Carbon Constrained Energy Planning (CCEP) for Sustainable Power

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Carbon Constrained Energy Planning (CCEP) for Sustainable Power Generation Sector with Automated Targeting Model Raymond E. H. Ooi, Dominic Chwan Yee Foo, Raymond R. Tan, Denny K. S. Ng, and Robin Smith Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/ie4005018 • Publication Date (Web): 07 Jun 2013 Downloaded from http://pubs.acs.org on June 24, 2013

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Industrial & Engineering Chemistry Research

Carbon Constrained Energy Planning (CCEP) for Sustainable Power Generation Sector with Automated Targeting Model Raymond E. H. Ooi1, Dominic C. Y. Foo1, *, Raymond R. Tan2 , Denny K. S. Ng1 , Robin Smith3 1

Department of Chemical and Environmental Engineering/Centre of Excellence for Green Technologies University of Nottingham Malaysia, Broga Road, 43500 Semenyih, Selangor, Malaysia 2

Chemical Engineering Department, De La Salle University 2401 Taft Avenue, 1004 Manila, Philippines

3

Centre for Process Integration, School of Chemical Engineering and Analytical Science The University of Manchester, United Kingdom

Abstract Carbon capture and storage (CCS) is one of the key technologies to mitigate greenhouse gas (GHG) emissions from stationary sources such as power plants. However, retrofitting power plants for carbon capture (CC) entails major capital costs as well as reduction of thermal efficiency and power output. Thus, it is essential for planning purposes to implement the minimal extent of CC retrofit in order to meet the energy requirement and grid-wide carbon emission targets. Recently proposed pinchbased techniques provide good insights for setting the minimum retrofit targets; however suffer with the limitation of simplification.

This paper presents an

optimisation-based automated targeting model (ATM) for carbon-constrained energy planning (CCEP), focusing on the deployment of CCS. The ATM incorporates the advantages of insight-based pinch techniques and mathematical optimisation approach. The applicability of ATM is demonstrated through the planning at sectoral level, as

*

Corresponding. Tel: +60-3-89248130, fax: +60-3-8924-8017. Emails: [email protected], [email protected], [email protected], [email protected], [email protected]

ACS Paragon Plus Environment


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well as discrete selection of power plant units for CCS deployment. Furthermore, this approach also allows CCS to be integrated not just via retrofit, but through new plants that are capture-ready at the outset; in particular, we consider the case of new bioenergy CCS (BECCS) which contribute negative emissions to a system. Hypothetical but representative case studies are solved to illustrate the proposed methodology. In particular, the results show that ATM provides good insights apart from identifying the minimum extent of retrofit. The identification of pinch point serves as a reference in which CC retrofit is justified.

Keywords: carbon capture and storage, greenhouse gas reduction, optimisation, process integration, pinch analysis, targeting.

1. Introduction

Recently, there has been increased research on efficiency improvements, fuel substitution and increased use of low-carbon or renewable energy for the generation of cleaner electricity. This trend is to address the issue that fossil fuels are the main contributors of high carbon dioxide (CO2) emission, and will continue to be the major contributors to the world’s overall power generation mix in the coming decades.[1-3 Replacing fossil fuels with the renewable energy (which are constrained by various limitations such as cost, geographic specificity and low availability) is often an unpopular decision. Besides, the fossil fuel-fired plants rely on mature technologies and have inherently better reliability and availability than renewable energy sources (e.g., wind, hydro, solar power etc.). At the same time, in many parts of the world,


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fossil fuel is still more socially acceptable to the general public than low-carbon nuclear energy. Thus, CCS potentially may allow extended use of the world’s fossil fuel reserves (including non-conventional gas resources) as CO2 emissions, rather than supply, become the limiting constraint to their exploitation. In addition, use of CCS with inherently carbon-neutral bioenergy offers the prospect of generating negative CO2 emissions4 which may be needed to achieve the necessary stabilization of atmospheric CO2 concentration to safe levels.

These factors have led to the widespread interest in retrofitting existing plants with carbon capture (CC) technology, such as oxy-fuel combustion, chemical looping combustion, pre-combustion capture using integrated gasification combined cycles, or post-combustion capture using flue gas scrubbing. These technologies can be used to capture 80 – 90% of CO2 from power plant exhaust gases, and subsequently compress it for secure storage in saline aquifers, depleted oil wells, inaccessible coal seams and other impervious geological formations.

As shown in the literature5-8, these

technologies will be available at commercial scale within the next decade.

Although CC provides various benefits as mentioned above, there are two main drawbacks of retrofitting power plants for CC.

Firstly, installing CC process

equipment entails additional capital costs. Wall7 estimates capital costs of power plants with CC to be 25 – 50% higher than that of comparable baseline plants. Secondly, additional power is required to operate the CC processes; therefore, the overall thermal efficiency and generation capacity of the plant is reduced due to this



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parasitic internal energy usage. It is estimated that this typically results in 15 – 20% reduction in power output.7 The additional capital cost and fuel consumption per unit of electricity output results in an increase in power generation cost, with some estimates ranging from 25 – 50%.5-8 At the macroscale level (i.e., power sector), it also becomes necessary to install additional or compensatory electricity generation to make up for the inevitable energy losses incurred by the CC system. Alternatively, this compensatory electricity may also be imported from contiguous regions, where geography and supply conditions allow. As a result, there is a need for methodologies to aid in planning and decision-making with respect to grid-wide deployment of CCS. Such techniques can be used at the policy level (e.g, by an energy department of ministry) to develop CCS priorities systematically. Note that the scale of such a problem is distinct from other process-level models for optimizing capture within power plants which is dealt with in the literature.9

Carbon-constrained energy planning (CCEP) is a relatively new area of research aiming to address carbon emission reduction issues systematically during energy planning stage. Several insight-based techniques were developed under the framework of carbon emission pinch analysis (CEPA). A graphical targeting tool known as energy planning composite curves was first presented by Tan and Foo10 to determine the optimum allocation of fossil fuels by minimising carbon-neutral energy sources. Two other equivalent algebraic tools were later developed, i.e. based on cascade analysis11 and composite table algorithm12 to overcome the limitations of the graphical tool10, such as inaccuracy problems. The concept was later extended to include segregated


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targeting with regions based on unique sets of energy sources using various insightbased tools.13-14 Alternatively, a superstructural-based optimisation technique was also developed for CCEP problems.15 Later, several works has been presented to demonstrate the application of the developed tools in energy planning for different countries and regions, such as New Zealand16 and China.17 Further work by Crilly and Zhelev18-19 also highlighted the need and applicability for emission targeting energy planning in Ireland for the electricity generation sectors.

To further reduce CO2 emission after optimum energy allocation, the deployment of CCS may be considered. Two sub-problems have been addressed in process systems engineering literature. First is the impact of CCS deployment on emissions and power supply. To date, a graphical technique20 based on CEPA has been developed for preliminary static planning of grid wide CCS planning in the power generation sector. The technique aims to minimise power losses associated with the deployment of CCS, and indirectly reduce the amount of make-up energy required for compensatory power, while meeting the overall energy demands and emission limits. Ooi et al.21 later extended this graphical technique20 for multi-period emission planning for Malaysia; while Ilyas et al.22 demonstrated its usage for CCS retrofit planning for the South Korea electricity sector. The second sub-problem is matching of CO2 sources with sinks, in which a few graphical and algebraic techniques have been presented to address this sub-problem. The graphical technique presented by Diamante et al.23 takes into account geological constraints of the CO2 reservoir, such as sink capacity and injectivity. In the recent work of Ooi et al.,24 both graphical and algebraic techniques



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were proposed for the optimum allocation between CO2 sink and sources, taking into consideration of time availability of the CO2 sink.

On the other hand, mathematical programming techniques offer a complementary approach to CEPA; these models have the advantage of accounting for detailed aspects of planning problem, while CEPA facilitates interpretation and insight-based decisionmaking. The model developed by Pękala et al.15 was also meant for CCEP with the deployment of CCS in power generations plants. However, similar to the graphical technique20, this early work is also limited to the types of CC technologies to be adopted, with fixed CO2 removal ratios and is not able to identify individual power plants for CC retrofit. These limitations were later overcome by the superstructural model developed by Tan et al.25 To address long term planning issue, Lee and Han26 developed a multi-period stochastic model for CCS planning for the energy sector in Korea. Also, Elkamel27 demonstrated that CCS can be considered as a low carbon option which can be added to the energy grid. More recent works on optimizationbased methods28-30, address source-sink matching of CO2 sources and sinks, taking into account geological constraints such as sink capacity and injectivity.

In this paper, an improved optimisation-based automated targeting model (ATM) is presented to address the optimum deployment of CCS in CCEP problem. The ATM shows more flexibility by allowing variations in the types of retrofit. By comparison, the earlier graphical approach20 is limited by the need to use lumped parameters that are assumed to apply uniformly to all plants in the system. In addition, the proposed


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model is extended to address CCS deployment issue for both sectoral planning level and discrete identification of power plants that needs to undergo CCS retrofit. Note that the ATM incorporates the concept of CEPA in an optimisation framework, in which CCS retrofit targets can be identified ahead of detailed planning, along with other insights. Note also that the ATM is linear, thus, global optimal solution can be found if a solution exists.

The rest of this paper is organised as follows. In the following section, a formal problem statement is first presented. The ATM is next presented to demonstrate the overall planning of CCS deployment at the sectoral level. Next, the presented model is extended by including binary variables for discrete selection of individual power plants for CC retrofit. The ATM is then extended to assess the application of bioenergy with CCS, which allows for the possibility of negative emissions in CCEP problems.

2. Problem Statement The CCEP problem with CCS deployment is formally stated as follows: •

The power generation sector of a given geographic or political region (e.g., a country or group of adjoining countries) consists of a mix of different power sources, which may be fossil-based (e.g., coal, oil or natural gas) or non-fossil (e.g., nuclear, wind, hydroelectric, biomass or solar).

•

Each component i of the power mix contributes a fixed quantity Fi to the total output, with an average carbon intensity, Ci.



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•

It is assumed that a total power requirement (FP) and an aggregate carbon emission limit (L) are set for the entire power sector of the region. In this work, L is set, which is lower than the actual current emissions (Σi FiCi). Thus, CC is used to reduce the sector emissions by retrofitting existing power plants. The magnitude for retrofit is dependent on the difference between Σi FiCi and L. As a simplifying approximation, it is assumed that CC retrofit achieves a fixed removal ratio (RRi), representing the fraction of the plants’ original CO2 emissions that are removed from flue gases. However these parameters can be made unique to different types of CC method adopted and also the type of power generation plant where CC is adopted. Note that this feature is not possible using the graphical method.20 Furthermore, CC retrofit also incurs a parasitic energy loss (EL), which leads to lower power output of the plant after retrofit. The energy loss ratio for power plant operated with fossil source i (Xi) is defined as the ratio of the power loss as compared with its original output.

•

The main task is to minimise the total energy loss when power plants are retrofitted for CC, while still achieving the carbon emission limit, L. This is done in such a way that the total extent of retrofit (Σi FRi) is minimized. The minimum extent of retrofit leads to lower capital costs as well as the reduction of additional electricity generation (or import) to compensate for losses due to energy demands of the CC retrofit. It is assumed that the resultant deficit in the electricity output is met either by building new, non-fossil plants with relatively low carbon intensities, or by importing power from nearby regions with surplus generation, since the main objective of the planning is to reduce aggregate


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sectoral emissions. In the latter case, the carbon footprint of the electricity import is accounted as part of the system emissions.

3. Automated targeting model (ATM) – basic framework The ATM was originally developed for the synthesis of industrial resource conservation network. A compilation of the various models are found in Foo.31 For CCEP, the earliest variant of ATM was proposed by Lee et al.,13 focusing on the optimum allocation of fossil fuels, i.e. without the deployment of CCS. In this work, ATM is extended for considering grid-wide CCS planning.

To consider CCS retrofit planning, ATM takes the overall framework as shown in Figure 1. As shown, the carbon intensities of all energy sources (including low- and zero-carbon, as well as retrofitted sources) and demand (i.e. total power requirement) are arranged in an ascending order. Note that a total of nine carbon intensity levels (k = 1 – 9 ) have been included, which represents those for retrofitted (CRi) and nonretrofitted (Ci) fossil fuel source i (coal – CL, natural gas – NG, and crude oil – OIL), as well as those for compensatory sources (CCRi) and net power output (CP).

Since energy losses are experienced in the retrofitted power plants, the resulting intensity levels of the retrofitted sources (CRi) are no longer the same as those before retrofit (Ci). Equation 1 is used to determine the intensity levels of the retrofitted sources i with CC (characterised by RRi), where energy loss (Xi) is incorporated:



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C Ri =

Ci (1 − RRi ) (1 − X i )

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(1)

On the other hand, the intensity of the energy demand is given by the division of the aggregate carbon emission limit (L) by the total power requirement (FP). Besides, a final fictitious carbon intensity level (an arbitrarily large value) is added to allow the calculation of residual CO2 load.

Power cascade is next performed across all carbon intensity levels, as shown in Figure 1(a), to ensure that the total amount of energy sources will meet the required energy demand. At each level, the difference between the total available energy sources and demands is first determined. The total available energy sources include those without CC retrofit (Σi Fi) and those that are retrofitted (with energy loss, i.e. FRi(1 – Xi)); while the total energy demands include the total power requirement (FP) and sources to be sent for retrofit (FRi). Next, the net power is cascaded from the higher (δk-1) to the lower carbon intensity levels (δk), given as in Equation 2.

δk = δk-1 + (Σi Fi + FRi(1 – Xi) – FP – FRi)k

∀k

(2)

Note that the net power flowrate (δk) can either take a positive or negative value, where positive indicates energy surplus and negative for deficit. Note also that in Figure 1, the energy source found at the first two intensity levels corresponds to the amount of low-carbon sources (FCRi) which are mainly contributed by renewable energy sources.


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The upper bound for retrofitted power sources is the total baseline power generation, given by Equation 3:

FRi ≤ Fi

∀i

(3)

Apart from power cascading, CO2 cascade (Figure 1 (b)) is also essential to ensure feasible CCS retrofit planning, in which the overall emission level is met based on the optimised power distribution. Hence, CO2 cascading is performed next. Within each carbon intensity interval, the CO2 load is given by the product of the net power from level k and the difference between two adjacent carbon intensity levels. As in the power cascade, residual of the CO2 load from each carbon intensity level k (εk) is cascaded down to the next level. Hence, residual CO2 load at kth carbon intensity level is determined using Equation 4.

εk = εk–1 + δk-1 (Ck – Ck-1)

∀k

(4)

where εk–1 is the residual CO2 load that is cascaded from intensity level k – 1.

Since there is neither net power nor CO2 load generated before the first carbon intensity level, these values are always set to zero (i.e. δ0 = ε1 = 0). Besides, all residual CO2 load εk must take non-negative values, which implies that a feasible CO2 cascade is achieved.11 Physically, a negative εk value indicates that the residual CO2 load is cascaded in the reverse direction (i.e. upwards) of the emission level, which is



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not possible. Therefore, Equation 5 is also included as a constraint in the formulation model.

εk ≥ 0

∀k

(5)

When the residual CO2 load is determined as zero in the model solution, a pinch point is observed at the CO2 intensity level k (εk = 0). The pinch represents the overall bottleneck of the problem, where the use of fossil fuels is maximised. It also implies that all power plant with fuel sources below the pinch (i.e. with higher intensity than the pinch) will be retrofitted with CCS. This distinctive insight is not possible with other superstructural-based optimisation models.15

Note that the above formulation is a linear programming (LP) problem, which can be readily solved to determine global optimal solution if a solution exists. The succeeding sections illustrate this approach using hypothetical but representative case studies, using emission levels based on realistic values of current technology.2 For the illustrative examples, the model is implemented and solved using Lingo v10.

4. Case Study 1 To illustrate the proposed targeting technique for CCEP, a case study is solved, with data given in Table 1.20 As shown, three fossil fuel and a zero-carbon sources are used to fulfill the total power requirement of 60 TWh/y, with the total CO2 load of 31.2 Mt/y. A carbon emission limit (L) of 15 Mt/y is set, which means a reduction of 16.2


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Mt CO2/y is needed. The example also assumes that CCS with RRi of 0.85 and energy loss (Xi) of 15% is used to retrofit all kinds of power plants.20 Based on Equation 1, the intensity levels for the retrofitted sources (CRi) are calculated in the last column of Table 1.

The optimisation objective for the problem is set to minimise the energy loss (EL), i.e. Minimise EL where energy loss is given by: EL = Σi FRi Xi

(6)

Solving the objective function in Equations 6 subject to the constraints in Equations 1 – 5 yields the minimum power loss of 2.94 TWh/y. The results of the ATM is shown using the cascade diagram in Figure 2. As shown, the retrofitted sources of 16.8 and 2.83 TWh/y are identified for coal and oil respectively. Note that the results match those that obtained using graphical targeting.20 Note also that the amount of energy available after retrofit for coal and oil are 14.28 and 2.41 TWh/y respectively, due to the associated energy losses (assuming at 15%). Note that the model preferentially selects power sources with high initial carbon intensities for CC retrofit.

The ATM model provides a good insights apart from locating the extent of retrofit required on each energy sources, since it incorporates the targeting concept of CEPA. In particular, the pinch point determines a cutoff point for carbon intensity at which CC retrofit is justified. Hence, the problem may be decomposed into below- and above-



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pinch regions (with carbon intensities higher and lower than that of the pinch, respectively), for which subsequent energy policy analysis may be facilitated. As described earlier, all power plants located in the below-pinch region will be fully retrofitted. This corresponds to all coal-fueled power plants, as shown in Figure 2. Note also that only a portion of power plants in the oil sector (at the pinch) are retrofitted.

5. ATM for Discrete Selection of Power Plants The targeting methodology presented in previous section is applied for CCEP of macroscale, where the retrofitted sources are selected and identified collectively by type of fossil source. The solution does not provide a discrete breakdown on the exact plant (within similar fossil sources) to be selected for retrofitting. In other words, it is uncertain which exact power plant(s) is to be selected for CC retrofit in the optimised solution.

In this section, the previously presented model is further extended to

demonstrate the applicability of the ATM technique in discrete modeling of CCS planning.

For discrete selection of power plants to be retrofitted, an additional constraint in Equation 7 is added to the ATM.

Binary variables with values of 1 and 0 are

introduced, indicating use and non-use of CC respectively:

FRi, n = bi, n Fi, n

∀i ∀n


(7)

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where bi, n = 1 or 0 and n = plant of fuel source i. This assumes that once a power plant is identified for retrofitting, the entire capacity of the power plant is considered for retrofitting. As such, a particular power plant unit is either selected for retrofitting or not selected, as the outcome of the specification of the binary variable. Note that in practical cases, one may also apply the same model to identify which individual generators of the power plant is to be retrofitted, as there may be more than one unit in a typical power plant.

The total energy to be retrofitted for each fuel source i of the power mix is the summation of all n power plants operating on the same fuel type, given in Equation 8. FRi = Σ n FRi,n

∀i

(8)

The result is a mixed integer linear programming model (MILP) which is also readily solved to global optimality with branch-and-bound solvers. Case Study 2 is solved via Lingo v.10 to illustrate this extension.

6. Case Study 2 In this case study, 10 power plants of various types of fuel sources is analysed. Their detailed breakdown on power generation and emission factor are presented in Table 2.24 As shown, they are five units of coal plants, three units of natural gas plants, and two units of oil plants. Assuming that these power plants are to supply the overall power requirement of 3100 MW for a country/region, they are emitting a total CO2



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load of 2490 t/h (21.8 Mt/y). This leads to an average carbon emission intensity of 0.803 t/MWh (= 2490 t/h / 3100 MW).

For this case study, a carbon emission limit (L) of 1100 t/h (9.6 Mt/y) is given.24 This translates to an average carbon footprint of 0.355 t/MWh (=1100 t/h / 3100 MW), i.e. approximately 44% reduction in CO2 emission is required. For simplicity, the case study assumes that oxy-fuel combustion is the CC technique to be used for all power plants, with removal ratio (RRi) of 0.90 and energy loss (Xi) of 25% .24

For this case, emission factor for compensatory energy (i.e., renewables) is fixed at 0.1 t CO2/MWh to account for minor GHG releases from such systems.24 Solving the objective function in Equations 6 subject to the constraints in Equation 1 – 5 and 7 – 8 yields the minimum energy loss (EL) of 412.5 MW. This amount of energy loss will be supplemented by the compensatory energy, which contributed by renewable energy sources. The results are shown in Figure 3 and details summarised in Table 3. Note that the results match those obtained using the superstructural model.24

Based on the results in Table 3, a total of five units of power plants (four out of five coal power plants and one out of two oil power plants) have been selected for CC retrofit to achieve a carbon emission limit (L) of 1100 t/h (9.6 Mt/y). All natural gas plants are not selected due to their relatively low carbon emission factor of 0.5 (t CO2/MWh) as compared to coal and oil plants, which have been prioritised for retrofitting.


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Note also that, the compensatory energy required to supplement the power loss has a low carbon emission factor (0.1 t CO2/MWh). This corresponds to renewables such as wind, hydroelectric, biomass, geothermal and solar power. In addition, nuclear energy may also be considered as part of the low-carbon options. Although power generation from these sources does not release CO2 directly, the carbon emission associated with large hydropower or nuclear plants comes mostly from indirect activities, such as the construction and decommissioning process.32

7. Bioenergy with Carbon Capture and Storage (BECCS) Bioenergy source is a form of renewable energy which is derived via biological carbon fixation, including electricity derived from the combustion of biomass. Such bioenergy has inherently low carbon emissions due to the almost closed carbon loop throughout the fuel cycle (i.e., photosynthesis offsets combustion emissions when the energy system is at steady state). Some emissions may occur through use of fossil energy as inputs within the supply chain (e.g., for harvesting and handling biomass). The usage of bioenergy is expected to reduce the GHG, besides revitalize economy of a country by increasing demand and prices or agricultural products.33 Since bioenergy by itself is inherently nearly carbon neutral, further use of CC will result in negative CO2 emissions, due to a net transfer of CO2 from the air into final storage.4 Such technology is known as bioenergy with carbon capture and storage (BECCS). Figure 4 shows an illustration on the flow of CO2 emission with BECCS where power generation from biomass source can be distributed between conventional plant and BECCS plant.



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Unlike the conventional plant, BECCS plant has a net CO2 emission as the amount or degree of CO2 captured (and sent for underground storage) exceeds CO2 released to the environment. For example, a plant using biomass (with carbon content of 45% and calorific value of 18 MJ/kg) at 32% thermal efficiency generates 1.03 kg/kWh of direct CO2 emissions, all of which is inherently recaptured in a steady-state system by carbon uptake during biomass growth. Life cycle based emissions of, say, 0.1 kg/kWh, represent emissions from biomass transport, as well as capital goods for the plant itself.2 Suppose that 80% of the emissions of this plant are captured; the resulting net emissions may then be computed as –0.72 kg/kWh (= 1.03(1 – 0.8) kg/kWh – 1.03 kg/kWh + 0.1 kg/kWh, where the three terms correspond to stack gas emissions, carbon fixation by biomass, and other emissions in the fuel cycle, respectively). These computations serve to give an order-of-magnitude sense of the level of emissions from BECCS systems, although exact values will depend on technical case-specific details. McLaren34 provided an overview on the global assessment for negative emission techniques which includes BECCS. This method of sustaining negative CO2 emission is highly potential and attracting substantial levels of research work lately. Based on Obersteiner et al.4 BECCS strategy has been proposed as an alternative of achieving net removal of atmospheric CO2 to achieve sustainable development. To illustrate the above concept, Case Study 2 is revisited.

8. Case Study 2 (revisited) In this case, the BECCS system with negative CO2 emissions is analysed. It is assumed that compensatory energy with negative CO2 emission (−0.6 t CO2/MWh) is to be


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produced from BECCS, to supplement the power loss of the power plants that undergo CC retrofit. Solving the objective function in Equations 6 subject to the constraints in Equation 1 – 5and 7 – 8 yields the minimum energy loss of 337.5 MW, with results shown in Table 4. As shown, a total of three coal plants (out of five plants) have been selected for CC retrofit to achieve the desired carbon emission limit (L) of 1100 t/h (9.6 Mt/y). Note that the use of BECCS in this case leads to negative emission of 203 t CO2/h, in which results in less power plants to be retrofitted, and also lower energy loss (as compared to the case where low carbon compensatory energy is used).

Due to the wide range of bioenergy sources with different emission intensity, sensitivity of the emission intensity is investigated to determine its effect on energy loss (and supplementary power indirectly). Figure 5 shows the results of sensitivity analysis, for intensity of compensatory power with CO2 emission factor ranging between −0.6 to 0.1 t CO2/MWh, which is solved by the ATM model. Note that Figure 5 shows that the relationship between emission factor with amount of compensatory energy is relatively linear, where renewable sources of lower CO2 emission intensity will lead to less amount of supplementary power to stay within the desired carbon emission limits.

9. Conclusion

A revised ATM has been developed for CCS deployment for power generation sector to meet regional or sectoral carbon emission limits. In this work, CCS may be deployed either through plant retrofit, or through the entry of new capture-ready plants;



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the case of BECCS with negative net emissions is considered to illustrate the latter case. In its simplest form, the methodology is equivalent to the previously developed graphical technique 20 , but allows for added flexibility since the objective function can be changed to suit case-specific considerations. Furthermore, additional variation can be introduced into the technological parameters such as carbon intensities and removal ratios. The proposed model results in linear formulation, so that any solution found is a global optimum. For discrete selection of power plant(s) to be retrofitted, the linear model is extended into mixed-integer linear program to achieve an overall CO2 emission limit.

References

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List of Tables Table 1

Data for Case Study 120

Table 2

Data for Case Study 224

Table 3

Results for Case Study 2 (with compensatory energy of low carbon emission)

Table 4

Results for Case Study 2 revisited (with compensatory energy from bioenergy of negative CO2 emission)


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Table 1. Data for Case Study 120 Energy source i Zero-carbon source Natural Gas Oil Coal Total

Fi Ci (TWh/y) (kg CO2/ kWh) 18 0 19.2 0.5 6 0.8 16.8 1 60 -

Σi FiCi (Mt CO2/y) 0 9.6 4.8 16.8 31.2


CRi (kg CO2/ kWh) 0 0.088 0.141 0.176 -


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Table 2: Data for Case Study 224 Plant

Power Plant Type

1 2 3 4 5 6 7 8 9 10 Total

Coal Coal Coal Coal Coal Natural Gas Natural Gas Natural Gas Oil Oil

Emission Factor (t CO2/ MWh) 1 1 1 1 1 0.5 0.5 0.5 0.7 0.7

Power Output (MW) 200 250 150 600 500 250 300 400 200 250 3100


Carbon emissions (t CO2/h) 200 250 150 600 500 125 150 200 140 175 2490

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Table 3: Results for Case Study 2 (with compensatory energy of low carbon emission) Plant

Power Plant Type

CC retrofit

1 2 3 4 5 6 7 8 9 10

Coal Coal Coal Coal Coal Natural Gas Natural Gas Natural Gas Oil Oil Compensatory

Yes No Yes Yes Yes No No No Yes No -

Emission Factor after Retrofit (t CO2/ MWh) 0.13 1 0.13 0.13 0.13 0.5 0.5 0.5 0.09 0.7 0.1

Final Power Output (MW)

Final Carbon emissions (tCO2/h)

150 250 112.5 450 375 250 300 400 150 250 412.5 3100

20 250 15 60 50 125 150 200 14 175 41 1100

Total



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Table 4: Results for Case Study 2 revisited (with compensatory energy from bioenergy of negative CO2 emission) Plant

Power Plant Type

CC retrofit

Final Power Output (MW)

Final Carbon emissions (t CO2/h)

No Yes No Yes Yes No No No No No

Emission Factor after Retrofit (t CO2/ MWh) 1.00 0.13 1.00 0.13 0.13 0.5 0.5 0.5 0.70 0.70

1 2 3 4 5 6 7 8 9 10

Coal Coal Coal Coal Coal Natural Gas Natural Gas Natural Gas Oil Oil Biofuel from BECCS

200 187.5 150 450 375 250 300 400 200 250

200 24 150 59 49 125 150 200 140 175

-

-0.6

337.5

-203

3100

1070

Total


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List of Figures Figure 1

Cascade diagram for CCS retrofit planning

Figure 2

Cascade diagram for CCS retrofit planning for Case Study 1

Figure 3

Cascade diagram for CCS retrofit planning for Case Study 2

Figure 4

CO2 emission with BECCS

Figure 5

Sensitivity of biofuel emission on compensatory power requirements – Case Study 2 (revisited)



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Page 30 of 34

δ0 = 0 k =1 k =2

FCR1

ε1 = 0

CCR1

δ1 FCR2

CCR1

ε2

CCR2

δ2 k =3

CRNG

CCR2

δ3 k =4

CROIL

CRNG

CRCL

CROIL

FP

CRCL

ε6

CL

δ6 k =7

FNG

CNG

CL FRNG

δ7 k =8

FOIL

COIL

FCL

CCL

ε7 CNG

FROIL

ε8 COIL

δ8 k =9

ε5

FRCL (1 – XCL)

δ5 k =6

ε4

FROIL (1 – XOIL)

δ4 k =5

ε3

FRNG (1 – XNG)

FRCL

δ9 CA (a)

ε9 COIL

ε10 (b)

Figure 1. Cascade diagram for CCS retrofit planning: (a) power cascade; (b) carbon cascade (annotations for subscript: CL = coal; NG = natural gas; OIL = crude oil; CRi = clean or non-fossil sources; A = arbitrary value)


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CO2 cascade (Mt CO2/y)

Power cascade (TWh/y)

δ0 = 0 k =1

FCR + EL = 20.94

ε1 = 0

CCR1

δ1 = 20.94 k =2

CCR1

ε2 = 1.05

CCR2

δ2 = 20.94 k =3

CCR2

ε3 = 1.85

CRNG

δ3 = 20.94

2.41 CROIL FROIL (1 – XOIL) δ4 = 23.35 14.28 CRCL FRCL (1 – XCL) δ5 = 37.63

k =4 k =5 k =6

FP = 60 FNG = 19.2 FOIL = 6 FCL = 16.8

ε5 = 3.78 CRCL CL

ε7 = 0.95

CNG COIL

FROIL

δ8 = 0 k =9

CROIL

ε6 = 6.54

δ7 = -3.17 k =8

ε4 = 2.96

CL

δ6 = -22.37 k =7

CRNG

CCL

FRCL

CNG 2.83 16.8

δ9 = 0 CA

Figure 2. Cascade diagram for CCS retrofit planning for Case Study 1


ε8 = 0 (pinch) COIL

ε9 = 0 CCL

ε10 = 0


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Page 32 of 34

CO2 cascade (Mt CO2/y)

Power cascade (TWh/y)

δ0 = 0 k =1

ε1 = 0

CRNG

δ1 = 0 k =2

CROIL

150 FROIL (1 – XOIL)

δ2 = 150 k =3

EL = 412.5

k =5

ε3 = 1.00

CCRenw 1087.5 CRCL FRCL (1 – XCL) δ4 = 1650

FP = 3100 FNG = 950

k =8

FOIL = 450 FCL =1700

ε4 = 19.74 CRCL

ε5 = 385.54 CL

ε6 = 175.29

CNG

δ6 = -500 k =7

CCRenw

CL

δ5 = -1450 k =6

ε2 = 0 CROIL

δ3 = 562.5 k =4

CRNG

COIL

FROIL δ7 = -250

CCL

FRCL

CNG 200 1450

δ8 = 0

ε7 = 75.29 COIL

ε8 = 0 (pinch) CCL

CA

Figure 3. Cascade diagram for CCS retrofit planning for Case Study 2


ε10 = 0

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Biomass cultivation

Biomass

Power supply to users

Conventional plant

CO2 released

Biomass

BECCS plant

CO2 captured

Underground

CO2 storage

Figure 4. CO2 emission with BECCS


Underground


420 400

Compensatory, MW

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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380 360 340 320 300 -1

-0.8

-0.6 -0.4 -0.2 Emission, tCO2 /MWh

0

0.2

Figure 5. Sensitivity of biofuel emission on compensatory power requirements – Case Study 2 (revisited)


Carbon Constrained Energy Planning (CCEP) for Sustainable Power

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