001.docx CHEMICAL ENGINEERING TRANSACTIONS VOL. 83, 2021 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Jeng Shiun Lim, Nor Alafiza Yunus, Jiří Jaromír Klemeš Copyright © 2021, AIDIC Servizi S.r.l. ISBN 978-88-95608-81-5; ISSN 2283-9216 Investment Decision Making for Carbon Capture and Storage Technology in High Efficiency, Low-Emission Coal-fired Power Plant via Dynamic Techno-Economic-Policy Evaluation Framework: Case Study in China Norhuda Abdul Manafa,*, Ali Abbasb aDepartment of Chemical and Environmental Engineering, Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia bSchool of Chemical and Biomolecular Engineering, The University of Sydney, Sydney NSW, Australia norhuda.kl@utm.my There are significant uncertainties pertaining to the future of coal power generation across the globe and especially for China. Being one of the largest coal consumers and CO2 emitters, China aims at addressing its national energy security while ensuring to achieve sustainable operations. In following its commitments to the Paris Agreement and in pursuit of near-zero emissions from power plants, China has actively moved towards high efficiency, low emission (HELE) coal-fired power plants. However, HELE technology alone cannot completely cut carbon emissions, and will require a mature CO2 emissions reduction technology such as carbon capture and storage (CCS) to significantly reduce the CO2 emissions within short to medium term. This paper conducts a preliminary evaluation for investment in HELE coal-fired power plant with CCS (HELE-CCS) in China, under the context of the Shenzhen emissions trading scheme. We carry out this preliminary analysis through mixed-integer non-linear programming (MINLP) framework, and by using publicly available power generation and carbon pricing data. Based on hypothetical scenarios, a HELE coal-fired power plant is found to contribute 55 % profit by selling the electricity. However, a substantial CO2 emissions cost is incurred due to the installation of CCS, leading to a high caution on investment. A more detailed analysis is warranted using real power plant data to confirm real business feasibility of HELE-CCS, including an expansive set of scenarios that also assess relevant appropriate government incentives and subsidies as well as variant financial models. 1. Introduction Coal is still dominating the world’s energy mix even though tremendous initiatives have been conducted to promote renewable and alternative energy resources. This scenario is due to increasing populations and rapid urbanization/industrialization, all which are resulting in enhanced energy demands. High efficiency, low emission (HELE) coal-fired power plants have emerged as one of the technological solutions to overcome this contemporary issue. Nevertheless, HELE technology alone is unable to completely mitigate greenhouse gas carbon emissions. Mature CO2 emissions mitigation technologies such as carbon capture and storage (CCS) are considered a key to significantly reduce the emissions within short to medium terms. In a joint climate statement with the USA (November’s Joint Announcement, The White House, 2016), the Chinese President Xi proclaimed to implement a national Emission Trading Scheme (ETS) (cap-and-trade scheme). This consequently meant that China would become the world’s largest carbon market, after the EU ETS. This declaration has sparked interest in studies related to ETSs and their implications on China’s economic, political, social, environmental and technology transitions. Deng and Wen (2019) suggested that China should learn from existing international carbon trading policy (e.g. EU ETS, California's cap-and-trade system) to minimize market risks and to ensure a successful implementation of national carbon trading scheme. Prior studies have also proven that preliminary design of carbon emissions schemes such as ‘carbon emissions trading only covering the pilots’ (Cui et al., 2014) and ‘unified carbon emissions trading market’ (Song et al., DOI: 10.3303/CET2183005 Paper Received: 26/06/2020; Revised: 23/09/2020; Accepted: 30/09/2020 Please cite this article as: Abdul Manaf N., Abbas A., 2021, Investment Decision Making for Carbon Capture and Storage Technology in High Efficiency, Low-Emission Coal-fired Power Plant via Dynamic Techno-Economic-Policy Evaluation Framework: Case Study in China, Chemical Engineering Transactions, 83, 25-30 DOI:10.3303/CET2183005 25 2018) can provide future insights on the health development of China’s emissions trading market. On the other hand, many studies have also been conducted pertaining to the diffusion of CCS technology in China. A real option approach was used by Wang et al. (2016) to analyse the investment decision of CCS by considering net present value and uncertainty factors including carbon price, fuel price, investment cost and government subsidy. Their study found that the increment on carbon price and government subsidy were able to offset the cost of emission reduction and capital cost of integrated plant (power plant integrated with post-combustion carbon capture (PCC)). They suggested that this coincides with increased investment opportunity of CCS towards the transition of clean coal technologies. Similarly, positive investment decision can be obtained if reasonable subsidy is provided to the coal-fired power plant with 20 % carbon emissions reduction based on China scenario in year 2030 (Chen et al., 2016). Recently, Ye et al. (2019) conducted a techno-economic analysis of a 600 MW supercritical plant with and without CCS located in China. They observed that the depreciation rate plays an important role in the economic assessment of carbon costs as well as to the investment option of CCS. High carbon price was required to ensure profitable investment of CCS. Nevertheless, the contribution of the capture and transportation stages towards global warming potential (GWP) will require construction of environmentally sustainable sorbents to ensure positive impact of the CCS technology diffusion in a near future (Zhang et al., 2019). All studies to date have dealt with several important uncertainties (i.e. carbon price, subsidies and so on) all while considering steady state/semi-dynamic scenarios, which increases the possibility to cause bias on the investment decision making. This current work is undertaken to underpin existing studies by demonstrating a framework under dynamic scenario analysis. The scenario involves utilization of time-transient uncertainties (hourly basis) such as carbon price, electricity price and load demand, all in China for the context of the cap-and-trade policy framework in Shenzhen. The present work extends authors’ previous work (Abdul Manaf and Abbas, 2019) by also considering capital cost of CCS and cost of CCS control and monitoring system (C & M). 2. Methodology 2.1 Dynamic techno-economic-policy evaluation framework A genetic algorithm (GA) with mixed-integer non-linear programming (MINLP) optimisation is formulated to predict investment opportunity of CCS based on a Shenzhen China scenario. This optimization-based framework is built on a temporal model of HELE-CCS plant and incorporates economic, environmental and policy dynamic constraints. This techno-economic-policy evaluation framework is adapted and improved from authors’ previous work (Abdul Manaf and Abbas, 2019). It comprises an optimization for net load matching mode with the aim (objective function) to maximize plant net profit (economic) as shown in Figure 1. Figure 1: A dynamic techno-economic-policy evaluation framework of HELE-CCS plant The framework generates two outputs; power plant net load and CO2 capture rate. These outputs are estimated based on the three inputs fed into the framework which are power plant gross load, electricity tariff 26 and carbon price at 30-min interval time. This framework is implemented in the software Matlab (Mathworks, USA). It involves execution of an optimization algorithm via a single computational technique. The computation time required for one scenario (one full year) is approximately 4–6 d. In this work, the operation of hybrid HELE-CCS plant are simulated under two scenarios of the present (2019) and forecast (2030) years. Dynamic data reflecting Shenzhen’s techno-economic-policy uncertainties are quantitatively appraised to examine the CCS investment decision via energy consumption rate (corroborates with power plant net load), CO2 capture rate and net profit composite. 2.1.1 Shenzhen cap-and-trade Shenzhen’s ETS pilot is managed and monitored by the National Development and Reform Commission (NDRC) which is responsible for the development of climate change policy in China. Each pilot applied different size of cap and carbon price depending on the type of industries involved and economic structure of the province. Table 1 delineates a cap-and-trade design formulated in MINLP model. Emission penalty is obtained from China Emission Exchange (2014) while other parameters are extracted from the existing information available in other China ETS pilot plants (e.g. Hubei, Beijing). In this work, Shenzhen is selected as a case study since the city is a pioneer in Chinese ETS pilot plant and has the largest historical carbon prices. Table 1: Preliminary information for the coal-fired power plant imposed with ETS (cap-and-trade) Project location Cap (t CO2/y) Penalty Free Allowance by government Shenzhen 50,000 3 x market price 60 % from the emission cap 2.1.2 Yearly HELE-CCS plant net profit CCS capital investment cost is extracted from studies conducted by Li et al. (2011). In this work, CCS capital cost component included pipeline (onshore and offshore) and injection costs to illustrate an actual total cost imposed to the investor. Cost of (C & M) is adapted from Jorge (2009). This cost includes service-oriented market and categorized under the service operator. One must take note that the cost assumptions made in this work are intended to reflect expectation of the future, based on the best information available at the time of writing. 2.1.3 Input variables Three variables (dynamic inputs) are considered in this investment decision evaluation: electricity tariff, carbon price and gross load demand. These three inputs have been selected due to their significant impact towards HELE coal-fired plant operation (supply and demand) and for consideration of CCS installation. Variation of electricity tariff and gross load demand may affect the power plant revenue and operation (Abdul Manaf and Ali Abbas, 2019), while fluctuation of carbon prices can jeopardize the feasibility (in economic aspect) of CCS technology in the long term (Wang et al., 2016). Electricity tariff in China is strictly regulated by the state government andt was charged at low rate and relatively more stable (Fan et al., 2014). This is for public appeasement and to prevent triggering elevation of inflation rates. Shenzhen’s electricity tariff for year 2019 is recorded at 52 CNY/MWh (CEIC, 2020) and data for carbon price are extracted from China Emission Exchange (2020). Coal specific cost for 2019 is at 2.5 CNY/GJ (Smil, 1988). Coal price is adopted from old resource due to the credibility of the suggested price to be accommodated with the HELE-coal plant model. Based on the new electricity distribution price, historical electricity tariff for year 2015, 2016 and 2017 are at 143.5 CNY/MWh, 143.3 CNY/MWh and 142.8 CNY/MWh (China Daily, 2015).Due to a scarcity of resources, those aforementioned was adopted to estimate the electricity tariff for year 2030 which is expected to reduce at 0.1 % (based on the percentage difference between 2015 to 2017) from 2019 onwards. This percentage estimation is underpinned with the study conducted by Huang et al. (2015) where they anticipated that the electricity production between 2013 to 2027 will keep stable. Similar assumption is made for forecasting carbon price (2030) since Shenzhen carbon trading scheme is already considered mature enough to sustain the trading process. Forecast carbon price is estimated to be reduced at 0.1 % from present year (2019). Due to limited information in obtaining actual gross load demand in Shenzhen HELE coal plant, plant gross load was estimated by using simple correlation between Shenzhen’s temperature (Weather Atlas, 2020) and energy demand, based on the fact that electricity demand is driven by the discomfort created by the temperature difference. This concept is proven based on the study conducted by Staffell and Stefan (2018). These three forecast inputs are considered reliable since those values are estimated from the present/historical trend by assuming minimum fluctuation in Shenzhen’s economic portfolio in year 2019 until 2030. 27 2.2 Investment decision procedure The main objective of this work is to estimate an investment opportunity of CCS technology for rapid investment decision by evaluating three criteria which are energy consumption rate, CO2 capture rate and net profit composite. In this work, HELE coal power plant is assumed to has built-in flexibility to be retrofitted with CCS. The investment decision is based on first year operation of CCS by assuming commissioning and testing has been done earlier and 5 % interest rate on the capital cost is incurred for 20 y. Annualized CCS and C&M costs are considered to ensure both costs are deducted from the net revenue of HELE coal-plant at the end of the financial year (assuming December 2019 and 2030). Take note that this study proposes investment decision making based on short term period (one year, in hourly basis). By means, all composited costs of plant revenue are calculated in hourly basis as illustrated in Eq(1). 𝐷𝐷𝑡𝑡 = ∏(𝑃𝑃𝑐𝑐 , 𝑃𝑃𝑒𝑒 ,𝑃𝑃𝑓𝑓) = 𝑀𝑀𝑀𝑀𝑀𝑀 �∫𝑃𝑃𝑒𝑒 ∗ � 𝑄𝑄𝑒𝑒 − 𝑄𝑄𝑝𝑝� ∗𝑑𝑑𝑑𝑑 − ∫(𝑃𝑃𝑐𝑐 ∗ 𝑄𝑄𝑐𝑐 ) ∗ 𝑑𝑑𝑑𝑑 − ∫(3 ∗ 𝑃𝑃𝑐𝑐 ∗ 𝑄𝑄𝑐𝑐𝑒𝑒) ∗𝑑𝑑𝑑𝑑 − 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝑜𝑜𝑝𝑝 − 𝐶𝐶𝐶𝐶𝐶𝐶𝑜𝑜𝑝𝑝− 𝐶𝐶𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐𝑝𝑝− 𝐶𝐶&𝑀𝑀𝑐𝑐𝑐𝑐𝑝𝑝 � (1) where Π depicts annual revenue of HELE coal-plant, 𝑃𝑃𝑒𝑒 , 𝑃𝑃𝑐𝑐 and 𝑃𝑃𝑓𝑓 denote time-dependent electricity tariff, carbon and coal prices. The 𝑄𝑄𝑒𝑒, 𝑄𝑄𝑝𝑝, 𝑄𝑄cand 𝑄𝑄𝑐𝑐𝑒𝑒 represent electricity output, total power penalty, CO2 emissions below allowable cap and CO2 emission exceeding allowable cap.. 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝑜𝑜𝑝𝑝, 𝐶𝐶𝐶𝐶𝐶𝐶𝑜𝑜𝑝𝑝, 𝐶𝐶𝐶𝐶𝐶𝐶𝑐𝑐𝑐𝑐𝑝𝑝 and 𝐶𝐶 & 𝑀𝑀𝑐𝑐𝑐𝑐𝑝𝑝 represent, coal plant operating cost, CCS operating cost, annualized CCS capital cost and annualized cost of C & M. All costs in 2030 are calculated based on the economic projection cost method via inflation rate. To drive CCS deployment towards zero emission from HELE coal plant, the improved evaluation framework is designed to maximize annual plant revenue by meeting the energy demand (gross load). 3. Result and discussions 3.1 Energy and CO2 capture performance of HELE-CCS plant In this work, investment decision is made based on the performance of HELE-CCS plant on technical terms such as energy and CO2 capture performance, on yearly basis. Energy performance is reflected by the reboiler thermal duty of retrofitted plant, where steam is extracted from the HELE turbine system to assist in the desorption regeneration process. Whilst CO2 capture performance is evaluated based on yearly amount of CO2 captured. Both criteria are appraised based on the trend and demand of uncertainties in key dynamic variables (e.g. carbon price, electricity tariff and load demand) in year 2019 and forecast year 2030. As illustrated in Figure 2, HELE coal plant emits almost the same amount of CO2 in present and forecast years while only capturing approximately 25 % of the CO2 emissions. Figure 2: Energy and CO2 capture performances of HELE coal-CCS plant This is due to the similarity of the dynamic variables featured in both years. There is enhanced energy performance in HELE-CCS plant in year 2030 operation compared to present operation reflected in 4 % improvement. Based on this prediction, it is relevant to take a negative investment decision for CCS-retrofitted since the plant unable to give significant operational (technical) and environmental contribution to HELE coal plant. 3.2 Net profit composite HELE coal-CCS plant Plant’s net profit composite is used to determine investment decision of CCS from short term economic point of view as illustrated in Figure 3. It can be observed that HELE coal plant contributed approximately 55 % 164,000 166,000 168,000 170,000 172,000 174,000 176,000 178,000 180,000 0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 2019 2030 R eb oi le r he at r eq ui re m en t (M W th /y ) G H G e m is si on s (t C O 2- eq /y ) Year Reboiler heat requirement (MWth/y) CO2 captured by CSS plant CO2 emission from HELE plant CO2 captured by CSS plant 28 profit from selling the electricity (CoE: cost of electricity) subjected to electricity and fuel costs at contemporary and forecast market conditions. While cost of CO2 emission features predominant composition (averaged 40 %) from the net profit composite which lead to negative continuity of the CCS project installation subsequently showed deficit in net profit of plant in year 2019 and 2030. Figure 3: Net profit composite of HELE coal-CCS plant. 3.3 Cap and trade profile of HELE coal-CCS plant Reliability of CCS investment option is conducted via policy evaluation such as cap and trade scheme. Figure 4 shows cap and trade profile in 2019 and 2030. Figure 4: Cost of CO2 emission based on cap and trade scheme for HELE coal-CCS plant It can be seen that HELE coal plant reached 60 % free allowance and emission cap in the month of January and February in 2019 and 2030. Based on this projection, HELE coal plant requires to pay huge amount of carbon credit with enormous penalty due to the excessive amount of CO2 emissions throughout the year, which render negative investment opportunity for CCS plant underpinning the analysis from net profit composite (Section 3.2). These circumstances influence by several aspects such as low carbon price and electricity tariff. It is anticipated that to encourage CCS installation, carbon price should be high enough to motivate maximum capacity of CCS plant (e.g. at > 90 % capture rate) between 99 CNY/t (Cui et al., 2014) and 293 CNY/t (USD 41/t) (Hu and Zhai, 2017). 4. Conclusions Based on the contemporary and forecast hypothetical scenarios (2019 and 2030), CCS has negative investment opportunity due to insignificant capture percentage (25 %) and energy performance (4 %) and imposition of substantial cost of CO2 emission. This is due to uncompetitive carbon price and electricity tariff at point of time, CCS operation being unable to achieve profitable operation in 2019 and 2030. To ensure a -40.0 -30.0 -20.0 -10.0 0.0 10.0 20.0 30.0 40.0 50.0 60.0 CoE CO2 Cost PP OPEX CSS OPEX CCS CAPEX C&M CAPEX N et p ro fit c om po si te ( % ) 2019 2030 0 10,000 20,000 30,000 40,000 50,000 60,000 Ja n Fe b M ar ch Ap ril M ay Ju ne Ju ly Au gu st Se pt O ct N ov De c Co st o f C O 2 em is io ns (C N Y) 2019 2030 2019, Pe na lty 2030, Pe na lty Penalty Emission cap Emission cap 29 positive investment of CCS in the future and relevancy of this technology towards sustainable development goal, a pragmatic approach is required. For instance, government can provide subsidy, tax reduction as well as maximizing the carbon price. Acknowledgments Authors wishing to acknowledge financial support from Universiti Teknologi Malaysia through Tier 2, project no Q.K130000.2643.15J77.2 References Abdul Manaf N., Ali A., 2019, Economic and environmental sustainability of low-carbon power generation: relevancy in the Malaysia Green Technology Master Plan (GTMP), Journal of Chemical Technology and Biotechnology, 94(5), 1425-1432. China Emissions Exchange, 2014, Annual Report on Shenzhen Emission s Trading Scheme First Year Operation, A Comprehensive analysis of Shenchen’s cap and trade program, Shenzhen, China. CEIC, 2020, 'Global Economic Data, Indicators, Charts and Forecasts accessed 23.4.2020. Chen H., Can W., Minhua Ye., 2016, An uncertainty analysis of subsidy for carbon capture and storage (CCS) retrofitting investment in China's coal power plants using a real-options approach, Journal of Cleaner Production, 137, 200-212. China Daily, 2015, China starts new electricity distribution pricing system, accessed 15.4.2020. China Emission Exchange, 2014, Annual Report on Shenzhen Emissions Trading Scheme First-Year Operation. Shenzhen, China. Cui L.B., Fan Y., Zhu L., Bi Q.H., 2014, How will the emissions trading scheme save cost for achieving China's 2020 carbon intensity reduction target?, Applied Energy, 136, 1043-52. Deng M.Z., Wen X.Z., 2019, Recognition and analysis of potential risks in China's carbon emission trading markets, Advances in Climate Change Research, 10(1), 30-46. Fan J., Dingtao Z., Yanri L., Jiuchang W., 2014, Carbon pricing and electricity market reforms in China, Clean Technologies and Environmental Policy, 16(5), 921-933. Hu B., Zhai H., 2017, The cost of carbon capture and storage for coal-fired power plants in China, International Journal of Greenhouse Gas Control, 65, 23-31. Jorge P., 2009, Monitoring and Control: Today’s market and its evolution till 2020, Office for Official Publications of the European Communities, Luxembourg. The White House, 2016, accessed 1.10.2019. Huang Y., Lei L., Xiaoming M., Xiaofeng P., 2015, Abatement technology investment and emissions trading system: a case of coal-fired power industry of Shenzhen, China, Clean Technologies and Environmental Policy, 17(3), 811-817. Li J., Liang X., Reiner D., Gibbins J., Lucquiaud M., Chalmers H., 2011, Financing New Power Plants 'CCS Ready' in China - a case study of Shenzhen city, Energy Procedia, 4, 2572-2579. Smil V., 1988., Energy in China's Modernization: Advances and Limitations, Sharpe Armonk Publishing Limited, New York, USA. Song Y., Dapeng L., Tiansen L., and Xiaoqiu S., 2018, How China's current carbon trading policy affects carbon price? An investigation of the Shanghai Emission Trading Scheme pilot, Journal of Cleaner Production, 181, 374-384. Staffell I., Stefan P., 2018, The increasing impact of weather on electricity supply and demand, Energy, 145, 165-78. Wang Q., Cuiyun G., Shuanping D., 2019, Effect of the Emissions Trading Scheme on CO2 Abatement in China, Sustainability, 11(4), 1055. Wang X., Lei D., 2016, Study on carbon capture and storage (CCS) investment decision-making based on real options for China's coal-fired power plants, Journal of Cleaner Production, 112, 4123-4131. Weather Atlas, 2020, accessed 23.4.2020. Ye B., Jingjing J., Yu Z., Junguo L., Kai W., 2019, Technical and economic analysis of amine-based carbon capture and sequestration at coal-fired power plants, Journal of Cleaner Production, 222, 476-487. Zhang S., Ran T., Linlin L., Lei Z., Jian D., 2019, Economic and Environmental Optimisation Framework for Carbon Capture Utilisation and Storage Supply Chain, Chemical Engineering Transaction, 76, 1-6. 30