Targeting for optimal grid-wide deployment of carbon capture and storage (CCS) technology
Introduction
Emissions of greenhouse gases (e.g., carbon dioxide (CO2), methane (CH4) and nitrogen dioxide (N2O), etc.) from industrial activities, such as fossil fuels (coal, oil, and natural gas) based power plants, have long been identified as the major contributors to greenhouse effect. The International Energy Agency (IEA, 2012a) estimated that global CO2 emissions from fossil-fuel combustion reached to 31.6 Gt in 2011. This represents an increase of 1.0 Gt, or 3.2% based on 2010 value. The projected emission by 2035 is estimated at 37 Gt CO2 (IEA, 2012a). Historically, developed industrialized countries have emitted the large majority of greenhouse gases. However, shares of emissions from developing countries have been rising very rapidly due to population and economic growth. The decomposition analysis of energy-based CO2 emissions has been carried out by Malla (2009). Meanwhile, Shrestha et al. (2009) studied the factors affecting CO2 emission of power sectors of developing countries in the Asia-Pacific region. The economic growth of developing countries, especially China and India, has resulted in higher energy demand and thus, higher levels of CO2 emissions. For example, Indian economy needs to grow at a rate of 8–10% per annum over the next 25 years to meet its human development goals and to eradicate poverty. With a sustained 8% growth, India's primary energy supply needs to increase by three or four times in 2031–2032, compared to base year of 2003–2004. During same time period, India's commercial energy supply would need to grow from 5.2% to 6.1% per annum. It is also projected that power generation capacity will increase to nearly 800 GW from the current capacity of around 160 GW (Grover and Chandra, 2006), with resulting implications of carbon emissions (Ghosh, 2010). There are similar concerns in other countries in Southeast Asia (Asian Development Bank, 2012). IEA (2012a) pointed out that the CO2 emissions from fossil fuel combustion in Asia Oceania have been growing at an average annual rate of 15.6% (during 1990–2010). As fossil fuel based power plants are going to dominate, rising carbon emissions are of serious concern.
Improved conversion efficiency, increased energy conservation as well as increased generation of electricity from low-carbon and renewable sources are primary steps toward mitigating emission of greenhouse gases (Weisser, 2007) as well as other important environmental metrics (Varun et al., 2009). Advantages of renewable-based power generation can include lower operating cost, reduced emissions, modularity, and ease of expansion. However, there are also disadvantages such as low energy density, high initial capital investment, and relatively poor reliability and availability, as compared to fossil fuel-fired plants that rely on mature technologies. Furthermore, in developing countries, with rapidly increasing energy demands, it may be impractical to shut down existing fossil fuel-fired plants before their projected economic life. For these reasons, fossil fuels are likely to be the major contributors to the world's overall electricity generation mix in the near future (Quadrelli and Peterson, 2007). Due to extensive demand for electricity in modern industrial, commercial and domestic activities, managing CO2 emissions from power generation is clearly essential to achieve sustainable consumption and production.
The above factors suggest retrofitting existing plants with carbon capture technology, such as oxy-fuel combustion, pre-combustion capture using integrated gasification combined cycles, or post-combustion capture using flue gas scrubbing. These technologies can be used to remove 80–90% of the CO2 from the flue gases of power plant, and subsequently compress it for secure storage in various reservoirs, like saline aquifers, depleted oil reservoirs, inaccessible coal seams and impervious geological formations (Davison et al., 2001). Such integrated systems are referred as carbon capture and storage (CCS) techniques. Research in different aspects of CCS involves a broad range of disciplines for addressing knowledge gaps pertaining to both CO2 sources and sinks (Todd, 2011). Engineering work has focused on developing capture processes with low energy penalties, while geologists have worked on assessing either global or region-specific storage potential (Pires et al., 2011). Engineering feasibility of CCS is described in Klemeš et al. (2006). The IEA has also projected that in order to achieve a desired emissions level of 14 Gt/y by 2050 at the lowest cost, 19% of the total reduction in emissions must come from CCS (IEA, 2010). Worldwide evaluation of geological reservoirs has estimated that over 236 Gt of CO2 can be captured and stored by 2050 (Stangelend, 2007).
Despite the environmental advantages of retrofitting power plants for carbon capture, there are also significant economic penalties that need to be considered during planning, especially as these changes may have significant effects on power generation cost. Firstly, installing carbon capture process requires additional capital investment. It is estimated that capital costs of power plants with carbon capture technology to be 25–50% higher than that of baseline power plants due to additional process equipment required (Wall, 2007). For example, in oxyfuel combustion, a major capital cost component is the air separation unit (ASU) needed to supply oxygen for firing (Wall et al., 2009). Secondly, parasitic power loss is incurred to operate the carbon capture processes, typically resulting in a thermal efficiency drop of 7–8% points, or about 15–20% reduction in electricity output (Wall, 2007). The additional capital cost and fuel consumption per unit of electricity output results in an increase in power generation cost, by a factor of about 25–50% (Steeneveldt et al., 2006). Similar specific results are reported for oxyfuel combustion in particular (Buhre et al., 2005). At the grid level, it thus becomes necessary to install additional or compensatory power plant for generating electricity to make up parasitic energy losses incurred by the carbon capture retrofits. It may be noted that compensatory power need not be from new plants. In places where countries are part of a common landmass (e.g., European Union) power deficit from CCS can be made up by importing electricity from a neighboring country (if the latter has surplus). These issues necessitate the use of decision aids for the large-scale deployment of CCS to ensure that any disadvantages or risks are properly managed. Previous techniques to the macro scale analysis of CCS systems have been demonstrated using life cycle assessment (LCA) (Koornneef et al., 2008), fuzzy LCA (Tan, 2008) and life cyle optimization (Tan et al., 2008). Mathematical programming models have also been developed for CCS systems, taking into account critical issues such as tradeoff between emissions and power cost (Tan et al., 2010), or energy price volatility and carbon emissions trading (Koo et al., 2011). Such models have been demonstrated using country case studies in The Netherlands (van den Broek et al., 2008) and Poland (Pękala et al., 2010). Other work has also focused on models for optimal matching of carbon sources and storage sites using both continuous (Tan et al., 2012, Lee and Chen, 2012) and discrete-time formulations (Tan et al., 2013).
Pinch analysis is an insight-based technique that was first developed for heat exchanger network synthesis (Hohmann, 1971) and became established as an important means of enhancing energy efficiency during the late 1970s (Linnhoff and Flower, 1978). The use of pinch analysis was further extended to mass exchanger network synthesis (El-Halwagi and Manousiouthakis, 1989), whose developments are summarized in a recent book (El-Halwagi, 2006). More recent years, the technique has been extended to address conservation of various material resources, e.g., water, based on fixed-load (Wang and Smith, 1994) and fixed-flowrate models (Manan et al., 2004, Foo et al., 2006a) as well as hydrogen in refineries (Alves and Towler, 2002) and other industrial gases (Foo and Manan, 2006). This period saw the development of a range of both graphical approach independently developed by two research groups (El-Halwagi et al., 2003, Prakash and Shenoy, 2005) and algebraic (Manan et al., 2004) variants of solutions techniques; the equivalence of both types of approaches was highlighted by such work as Bandyopadhyay et al. (2006) and later by Pillai and Bandyopadhyay (2007). Property integration also emerged as a special case wherein functional properties rather than composition define the quality of material streams (e.g., Kazantzi and El-Halwagi, 2005), which was later extended for use in conjuction with cascade based tabular approaches (Foo et al., 2006b).
Pinch analysis was also used in emission targeting in the framework of total site analysis, which was first proposed by Smith and Delaby (1991). The concept has been extended to CO2 emissions targeting (Linnhoff and Dhole, 1993) as well as targeting various energy streams and emissions (Dhole and Linnhoff, 1993). Further interest in total site integration led to developments for batch and semi-batch operations (Klemeš et al., 1997), for plant expansion (Gorsek et al., 2006) as well as locally integrated energy systems (Perry et al., 2008) which developed as a result of the increasingly apparent need to reduce industrial greenhouse gas emissions from the late 1990s onwards. Although emissions targeting by pinch analysis was introduced in those studies, the early applications were limited specifically to optimization within industrial facilities, and not to national energy sectors. A graphical pinch-based targeting tool called the energy planning composite curves has been proposed in carbon emission pinch analysis (CEPA) to determine the minimum amount of carbon-neutral energy source to fulfill the energy demands while maintaining the emission limits (Tan and Foo, 2007). The targeting tool has further extended into the application of low-carbon energy source and segregated planning scenario (Lee et al., 2009). Formal proof of segregated targeting principles for such problems was also provided by Bandyopadhyay et al. (2010). An equivalent algebraic targeting tool was also developed to address applications with carbon and land footprint limits (Foo et al., 2008). Successful applications of these targeting tools have been reported in Ireland (Crilly and Zhelev, 2008a), New Zealand (Atkins et al., 2010) and China (Jia et al., 2009). Further extensions of these applications have also been demonstrated based on water (Tan et al., 2009a) and emergy constraints (Crilly and Zhelev, 2008b). The emergy aspect has even been extended for segregated targeting (Bandyopadhyay et al., 2010) and algebraic implementation (Shenoy, 2010).
An important extension of CEPA technique is the establishment of graphical targeting tool for grid wide CCS planning in the power generation sector (Tan et al., 2009b). Based on the characteristics of CCS, such technologies can be modeled as having fixed CO2 removal ratio (Tan et al., 2009b). The technique aims to determine the minimum compensatory power when CCS retrofit is planned for the power generation sectors. However, the main limitation of the work is that, it assumes compensatory power being generated from renewable sources with zero CO2 intensity. In practice, renewable resources have non-zero carbon emissions when their life cycle considerations are taken into account. In addition, fossil-fuel based power plants may also be used to compensate the parasitic power loss. A recent attempt to address some of these issues was the automated targeting technique, which pinch targeting concept is incorporated into optimization framework (Ooi et al., 2013a) or purely graphical (Diamante et al., 2013). In the work of Ooi et al. (2013b), both graphical and algebraic techniques were proposed for the optimum allocation between CO2 sink and sources governed by time constraint. On the other hand, Diamante et al. (2013) performed a pinch-based targeting and sensitivity analysis for optimal CO2 allocation between sources and sinks, especially considering uncertainties in geographic properties of the latter.
In this work, a new targeting procedure for CCS retrofit planning for the electricity generation sector is presented. The objective is to determine how much of the total power generation plant is to be retrofitted with CCS to satisfy the carbon emission limit, governed by the national or regional commitments, along with the minimum requirement of compensatory power accounted for the parasitic power loss due to CCS technology. This work will overcome the limitation in the work of Tan et al. (2009b), in which the parasitic power loss is compensated by renewable power sources with zero CO2 intensity. As mentioned earlier, parasitic power loss may be supplemented by renewable resources with non-zero carbon emissions, and also fossil-fuel based power plants. Therefore, the work of Tan et al. (2009b) cannot be used to locate the target when emission from parasitic power is taken into consideration. The objective of this work is to develop a general targeting technique which can consider the generation of compensatory power from any kind of sources (fossil or non-fossil fuel based). It is worth mentioning that this newly proposed technique is developed based on the analogy with the algebraic procedures of source composite curve (Bandyopadhyay et al., 2006) and waste composite curve (WCC) (Bandyopadhyay, 2009), developed for resource conservation networks. The new technique is demonstrated on case studies based on electricity generation scenarios in India and the Philippines.
Section snippets
Problem statement
The general static planning problem of optimal grid-wide deployment of CCS technology may be stated as follows. In a power generation sector of a given geographical region (e.g., a country, regional grid, etc.), a set of Ns power generating sources is given. The power generating sources may be fossil fuel (e.g., coal, oil or natural gas) or non-fossil fuel-based (e.g., nuclear, wind, hydroelectric, biomass or solar). Each source is comprised of a number of individual power plants of various
Targeting technique for minimum CO2 removal and compensatory power requirement
Bandyopadhyay et al. (2006) proposed pinch analysis-based techniques, i.e. source composite curve (SCC) and wastewater composite curve (WCC), for targeting the minimum fresh water and treatment flow rate requirement for water management problems. Note that SCC and WCC are the graphical representations of the developed algebraic techniques for minimum fresh water and treatment flow rate targets, respectively. Both Tan and Foo (2007) and Shenoy and Shenoy (2012) reported that CEPA is structurally
Case studies
The developed techniques are illustrated through the following case studies based on power generation scenarios in India and the Philippines.
Sensitivity analysis
Note that the derived results for each scenario may vary depending on the emissions limit, value of RR, parasitic energy loss (EL) and the fuel resource considered for compensatory power. In practice, this technique can be used to evaluate the tradeoff between emission limits (L) and the required compensatory power (Pr) having various CCS technology (different RR and EL values) as well as compensatory power sources (different values of Cr) for planning purposes. Even some times it is possible
Conclusion
Due to the evident link between electricity use, climate change and sustainable development, it is essential to effectively deploy clean technologies such as CCS at the grid level. Both graphical and algebraic targeting techniques which originally developed for resource conservation problem have been extended for preliminary static planning of grid-wide CCS retrofits in the power generation sector. The proposed techniques can be used to determine the minimum extent of retrofit of power plants
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