DOI: 10.3303/CET25120032 Paper Received: 14 May 2025; Revised: 20 August 2025; Accepted: 4 September 2025 Please cite this article as: Lau C.Y.F., How B.S., Moser I., Andiappan V., 2025, Optimal Energy Resource and Storage Planning for Decarbonisation, Chemical Engineering Transactions, 120, 187-192 DOI:10.3303/CET25120032 CHEMICAL ENGINEERING TRANSACTIONS VOL. 120, 2025 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Bing Shen How, Viknesh Andiappan, Denny K.S. Ng, Hon Loong Lam, Petar S. Varbanov Copyright © 2025, AIDIC Servizi S.r.l. ISBN 979-12-81206-21-2; ISSN 2283-9216 Optimal Energy Resource and Storage Planning for Decarbonisation Clarence Yii Fang Laua, Bing Shen Howa, Irene Moserb, Viknesh Andiappana,* aResearch Centre for Sustainable Technologies, Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak, Jalan Simpang Tiga, 93350 Kuching, Sarawak, Malaysia bSchool of Software and Electrical Engineering, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, Victoria, 3122, Australia vmurugappan@swinburne.edu.my Renewable energy sources and carbon dioxide reduction technologies play a crucial role in mitigating emissions and promoting cleaner energy. However, the challenges posed by the intermittent nature of renewable generation, energy-intensive nature of carbon dioxide reduction technologies, and their high costs threaten the energy security. This project aims to develop an energy planning model to facilitate the development of clean and sustainable energy strategies. A case study focusing on residential energy system is presented, where the model proposes an optimal configuration, which is later assessed for its energy mix and potential impacts from decarbonisation efforts. In the absence of emissions reduction targets, the optimal system yields an emissions factor of 0.10 kgCO2/kWh and an electricity cost of 1.70 cent/kWh. However, when the emissions reduction levels are mandated for decarbonisation, the optimal system shifts towards cleaner energy sources and, with stricter emissions targets, adopt carbon dioxide removal technologies. Under 25 % and 50 % emissions reduction scenarios, the emissions factor decreases to 0.08 kgCO2/kWh and 0.05 kgCO2/kWh respectively, whereas the cost of electricity climbs from 1.79 cent/kWh to 1.88 cent/kWh. 1. Introduction The energy sector is responsible for roughly 34 % of global greenhouse gas emissions, primarily due to fossil fuels combustion (IPCC, 2023). Despite the ongoing warming climate, the growth of clean energy is being outpaced by economic development, causing overall emissions to continue rising. Renewable energy plays a critical role in reducing greenhouse gas emissions while meeting energy demands (Owusu and Asumadu- Sarkodie, 2016). However, renewable energy systems face challenges such as high costs and intermittent energy production. Alternatively, Carbon Dioxide Removal (CDR) technologies offer a vital solution for removing atmospheric emissions and offsetting residual emissions, contributing to climate change mitigation efforts (Javadi et al., 2024). While CDR technologies can effectively reduce emissions, they are energy-intensive and expensive. These challenges threaten the capability of the power sector to provide adequate, affordable, and clean energy. Given the urgency to decarbonise the power sector, energy planning tools are essential in shaping strategies for developing sustainable and clean energy generation in the future. Energy planning models are computational tools used to optimise energy systems and assess different energy scenarios (Akpahou et al., 2024). They play a key role in identifying cost-effective and sustainable strategies while providing insights into the outcomes of various policy measures. As such, EnergyPLAN allows decision-makers to assess various energy scenarios and generation strategies while accounting factors like energy demand growth, resource availability, technology costs, environmental constraints and policy objectives (Lund et al., 2021). Besides, Hybrid Optimisation Model for Electric Renewables, which enables users to design, simulate and evaluate different hybrid power systems configurations (Chisale et al., 2023). Furthermore, Suhail et al., (2022) developed an energy planning model designed to identify optimal decarbonisation strategies while incorporating various energy sources, alternative feedstocks and carbon capture systems. Lastly, the open source energy modelling system focuses on detailed power representations and multi-resource systems, incorporating materials, financial flows and energy dynamics (Howells et al., 2011). Based on past studies, there is limited storage 187 options considered to mitigate the intermittency of renewable energy system. Most previous models also focus on employing low carbon energy generation technologies to minimise carbon emissions without considering the potential of incorporating carbon dioxide removal technologies as a pragmatic solution for emissions mitigation. In response, this work presents an energy planning model that can provide optimal energy planning solutions, while incorporating storage and carbon dioxide removal technologies simultaneously. 2. Methodology The model incorporates a set of technologies n ∊ j, j’, s, representing conversion technology j ∊ J, carbon dioxide removal (CDR) j’ ∊ J’ and storage s ∊ S. Feed i ∊ I with flow rate F is sent to technology j to produce output k ∊ K (e.g., electricity (or k=1) and emissions (or k=2)) at time period t ∊ T. Output k can be dispatched to meet the demand (F𝑡𝑡Demand), sent to CDR j’ for emissions reduction, or stored in storage s. The model is formulated in mixed-integer linear programming to minimise the total annualized cost of the optimal energy system (see Eq(1)), which comprises three terms: (i) cost of feed entering technology j, scaled by fraction of occurrences α𝑡𝑡, the proportion of each time period t to account for its duration, (ii) capital expenditures of technology n, proportional to 𝐹𝐹𝑛𝑛𝑛𝑛Max, and (iii) operating expenditures of technology n, based on 𝐹𝐹𝑛𝑛𝑛𝑛𝑡𝑡 , scaled by α𝑡𝑡. The capital expenditures are subjected to an annualised cost factor (ACF) of 0.0433, assuming a 3 % discount rate (BNM, 2025) and 40 year system lifespan. Eq(2) determines the maximum flow rate of output k (𝐹𝐹𝑛𝑛𝑛𝑛Max), by comparing each flow rates of output k in or out of technology n across all the time periods (𝐹𝐹𝑛𝑛𝑛𝑛𝑡𝑡 ) for the highest value. On the other hand, Eq(3) and Eq(4) ensure the assigned electricity demands (F𝑡𝑡Demand ) and the emissions constraints (EmissionLimit) are satisfied. min (𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶Total = �������𝐹𝐹𝑖𝑖𝑖𝑖𝑡𝑡 × D𝑖𝑖𝑡𝑡 × α𝑡𝑡� + �ACF × 𝐹𝐹𝑛𝑛𝑛𝑛Max × C𝑛𝑛𝑛𝑛� + (𝐹𝐹𝑛𝑛𝑛𝑛𝑡𝑡 × O𝑛𝑛𝑛𝑛 × α𝑡𝑡)� 𝐾𝐾 𝑛𝑛=1 𝐽𝐽 𝑖𝑖=1 𝐼𝐼 𝑖𝑖=1 𝑇𝑇 𝑡𝑡=1 𝑁𝑁 𝑛𝑛=1 ) (1) 𝐹𝐹𝑛𝑛𝑛𝑛Max ≥ 𝐹𝐹𝑛𝑛𝑛𝑛𝑡𝑡 ∀𝑛𝑛 ∀𝑘𝑘 ∀𝐶𝐶 (2) F𝑡𝑡Demand ≤ 𝐹𝐹𝑛𝑛=1𝑡𝑡 ∀𝐶𝐶 (3) �𝐹𝐹𝑛𝑛=2𝑡𝑡 𝑇𝑇 𝑡𝑡=1 ≤ EmissionLimit (4) Eq(5) represents the balance between the available feed flow rate (F𝑖𝑖𝑡𝑡 ) and the total feed flow rate distributed to technology j (𝐹𝐹𝑖𝑖𝑖𝑖𝑡𝑡 ) at period t. Thereafter, the production rate of output k from technology j at period t (𝐹𝐹𝑖𝑖𝑛𝑛𝑡𝑡 ) is determined using Eq(6), where the respective conversion rate is defined as X𝑖𝑖𝑖𝑖𝑛𝑛. Subsequently, the outlet flow of output k (𝐹𝐹𝑖𝑖𝑛𝑛𝑡𝑡 ) is subjected to transmission efficiency (E𝑖𝑖𝑛𝑛), which refers to the fraction of output k successfully delivered by technology j after accounting for the transmission losses. Then, 𝐹𝐹𝑖𝑖𝑛𝑛𝑡𝑡 is either dispatched to meet the demand (𝐹𝐹𝑖𝑖𝑛𝑛𝑡𝑡Out), distributed across all CDR technology j’ (𝐹𝐹𝑖𝑖𝑛𝑛𝑖𝑖′𝑡𝑡 ) for emissions reduction, or across all technology s for storage (𝐹𝐹𝑖𝑖𝑛𝑛𝑗𝑗𝑡𝑡𝑆𝑆𝑡𝑡𝑆𝑆𝑆𝑆𝑆𝑆), as outlined in Eq(7). F𝑖𝑖𝑡𝑡 ≥�𝐹𝐹𝑖𝑖𝑖𝑖𝑡𝑡 𝐽𝐽 𝑖𝑖=1 ∀𝑖𝑖 ∀𝐶𝐶 (5) 𝐹𝐹𝑖𝑖𝑛𝑛𝑡𝑡 = �𝐹𝐹𝑖𝑖𝑖𝑖𝑡𝑡 × X𝑖𝑖𝑖𝑖𝑛𝑛 𝐼𝐼 𝑖𝑖=1 ∀𝑗𝑗 ∀𝑘𝑘 ∀𝐶𝐶 (6) 𝐹𝐹𝑖𝑖𝑛𝑛𝑡𝑡 × 𝐸𝐸𝑖𝑖𝑛𝑛 = 𝐹𝐹𝑖𝑖𝑛𝑛𝑡𝑡Out + ��� 𝐹𝐹𝑖𝑖𝑛𝑛𝑖𝑖′𝑡𝑡 𝐽𝐽′ 𝑖𝑖′=1 + �𝐹𝐹𝑖𝑖𝑛𝑛𝑗𝑗𝑡𝑡𝑆𝑆𝑡𝑡𝑆𝑆𝑆𝑆𝑆𝑆 𝑆𝑆 𝑗𝑗=1 � 𝐽𝐽 𝑖𝑖=1 ∀𝑗𝑗 ∀𝑘𝑘 ∀𝐶𝐶 (7) Eq(8) dictates the amount of electricity flowing into CDR technology j’ at period t (𝐹𝐹𝑛𝑛1𝑖𝑖′𝑡𝑡 ). CDR technology j’ consumes electricity to capture emissions based on ratio N𝑖𝑖′, which is the unit flow of emissions (𝐹𝐹𝑛𝑛2𝑖𝑖′𝑡𝑡 ) per electricity into CDR technology j’ (𝐹𝐹𝑛𝑛1𝑖𝑖′𝑡𝑡 ). Afterward, Eq(9) determines the remaining emissions flow from CDR technology j’ at period t (𝐹𝐹𝑛𝑛2𝑖𝑖′𝑡𝑡 Remain) by subjecting the inlet flow of emissions (𝐹𝐹𝑛𝑛2𝑖𝑖′𝑡𝑡 ) with the remaining fraction of removal efficiency (E𝑖𝑖′). E𝑖𝑖′ refers to the efficiency of CDR technology j’ in capturing emission. Full consumption of electricity by CDR technology j’ (𝐹𝐹𝑛𝑛1𝑖𝑖′𝑡𝑡 Consume) is assumed. 188 𝐹𝐹𝑛𝑛2𝑖𝑖′𝑡𝑡 = 𝐹𝐹𝑛𝑛1𝑖𝑖′𝑡𝑡 × 𝑁𝑁𝑖𝑖′ ∀𝑗𝑗′ ∀𝐶𝐶 (8) 𝐹𝐹𝑛𝑛2𝑖𝑖′𝑡𝑡 Remain = 𝐹𝐹𝑛𝑛2𝑖𝑖′𝑡𝑡 (1 − E𝑖𝑖′) ∀𝑗𝑗′ ∀𝐶𝐶 (9) On the other hand, the inventory of electricity in storage s at period t (𝑆𝑆𝑛𝑛1𝑗𝑗𝑡𝑡 ) is calculated using Eq(10). Storage s can either store the distributed output at period t (𝐹𝐹𝑛𝑛1𝑗𝑗𝑡𝑡𝑆𝑆𝑡𝑡𝑆𝑆𝑆𝑆𝑆𝑆), or discharge the output stored at the previous period (t–1) as 𝐹𝐹𝑛𝑛1𝑗𝑗𝑡𝑡Withdraw. The inventory of electricity in storage s at the previous period t–1 (𝑆𝑆𝑛𝑛1𝑗𝑗(𝑡𝑡−1) ) is projected to loss through self-discharge. Thus, 𝑆𝑆𝑛𝑛1𝑗𝑗(𝑡𝑡−1) is subjected to the remaining fraction of E𝑗𝑗, the electricity losses of storage s over time through self-discharge. The flow rate of electricity into and out of storage s is also subjected to its charging efficiency E𝑛𝑛1𝑗𝑗Store and discharging efficiency E𝑛𝑛𝑗𝑗Withdraw. E𝑛𝑛1𝑗𝑗Store refers to the efficiency of charging electricity into storage s whereas E𝑛𝑛𝑗𝑗Withdraw refers to the efficiency of discharging electricity from storage s. 𝑆𝑆𝑛𝑛1𝑗𝑗𝑡𝑡 = 𝑆𝑆𝑛𝑛1𝑗𝑗(𝑡𝑡−1) (1 − E𝑗𝑗) + �𝐹𝐹𝑛𝑛1𝑗𝑗𝑡𝑡𝑆𝑆𝑡𝑡𝑆𝑆𝑆𝑆𝑆𝑆 × E𝑛𝑛𝑗𝑗Store� − � 𝐹𝐹𝑛𝑛1𝑗𝑗𝑡𝑡Withdraw E𝑛𝑛𝑗𝑗Withdraw� ∀𝐶𝐶 ∀𝐶𝐶 (10) Eq(11) stated that the total flow of output k at time t (𝐹𝐹𝑛𝑛𝑡𝑡 ) is determined by summing the flow rate of output k dispatched directly from technology j (𝐹𝐹𝑖𝑖𝑛𝑛𝑡𝑡Out), the remaining flowrate of output k after employing CDR technology j’ t (𝐹𝐹𝑛𝑛𝑖𝑖′𝑡𝑡 Remain) and the stored output k withdraw from storage s (𝐹𝐹𝑛𝑛𝑗𝑗𝑡𝑡Withdraw) at each time period. 𝐹𝐹𝑛𝑛𝑡𝑡 = �𝐹𝐹𝑖𝑖𝑛𝑛𝑡𝑡Out 𝐽𝐽 𝑖𝑖=1 + � 𝐹𝐹𝑛𝑛𝑖𝑖′𝑡𝑡 Remain 𝐽𝐽′ 𝑖𝑖′=1 + �𝐹𝐹𝑛𝑛𝑗𝑗𝑡𝑡Withdraw 𝑆𝑆 𝑗𝑗=1 ∀𝑘𝑘 ∀𝐶𝐶 (11) The flow rate of output k into or out of technology n at time period t (𝐹𝐹𝑛𝑛𝑛𝑛𝑡𝑡 ) is constrained by its lower (F𝑛𝑛𝑛𝑛𝑡𝑡Lower) and upper bound (F𝑛𝑛𝑛𝑛𝑡𝑡 Upper), as shown in Eq(12). The binary variable 𝐴𝐴𝑛𝑛𝑡𝑡 is responsible for the selection of technology n at period t. F𝑛𝑛𝑛𝑛𝑡𝑡 Upper × 𝐴𝐴𝑛𝑛𝑡𝑡 ≥ 𝐹𝐹𝑛𝑛𝑛𝑛𝑡𝑡 ≥ F𝑛𝑛𝑛𝑛𝑡𝑡Lower × 𝐴𝐴𝑛𝑛𝑡𝑡 ∀𝑛𝑛 ∀𝐶𝐶 (12) 3. Case Study A residential case study is conducted to evaluate the model’s performance in addressing short-term optimisation challenges by meeting the electricity demands of the population. Figure 1(a) presents the hourly electricity consumption of 123.7 k households in the urban area of Kuala Lumpur, Malaysia. Figure 1: (a) Hourly electricity demand (Aqilah et al., 2021); (b) Case study superstructure The households are grouped into three categories: high-income; medium-income; and low-income, based on their respective income range (KRI, 2018). High-income households make up the largest portion, accounting for 49 % of the population, followed by medium-income households at 45 %, with the remainder filled with low- income households. Each household category is projected to have varying electricity consumption patterns, influenced by differences in lifestyle, number of occupants and income levels (Aqilah et al., 2021). Accordingly, electricity consumption is categorised based on these household groups, determined by multiplying the number of households in each category by their respective consumption rates. Figure 1(b) shows the superstructure developed for the case study. The superstructure comprises of energy sources, energy conversion plants, energy storage and emissions reduction technologies. The energy sources that are widely available, along with their conversion plants are included in the superstructure to generate electricity for the population. Energy 189 storage, includes a variety option of well-established battery energy storage systems, are included to store electricity. Emissions reduction or Carbon Dioxide Removal (CDR) technology is included as a viable option for addressing tightening emissions constraints. The optimal energy system is then assessed for its energy mix, cost implications and potential impacts from decarbonisation. Table 1 summarises the technical and economic data of the technologies illustrated in the superstructure. 4. Results and Discussions Figure 2 presents the energy mix of the optimal energy system alongside its associated storage condition. The Total Annualised Cost Factor (TACF) encapsulates the summation of all annual capital, operational and fuel expenditures associated with the electricity production across an energy plant. As seen in Figure 2(a), the optimal system employs a mix of hydropower, solar photovoltaic (PV) and natural gas energy plants to address the electricity demand. Among the generation sources, solar PV offers a lower TACF at 10.97 USD/MWh, compared to natural gas (14.16 USD/MWh) and coal-fired plants (23.73 USD/MWh). However, their reliance on sunlight confines their operational availability to daylight hours (7am to 5pm) only, corresponding to time periods t24 to t10 (numerical values after “t” refers to the time periods shown in Figure 1(a)). As a result, solar energy contributes only 6.7 % to the total electricity demand across all time periods, necessitating other power sources (i.e., hydropower and natural gas) to ensure uninterrupted supply, especially at the periods of high demand. Hydropower, with the lowest TACF at 9.83 USD/MWh, emerges as the main electricity contributor, covering 62.1 % of total demand. Despite its cost advantage, its capacity is capped at 50 MW, restricting its ability to solely meet surging demand, notably the higher demand of 67.5 MW at t11. In response, natural gas power plant with a capacity of 39.5 MW, bridge the shortfall while accounting for 26.9 % of the total demand. Nonetheless, the combined capacity of hydropower and natural gas (89.5 MW) fall shorts when the demand peaks to 107.1 MW at t15, necessitating storage technologies for peak-shaving. Figure 2(b) depicts that surplus electricity stored during off-peak periods, mainly generated from hydropower (54.2 MWh), owing to its lower operating cost (3.60 USD/MWh) compared to 3.70 USD/MWh for natural gas. Meanwhile, natural gas generates an additional 21.4 MWh of electricity for storage, while solar, limited by its restricted availability, supplies 18.0 MWh only. This surplus is stored across a mix of battery technologies, including sodium sulfur (NAS), lithium ferrophosphate (LFP) and lead-acid (Pb-A) batteries, later discharged during high-demand periods (t13 to t19) to reduce peak loads. Although LFP are the most economical, with a TACF of 10.18 USD/MWh, followed by Pb- A at 12.05 USD/MWh, both have limited capacities (i.e., Pb-A: 40 MWh; LFP: 40 MWh). Thus, NAS batteries, the third most economical option at a TACF of 16.77 USD/MWh, are deployed to ensure adequate electricity stored for peak-shaving, mitigating the necessity for coal-fired power generation and its associated costs. Figure 2: (a) Energy mix of optimal energy system and (b) its storage condition Figure 3: Effects of emissions constraints on (a) energy mix; (b) cost of electricity 190 Table 1: Technical and economic data of technologies Technology Conversion Efficiency Capital cost factor Operating cost factor Capacity Reference Coal power plant 0.43 kWhe/kWhIn 0.35 kgCO2/kWhIn 1.2 %Loss to Transmission 2,481.81 USD/kWe 11.48 USD/MWhe.y 50 MWe (Bellotti et al., 2019) Natural gas power plant 0.43 kWhe/kWhIn 0.35 kgCO2/kWhIn 2,119.44 USD/kWe 3.70 USD/MWhe.y 40 MWe (Oh et al., 2021) Solar power plant 0.15 kWhe/kWhIn 706.82 USD/kWe 2.59 USD/MWhe.y 40 MWe (IRENA, 2023) Hydropower plant 0.72 kWhe/kWhIn 1,261.42 USD/kWe 3.60 USD/MWhe.y 50 MWe Sodium sulfur battery - 90 %Charge 90 %Discharge 1 %Daily Loss 2,916.52 USD/kWe 2.37 USD/kWhe.y 40 MWhe (Borerwe and Longe, 2025) Lead acid battery 90 %Charge 89 %Discharge 5 %Daily Loss 1,999.16 USD/kWe 2.18 USD/kWhe.y 40 MWhe Lithium ferrophosphate battery 95 %Charge 95 %Discharge 2 %Daily Loss 1,585.56 USD/kWe 2.35 USD/kWhe.y 40 MWhe Vanadium redox flow battery 90 %Charge 89 %Discharge 1 %Daily Loss 3,005.83 USD/kWe 2.47 USD/kWhe.y 40 MWhe Membrane carbon capture system 7.20 kgCO2/kWhe 99.8 %Capture 2,937.03 USD/(kgCO2/h) 1.19 USD/kgCO2.y 25 tCO2/h (Adhikari et al., 2023) Figure 3 illustrates how different emissions reduction targets affect the optimal energy system, specifically reducing its emissions by 25 % and 50 %. In the absence of emissions reduction requirement (0 %), the baseline system achieves an emissions factor of 0.10 kgCO2/kWh and delivers an electricity at a cost of 1.70 cent/kWh. When a 25 % emissions reduction is mandated, the energy mix transitions towards cleaner sources. Solar power sees a significant boost, contributing 9.7 % of total electricity generation, as shown in Figure 3(a). This increment not only curbs emission, but also reduces the reliance on fossil fuel energy, particularly natural gas which drops to 26.0 %. Simultaneously, the share of storage rises to 5.9 % to support more usage of solar energy outside sunlight hours (i.e., t24 to t10), consequently reduce hydropower’s share to 58.4 %. However, the limited capacity and operational availability restrict the capability of green energy plants (i.e., solar and hydropower) to meet further emissions reduction on their own. In response, the system adopts membrane-based carbon capture technologies to further mitigate the carbon footprint. At a 25 % reduction target, the capture system sequesters 35 t of CO2, consuming 4.9 MWh of electricity and reducing the emissions factor to 0.08 kgCO2/kWh. A more stringent 50 % reduction target necessitates higher capture efforts, with 83 t of CO2 captured at an energy consumption of 11.6 MWh, further reducing emissions to 0.05 kgCO2/kWh. However, solar plants lack the sufficient operational availability to power the energy-intensive capture technology while addressing the higher demands. Therefore, the optimal system increases the electricity generation from hydropower, raising its share to 61.2 % whereas solar drops to 7.9 %. Storage experiences a slight increase to 6.0 % while the natural gas usage falls to 25.0 %. Meanwhile, these shifts and reliance on carbon capture system increase the cost of electricity, which refers to the total expenses associated in generating electricity. The total expenses encompass capital expenditures (CAPEX), operating expenditures (OPEX) and fuel expenditures (FUEX). Figure 3(b) depicts that the cost of electricity climbs from 1.79 cent/kWh at 25 % reduction target to 1.88 cent/kWh at a 50 % reduction target. 191 5. Conclusions This work presents an energy planning model developed to support the transition towards sustainable and low- carbon energy future, demonstrated through a residential case study. After evaluating the availability, capacity, and costs of all technologies within its superstructure, the model identifies cost-effective energy generation options that ensures adequate and affordable electricity supply. Additionally, surplus electricity is stored in cost- efficient storage options during off-peak hours and dispatched during peak hours, employing peak-shaving strategy to reduces the need for costly additional generation capacity. When emissions reduction targets are enforced, the model prioritises a shift towards greener energy sources. Under more stringent emissions targets, carbon dioxide removal technology, powered by excess electricity, is introduced to offset emissions further. Overall, the model demonstrates strong potential to facilitate the development of cleaner and cost-efficient energy systems under tight environmental constraints. Future research could extend this work by integrating emissions removal policies, such as carbon pricing, financial subsidies and grants, tax incentives and exemptions, to assess their influence on decarbonisation strategies. Acknowledgments The authors would like to acknowledge the financial support from Swinburne Tuition Fee Award. 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