTEMS PLANNING MODEL FOR THE TARGET AREA
DB
Absorption
Components refrigerator
and
_~~~ti!l~ ~I!i~_________________________ _ _?_l~~~~ _~:~~_ ::!i:i~::~:~::: .~?}~~: __ Heat pump system with accumulation equipment
heat
-El~ctri~ -~ib~- refug~;:~tor -.+.- Boifer+ CGS(FC or GE) + Absorption refrigerator DHC (Distinct Heating & Cooling)
*Suffix I: The capacity of CGS is determined as a half of the given demand peak. *Suffix 2: CGS is operated such a way that the electricity supply from the commercial power grid be constant throughout the day.
There are several energy systems including the DHC system at each customer in the target area. This chapter describes how to determine the optimal ratio of energy systems in the area.
3.1 Assumed customer types and energy system options
system, are introduced into individual customers. Therefore, the system performances are not changed at spatial points. However, as the location of the block becomes far from the central DHC plant, the performance of the DHC system is reduced due to higher transportation cost of thermal energy. Thus, there is an optimal area for the DHC system.
In this model, five types of customers (Office, Hotel, Hospital, Retail store and Restaurant) in business and commercial sector and two types of customers (Detached house and Apartment house) in residential sector are assumed. Also, it is assumed that each customer type has identical end-use demand. Moreover, the customer distribution in the target area are represented by the floor area by customer types for each block which divided by streets.
3.2 Formulation The three objective functions taken into account in this model are as follows: Cost = EnergyCost + EquipmentCost PrimaryEnergyConsumption (MJ) =Oil + CityGas +(1 A) . 2450' Electricity
This model takes into account some typical energy systems in the residential sector and the business and commercial sector shown in Table I and Table 2, respectively. Because the SLR system in Table I contains the solar energy supply systems, the characteristics are generally lower CO2 emission and higher cost. Also, the FC system in Table 2 is lower CO2 emission and higher cost than the GE system. These energy systems, except for the DHC
(1) (2)
CO 2 emission (kg) =A.. 0.295' Oil(MJ) +A.. 0.211' CityGas(MJ) +A.. 0.414' DayElectricity(MJ) +A.. 0.313' NightElectricity(MJ) (3) where, A.=(l/4.186).
189
As for electricity, the CO 2 emission coefficients of day time and night time are different because of the different operating condition of generators. In order to obtain Pareto optimal solutions, the weighted sum of above three indices is minimized in this model. In this model, all objectives are converted into cost as follows:
Pump= L
K
. =a pr!
where, TJph energy
K
-
C02 -
f3 lOO() Ccarbon
/
m
)
(TJ~) :
(8)
Scaling factor of hot (cold) thennal
The above scaling factors (TJk TJph, TJ~) are key parameters in this model and, these are coordinated by the results obtained in sub-model 2. To evaluate the DHC system perfonnance more accurately, the modified scaling factors are used when the sub-model I is to be applied agam.
(4)
Ccarbon · O.58(lon l kL) IOOO . 4.186.9250(MJ I L)
·lTJph(LLH~. , y
+TJ~( ~~C~., J)
ObjectiveFunction = Cost(yen) + K pri ' (Primal energy cunsumption(MJ»
+ K C02 ' (C0 2 emission(ton» where,
'i
IEDHC
(5) (6)
It should be noted that
is the conversion factor from the CO 2 emission to the cost (yen/ton) and may be regarded as a possible environmental tax or penalty. Using Eqn. (5), the primary energy consumption can be converted into CO2 emission by the heat quantity of crude oil. The a and f3 are arbitrary weights for which model-users can specify according to their preference. Ccarbon
• •• , Piping network (Sub-model I) Piping network (Sub-model 2)
3.3 Simplified transportation model o/thermal energy
Fig. 2 Difference of piping model in sub-model 1 & 2 To reduce the computational effort, this sub-model uses simplified transportation model for thermal energy and, the strict evaluation is performed in next sub-model 2. In the simplified model, it is assumed that the piping is laid from central CGS plant to each block in the DHC area by a straight line as shown in Fig. 2. Moreover, the fixed cost of piping is assumed to be proportional to the maximum thermal demand and the length of piping respectively. The fixed cost of piping are as follows: Piping =TJ1 '
L
ieDHC
'i
.{max(H~., )+max( C~., )} I
4.
PLANNING MODEL FOR DISTRlCT HEAT SUPPLY INFRASTRUCTURE
The sub-model 2 focuses only on the DHC system area determined in sub-model I. This chapter describes how to configure the piping infrastructure along the streets to evaluate the thermal transportation cost strictly. As for the network model, piping and folk point can be represented by branch and node, respectively. It is assumed that the block in DHC system area can be supplied with hot and cold thermal energy from some nodes surrounding the block. Therefore, it must be determined how the folk points were connected each other to cover all blocks in the DHC system area.
(7)
I
where, Jim,1: Hot thermal energy, C m,1 : Cold thennal energy, TJA. : Scaling factor of piping cost, ( subscript, i: Block, m : Customer type, t : time) Also, under assumptions that the pumping energy is proportional to the square of thermal demand and to the length of transportation, the pumping energy for transporting thermal energy at time t can be written as follows :
The objective of this sub-model is minimization of annual total cost. The annual total cost consists of capital cost for the equipments in DHC plant and the piping, and the demand charge and the energy
190
charge for electricity and city-gas. Generally, capacity of the equipment in DHC plant and the piping can be efficiently utilized by gathering many blocks. Considering the scale merit of the capacity, the equipment capacity and the piping capacity cost are expressed as follows:
(l=I···L)
100%
80%
~ ~
.. IQ
.r.
.DHC DELE DSLR .CNV
60%
en
E .;; 40%
(9)
c-
o
20%
(10)
0% 4
(11 )
7
10
Cc.roon:Conversion factor
[* 1O'yen / ton]
(12)
Fig. 3
Optimal share valiation of energy systems in Residential houses
where, qr: energy flow at piping I, 8/: place (I) or not (0) piping I, Qnm OUI: output energy from equipment m block n,
100%
rn: place (1) or not (0) the DHC plant at block n 80%
It is possible that the objective function in this sub-model is extended to the multi-objective function in the as sub-model 1. The authors are going to perform this extension in the future .
~ ~
..
60%
"i
40%
IQ
.DHC
.r.
DHP
en
E
D FC2
BFCl DGE2 .GEl .ER
0
This sub-model is a mixed integer planning problem consisting of the (0-1) discrete variables which represent locations of piping and DHC plant, and the continuous variables which represent energy flow in piping network and DHC plant. As a combinatorial optimization solver, sub-model 2 is solved by using GAMS/CPLEX. 5.
20%
DARH
0% 4
7
10
ER
C~_:Coversion factor [*10'yen/ ton]
Fig. 4
NUMERICAL SIMULATIONS
Optimal share valiation of energy systems in Business and Commercial sector
c::::::::J Radius of DHC area ~
5.1 Simulation data in a Target area This chapter shows the simulation results in the case that the proposed model is applied to a city in Osaka, Japan. The target area is 2 kilometers square at the center of the city containing 632 blocks. The floor area of customers was set at each block by investigating the actual city. Both a and f3 in Eqn. (6) are chosen to be equal to 1, and Ccarbon is changed as a parameter.
~
Reduction rate of C02 Redution rate of Primary Energy
~
Reduction rate of Cost
1200 1000
60 40 ~ 20 G) ....., 0 '-c: -20·2 ::> -40] 0::: -60 -80
] 800 (J)
::>
'0
600
'" 400
()
0:::
200 0
5.2 Simulation results
4
5
6
7
8 10
*
4
Cca.t>on:Converion factor[ 10 yen/ton]
Figure 3 and 4 shows the optimal share of energy system in Residential sector and Business and
Fig. 5
191
Reduction rates of evaluating indices
and the piping network planning sub-model, and the coordination method between the two sub-models. Using a complementary relationship between the two sub-models, the detail planning can be efficiently obtained by means of the coordinated solution. The proposed planning model has been applied to a city in Japan. The numerical simulations identified the varIOus Pareto optimal energy system configuration including DHC system. 7.
The authors express their appreciation to Mr. S. Raita at University of Osaka Prefecture and Mr. S. Kato at Osaka University for their work on numerical computations and display of the results. This research is financially supported by the Research for The Future Program of the Japan Society for the Promotion of Science (JSPSRFTF97PO I 002).
Fig. 6 Piping network planning (Gray block: the DHC system block, Black line: the optimal piping) Commercial buildings, respectively. In Figure 3, the economical energy systems "DHC"&"CNV" have large share in the case of low conversion factor (40 thousands yen/ton). As the conversion factor increases, the low emission type energy system "SLR" has got the large share. Also, as increasing the value of Carbon in Business and Commercial sectors, the energy system options "HP" and "FC" have got the large share in Figure 4. This is because commercial electric energy in day-time with large CO2 emission, is not consumed in the heat pump energy system. In Fig. 5, the reduction rate of the primary energy consumption and the CO2 emission are improved gradually as a conversion factor Ccarbon increases. However, it follows that the cost index becomes worse, because energy system options "SLR" and "HP" are generally expensive.
8.
In the case when the conversion factor Ccarbon is set as 70,000 (yen/ton), the piping network planning by using sub-model 2 is shown in Figure 6. Considering the realistic network constraints along the street, the solution is considered to be reasonable. 6.
ACKNOWLEDGEMENT
CONCLUSION
This paper has presented a planning model for the integrated energy service system, consisting of three sub-models. Particularly, this paper has described the energy system planning sub-model
192
REFERENCES
R.Takao et.a!' (2000). An Energy Systems Planning for Urban Area Considering Customer Distribution. Proc. of the Conference on Energy System, Economics and environment, Tokyo, Japan, pp.217-222 (2000) (in Japanese) K.lto et.a!' (2000) . Optimal Planning of a District Heat Supply Infrastructure by a Decomposion Method. Proc. of the Conference on Energy System, Economics and environment, Tokyo, Japan, pp.217-222 (2000) (in Japanese) K.Nara. & lHasegawa (1999). Configuration of New Power Derivery System for Reliable Power Supply. Proceedings of IEEE Power Engneering Society Meeting, Summer pp.248-253 (1999) K. Nara & J.Hasegawa (1997). A New Flexible, Reliable and Intelligent Electrical Energy of Electrical Delivery System. Trans. Engineering in Japan, Vo1.l21, No.l, pp.26-34 (1997) K.Tsuji et.a!' (1999). Distributed Autonomous Energy Systems Planning for Urban Area. Proceedings of New Energy Systems and Conversions (NESC'99), Osaka, Japan, pp.455-460 (1999)
A PLANNING MODEL OF INTEGRATED ENERGY SERVICE SYSTEMS FOR A SPECIFIC AREA Hideharu Sugihara*, Ryo Takao*, Kiichiro Tsuji*, Ryohei Yokoyama** , Koichi Ito** and Koichi Nara*** * Graduate School of Engineering, Osaka University, 2-1 Yamada-oka, Suita, Osaka 565-0871 JAPAN (E-mail: [email protected]) ** Graduate School of Engineering, University of Osaka Prefecture, 1-1 Gakuen, Sakai, Osaka 599-8531 JAPAN *** Department of Engineering, Ibaraki University, 1-1 Nakanarisawa, Hitachi, Ibaraki 316-8511 JAPAN
Abstract: The integrated energy service system for an specific area is supposed to deliver electric and thermal energy in an integrated manner for purpose of reducing cost, primary energy consumption and CO2 emission. This paper proposes a planning model for the integrated energy service, consisting of three sub-models. First problem is to determine the optimal share of energy system options including the District Heating and Cooling (DHC) system. Next, for the area with the DHC system, the piping network for the transportation of thermal energy is determined to transport thermal energy. Finally, electric energy delivery network is determined. Particularly, this paper focuses on a coordination procedure of the energy system planning sub-model and the piping network planning sub-model. Copyright @20011FAC Keywords: Large-scale systems, Multi-objective optimization, Trade off, District heating, Pipelines, Coordination
1. INTRODUCTION
dispersed-type generators is thermal energy as well as electricity, the facility expansion planning for both electric utilities and city-gas utilities may have mutual influence on the other planning. Therefore, the coordinated planning and operation of mUltiple energy suppliers will be necessary to achieve the best performance from economic and environmental viewpoints. Also, from legal point of view, the deregulation of energy industry has been performed in many countries. Through M&A (Merger and Acquisition) among energy supply companies in different type of business, it is possible to implement an integrated energy service that supplies electricity, city-gas and heat in an integrated manner to a specific area.
Energy demand in the business and commercial sector and the residential sector of Japan is still increasing, although the global warming issue is highly concerned. To attain the target of green house gases reduction including CO2, it is very important that the efficient energy supply systems are designed in an urban area where these sectors are concentrated. Also, the technical development of dispersed-type generators such as Gas Turbine, Fuel Cell and so on, have significantly advanced in recent years. As a result, the dispersed-type generators have been a powerful option of energy systems in urban areas. Because the output of
187
The planning and operation of the integrated energy service system is a very large scale system problem. Therefore, in this paper, the problem is divided into three sub-models and a mutual coordinated solution among these sub-models is adopted. First of all, one sub-model determines what is the proper ratio of energy systems to improve the economic performance, the primary energy consumption and the CO2 emission in the area The next sub-model determines the piping network infrastructures to deliver thermal energy in the partial area selecting Distinct Heating and Cooling (DHC) system as an energy system option. Finally, the optimal network configuration for delivering electric energy is determined in another sub-mode!. The authors have already developed the individual sub-models in R.Takao et.al. (2000), K.lto et.a!. (2000) and K.Nara et.a!. (2000). This paper describes the each sub-model briefly and how to coordinate among the sub-models to optimize the multi-objective function in an urban area. However, due to the lack of space, this paper mainly focuses a thermal energy part of overall planning mode!. Also, in the numerical simulations, the authors report the results that the planning model is applied to a certain city in Japan.
energy system option, this paper assumes the DHC (Distinct Heating and Cooling) system that Gas-Turbine cogeneration system at a certain block supplies thermal energy to the surrounding blocks by piping network. In general, the piping network is laid along the streets. Therefore, to evaluate the three indices of the DHC system accurately, transportation cost of thermal energy must be evaluated based on realistic piping route. However, as the computational effort is too large, the sub-model 1 uses a simplified model about the transportation of thermal energy. Next, the sub-model 2 focuses only on the area selecting the DHC system and determines the optimal route of piping network to transport the thermal energy from central cogeneration plant to all blocks in the area. Consequently, the sub-model 2 is able to calculate the transportation cost strictly by using the cost of and electric energy for pump. Returning the calculated transportation cost to sub-model I, the sub-model 1 determines the optimal share of the energy system including DHC system again. In other words, the area selecting the DHC system is modified based on the strict transportation cost. In consequence of the above calculation process, electric energy demand and dispersed-type generators for each block of the area are determined based on the optimal share of energy systems. Many dispersed-type generators may be allocated In various blocks of the area. Therefore, the electric power flow in the area can become quite complex; a reverse power flow from the customer to the supply network can occur. The configuration of the conventional distribution network is not necessarily suitable in order to maintain the supply reliability to the customers. The concept of Flexible, Reliable, Intelligent Electric Energy Delivery System (called as FRIENDS) has been proposed by K. Nara & J.Hasegawa (1997). Here, the FRIENDS is assumed as an desirable electric delivery system and the network configuration of the FRIENDS is determined in sub-model 3 (K.Nara, & lHasegawa, 1999).
2. PLANNING FRAMEWORK OF INTEGRATED ENERGY SERVICE SYSTEM Under the assumption of providing the integrated energy service to customers in a specific area, this chapter describes the overall framework for planning of the service system. The proposed flowchart of planning models is shown in Fig. l . As input data of this planning, daily end-use demand curve per floor area of each customer and, spatial distribution of customers, are used in the energy system planning sub-model (sub-model I in Fig. I ). In the sub-model I , the optimal share of energy system options is determined at each customer based on mUlti-objective optimization. As energy system options, this paper considers the gas engine system, fuel cell system, etc. in Business and Commercial sector, and Photovoltaic solar generation system etc. in Residential sector. As each energy system has different performance of three evaluation indices to other systems, there is an optimal energy system share to minimize the weighting sum of the three indices. Also, as an
Particularly, this paper focuses on a thennal energy part of this overall planning mode!. The sub-model I &2 are described in the following chapters.
188
Table 1 Energy system options in residential houses. Sub-model 1 Energy system planning for the target area ·Specification of ORe area "Energy demand in the DHC area Sub
Components Air-conditioner + Stove + Gas-boiler
SJ!!!!..bols CNV SLR
-CNV -"+ --S~iar --g~~er~ti~~ -syste~- -+-
ELE
-Air-co~di~ -.+.- El~-~tri~ -h~t- ~~t~;: -stipply-
Solar-type hot water supply system
· Transportation cost of tbermal cner&y
system + Electric cookil!.[ ~atus DHC (Distinct Heating & Cooling)
______~______~________- ,
DR
Piping network planning for thermal energy "Electric load curve at each block "allocation and
0
eration of DGs
Table 2 Energy system options in business &commercial buildings
Szmbols ARH
Fig. I
ER HP
Flowchart of overall planning model
GEI*,GE2*, FCI *, FC2* 3.
ENERGY SYSTEMS PLANNING MODEL FOR THE TARGET AREA
DB
Absorption
Components refrigerator
and
_~~~ti!l~ ~I!i~_________________________ _ _?_l~~~~ _~:~~_ ::!i:i~::~:~::: .~?}~~: __ Heat pump system with accumulation equipment
heat
-El~ctri~ -~ib~- refug~;:~tor -.+.- Boifer+ CGS(FC or GE) + Absorption refrigerator DHC (Distinct Heating & Cooling)
*Suffix I: The capacity of CGS is determined as a half of the given demand peak. *Suffix 2: CGS is operated such a way that the electricity supply from the commercial power grid be constant throughout the day.
There are several energy systems including the DHC system at each customer in the target area. This chapter describes how to determine the optimal ratio of energy systems in the area.
3.1 Assumed customer types and energy system options
system, are introduced into individual customers. Therefore, the system performances are not changed at spatial points. However, as the location of the block becomes far from the central DHC plant, the performance of the DHC system is reduced due to higher transportation cost of thermal energy. Thus, there is an optimal area for the DHC system.
In this model, five types of customers (Office, Hotel, Hospital, Retail store and Restaurant) in business and commercial sector and two types of customers (Detached house and Apartment house) in residential sector are assumed. Also, it is assumed that each customer type has identical end-use demand. Moreover, the customer distribution in the target area are represented by the floor area by customer types for each block which divided by streets.
3.2 Formulation The three objective functions taken into account in this model are as follows: Cost = EnergyCost + EquipmentCost PrimaryEnergyConsumption (MJ) =Oil + CityGas +(1 A) . 2450' Electricity
This model takes into account some typical energy systems in the residential sector and the business and commercial sector shown in Table I and Table 2, respectively. Because the SLR system in Table I contains the solar energy supply systems, the characteristics are generally lower CO2 emission and higher cost. Also, the FC system in Table 2 is lower CO2 emission and higher cost than the GE system. These energy systems, except for the DHC
(1) (2)
CO 2 emission (kg) =A.. 0.295' Oil(MJ) +A.. 0.211' CityGas(MJ) +A.. 0.414' DayElectricity(MJ) +A.. 0.313' NightElectricity(MJ) (3) where, A.=(l/4.186).
189
As for electricity, the CO 2 emission coefficients of day time and night time are different because of the different operating condition of generators. In order to obtain Pareto optimal solutions, the weighted sum of above three indices is minimized in this model. In this model, all objectives are converted into cost as follows:
Pump= L
K
. =a pr!
where, TJph energy
K
-
C02 -
f3 lOO() Ccarbon
/
m
)
(TJ~) :
(8)
Scaling factor of hot (cold) thennal
The above scaling factors (TJk TJph, TJ~) are key parameters in this model and, these are coordinated by the results obtained in sub-model 2. To evaluate the DHC system perfonnance more accurately, the modified scaling factors are used when the sub-model I is to be applied agam.
(4)
Ccarbon · O.58(lon l kL) IOOO . 4.186.9250(MJ I L)
·lTJph(LLH~. , y
+TJ~( ~~C~., J)
ObjectiveFunction = Cost(yen) + K pri ' (Primal energy cunsumption(MJ»
+ K C02 ' (C0 2 emission(ton» where,
'i
IEDHC
(5) (6)
It should be noted that
is the conversion factor from the CO 2 emission to the cost (yen/ton) and may be regarded as a possible environmental tax or penalty. Using Eqn. (5), the primary energy consumption can be converted into CO2 emission by the heat quantity of crude oil. The a and f3 are arbitrary weights for which model-users can specify according to their preference. Ccarbon
• •• , Piping network (Sub-model I) Piping network (Sub-model 2)
3.3 Simplified transportation model o/thermal energy
Fig. 2 Difference of piping model in sub-model 1 & 2 To reduce the computational effort, this sub-model uses simplified transportation model for thermal energy and, the strict evaluation is performed in next sub-model 2. In the simplified model, it is assumed that the piping is laid from central CGS plant to each block in the DHC area by a straight line as shown in Fig. 2. Moreover, the fixed cost of piping is assumed to be proportional to the maximum thermal demand and the length of piping respectively. The fixed cost of piping are as follows: Piping =TJ1 '
L
ieDHC
'i
.{max(H~., )+max( C~., )} I
4.
PLANNING MODEL FOR DISTRlCT HEAT SUPPLY INFRASTRUCTURE
The sub-model 2 focuses only on the DHC system area determined in sub-model I. This chapter describes how to configure the piping infrastructure along the streets to evaluate the thermal transportation cost strictly. As for the network model, piping and folk point can be represented by branch and node, respectively. It is assumed that the block in DHC system area can be supplied with hot and cold thermal energy from some nodes surrounding the block. Therefore, it must be determined how the folk points were connected each other to cover all blocks in the DHC system area.
(7)
I
where, Jim,1: Hot thermal energy, C m,1 : Cold thennal energy, TJA. : Scaling factor of piping cost, ( subscript, i: Block, m : Customer type, t : time) Also, under assumptions that the pumping energy is proportional to the square of thermal demand and to the length of transportation, the pumping energy for transporting thermal energy at time t can be written as follows :
The objective of this sub-model is minimization of annual total cost. The annual total cost consists of capital cost for the equipments in DHC plant and the piping, and the demand charge and the energy
190
charge for electricity and city-gas. Generally, capacity of the equipment in DHC plant and the piping can be efficiently utilized by gathering many blocks. Considering the scale merit of the capacity, the equipment capacity and the piping capacity cost are expressed as follows:
(l=I···L)
100%
80%
~ ~
.. IQ
.r.
.DHC DELE DSLR .CNV
60%
en
E .;; 40%
(9)
c-
o
20%
(10)
0% 4
(11 )
7
10
Cc.roon:Conversion factor
[* 1O'yen / ton]
(12)
Fig. 3
Optimal share valiation of energy systems in Residential houses
where, qr: energy flow at piping I, 8/: place (I) or not (0) piping I, Qnm OUI: output energy from equipment m block n,
100%
rn: place (1) or not (0) the DHC plant at block n 80%
It is possible that the objective function in this sub-model is extended to the multi-objective function in the as sub-model 1. The authors are going to perform this extension in the future .
~ ~
..
60%
"i
40%
IQ
.DHC
.r.
DHP
en
E
D FC2
BFCl DGE2 .GEl .ER
0
This sub-model is a mixed integer planning problem consisting of the (0-1) discrete variables which represent locations of piping and DHC plant, and the continuous variables which represent energy flow in piping network and DHC plant. As a combinatorial optimization solver, sub-model 2 is solved by using GAMS/CPLEX. 5.
20%
DARH
0% 4
7
10
ER
C~_:Coversion factor [*10'yen/ ton]
Fig. 4
NUMERICAL SIMULATIONS
Optimal share valiation of energy systems in Business and Commercial sector
c::::::::J Radius of DHC area ~
5.1 Simulation data in a Target area This chapter shows the simulation results in the case that the proposed model is applied to a city in Osaka, Japan. The target area is 2 kilometers square at the center of the city containing 632 blocks. The floor area of customers was set at each block by investigating the actual city. Both a and f3 in Eqn. (6) are chosen to be equal to 1, and Ccarbon is changed as a parameter.
~
Reduction rate of C02 Redution rate of Primary Energy
~
Reduction rate of Cost
1200 1000
60 40 ~ 20 G) ....., 0 '-c: -20·2 ::> -40] 0::: -60 -80
] 800 (J)
::>
'0
600
'" 400
()
0:::
200 0
5.2 Simulation results
4
5
6
7
8 10
*
4
Cca.t>on:Converion factor[ 10 yen/ton]
Figure 3 and 4 shows the optimal share of energy system in Residential sector and Business and
Fig. 5
191
Reduction rates of evaluating indices
and the piping network planning sub-model, and the coordination method between the two sub-models. Using a complementary relationship between the two sub-models, the detail planning can be efficiently obtained by means of the coordinated solution. The proposed planning model has been applied to a city in Japan. The numerical simulations identified the varIOus Pareto optimal energy system configuration including DHC system. 7.
The authors express their appreciation to Mr. S. Raita at University of Osaka Prefecture and Mr. S. Kato at Osaka University for their work on numerical computations and display of the results. This research is financially supported by the Research for The Future Program of the Japan Society for the Promotion of Science (JSPSRFTF97PO I 002).
Fig. 6 Piping network planning (Gray block: the DHC system block, Black line: the optimal piping) Commercial buildings, respectively. In Figure 3, the economical energy systems "DHC"&"CNV" have large share in the case of low conversion factor (40 thousands yen/ton). As the conversion factor increases, the low emission type energy system "SLR" has got the large share. Also, as increasing the value of Carbon in Business and Commercial sectors, the energy system options "HP" and "FC" have got the large share in Figure 4. This is because commercial electric energy in day-time with large CO2 emission, is not consumed in the heat pump energy system. In Fig. 5, the reduction rate of the primary energy consumption and the CO2 emission are improved gradually as a conversion factor Ccarbon increases. However, it follows that the cost index becomes worse, because energy system options "SLR" and "HP" are generally expensive.
8.
In the case when the conversion factor Ccarbon is set as 70,000 (yen/ton), the piping network planning by using sub-model 2 is shown in Figure 6. Considering the realistic network constraints along the street, the solution is considered to be reasonable. 6.
ACKNOWLEDGEMENT
CONCLUSION
This paper has presented a planning model for the integrated energy service system, consisting of three sub-models. Particularly, this paper has described the energy system planning sub-model
192
REFERENCES
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