6t International Conference on Hydrogen roduction UOIT Osawa, Ontario, Canada OTIMAL OERATION OF AN ENERGY HUB NETWORK IN THE CONTEXT OF HYDROGEN ECONOMY 1, 2, 3* Maroufmasat Azade, 2 Fowler Micael, 2 Elkamel Ali, 3 Sattari Kavas Sourena 1 Sarif University of Tecnology, Energy Engineering department, Azadi Street, Teran, Iran 2 University of Waterloo, Department of Cemical Engineering, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada 3 Sarif University of Tecnology, Sarif Energy Researc Institute, Teymouri Blvd., Tarast, Teran, Iran ABSTRACT *Corresponding Autor E-mail: azade.masat@gmail.com Tis paper aims to develop a generic matematical model for te optimal energy management of future communities were ydrogen is used as an energy vector to carry and store energy. Eac ub is an energy model of one part of a given community, including a commercial center, residential complex, a scool, or even a fuel station. Te study investigates te optimal operation of different energy conversion and storage tecnologies in order to meet te demand of energy. Te results sowed tat te optimum size of electrolyser and ydrogen tank for supplying te ydrogen demand in te energy ub network is two 290 kw electrolysers and four 30 kg tanks, respectively. Te average daily strike price of electricity by wic te electrolyser operates is 0.036 $ kw -1 and fails to operate wen te average ourly Ontario electricity price is iger tan 0.13$ kw -1. Te levelized cost of ydrogen produced by ydrogen fuelling station is an estimated to be 6.74 $ kg -1. Moreover, te optimal operation of energy conversion and energy storage tecnologies witin eac ub and te optimal interaction between energy ubs wit in te network are also investigated. Keywords: Hydrogen economy, Smart energy network, Mixed integer programming, Operational and design optimization, Electrolyzer, Energy ub. INTRODUCTION Te use of distributed generation is expanding wit te increase in renewable energy generation and a move towards developing a smart energy network, suc as migt be used witin a community (Akorede et al., 2012). Also, te energy demands of a community must be considered in a more olistic and compreensive manner, tus electrical, eat and transportation demands must be taken in to consideration. Mainly wit te emergency of ydrogen fuel cell veicles, plug-in ybrid veicles, and electric veicles, transportation energy demand must be considered wit an overall energy system. Communities and networks of facilities wit distributed generation tecnologies present different energy flow problems. Serious consideration to te management of energy must be analyzed wen using different energy sources suc as natural gas, electricity, eat, and ydrogen. Energy vectors witin a network can be excanged and perform oter key functions, suc as energy arbitrage and storage. A combined eat and power (CH) system, for example a turbine operating on natural gas, can simultaneously produce electricity and eat wic an electrolyzer operating witin te network can satisfy te demands of bot ydrogen transportation fleets and tat of eat. Wit tis in mind, a key aspect to create an affordable and cleaner energy system is te development of an integrated, multi-node system wit multiple energy vectors. In tis system, electricity, fuel, eat, cooling, and transportation, optimally interact wit one anoter at various levels; e.g., in a local district, city or region. Tis gives an opportunity to improve te tecnical, economic and environmental performance of te system in comparison wit traditional energy systems were different sectors are implemented independently. Tis improvement can take place at different stages, suc as at te operational and te planning levels (Mancarella & ierluigi, 2014; Cicco & Mancarella, 2009). Terefore, an integrated study of energy systems is required to properly take into account energy conversion tecnologies and possible energy storage of te different energy carriers to reac a more efficient level of system operation. 1
6t International Conference on Hydrogen roduction UOIT Osawa, Ontario, Canada Traditionally, energy resources suc as natural gas and electricity ave been used independent of eac anoter. However, in te recent years a growing appeal to supplement te isolated use of energy for an integrated form of energy usage to improve efficiency and reduce environmental impact as arisen. Energy integration is increasingly sougt after particularly in te wake of two core callenges: te rise of energy demand and environmental concerns (IEA, 2014). Te move towards a ydrogen economy, wereby ydrogen is used for transportation and utility-scale energy applications is of great deal of interest to bot industry and te academe. Hydrogen is favoured since as a renewable source of energy, it is limitless and dependable in supply particularly for te generation of electricity, wile still possessing te ability to arness energy from myriad resources akin to fossil fuels. Moreover, ydrogen may act as an energy vector for applications related to transport reduces environmental adversities suc as greenouse gas emission and urban pollution (Hajimiraga, et al., 2007). In addition, ydrogen as an energy carrier is desired even from te outlook of a power grid management and competitive markets for its great energy storage potential and its cost difference between peak and low price ours, respectively (Yang, 2008). To clarify, ydrogen can be produced troug te- ceaper off-peak power and can be consumed by ydrogen veicles or it can be stored and converted to electricity wen te price of power is ig (Felder et al., 2006). However, tere are still callenges to setting up te ydrogen economy in te fields of production, distribution, storage and consumption (Crabtree et al., 2004). Given te many potential advantages of ydrogen as an energy carrier, tere is a substantiated basis to furter investigate and study ydrogen use. A possible future acievement of te ydrogen economy would exist in te context of integrated energy systems were all te energy carries are networked and ave synergy among one anoter. Energy ubs are implemented to simultaneously monitor integrated energy sources. An energy ub acts as an interface between different energy sources, suc as electricity, natural gas, eat, and ydrogen using energy storage and conversion tecnologies (Geidl et al. 2007; 2007). Eac ub contains various energy conversion tecnologies, suc as transformers, combined eat and power systems, electrolyzers, as well as fuel cells, and energy storage tecnologies, suc as batteries, ydrogen and potentially compressed air. Since different energy conversion tecnologies inside te ub ave different caracteristics and costs, an optimal network including energy storage and energy excange system sould be studied. Te proposed energy ub networks consider te optimal contribution and excange of eac energy source and energy carrier in order to meet te required demand at minimum cost. Herein te modeling and optimization of a network of energy ubs, wit an empasis on developing ydrogen infrastructure and urban energy systems, is studied so tat a new approac for energy management of te future of urban energy systems. Due to te integrated nature of energy ubs, tere is a potential to model and optimize different energy flows simultaneously. Te production and use of ydrogen are considered in urban energy systems, were, electricity, natural gas, as well as solar energy are te main energy sources and electricity and ydrogen, and termal energy are demands. Te model of an energy ub found in tis paper utilizes a framework created by Geidl (2007) wile focusing specifically on te ydrogen production and utilization, and distribution in te smart energy network. Te remaining sections of tis paper are organized as follows. In te 2 nd section, te problem and optimization goals are described. Next, te matematical modeling approac to optimize te network of ubs is described. In te 4 t section, te model is applied to a case study of four energy ubs working togeter. In te final sections, te results and conclusions are presented. ROBLEM DESCRITION An energy ub network is comprised of different energy ubs tat are connected to eac oter troug te excange of energy in te form of te district eat, electricity, as well as ydrogen. Te ubs can be connected by different distribution networks and caracterized by teir electrical and termal demand profiles. In eac ub, a variety of energy conversion tecnologies including tose fed by natural gas suc as combined eat and power systems, Boilers or tose fed by renewable energy resources suc as solar potovoltaic, solar termal collectors are exploited to best meet energy demands. Unique to tis analysis is te consideration of a ydrogen demand and an energy ub of ydrogen refuelling station generating ydrogen from alkaline electrolysers to meet te ydrogen demand of industrial forklift and ligt duty ydrogen veicle.. In tis study, te autors investigate ow to optimally design a ydrogen refuelling station in an urban area were energy ubs can excange teir surplus energy wit one anoter. Te 2
6t International Conference on Hydrogen roduction UOIT Osawa, Ontario, Canada generation of ydrogen in a distributed fasion is advantageous as it eliminates te need for pipeline or tanker truck distribution, wile making use of existing electrical distribution system infrastructure. Hydrogen generated in tis manner also eliminates inefficiencies associated wit liquefaction and storage (and te associated boil-off). In order to optimally design te ydrogen refuelling station, tis study addresses te following points: te optimum size of te electrolyser and ydrogen tank for meeting ydrogen demand in te energy ub network; te capacity factor of te electrolyser; te optimal operation of energy conversion and energy storage tecnologies witin eac ub; te optimal interaction between energy ubs in te network; te strike price of electricity were te electrolyser operates; and te levelized cost of ydrogen. Accordingly, te superstructure of te proposed model is presented in te figure 1. Hourly energy demand, market and tecnical information, and te properties of te energy storage and conversion systems are inputs to te model. Te objective function of te model is to reduce annual operational and maintenance cost and te capital cost of te ydrogen fuel station according to pysical and termodynamic constraints. Te multi period mixed integer dynamic optimization model will be developed in te General Algebraic Modeling Software (GAMS) and te simulation is conducted over a year wit ourly resolution. Te number of eac tecnology cosen is te decision variables wile te operating variables are related to ow eac energy ub is run. Te result of tis optimization is an optimal operation plan, te optimal design of te ydrogen refuelling ub and an estimated of greenouse gas emissions. MATHEMATICAL MODEL Objective Function Te objective function seeks to minimize te total operational and maintenance costs of te network of energy ubs and te capital cost of Energy Hub 4, ydrogen refuelling station, as expressed in equation 1. Te cost of eac energy ub includes all costs related to operational, maintenance, fuel, minus te revenue of eac energy ub for te sale of surplus energy to oter ubs, as presented in equations 2-4. Were, f c j, are te unit costs of operation and fuel at ub j, respectively. s,i,j,m, and E and s,j,m, denote te operating loads of eac tecnology and te vector of input energy carrier, respectively. Te total capital cost related to te ydrogen fuel station, energy ub 4, is presented as te sum of te cost of its electrolyzers and ydrogen storage tanks in equation 5. Te capital recovery factor, CRF, is employed in equation 6 to relate tis total cost to te considered time orizon of n years. Te annual cost incurred for installing a single electrolyser unit is sown in equation 7 were, c is te capital cost of a single electrolyser wic is calculated in equation 8 and elec,h 2,rated elec,unit n is its rated ydrogen production. Since te electrolyser s lifetime is sorter tan te project s study period, te replacement cost c will be incurred on te elec,rep,unit c op j 't n year of te operation of te plant as sown in equation 8 (eng, 2013; Amos, 1998; Saur,2008). Te Cemical Engineering lant Cost Index (CECI) is used so tat te conversion of te cost from te base year to te study year of te project is possible. s s s CECI 2013 4 Z ( Costop Cost fuel Income ).Cost cap. CRF s CECI base year (2) Cost E c Cost s op op s, i, j, m, j m i j c (3) s f fuel s, j, m, j, m j (1) 3
6t International Conference on Hydrogen roduction UOIT Osawa, Ontario, Canada Figure 1: Te superstructure of te proposed model. s sell Income Ts, i, m, price (4) s, i, m, m i Cost N c NS c 4 cap cap. stor Cap 4, elec elec 4,H2 H2 (5) n r.(1 r) CRF n (1 r) 1 c cap elec celec, unit celec, rep, unit c 0.6156 elec, unit elec, H2, rated c elec, rep, unit 224490.(2 n ).5 c (1 r) elec, unit ' n (6) (7) (8) Constraints Te objective function defined by equations 1 troug 8 is constrained troug te following equations based on energy balances trougout te system as well as losses from energy conversion. Energy balance of energy ub Te energy balance among demands and input energy flow at eac unit of te network is given in equation 9. L C.I. b. Q Q c dis s, i, m, s, i, j s, j s,j, m, s, i, m, s, i, m, Were, L i corresponds to te vector of energy production by eac ub, denotes te vector of input s,j,m, energy carrier and C is a coupling matrix. Its array can be te zero, individual efficiencies or te s,i,j combination of energy efficiencies. Assuming te carging and discarging status of te system, te output energy carriers can be calculated according to te following equations. M ( t) M ( t 1) A c Q c ( t) A dis Q dis ( t) M (10) s, i, m, s, i, m, s, i s, i, m, s, i s, i, m, s, i, m, (9) 4
6t International Conference on Hydrogen roduction UOIT Osawa, Ontario, Canada L D T (11) s, i, m, s, i, m, s, i, m, Were D denotes te individual energy ub demand, and Ts,i,m, is related to te surplus energy sold s,i,m, to oter energy ubs in te urban energy network. Network Balance Equation In equation set 12, te balance of energy witin te network is defined. Teses equations represent te energy excange in te network of energy. T Tr for i elec, eat, H s, i, m, sk, i, m, 2 ks Tr. f (d ) for i, j elec, eat, H network s, j, m, ks, i, m, ks 2 ks Were, sk, i, m, Tr,,,, Tr is te energy flow wic excange between energy ub s and ub k, ks ks i m (12) f(d ) is te network energy loss between ubs k and s as a function of teir distance, d ks and s, j, m, is te amount of energy delivered to ub s from oter ubs witin te network per unit time. Energy flow limit In order to guarantee tat te flow of electrical and termal energy and te flow of ydrogen and natural gas can be andled by te proposed infrastructure of te network of energy ubs, it is necessary to define energy flow limits. Te flow limits at eac energy ub for eac energy patway, is defined in equation 13 below. min s, j s, j, m, s, j Energy Network limit Regarding te network constraints, te energy flow between eac energy ub must stay between a minimum and a imum value, as sown below in equation 14. Tis ensures tat te flow of energy simulated ere can be andled by te network infrastructure. Tr Tr Tr (14) min sk, i ks, i, m, sk, i Energy conversion tecnology limit Eac energy conversion tecnology operates witin a range of efficiencies. In equation 15, te conversion tecnology used for eac energy stream at eac energy ub is defined as between a imum and minimum. E E E (15) min s, j s, i, j, m, s, j Energy storage and power excange limits Tese constraints are proposed on te capacity and te excange energy of eac energy storage tecnology in equation 16. min M M ( t) M m, t (16) m m m Q Q ( t) ( t). Q dis dis, min dis dis dis, m m m m m Q Q ( t) ( t). Q c c, min c c c, m m m m m c,min dis,min c, dis, Were, tecnology and Q,Q,Q, Q m m m m are te minimum and imum energy flows troug te m t storage M,M are te minimum and te imum levels of te energy stored in te m t min m m (13) 5
6t International Conference on Hydrogen roduction UOIT Osawa, Ontario, Canada storage system. Since te energy storage tecnology cannot be carged and discarged simultaneously, dis c two binary variables ( ( t), ( t) ) are introduced for eac storing tecnology at eac time t in equation m m 17. dis c ( t) ( t) 1 m, t (17) m m Sustainability constraint Te equation below illustrates tat te stored energy at te beginning and end of te eac period are equal in magnitude. Te time tat te energy is stored is unique for eac energy storage tecnology, owever. For example, te most optimal time period for te eat storage tank may be as sort as 1 day wile ydrogen storage may be optimally stored for as long as 1 year. M M (18) s, i,0 s, i, T Energy Conversion Tecnology Modeling In order to calculate te amount of ydrogen tat is produced by te electrolyzer, te relation below, equation 19, is used.. E (19) s,elec H, m, El s,elec, H 2 2 FC, m, Were, E is te ydrogen produced by te electrolyzer in kg -1, s,elec,h 2 FC,m, is te power s,elec H 2,m, consumed to produce a ydrogen and wic can be obtained from equation 20. Were, elec, H2, rated El is te conversion rate for ydrogen production from input power, E rated is te rated input power of electrolyser and n rated capacity of electrolyser in kmol -1 wic is multiplied by te molecular weigt of ydrogen to convert kmol -1 into kg -1 wic as a value of two (Sarif, 2014). E f n s,elec H, m, 2 s,elec, H2,, 2 FC m elec, H2, rated elec, rated Te actual power required by te compressor to compress te ydrogen is calculated as (20) comp in equation 21. Were, ηcomp is te isentropic efficiency of te compressor and W comp, t is te isentropic work consumed by te compressor. Tis isentropic work, or required energy, is calculated by equation 22 (eng, 2013). 2 E comp s,elec, H2 FC, m, W comp, t comp (21) W k1 k k p out comp, t zrt in 1 2( k 1) pin (22) Were, is te compressibility factor of ydrogen as a function of temperature and pressure; k is te eat capacity ratio of te compression gas; p out and p in are te incoming and outgoing pressure of ydrogen, and R is te universal gas constant. In order to calculate te amount of termal energy produced by a boiler or furnace, equation 23, is considered; were boiler is te estimated constant termal efficiency of te boiler, LHVNG is te low eat value of natural gas, is te amount of natural gas consumed and E is te eat produced by te boiler. s,ng boiler,m, s,ng boiler,eat,m, 6
6t International Conference on Hydrogen roduction UOIT Osawa, Ontario, Canada..LHV E (23) s, NGboiler, m, boiler NG s, NGboiler, eat, m, Te overall efficiency of a cogeneration system depends on te type of prime mover, its size, and te temperature at wic te recovered eat can be utilized. Also, te efficiency of te system depends on te condition and operating regime of te cogeneration unit. Te power and termal energy produced by cogeneration systems are obtained from equation set 24, below were constant efficiencies are assumed for all operating conditions. In addition, as illustrated by Reider et al. (2014) a fixed ratio of power and eat production is implemented...lhv E s, NGCH, m, CHelec NG s, NGCH,elec, m,..lhv E s, NGCH, m, CHTermal NG s, NGCH,eat, m, A linear matematical model, outlined in Equation set 25, is employed to determine te amount of energy arvested from te potovoltaic solar panels wereby te output power is proportional to te area ( A s, V ), te efficiency of te panel ( is te V-solar binary ), and te ourly solar irradiation ( I ). Here,, V variable wic is 1 if its operating temperature is witin te range of -40 ⁰C and 85 ⁰C, oterwise it is assumed te value of zero. Also, A is te imum area of te V. s, Solarelec, m, V s, solar s,sun,elec, m, s, Solarelec, m, s, V A A s, V s,v.. E I. A s,v In addition to te electrical contribution of te potovoltaic cells, termal solar energy can also be arnessed. Te termal energy generated by te solar collector is presented ere in equation 26.. E (26) s, Solareat, m, SC s,sun,eat, m, I. A s, Solareat, m, s, collector SC is te efficiency of collector assuming constant in all operating period, and As, collector Were, s solar (24) (25) is te collector area. Using tese 26 equation sets, a case study is analyzed wereby 4 energy ubs are tied togeter in an urban energy network. In tis case study, te optimal size of ydrogen refuelling station and te operating conditions of all energy ubs are determined by minimization of total cost for all te energy ubs. CASE STUDY Employing te model described in te preceding section, energy ubs are considered as part of an urban smart energy network in te Canadian province of Ontario. In Figure 2, below, te four energy ubs are illustrated; Energy ub 1 is a scool wic as a 530 kw boiler and solar V of 50 m 2. Energy ub 2 is a food distribution center wic as a 300 kw CH, a 147 kw boiler, eat storage tank, as well as a 100 m 2 potovoltaic system. Energy ub 3 is a residential complex wic uses a 100 kw CH, a 300 kw boiler, a 80 m 2 solar collector, and a 80 m 2 solar V to generate eat and electricity for te building. Finally, Energy ub 4 is a ydrogen refuelling station for ydrogen fuel cell veicles and forklifts wic utilizes electrolyser to provide ydrogen fuel in order to supply te ydrogen demand of oter ubs. Te size of electrolyser and ydrogen storage tank is unknown wic is optimally determined by te optimization model. Te input energy flow to eac ub can be in te form of natural gas, solar energy, grid electricity, eat or electrical and termal energy from neigbouring ubs, as sown in Figure 2. Te excange of energy between ubs energy ubs follows two simple rules. Te first rule is tat eac ub is proibited from purcasing and selling energy at te same time. Tis ensures tat ubs are not selling energy at a ig price from one ub wile buying it at a lower price from anoter neigbour. Te second rule is tat te prices of te different energy carriers are assumed to be fixed. Tese energy prices are calculated according to te market price, evaluated at eac ub. To illustrate, Time-of-use (TOU) electricity prices are employed for te residential and scool energy ubs (IESO, 2014), wile te ourly Ontario electricity prices (HOE) are used for large consumer base suc as te food distribution center and te ydrogen refuelling station. Te price of 7
6t International Conference on Hydrogen roduction UOIT Osawa, Ontario, Canada natural gas, for instance, including storage and transportation carges is 0.22$ per m 3 (Ontario Energy Board, 2014). Te energy demand for eac energy ub is sown below in figures 3 and 4. For simplicity s sake, te energy demand is presented by one day from eac season; owever, te model is run for every our of one ypotetic year. 100 forklifts are considered for te food distribution center and fifty ydrogen veicles are assumed for te residential ub (Alto, 2008). Te ydrogen demand of tis forklifts and ligt duty veicles are plotted in te figure 5. Te ourly profile of ydrogen demand at refuelling station for ligt duty veicles were obtained from ydrogen andbook (Elgowainy et al., 2014). Figure 2: Scematic view of te network of studied energy ubs Te refueling station is located at te address of te forklifts near te food distribution center. For te forklifts, consequently, a regular peaking pattern is likely to occur, instead of a random refuelling pattern. Wit te peak pattern, tere are two peak refuelling times wic is related to te teir two working sifts during te day; te first peak occurs during te pre-commute ours of 7-9 am wile te second occurs during te after-work ours of 5-7 pm (Hajimiraga, et al., 2007). An interest rate of 8% is considered for an assumed 20 year lifetime of te project. Figure 3: Termal energy demand of Energy Hubs. Figure 4: Electrical energy demand of energy ubs. Figure 5: Hydrogen demand of energy ubs 8
6t International Conference on Hydrogen roduction UOIT Osawa, Ontario, Canada Te refueling station is located at te address of te forklifts near te food distribution center. For te forklifts, consequently, a regular peaking pattern is likely to occur, instead of a random refueling pattern. Wit te peak pattern, tere are two peak refueling times wic is related to te teir two working sifts during te day; te first peak occurs during te pre-commute ours of 7-9 am wile te second occurs during te afterwork ours of 5-7 pm (Hajimiraga, et al., 2007). An interest rate of 8% is considered for an assumed 20 year lifetime of te project. In order to optimally design and operate te ydrogen refuelling station at energy ub 4, different components including electrolyzers, ydrogen storage tanks must be considered. Te design of tis energy ub is predicted by determining te number of eac component of fixed unit size needs to be installed in order to optimally meet te energy demands trougout te network. In tis study, te autors consider an electrolyzer system based on te operating caracteristics of a HySTAT-60 alkaline electrolyzer developed by Hydrogenics Corporation. Te ydrogen production rate of a unit is approximately 5.59 kg -1 at a rated power of 290 kw (Sarif, 2014). In te course of a day, 100 forklifts and fifty ligt duty veicles may be fuelled. Te daily consumption of forklift sould be 0.9 kg according to data from Walmart, wile te ligt duty ydrogen veicles (HV) using 0.6 kg of ydrogen wic is te daily fuel requirement tat require a 6000 psig on-board tank fill pressure (Elgowainy et al., 2014).Te unit size of ydrogen storage tank cosen for te model is 30 kg at 6000 psig and 298 K. In Table 1, te properties of te energy generation tecnologies are given. In tis model, te solar irradiation data is related to te region in te sout of Ontario. Moreover, te simulation is conducted over a year wit ourly resolution. Te capital cost of te equipment is amortized over 20 years. Solar collector Table 1: Key tecnology specification Type Area Efficiency Flat plate and evacuated tube (SRCC 100-2004-001 A) ( Canadian Solar,2014) Solar V CS6-256mm V-Solar module ( Canadian Solar, 2014) 2 m 2 based on te gross area 34% ( Martin, 2001) based on te gross area 1.5 m 2 13% ( Weber et al.,2011) CH Reciprocating internal combustion engines - Electric Termal 30-35% 44-51% Boiler - - - 80-90% Compressor reciprocating compressor - 87.5% (eng, 2013) - In Table 2, below, te cost and emissions factors for te CH, boiler, solar collector, potovoltaic system and ydrogen tank are given. Tis data can ten be used in te optimization of te objective function, given in te prior section. Operation and maintenance cost Variable cost($ kw -1 ) Table 2: Cost and emission factors CH Boiler Solar collector 0.01 (Weber et al.,2011) Solar V 0.027.01 0.01(Weber et al.,2011) Fixed cost ($ kw -1 yr -1 ) - - - $21 (NREL, 2014) Hydrogen Tank Capital Cost - - - - 1760 $ kg-1 CO 2 emissions(kg kw -1 ) Grid Electricity Ontario s natural gas 0.177 0.187(Te climate Registry, 2014) RESULT AND DISCUSSION By optimizing te model defined in te previous sections, te following results, as sown in Table 3 are arrived at. Table 3: Caracteristic of optimum model Annual cost of optimal system $1,468,201 Annual CO 2 emissions 3,685tonnes 9
6t International Conference on Hydrogen roduction UOIT Osawa, Ontario, Canada Number of 290 kw alkaline electrolysers 2 Number of 30 kg ydrogen storage tanks 4 Wit a levelized cost of ydrogen, calculated to be approximately 6.74$ kg -1 it was found tat te annualized cost of te system is just below $1.5 million. Te annual total emission of te energy ub network is found to be 3,685 tonnes of CO 2. Figure 6: Electrolyser and ydrogen storage operation condition in 300- our. Te 300 ours of electrolyser and ydrogen storage operation are presented, along wit te HOE, in figure6. Note tat te simulation was conducted over 8760 ours, but 300 ours is sown for simplicity. As expected te production and storage of ydrogen are timed suc tat wen te energy price increased, te stored ydrogen decreases and te electrolyzer decreases production. In Figure 7, it can be seen tat wen te average price of electricity is as low as 0.036 $ kw -1, te electrolyser operates, wile it does not operate wen te average HOE is iger tan 0.13 $ kw -1. Te daily average capacity factor of te electrolyser is sown in te Figure 8. It is clear tat te wen te price of electricity increases, te capacity factor of it will be decreased. Tis occurs in order to reduce te cost of producing ydrogen by using off-peak energy. Figure 7: Average daily strike price of electricity for electrolyser operation Figure 8: Average capacity factor of electrolyser 10
6t International Conference on Hydrogen roduction UOIT Osawa, Ontario, Canada Te nature of te excange of termal and electrical energy between te energy ubs is sown in figure 9. Surplus energy can be excanged between energy ubs in urban energy network. Energy ub 2, for example transfers its surplus eat to oter energy ubs. Energy ub 3 also excanges its surplus eat to te oter ubs due to te use of CH in te system. Te eat recovered by te electrolyser is also transferred to ub 3 and ub 1.Te surplus electricity transfer from ub 3 to ub 1 is also sown on te rigt side of Figure 9. Tis figure illustrates tat in ubs were distributed energy systems suc as CH or solar tecnologies are operated, te operational cost will be decreased by te excange te surplus energy to oter ubs. Tere is no electricity excange under optimal conditions to Energy ub 4, te ydrogen fuel refuelling station. Te reason tat te price of electricity for tis ub is based on te HOE wic is lower tan te electricity price from CH wic is operated based on natural gas. Conclusion Figure 9: Average daily termal energy and electricity excange between energy ubs Te purpose of tis paper is to analysis te benefit of a distributed ydrogen energy production system witin te context of interaction in a smart urban energy network of energy ubs. Tis analysis was carried out wit te development of a generic matematical model for te optimal operation of future communities; were ydrogen is used as one of te energy vectors in te system to carry and store energy. In tis study, te optimal design and operation of ydrogen refuelling station is investigated in future urban area were ubs can excange te surplus energy between one anoter. Energy ub concept is employed tat systematically considers te energy requirements implemented. As case study of four energy ubs are considered tat te one is ydrogen refuelling station to supply te demand of forklift and ydrogen veicles in te network. Te oter nodes witin te system of energy ubs are: a fres food distribution center tat operates 100 ydrogen lift trucks, residential complex and scool. It is also assumes tat 50 ligt duty veicles for residential complex. A multi period mixed integer dynamic optimization model as been developed in te General Algebraic Modeling Software (GAMS) were te objective function seeks to optimize te total operational and maintenance cost of te network and te capital cost of te energy ub 4 ydrogen refuelling station by number of electrolysers, number of ydrogen tanks, and operational conditions (i.e. availability of key tecnology and energy excange between ubs). Te optimum size of electrolyser and ydrogen tank for supplying te ydrogen demand in te energy ub network in te proposed case study is calculated 2 electrolyser units of 290 kw and 4 ydrogen storage tank unit of 30 kg, respectively. Te average daily strike price of electricity were electricity is purcased from te grid to operate te electrolyser is $0.036 per kw and wen te average Ontario electricity price is iger tan 0.13$ kw -1 te electrolysor cease to operate. Te levelized cost of ydrogen produced by ydrogen fuelling station is estimated to be $6.74 per kg. Moreover, te optimal operation of energy conversion and energy storage tecnologies witin eac ub and te optimal interaction between energy ubs witin te network are te oter results of te optimal model. References Akorede, M.F., H. Hizam, and E. ouresmaeil(2010), Distributed energy resources and benefits to te environment. Renewable and Sustainable Energy Reviews, 14(2): p. 724-734. Alto,.(2008), Feasibility of Electrolyzer Based Home Refueling System for Advanced lug-in Hydrogen Veicle Applications, ERI, CA, E-25770/C12457. Amos, W. A. (1998), Cost of storing and transporting ydrogen (Tecnical Report), NREL, National Renewable Energy Laboratory. Canadian Solar (2014), Evacuated Solar Tube Tecnology, Retrieved July 2014, from ttp://www.canadiansolartecnologies.ca/solar-tecnology/ 11
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