DOI: 10.3303/CET24114091 Paper Received: 10 June 2024; Revised: 22 August 2024; Accepted: 9 November 2024 Please cite this article as: Fadhil M.F.S.A., Liew P.Y., Zaharuddin H.H., Mah A.X.Y., Ho W.S., Woon K.S., 2024, Hydrogen Supply Chain Optimisation with Green Electricity Generation for Energy Trading Between Industrial Parks, Chemical Engineering Transactions, 114, 541-546 DOI:10.3303/CET24114091 CHEMICAL ENGINEERING TRANSACTIONS VOL. 114, 2024 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Petar S. Varbanov, Min Zeng, Yee Van Fan, Xuechao Wang Copyright Β© 2024, AIDIC Servizi S.r.l. ISBN 979-12-81206-12-0; ISSN 2283-9216 Hydrogen Supply Chain Optimisation with Green Electricity Generation for Energy Trading Between Industrial Parks Muhammad Fakhri Salahuddin Ahmad Fadhila, Peng Yen Liewa,*, Hazirah Hanim Zaharuddina, Angel Xin Yee Mahb, Wai Shin Hoc, Kok Sin Woond aDepartment of Chemical and Environmental Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia (UTM) Kuala Lumpur, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia bNUS Environmental Research Institute, National University of Singapore, 138602, Singapore cFaculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia dSchool of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900 Sepang, Selangor, Malaysia pyliew@utm.my Hydrogen can complement renewable energy to produce green electricity and green hydrogen. Most supply chain studies do not consider hydrogen demand in industrial zones and have never considered hydrogen as an energy source to meet industry's energy needs. This paper develops an optimal Hydrogen Supply Chain (HSC) network to minimise the overall cost of the energy system, considering the energy trading option across industrial zones. Grey and blue hydrogen are included with natural gas as the Steam Methane Reforming (SMR) plant feedstock and SMR with the Carbon Capture Storage (CCS) plant. Green electricity production from solar, wind, hydroenergy, and biomass gasification are considered to produce green hydrogen in this study. Through AIMMS modelling and optimisation, it was determined that the total daily cost (TDC) of the HSC network is 2,464 million USD/d. Hydrogen trading is essential to minimise the energy consumption of hydrogen production plants. Hydrogen trading is forcing one industrial zone with excess hydrogen to export to other industrial zones with insufficient hydrogen to meet its energy demand. In simple terms, hydrogen trading does not fully rely on hydrogen production plants but relies on transportation technology to import and export hydrogen for other industrial zones. With that, hydrogen trading can minimise the TDC of the HSC network and minimise the energy consumption from conventional hydrogen production plants. According to the results, the main exporter of hydrogen is industrial zone G2, with 115.57 t/d of hydrogen. 1. Introduction Many countries focus on developing a more sustainable and clean energy supply by exploring and getting more low-carbon and renewable energy sources, such as solar, wind, hydro and geothermal. Reliance on fossil fuels increases greenhouse gas (GHG) emissions and causes climate change. The transition of energy systems from fossil-based to renewable energy is essential for sustainable development. Hydrogen is a critical player in the energy transition due to its potential as a clean and sustainable energy carrier (Guilbert and Vitale, 2021). Hydrogen could decarbonise the energy sector through its utilisation as fuel cell EV fuel, energy storage, and power generation. Hydrogen is a potential future fuel as it has a clean emission with high gravimetric energy density (Mah et al., 2020). Despite being an important feedstock for the chemical industry, it can also be a suitable energy storage medium compared to conventional batteries. A hydrogen supply chain (HSC) network comprises four influential echelons: production, storage, transportation, and distribution. The shift to a hydrogen economy highly depends on the HSC, which presents opportunities and constraints differently. Sgarbossa et al. (2023) give the planning and outline for a cleaner hydrogen supply chain network to support renewable energy development. Corresponding to demand fluctuation, Yoon et al. (2022) improved the research by considering multi-period optimisation of the HSC network. Erdoğan and GΓΌler (2023) applied the model for a case study in Turkey. These studies include all possible hydrogen production 541 technologies to see the optimal pathway to produce hydrogen. To improve the current studies, the capital cost can be eliminated using the current natural gas pipelines and infrastructure to minimise the total daily cost of the HSC network. However, none of these studies consider the need for green electricity and hydrogen fuel in industrial zones to meet the energy demand. Although energy management has its optimisation strategy, optimising the hydrogen supply chain can also be considered one of the solutions to minimise energy consumption. While previous research has primarily focused on utilising hydrogen in the transportation industry, this paper takes a different approach. This study develops an optimisation model to quantify the potential use of hydrogen to fulfil the energy demand of several industrial zones through hydrogen trading and green electricity-based hydrogen production. 2. Methodology 2.1 Superstructure of the hydrogen supply chain The feedstock for hydrogen production comes from natural gas, biomass, and solar energy. The feedstock selected is based on their availability in Malaysia. Natural gas is a source for large petrochemical plants, while biomass such as empty fruit bunches (EFB) will be used for gasification. Solar energy harvested using PV panels will be used as an electricity source for electrolysis to produce green hydrogen. Wind energy considers the velocity of wind, while hydro-energy considers the flow rate of water as a source to generate green electricity. The green electricity from these renewable sources will fulfil hydrogen and electricity demand in each industrial zone. The superstructure of the HSC network for this study is illustrated in Figure 1. Figure 1: Generic superstructure of hydrogen supply chain with green electricity generation 2.2 Mathematical modeling The mixed integer linear programming (MILP) is extended from Mah et al. (2020), where all possible hydrogen production technologies available in Malaysia were considered for electricity generation from hydrogen and renewable energy. 2.2.1 Production and Trading Constraints The mass balance of hydrogen production in the HSC network is represented in Eq.(1). 𝑃𝑇𝑖𝑔 = βˆ‘π‘™π‘”β€²(𝑄𝑖𝑙𝑔𝑔′ βˆ’ 𝑄𝑖𝑙𝑔′𝑔) + (𝐻2𝑖𝑔 π‘‘π‘œπΈ βˆ’ 𝐻2𝑖𝑔 π‘“π‘Ÿπ‘œπ‘šπΈ ) + 𝐷𝑇𝑖𝑔 βˆ€ 𝑖, 𝑔; 𝑔 β‰  𝑔′ (1) Where PTig is the total production of hydrogen in form i in industrial zone g, Qilgg' is the amount of hydrogen form i delivered from industrial zone g to industrial zone g' through transportation mode l, Qilg'g is the amount of hydrogen form i exported from industrial zone g' to industrial g through transportation mode l, and DTig is the hydrogen demand in form i in industrial zone g. As shown in Eq(2), the total hydrogen production from steam methane reforming (SMR), SMR with carbon capture storage (CCS) and biomass gasification, PTig is equal to 542 the sum of hydrogen produced from all types of production plants that utilise natural gas and biomass as feedstock. The hydrogen production rate, PRpig is constrained with minimum and maximum capacities, as shown in Eq(3). 𝑃𝑇𝑖𝑔 = βˆ‘π‘π‘ƒπ‘…π‘π‘–π‘” βˆ€ 𝑖, 𝑔 (2) π‘ƒπΆπ‘Žπ‘π‘π‘– π‘šπ‘–π‘›π‘π‘ƒπ‘π‘–π‘” ≀ 𝑃𝑅𝑝𝑖𝑔 ≀ π‘ƒπΆπ‘Žπ‘π‘π‘– π‘šπ‘Žπ‘₯𝑁𝑃𝑝𝑖𝑔 βˆ€ 𝑖, 𝑔 (3) where NPpig is the number of hydrogen plant p producing hydrogen form i in industrial zone g, π‘ƒπΆπ‘Žπ‘π‘π‘– π‘šπ‘–π‘› is the minimum capacity of hydrogen plant p producing hydrogen form i, while π‘ƒπΆπ‘Žπ‘π‘π‘– π‘šπ‘Žπ‘₯ is the maximum capacity of hydrogen plant p producing hydrogen form i. 2.2.2 Storage constraints The storage capacity of the hydrogen is represented in Eq(4). Eq(5) shows the different types of hydrogen storage capacity, and Eq(6) represents that hydrogen stored should be constrained with allowable limits. 𝑆𝑇𝑖𝑔 = 𝛽(𝐷𝑇𝑖𝑔 + 𝐻2𝑖𝑔 π‘‘π‘œπΈ + βˆ‘π‘™π‘”β€²(𝑄𝑖𝑙𝑔𝑔′ ) βˆ€ 𝑖, 𝑔 (4) 𝑆𝑇𝑖𝑔 = βˆ‘π‘ π‘†πΌπ‘ π‘–π‘” βˆ€ 𝑖, 𝑔 (5) π‘†πΆπ‘Žπ‘π‘ π‘– π‘šπ‘–π‘›π‘π‘†π‘ π‘–π‘” ≀ 𝑆𝐼𝑠𝑖𝑔 ≀ π‘†πΆπ‘Žπ‘π‘π‘– π‘šπ‘Žπ‘₯𝑁𝑆𝑠𝑖𝑔 βˆ€ 𝑝, 𝑖, 𝑔 (6) where STig is the total hydrogen produced in the form i in industrial zone g and Ξ² is the storage holding period. π‘†πΆπ‘Žπ‘π‘ π‘– π‘šπ‘–π‘› is the minimum capacity of hydrogen produced in the form i stored in storage s, while π‘†πΆπ‘Žπ‘π‘ π‘– π‘šπ‘Žπ‘₯ is the maximum hydrogen storage capacity in the form i stored in storage s. NSsig is the amount of storage used to store hydrogen form i in storage s in industrial zone g. 2.2.3 Transportation constraints The hydrogen transported between industrial zones for hydrogen trading must be within the allowable limits is represented in Eq(7). Eq(8) indicates that one industrial zone cannot simultaneously receive and send hydrogen from/to an industrial zone at a time. 𝑄𝑖𝑙 π‘šπ‘–π‘›π‘‹π‘–π‘™π‘”π‘”β€² ≀ 𝑄𝑖𝑙𝑔𝑔′ ≀ 𝑄𝑖𝑙 π‘šπ‘Žπ‘₯𝑋𝑖𝑙𝑔𝑔′ βˆ€π‘–, 𝑙, 𝑔, 𝑔′; 𝑔 β‰  𝑔′ (7) 𝑋𝑖𝑙𝑔𝑔′ + 𝑋𝑖𝑙𝑔′𝑔 ≀ 1 βˆ€π‘–, 𝑙, 𝑔, 𝑔′; 𝑔 β‰  𝑔′ (8) where 𝑄𝑖𝑙 π‘šπ‘–π‘› is the minimum amount of hydrogen form i through transportation mode l, while 𝑄𝑖𝑙 π‘šπ‘Žπ‘₯ is the maximum amount of hydrogen form i through transportation mode l. 𝑄𝑖𝑙𝑔𝑔′ is the flow rate of hydrogen form i transported via transportation mode l from industrial zone g to industrial zone g'. Xilgg' is the binary determinant for hydrogen in form i transportation via transportation mode l from industrial zone g to industrial zone g', while Xilg'g is the binary determinant for hydrogen in form i transportation via transportation mode l from industrial zone g' to industrial zone g. 2.2.4 Green electricity system The green electricity produced from solar, wind and hydro energy are represented in Eqs.(9-11). Eq(12) shows the total green electricity produced from renewable energy. 𝐸𝑔 𝑆 = Rg S PVg A 𝑃𝑉𝐸𝐹𝐹 𝐼𝑁𝑉𝐸𝐹𝐹 βˆ€ 𝑔 (9) 𝐸𝑔 𝑀 = 0.5CpΟπ‘ŽR2π‘Šg 𝑉3 𝐼𝑁𝑉𝐸𝐹𝐹 βˆ€ 𝑔 (10) 𝐸𝑔 𝐻 = 𝐻g 𝑄 ρ𝑀gH𝐻𝑇𝐸𝐹𝐹 𝐼𝑁𝑉𝐸𝐹𝐹 βˆ€ 𝑔 (11) 𝐸𝑔 𝑅𝐸 = 𝐸𝑔 𝑆 + 𝐸𝑔 𝑀 + 𝐸𝑔 𝐻 βˆ€ 𝑔 (12) where 𝐸𝑔 𝑆 is the solar energy, 𝐸𝑔 𝑀 is the wind energy and 𝐸𝑔 𝐻 is hydro energy. For solar energy, 𝑅𝑔 𝑆 is solar radiation, 𝑃𝑉𝑔 𝐴 is the area of PV panel, PVEFF is the PV efficiency and INVEFF is the inverter efficiency. For wind energy, Cp is the coefficient of performance, ρa is air density, R is the blade length, and π‘Šg 𝑉 is the wind speed. 543 For hydro energy, 𝐻g 𝑄 is flowrate of water, πœŒπ‘€ is water density, H is the height of the damp, and 𝐻𝑇𝐸𝐹𝐹 is the hydro turbine efficiency. Eq(13) shows that renewable energy can be used to meet the demand for electricity in industrial zones or converted to H2. Eq(14) indicates the industrial zone electricity demand that can be satisfied by green electricity and hydrogen electrolysis. 𝐸𝑔 𝑅𝐸 = 𝐸𝐷𝑔 + 𝐸𝑔 π‘‘π‘œπ»2 βˆ€π‘” (13) 𝐷𝐸𝑇𝑔 = 𝐸𝐷𝑔 + 𝐸𝑔 π‘“π‘Ÿπ‘œπ‘šπ»2 βˆ€π‘” (14) where EDg is the green electricity generated, and DETg is the total industrial zone electricity demand. 𝐸𝑔 π‘‘π‘œπ»2 is the amount of electricity used in industrial zone g to produce hydrogen and 𝐸𝑔 π‘“π‘Ÿπ‘œπ‘šπ»2 is the amount of green hydrogen produced in industrial zone g. 2.2.5 Fuel cell and electrolysis system The amount of electricity converted from H2 is shown in Eq(15). Eq(16) indicates the yield of the electrolysis process. The sum of hydrogen produced from each electrolysis plant should balance the total hydrogen generated from electricity, as illustrated in Eq(17). Eq(18) represents the capacity of an electrolysis plant bound by its lower and upper production limits. 𝐸𝑔 π‘“π‘Ÿπ‘œπ‘šπ»2 = βˆ‘π‘– 𝐻2𝑖𝑔 π‘‘π‘œπΈ π‘Œπ»2π‘‘π‘œπΈ 𝐼𝑁𝑉𝐸𝐹𝐹 βˆ€π‘” (15) βˆ‘π‘–π»2𝑖𝑔 π‘“π‘Ÿπ‘œπ‘šπΈ = 𝐸𝑔 π‘‘π‘œπ»2 π‘ŒπΈπ‘‘π‘œπ»2 βˆ€π‘” (16) 𝐻2𝑖𝑔 π‘“π‘Ÿπ‘œπ‘šπΈ = βˆ‘π‘’π‘ƒπΈπ‘’π‘–π‘” βˆ€π‘–, 𝑔 (17) πΈπΆπ‘Žπ‘π‘’π‘– π‘šπ‘–π‘›π‘πΈπ‘ π‘–π‘” ≀ 𝑃𝐸𝑒𝑖𝑔 ≀ πΈπΆπ‘Žπ‘π‘π‘– π‘šπ‘Žπ‘₯𝑁𝐸𝑒𝑖𝑔 βˆ€ 𝑒, 𝑖, 𝑔 (18) where 𝐻2𝑖𝑔 π‘‘π‘œπΈ is hydrogen form i converted to electricity in industrial zone g and π‘Œπ»2π‘‘π‘œπΈ is the yield of hydrogen converted to electricity through fuel cells. π‘ŒπΈπ‘‘π‘œπ»2 is the yield of hydrogen produced per electricity consumed through electrolysis. The production rate of hydrogen form i at electrolysis plant e in industrial zone g, 𝑃𝐸𝑒𝑖𝑔 is bounded within the minimum, πΈπΆπ‘Žπ‘π‘’π‘– π‘šπ‘–π‘› and maximum, πΈπΆπ‘Žπ‘π‘π‘– π‘šπ‘Žπ‘₯ indicates the maximum of the plant's capacity. NEeig is the number of electrolysis plants e producing hydrogen form i in industrial zone g. 2.2.6 Objective function The objective function of this case study is shown in Eq(19), which is to minimise the total daily cost of the HSC network. Eqs. (20-21) shows facility operating and capital costs. 𝑇𝐷𝐢 = (𝐹𝐢𝐢 + 𝑇𝐢𝐢)/(𝛼 βˆ™ 𝐢𝐢𝐹) + 𝐹𝑂𝐢 + 𝑇𝑂𝐢 (19) 𝐹𝑂𝐢 = βˆ‘π‘” [ βˆ‘π‘– ( βˆ‘π‘(π‘ˆπ‘ƒπΆπ‘π‘– + 𝐹𝑆𝑃𝑝)𝑃𝑅𝑝𝑖𝑔 + βˆ‘π‘ π‘ˆπ‘†πΆπ‘ π‘–π‘†πΌπ‘ π‘–π‘” + βˆ‘π‘’π‘ˆπΈπΆπ‘’π‘–π‘ƒπΈπ‘’π‘–π‘”) + 𝑃𝑉𝑔 𝐴 𝑃𝑉𝐸𝐹𝐹 π‘ƒπ‘‰π‘ˆπΆ + 𝐹𝐢𝑔 𝐢𝐴𝑃 πΉπΆπ‘ˆπΆ ] (20) 𝐹𝐢𝐢 = βˆ‘π‘” [ βˆ‘π‘– ( βˆ‘π‘π‘ƒπΆπΆπ‘π‘–π‘π‘ƒπ‘π‘–π‘” + βˆ‘π‘ π‘†πΆπΆπ‘ π‘–π‘π‘†π‘ π‘–π‘” + βˆ‘π‘’πΈπΆπΆπ‘’π‘–π‘πΈπ‘’π‘–π‘”) + 𝑃𝑉𝑔 𝐴 𝑃𝑉𝐸𝐹𝐹 𝑃𝑉𝐢𝐢 + 𝐹𝐢𝑔 𝐢𝐴𝑃 𝐹𝐢𝐢𝐢 + 𝐼𝑁𝑉𝑔 𝐢𝐴𝑃 𝐼𝑁𝑉𝐢𝐢 ] (21) where TDC is total daily cost, FCC is facility capital cost, TCC is transportation capital cost, Ξ± is network operating period, FOC is facility operating cost, and TOC is transportation operating cost. CCF is the capital change factor, which refers to the payback period of capital investment. UPCpi is the unit production cost of hydrogen plant p producing hydrogen form i, FSPp is the feedstock price of hydrogen plant p, USCsi is the unit storage cost of storage unit s for hydrogen form i, UECei is the unit electrolysis cost for electrolysis plant e producing hydrogen form i, PVUC is unit operating cost of PV system, FCUC is unit operating cost of the fuel cell system. PCCpi is the capital cost of hydrogen plant p producing hydrogen form i, SCCsi is the capital cost of storage unit s for hydrogen form i, ECCei is the capital cost of electrolysis plant e producing hydrogen form i, PVCC is the PV system's capital cost, FCCC is the fuel cell's capital cost, INVCC is the inverter's capital cost. The transportation capital cost is shown as follows: 544 π‘π‘‡π‘ˆπ‘”π‘Ÿπ‘–π‘‘π‘–π‘™π‘”π‘”β€² = [ 𝑄𝑖𝑙𝑔𝑔′ π‘‡π‘€π΄π‘™π‘‡πΆπ‘Žπ‘π‘–π‘™ ( 2𝐴𝐷𝑔𝑔′ 𝑆𝑃𝑙 + πΏπ‘ˆπ‘‡π‘™)] βˆ€ 𝑖, 𝑙, 𝑔, 𝑔′ (22) 𝑇𝐢𝐢 = βˆ‘π‘–π‘™π‘”π‘”β€² π‘π‘‡π‘ˆπ‘”π‘Ÿπ‘–π‘‘π‘–π‘™π‘”π‘”β€²π‘‡π‘€πΆπ‘–π‘™ (23) where NTUgridilgg' is the number of transportation units of hydrogen form i via transportation mode l from industrial zone g to g', TMAl is the availability of transportation mode l, TCapil is capacity of transportation l carrying hydrogen form i, ADgg' is the distance between industrial zone g to g', SPl is the average speed of transportation mode l, LUTl is the load/ unload time for transportation mode l, and TMCil is the unit cost of transportation mode l carrying hydrogen form i. Eqs(24-28) indicate the component in transportation operating cost. 𝐹𝐢 = βˆ‘π‘–π‘™π‘”π‘”β€² 𝐹𝑃𝑙( 2𝐴𝐷𝑔𝑔′𝑄𝑖𝑙𝑔𝑔′ πΉπΈπ‘™π‘‡πΆπ‘Žπ‘π‘–π‘™ ) (24) 𝐿𝐢 = βˆ‘π‘–π‘™π‘”π‘”β€² π·π‘Šπ‘™ [ 𝑄𝑖𝑙𝑔𝑔′ π‘‡πΆπ‘Žπ‘π‘–π‘™ ( 2𝐴𝐷𝑔𝑔′ 𝑆𝑃𝑙 + πΏπ‘ˆπ‘‡π‘™)] (25) 𝑀𝐢 = βˆ‘π‘–π‘™π‘”π‘”β€² 𝑀𝐸𝑙( 2𝐴𝐷𝑔𝑔′𝑄𝑖𝑙𝑔𝑔′ π‘‡πΆπ‘Žπ‘π‘–π‘™ ) (26) 𝐺𝐢 = βˆ‘π‘–π‘™π‘”π‘”β€² 𝐺𝐸𝑙 [ 𝑄𝑖𝑙𝑔𝑔′ π‘‡π‘€π΄π‘™π‘‡πΆπ‘Žπ‘π‘–π‘™ ( 2𝐴𝐷𝑔𝑔′ 𝑆𝑃𝑙 + πΏπ‘ˆπ‘‡π‘™)] (27) 𝑇𝑂𝐢 = 𝐹𝐢 + 𝐿𝐢 + 𝑀𝐢 + 𝐺𝐢 (28) where FC is the fuel cost, LC is the labour cost, MC is the maintenance cost, GC is the general cost. FPl is the fuel price of transportation mode l, FEl is the fuel economy of transportation mode l. DWl is the drive wages, LUTl is the loading and unloading time of transportation mode l, SPl is the average speed of transportation mode l, MEl is the maintenance expenses, and GEl is general expenses, which include transportation mode l insurance, license and registration. 3. Case Study The case study has eight industrial zones across Peninsular Malaysia; the industrial areas are assumed to have the capacity to generate grey, blue, and green hydrogen and green electricity to support the energy demand. The industrial zones can import and export hydrogen energy to minimise overall energy consumption. The hydrogen demand for eight industrial zones is estimated (Neuwirth et al., 2022). The natural gas price is obtained from BEIS UK (2021), which produces 1.25 USD/kg H2. Biomass prices for medium-scale and large-scale plants were obtained from Cook and Hagen (2024), which are 2.00 USD/kg H2 produced and 4.00 USD/kg H2 produced. The fuel economy of transportation mode l, truck, is 2.3 km/L of diesel used (Webfleet, 2020). The data on the PV system is assumed based on Elshurafa et al. (2019), and the PV area is assumed to be 1 % of the industrial zone area. Wind power and hydropower energy sources are not considered in the case study because wind power is not feasible in the studied regions. Hydropower is commonly connected to the national power grid, where industrial zones cannot operate any hydropower plant. Table 1 outlines the energy demand and parameters used. The MILP model is solved in Windows 11 Pro 64-bit operating system MSI machine with Intel(R) Core(TM) i5-12450H CPU @ 2.00GHz to 4.40GHz and 16GBDDR5 by using BARON solver accessed with AIMMS version 24.2.1.0. The CPU time to solve the model with 581 variables and 797 constraints is 11.55 s. Through AIMMS optimisation, the optimal total daily cost of HSC is determined to be 2,464 M USD/d. Table 1 Energy demands for each industrial zone No Industrial Zone Solar radiation (kWh/d) PV area (m2) Electricity demand (kWh/d) H2 demand (kg/d) G1 Pasir Gudang 3,596,000 4.45 46,608,676 502,420 G2 Pengerang 34,840,000 4.454 19,983,827 1,355,629 G3 Kulai 7,560,000 4.477 29,648,352 153,893 G4 Melaka 16,520,000 4.684 89,704,599 989,609 G5 Port Dickson 6,050,000 4.878 11,585,207 1,495,710 G6 Klang 6,270,000 4.731 98,897,683 192,367 G7 Kemaman 25,360,000 4.815 19,783,029 695,889 G8 Kuantan 29,600,000 4.929 49,405,190 167,685 545 According to the optimisation results, eight hydrogen plants are in different industrial zones. However, it is worth noting that there are only two electrolysis plants, specifically at G2 and G7. The production technology for eight industrial zones primarily relies on steam methane reforming plants due to the lower cost of natural gas as a feedstock than biomass. The main industrial zones that produce green hydrogen from green electricity through electrolysis are G2 and G7. The electrolysis plant will only be operational if extra energy is available after meeting the electricity demands of the nearby industrial zone. The primary importer for the HSC network is G6 (Klang), which receives 102.5 t/d of hydrogen. The main exporter for this HSC network is G2 (Pengerang), which distributes hydrogen to G3 and G6 with a total of 115.57 t/d of hydrogen because G2 has excess hydrogen after satisfying local energy demand. In this case, each industrial zone has its production plant to fulfil the energy demand. Apart from that, the G8 industrial zone can stand on its own. Despite that, hydrogen trading is essential to minimise the TDC. When the TDC is minimised, energy reduction is also applied since the hydrogen plant consumes more energy than distributing hydrogen by using transportation such as trucks. Table 2 Hydrogen trading network between industrial zones Sender G1 - Pasir Gudang (kg/d) G2 - Pengerang (kg/d) G4 - Melaka (kg/d) G5 - Port Dickson (kg/d) G7 - Kemaman (kg/d) Receiver G3 - Kulai 77,580 G6 - Klang 10,985 37,989 20,382 27,298 5,845 4. Conclusions The model presented in this paper shows the application of hydrogen energy to meet the demand for hydrogen fuel and electricity in the industrial zones, focusing on modelling the conversion of hydrogen energy from green, blue and grey resources. The model can optimise the supply chain for an industrial zone and determine the hydrogen energy trading network across industrial zones. This research is essential in investigating the industry's potential in hydrogen energy. Future research must be conducted as a guideline and framework for Malaysia to achieve a hydrogen economy. 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