CHEMICAL ENGINEERING TRANSACTIONS VOL. 81, 2020 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Petar S. Varbanov, Qiuwang Wang, Min Zeng, Panos Seferlis, Ting Ma, Jiří J. Klemeš Copyright © 2020, AIDIC Servizi S.r.l. ISBN 978-88-95608-79-2; ISSN 2283-9216 Optimal Electricity Trading with Carbon Emissions Pinch Analysis Raymond R. Tana,*, Neil Stephen Lopezb, Dominic C. Y. Fooc aChemical Engineering Department, Gokongwei College of Engineering, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines bMechanical Engineering Department, Gokongwei College of Engineering, De La Salle University, 2401 Taft Avenue, 0922 Manila, Philippines cDepartment of Chemical and Environmental Engineering, University of Nottingham Malaysia Campus, Malaysia, Broga Road, 43500 Semenyih, Selangor, Malaysia raymond.tan@dlsu.edu.ph Many countries face the challenge of cutting greenhouse gas (GHG) emissions from electricity generation while coping with increasing demand. This problem is especially pronounced in developing countries with growing energy consumption. Commitments to cut GHG emissions can be met by increasing the share of low- carbon sources such as renewable and nuclear energy, but rapidly scaling up the capacity of such sources can be difficult. In a region of contiguous countries with heterogeneous levels of GHG intensity, electricity trading can be used to reduce the need for new power generation capacity. A country with a low-carbon power mix can export to its neighbours and thus reduce the need for new generation capacity in those countries. In this work, a variant of Carbon Emissions Pinch Analysis (CEPA) is developed for optimizing regional electricity trading to meet GHG emissions cuts. The method is demonstrated with the case of the Association of Southeast Asian Nations (ASEAN). 1. Introduction Due to the clear urgency of climate change as a global environmental problem, the optimization of Carbon Management Networks (CMNs) has become an important emerging sub-area of Process Integration (PI) (Tan and Foo, 2018). The potential of PI, and specifically Pinch Analysis (PA), for determining emissions reduction potential in Total Sites (TS) was recognized in the early 1990s before climate change became widely recognized as a cause for concern (Dhole and Linnhoff, 1993). Although PI was originally motivated primarily by the need to reduce costs associated with energy consumption, sustainability issues have become increasingly important considerations as PI diversified through four decades of development. This trend can be seen in the scope of issues addressed by contributions in a handbook dedicated to the topic (Klemeš, 2013), and more recent developments are surveyed in a review article (Klemeš et al., 2018). Carbon emissions pinch analysis (CEPA) was first proposed by Tan and Foo (2007) for energy planning with greenhouse gas (GHG) emissions constraints. The original methodology used a graphical PA approach, but subsequent contributions developed algebraic (Foo et al., 2008) and Automated Targeting Method (ATM) variants (Lee et al., 2009). The temporal aspect was introduced by Atkins et al. (2010) to allow the use of CEPA for long-term planning. Related approaches based on Mathematical Programming (MP) (Pękala et al., 2010) and P-graph (Tan et al., 2017) have also been developed. Francisco et al. (2014) proposed the Carbon Sources Diagram technique that allows the generation of an optimal CMN without the need to establish the target beforehand. Tan et al. (2018) combined economic input-output analysis and CEPA to allow carbon- constrained energy planning to be based on economic sectors. Variants of CEPA were also developed to account for other sustainability metrics, such as land footprint (Foo et al., 2008), water footprint (Tan et al., 2009a), emergy (Bandyopadhyay et al., 2010), and inoperability risk (Tan and Foo, 2013). The limitation of being able to deal with just one sustainability aspect at a time was addressed in recent efforts to develop multi-dimensional variants (Jia et al., 2016). Patole et al. (2017) proposed to use a weighted aggregate DOI: 10.3303/CET2081049 Paper Received: 28/04/2020; Revised: 10/05/2020; Accepted: 13/05/2020 Please cite this article as: Tan R.R., Lopez N.S., Foo D.C.Y., 2020, Optimal Electricity Trading with Carbon Emissions Pinch Analysis, Chemical Engineering Transactions, 81, 289-294 DOI:10.3303/CET2081049 289 sustainability index to allow graphical PA to handle multiple dimensions simultaneously. Sinha and Chaturvedi (2018) developed a graphical bi-objective approach to minimize energy use and carbon footprint. Lee et al. (2019) proposed an MP model for multi-footprint energy planning problems. CEPA and its variants has been used by different research groups to optimize energy systems in Ireland (Crilly and Zhelev, 2008), New Zealand (Atkins et al., 2010), India (Krishna Priya and Bandyopadhyay, 2013), the United States (US) (Walmsley et al., 2015a), China (Li et al., 2016), the United Arab Emirates (UAE) (Lim et al., 2018), the Baltic States (Baležentis et al., 2019), Nigeria (Salman et al., 2019), Taiwan (Lee et al., 2019), and the European Union (EU) (Su et al., 2020). It has also been used for specific sectors or subsystems, such as industrial parks (Jia et al., 2009), electricity generation with CO2 capture and storage (CCS) (Tan et al., 2009), transportation systems (Walmsley et al., 2015b), negative emissions technologies (NETs) (Foo, 2017), chemical production (Qin et al., 2017), and municipal solid waste (MSW) management (Jia et al., 2018). Andiappan et al. (2019) discussed the potential of CEPA as a tool to guide the development of policies for low-carbon growth. The review article by Foo and Tan (2016) gives a detailed survey of key developments in CEPA and allied topics in the decade following the publication of the initial paper (Tan and Foo, 2007). A comprehensive tutorial on CEPA can be found in a recently published book (Foo and Tan, 2020). In this paper, a variant of CEPA is developed for the novel problem of optimal planning of electricity trading among countries or regions. This strategy allows for export of low-carbon electricity to displace capacity in other countries with higher grid carbon intensity (Lopez et al., 2018); trade can also occur among regions in a single country (de Chalendar et al., 2019). The methodology finds the minimum target for the total zero-carbon electricity generation required by all the countries or regions in the system, and then determines the optimal electricity imports and exports to meet the target. The rest of this paper is organized as follows. Section 2 defines the formal problem statement. Section 3 describes the steps in the graphical procedure. Section 4 applies the methodology to a simple tutorial example, while Section 5 presents a more complex application to geographically contiguous countries in the Association of Southeast Asian Nations (ASEAN). Finally, Section 6 gives the conclusions and further prospects for future work. 2. Problem statement The specific problem addressed by this work can be formally stated as follows. Given: • A system consisting of m countries which at present do not trade electricity; • For each country in the system, the current electricity generation/demand, the average CO2 intensity, and the total CO2 emissions from electricity generation; • For each country in the system, the estimated future electricity demand (typically larger than the current level), the future average CO2 intensity limit at the end user (lower than the current level), and the corresponding limit on the total CO2 footprint of electricity; The objective is to target the minimum amount of new zero-carbon electricity generation capacity to be shared by all the countries in the system, but allowing them to meet their future increased demand for electricity with reduced CO2 intensity. By allowing countries with lower grid CO2 intensity levels to export to their neighbours, the requirement for new zero-carbon electricity can be lower than if each country seeks to meet its demands in isolation from the others. Note that zero-carbon electricity, in this case, refers to sources such as renewables, whose emissions intensity levels are much lower than those of fossil-based electricity (Tan and Foo, 2007). Figure 1: Electricity trading problem superstructure for two countries The corresponding superstructure for this problem is shown in Figure 1. The current installed capacity in the different countries act as the sources, while future demands of the countries are as the sinks. Additional zero- 1 2 1 2 Z Current Capacity Future Demand New Zero-C Generation Capacity 290 carbon generation capacity may be needed in the system to meet increased demand or reduce CO2 intensity, but is to be minimized through electricity trading. 3. Graphical procedure The steps in the graphical procedure are similar to standard CEPA (Tan and Foo, 2007) and are as follows: • Step 1. The sources are arranged in order of increasing CO2 intensity. • Step 2. The demands are arranged in order of increasing CO2 intensity. • Step 3. The sources are plotted in sequence to form the source composite curve (SCC), with cumulative electricity generation as the x-axis and cumulative CO2 emissions as the y-axis. • Step 4. The demands are plotted in sequence to form the demand composite curve (DCC) using the same coordinates as the SCC. • Step 5. The SCC and DCC are superimposed and the relative orientations are inspected. A feasible solution occurs if the SCC is entirely below the DCC and if its horizontal span at least equals that of the DCC. • Step 6. If the initial solution is infeasible, the SCC is shifted horizontally to the right until the conditions for feasibility are satisfied. The smallest horizontal shift needed to ensure feasibility is the target. • Step 7. The electricity trading matrix may be determined from the final orientations of the SCC and DCC. Detailed steps are omitted here for brevity, but a full description can be found in the book by Foo and Tan (2020). This procedure is illustrated with case studies in the next two sections. 4. Case study 1 This case study presents a simple example with three countries for illustrative purposes. The system data are given in Table 1. In the current state, all countries are assumed to be self-sufficient, so the capacity is equivalent to demand. It can be seen that the desired future state entails demand growth of 25 %, 0 %, and 25 % for the three countries, respectively. At the same time, the countries seek to reduce CO2 intensity by 40 %, 50 %, and 10 %. The countries aim to meet the future conditions by supplementing the current electricity generation mix with new zero-carbon capacity in the form of renewables. Table 1: System data for Case Study 1 Country Current capacity (TWh/y) Current CO2 intensity (Mt/TWh) Current CO2 emissions (Mt/y) Future demand (TWh/y) Future CO2 intensity limit (Mt/TWh) Future CO2 emissions limit (Mt/y) 1 60 0.40 24.00 75 0.24 18.00 2 40 0.70 28.00 40 0.35 14.00 3 20 0.90 18.00 25 0.81 20.25 Without electricity trading, each country will need to install new generation capacity for itself in order to reduce CO2 intensity, meet the increased demand, or both. For example, Country 1 will need to phase out 25 % of its current generation capacity to reduce CO2 emissions by the same factor. It is assumed that there is no differentiation of the components of the current energy mix. Country 1 will need 15 TWh/y (60 TWh/y x 25 %) of new capacity just to replace this lost output, and an additional 15 TWh/y to meet the incremental demand. Thus, a total of 30 TWh/y of zero-carbon electricity generation capacity will be needed to satisfy the future demand and emissions limit. Similar calculations for Country 2 will yield a requirement of 20 TWh/y. Finally, Country 3 can meet its new demand by installing 5 TWh/y of additional capacity without even reaching its emissions limit. Without electricity trading, the system needs a total of 55 TWh/y (30 TWh/y + 20 TWh/y + 5 TWh/y) of new zero-carbon electricity generation capacity. If electricity trading is allowed, the amount of new capacity needed can be reduced. Application of Steps 1–5 of the procedure described in the previous section gives an infeasible solution, as shown in Figure 2a. Note that the SCC is above the DCC, and its horizontal span is shorter. Applying Step 6 gives an optimal result as shown in Figure 2b. The target is 43.6 TWh/y, which is 20.7 % lower than the requirement without electricity trading. Countries 1 and 2 are below the Pinch Point; the significance of this result is discussed later. It can be seen that Country 3 in the DCC is above the Pinch Point, which indicates that its emissions limit are not reached by this configuration. 291 The actual electricity trading scheme that satisfies this target can be found using Step 7. The allocation can be found by inspection in the case of this simple example, but can also be done algorithmically; details of this step are described elsewhere (Foo and Tan, 2020). (a) (b) Figure 2: (a) Initial and (b) Optimal Pinch Diagrams for Case Study 1 The resulting electricity trading matrix is shown in Table 2. The total new capacity of zero-carbon electricity generation (43.6 TWh/y) is allocated only to Countries 1 and 2, which are below the Pinch Point. This result corresponds the Golden Rule of PA forbidding cross-pinch transfer of streams in an optimal system. Country 1 retains its current generation capacity, but uses only 75 % for itself, and exporting 25 % to Country 2. Country 2 uses 11.4 TWh/y of its capacity internally, exports enough electricity to supply the entire 25 TWh/y demand of Country 3, and an excess capacity of 3.6 TWh/y is unused. This excess capacity corresponds to power plants to be shut down or held in reserve. Country 3 shuts down all of its power plants and relies on imports from Country 2 to satisfy all of its requirements. It should be noted that the PA solution represents the physical limit based on energy and carbon balances, and does not account for non-physical aspects such as energy security. In practice, the solution to be implemented may lie between the two extremes of complete self- sufficiency (i.e., no electricity trading) and the physical optimum reported here. Table 2: Optimal electricity trade matrix for Case Study 1 (values in TWh/y) Country 1 Country 2 Country 3 Excess New zero-C capacity 30 13.6 0 0 Country 1 45 15 0 0 Country 2 0 11.4 25 3.6 Country 3 0 0 0 20 5. Case study 2 This case study applies this methodology to the case of six geographically contiguous countries in ASEAN. The system data is shown in Table 3. Final consumption energy data is used from IEA (2019). CO2 intensity is obtained by calculating actual emissions based on the power generation mix. Future power demand and CO2 emissions are estimated in the near term, considering the ASEAN Energy Outlook (ASEAN Centre for Energy, 2017) and the individual country renewable energy commitments (IEA, 2017). Table 3: System data for Case Study 2 Country Current capacity (TWh/y) Current CO2 intensity (Mt/TWh) Current CO2 emissions (Mt/y) Future demand (TWh/y) Future CO2 intensity limit (Mt/TWh) Future CO2 emissions limit (Mt/y) Vietnam 194.04 0.360 69.85 242.42 63.03 0.260 Myanmar 21.05 0.380 8.00 25.65 8.34 0.325 Singapore 50.24 0.440 22.11 58.07 16.85 0.290 Cambodia 8.09 0.520 4.21 9.41 4.33 0.460 Thailand 207.94 0.570 118.53 249.76 129.87 0.520 Malaysia 163.29 0.660 107.77 191.68 76.67 0.400 292 Table 4: Optimal electricity trade matrix for Case Study 2 (values in TWh/y) Vietnam Myanmar Singapore Cambodia Thailand Malaysia Excess New zero-C capacity 67.3 2.7 17.4 1.1 15.8 75.4 0 Vietnam 175.1 19.0 0.0 0 0 0 0 Myanmar 0 4.0 17.1 0 0 0 0 Singapore 0 0 23.6 0 26.7 0 0 Cambodia 0 0 0 8.1 0 0 0 Thailand 0 0 0 0 207.3 0.7 0 Malaysia 0 0 0 0.2 0 115.6 47.5 Following the same steps as in the previous case study, it is possible to determine the target of 179.9 TWh/y of new zero-carbon electricity generation capacity, and an optimal electricity trading scheme for these six countries which meet their future requirements and emissions limits. Due to space constraints, the Pinch Diagram is not shown here. The resulting electricity trading matrix is given in Table 4. The results show that even with electricity trading in place, each country would still need to install some amount of zero-carbon generation capacity to meet its emissions target. It also suggests that Malaysia should shut down (or utilize as reserve) 47.5 TWh/y of its current capacity. In this scenario, the region would trade approximately 71.6 TWh/y of electricity. 6. Conclusions This paper has developed a CEPA approach for planning optimal electricity trading. The new methodology can identify the optimal target and determine the corresponding electricity import and export matrix for a given set of countries or regions with self-defined emissions limits. Two case studies were solved to illustrate the procedure – one for tutorial purposes, the other to demonstrate applicability to a real data set from ASEAN. Future work can focus on the development of a multi-period or dynamic extensions, which will allow progressive emissions cuts and power plant phase-out to be dealt with. References Andiappan, V., Foo, D.C., Tan, R.R., 2019, Process-to-Policy (P2Pol): using carbon emission pinch analysis (CEPA) tools for policy-making in the energy sector, Clean Technologies and Environmental Policy, 21, 1383–1388. ASEAN Centre for Energy, 2017, The 5th ASEAN Energy Outlook 2015–2040. Atkins, M.J., Morrison, A.S., Walmsley, M.R.W., 2010, Carbon Emissions Pinch Analysis (CEPA) for emissions reduction in the New Zealand electricity sector, Applied Energy, 87, 982–987. Baležentis, T., Štreimikienė, D., Melnikienė, R., Zeng, S., 2019, Prospects of green growth in the electricity sector in Baltic States: Pinch analysis based on ecological footprint, Resources, Conservation and Recycling, 142, 37–48. Bandyopadhyay, S., Sahu, G.C., Foo, D.C.Y., Tan, R.R., 2010, Segregated targeting for multiple resource networks using decomposition algorithm, AIChE Journal, 56, 1235–1248. Crilly, D., Zhelev, T., 2008, Emissions targeting and planning: An application of CO2 Emissions Pinch Analysis (CEPA) to the Irish electricity generation sector, Energy, 33, 1498–1507. de Chalendar, J.A., Taggart, J., Benson, S.M., 2019, Tracking emissions in the US electricity system. Proceedings of the National Academy of Sciences of the United States of America, 116, 25497–25502. Dhole, V.R., Linnhoff, B., 1993, Total site targets for fuel, co-generation, emissions, and cooling, Computers & Chemical Engineering, 17, S101–S109. Foo, D.C.Y., Tan, R.R., Ng, D.K.S., 2008, Carbon and footprint-constrained energy sector planning using cascade analysis technique, Energy, 33, 1480–1488. Foo, D.C.Y., Tan, R.R., 2016, A review on process integration techniques for carbon emissions and environmental footprint problems, Process Safety and Environmental Protection, 103, 291–307. Foo, D.C.Y., 2017, Extended graphical technique for the evaluation of carbon dioxide emission reduction projects, Process Integration and Optimization for Sustainability, 1, 269–274. Foo, D.C.Y., Tan, R.R., 2020, Process Integration approaches to planning carbon management networks, CRC Press, Boca Raton, FL, USA. Francisco, F.D.S., Pessoa, F.L.P., Queiroz, E.M., 2014, Carbon sources diagram – A tool for carbon- constrained energy sector planning, Chemical Engineering Transactions, 39, 1495–1500. IEA, 2017, Southeast asia energy outlook, accessed 17.10.2019. 293 IEA, 2019, Energy Data and Statistics accessed 17.10.2019. Jia, X.P., Liu H.C., Qian, Y., 2009, Carbon Emission Pinch Analysis for energy planning in chemical industrial park, Modern Chemical Industry, 29, 81–85. Jia, X., Li, Z., Wang, F., Foo, D.C.Y., Tan, R.R., 2016, Multi-dimensional Pinch Analysis for power generation sector in China, Journal of Cleaner Production, 112, 2756–2771. Jia, X., Wang, S., Li, Z., Wang, F., Tan, R.R., Qian, Y., 2018, Pinch Analysis of GHG mitigation strategies for municipal solid waste management: A case study on Qingdao City, Journal of Cleaner Production, 174, 933-944 Klemeš, J.J. (Ed.), 2013, Handbook of Process Integration (PI): Minimisation of Energy and Water Use, Waste and Emissions, Elsevier/Woodhead Publishing, Cambridge, UK. Klemeš, J.J., Varbanov, P.S., Walmsley, T.G., Jia, X., 2018, New directions in the implementation of Pinch Methodology (PM), Renewable and Sustainable Energy Reviews, 98, 439–468. Krishna Priya G.S., Bandyopadhyay S., 2013, Emission constrained power system planning: A pinch analysis based study of Indian electricity sector, Clean Technologies and Environmental Policy, 15, 771–782. Lee, S.C., Ng, D.K.S., Foo, D.C.Y., Tan, R.R., 2009, Extended Pinch targeting techniques for carbon- constrained energy sector planning, Applied Energy, 86, 60–67. Lee, J.-Y., Lin, H.-F., 2019, Multi-Footprint Constrained Energy Sector planning, Energies, 12, 2329. Li, Z., Jia, X., Foo, D.C.Y., Tan, R.R., 2016, Minimizing carbon footprint using Pinch Analysis: The case of regional renewable electricity planning in China, Applied Energy, 184, 1051–1062. Lim, X., Foo, D.C.Y., Tan, R.R., 2018, Pinch Analysis for the planning of power generation sector in the United Arab Emirates: A climate-energy-water nexus study, Journal of Cleaner Production, 180, 11–19. Lopez, N.S.A., Biona, J.B.M.M., Chiu, A.S.F., 2018, Electricity trading and its effects on global carbon emissions: A decomposition analysis study, Journal of Cleaner Production, 195, 532–539. Patole, M., Bandyopadhyay, S., Foo, D.C.Y., Tan, R.R., 2017, Energy sector planning using Multiple-Index Pinch Analysis, Clean Technologies and Environmental Policy, 19, 1967–1975. Pękala, Ł.M., Tan, R.R., Foo, D.C.Y., Jeżowski, J.M., 2010, Optimal energy planning models with carbon footprint constraints, Applied Energy, 87, 1903–1910. Qin, Z., Tang, K., Wu, X., Yu, Y., Zhang, Z., 2017, Product-based carbon constraint energy planning with Pinch Analysis for sustainable methanol industry in China, Chem. Eng. Trans., 61, 103–108. Salman, B., Nomanbhay, S., Foo, D.C.Y., 2019, Carbon emissions pinch analysis (CEPA) for energy sector planning in Nigeria, Clean Technologies and Environmental Policy, 21, 93–108. Sinha, R.K., Chaturvedi, N.D., 2018, A graphical dual objective approach for minimizing energy consumption and carbon emission in production planning, Journal of Cleaner Production, 171, 312–321. Su, W., Ye, Y., Zhang, C., Baležentis, T., Štreimikienė, D., 2020, Sustainable energy development in the major power-generating countries of the European Union: The Pinch Analysis, Journal of Cleaner Production, 120696. Tan, R.R., Foo, D.C.Y., 2007, Pinch Analysis approach to carbon-constrained energy sector planning, Energy, 32, 1422–1429. Tan, R.R., Foo, D.C.Y., Aviso, K.B., Ng, D.K.S., 2009a, The use of Graphical Pinch Analysis for visualizing Water Footprint Constraints in biofuel production, Applied Energy, 86, 605–609. Tan R.R., Ng D.K.S., Foo D.C.Y., 2009b, Pinch Analysis approach to carbon-constrained planning for sustainable power generation, Journal of Cleaner Production, 17, 940–944. Tan R.R., Foo, D.C.Y., 2013, Pinch Analysis for sustainable energy planning using diverse quality measures, In: Klemeš, J.J. (Ed.), Handbook of Process Integration (PI): Minimisation of Energy and Water Use, Waste and Emissions, pp. 505–523, Elsevier/Woodhead Publishing, Cambridge, UK. Tan, R.R., Aviso, K.B., Foo, D.C.Y., 2017, P-graph and Monte Carlo simulation approach to planning Carbon Management Networks, Computers and Chemical Engineering, 106, 872–882. Tan, R.R., Aviso, K.B., Foo, D.C.Y., 2018, Carbon emissions pinch analysis of economic systems, Journal of Cleaner Production, 182, 863–871. Tan, R.R., Foo, D.C.Y., 2018, Process integration and climate change: from carbon emissions pinch analysis to carbon management networks, Chemical Engineering Transactions, 1–6. Walmsley, M.R.W., Walmsley, T.G., Atkins, M.J., 2015a, Achieving 33% renewable electricity generation by 2020 in California, Energy, 92, 260–269. Walmsley, M.R.W., Walmsley, T.G., Atkins, M.J., Kamp, P.J.J., Neale, J.R., Chand, A., 2015b, Carbon Emissions Pinch Analysis for emissions reductions in the New Zealand transport sector through to 2050, Energy, 92, 569–576. 294