A techno-economic quantification of carbon reduction strategies in the Trinidad and Tobago power generation sector using Carbon Emission Pinch Analysis (CEPA)

Abstract Trinidad and Tobago’s (T&T) conditional commitment to the Paris Agreement requires an overall power emission avoidance (EAT) of 28.7 MtCO2-e from Business-As-Usual by 2030, dependent on international financing. T&T has outlined several initiatives to achieve this, including zero-carbon renewable energy (RE) introduction. However, other technologies such as Carbon Capture and Storage (CCS) can also be used in support of achieving EAT. Using a specific scenario (S3), this study assesses the techno-economics of CCS within the sector to minimize the requirement of RE using a carbon measuring tool called Carbon Emission Pinch Analysis (CEPA) to achieve EAT. Local power plants were screened, and a CCS retrofit was then technically designed using a validated software called Aspen HYSYS. Multi-period CEPA methodology was then applied to quantify ∼17% of grid energy from RE along with CCS to achieve EAT. Economic models were also used to determine the grid unit cost of emission abatement for S3 to be 64 USD/tCO2-e; a doubling of initial projection requirements. With T&T’s current dynamics, these findings can help guide actions to reduce the requirements of RE onto the grid through the supplemental introduction of CCS to achieve its EAT.


Introduction
The impact of anthropogenic carbon emissions on the environment has prompted governments worldwide to take unprecedented actions and allocate significant resources to reduce the effects of climate change.However, significant challenges and gaps present relating to emission mitigation strategies [1].The primary source of these emissions is the burning fossil fuels to meet the energy demands in the industrial, power generation, and transport sectors [1].Coal, oil, and natural gas are feedstock fuels for power plants that contribute approximately 40% of global CO 2 equivalent (CO 2e) emissions [2].According to the International Energy Agency (IEA) reports, this is projected to increase annually by 2%, unless significant investment into decarbonization is made in the short term [3].To address the dependence on fossil fuels and close gaps on emission reduction pledges, governments have committed to the Paris Agreement, which requires countries to develop Nationally Determined Contributions (NDCs) outlining their plans for mitigating carbon emissions to achieve an established global threshold through various mitigation and adaptation techniques [4].However, countries face challenges in meeting these targets due to technical and financial constraints.Flexible methodologies are therefore required to support each country, especially Small Island Developing States (SIDS), in meeting their NDCs.
Trinidad and Tobago (T&T) has identified numerous avenues to achieve its conditional NDC of avoiding (abating) 28.7 million tonnes of CO 2 equivalent (MtCO 2 -e) from Business-As-Usual (BAU) levels by the year 2030 within its power generation sector.These initiatives are estimated to cost approximately USD 945 million [5] and account for almost 50% of the financing required to meet T&T's NDC commitment, at around USD 33 per tonne of CO2-e grid emissions abated [6].Therefore, to achieve this target, plans must consider both technical and economic aspects of carbon mitigation.
T&T's Third National Communication outlines nine major proposed initiatives and projects related to the power generation sector, including policy reform, renewable energy (RE) integration into the national power grid, and energy-efficient practices [7].However, there is a gap in identifying the direct impact of these initiatives on BAU emissions to achieve the 2030 NDC for the sector.While the initiatives suggest potential grid emissions abated, there is a dissociation in comparing the energy output's offset of BAU emissions within the national grid.Furthermore, the proposed initiatives primarily focus on zero-carbon energy sources and policy changes to facilitate their adoption, and do not consider established technologies for integration into existing infrastructure to reduce carbon emissions and improve plant efficiencies.
Recent work done by Ramsook et al. [8] explored using the method of carbon constrained energy planning (CCEP) through a case study within T&T's power generation sector, which quantified that 47% of zero-carbon energy is required along with the dispatch optimization of existing power plants to achieve its 2030 NDC.However, given the total reliance on natural gas for power generation, a sudden transition to zero-carbon sources may not be as pragmatic as a gradual transition with limitations arising from reliability and policy changes [9].
In the global context of decarbonization, emerging technologies are being developed to reduce the carbon footprint of existing fossil fuel plants and expedite the transition to decarbonization [10].One such technology is Carbon Capture and Storage (CCS) [11,12], which has been recognized as a critical tool for mitigating global climate change and reducing carbon intensities in both industry and power generation sectors [11].As of 2018, there have been 299 CCS projects worldwide, with 93 facilities currently in operation [13].Of these projects, 110 have been identified for post-combustion capture for power generation sources, with the remainder targeting unidentified sources, pre-combustion, and industrial processes.
However, the relatively high cost of current CCS operations remains a significant obstacle to its widespread deployment [14].Additionally, the introduction of CCS within power generation plants results in parasitic energy loss, which decreases the plant energy output.Despite significant research and development for CCS implementation within T&T's petrochemical sector [15], there is a dearth of literature assessing its applicability to the power generation sector.
Given that the interconnectedness of proposed projects, established technologies, and the conditional NDC target are all interlinked, a systematic analysis for effective CCEP strategy in T&T is required.Moreover, quantifying the activity level of zero-carbon energy required to meet this NDC target is essential when considering CCS.Due to the current heavy dependence on natural gaspowered plants, CCS can be explored as a shortterm option to meet the 2030 NDC target which can minimize the need for a large amount of zerocarbon energy.It is important to note the terminology used throughout T&T's NDC and decarbonization research.The NDC language interchanges the use of "avoided" and "reduced" [5].However, a study by Rubin E.S. [16] noted the term "avoidance" with specific power plants with CCS, using data such as cost of electricity, and "abate/reduce" with CO 2 -e reduction strategies involving one or more type of carbon reduction strategy."Avoid", as used in T&T's NDC, is referred to as "abated" in this study; due to its affiliation with singular power plants (with CCS) using data points of cost of electricity for power plants which were not gathered in this research.
Techno-economic evaluations can be conducted to assess the cost comparison of both options and shed light on the financial performance of carbon abatement.This study explores these items through a popular CCEP method called Carbon Emission Pinch Analysis (CEPA) to evaluate the effectiveness of dispatch optimization, CCS, and RE to achieve T&T's 2030 NDC target for its power generation sector.Considering CCS's applicability in T&T and RE resource commitments, this study aims to evaluate the multi-period cost implications of these actions to compare economic performance and emission avoidance.
CEPA is a widely accepted CCEP method using traditional process integration pinch analysis principles developed for macro-scale problems, first proposed by Tan and Foo [17].This approach involves gathering data for and constructing demand and supply composite curves using the carbon intensity of energy sources as the gauge of value for energy streams.The objective is to identify the energy allocation scheme needed to meet the specified emission limits using the minimum quantity of zero-emission energy resources [17].Since its introduction, CEPA has been widely adapted to various global energy sectors including transport [18,19] and power generation [20][21][22].A further study by Andiappan et al. [23] demonstrates the relevance of CEPA as an effective tool, which cites its ability to inform policy makers for carbon constrained energy planning providing the framework in which various stakeholders can use CEPA to develop strategies to address carbon mitigation issues.
Graphical CEPA has been widely used compared to other methods due to its illustrative and simplistic approach to CCEP problems.Cossutta et al. [24] use graphical energy planning pinch diagrams to represent visual pinch methods highlighting pinch point emissions and energy usage for the UK's power sector.They observed several single-period scenarios and were able to describe the outcomes within a single pinch diagram for illustration.They also use stacked columns to represent the overall emissions for each analyzed scenario, classifying supply sources by energy type.Other recent work by Nair et al. [25] demonstrated an extended graphical approach for implementing negative carbon emission technologies to replace or reduce the requirement of new energy producing negative emission technologies to optimize decarbonization while minimizing the impact on existing infrastructure.For effective sectoral planning however, the dynamics of multi-period would be most useful to observe the periodic changes in dynamics of energy supply and demand.
Ooi et al. [26] first explored extensions of CEPA's graphical approach to consider multi-period adaptations.Their work introduces an expansion of Tan and Foo's [17] original study using multi-period graphic and automated targeting modeling techniques.Ramsook et al. [8] used a multi-period approach to assess the impact of CEPA within T&T's power generation sector which is particularly helpful in analyzing periodic changes in energy supply to carbon emissions.Their method, derived from work done by Ooi et al. [26], is advantageous when meeting specified emission limits over time using multiple fuel sources for T&T.Ramsook et al.'s [8] case studies entailed gathering power plant data, calculating power plant carbon emissions and intensities, projecting a business as usual (BAU) model, and applying CEPA through two ( 2) scenarios (S1 and S2).S1 identified the impact on emission abatement of power plant optimization based on carbon intensity, and S2 quantified the amount of zero-carbon energy required to achieve T&T's computed emission limits based on the 2030 NDC target for power generation.Their work found that dispatch optimization alone was insufficient to meet its target (S1), and approximately 4.75 TWh/year of zero-carbon energy from 2022 is needed (S2).Other recent multi-period studies have been also explored for power sectors using alternative algebraic targeting models (ATM) for CEPA rather than the widely used graphical methods.Nair et al. [27] use an ATM for the integration of RE sources with CCS during CCEP for sectoral planning to eliminate the iterative procedure of graphical CEPA to provide rigorous targets through a case study.Nair et al. [28] further expand on this research with the use of ATM models to determine the optimum deployment of energy sources for CO2-intensive industries other than the power sector.The ATM model was used to overcome the non-linearity in the carbon intensity calculations to demonstrate the time-dependent deployment of some technologies over others.While ATMs have been used to overcome the limitations of repetitive graphical methods, the latter is cited in these papers as a fundamental linear baseline for CEPA.Additionally, ATMs become most useful with large time periods of analysis, and additional variables in energy planning such as detailed economics and multiple case study analysis scenarios.With the fundamentals of CEPA being a linear, graphical problem, it is important to understand how reduction strategies such as CCS can be adapted with existing infrastructure via retrofit.
Tan et al.'s [29] proposed a methodology that integrates CCS with CEPA, to optimize retrofitting strategies in the power generation sector for new and existing power plants.Their study addresses the challenge of meeting aggregate carbon emission targets while minimizing the need for power plant retrofit.The proposed approach considers energy losses associated with CCS and the introduction of zero-carbon energy sources into the macro-grid to compensate for achieving an emission limit.Nevertheless, limitations in their method arise from the assumption of retrofitting only the most carbon-intensive sources, which may not be applicable to smaller-scale operations in T&T.Additionally, the method only considers a single period and fails to account for changes in energy demand over time and economic considerations for CCS and RE.Thus, a comprehensive CCEP that incorporates these elements is required for a more holistic analysis to provide decisionmakers with relevant details.Still not addressed in the studied literature are the techno-economic factors of these actions over time and the application of CCS retrofit to specific power plants rather than macro-scaled energy sources, using CEPA.These details will provide decision-makers with deeper analysis that can drive decisive actions if the emission abatement targets are achieved.Extensive research has also been explored to highlight the techno-economic considerations needed for all elements of CCS to be costed accurately.
Rubin et al. [14] cite that capture costs for natural gas plants can account for $85% of the CCS value chain cost, with dependent factors being volumes, distance from the sink, and classification of the sink.Roussanaly's [30] study cites standard calculations for costing CO 2 capture in the power generation sector and uses it to develop abatement costing methods for other sectors.More recently, Roussanaly et al.'s [31] study further expand on costing considerations for the technoeconomic evaluations of CCS retrofits for various sectors by exploring the commonly overlooked aspects of existing techno-economic studies.They cite the importance of technical simulation for capture plants to factor in case-by-case factors of utility rates, host plant energy loss, and flue gas treatment.Furthermore, they highlight the importance of factoring in distance and volume when using unit cost per tonne of CO 2 for transport and storage costs.
Therefore, careful considerations through established models and software must be made around the technical design and economic costing of capture plants, as varying assumptions can severely affect overall economics.Aspen Technology has been widely used in studies for carbon capture simulation to provide baseline technical and economic details.Petrovic et al. [32] use Aspen Plus simulation software to design a capture plant retrofit for a gas turbine power plant that uses amine solution to remove CO 2 from the existing plant's flue gas stream.Kheirinik et al. [33] used Aspen HYSYS (another version of Aspen software) to accurately design CO 2 capture plants for postcombustion capture plant retrofits and baseline economics of equipment costs and operational expenditure.Liang et al. [34] cited Aspen HYSYS, along with several others as viable options for technical CO 2 capture.
Lars Erik Øi [35] utilized Aspen HYSYS to simulate CO 2 removal using amine absorption from a gas-based power plant.Aspen HYSYS is demonstrated as a valuable tool to design and achieve baseline technical designs and economics for equipment, raw materials required, and overheads.Still, it is limited by not providing full economics for the capture plant and the components of transport and storage and not reliable enough for commercial optimization scenarios to conduct life-cycle assessments.Several studies have identified the limitations for optimization of economics using Aspen HYSYS [36][37][38].Further optimization software such as LINGO can help create a surrogate model that has the best operating conditions while satisfying technical thermodynamic parameters and flow rates [37].It is important to note however, that this optimization software would require the collection of more detailed system parameters, and useful in more mature planning phases of integration and engineering design as noted by Santos [38] who uses communication approaches between Aspen HYSYS and other software tools for optimization which can provide more specific details for economic modeling rather than general baseline information from Aspen HYSYS.
Silla [39] addresses these limitations of the financial modeling by providing economic models using equipment, utility, and raw material costs gathered from Aspen HYSYS.They use economic factors to compute the total fixed capital investment (CAPEX) and operational expenses (OPEX).Smith et al.'s [40] factors for storage provide a unit cost baseline range that can be applied to T&T through their technical analysis for US storage sites.They note that factors such as permeability and depth of storage wells affect the costs of injection and storage.
Extensive research has also been conducted for RE costing, with comprehensive international annual reviews [41] and software [42] being used globally.However, the most simplistic approach to RE costing would be using power output to compute CAPEX and OPEX costs, such as NREL's [42] free software online, whereby users can use economic factors to determine expenses based on installed capacity.
With 100% of T&T's current power coming from fossil-fuelled sources, a sudden move to RE supplies may not be as pragmatic as other SIDS.T&T solely relies on natural gas to power its industrial presence, which accounts for >60% consumption of electricity produced.This high dependence on fossil fuels has resulted in T&T's place within the top 20 global ranking in annual CO 2 emissions per capita and CO 2 emissions per GDP [43].T&T accounts for approximately 35% of the cumulative annual emissions within the Caribbean region [8].

CCS in T&T
The involvement of CCS in T&T has historically been driven by increased oil production, however recent developments by both public and private companies have noted the importance of permanent storage of CO 2 rather than its use for enhanced oil recovery (EOR).
Between 1973 to 1990, T&T's local petroleum company at the time, Petroleum Company of Trinidad and Tobago (Petrotrin), first conducted four (4) immiscible CO 2 pilot flooding projects within Trinidad's Forest Reserve and Oropouche fields [44].The CO 2 injected into the wells were produced from a local ammonia plant 25 miles away and piped to the oilfield sites where it was injected into thick sands of variable continuity containing medium gravity crude.In the four cases, oil production increases were observed, in some cases even many years after discontinuation of CO 2 injection due to supply interruptions.Between 2009 and 2010, a small pilot project was conducted in southern Trinidad which also observed positive results in oil recovery [15].A small pilot project was also operated by a local privately owned contractor in 2020 which injected CO 2 continuously to recover additional oil.Geographically, all T&T's involvement in CCS has been in its southern oil fields, with its CO 2 originating from its central petrochemical industrial area as highlighted in Figure 1.
From 2012, there has been an uptick within T&T government's involvement with CCS integration through numerous funded local studies and partnerships with private operators [45].Notably in 2018, A Carbon Management Study was conducted and quantified economic requirements through models for CCS's feasibility within different local oilfields for EOR.In 2021, a local steering committee for CCS was established to manage the implementation of large-scale CO 2 EOR projects through private stakeholder partnerships.In 2021, the National Storage Atlas project commenced which aims address current gaps T&T's CO 2 storage potentials via oil wells, caprock, and aquifers by quantifying volumes, dimensions, and thermodynamic requirements of CO 2 to be stored or utilized within geological formations.Additionally, the study aims to conduct an analysis on technical, logistical, legislative, and regulatory gaps for the CCS value chain in T&T.In 2022, the UNFCCC plans to deliver a study to identify and describe the key elements which may influence the choice of carbon pricing and design in T&T to assist in CCS decision making and feasibility studies.

CCS process flow
Capture technologies refer to the physical or chemical removal of CO 2 from a process stream using sorbents, solvents, membranes, or refrigeration techniques.Capture can be done in at least three ways within the process stream post-combustion capture, pre-combustion capture, or oxyfuel combustion systems.The most widely used capture method for natural gas processes of the three methods is post-combustion capture using amine solvents due to its experience in the industry, low relative costs, and applicability to fossilfuelled power generation plants [12].
The flue gas enters the bottom of the absorber at high pressure and low temperature (optimal conditions for absorption), where it is washed in a multi-tray absorber by a water-amine solution.
Here, the absorption reaction occurs where most of the CO 2 is absorbed into the amine solution; the most commercialized one being monoethanolamine (MEA) with more than 60 years used mainly by large-scale chemical industries [12].The rich amine (CO 2 , H 2 O, Amine) solution then exits the bottom of the absorber, where it is then passed through a heat exchanger and is heated before entering a multi-staged distillation column.A reboiler heats the stream at the bottom in the distillation column and strips the CO 2 from the rich amine stream.The CO 2 -rich solvent is regenerated using a counter-flowing stream.The lean amine leaves the bottom of the regenerator and is recycled by adding makeup amine and water.The final mixture of lean amine solution is then reused as the top inlet in the absorber.The overall highlevel process flow for this capture process is illustrated in Figure 2.
Typically, once captured, CO 2 is transported via pipeline to the storage site.Storage locations are usually depleted oil and gas wells, inaccessible coal seams, saline aquifers, and other geological cap rock structures [12].CCS retrofit can drastically reduce a power plant's carbon emissions; however, the limitations arise in the increased capital costs (about 25-50% higher) and parasitic energy requirements (7-8%) from the host plant [46].Given these limitations, a wide range of studies has been conducted to assess the technical and financial impacts of CCS on natural gas power plants-specifically as it relates to application within T&T's power generation sector.
In the Global CCS Institute's [47] review for CCS in T&T, power sector regulation gaps and policy issues were identified and explored, highlighting the regulatory limitations for CCS application.Boodlal et al. [48] demonstrated the techno-economic design of CCS systems for both power generation and industrial applications which uses  Aspen HYSYS for technical designs and Silla's [39] for economic modeling.This study provides a baseline analysis which demonstrates the technical costing inputs from the Aspen HYSYS software influencing Silla's [39] economic modeling, to provide the unit cost of emissions captured for a natural gas power plant example within T&T.Boodlal et al. [49] successfully used technical methods in their study to quantify unit costs of CCS within the industrial sector for transport components specific to T&T.In their study, they highlight the variables and parameters needed to successfully quantify the capital and operational expenditures of these CO 2 transport in T&T using various volumes and distances to conduct a sensitivity analysis on potential costs.Boodlal et al. [50] further cite the CCS value proposition to T&T as a pragmatic transitionary medium to RE for the fossil fueldependent country through further similar technoeconomic analysis of the CCS value chain within the ammonia sector.
Techno-economic analysis projects have also been explored by the MEEI [45], highlighting the feasibility of capturing projects from point sources within T&T's energy sector using proprietary screening tools for selecting suitable storage locations for CCS.This was developed using reservoir data, and economic models were then designed based on input/output data for the CCS value chain.Selection of appropriate power plants for CCS retrofit have also been discussed which selects the most carbon intensive power plants for retrofit in the adapted macro-scaled problems.A study by Tan et al. [51] addresses the shortcomings of selecting the most carbon intensive power plants through adapted mixed integer linear programming models to incorporate other energy planning variables such as the incremental associated increase in cost of electricity.

Problem statement
This study marries the highlighted methods of multi-period CEPA and modeling of CCS & RE to identify the technical requirements, and associated costs for achieving the macro-scaled target for T&T's 2030 NDC.This work aims at addressing the existing dissociation between technical design and the macro-scaled carbon emission planning technique called CEPA to achieve carbon abatement target of 28.7 MtCO 2 -e according to T&T's NDC for its power generation sector (EA T ).Prior applications of CEPA have solely focused on its ability to assess actions to abate emissions for macro-scaled planning without the introduction of technical engineering design.This study introduces technical designs to this already reputable CCEP technique to provide decision makers with further avenues for analysis and pragmatic planning for low carbon futures.It is important to note that CCS was not used in this study as a mean to totally meet the 2030 NDC, however as an avenue to reduce the transition to RE energy as highlighted in Tan and Foo's original methodology [17].The following objectives for this study are outlined below using T&T's data as an example: Outline baseline period grid emissions (PE) of each power plant using Ramsook et al.'s [8] data for T&T power plants Selection of power plant (i) most applicable for CCS retrofit based on PE i as screening criteria.PE i between the years (t) 2024-2030 based on total grid emissions for an emission factor (EF i ) power plant optimization scenario from prior work.Technical design of CCS retrofit using Aspen HYSYS for the selected power plant to determine parasitic energy (EP CCS ), plant load factor (PLF), EF i , new energy output (EO CCS ) and emission capture rate (EC) of CO 2 Application of multi-period CEPA to assess the impact of CCS retrofit on grid emissions abated (EA) and EO and determine the minimum amount (TWh/year; MW) of zero-carbon energy (RE) required to meet the 2030 NDC target (EA T ) Aspen HYSYS economizer to determine the capital (CAPEX Capture ) and operational (OPEX Capture ) costs of capture retrofit for selected power plant for retrofit (TC Capture ).Application of appropriate unit costs for transport (TC Transport ) and storage (TC Storage ) to determine the overall costs of CCS (TC CCS ) Internationally recognized costing models and unit cost breakdowns for RE economic modeling (TC RE ) Economic modeling (CCS & RE) to determine the unit costs of abatement (UEA) of necessary actions to meet EA T

Methodology
The overall steps taken proposed in this study are represented in Figure 3. CEPA studies cited within the literature have focused solely on high-leveled macro-scale planning.However, this study uses available technical process engineering design tools to further expand the analysis into the cost implications of emerging technologies within the national power generation grid for carbon abatement.

Baseline CEPA data
This step was done to compile the data points needed to inform the subsequent technical design and multi-period CEPA modification.The EFs for each power plant (i) were computed using IPCC [52] Tier 1 guidelines using collected fuel usage and energy output data from public archives.This data was also used to compute the total grid PE, grid emission factor (GEF), and EF i .
This method follows Ramsook et al.'s [8] case report Scenario 1 (S1) technique which uses Ooi et al.'s [26] multi-period method to conduct CEPA with no change in energy supply for the national grid to meet the 28.7 MtCO 2 -e (EA T ).The annual grid emission limit (EL) is noted for each year to achieve EA T .To conduct CEPA following this method [8], demand and supply curves were constructed.For the demand curve, the overall grid energy demand (ED BAU ) and discretized grid emission factor (GEF BAU ) were used to calculate the business-as-usual grid period emissions (PE BAU ) by year.The supply composite curve data represents the energy supply (ES i ) and emissions at 100% capacity (PE i ) for each power plant.In both curves, the x-axis represents energy output (TWh) and the yaxis represents overall emissions (MtCO 2 -e).Once constructed following Ooi et al.'s [26] method, the compounded sum of each multi-period pinch point (PP S1, t ) was then read graphically for each year.Cossutta et al.'s [24] graphical method of illustrating carbon pinch diagrams is used to represent the EA via a waterfall chart embedded into the pinch representation bounded by the first and last years of analysis in this study.However, the excess capacity will be left on the graph as an illustration for calculating the excess load capacity and the PLF for the national grid.These calculations are represented by Eqs. ( 1) and ( 2): where In their study, S1 is used as the BAU scenario where no additional energy supply is added; only optimization of energy outputs based on EF i .The summary findings of S1 are then illustrated via a waterfall chart showing EA S1, t highlighting the PP S1 of the first and last year of the period as examples.

Screening power plants for CCS retrofit
This screening of power plants is conducted to determine the most appropriate power plant for capture retrofit in the national grid using volume of emissions over time as the selection criteria.The period grid emissions (PE S1, i ) were calculated for each i with the assumption that the construction for the CCS retrofit will take 2-years and be commissioned in the year 2024.The discretized EF i was used with the total energy output (ED S1, i ) for each plant over the years of analysis to quantify the PE S1, i .This calculation is represented by Eq. (3).
For each t, the EF, PLF, and PE S1, i were computed.The PLF i for each year is represented by Eq. (4) using the overall ES.This equation was replicated to calculate PLFs for other scenarios.
Other factors which affect the selection criteria [12] but not used in this study such as age and approximate distance from CO 2 sinks were also noted for reference.The most suitable selection is made solely on the quantity of emissions projected over the period until 2030.Power plant's thermal efficiency is also calculated from the source data to highlight the efficiencies of generating power per unit of energy used [53]; however, it was not used in the decision making of the selection of power plant for the CO 2 capture retrofit.For this study, the selection of the most appropriate power plant was made by volume of CO 2 emitted alone (PE S1, i ), however the thermal efficiency illustration provides contextual background on the performance of local power plants and opportunity for future screening criteria to be adapted.It is important to note for this study, CCS was not selected to achieve the EA T , but an avenue to decrease the load required from zero-carbon energy.The annual volume flow of the PE S1, i for the selected power plant would then be used as the input flow stream to the technical design simulation since emission stack flow data at 100% capacity for each i was not available.

Technical design of CCS
Aspen HYSYS (Version 8.8) was used to create a detailed capture plant retrofit technical design.The software simulation with the key objectives to: Determine the baseline technical requirements to optimally capture CO 2 emissions from a selected power plant like studies by Rubin et al. [14] (85%-95% removal of CO 2 ).Identify the EP CCS a capture retrofit would have on a host plant and compare to cited literature.This will also indicate supplementary energy needed to meet grid projections.Determine capital costs and operational costs for a capture plant designed for the selected power plant.
It is important to note that optimization modeling software cited in the literature were not used in this study and could be possibly adapted at further stages of commercial implementation.Aspen HYSYS was used to determine the preliminary, baseline techno-economic parameters to inform macro-scaled decision makers, and detailed optimization models can be further utilized to inform technical planners, engineering design teams, and other stakeholders on the economics of detailed lifecycle analysis.To initiate the simulation, the flue gas emission stack compositional (mole % distribution) for the selected power plant were gathered and compared to published work [54] for reference.It is assumed the elements within the flue gas compositional stream are distributed relative to their mole % and mass flow rates.Using the process simulation environment within Aspen HYSYS, and following the unit operation flow gathered from literature, a capture plant was designed to recover a near pure stream of CO 2 -e emissions for the selected power plant.10% Monoethanolamine (MEA) solution was selected as the solvent to capture the CO 2 from the flue gas exit stream as one of the most popularly used solvents for removal [12] when simulating within Aspen HYSYS [35,55].The "Acid Gas" fluid package was selected to incorporate the chemical equations needed for amine solution treatment.It was assumed that the flue gas from the power plant enters the system at optimal conditions for capture and the design of pre-treatment operations to meet CO 2 capture were not factored in.The compositional, thermodynamic, and CO 2 mass flow rates were recorded at the major inlet and outlet streams of the HYSYS simulation.The absorber and distillation column were optimized for maximum removal ratios based on literature sources [12,14].Built in Aspen HYSYS optimizers for capture simulations were utilized to ensure the process parameters were consistent for optimal recovery of CO 2 (>85%) consistent with international cases cited [14].Additionally, it is assumed that only CO 2 is captured from the exhaust stream, and other compounds such as CH 4 are negligible in the capture stream.The parasitic energy (EP CCS, i ) is obtained from the Aspen HYSYS utility section which automatically calculates energy requirements of the capture retrofit based on the design specifications.The emissions captured (EC) for the year were noted by the total mass flow rate of emissions exiting the capture plant to be sent for transport to the nearby sink.The cleaned flue gas emissions were assumed to be vented to the atmosphere.The new annual energy output (EO CCS ) for the retrofitted power plant is calculated from Eq. (5).
Annually the new EF CCS is calculated using Eq.(6).
where the new annual PE CCS for the selected power plant is denoted by Eq. (7).
The EO CCS and PE CCS is then used to formulate the new supply composite curve for each t of CCS operation in this study; between 2024 and 2030.The technical design outputs of the capture plant provide the new input variables to conduct multi-period CEPA.A summary of the data points used from Aspen HYSYS in the follow sections are illustrated in Figure 4.
The EC is assumed to be piped to a nearby depleted oil well in Trinidad for permanent storage.The technical design requirements of pipelines and injection equipment was not factored in within this study and only factored in the following sections from a unit cost basis.For transport, local technical studies provided a range of unit costs (USD/tCO 2 -e) for pipeline transport of CO 2 [49].For injection, Smith et al.'s [40] study provided a range of unit costs for injection from case studies across the US for oilfields.It is assumed that all the emissions captured are transported and injected in steady state.

Multi-period CEPA
This method aims at using the Aspen HYSYS information to modify multi-period CEPA analysis to minimize the RE requirements to meet EA T .Following Ooi et al.'s [26] graphical method of targeting multi-period emissions, the annual demand and supply composite curves are plotted to graphically read off the pinch points (PP CCS, S1 ).The overall grid emissions abated from the baseline study together with CCS retrofit (EA CCS, S1 ) was calculated using Eq. ( 8) where the initial year and final year of pinch analysis are 2022 and 2030, respectively.
This CEPA is conducted to illustrate the impact of CCS onto grid EA and the offset energy requirements resulting from EP CCS .To quantify the direct impact of CCS onto grid EA, Eq. ( 9) is used.
The EA CCS demonstrates the overall impact on grid emissions from a CCS plant and the offset energy requirements from more carbon intensive sources.It is important to note that the EA CCS is not equal to the EC directly from the capture plant due to this offset requirement from more carbon intensive power plants.
To supplement the unwanted effects of EP CCS and achieve the EA T , CEPA is then conducted using zero-carbon energy added to the supply composite curve following Ramsook et al.'s [8] method.An EF of 0 MtCO 2 -e/TWh is used for the added zero-carbon energy resource and is denoted by EF RE .
Scenario 3 (S3) was used to denote the case of CCS implementation within Power Plant A and zerocarbon energy to meet EA T .S3 represents an extension of Ramsook et al.'s [8] case studies which occur for the same periods and energy demand matrices.
The aggregated planning CEPA approach with overall emission limits (EL) is followed using Ramsook et al.'s approach to Tan and Foo's [17] initial study and Ooi et al.'s [26] multi-period method.Zero-carbon energy (EO RE ) is added to the supply composite curve to achieve the pinch point to satisfy the annual EL to meet EA T .The graphical pinch points are denoted by PP S3 (Eq.( 11)) for S3 and are used to compute the annual values summed to equate to the EA in Eq. (10).S3 represents the case where the total EA S3 is equal to EA T denoted by Eq. (12). where and The grid emissions abated from the supplemented RE (EA RE ) to meet EA T is calculated using Eq. ( 13).
Using Eq. ( 4), the overall grid PLF and GEF can then be calculated for this CEPA scenario to be compared with the findings referenced in their S2 study which uses solely zero-carbon energy to achieve the EA T .The calculation of these EA and EC values would then be used to subsequently determine unit costs for comparison between zero-carbon and CCS technology.

Economic modeling of CCS
Equipment costs in USD for the CCS retrofit were taken from the Aspen HYSYS simulation's costing function.Silla's [39] method for financial operational modeling was then applied using the equipment costs as the input for the "Fluid Processing" option for economic evaluation.This method uses the gathered equipment costs multiplied by various factors to determine the total fixed capital investment (CAPEX) through the direct (piping, electrical, utilities, etc.) and indirect (design, construction, contingency, etc.) costs.The direct operational costs (OPEX) were calculated using local rates for labor and utilities multiplied by raw material and utility usage data from the Aspen HYSYS simulation (power consumed, water usage, MEA usage, etc.).The indirect OPEX costs (taxes, insurance, administration, etc.) were determined using modeling indices outlined by Silla [39].A cash depreciation rate of 12% was used according to estimates used by Silla [39] for processing plants.The total cost of capture for the specified timeline is denoted by Eq. ( 14).
The unit cost of capture for CO 2 would be calculated using Eq.(15).
The unit transport (UC Transport ) was used based on the selected power plant's distance and EC as identified from the graphical illustrations conducted by Boodlal et al. [49].The storage costs were assumed based on rate of injection of CO 2 per year according to Smith et al.'s assumptions [40].
The overall total cost of the CCS stream for the years of operation is therefore denoted using Eq. ( 16).
where the total cost of transport (TC Transport ) and storage (TC Storage ) is computed using Eqs.( 17) and (18).
The unit cost of CO 2 removal for capture, transport, and storage is then denoted by Eq. (19).

Economic modeling of RE
Using a capacity factor of 25% [56], solar PV installed capacity (IC) is determined (MW).The unit CAPEX cost (CAPEX RE ) is assumed to be 1229 USD/kW and the annual OPEX cost (OPEX RE ) 22 USD/kW-year from globally established industry trends [42].Local technical estimates for macroscaled solar PV costs were not available.The overall useful lifecycle (LC) of 25 years will be used for the solar power to depreciate the capital investment.These variables were used to calculate the total cost of solar PV RE (TC RE ) in Eq. (20).
For comparison, Eq. ( 20) was also applied to S2 for Ramsook et al.'s [8] case report which satisfies EA T using solely RE along with the baseline S1.This was accomplished by applying the factor of LC and using the S2's computed IC result to determine this scenario's TC RE .

Unit cost of grid emissions abated
The total unit cost of grid emissions abated (UEA) is calculated using the lifecycle costs calculated for CCS, RE, and the total S3 using Eqs.( 21)-( 23), respectively.It is assumed that no cost is incurred for optimization of power plants based on carbon intensity given T&T's interconnected grid.
These factors are then compared to assess the viability of actions to achieve the overall EA T .Eq. ( 22) is also repeated for Ramsook et al.'s [8] S2 case report to compute the UEA using the TC RE calculated from the step prior.
According to Rubin et al.'s [14] study, the range of costs of CO 2 for natural gas combined cycle plants are $40% from the mean cited cost of 74 USD/tCO 2 .For this study, this range of cost variance is applied to the cost of capture to observe the effects on the associated overall UEA.The unit costs of transport, injection, and RE are kept constant.

Results
Each subsequent section illustrates the results in the logical order presented in the adapted methodology using T&T's baseline data.The results highlight the interpretation of the baseline data used and the screening criteria applied to select a power plant for capture retrofit.This is followed by a breakdown of the intricacies of the technical Aspen HYSYS simulation and model parameters used.Multi-period CEPA was then illustrated to identify the minimum amount of RE needed along with CCS to meet the EL established for the EA T .The economic modeling results were then presented for each action and compared using the unit cost assessment.

Baseline data representation
Baseline data for the study was first collected for power plants within T&T sourced from Ramsook et al.'s [8] case study and used as input values throughout the methodology for the same period.The data represented in Figure 5 was computed using Eq. ( 2) which represents the annual PE BAU using the ED projection for the years of analysis.This data was used to construct the annual demand composite curves and emission limits following Tan and Foo's [17] CEPA method.The figure illustrates the annual x-axis and y-axis values of ED and PE BAU respectively used to construct the composite curves.The annual EL is needed to provide the annual constraint variable to achieve EA T .The power plant data collected in Table 1 is used to develop the baseline supply composite curve.
The baseline summary data for S1 can be represented by Figure 6 for this study.The figure illustrates the determination of pinch points PP S1, 2022 and PP S1, 2030 to the supply composite curve highlighting the power plants denoted by A, B, C, and D in order of carbon intensity represented by the gradient of each straight line.
Using Eq. ( 1), the REA S1 calculated was 2.34 MtCO 2 -e between the years (t) 2022 and 2030 as shown in the waterfall chart embedded in Figure 6. Figure 6's foreground illustrates the PP S1 for the time bounds of this study: 2022 and 2030.Using this baseline CEPA data, the power plant's PE i were then screened for applicability for CCS.

Screening power plants for CCS retrofit
Table 2 shows the findings of EF, PLF, and PE for each i along with the age at the point of CCS commission and approximate pipeline distance from the sink source using Eqs.( 3) and (4).A carbon sink source of a depleted oil well located in the southern area of Trinidad is selected based on prior research conducted [48].The PE S1, i value from Eq. ( 4), which is nearest to EA T was selected to be most applicable for CCS retrofit.Other general criteria of distance from sink, plant age, emission factor, and load factor were noted but not used to select the most suitable power plant for CCS.
Power plant A is selected for CCS retrofit from Table 2.It is important to note the PE S1, A is not sufficient for the overall grid emissions amount required to be abated to achieve EA T but will significantly alleviate the amount of zero-energy capacity needed.Other noted factors referenced are its relative age and closeness to the selected sink area.The thermal efficiencies of the power plants show an observable upward trend from 2012 which can be attributed to the increased utilization rate of power plant A from the same year in Figure 7.
This trend validates the increased efficiency of combined cycle technology over conventional gas and steam turbines (single cycle) and highlights T&T's increased output per unit of fuel used.The thermal    efficiency however was not selected as part of the screening criteria, where logically, a lower efficient plant may be selected over a higher one.Because of power plant A's high volume of emissions, even though it was the most thermal efficient plant.Following the highlighted methodology, the annual PE S1, A is used as the input data for the Aspen HYSYS v8.8 simulation of CCS.

Technical design of CCS
Following the methodology, the unit operation design and optimization was simulated for a CCS capture plant for the selected power plant A within Aspen HYSYS v8.8 process simulation software.The flue gas compositional data for Power Plant A was gathered from emission stack data sources and compared to other studies [54].The actual information for power plant A was collected from secondary sources and verified with similar natural gas combined cycle power plants worldwide [54].Figure 8 illustrates the process simulation and four main highlighted streams.
The process flow follows the cited methodology of the separation of CO 2 from the flue gas stream (absorber) using MEA from the flue gas stream, and regeneration of a lean amine and almost pure CO 2 stream (regenerator).The stream breakdown for the main process flow streams from Aspen HYSYS were noted in Table 3 which compares the Aspen HYSYS input data with a similar reference plant for combined cycle.This comparison validates the input information used in this study compared with a similar plant [47].
The results of the simulation plant were denoted in Table 4 and compared to reference studies [14].Before and after CCS retrofit, power plant A's annual performance was noted.The result of the simulation demonstrates the accuracy of the Aspen HYSYS simulation conducted in this study through the relative similarity in findings from the extensive work conducted by Rubin et al. [14] in Table 4; particularly through the recovery rate of CO 2 , energy output loss ratio, and CO 2 -e reduction ratio.Table 5 illustrates the results of Eqs. ( 5)- (7).The comparison of this study's Aspen HYSYS results are validated with similar performances for other combined cycle capture plants referenced by Rubin et al. [14].
Using the Aspen HYSYS simulation results, and applying Eqs. ( 5)-( 7), the following results were collected for power plant A.
The significant EP CCS represents a corresponding decline in national grid supply from power plant A that will now be used to power the CCS retrofit in this study.The compounded PE CCS, S1 for power plant A has now decreased by 88% due to the EC simulated.ED CCS, S1 and PE CCS, S1 are now used as the x-axis and y-axis values respectively to construct the supply composite curves following the CEPA multi-period method.Power plant A is now denoted in the CEPA analysis as A CCS .
Multi-period CEPA Equation ( 8) was used to compute the results within Figure 9.It illustrates the targeting emissions method of the CEPA analysis with CCS retrofitted in power plant A over the period.It illustrates the compounded abatement of emissions (EA CCS, S1 ) via the embedded waterfall chart and illustrates the resultant changes CCS has on power plant A's EF and EO.Because of the EF CCS for power plant A, the supply composite curve now has a lowered overall EF post CCS retrofit; represented by a decrease in gradient represented by A CCS .
Commissioned in the year 2024, the effects of CCS are demonstrated by the increase EA versus the baseline scenario.It is important to note the change CCS incurs onto the national grid.From a carbon intensity point, it significantly decreases the supply EF; however due to the EP CCS this impact is lessened due to the increased utilization of the more carbon intensive Power Plant D. Following Eq. ( 9), the EA CCS, S1 is used to calculate EA CCS highlighted in Table 6.
This preliminary CEPA result demonstrates CCS's inability to achieve EA T by $45% when used in T&T's largest power plant.This is mainly due to the decreased output of A incurred by EP CCS as well as the increased utilization of D with a PLF of $27% by 2030.It is also important to note the   Following the S3 CEPA planning method, zerocarbon energy is added into the supply composite curve, denoted by RE.This action will offset the effects from power plant D, negate the effects of EP CCS on power plant A, and achieve the EA T .Figure 10 demonstrates results of Eqs. ( 10) and ( 11) for RE's addition in the year 2022 to achieve Eq. ( 12) target.1.9 TWh/year of RE (EO RE ) is added in the first year of analysis and is represented in the supply composite curves for each t. Figure 10 also demonstrates this addition to meet the EL's which is represented by PP S3, t (Eq.( 11)).
The waterfall graph embedded in Figure 7 highlights the annual grid emissions abated for this scenario by EA S3, t .which was calculated using Eq.(10).The results of Eqs.(10) and (13) are found in Table 7 from the CEPA analysis highlighting this method was successfully applied to achieve T&T's 2030 NDC emission abatement target.
Using the cited capacity factor for solar PV, 868 MW of RE capacity is required (IC) to meet EA T ; approximately 60% less than the capacity calculated by Ramsook et al. [8] for the same period of analysis.This finding demonstrates the high potential of CCS integration within the power grid and its implications of use to ease the transition to zero-carbon energy within the national grid as alluded to by Boodlal et al. [48].The grid PLF and GEF for this scenario were also compared to Ramsook et al.'s [8] case report in Table 8 to achieve the same EA T .
The PLF average for the period indicates the increased use of existing infrastructure to achieve EA T compared to the CEPA scenario conducted Ramsook et al. [8] which satisfies EA T using solely zero-carbon energy.The period average GEF denotes the success of this method's approach of prioritizing CCS and supplementing RE to achieve EA T .

Economic modeling
Following the cited method of gathering equipment costs from Aspen HYSYS v8.8 and applying Silla's [39] factors, the CAPEX and OPEX were calculated for the simulated capture plant; a full breakdown can be found within the Appendix.The findings for Eqs. ( 14)- (19) were summarized in Table 9 which represents the entire breakdown of costs for the simulated plant design, pipeline transport, and injection for storage into the sink.The complete breakdown of the financial operational model is captured in the Appendix.The UC Transport was selected at 10.6 USD/tCO 2 from technical transport studies for T&T for distances <40km.A UC Storage of 5.59 USD/tCO 2 was used assuming steady state injection over the period of operation.Permeability and depth of well data which were also factors for selecting unit costs of storage were not factored in due to lack of data for T&T.The findings successfully link the technical design aspects of a retrofit capture plant for power plant A and provide the baseline data for the economic modeling highlighted by Silla [39].It is also important to note that the UC Capture represents $77% of the overall unit cost of the CCS components; with transport and injection accounting for 7% and 16% respectively.This highlights the importance the capture component of CCS and need to ensure this aspect is properly designed to reduce costing variation.These techno-economic findings represent an avenue to address the highleveled planning gaps previously mentioned in CEPA studies.This work demonstrates the economic assessment of the impact of detailed plant process integration and optimization techniques on macro-scaled emission targets, which has not yet been explored in the known literature.
Using the capacity factor conversion for solar PV, the parameters for Eq. ( 20) is represented in Table 10 along with the computed TC RE for the period.Following the method and providing a reference point, Ramsook et al.'s [8] S2 IC case report capacity was also costed to compute this scenario's TC RE .
The results for the costing of RE over the period demonstrate the success of the equations highlighted using the computed IC and EO RE .The unit costs comparison reveals unit costing superiority of RE compared to CCS for this scenario mainly due to its CAPEX RE depreciation over its LC.This method also reinforces the traditional approach of macro-scaled multi-period CEPA with technical design and economic models to provide decision makers with the insights needed to achieve the established EA T .Additionally, no cost is assumed for the baseline S1 which optimizes the power plants based on EF i since the island is electrically interconnected.
To achieve EA T following this method would cost T&T $93% greater than budgeted costs [6].This is mainly due to the introduction of CCS retrofit within T&T's largest power plant to achieve the short-term reduction goal.However, considering the $55% cheaper unit costs of solar PV, considerations can be made to have EA T met mainly through RE.Ramsook et al.'s [8] case report capacity was also costed and used to compute the overall UEA for S2 which uses solely zero-carbon energy to achieve the same EA T .

Recommendations
The economics of this study can further be built upon by the calculation of cost of electricity implications with the capital and operational financials presented in this work.Future work will determine the cost of avoidance of CO 2 by factoring in data points of cost of electricity generation of power at local power plants and specific to RE costs in T&T.Currently, T&T has a significantly lower cost of electricity compared to other SIDS due to local subsidies from fossil fuel electricity which will have . UEA results of Eq. ( 23) (with sensitivity of costs of capture retrofit) compared to budgeted values [6].
a negative impact on increasing the feasibility of RE integration [57].This will facilitate the calculation rate of return for electricity generation from RE, negative impacts on electricity costs of CCS on power plants proposed for retrofit, and economic returns of CO 2 injection for topics such as enhanced oil recovery (EOR).Future work is proposed to be conducted to factor in these aspects with multi-period CEPA to inform cross functional decision makers for electricity costs, oil recovery, and local subsidy adjustments.
Additional selection criteria can also be economically weighted and factored into the decision making for power plant selection for CCS such as efficiency, distance from sink, and composition of CO 2 from flue gas streams.Furthermore, optimization modeling software such as LINGO can be used for detailed economic planning at the planning phase of work informed by detailed lifecycle analysis of the CCS retrofit.This will allow the progression of the preliminary data presented in this study to validated optimization techniques to ensure reliable optimization results.
Other emerging technologies such as heat recovery steam generation (HRSG) have also been cited as viable options to reduce the GEF.HRSG like CCS would potentially be a retrofit to existing single-cycle natural gas turbines which recover waste heat for steam-turbine engines; thus, producing more power for the same unit of natural gas.Future work will explore HRSG retrofits to be screened like criteria established in this study and the effects on PE assessed using multi-period CEPA similarly.The techno-economics of this method can then be assessed to determine UEA for comparison to CCS and RE.Finally, recommendations can be made based on this cost comparison for economic optimization to achieve the EA T .
This study considers only the cost of storage of the captured carbon and not its potential payback from popular methods used worldwide called EOR.Given T&T's history of being an oil production economy and recent global trends in industry for CO 2 -EOR, the opportunity to expand this work is present to consider increased oil production recoveries for local depleted oil wells.EOR provides an attractive economic payback factor for CCS retrofit through increased oil production of depleted wells in addition to storing the CO 2 captured.However, oil well sink classification, data collection, and recovery factor screening is needed to ensure success in this proposed expansion of scope.Furthermore, literature highlights EOR as being counter-intuitive to the aim of reducing emissions in which CO 2 is used to produce more fossil fuels which in turn increase the country's carbon footprint.These cost implications can also be further expanded to calculate the new cost of electricity generated for power plant A with CCS retrofit; the requirements of which were not gathered for this study.Further opportunity for expansion lies in gathering actual plant data for costs of generating electricity and projecting the future costs if retrofit is considered.
It is also essential to extend the analysis beyond 2030 and consider the implications for global decarbonization goals for the year 2050.To improve upon the existing study, the following recommendations can be considered: Future Scenarios: Incorporate different projections that account for potential changes in technology advancements, policy frameworks, and energy demand patterns which will facilitate a more robust analysis of the potential contributions and limitations of CCS and renewable energy sources in achieving GHG reduction targets by 2050.Comprehensive Economic Assessment: Conduct a comprehensive economic assessment that includes the lifecycle costs and benefits associated with CCS and renewable energy technologies.This should consider factors such as capital investments, operational costs, maintenance, and potential revenue streams, ensuring a holistic understanding of the economic feasibility and viability of these technologies over the long term.Furthermore, it should also consider the economic benefits of having RE technology as a long-term solution.Integration and System Optimization: Assess the integration and optimization of CCS and RE within the broader energy system.Consider the impact on power generation, transmission, and distribution infrastructure, as well as the potential synergies or trade-offs between different energy sources.This analysis should account for grid stability, flexibility, and the role of energy storage technologies in accommodating the variability of renewable energy sources.Policy and Regulatory Frameworks: Evaluate the existing policy and regulatory frameworks and propose recommendations for enhancing their effectiveness in promoting the deployment of CCS and RE technologies which considers the need for supportive policies, incentives, and long-term planning strategies that foster investment and innovation in sustainable energy solutions.
By extending the analysis to the year 2050 and implementing these recommendations, the study can provide valuable insights into the long-term implications of CCS and RE on emission reduction targets.This will assist policymakers, industry stakeholders, and decision-makers in formulating sustainable and effective strategies to address climate change challenges and achieve a low-carbon future.

Conclusion
This paper successfully applies a first of its kind approach to demonstrate the linkage of information of macro-scaled insights from graphical multiperiod CEPA to simulate a baseline techno-economic scenario using Aspen HYSYS software, with the T&T's 2030 Paris Agreement EA T as case data.The overall aim of minimization the zero-carbon energy capacity introduced within the national grid using CCS within existing fossil fuel power generation was achieved.By gathering and analyzing relevant quantitative power plant data, this study concludes that significant, short-term investments are needed to activate changes in T&T's current energy supply to meet future energy demands and GHG avoidance targets versus BAU projections.This study introduces the use of Aspen HYSYS as a technical tool to conduct macro-scaled planning for process integration within existing power plant and can be used by decision makers to benchmark proposed activities and economic costs for carbon reduction targets.The methodological steps presented in this work allow it to be a flexible mathematical instrument that can be adapted to other critical areas where GHG avoidance is essential to combat the expected effects of climate change.This adaptable and flexible ability will assist in closing informational gaps needed by decision-makers to implement impactful strategies to achieve international GHG avoidance commitments made by countries under the Paris Agreement.This finding of this study provides decision makers with key economic information since T&T's power generation NDC target remains conditional on international financing.Studies like this can be conducted and strengthened with actual data points to allow national decision makers to answer the key points of pragmatic NDC targets, technical requirements to achieve proposed targets, and indicative economic requirements.

Disclosure statement
No potential conflict of interest was reported by the authors.

Appendix
Costing breakdown using equipment costs, utilities, and raw material rates from Aspen HYSYS applied to economic factors [39].

Figure 1 .
Figure 1.Map of T&T highlighting historical CCS injection sites and main CO 2 sources relative to oil and gas field operations.

Figure 3 .
Figure 3. Overview of methodology to link CEPA and techno-economics.

Figure 4 .
Figure 4. Data points used from Aspen HYSYS in following methodology sections.

Figure 6 .
Figure 6.Graphical representation of CEPA and grid emissions abated for baseline data used [8].

Figure 7 .
Figure 7. Thermal efficiencies of power plants and T&T's national average.

Figure 8 .
Figure 8. Process overview of Aspen HYSYS v8.8 simulation for capture plant at power plant A.

Table 2 .
PEs, EFs, and PLFs for baseline S1 used to select i for CCS retrofit.

Table 3 .
Stream breakdown within Aspen HYSYS simulation for capture retrofit at power plant A.

Table 4 .
Aspen HYSYS CCS simulation results compared to global Averages.

Table 5 .
Energy and emission parameter effects of CCS retrofit on power plant A.

Table 6 .
Grid emissions abated summary of CCS on national grid.

Table 7 .
Grid emissions abated for S3 compared to RE's performance.

Table 9 .
Costing breakdown of capture retrofit for power plant A.Unit cost of grid emission abatementHaving successfully calculated TC CCS and TC RE , Eqs. (21)-(23) can be applied to determine the unit costs of carbon abatement to compare both methods.The values of UEA CCS and UEA RE were determined to be 95.41 and 42.64 USD/tCO 2 -e