An integrated decision support model for design and operation of a wind-based hydrogen supply system

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 6 ) 1 e1 7

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An integrated decision support model for design and operation of a wind-based hydrogen supply system Minsoo Kim, Jiyong Kim* Department of Energy and Chemical Engineering, Incheon National University, 119, Academy-ro, Yeonsu-gu, Incheon 406-772, Republic of Korea

article info

abstract

Article history:

We aim to develop a new optimization-based approach for the strategic planning of a

Received 11 August 2016

renewable hydrogen supply system using onshore and offshore wind energy. To achieve

Received in revised form

this goal, we develop an optimization model to design and analyze a wind-powered

26 September 2016

hydrogen supply (WPHS) system using a mixed-integer linear programming technique.

Accepted 22 October 2016

In this model, we include decision variables to account for a wide range of issues regarding

Available online xxx

the proposed system, from the technical selection of the wind turbine and wind farm layout design to strategies for onshore/offshore wind farm and hydrogen supply network

Keywords:

development. To illustrate the capability of the proposed approach, we present a case

Wind energy

study pertaining to the design of the WPHS system for the road transportation sector of Jeju

Renewable energy

Island, Korea. Using the proposed approach, we are able to i) identify the configuration and

Hydrogen supply

operational practices of an optimal WPHS system, ii) analyze the cost distribution and

Optimization

major cost drivers through economic evaluation, and iii) provide decision-making guidance

Korea

to stakeholders and policy makers for planning an economically viable and a sustainable hydrogen supply system using various policies to encourage offshore wind farm development. © 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Introduction Recently, global concerns about climate change and the depletion of fossil fuels have led to considerable research toward developing sustainable energy supply systems [1]. The hydrogen economy has been identified as one of the most attractive replacement alternatives for the current fossil fuelbased energy system [2]. Not only is hydrogen an environmentally clean energy resource, it is a flexible energy carrier that can convert energy from primary energy sources to

different end-user energy forms, such as electricity, heat, and chemicals. A large number of studies in the literature have dealt with problems regarding the design and analysis of hydrogen-based energy supply systems and related infrastructure [2e9]. The merits of hydrogen as an energy carrier can be further enhanced when it is produced from renewable energy sources (RES) such as biomass, wind, and solar energy. Many prominent researchers have attempted to investigate the feasibility of renewable hydrogen systems and to analyze the economics with regard to installation and operation of

* Corresponding author. Fax: þ82 2 835 0797. E-mail address: [email protected] (J. Kim). http://dx.doi.org/10.1016/j.ijhydene.2016.10.129 0360-3199/© 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article in press as: Kim M, Kim J, An integrated decision support model for design and operation of a wind-based hydrogen supply system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.10.129

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renewable hydrogen supply infrastructure. For example, Dagdougui et al. developed a decision support system for the establishment of on-site renewable hydrogen production systems from solar and wind energy [10] and a hydrogen distribution network [1], respectively. Almaraz et al. constructed new optimization models for the design and analysis of a hydrogen supply chain from various renewable energy sources [11,12]. Notably, there are many studies on the design of stand-alone renewable hydrogen systems, that is, off-grid or without involving the transport of energy from/ to other regions [13e15]. In particular, hydrogen produced from wind power provides many benefits, including improved energy security and sustainability, along with reduced carbon dioxide emissions. For instance, hydrogen produced from wind energy can be directly used in fuel cell vehicles (FCVs). This wind-hydrogenFCV pathway emits no carbon dioxide (or very little carbon dioxide as a result of hydrogen production and vehicle manufacturing), thus avoiding the addition of greenhouse gas emissions that worsen global warming [16]. The design of hydrogen supply systems using wind energy is highly complex owing to the presence of a number of alternative options (e.g., different locations, timing, types) and parameter uncertainty (e.g., intermittent wind power and predicted demand). While many approaches and models have been developed, no study comprehensively addresses all the problems that may arise in a renewable hydrogen supply system. Some studies have addressed the design problem without considering operational issues pertaining to the design, whereas other studies have paid heed to operation problems while making simple assumptions with regard to design, such as fixed location. Recently, Samsatli and coworkers developed a new optimization model to design an integrated wind-power-based hydrogen supply chain. They identified the optimal supply network and analyzed its economic performance by simultaneously determining the design and operation issues and considering various practical problems such as changeable wind speed, and energy transport and storage [16,17]. While they addressed many issues related to the design and downstream operation (i.e., the hydrogen supply network), it is vital that issues pertaining to design and upstream operation (i.e., the wind farm) should also be resolved via a simultaneous decision-making process. For example, when we determine the capacity and location of a wind farm, its deployment should be determined to achieve the best possible performance of the wind farm, which would improve the economics of the entire system. For instance, the spacing of wind turbines in a wind farm is a critical factor in calculating the actual electricity output of the farm, as one would need to consider the loss due to the wake effect [10,18e21]. Besides, the occupied area is an important factor in the design of a hydrogen supply chain, since land availability for establishing large wind farms is typically limited [10,18,22e24]. Therefore, this paper proposes a new approach capable of addressing all design issues pertaining to a wind-based hydrogen supply system (from determining the type of wind turbine, and spacing and layout of a wind farm to typical matters regarding supply chain problems) as well as operational strategies.

Problem statement Aims and methodology The main objectives of this study are to i) develop an optimization model to design and analyze a wind-powered hydrogen supply (WPHS) system, and ii) provide strategically useful solutions for planning the WPHS system, such as system configuration, capital investment, and logistics operation management. In achieving the goals, we develop a new mathematical model based on the mixed-linear programming (MILP) technique, and use the model to predict results under different scenarios of the future energy system. We apply the proposed model to a design problem of the WPHS system on Jeju Island, Korea, as a case study. Furthermore, to investigate parameters which have the significant impact on the total network cost, this study analyzes the sensitivity of major parameters. The main steps in this study are summarized in Fig. 1.

Fig. 1 e Main steps for design and analysis of a windpowered hydrogen supply system.

Please cite this article in press as: Kim M, Kim J, An integrated decision support model for design and operation of a wind-based hydrogen supply system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.10.129

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WPHS system For the design of the WPHS system, we generate the system structure along with major activities from electricity generation and transmission to hydrogen production, storage, and transportation. Fig. 2 shows the schematic structure of the WPHS system, which consists of two main divisions: wind power and hydrogen supply chain. The wind power supply chain includes two different wind power sources such as onshore/offshore wind farms. These wind farms have different wind turbines sizes and layout structures, and are installed to generate electricity. Then, the generated electricity is transmitted to central or on-site hydrogen production facilities to convert it into hydrogen. In the hydrogen supply chain, we consider typical hydrogen supply activities such as hydrogen production, transportation, storage, and dispensing. We consider a single facility, called a water electrolysis plant, of different capacities. The plant converts wind-powered electricity into renewable hydrogen. As the name implies, hydrogen storage systems (i.e., hydrogen terminals) store the hydrogen produced by the water electrolysis plants and distribute the stored hydrogen to fueling stations in adjacent regions. The fueling stations meet the final hydrogen demand (that of hydrogen fuel cell vehicles or HFCVs). We consider two types of fueling stations. The first is capable of producing hydrogen on-site at a small scale as well as dispensing hydrogen to HFCVs, and the other is a dispensing station only.

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The optimization problem to be solved is summarized as follows. Given:  Regional data, such as hydrogen demand, area (size), land availability, and distance between different regions.  Resource data such as spot and average wind speeds.  Technical and economic data such as the conversion efficiency, processing capacity, loss factor, packing factor, lifetime, area, and capital and operating costs. Determine:  Whether an onshore and/or offshore wind farm should be constructed.  Number, size, and location of wind farms and their deployment specifications, including wind turbine type, space between the turbines, number and height of the turbines, and location and layout of the wind farm.  Number, capacity, and location of the hydrogen production, storage, and dispensing facilities.  Transmission connectivity from wind farms to the electricity grid.  Transportation mode, and amount of hydrogen transferred between the hydrogen production and storage facilities, and between the storage facilities and refueling station.  Total amount of electricity and hydrogen produced.

Fig. 2 e Superstructure of the WPHS system and corresponding decision problems. Please cite this article in press as: Kim M, Kim J, An integrated decision support model for design and operation of a wind-based hydrogen supply system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.10.129

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The following assumptions are made for designing the WPHS system:  There are no product (electricity and hydrogen) transmission losses.  An onshore wind farm can be established only on grassland, whereas two options exist for the offshore site: shallow or transitional wind farms.  The sites for the hydrogen production, storage, and dispensing facilities are not considered, because the area occupied by them is significantly small compared to that needed for the wind farm.  The lifetime of all the facilities is assumed to be 15 years, except for vehicles used to deliver the hydrogen (10 years) and the electricity transmission network (25 years).  Loans provide 100% of the initial capital investment. The interest rate and loan payback term are assumed to be 8% and the lifetime of the facility, respectively [25].  The holding factor of the hydrogen storage facility is 3 days.  The electricity transmission cost is related to the distance between connected regions and not the transmitted amount. The capital cost for installation of a new electricity transmission network is assumed to be $1.875 million per kilometer [26].

Electricity production and conservation For every region (i.e., onshore, shallow, and transitional offshore), the amount of electricity generated from wind turbine i2IWT in region j2J with wind farm layout k2K at height m2M is calculated using Eq. (4): XEijkm ¼

ð1  fk ÞNWT ijkm OPijm 1000

Z

vout in

OPijm ¼ Z vvout vin

TEðvÞijm

ci2IWT ; j2J; m2M

Constraints Demand constraints The total hydrogen demand in region j2JO (DETj ) is equal to the sum of the demand in region j2JO , satisfied by hydrogen imported from other regions (DIj ) and the demand in region j2JO , satisfied by local hydrogen production (DLj ): DETj ¼ DIj þ DLj

cj2JO

(1)

In Eq. (1), region j2JO denotes the set of regions that can establish all facilities except the offshore wind turbine. The amount of hydrogen imported to fueling station i2IFS in region j2JO from terminal in region j0 2JO by transportation modes Htf l2LH (Qij0 jl ) should be larger than the demand satisfied by hydrogen imported to j2JO from the other regions (DIj ): DIj 

X X X i2IFS

Htf

Qij0 jl

cj2JO

(2)

j0 2JL l2LH

The demand satisfied by local hydrogen production in region j2JO is given by DLj 

X

XHo ij

cj2JO

(3)

i2IOE

where XHo ij is the amount of hydrogen produced by on-site hydrogen electrolysis facility i2IOE in region j2JO .

(5)

WPðvÞjm

TEðvÞijm is the turbine energy of wind turbine i2IWT in region j2J at height m2M for each of wind speed v, which lies between the cut-in (vin ) and cut-out (vout ) wind speed. WPðvÞjm is the Weibull probability distribution for each wind speed v in region j2J at height m2M. WPðvÞjm explains the statistical behavior of the wide range of wind speeds. TEðvÞijm and WPðvÞijm are calculated as follows:

WPðvÞjm ¼

The proposed model is formulated using the MILP technique. The following sections discuss the major constraints and objective function.

(4)

where fk is the array loss dependent on the wind farm layout WT in region k2K, and NWT ijkm is the number of wind turbines i2I j2J having layout k2K at height m2M. The output power from a wind turbine i2IWT in region j2J at height m2M(OPijm ) is calculated using Eq. (5):

TEðvÞijm ¼ WPðvÞjm PCðvÞi

Optimization model

ci2IWT ; j2J; k2K; m2M

ci2IWT ; j2J; m2M

  ε   ε1 ε v v exp  cj2J; m2M ujm ujm ujm

(6)

(7)

where PCðvÞi represents the power curve for each of wind speed v of wind turbine i2IWT , and K is the dimensionless shape parameter, and ε is the Weibull shape factor showing the extent of peaks in the wind distribution. ujm is the Weibull scale factor related to region j2J and height m2M, which indicates the windiness of the selected location. ujm is calculated with Eq. (8): WSjm  ujm ¼  G 1 þ 1ε

cj2J; m2M

(8)

where WSjm and GðxÞ are the wind speed in region j2J at height m2M and the gamma function, respectively. GðxÞ is expressed as follows: Z∞ GðxÞ ¼

tx1 et dt

(9)

0

WSjm can be calculated through the reference height, wind velocity at that height, and wind shear exponent. WSjm is calculated using Eq. (10): WSjm ¼

d  HHm WSj50 50

cj2J; m2M

(10)

where HHm , d, and WSj50 are the height of wind turbine m2M, power law shear exponent, and wind speed measured at a height of 50 m in region j2J, respectively. The total amount of electricity generated in region j2J (XTE j ) is equal to the sum of the electricity generated from wind

Please cite this article in press as: Kim M, Kim J, An integrated decision support model for design and operation of a wind-based hydrogen supply system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.10.129

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turbine i2IWT in region j2J with wind farm layout k2K at height m2M: XTE j ¼

X X X

cj2J

XEijkm

(11)

i2IWT k2K m2M

The energy balance of the total produced electricity is also equal to: XTE j ¼

X X X

QijjEwc 0l þ

j0 2JO i2ICE l2LE

X X X

QijjEwo 0l

cj2J

(12)

j0 2JO i2IOE l2LE

X

SHij 

i2ITE

X X X i2IFS

j0 2JO

Htf

Qijj0 l

cj2JO

5

(19)

l2LH

In a similar manner, in Eq. (15), the amount of stored hydrogen is constrained by the number of terminals i2ITE and bound between the minimum and maximum storage capacand Scapmax , respectively): ities of terminal i2ITE (Scapmin i i H max TE Scapmin NTE Nij i ij  bSij  Scapi

ci2ITE ; j2JO

(20)

and QijjEwo are the amount of electricity exported where QijjEwc 0l 0l from region j2J to central electrolysis facility i2ICE and to onsite electrolysis facility i2IOE in region j0 2JO by transportation mode l2LE , respectively.

where b denotes the average number of days' worth of storage in order to explain demand variations. In this paper, we assume that the on-site electrolysis, including hydrogen storage technology, has no inventory period owing to its low capacity.

Hydrogen production and conservation

Transportation operation

Hydrogen can be produced from central or on-site electrolysis using the electricity generated by wind turbines. The amount of hydrogen produced from the central electrolysis facility and on-site electrolysis are calculated using Eqs. (13) and (14), respectively:

The flow rate of hydrogen transported between different regions is bound between the small number (SN) and big number (BN):

XHc ij

¼

XX

QijEwc 0 jl hi

ci2I ; j2J

O

(13)

QijEwo 0 jl hi

ci2IOE ; j2JO

(14)

CE

j0 2J l2LE

XHo ij ¼

XX j0 2J l2LE

Ho where XHc ij and Xij are the amount of hydrogen produced from central electrolysis facility i2ICE and on-site electrolysis facility i2IOE in region j2JO , respectively. hi is the energy conversion efficiency of electrolysis i2ICE ; IOE . The amount of produced hydrogen is constrained by electrolysis i2ICE ; IOE and bound between the minimum and and maximum production capacities of i2ICE ; IOE (Pcapmin i , respectively): Pcapmax i

Htf

Yijj'l LN  Qijj0 l  Yijj0 l BN

ci2IFS ; fj; j0 g2JO ; l2LH ; jsj0

where Yijj0 l is a binary variable that represents the transportation of hydrogen from the terminal to the fueling station between regions. Yijj0 l is 1 if the hydrogen is to be transported from region j2JO to fueling station i2IFS in region j0 2JO by transportation mode l2LH , and 0 otherwise. Logically, the flow of hydrogen between regions must occur in one direction only. For instance, a particular region imports hydrogen from other regions or exports hydrogen to other regions, but not both. Eqs. (22)e(25) indicate the constraints of the regional transportation network. Yijj0 l þ Yij0 jl  1 ci2IFS ; fj; j0 g2JO ; l2LH ; jsj0 ci2IFS ; fj; j0 g2JO ; l2LH ; jsj0

Yijj0 l  Vij

ci2IFS ; fj; j0 g2JO ; l2LH ; jsj0

Hc max CE Pcapmin NCE Nij i ij  Xij  Pcapi

ci2ICE ; j2JO

(15)

Yijj0 l  Wij

Ho max OE Pcapmin NOE Nij i ij  Xij  Pcapi

ci2IOE ; j2JO

(16)

Vij þ Wij  1 ci2IFS ; j2JO

The total amount of hydrogen produced from central electrolysis facility i2ICE in region j2J is equal to the total hydrogen transported from region j2JO to terminal i2ITE in region j0 2JO by transportation mode l2LH (QijjHct 0 l ): X

XHc ¼

i2ICE

X X X

cj2JO

QijjHct 0l

(17)

(21)

(22) (23) (24) (25)

where Vij and Wij are binary variables. Vij is 1 if hydrogen is to be exported from region j2JO to fueling station i2IFS in other regions, and 0 otherwise. Wij is 1 if hydrogen is to be imported to fueling station i2IFS in region j2JO from other regions, and Htf 0 otherwise. Furthermore, Eqs. (21)e(25) apply not only Qijj0 l Ewc Ewo Hct but also all flows (i.e., Qijj0 l , Qijj0 l , and Qijj0 l ).

i2ITE j0 2JO l2LH

Fueling station operation Storage operation The amount of hydrogen stored in terminal i2ITE in region j2JL (SH ij ) is equal to the amount of hydrogen imported from region j0 2JO to terminal i2ITE in region j2JO by transportation mode l2LH (QijHct 0 jl ): SHij

¼

X X

QijHct 0 jl

0

ci2I ; j 2J TE

O

The number of fueling stations i2IFS in region j2JO is determined by DIj and the capacity of the fueling station i2IFS (Fcapmax ), as follows: i NFS ij 

DIj Fcapmax i

ci2IFS ; j2JO

(26)

(18)

j2JO l2LH

The total amount of stored hydrogen in region j2JO should be greater than the amount of hydrogen transported from region j2JO to fueling station i2IFS in region j0 2JO by transHtf portation mode l2LH (Qijj0 l ):

Land limitation The total area occupied by the wind farm should not be larger than the area available in region j2JO (AAj ): X X X i2IWT

NWT ijkm OAik  AAj

cj2JO

(27)

k2K m2M

Please cite this article in press as: Kim M, Kim J, An integrated decision support model for design and operation of a wind-based hydrogen supply system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.10.129

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where OAik is the occupied area of a wind turbine i2IWT present in wind farm layout k2K, and OAik is calculated using Eq. (28):

We construct a cost function of height according to wind turbine type in order to simplify the representation of the cost. TCim is given by

OAik ¼ qk RD2i

TCim ¼ ai HHm þ bi

ci2IWT ; k2K

(28)

ci2IWT ; m2M

(35)

where qk is the coefficient depending on wind farm layout k2K, and RDi is the rotor diameter of wind turbine i2IWT . The area available in region j2JO (AAj ) is given by the following equation:

where ai and bi are the slope and intercept of the tower cost function according to wind turbine i2IWT , respectively. BCijm is determined by the type, region, and height of the wind turbine. Similarly, BCijm is given by

AAj ¼ gGAj

BCijm ¼ cij HHm þ dij

cj2JO

(29)

where GAj and g are the grassland area in region j2JO and land regulation, respectively.

ci2IWT ; j2J; m2M

(36)

where cij and dij are the slope and intercept of the balance cost function according to wind turbine i2IWT in region j2J, respectively.

Other constraints Logically, the wind farm in region j2J must consist of one type of wind turbine i2IWT in layout k2K at height m2M. This constraint can be expressed as follows: Rijkm SN  XEijkm  Rijkm BN ci2IWT ; j2J; k2K; m2M X X X

Rijkm  1 cj2J

Transportation capital cost The transportation capital cost (TCC) consists of the capital cost of hydrogen and electricity transportation (THC and TEC, respectively):

(30) TCC ¼ THC þ TEC (31)

i2IWT k2K m2M

where Rijkm is a binary variable. Rijkm is 1 if electricity is to be produced by wind turbine i2IWT in region j2J with wind farm layout k2K at hub height m2M, and 0 otherwise.

where THC is determined by the amount of hydrogen transHtf ported between regions (QijjHct0 l and Qijj0 l ), delivery distance betransport availability (TMAl ), tween regions (Ljj0 ), transportation capital cost (TMCl ), transport capacity (Tcapl ), average speed (SPl ), and loading/unloading time (LUTl ). THC is calculated using Eq. (38):

Objective function THC ¼ The objective of the proposed model is to minimize the total daily cost of the WPHS system. TDC (total daily cost) is composed of FCC (facility capital cost), TCC (transportation capital cost, FOC (facility operating cost), and TOC (transportation operating cost):

(37)

  Hct P P P Qijj0 l TMCl 2Ljj0 þ LUTl SPl i2ITE j2JO j0 2JO TMAl Tcapl

  Htf P P P Qijj0 l TMCl 2Ljj0 þ þ LUTl cl2LH SPl i2IFS j2JO j0 2JO TMAl Tcapl

(38)

The first term on the right-hand-side of Eq. (32) is divided by the capital charge factor (CCF) and 365 days (a) to find the cost per day.

The first and second terms in Eq. (38) represent the transportation capital cost associated with the flow rate of hydrogen between the central electrolysis facility and the terminal (primary transportation), and the flow rate of hydrogen between the terminal and fueling station (secondary distribution), respectively. Transportation capital cost of electricity (TEC) is given by:

Facility capital cost

TEC ¼

TDC ¼

FCC þ TCC þ FOC þ TOC aCCF

(32)

The facility capital cost (FCC) is the sum of the capital costs of production, storage, and dispenser facilities. FCC is calculated by the number of facilities and the capital cost of the corresponding facility: FCC ¼

P P P P i2IWT j2J k2K m2M

þ

P P

i2IOE j2JO

NOE ij FCi þ

X X

NWT ijkm WCijm þ

X X

i2ITE

j2JO

NCE ij FCi

X X

i2ICE j2JO

NTE ij FCi þ

i2IFS

NFS ij FCi

(33)

j2JO

where WCijm is the capital cost of wind turbine i2IWT in region j2J at height m2M, and FCi is the capital cost of electrolysis i2ICE ; IOE , terminal i2ITE , and fueling station i2IFS , respectively. Specifically, WCijm consists of four types of cost: tower cost (TCim ), rotor cost (RCi ), drive cost (DCi ), and balance cost (BCijm ):

 X X X wo Mwc jj0 l þ Mjj0 l Ljj0 TMCl

(39)

j2J j0 2JO l2LE wo where Mwc jj0 l and Mjj0 l are binary variables that represent the electricity connectivity from the wind turbine to central wo electrolysis facility between regions. Mwc jj0 l or Mjj0 l is 1 if electricity is to be transported from the wind turbine in region j2J to the central electrolysis facility or on-site electrolysis facility in regions j0 2JO by transportation mode l2L, and 0 otherwise. wo Mwc jj0 l are Mjj0 l obtained using Eqs. (40) and (41).

Mwc jj0 l SN 

X

wc QijjEwc cj2J; j0 2JO ; l2LE 0 l  Mjj0 l BN

(40)

cj2J; j0 2JO ; l2LE

(41)

i2ICE

Mwo jj0 l SN 

X

wo QijjEwo 0 l  Mjj0 l BN

i2IOS

Facility operating cost WCijm ¼ TCim þ RCi þ DCi þ BCijm ci2I

WT

; j2J; m2M

(34)

The facility operating cost is the sum of the operating costs of each facility. FOC is calculated as follows:

Please cite this article in press as: Kim M, Kim J, An integrated decision support model for design and operation of a wind-based hydrogen supply system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.10.129

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P PP

FOC ¼

i2IWT j2J

þ

P P

i2IOS j2JO

k

X X

XEijk UOCi þ

XHc ij UOCi

X Xi2I j2J X X max THij UOCi þ NFS ij UOCi Fcapi CE

XHo ij UOCi þ

O

i2ITE j2JO

i2IFS j2JO

(42) where UOCi is the unit operating cost of facility i2I.

Transportation operating cost The total transportation operating cost is determined from Eq. (43):

Energy system scenarios

TOC ¼ FC þ LC þ MC þ GC

(43)

TOC is classified into costs of fuel (FC), labor (LC), and maintenance (MC), as well as general costs (GC). The fuel cost is X X X X

FC ¼

i2ITE

j2JO

X



j0 2JO

FPl

l2LH

FPl

2Ljj0 QijjHct0 l

! þ

FEl Tcapl Htf !

l2LH

X X X i2IFS j2JO j0 2JO

2Ljj0 Qijj0 l

(44)

FEl Tcapl

where FPl and FEl are the fuel price and fuel economy of the transportation mode l2LH , respectively. The labor cost is  ! 2Ljj0 DWl þ LUTl LC ¼ Tcapl SPl i2ITE j2JO j0 2JO l2LH ! Htf  Qijj0 l 2Ljj0 P P P P þ DWl þ LUTl Tcapl SPl i2IFS j2JO j0 2JO l2LH QijjHct 0l

P P P P

(45)

where DWl is the driver's wage for the transportation mode l2LH . The maintenance cost is MC ¼

X X X X i2ITE j2JO j0 2JO l2LH



X

MEl

l2LH

MEl

2Ljj0 QijjHct0 l

FEl Tcapl ! Htf

! þ

X X X i2IFS j2JO j0 2JO

2L Qijj0 l jj0

Hydrogen demand and wind potential data

where MEl is the maintenance expenses incurred for the transportation mode l2LH . The general cost GC ¼

i2ITE j2JO j0 2JO l2LH

P P P P i2IFS j2JO j0 2JO l2LH

GEl Htf

GEl

Qijj0 l

Tcapl

Korea has long been participating in international conventions for environmental protection, in particular action against climate change, and thus, the country has established and implemented comprehensive policies and programs to reduce carbon dioxide (CO2) emissions. Of the various government policies for reducing CO2 emissions, we adopt the Btu tax, which imposes tax on conventional primary energy source usage (e.g., crude oil and coal), since this taxation policy is one of the most efficient ways to reduce fossil fuel consumption, and thereby, CO2 emissions [28]. Based on our previous work [28], we set two different tax rates: 1.5 and 3.0 $/ mmbtu. Establishing a new energy system requires ensuring appropriate land use for energy production [29,30]. Thus, land for establishing wind farms is a crucial design factor in the WPHS system at Jeju Island, which has limited land area (approximately 1,850 km2). Under these circumstances, we assume three different Korean government regulations on land use for energy production: No regulation, Modest regulation, and Hard regulation, which mean that 100%, 50%, and 25% of the grassland may be used for energy production, respectively. By combining two different tax rates and three land use regulations, we generate six scenarios, are as shown in Table 2. Accordingly, the technologies listed in Table 1 are selected and integrated to form the optimal configurations of the WPHS system in the six generated scenarios.

(46)

FEl Tcapl

P P P P

the Korean provinces with the highest RES potential, in particular, wind power; 60% of the wind potential in Korea is concentrated at Jeju Island and it offshore area [27]. Accordingly, the Korean government announced a wind power utilization plan, which supplies 30% of the total electricity demand of Jeju Island (18,450 TJ annually) using wind power generation. In this section, we explain the versatility and capability of the proposed approach for modeling and designing the WPHS system. We refer to five activities and seven technologies, as summarized Table 1.

QijjHct 0l Tcapl

2Ljj0 þ LUTl SPRl !!

!! þ (47)

2Ljj0 þ LUTl SPRl

where GEl is the general expense incurred for the transportation mode l2LH .

To obtain the daily hydrogen demand of Jeju Island, this study adopts the estimation method used in our previous work [28]. Using various scenarios, we estimated the hydrogen demand of Korea in 2044 to be 4.6 to 7.6  106 ton/year under the tax rate 1.5 and 3.0 $/mmbtu and a modest oil price, respectively. In the current study, we calculate the hydrogen demand of Jeju Island by multiplying the population rate of Jeju Island

Table 1 e Major activities and selected technologies in the WPHS system of Jeju Island. Activity

Case study: the renewable hydrogen supply chain at Jeju Island

Electricity producing Hydrogen producing

We apply the proposed approach to the design problem of the WPHS system at Jeju Island, Korea. Jeju Island as the largest island is the southernmost part of Korea. Jeju Island is one of

Hydrogen storing Hydrogen dispensing Hydrogen transport

Technology Onshore/offshore wind farms Central/on-site electrolysis facilities Terminal Fueling station Tanker truck

Please cite this article in press as: Kim M, Kim J, An integrated decision support model for design and operation of a wind-based hydrogen supply system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.10.129

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Table 2 e Scenarios for the design of the WPHS system at Jeju Island, Korea. Sc. 1 2 3 4 5 6

Carbon taxation

Land use

Low Low Low High High High

No regulation Modest regulation Hard regulation No regulation Modest regulation Hard regulation

offshore wind farm has been installed already as well as its generation capacity. This study refers to wind speeds measured at 50 m above ground level [31]. We assume that the selection of offshore spots does not pose any other limitations. Shallow and transitional offshore locations are located 30 m and 60 m away from coast of Region 1, respectively [33]. Fig. 3 also shows the grassland area that can be used for onshore wind farm installation at Jeju Island [32].

Technology data (1.18%) to the previously estimated Korean hydrogen demand. The resulting hydrogen demand for the twelve regions of Jeju Island, subject to a tax of 1.5 and 3.0 $/mmbtu under medium oil pricing, is summarized in Table 3. Note that the regional demand is calculated based on the population ratio of each region. Fig. 3 presents the information of 14 (two offshore and twelve onshore) regions [31,32]. Note that the selected offshore spots are well known as locations with the highest wind potential, and thus, we select these spots as candidates for the offshore wind farms. Thus, the decisions related to offshore wind farms in this study pertain to whether the

Table 3 e Regional hydrogen demand of Jeju Island under two taxation scenarios (ton/day) [28].

R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12

Name

Low tax

High tax

Hangyeong Hallim Aewol Jeju Jocheon Gujwa Seongsan Pyoseon Namwon Seogwipo Andeok Daejeong Total

2.1 5.2 7.5 85.5 5.3 3.7 3.6 2.8 4.6 21.8 2.5 4.4 149.1

3.5 8.6 12.3 141.5 8.7 6.2 6.0 4.6 7.7 36.2 4.2 7.2 246.7

The technical parameters and cost information of the wind turbines are summarized in Table 4. We adopt the capital cost of wind turbines from an existing database [33,34]. In order to simplify this capital cost, we estimate the slope and intercept of the cost functions of the tower and balance through proportional expressions, as shown in Eqs. (35) and (36) in Section 'Optimization model'. In particular, the cost for the balance is related to not only the height of the tower but also the location of the wind turbine (i.e., onshore, shallow, or transitional offshore). Fig. 4 shows the power curve of the selected wind turbines [33]. Note that we use Weibull distribution to calculate the mean electric power by the wind turbine; Weibull distribution is a widely used statistics method to describe wind speed variation thereby assessing the wind power potential [35,36]. We set the Weibull shape factor and power law shear exponent to 2.1 and 0.1, respectively. The density of the wind turbines at a wind farm can be expressed by the distance between the turbines. Thus, the array loss of a wind farm can be expressed using the distance between the wind turbines [20]. For example, Fig. 5 illustrates the array loss estimations, wherein Model A adopts the conventional array efficiency using the inner boundary layer theory; Model B refers to a large wind farm correction model, calculated by GH WindFarmer and using eddy viscosity; Model C is the simple Park model; and Model D is an experimental model. We consider Model B, which is relatively suitable for systematic design and

Table 4 e Parameters used to estimate the costs of wind turbine technologies [33,34].

Fig. 3 e Regional average wind speed and available land (offshore and onshore) area for wind farm installation [31,32].

Rated power (kW) Diameter (m) Min. height (m) Max. height (m) Operating cost ($/MWh) Rotor cost (103$) Drive cost (103$) Cost coefficient (Eqs. (35) and (36)) ai bi ci,onshore ci,off-shallow ci,off-transitional di,onshore di,off-shallow di,off-transitional

Small

Large

1300 60 70 90 0.4 194 686

2500 100 110 130 0.4 695 1568

2.54 2.8 1.16 0.68 0.68 461 1981 3073

7.06 1.8 2.08 2.02 2 831 3903 5925

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Fig. 4 e Power curves of wind turbine technologies [34].

production, whereas the fueling station cost in Table 5 denotes the costs for storage and dispensing only (i.e., it excludes the cost of production) [37]. The operating cost was estimated based on the following assumptions: 33.3 kWh of electricity for 1 kg of hydrogen production, 66.5% of the efficiency of the electrolysis (i.e., central and on-site) system, and 68 atm (~1000 psig) of the operating pressure of the hydrogen technologies [38]. In this study, we consider only one type of mode, a tank truck, for hydrogen transportation, volcanic soil [3,8,39]. The technical parameters used for the capital and operating costs are summarized in Table 6 [3,6,8]. Note that while the processing efficiency of most technologies and activities is taken into account in this study, minor efficiencies which is negligible in comparison with other major activities, such as losses during storing and transporting hydrogen, are excluded [16,40]. Finally the delivery distances for the twelve regions of Jeju Island are measured as the real road distance (center to center). These distances are summarized in Table S1 in the Supplementary Information.

Results and discussion We implement the proposed model in the General Algebraic Modeling System (GAMS) environment and use the MILP solver of CPLEX.

Optimal network of the WPHS system

Fig. 5 e Array loss calculated using the distance between the wind turbines [20].

Fig. 6 summarizes the optimal configuration of the WPHS system considering different land regulations and a low energy tax (i.e., scenarios 1, 2, and 3). Fig. 6(a) shows the optimal configuration along with the flows pertaining to scenario 1. Region 1 would have a significant number of wind turbines (311 large turbines of height 115 m, and distance between two

optimization compared to the other models [20]. The array loss estimated using model B is changed from 6.5% to 19.0% according to the distance between the wind turbines, which ranges from 4D  4D (16D2) to 12D  12D (144D2), where D denotes the diameter of the blade of a wind turbine, as shown in Fig. 5. In this study, the cost information of the hydrogen technologies, including production, storage, and dispense, are estimated based on the results of economic analyses in the literature [3,6,8,37,38]. The obtained values are summarized in Table 5. The capital cost of the on-site production facility includes costs for storage and dispense as well as the cost of

Table 6 e Capital and operating costs of a tanker truck [3,6,8]. Capacity (ton/trip) Average speed (km/h) Mode availability (h/day) Load/unload time (h/trip) Driver wage ($/h) Fuel price ($/kg of H2) Maintenance expenses ($/km) General expenses ($/day) Fuel economy (km/kg of H2) Capital cost (106$)

4 55 18 2 23 3.4 0.1 8.2 80.5 0.5

Table 5 e Technical and economic parameters of the hydrogen facilities [6,8,38]. Central electrolysis

Capacity (ton/d) Capital cost (106$) Operating cost ($/ton)

Small

Large

5 25.4 27

15 49.2 27

On-site electrolysis

1 14.7 42

Terminal

Fueling station

Small

Large

5 13.3 15

15 25.8 15

1 5.1 15

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Fig. 6 e Optimal configuration of the WPHS system, and electricity and hydrogen flows for different land regulations under a low energy tax scenario.

turbines being 4D  4D) because of its wind speed, which is the highest of all regions except offshore regions. Although the best suitable site for establishing a wind farm is located in region 1, the amount of electricity produced in region 1 would be 5597.2 MWh/day, about 71.3% of the total electricity production. This is attributed to the lack of area needed for establishing the wind farm. The rest of the required electricity (28.7%) are generated from the wind farms established in regions 2 and 7. Note that region 12, which has the second highest wind speed, is not selected as the electricity production site owing to the high electricity transportation cost to eastern areas such as regions 6 and 7. For hydrogen production in scenario 1, 11 large central and 4 on-site electrolysis facilities need to be installed to satisfy the hydrogen demand. The amount of hydrogen produced by the central electrolysis facility would be 145.2 ton/day, accounting for 96.6% of the total hydrogen production. This may be attributed to the low production cost. Although region 4 requires the highest

hydrogen demand, no wind turbines would be erected here as its wind potential is the second lowest of all regions. Thus, electricity needs to be imported from regions 1, 2, and 7 to region 4, to produce hydrogen in region 4. This means that in order to meet the hydrogen demand in a region that has low wind potential, the transportation of electricity from another region having high wind potential must be cheaper than the establishment of a wind farm in that region. The optimal configuration and operation results pertaining to modest land regulation combined with low energy tax (i.e., scenario 2) are different from the results obtained for no land regulation. The number of wind turbines and the amount of electricity produced in region 1 significantly decrease owing to the land regulation, as shown in Fig. 6(b). The number of wind farm sites increases compared to scenario 1. Consequently, the number of on-site electrolysis facilities increases because hydrogen production from on-site electrolysis facilities in regions having a wind farm reduces the costs of electricity and

Please cite this article in press as: Kim M, Kim J, An integrated decision support model for design and operation of a wind-based hydrogen supply system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.10.129

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hydrogen transportation. These results show that as the land regulation is made more stringent, the amount of hydrogen produced from on-site electrolysis increases, but because of economies of scale, the amount of hydrogen produced by the central electrolysis facility still exceeds that generated by an on-site electrolysis facility. The hydrogen demand in regions 2 and 7 is fulfilled by local production. The results pertaining to stringent land regulation combined with low energy tax (i.e., scenario 3) appear in Fig. 6(c). In comparison with the results of scenario 2, the amount of electricity produced in regions 1 and 12 is halved. On the other hand, the number of electricity production sites increases. Thus, as the electricity supply system becomes more decentralized, regions 1, 2, 6, 7, 8, and 12 can fulfill their own demand using the hydrogen produced by their respective on-site electrolysis facilities. Furthermore, wind turbines tend to be erected at a considerable height despite the expense for the towers. Notably, the height of a wind turbine in region 1 would need to increase from 110 m to 120 m in order to generate

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more electricity. This result suggests that a trade-off occurs between the erecting of taller wind turbines at sites having high wind potential and that of shorter wind turbines at sites with low wind potential. The results of the optimal network for a combination of different land regulations and high energy tax (i.e., scenarios 4, 5, and 6) are summarized in Fig. 7. The findings for the high energy tax scenarios differ from those for low energy tax scenarios. First, the number of wind turbines, electrolysis facilities, and terminals increase owing to the increased hydrogen demand. Specifically, the amount of hydrogen produced by onsite electrolysis facilities increases because of the more decentralized nature of the electricity supply system. Furthermore, variations in wind farms characteristics, such as the height and farm layout, are clearly observed in the hightaxation scenario. As per Fig. 7, the height of the wind turbine towers would increase when the land regulation is tightened. The selection of wind farm layout also becomes narrower despite high array losses. For instance, the layout of a

Fig. 7 e Optimal configuration of the WPHS system, and electricity and hydrogen flows for different land regulations under a high energy tax scenario. Please cite this article in press as: Kim M, Kim J, An integrated decision support model for design and operation of a wind-based hydrogen supply system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.10.129

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wind farm in region 2 is 8D  8D in the scenario of no land regulation, but for the other two scenarios, the layout in region 2 changes to 5D  5D and 4D  4D, respectively. This means that the inefficient production of electricity in a region having high wind potential is more economical than the efficient production of electricity in a region with low wind potential. Notably, in scenario 6, we observe a conspicuous change in the configuration of the electricity supply system compared to other scenarios, as shown in Fig. 7(c). Thirty-five wind turbines (large-sized, of height 120 m, and spaced 12D  12D) would need to be established in the shallow offshore area because of the lack of grassland in regions with relatively high wind potential. In general, the cost of offshore wind turbines exceeds that of onshore wind turbines. This means that despite the expense associated with offshore wind turbines, it is more economical to establish off-shore wind farms compared to on-shore regions with low wind potential. Fig. 8 clearly shows the regional distribution of the produced electricity in different scenarios. First, as the land regulation becomes more stringent, the amount of electricity produced in regions 1 and 12 significantly decreases. Under a high energy tax and stringent land regulation (i.e., scenario 6), region 6 becomes the main electricity production site although region 1 has the highest wind potential. The amount of electricity produced in region 6 is 3.7 GWh/day, about 26.9% of the total electricity production. Furthermore, the paucity of area available for erecting wind turbines favors the inclusion of offshore wind turbines in the electricity supply system, the

amount of electricity produced from shallow offshore farms being 0.7 GWh/day, 5.0% of the amount in scenario 6.

Economic analysis of the WPHS system Cost breakdown The costs required to design and operate the WPHS system in different scenarios are summarized in Table 7. The capital cost of the wind turbines is the main cost component across all scenarios: its contribution ranges from 45.7 to 52.6% of the total network cost. When the land regulation becomes more stringent, the contribution of the capital cost of wind turbines increases. The land use limit in regions with high wind potential necessitates the erecting of additional wind turbines, even in regions with relatively low wind potential. Accordingly, land regulation and energy taxation determine the hydrogen demand, and thus, they are the main factors affecting the levelized cost of the WPHS system. Table 7 shows the breakdown in the costs of centralized and on-site facilities, assuming that the land regulation applies. It is clear that in the stricter land regulation condition, the higher proportion of the on-site facilities is observed. For example, the proportion of the capital cost of an on-site electrolysis system within the total network cost increases from 1.5% (scenario 1) to 6.5% (scenario 3). The calculated levelized cost of hydrogen is 8.9e10.1 $/kg of hydrogen. Note that the total cost is the network cost, which includes the capital and operating costs of not only

Fig. 8 e Regional electricity production per scenario (GWh/day).

Table 7 e Scenario-wise breakdown of the total cost of the WPHS system ($/day). Scenario Capital cost Wind turbine Central electrolysis Terminal On-site electrolysis Fueling station Hydrogen transportation Electricity transportation Operating cost Wind turbine Central electrolysis Terminal On-site electrolysis Fueling station Hydrogen transportation Total network cost Levelized cost($/kg of H2)

1

2

3

4

5

6

6.1  1.7  2.3  2.1  2.4  2.0  2.1 

105 105 104 104 105 103 104

6.7 1.6 2.4 7.0 2.2 2.0 9.1

      

105 105 105 104 105 103 103

7.0  1.6  2.2  1.1  2.0  1.9  1.4 

105 105 105 105 105 103 104

1.0 2.5 4.0 7.7 3.7 3.3 2.4

      

106 105 105 104 105 103 104

1.1  2.9  3.8  1.0  3.7  3.3  3.7 

106 105 105 105 105 103 104

1.3 2.5 3.6 1.6 3.5 3.1 2.5

      

106 105 105 105 105 103 104

3.2  3.9  2.2  1.8  2.2  5.4  1.3  8.9

103 103 103 103 103 103 106

3.2 3.6 2.0 6.3 2.0 5.7 1.4 9.3

      

103 103 103 102 103 103 106

3.2  3.4  1.9  9.9  1.9  5.0  1.4  9.6

103 103 103 102 103 103 106

5.3 6.2 3.5 6.9 3.5 8.7 2.2 8.9

      

103 103 103 102 103 103 106

5.3  6.1  3.4  9.0  3.4  8.8  2.3  9.4

103 103 103 102 103 103 106

5.3  5.7  3.2  1.4  3.2  8.2  2.5  10.1

103 103 103 102 103 103 106

Please cite this article in press as: Kim M, Kim J, An integrated decision support model for design and operation of a wind-based hydrogen supply system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.10.129

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major facilities (e.g., wind farm and hydrogen production facility) but also all the required infrastructure (e.g., a newly installed electricity network and hydrogen fueling station). Accordingly, the levelized cost calculated in this study seems to be higher than the corresponding values in the literature [10,13,15]. The other interesting result in Table 7 is that the levelized cost increases as hydrogen demand increases (for instance, it is 9.6 $/kg of hydrogen in scenario 3 and 10.1 $/kg in scenario 6). Generally, system economics tend to improve with larger demand owing to economies of scale. However, such an effect is not observed in our case results. Unlike other renewable sources (e.g., solar), an unlimited amount of electricity cannot be generated at the minimum cost because of the land use limitation. Accordingly, to meet the hydrogen demand, additional electricity would need to be generated by other regions with relatively low wind potential, thereby increasing the expense in spite of the effect of economies of scale.

Sensitivity analysis To investigate the parameters which have the large effect on the economics of the WPHS system, we carry out the sensitivity analysis of the major cost drivers on the total network cost. We considers ±30% changes of seven parameter

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discussed in this Section. As shown in Fig. 9, the levelized cost of hydrogen is the significant sensitive to the wind speed, the interest rate, and the unit cost of the wind turbines across all the scenarios. This is because the cost structure of the WPHS system is completely dominated by the capital costs, in particular the wind turbine (See Table 7). It is also observed that the effect of the change of the wind speed tends to increase according to the land regulation (i.e., the lack of available land). For example, while the interest rate is the biggest cost-driver in the case of no land regulation (scenarios 1 and 4), the wind potential becomes the most crucial factor determining the economics of the WPHS system in the hard land regulation (scenarios 3 and 6). On the other hand, the effects of the change in parameters related to the centralized hydrogen supply system (i.e., central electrolysis, terminal, and fueling station) are not sensitive to the total network cost in comparison with the cost drivers related to the wind turbine. Finally Fig. 9 shows that the effect of the selected parameters on the total network cost varies considerably in each scenario.

Competiveness of the WPHS system In the previous section, we solved the design problem of the WPHS system, using the optimization model discussed in

Fig. 9 e Sensitivity analysis (±30%) of the major parameters in (a) scenarios 1; (b) scenario 2; (c) scenario 3; (d) scenario 4; (e) scenario 5, and; (f) scenario 6. Please cite this article in press as: Kim M, Kim J, An integrated decision support model for design and operation of a wind-based hydrogen supply system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.10.129

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Section 'Optimization model'. We now propose three new modified optimization models to verify the capability of our integrated approach with regard to decision-making on the design of an entire WPHS system. While the base model in Section 'Optimization model' addresses all the major decision variables (i.e., the location and layout of wind farms, types of wind turbines, and configuration of the hydrogen supply system), the new optimization models presented in this section determine the decision variables. Notably, one of the major decision variables related to wind-power strategy is fixed. As shown in Fig. 10(a), we minimize the total network cost of the WPHS system after we assume the location of the wind farms (i.e., in R1, R2, and so on, or transitional offshore). This optimization model is referred to as alternative model #1. Accordingly, we execute alternative model #1 separately 84 times to calculate the total required cost (TDC1) for the WPHS system in a given region. We consider 14 regions under 6 different scenarios (see Table 2 and Fig. 3). Similarly, we define alternative model #2 to calculate the minimized cost (TDC2) with given wind turbine specifications (turbine size and tower height), and alternative model #3 for a given wind farm layout. The numerical results of the three optimization models are summarized in Tables S2eS4 in the Supplementary Information. We discuss the specifics of the comparison between the three models as well as the base model.

The levelized cost of hydrogen in this analysis ranges from 8.9 to 13.1 $/kg of hydrogen. Fig. 10(b) presents the levelized cost of hydrogen calculated by the three alternative models. When the location of a wind farm is fixed in a certain region, the levelized cost ranges from 9.85 to 13.1 $/ kg of hydrogen, which is far higher than the range obtained using the base model in scenarios 1e6 (9.85e13.1 $/kg of hydrogen; marked in gray color in Fig. 10(b)). This means that the selection of the wind farm location is one of the most crucial factors determining the overall economics of the WPHS system. Fig. 10(b) also reveals that the final costs of alternative models #2 and #3 are higher than that of the base model. In particular, it is worth noting that the lowest cost among all the alternative models is higher than that of the base model. This means that the integrated approach proposed in this study can provide a global solution, which cannot be explored using the three alternative models separately. Therefore, it is obvious that the integrated approach not only provides a convenient design platform for the WPHS system, by unifying the separated tasks (wind turbine selection, wind farm layout design, and onshore/offshore wind farm location) but also allows one to obtain a systematically comprehensive solution unlike the results for distinctive tasks.

Fig. 10 e Comparison of the alternative models and economic effects of the integrated approach for designing the WPHS system.

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Conclusions This study proposed an integrated approach for the design and analysis of a WPHS system. To achieve this goal, we first developed a new optimization model capable of dealing with a wide range of issues pertaining to the WPHS system, from wind turbine (size and height) selection to the wind farm layout and the strategic (onshore/offshore) allocation of wind farms. We then applied the proposed approach to the design problem of the WPHS system for the road transportation sector of Jeju Island, Korea. Furthermore, we generated six scenarios of the hydrogen economy and investigated the effect of external factors related to economics of the WPHS system, such as the uncertainty of hydrogen demand and government policy regarding area limitations for the establishment of wind farms. The major findings from the case study are as follows.  The type of wind turbine and wind farm layout are sensitive to not only wind potential and hydrogen demand but also government regulation on land use for energy production.  Onshore allocation is preferable to offshore wind farms owing to expensive capital cost of the latter despite their superior wind potential.  Regarding the design of the WPHS system, a centralized hydrogen production system provides better economics than a regionally distributed production system, owing to economies of scale.  The wind speed, interest rate, and the capital cost of wind turbines are the crucial factors determining the economics of the WPHS system and their impacts vary according to scenarios, in particular the land use regulations for energy production.  The approach proposed in this study is useful to obtain a comprehensive solution on the design and operation of the WPHS system, as it integrates issues pertaining to scale and thus improves the decision-making ability. Note, however, that even though onshore electricity generation and a centralized hydrogen supply system are identified as the most suitable options for a hydrogen supply chain system at Jeju Island, the results might change depending on the applicable land regulation and hydrogen demand. Furthermore, the comparison of the integrated and individual optimizations shows that the location and height of the turbines as well as layout of the wind farm are also important factors with regard to the total network cost; this cost may reduce by 0.3e29.1% compared to individual optimization of each factor. Finally, the results for the WPHS system are liable to change according to the input data. However, the proposed approach is generic enough to be applied to the WPHS system in different regions and countries. We proposed an integrated decision-making platform to help design and operate the WPHS system, and analyze its major cost drivers. While the proposed approach is undoubtedly useful, further research based on our results may proceed to address the practical barriers against the establishment of the WPHS system: for example, the connection to existing

15

facilities, the prediction of precise hydrogen demand, limited government investment, trade-offs with the sustainability of the surrounding ecosystem, and the consideration of hydrogen safety and environmental issues.

Acknowledgement This research was supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (NRF-2014R1A1A2058904).

Appendix A. Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.ijhydene.2016.10.129.

Nomenclature Sets i j k l m

the the the the the

Subsets i2ICE i2IFS i2IOE i2ITE i2IWT j2JO j2JS j2JT l2LE l2LH

central electrolysis facility hydrogen fueling station on-site electrolysis facility hydrogen terminal wind turbine onshore shallow offshore transitional offshore electricity transportation hydrogen transportation

set of facilities set of regions set of wind farm layouts set of transportation modes set of height of wind turbine

Parameters available area in region j2JO , km2 AAj BCijm balance cost of wind turbine i2IWT in region j2J at height m2M, $ drive cost of wind turbine i2IWT , $ DCi T DEj total hydrogen demand in region j2JO , ton/d driver's wage for the transportation mode l2LH , $/h DWl capital cost of facilities i2ICE ; IOE ; ITE and IFS , $ FCi fuel economy of the transportation mode l2LH , km/ FEl kg of H2 FPl fuel price of the transportation mode l2LH , km/kg of H2 GAj grassland area of region j2JO , km2 GEl general expenses incurred for the transportation mode l2LH , $/d height of wind turbine m2M, m HHm delivery distance between regions j2J and j0 2J, km LLjj' MEl maintenance expenses incurred for the transportation mode l2LH , $/km

Please cite this article in press as: Kim M, Kim J, An integrated decision support model for design and operation of a wind-based hydrogen supply system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.10.129

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OAik OPijm PCðvÞi RDi RCi SPl TCim TEðvÞijm WCijm WPðvÞjm WSjm WSj50 Fcapmin i Pcapmax i Pcapmin i Scapmax i Scapmin i TMCl Tcapl UOCi ai bi cij dij fk qk hi ujm ε GðxÞ a b g d vin vout K BN SN CCF

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 6 ) 1 e1 7

occupied area of a wind turbine i2IWT present in wind farm layout k2K, km2 output power from a wind turbine i2IWT in region j2J at height m2M, MWh/d power curve for each of wind speed v of wind turbine i2IWT rotor diameter of wind turbine i2IWT , m rotor cost of wind turbine i2IWT , $ average speed of hydrogen transportation mode l2LH , km/h tower cost of wind turbine i2IWT at height m2M, $ turbine energy of wind turbine i2IWT in region j2J at height m2M for each of wind speed v, MWh/d capital cost of wind turbine i2IWT in region j2J at height m2M, $ Weibull probability distribution for each of wind speed v in region j2J at height m2M wind speed in region j2J at height m2M, m/s wind speed measured at a height 50 m in region j2J, m/s maximum storage capacity of fueling station i2IFS , ton/d maximum production capacity of central electrolysis facility i2ICE and on-site electrolysis i2IOE , ton/d minimum production capacity of central electrolysis facility i2ICE and on-site electrolysis i2IOE , ton/d maximum storage capacity of terminal i2ITE , ton/d minimum storage capacity of terminal i2ITE , ton/d capital cost of transportation mode l2L, $ transport capacity of hydrogen transportation mode l2LH , ton/d unit operating cost of facility i2I, $/unit slope of the tower cost function according to wind turbine i2IWT intercept of the tower cost function according to wind turbine i2IWT slope of the balance cost function according to wind turbine i2IWT in region j2J intercept of the balance cost function according to wind turbine i2IWT in region j2J array loss dependent on wind farm layout k2K coefficient depending on wind farm layout k2K energy conversion efficiency of electrolysis i2ICE ; IOE , % Weibull scale factor in region j2J at height m2M Weibull shape factor gamma function network operating period, d/yr storage holding period, d land regulation, % power law shear exponent cut-in wind speed, m/s cut-out wind speed, m/s dimensionless shape parameter big number small number capital charge factor

Continuous variables demand in region j2JO satisfied by hydrogen DIj imported from the other regions, ton/d

DLj Ewc Qijj 0l

Ewo Qijj 0l

Hct Qijj 0l

Htf

Qijj0 l

SH ij XEijmk

XTE j XHc ij XHo ij FCC FOC TCC TDC TEC THC TOC

demand in region j2JO satisfied by local hydrogen production, ton/d amount of electricity transported from region j2J to central electrolysis i2ICE in region j0 2JO by transportation mode l2LE , MWh/d amount of electricity transported from region j2J to on-site electrolysis i2IOE in region j0 2JO by transportation mode l2LE , MWh/d amount of hydrogen transported from region j2J to terminal i2ITE in region j0 2JO by transportation mode l2LH , ton/d amount of hydrogen transported from region j2J to fueling station i2IFS in region j0 2JO by transportation mode l2LH , ton/d amount of hydrogen stored in i2ITE in region j2J, ton/d amount of electricity generated from wind turbine i2IWT in region j2J by layout of wind farm k2K at height m2M, MWh/d total amount of electricity generated in region j2J, MWh/d amount of hydrogen generated from central electrolysis facility i2ICE in region j2JO , ton/d amount of hydrogen produced from on-site electrolysis facility i2IOE in region j2JO , ton/d facility capital cost, $ facility operating cost, $/d transportation capital cost, $ total daily cost, $/d capital cost of electricity transportation, $ capital cost of hydrogen transportation, $ hydrogen transportation operating cost, $/d

Integer variables number of central electrolysis facility i2ICE in region NCE ij j2JO FS Nij number of fueling station i2IFS in region j2JO OE Nij number of on-site electrolysis facility i2IOE in region j2JO NTE number of hydrogen terminal i2ITE in region j2JO ij NWT number of wind turbine i2IWT in region j2J having ijkm wind farm layout k2K at height m2M Binary variables 1 if electricity is to be produced from wind turbine Rjjkm i2IWT in region j2J with wind farm layout k2K at hub height m2M, 0 otherwise 1 if electricity is to be transported from wind turbine Mwc jj0 l in region j2J to central electrolysis facility in regions j0 2JO by transportation mode l2L, 0 otherwise. 1 if electricity is to be transported from wind turbine Mwo jj0 l in region j2J to on-site electrolysis facility in regions j0 2JO by transportation mode l2L, 0 otherwise. 1 if hydrogen is to be exported from region j2JO to Vij fueling station i2IFS in other regions, 0 otherwise 1 if hydrogen is to be exported from region j2JO to Wij fueling station i2IFS in other regions, 0 otherwise 1 if the hydrogen is to be transported from region Yijj'l j2JO to fueling station i2IFS in region j0 2JO by transportation mode l2LH , 0 otherwise

Please cite this article in press as: Kim M, Kim J, An integrated decision support model for design and operation of a wind-based hydrogen supply system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.10.129

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 6 ) 1 e1 7

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Please cite this article in press as: Kim M, Kim J, An integrated decision support model for design and operation of a wind-based hydrogen supply system, International Journal of Hydrogen Energy (2016), http://dx.doi.org/10.1016/j.ijhydene.2016.10.129