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Hybrid approach for carbon-constrained planning of bioenergy supply chain network

Citation for published version: Leong, H, Leong, H, Foo, DCY, Ng, LY & Andiappan, V 2019, 'Hybrid approach for carbon-constrained planning of bioenergy supply chain network', Sustainable Production and Consumption, vol. 18, pp. 250- 267. https://doi.org/10.1016/j.spc.2019.02.011

Digital Object Identifier (DOI): 10.1016/j.spc.2019.02.011

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Download date: 28. Sep. 2021 1 Hybrid Approach for Carbon-constrained Planning of Bioenergy

2 Supply Chain Network 3

4 Huini Leong a, Huiyi Leong a, Dominic C. Y. Foob, Lik Yin Nga, Viknesh Andiappana,* 5 aSchool of Engineering and Physical Sciences, Heriot-Watt University Malaysia, 62200, Putrajaya, 6 Wilayah Persekutuan Putrajaya, Malaysia 7 bDepartment of Chemical and Environmental Engineering/Centre of Excellence for Green 8 Technologies, The University of Nottingham Malaysia, Jalan Broga Road, 43500 Semenyih, 9 Selangor, Malaysia 10 Abstract 11 With increasing emphasis on sustainable development, developing countries are required to adapt 12 more sustainable approaches to energy policy-making. Bioenergy supply chain planning 13 methodologies can provide viable frameworks for policy-making. However, current

14 methodologies lack the ability to simultaneously consider CO2 emission reduction targets while 15 design a bioenergy supply chain. As such, the objective of this work is to present a hybrid 16 methodology that combines both carbon emission pinch analysis with superstructure-based 17 optimisation technique. The proposed methodology was demonstrated via a palm-based case study, 18 focused on the state of Selangor, Malaysia. The case study was broken into two scenarios. The 19 first scenario accounted for an output-driven energy policy, whereby the electricity output of the 20 bioenergy supply chain network (BSCN) is prioritised and the corresponding emission reduction 21 is analysed. Results from the first scenario suggests that the optimised BSCN with 5,040 TJ output

22 could reduce CO2 emission intensity by 9.71%. The second scenario focused on an emission-driven 23 policy. Emission-driven policy establishes the emission reduction targets first and then determines 24 the corresponding BSCN to achieve it. Results from this scenario indicate all oil palm plantations

25 could afford to operate on low growth factors of up to 0.8 to avoid sharp drops in possible CO2 26 reductions for the BSCN. This policy was explored further by conducting sensitivity analysis on 27 the agricultural growth and biomass export factors respectively. The analyses found that the 28 optimised BSCN experience minimal change in costs when plantations have growth factors 29 beyond 1.1. Lastly, analysis was performed to evaluate the range of technologies chosen based on 30 the electricity output. The analysis found that power plant technologies were favoured more as 31 compared to combined heat and power systems.

32 Keywords: carbon emission pinch analysis, superstructural optimisation, output-driven, 33 emission-driven, process integration, palm biomass.

CEPA: Carbon emission pinch analysis; ATM: Automated targeting model; BSCN: bioenergy supply chain network; EFB: empty fruit bunch; PKS: palm kernel shell; PMF: palm mesocarp fiber; POME: palm oil mill effluent; CHP: combined heat and power; PP: power plant 34 35 *Corresponding. Emails: [email protected] (Huini Leong); [email protected] (Huiyi 36 Leong); [email protected] (D. C. Y. Foo); [email protected] (L. Y. Ng); 37 [email protected] (V. Andiappan). 38 39 1 Introduction 40 In December of 2015, governments of the world made history in Paris by pledging to stop 41 climate change at the 21st Conference of Parties (COP21). After two weeks of negotiations, the 42 Paris Agreement was adopted by all participating nations (UN Framework Convention on Climate 43 Change, 2015a). Since the Paris Agreement, many countries have been developing sector-specific 44 plans and policies to achieve their emissions reduction commitments (UN Framework Convention 45 on Climate Change, 2018). In fact, leaders of many countries recently met at the 24th Conference 46 of Parties (COP24) to set out a global climate agreement on how to implement the Paris 47 Agreement (UN Framework Convention on Climate Change, 2018). For many countries, 48 renewable electricity plays an important role and many countries have either set targets or started 49 along this path (UN Framework Convention on Climate Change, 2015b). Plans for renewable 50 energy deployment requires consideration of many short- and long-term actions. These decisions 51 would be a big departure from typical governmental decisions and policy-makers must rely on 52 various types of analysis tools for additional insights and to explore trade-offs involved. However, 53 specific analysis tools are required for developing countries. Developing countries face hurdles in 54 keeping carbon reductions commitments due to lack of financial resources (UN Framework 55 Convention on Climate Change, 2017). This in turn, would deter developing countries to consider 56 capital intensive projects for renewable energy deployment. Therefore, it is imperative to set up 57 decision-making tools that is not only suited for developed countries but also for developing 58 countries.

59 Among the various developed tools, carbon emissions pinch analysis (CEPA) has gained good 60 attention since its inception (Tan and Foo, 2007). CEPA is a planning tool developed for macro 61 level energy planning based on carbon emission reduction targets, built on the same principles of 62 the well-established pinch analysis and process integration tools (Klemeš, 2013; Linnhoff et al., 1982). 63 In their seminal work, Tan and Foo (2007) proposed a graphical tool known as energy planning pinch 64 diagram to determine the minimum renewable energy resources, while maximising the conventional 65 fossil fuels. The main limitation of this early work is that, renewable energy has been assumed 66 with zero carbon intensity. This limitation was later overcome in their latter work (Lee et al., 2009), 67 where the energy planning pinch diagram was revised for renewable energy sources with low 68 carbon intensity. To overcome the cumbersomeness of graphical techniques, algebraic technique 69 (Sahu et al., 2014) and automated targeting model (ATM) (Lee et al., 2009) have also been proposed. 70 For the latter, CEPA concept was incorporated into an optimisation framework and the ATM was 71 solved to determine required the renewable energy resources. Since then, CEPA has been used 72 for energy planning in both developed and developing countries. Examples of CEPA approaches 73 for developed countries include New Zealand (Atkins et al., 2010) and Ireland (Crilly and Zhelev, 74 2010) while examples for developing countries include China (Jia et al., 2010) and India (Priya and 75 Bandyopadhyay, 2013). Tan et al. (2016) then extended the CEPA approach to consider near- 76 optimal solutions and alternative network solutions using process graph method. More recently, Lim 77 et al. (2018) presented a CEPA approach for the power sector in the United Arab Emirates. 78 Following this, Baležentis et al. (2019) developed a CEPA approach that is based on ecological 79 footprint and applied it on the power sector in Baltic States.

80 The mentioned CEPA approaches provide useful insights on planning for a given energy 81 sector at the regional scale. Whilst regional scale planning can provide interesting insights to 82 allocate renewable energy resources, it is important to consider the optimisation of renewable 83 energy resource supply chains as well. Several contributions have been developed in the past for 84 the optimisation of renewable energy resource supply chain. These contributions often based their 85 work on mathematical programming approaches. For instance, Dunnett et al. (2008) adapted a 86 model previously developed by Almansoori and Shah (2009) for a biomass-to-ethanol supply chain. 87 In this model, full spatial representations are utilised with the aid of echelons to investigate the 88 trade-offs between centralised and decentralised pre-processing of biomass. Echelons were used 89 to break biomass supply chain stages into several levels. Later, Čuček et al. (2010) developed a 90 biomass supply chain model which uses a multi-echelon structure. This model was then extended 91 by Cucek et al. (2014) to consider 12 one-month periods, seasonality, purchase of raw materials 92 and intermediate storage (with losses). Čuček et al. (2012) presented a multi-objective optimisation 93 model for regional biomass supply chains, where economic performance, environmental and social 94 footprints were considered. Results from this model suggested that biomass energy required more 95 water, transportation and chemical input as compared to fossil energy. On the other hand, How 96 et al. (2016) developed a transportation decision-making tool to optimise biomass supply chains 97 with vehicle capacity constraints such as weight and volume. Ng and Maravelias (2017) proposed 98 a multi-period model based on mixed integer linear programming (MILP) for the design and 99 operational planning of cellulosic biofuel supply chains in the United States. In this work, the 100 MILP model considers multi-year horizons for biomass selection and allocation, technology 101 selection and capacity planning at regional depots and biorefineries (Ng and Maravelias, 2017). 102 Meanwhile, Zore et al. (2017) developed a new metric for multi-criteria evaluation of sustainable 103 supply networks, known as sustainability profit. Sustainability profit is composed of economic, 104 environmental and social indicators whereby environmental and social indicators are measured 105 based on its monetary unit equivalence. More recently, How and Lam (2018) presented a 106 debottlenecking approach using Principal Component Analysis to identify sustainability 107 bottlenecks in a biomass supply chain and subsequently removing them. Zulkafli and Kopanos 108 (2018) developed a general spatial optimisation framework based on a modified state-task network 109 representation to design energy supply chain networks. Meanwhile, Gonela (2018) presented a 110 stochastic mixed integer linear optimisation approach for hybrid electricity supply chains with 111 special attention towards carbon emission schemes. Campanella et al. (2018) developed a 112 modelling framework for forest supply chains. The framework considered the location and size of 113 each production facility, the amounts of products and residues to be generated, and all the material 114 flows within the network.

115 The abovementioned papers consider carbon-constrained planning and biomass supply chain 116 planning as separate tasks, independent of each other. In reality, the decisions made in carbon 117 emission reductions planning would affect decisions in biomass supply chain planning. It is worth 118 noting that some biomass supply chain approaches have considered minimising environmental 119 impacts. This however, fails to capture how much carbon emissions were reduced. Essentially, 120 renewable energy resource supply chains play an important role in materialising the carbon 121 reduction targets set by CEPA. In this sense, Li et al. (2016) developed a combined CEPA and 122 biomass supply chain network synthesis approach to minimise carbon footprint in China. 123 However, Li et al. (2016) only considered biomass collection efficiency for the biomass supply 124 chain synthesis. More recently, an input-output analysis approach was incorporated into CEPA by 125 Tan et al. (2018). This contribution analysed fixed industrial sectors via input-output analysis and 126 its corresponding carbon emissions via CEPA.

127 The approaches that combined CEPA and biomass supply chain planning prove to be useful 128 tools. However, there is room for improvement. For instance, these approaches are generic and 129 assume that capital is an infinite resource for a given nation. In reality, developing nations face 130 major challenges in securing capital to mobilise a nation-wide bioenergy supply chain to meet 131 carbon emission reduction targets. Alongside this, it was observed that the level of decisions in 132 supply chain planning for carbon emission reduction remains an area which needs further attention. 133 Supply chain decisions are divided into three levels; strategic, tactical and operational (De Meyer 134 et al., 2014). A vast majority of the papers discussed focused on either strategic or tactical decisions 135 but not both simultaneously. In the context of supply chain planning for carbon emission 136 reduction, it is imperative to consider strategic and tactical decisions simultaneously.

137 As such, the objective of this paper is to present a methodology that is able to simultaneously 138 plan carbon emission reduction targets and bioenergy supply chain networks (BSCN) via the 139 combined use of CEPA and superstructural optimisation. Essentially, the methodology proposed 140 in this paper offers two approaches. The first is known as output-driven approach, which prioritises 141 the output of the BSCN via superstructural optimisation and its corresponding emission reduction 142 is determined through CEPA. The output-driven approach is particularly suited for developing 143 countries with limited resources and are looking to take a cautious approach towards renewable 144 energy. On the other hand, the second approach, otherwise known as emission-driven approach 145 focuses on targeting emission reductions via CEPA and subsequently establishing a cost effective 146 BSCN through superstructural optimisation. The emission-driven approach caters for developed 147 countries that have healthier financial positions and are able to invest in capital intensive renewable 148 energy deployment. Note also that both output-driven and emission-driven approaches address 149 strategic and tactical supply chain planning decisions.

150 The outline of this paper is as follows: a formal problem statement is provided in the next 151 section to describe the problem addressed. Following this, a detailed account of the proposed 152 methodology is provided. Subsequently, two case studies are solved to show applicability of the 153 proposed methodology. Results from these two case studies are extensively analysed and key 154 insights are discussed. Finally, implications, conclusions and future research works are provided 155 at the end of the paper.

156

157 2 Problem Statement 158 As mentioned previously, developing countries face hurdles to adhere to carbon reductions 159 commitments because of lack of financial resources (UN Framework Convention on Climate 160 Change, 2017). Thus, it is important to develop decision-making tools that are not only applicable 161 to developed countries but also for developing countries. For developed countries, it is easier to 162 analyse the required renewable energy output needed to achieve carbon emission reductions agreed 163 at the international stage. Meanwhile, developing countries would rather take a cautious approach 164 towards capital investment to honour carbon emission reduction commitments. To do address 165 these problems, this work proposes a methodology that considers the following;

166  For developed countries: determination of required renewable energy output and 167 corresponding BSCN based on emission reduction target. 168  For developing countries: determination of corresponding carbon emission reductions 169 based on optimal BSCN with minimised cost.

170 The following section describes the methodology proposed in this work. 171 3 Methodology 172 The proposed methodology is shown in Figure 1.

START

Emission-Driven Output-driven or Output-Driven emission-driven?

Superstructural Optimisation Carbon Emission Pinch Analysis Determine the optimal bioenergy supply Target required renewable energy output chain network (BSCN) based on defined based on emission reduction target output

Superstructural Optimisation Carbon Emission Pinch Analysis Determine optimal bioenergy supply Determine corresponding emission chain network (BSCN) to deliver reduction based on output of BSCN

required renewable energy output

Readjust requirementsReadjust Readjustrequirements Emission No No BSCN cost reduction acceptable? acceptable?

Yes Yes Recommend BSCN design

END 173

174 Figure 1 Methodology Flowchart

175 As shown in Figure 1, the hybrid methodology proposed in this work is generally made up of 176 two techniques, i.e., CEPA and superstructural optimisation. CEPA is implemented through the 177 automated targeting model (ATM), where the minimum renewable energy target is determined through 178 an optimisation framework (Lee et al., 2009). Even though ATM identifies the same target as the 179 energy planning pinch diagram (Tan and Foo, 2007), it overcomes the cumbersomeness of the 180 latter, and allows interaction with other optimisation techniques, such as superstructural 181 optimisation (Foo and Tan, 2016). Superstructural optimisation is used in this work to enumerate 182 several potential technological pathways that convert raw materials to products and/or energy 183 (Andiappan, 2017). These pathways are then modelled by a mathematical model with the aim of 184 determining an optimal pathway. Superstructural optimisation has been widely used in areas such 185 as design of utility systems (Yeomans and Grossmann, 1999), eco-industrial parks (Andiappan et 186 al., 2016) and biorefineries (Ng et al., 2015). 187 The proposed methodology in Figure 1 contains two approaches to design a BSCN, i.e. 188 emission-driven and output-driven. The choice of approach would depend on the intended objective.

189 The emission-driven approach begins by targeting a desired percentage reduction in CO2 emissions 190 via ATM. Based on the targeted emission reduction, the required electricity output is calculated. 191 This required electricity output is then exported to the superstructure model to screen and select 192 an optimal BSCN with minimum cost. If the cost of the optimal BSCN is within an acceptable 193 range, it can be accepted for recommendation. Otherwise, the emission reduction must be revised 194 to a more lenient target. This approach is particularly suitable for developed countries with 195 substantial capital, looking to analyse several carbon emission scenarios and their corresponding 196 impact on the optimal BSCN design.

197 Alternatively, the output–driven approach may be adopted. In this case, it is imperative to 198 decide a suitable electricity output target accounting the number of participating biomass sources 199 (i.e., plantations, suppliers). Following this, the superstructural approach determines an optimal 200 BSCN based on the targeted electricity output at minimum cost. Next, the targeted electricity

201 output is exported to ATM to obtain the corresponding percentage of CO2 reduction. If 202 reductions are deemed unacceptable, the BSCN optimisation must be revisited. In this case, a 203 lower electricity output should be defined. This approach is suitable for developing countries with 204 limited financial resources.

205 It is also worth noting that this methodology considers strategic and tactical planning 206 decisions within the BSCN. Specifically, the strategic decision variables considered are flow of 207 biomass within the BSCN, capital and operating costs, technology and capacity selection, biomass 208 availability and emission reductions. Meanwhile, tactical decision variables considered are biomass 209 production via growth factors, biomass export factors and the selection of biomass utilisation 210 technologies.

211 Following sub-sections outline details for both techniques, i.e. ATM and superstructure 212 optimisation.

213 3.1 Model Nomenclature 214 Indices

215 k Index for levels

216 S Index for supply

217 D Index for demand 218 h Index for resources

219 g Index for plantations

220 i Index for biomass

221 f Index for facility/export

222 j Index for energy system/export

223 e Index for energy

224

225 Parameters

226 ES Energy supply

227 ED Energy demand

228 Agh Area of plantation g planted with of resource h

229 Ygh Yield of resource h from plantation g

230 Ggh Growth factor of resource h from plantation g

Res,Av 231 Fgh Available amount of resource h from plantation g

232 αghf Fraction of the available resources h intended for export from plantation g

233 Vhfi Conversion of resource h to biomass i

234 αij Fraction of the produced biomass i intended for export from technology j

235 Vije Conversion of biomass i to energy e

236 VCj Variable cost for energy system j per unit energy produced

237 CRFj Capital recovery factor for energy system j

238 FCj Fixed cost for energy system j per unit energy produced

239 OFCghf Cost per unit flow of resource h transported from plantation g to facility f

240 OFCij. Cost per unit flow of biomass i transported to energy system j

241 dghf Distance to transport resource h transported from plantation g to facility f

242 dij Distance to transport resource i transported to energy system j

CHP/PP, min 243 Fij Minimum operating capacity for a given energy system j

CHP/PP, max 244 Fij Maximum operating capacity for a given energy system j

245

246 Variables

247 ERE Minimum renewable energy target

248 k Residual energy at each emission level k

249 εk Residual load at each emission level k

Trans 250 Fghf Flow of resource h transported from plantation g to facility f

Exp 251 Fghf Flow of resource h exported from plantation g

Bio 252 Fi Flow of biomass i produced

CHP/PP 253 Fij Flow of biomass i transported to energy system j

Exp 254 Fij Flow of biomass i exported from facility f

Energy 255 Fje Amount of energy e produced at energy system j

256 bj Integer denoting existence of energy system j in the supply chain

257 CAP Annualised capital costs of the supply chain

258 OPEX Operational costs of the supply chain

259

260 3.2 CEPA 261 In order to determine the minimum renewable energy target for a carbon-constrained energy 262 planning scenario, CEPA may be utilised. As discussed earlier, CEPA may be implemented 263 through energy planning pinch diagram or ATM. For better interactions with the superstructural 264 approach, ATM is utilised here. The basic framework of the ATM is given in Figure 2(a). First

265 step of the procedure is to arrange emission factors (Ck) in an ascending order, with all energy

266 demands (ED) and supplies (ES) located at their respective levels k. The main objective is to

267 determine the minimum renewable energy target (ERE), as given in Eq. (1): Minimum = ERE (1) 268

269 In the energy cascade, the residual energy that leaves an emission level k (k) is given by the 270 summation of the residual energy from previous level with the net energy at each level:

k = k–1 + (∑i ES – ∑j ED)k k (2) 271

272 In the CO2 load cascade, the residual load at each level k (εk) is given by the product of the residual

273 energy of previous level (k-1) with the difference of its adjacent levels:

k = k–1 +  k–1 (Ck – Ck–1) k (3) 274

Energy CO2 load cascade cascade Demand ε composite E C 1 = 0 RE 1 curve

δ C 1 1 ε E C 2 S1 2 δ C Pinch

2 2

emission emission

ε 2 C E 3 3 D1 δ C CO 3 3 ε Supply C E 4 4 D2 composite δ C curve 4 4 E ε S2 C 5 5 δ C 5 5 Minimum Excess fossil E C ε renewables, ERE sources S2 6 6 (a) (b) 275

276 Figure 2 CEPA tools: (a) Generic framework of ATM; (b) energy planning pinch 277 diagram

278 Note that the energy sources entering the first level (i.e. δ0 = ERE), as well as that leaving the last 279 level must take positive values:

δk ≥ 0 k = 0, n (4) 280

281 Note also that the CO2 emission loads for all levels should take non-negative values: 1 = 0, k ≥ 0 k = 2, 3,… n (5) 282

283 The above formulation is a linear problem (LP). ERE can be determined by solving Eq. (1), subject 284 to the constraints in Eqs. (2) – (5).

285 One may also display the results of ATM in the graphical tool of CEPA, i.e. energy planning 286 pinch diagram. A generic form of the latter is given in Figure 2 (b). As shown, the opening of the 287 left of the energy planning pinch diagram corresponds to the minimum renewable energy target as

288 identified by the ATM, i.e. ERE, while that on the right corresponds to excess fossil fuels, which

289 cannot be utilised due to CO2 emission constraint.

290

291 3.3 Superstructure Optimisation 292 In this section, the generic framework for superstructure optimisation is discussed. In the 293 case of emission-driven approach, superstructure optimisation is used to screen and select an optimal 294 bioenergy supply chain network (BSCN) with minimum cost, while targeting the minimum

295 renewable energy (ERE) determined by ATM. As for the output-driven approach, superstructure 296 optimisation is used to determine the BSCN with minimum cost, followed by determining the

297 corresponding CO2 emission reduction via ATM.

Plantation Resource Facility Biomass CHP/PP Energy Export Export f = 1 j = 1

g = 1 h = 1 f = 1 i = 1 j = 1 e = 1

g = G h = H f = F i = I j = J e = E

Plantation Power generation Facility Side side Side Legend Transported Within Process 298

299 Figure 3 Generic Superstructure for Bioenergy Supply Chain 300 The generic superstructure in Figure 3 starts with plantations g є G. In plantations g є G, resources

Res,Av 301 h are planted with an area Agh. The available amount of resource h, Fgh is produced at a yield

302 of Ygh and growth factor Ggh as shown in Eq. (6). Growth factor Ggh represents the potential

303 increase or decrease in yearly yield for resources h. Note that the values set for Ggh would depend 304 on the plantation performance. If there are possibilities of disruption (e.g., the water crisis, 305 droughts), the growth factor can be set below 1. On the other hand, if the plantations yield higher 306 amounts of resource h (than its prior period), the growth factor can be set to more than 1.

Res,Av FAYGgh gh gh gh gh (6) 307

308 The available resource h can be transported to another facility to undergo conversion or exported 309 for further use as shown in Eq. (7). The total flow of resource h transported to facility f is given

Trans Exp 310 by Fghf . Meanwhile, the flow of resource h exported is denoted by Fghf .

FF Res,Av Trans Exp FghFF ghf ghf (7) ff11 311

Exp Exp 312 Fghf is determined by Eq. (8). As shown, Fghf is computed based on the fraction ( αghf ) of the 313 available resources h intended for export from plantation g.

Exp Res,Av FFghf α ghf gh g  h,  f  Export (8) 314

315 The flow of resource h transported to facility f is then converted to biomass i as shown in Eq. (9).

316 The conversion of resource h to biomass i is given by Vhfi. The flow of biomass i produced at

Bio 317 facility f is represented by Fi .

GF Trans Bio FFghfV hfi i i h (9) gf11

318

Bio Bio 319 Following this, Eq. (10) then shows the split of Fi further downstream. As shown, the flow Fi 320 can be split for transport to an energy system j (i.e., CHP: combined heat and power; PP = power

CHP/PP Exp 321 plant) Fij or for export, Fij . JJ Bio CHP/PP Exp FFFi ij ij i (10) jj11 322

Exp 323 Similar to Eq. (8), Fij is determined in Eq. (11), whereαij is the fraction of the produced biomass 324 i intended for export from facility f.

Exp Bio FFij α ij i ij,   Export (11) 325

326 The flow of biomass i transported to energy system j is then converted to energy e as shown in Eq.

327 (12). The conversion of biomass i to energy e is given by Vije. The amount of energy e produced

Energy 328 at energy system j is given by Fje .

I CHP/PP Energy  FFijV ije je ej (12) i1 329

330 It is important to note that the sum of is the total renewable energy produced by the BSCN.

331 In this respect, the sum of is equal to ERE from the ATM as shown in Eq. (13). Eq. (13)

332 represents the linkage between ATM and the superstructure optimisation.

JE Energy EFRE   je (13) je11 333

334 The annualised capital costs of the supply chain (CAP) is computed using Eq. (14). CAP is 335 determined based on the capacity of energy system j, which is calculated using the energy produced

Energy 336 Fje and the variable cost per unit energy produced VCj. CRFj is the capital recovery factor for

337 energy system j. Meanwhile, the fixed cost (FCj) is activated using a binary integer bj which denotes

338 the existence of energy system j in the supply chain. bj is governed by the following Eq. (15), which 339 shows the upper and lower capacities for a given energy system j.

JE Energy CAP FjeVC j  b j  FC j  CRF j (14) je11 340

CHP/PP, min CHP/PP CHP/PP, max FFijb j  F ij  ij  b j (15) 341 342 Aside from this, the operational costs of the supply chain (OPEX) is computed using Eq. (16). 343 OPEX is determined based on the transportation costs, which is calculated using the flow

Trans CHP/PP 344 transported Fghf and Fij . The cost per unit flow transported is given by OFCghf and OFCij.

GHFIJ Trans CHP/PP OPEX FghfOFC ghf d ghf  F ij OFC ij d ij  (16) g1 h  1 f  1 i  1 j  1 345

346 The total annualised cost (TAC) is given by the summation of CAPEX and OPEX, given as in Eq. 347 (17).

TAC = CAP + OPEX (17) 348

349 Note that the superstructure model in Equations (6)-(16) is a mixed integer linear program (MILP). 350 It is also important note that Eq. (13) is a key constraint in linking both ATM and superstructure 351 optimisation.

352 Following this, the proposed methodology in Figure 1 and formulations shown Sections 3.2 353 – 3.3 are demonstrated in the following section using a case study based in Malaysia.

354 4 Case Study 355 During COP21, the Malaysian government pledged to reduce its greenhouse gases emission 356 intensity per unit gross domestic product from levels in year 2005, by 45% in 2030 (Begum, 2017). 357 Assuming a gross domestic product growth rate of 4% every year, the country would need to 358 achieve a reduction in 85 Million t of greenhouse gas emission per year for a sustainable low carbon 359 future (Yap, 2017). In the effort of promoting green technology, then Ministry of Energy, Green 360 Technology and Water launched the National Renewable Energy Policy and Action Plan 2010 361 (SEDA, 2018). This policy aimed to enhance national electricity supply security by increasing 362 effective use of renewable resources. The Malaysian government targeted to achieve a green 363 energy feed-in rate of 37,440 TJ by 2020. However, the outcome of general elections in 2018 364 brought change in Malaysian government agencies and policy-makers. During this transition 365 period, many projects are being re-evaluated and/or kept on hold. Nevertheless, carbon intensity 366 reduction and the future of the renewable energy sector in Malaysia remains a key focus as stated 367 in an official address by the newly appointed Minister of Energy, Technology, Science, Climate 368 Change and Environment (Brown, 2018). Among the available clean energy resources, oil palm 369 biomass is abundantly found in Malaysia. Since 2017, the oil palm industry in Malaysia holds 5.81 370 million hectares of plantation land. From here, it is worth noting that this industry is a major 371 contributor to the gross domestic product of the Malaysian agricultural sector (Ministry of Foreign 372 Affairs, 2018). In fact, Malaysia accounts for around 39% of world oil palm production and this 373 figure was expected to rise by 3% in 2018 (MPOC, 2018). Crude palm oil is extracted from fresh fruit 374 bunches via palm oil milling processes. During this process, huge amount of biomass wastes is 375 produced. These wastes comprise of empty fruit bunches (EFB), palm kernel shell (PKS), palm mesocarp 376 fiber (PMF), and wastewater with high pollution load, known as palm oil mill effluent (POME). 377 Besides, oil palm trunk (OPT) and oil palm fronds (OPF) are also part of the biomass wastes during 378 the replanting of oil palm tree (Sadhukhan et al., 2018).

379 This case study focuses planning renewable energy resources for Selangor, a densely populated 380 state in the Malaysian Peninsular. In fact, Selangor has an urbanisation rate of 91.4% since 2010. 381 Thus, it is imperative that the state of Selangor considers a sustainable strategy for its energy sector 382 (Ministry of Natural Resources and Environment Malaysia, 2015).

383 In this case study, 7 palm oil mills, their plantations and one substation were considered. The 384 locations of these sites are shown in Table 1 and are illustrated in Figure 4. As mentioned 385 previously, these palm oil mills and plantations generate biomass as waste from their operations. 386 The amount of biomass wastes generated by each plantation is summarised in Table 2 (Ministry 387 of Natural Resources and Environment Malaysia, 2015). Note that the area of plantations and

Bio 388 amount of biomass correspond to parameters Agh and Fi respectively. Also, the distance to

389 substation is represented by the parameter dij.

390 Table 1 GPS Coordinates and Distances of Mills and Plantations from TNB substation

Distance to Mills and Plantations Latitude Longitude substation (km) Jugra 2.845557 101.467958 28 Eng Hong 2.860081 101.476569 30 Sime Darby East 2.883333 101.436136 24 Banting 2.728394 101.494616 50 Kampung Kuantan 3.344463 101.290602 77 Tuan Mee 3.265013 101.463790 42 Seri Ulu Langat 2.851192 101.650403 32 TNB Substation 2.9365 101.58014 - 391 392

393 Figure 4 Location of Mills and TNB Substation

394 Table 2 Amount of Biomass Available based on Total Area of Plantation.

Area EFB PMF PKS POME OPT OPF Location (ha) (t/h) (t/h) (t/h) (m3/h) (t/h) (t/h) Jugra 6.29 13.49 8.84 3.23 4.1 236 78 Eng Hong 5.03 10.79 7.07 2.58 3.3 189 63 Sime Darby East 5.66 12.14 7.96 2.91 3.7 212 70 Banting 2.52 12.14 7.96 2.91 3.7 212 70 Kampung Kuantan 5.66 5.40 3.54 1.29 1.6 94 31 Tuan Mee 2.52 7.28 4.77 1.74 2.2 127 42 Seri Ulu Langat 3.40 12.14 7.96 2.91 3.7 212 70 395

396 The aim of this case study is to design an optimal bioenergy supply chain network (BSCN) to

397 produce electricity from biomass, considering costs and CO2 emission reductions. Within the 398 BSCN, this case study assumes that a centralised energy system needs to be installed near the 399 substation in Figure 4. The electricity generated from the centralised system will then be 400 transmitted to the substation via a feed-in tariff scheme with the national grid. Figure 5 (a) and (b) 401 show the superstructures developed based on the mills and plantations considered in Figure 4. 402 Figure 5 (b) considers the decision of investing in includes different electricity generation systems 403 i.e., power plants (PPs) and combined heat and power (CHP) systems. The following is a summary 404 of the six biomass utilisation pathways considered in Figure 5;

405 EFB Pathway: The highest amount of biomass waste from mills is EFB. EFB contains high 406 moisture content which would subsequently affect its calorific value. As such, several pre- 407 treatment technologies such as hot air drying, superheated steam drying, and size reduction are 408 evaluated. The dried EFB emerging from the selected technologies are then directed into a gasifier 409 to produce syngas. Syngas is then utilised in a PP system to generate electricity or in a fludised bed 410 boiler (CHP system) to generate steam and electricity. To minimise the operating cost and to 411 ensure the technologies are self-sustained, it is assumed that part of the electricity and steam 412 generated is recycled to the dryer for the drying process.

413 PKS Pathway: The other biomass product exiting the palm oil mill comprises PKS. PKS is 414 considered the most effective biomass fuel due to its high calorific value. In Figure 5(b), PKS may 415 be utilised in PP system that converts it into syngas through a gasifier and later fed into a gas 416 turbine for electricity. Alternatively, PKS can be combusted in a fludised bed boiler for steam 417 generation in a CHP system.

418 PMF Pathway: Similarly, PMF will undergo the same process route as PKS to generate electricity 419 in a PP or steam and electricity in a CHP. However, it is less efficient for PMF to be used as a 420 biomass fuel compared to PKS. This is because its low calorific value is relatively lower compared 421 to PKS.

422 POME Pathway: This is a pathwy where highly polluting wastewater emerging from the mill, 423 POME is treated in a digester pond to produce value added methane gas. This methane gas is 424 then combusted either in a gasifier (PP system) or a fludised bed boiler (CHP system) for 425 production of electricity and/or steam respectively.

426 OPT Pathway: OPT is biomass waste from oil palm plantations. It is obtained directly from 427 plantations without processing in palm oil mills. This feedstock is available in abundance but 428 possess very high density to be transported. In Figure 5(b), OPT can follow similar paths to both 429 PKS and PMF to produce electricity and steam through PP or CHP systems respectively.

430 OPF Pathway: Similar to OPT, OPF is also a biomass waste directly obtained from the plantation. 431 Research has shown that OPF contains large amount of sugar, proposing it to be a perfect 432 feedstock to a gasifier (Roslan et al., 2014). This syngas generated would then be directed into a 433 PP or a CHP for the generation of electricity and/or steam respectively. 434 Export: This path is allocated to accommodate the biomass waste products from each plantation 435 for export markets such as Japan, South Korea, China, Thailand and Europe. This is represented

436 by export factors αghf and αij in the proposed methodology.

437

438 Legend FFB PL1- Banting plantation EFB PL2- East Palm plantation PL 7 PMF PL3- Eng Hong plantation PL 1 PL4- Tuan Mee plantation PKS POM 7 PL5- Jugra plantation OPT PL6- Seri Hulu Langat plantation POM 1 OPF PL7- Kg. Kuantan plantation POM – Palm oil mill Export

PL 6 PL 2

Centralised Energy System POM 6 POM 2

POM 3

POM 5 PL 3

PL 5 POM 4 PL 4 439 440 Figure 5 (a) Superstructure of Bioenergy Supply Chain

441

442

Electricity Hot Air Drying PP PP PMF Superheated EFB Steam Drying

CHP CHP CentralisedEnergy System Boundary Size Reduction

PP PKS PP OPF CHP

CHP

PP OPT

CHP 443

444 Figure 5 (b) Superstructure of Centralised Energy System in Bioenergy Supply Chain 445 Tables 3 and 4 show other technical considerations considered in this case study;

446 Table 3 Technical Considerations for Technologies in Scenario 1

Technology Data Ref Hot Air  Moisture content of EFB before and after (Aziz et al., 2011) Drying the dryer are 60% and 10% respectively

 Efficiency of 85.93% (Vhfi)  Requires 2.31 MJ/kg biomass from PP or CHP Superheated  Moisture content of EFB before and after (Aziz et al., 2011) Steam Drying the dryer are 60% and 10% respectively

 Efficiency of 86.72% (Vhfi)  Requires 2.18 MJ/kg biomass from PP or CHP

PP  Efficiency is 40% (Vije) (Loh, 2017)  Maximum power produced by the PP is CHP/PP, max 419.4 TJ ( Fij ) CHP  Efficiency is 48.9% (with 26.5% for heat and (Loh, 2017) 22.4% for electricity) (Vije)  Maximum power produced by the PP is 1175.94 TJ ( ) Gasifier  Maximum weight of biomass waste handled (Forbes International Co. is 1,612 t ( ) LTD, 2018). 447

448 Table 4 Technical Considerations for Supply Chain in Scenario 1

Growth factors (Ggh) 1 for all plantations

Export factors (αghf and αij) 0 for all plantations and mills -1 Biomass transportation costs (OFCghf and OFCij) 0.23 USD t km travelled (Zafar, 2018)

CO2 emission factor for Selangor (Ck) 66 million t per TJ (Loh, 2017) 449

450 Calorific values of each biomass considered in the model are given in Table 5. It is important 451 to note that the calorific value considered in the MILP model takes into account of the efficiency 452 of equipment are used. The model also includes cost of equipment used for each pathway (Table 453 6). The fixed cost indicate the capital and installation cost of the equipments, while variable cost 454 includes operating and maintanence cost. 455 Table 5 Calorific Value of Biomass before and after Incorporating Equipment Efficiency.

Actual Heat loss Final CV Final CV for CHP Pathway CV for drying for PP **(MJ/kg or MJ/m3) Ref (MJ/kg) (MJ/kg) **(MJ/kg) Electricity Steam EFB (Hot Air Drying) 17.81 2.31 4.814 3.99 2.41 (Aziz et al., 2011) EFB (Superheated Steam Drying ) 17.81 2.18 4.944 3.99 2.54 (Aziz et al., 2011) EFB (Size Reduction) 16.5 - 6.6 3.70 4.37 (EPA, 2018) PKS 20.09 - 8.036 4.50 5.32 (Paul et al., 2015) PMF 14.51 - 5.804 3.25 3.85 (Sarawak Energy, 2018) POME 22 - 8.8 4.93 5.83 (Onoja et al., 2018) OPT 17.47 - 6.988 3.91 4.63 (Rashid et al., 2017) OPF 5.15 - 2.06 1.15 1.36 (EPA, 2018) 456 *CV – Calorific value, **corresponds to Vije

457 Table 6 Equipment Cost for Each Pathway.

Variable cost, VCj Fixed cost, Pathway (USD/ton or Ref FCj (USD) USD/kW) EFB (Hot Air Drying) 446,455.75 0.0245 (IRENA, 2013) EFB (Superheated Steam Drying) 446,455.75 0.0245 (IRENA, 2013) EFB (Size Reduction) 674,000 20 (IRENA, 2013) PKS 14,250,000 3700 (Forbes International Co. LTD, 2018) PMF 14,250,000 3700 (Forbes International Co. LTD, 2018) POME 451,655 4350 (MP Energy, 2018) OPT 14,250,000 3700 (MP Energy, 2018) OPF 14,250,000 3700 (MP Energy, 2018) PP 18,000,000 211.6 (Loh, 2017) CHP 11,780,000 7670 (Loh, 2017)Error! Reference source not found. 458 459 For this case study, the formulated MILP model was solved in two scenarios. As entailed in 460 Figure 1, the first scenario goes deeper into the output-driven approach. Meanwhile, the second 461 scenario focuses on emission-driven approach. A detailed discussion of these two scenarios is 462 provided in the following sub-sections.

463

464 4.1 Scenario 1 – Output-Driven Approach 465 Objective for Scenario 1 is to determine the optimal BSCN with minimum cost for a targeted 466 electricity output via superstructure optimisation. Following this, the corresponding carbon 467 emission reduction of the BSCN is determined via ATM.

468 Using the proposed framework in Figure 1 and Section 3, a mixed integer linear programming 469 (MILP) model was formulated. Note that the MILP model was formulated based on the pathways 470 shown in Figures 5 (a) and (b) in a optimisation software (i.e., LINGO version 17.0) on Acer 471 Laptop with 4 GB RAM and Intel Core i5@ 1.70 GHz Processor. The MILP model consists of 472 231 variables, 30 integers and 250 constraints. The MILP model was solved by minimising the 473 TAC in Eq. (17), subject to the constraints in Eqs. (1) – (16). Results from minimising Eq. (17)

474 computed a BSCN with a minimum cost of 7.2 trillion USD. The corresponding ERE computed 475 for this optimal BSCN was 5040 TJ. The computed 5040 TJ electricity output was then exported

476 to CEPA to determine the subsequent CO2 emission reduction. Results of CEPA are shown in 477 ATM (Figure 6) and the energy planning pinch diagram (Figure 7). It is also important to note 478 that Figure 6 and 7 both indicate 5040 TJ equivalent amount of electricity generated using coal

479 may be reduced, leading to lower CO2 emissions (solid lines in Figure 7). The energy planning

480 pinch diagram in Figure 7 shows that CO2 emission is reduced from 5.5 to 4.9 Mt/y; the latter 481 corresponds to an intensity of 59.6 t/TJ (= 4,916 kt/82,490 TJ). This means that with the

482 aforementioned BSCN could reduce the current carbon intensity from 66.0 to 59.6 t CO2/TJ, 483 which is a 9.71% reduction from the original staus quo.

484 Table 7 shows the optimal flows of biomass transported from the plantations to the 485 centralised energy system within the BSCN. As shown in Table 7, EFb and OPF are the most 486 utilised biomass. Most notably, it can e seen that OPT was ony utilised from one plantation. OPT 487 is heavy to transport. In this respect, the model chose the shortest and cheapest OPT 488 transportation route; Sime Darby East plantation to the centralised energy system. The optimal 489 flow of biomas transported was then allocated to several technologies within the centralised energy 490 system (as illustrated in Figure 8). In Figure 8, it can be seen that all EFB is sent to the PP system. 491 Moreover, Figure 8 shows that the distribution of EFB and PKS to PP systems was higher as 492 compared to the CHP systems. However, the capacity constraint of the PP systems inhibited them 493 from receiving more biomass. As a result, the remaining biomass (i.e., PMF, OPF and OPT) were 494 directed to CHP systems to produce more electricity. Note that the total electricity output in Figure 495 8 is from biomass. The remaining 194.42 TJ electrcity output comes from the POME source at 496 the each palm oil mill.

497 It is impotant to note that the growth factor for all plantations were fixed at 1 and the export 498 factor for all plantations are set to 0. However, these assumptions made are very unlikely in reality, 499 especially with respect to a developing nation like Malaysia. The effect of varying both parameters 500 will be discussed in the next section using the emission-driven approach.

501

Emission Factor Supply (S)/ Energy CO2 Load Cascade (t CO2/ TJ Demand (D) Cascade (TJ) (t)

0 0 0 Srenewable = 5,040 5,040

51.6 Snatural gas = 60,176 260,064

65,216

66.0 Dcurrent demand = -82,491 783,736

-17,274

105.0 Scoal = 22,314 0

5,040 (Pinch) 502

503 Figure 6 Results of ATM for BSCN in Scenario 1 504 Table 7 Amount of Biomass Utilised from each Source in Scenario 1

Source EFB (/h) PKS (t/h) PMF (t/h) POME (m3/h) OPT (t/h) OPF (t/h)

Jugra 9.9 2.5 5.9 0.029 0.0 70.4 Eng Hong 9.9 2.5 5.9 0.029 0.0 70.4 Sime Darby East 8.8 2.2 5.2 0.026 62.8 62.6 Banting 11.0 2.8 6.5 0.033 0.0 0.0 Kampung Kuantan 4.4 1.1 2.4 0.013 0.0 0.0 Tuan Mee 5.9 1.5 3.5 0.018 0.0 0.0 Seri Ulu Langat 9.9 2.5 5.9 0.029 0.0 10.3 Total 59.8 14.9 35.3 0.177 62.8 213.7 505

506 82,490 87,530 6,000

5,448

5,000 4,916

4,000 Coal

3,105 3,000

Demand

emission (x1000 t/y) (x1000 emission 2 2,000 CO Natural gas

1,000

0 5,040 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 Biomass Electricity (TJ) 507

508 Figure 7 Pinch Analysis for base case (dotted lines) and Scenario 1 (solid lines).

509 510 511 5,040 TJ 14.5 ton/h 419.4 TJ PP 35.3 ton/h PMF 29.4 ton/h PP SSD 755.49 TJ 742.53 TJ 59.8 ton/h EFB CHP 336.09 TJ 20.7 ton/h SR 30.4 ton/h 10.4 ton/h 419.4 TJ PP 14.9 ton/h PKS 521.17 TJ 419.4 TJ 40.7 ton/h PP 1,413.81 TJ 213.7 ton/h OPF CHP 4.5 ton/h 101.77 TJ CHP 994.41 TJ 172.9 ton/h 12.0 ton/h 419.4 TJ PP 62.8 ton/h OPT 1,412.52 TJ CHP 50.8 ton/h 993.12 TJ 512

513 Figure 8 Optimal Allocation of Biomass within Centralised Energy System in Scenario 1

514 515 516 517 518 4.2 Scenario 2 – Emission-Driven Approach 519 The oil palm industry in Malaysia faces many difficulties; one of those being the limitation in 520 increasing fresh fruit bunch yields in plantations. This limitation may be related to the effect of 521 drastic weather conditions (i.e., drought, haze, etc.), disease attacks and poor water quality at 522 plantations. Fresh fruit bunch yields are crucial as they influence the amount of biomass generated 523 at the POM process. As such, Scenario 2 analyses various growth factors to determine its impact 524 on the decisions with regards to BSCN design. Note that growth factors below 1 are set to 525 consider possibilities of disruption (e.g., the water crisis that is common in Malaysia). On the other 526 hand, growth factor that is more than 1 indicates that the plantations are yielding higher crops than 527 its previous period.

528 The growth factors considered for Scenario 2 are shown in Table 8. In Case 1, the growth 529 factor for Seri Ulu Langat plantation is varied within the range of 0.6 – 0.8. Case 2, on the other 530 hand varied growth factors in the same range, but for two plantations, i.e. Seri Ulu Langat and 531 Tuan Mee. In Case 3, the same range of growth factors were varied for three plantations i.e. Seri 532 Ulu Langat, Tuan Mee and Kampung Kuantan.

533 Table 8 Growth Factors for Scenarios 2.

Eng Sime Darby Kampung Tuan Seri Ulu Jugra Banting Hong East Kuantan Mee Langat 1 0.9 0.9 0.9 0.9 0.9 0.6 Case 1 1 0.9 0.9 0.9 0.9 0.9 0.7 1 0.9 0.9 0.9 0.9 0.9 0.8 1 0.9 0.9 0.9 0.9 0.6 0.6 Case 2 1 0.9 0.9 0.9 0.9 0.7 0.7 1 0.9 0.9 0.9 0.9 0.8 0.8 1 0.9 0.9 0.9 0.6 0.6 0.6 Case 3 1 0.9 0.9 0.9 0.7 0.7 0.7 1 0.9 0.9 0.9 0.8 0.8 0.8 534

535 Following this, the energy planning pinch diagram and ATM is used to determine the required

536 bioenergy to achieve a given CO2 emission reduction target. Once the required bioenergy is 537 determined, it is exported to to superstructure model where constraints in Eqs. (1)-(16) are solved 538 by minimising the TAC in Eq. (17). This was procedure repeated for growth factors in the 539 aforementioned Cases 1 – 3. Note that the model formulated here was also solved in the same 540 optimisation software (i.e., LINGO version 17.0) on Acer Laptop with 4 GB RAM and Intel Core 541 i5@ 1.70 GHz Processor. Like in Scenario 1, the MILP model consists of 231 variables, 30 542 integers and 250 constraints.

543 Results for Cases 1–3 are shown in Figures 9 – 11. Figure 9 shows the energy planning pinch 544 diagram particularly for Case 3 in Scenario 2, where the growth factor is 0.6 for three plantations.

545 The energy planning pinch diagram shows that CO2 emissions were reduced from 5.45 to 4.96 546 Mt/y. The latter corresponds to an intensity of 60.1 t/TJ (= 4,960 kt/82,490 TJ). The electrcity

547 output required to achieve this CO2 emission reduction is 4,644 TJ. Subsequently, the optimal 548 BSCN with minimum TAC was determined for the 4,644 TJ electricity output. Figure 10 (a)-(c) 549 indicate the results obtained for the BSCNs in Case 1- 3 respectively. In Figure 10 (a), it is found

550 that the lowest CO2 reduction achieved is 9.26% when the growth factor of one plantation in the

551 BSCN is 0.6. The lowest CO2 reduction of 9.16% is obtained in Figure 10 (b) when growth factors 552 of two plantations are 0.6. When three plantations in the BSCN have growth factors of 0.6, the

553 lowest attainable CO2 reduction is 8.95% (Figure 10 (c)).

554

82,490 87,134 6,000

5,448

5,000 4,960

4,000 Coal

3,105 3,000

Demand

emission (x1000 t/y) (x1000 emission 2 2,000 CO Natural gas

1,000

0 4,644 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 Biomass Electricity (TJ) 555

556 Figure 9 Pinch Analysis for base case (dotted lines) and Scenario 2 (solid lines).

557

558 (a) 9.38% 4870 9.36% 4860 9.34% 4850 4840 9.32% 9.36% 4830 9.30% Reduction (%) Reduction 4820 2 9.28% 9.30% 4810 CO 9.26% 4800 9.24% 9.26% 4790 9.22% 4780 (TJ) Output Electricity 9.20% 4770

0.8 0.7 0.6 Percentage of Percentage Growth Factor for One Plantation in Case 1

Growth Factors Electricity Output (TJ) 559

(b) 9.35% 4860 4840 9.30% 9.33% 4820 9.25% 4800

Reduction (%) Reduction 9.20% 4780 2 9.22% 4760 CO 9.15% 9.16% 4740 9.10% 4720 (TJ) Output Electricity 9.05% 4700 0.8 0.7 0.6 Percentage of Percentage Growth Factor for Two Plantations in Case 2

Growth Factors Electricity Output (TJ) 560

(c) 9.30% 4850 9.25% 9.20% 4800 9.15% 9.10% 9.26% 4750 9.05%

Reduction (%) Reduction 4700

2 2 9.00% 8.95% 9.05%

CO 4650 8.90% 8.85% 8.95% 4600 8.80% (TJ) Output Electricity 8.75% 4550 0.8 0.7 0.6

Percentage of Percentage Growth Factor for Three Plantations in Case 2

Growth Factors Electricity Output (TJ) 561 562 Figure 10 Electricity Output and CO2 Reduction from the Status Quo using Varied 563 Growth Factors for; (a) Case 1 – One Plantation, (b) Case 2 – Two Plantations, (c) Case 3 564 – Three Plantations

565 Meanwhile, Figure 11 shows a comparison of electricity outputs across all three cases 566 according to growth factors. As shown, a decreasing trend in electricity output was observed from 567 Case 1 to 3. This is because the number of plantations with varied growth factors increase from 568 Case 1 to 3. This would subsequently reduce the amount of biomass available for utilisation and 569 electricity generation. Moreover, it was observed that there was a significant drop in electricity 570 output between instances where three plantations have growth factors of 0.8 and 0.7 (as indicated 571 in Figure 11). This signifcant drop provides insight on the level to which the BSCN can tolerate 572 in terms of reduction in growth factors. Essentially, Figure 11 suggests that in the case where three 573 plantations were to experience reduced growth factors, the lowest tolerable growth factor without 574 experiencing a significant drop in electricity output is 0.8. Aside from this, the distance between 575 0.7 and 0.6 lines are relatively closer. This indicates a low difference in electricity produced and

576 reduction in CO2 between the two cases. In general, the electricity outputs obtained in all three 577 cases are lower than Scenario 1 (5040 TJ). This is because Scenario 1 considered growth factors

578 of 1 for all plantations. In addition, it can be concluded that the electricity output and CO2 579 reductions decrease when growth factors decreases.

4900 When GF = 0.8 for 4850 three plantations

4800

4750 Steep drop

4700

4650

Electricity (TJ) Output 4600 When GF = 0.7 for three plantations 4550

4500 Case 1 - One Plantation Case 2 - Two Plantations Case 3 - Three Plantations

Growth Factor=0.7 Growth Factor=0.6 Growth Factor=0.8 580

581 Figure 11 Comparison of Electricity Output between Cases 1, 2 and 3

582 583 4.2.1 Sensitivity Analysis – Growth Factor 584 To evaluate the effect of the growth factors on the BSCN, sensitive analysis is carried out. 585 The sensitivity analysis used similar growth factors ranging between 0.7 – 1.3 for all plantations. 586 Results of the sensitivity analysis are shown in Figures 12 and 13. As shown, the lowest possible 587 reduction in carbon dioxide emission is 8.7%. This corresponds to an electricity output of 4500 588 TJ, as shown in Figure 13. Aside from this, growth factors of up to 1.3 was analysed to study its 589 impact on the cost of the BSCN. According to Figure 12, the cost approaches a constant trend 590 when growth factors increase beyond 1.1. This shows that there BSCN is able to experience an 591 increase in growth factors from 1.1 to 1.3 in all plantations without a steep increase in costs.

592 However, the increase in CO2 reduction becomes less obvious from growth factors of 1.1 onwards. 593 This might be due to the less obvious increase in electricity output starting from growth factor 1.1 594 (indicated in Figure 13). The constraint here is the capacity of equipment. Besides this, CHP 595 systems were not prioritised because of their relatively lower electricity generation efficiency. But 596 when PP systems reach their maximum capacity, CHP systems are required to supply the 597 remaining portion of electricity output targets.

7.60E+12 11 Cost stagnates 7.40E+12 10.5 7.20E+12 10 7.00E+12

9.5 (%) Reduction 6.80E+12 2 9

Cost (USD) Cost 6.60E+12 8.5 6.40E+12 8 6.20E+12 CO of Percentage

6.00E+12 7.5 0.7 0.8 0.9 1 1.1 1.2 1.3 Growth Factor Cost Reduction in CO2 (%) 598

599 Figure 12 Further Analysis of Cost and Percentage of CO2 Reduction on Different 600 Growth Factor. 5600 5400 5400 5310 5220 5200 5040 5000 4860 4800 4680

4600 4500

Electricity Produced(TJ) 4400

4200

4000 0.7 0.8 0.9 1 1.1 1.2 1.3 Growth Factor 601

602 Figure 13 Further Analysis of Electricity Output Based on Different Growth Factor.

603

604 4.2.2 Sensitivity Analysis – Export Factor 605 Apart from growth factors, a sensitivity analysis was performed on export factors of biomass. 606 Among all the biomass included in the model, PKS is identified to be the highest exportation 607 quantity of biomass waste. This is because PKS has low weight and relatively high calorific value, 608 which allows transportation of high energy content per t of biomass. Malaysia exports up to 609 135,000 t of PKS (which is 32% of Malaysia’s annual PKS export) annually to the world (Zainul, 610 2017). Besides, The Edge Financial Daily also reported that Malaysia is currently holding 54% of 611 market share for PKS in Japan, and companies still intend to increase the annual output to Japan 612 (Laohalidanond, 2011). As the export quantity increases significantly, the electricity produced by 613 PP or CHP would be crucially affected. This would directly impact the Malaysian government’s

614 aim to reduce its CO2 emissions per gross domestic product. The analysis on export factor of

615 PKS from no export (0) to fully export (1) is shown in Figure 14. As observed in Figure 14, CO2 616 reductions experience a drop between export factors 0.3–0.5. This provides insight on the range 617 of biomass export that Selangor should avoid if the aim is to generate large amounts of electricity. 618 The amount of electricity produced is also relatively lower after the export factor of 0.5 as shown 619 in Figure 14. 9.80% Sudden Drop 5100

9.60% 5000

9.40% 4900

9.20% 4800

Reduction Reduction 2

CO 9.00% 4700 5040 5004 4968 4932 8.80% 4878 4600 4806 4752 8.60% 4698 4500 (TJ) Produced Electricity

Percentage of Percentage 4626 4572 8.40% 4400

8.20% 4300 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Export Factor of PKS

Electricity Produced (TJ) Percentage of CO2 Reduction 620 621 Figure 14 Further Analysis of Electricity Output Based on Different Export Factor.

622 In addition to this, export factors were analysed further via five sub-cases. These five sub- 623 cases were analysed to better understand the effect of exporting individual biomass on the 624 electricity output of the BSCN. The first case shown in Table 9 is carried out to study the 625 importance of OPT and OPF. OPT and OPF are less favourable as they are too heavy to be 626 transported even though they are available abundantly. The low calorific value of OPF contributes 627 to this decision too. Furthermore, the weight of OPT, constraints on size and cost of equipment, 628 also add to the low feasibility of investing in OPT path. The outcome of base case as displayed in 629 Table 5 comply with this insight. Hence, OPT and OPF has the lowest priority in the model. 630 Results in Table 10 further supports this insight. It is observed that as the export for OPT and 631 OPF increases, the effect on electricity output is minimal. In this sense, it would be sensible to 632 direct OPT and OPF residues for other uses.

633 The second and third sub-cases are then compared together. The result demonstrated that 634 when the amount of exported PKS increases to 0.3, the electricity output will decrease (as shown 635 in Case 3 of Table 10). This holds true as the calorific value for PKS is the highest among all 636 biomass available in the palm oil industry. However, in terms of electricity output, the difference 637 between both cases are minimal. This is because the huge amount of EFB and PMF was available 638 to compensate for the loss caused by high PKS export. Proceeding to the Cases 4 and 5, both 639 electricity outputs were similar. This is because the loss from PKS was compensated by PMF and 640 EFB here as well. This strengthens the conclusion that quantity of PMF and EFB was favoured 641 over PKS since it was highly exported in these scenarios.

642 Table 9 Export Factor of Different Cases.

EFB PKS PMF OPF OPT

Case 1 0.2 0.2 0.2 0.3 0.3 Case 2 0.3 0.2 0.3 0.1 0.1 Case 3 0.2 0.3 0.2 0.1 0.1 Case 4 0.3 0.3 0.2 0.1 0.1 Case 5 0.2 0.2 0.3 0.1 0.1 643

644 Table 10 Result for Different Cases.

Electricity Output (TJ) Reduction in CO2 (%)

Case 1 4698 9.05 Case 2 4626 8.92 Case 3 4554 8.78 Case 4 4590 8.85 Case 5 4608 8.88 645

646 4.2.3 Technology Analysis 647 Lastly, the technologies chosen for the BSCN were analysed in detail. This is to list the 648 possible technologies and to analyse the trend of technologies selected when electricity output is 649 increased. The pathways chosen at different targeted electricity output are shown in Figure 15. In 650 Figure 15, superheated steam drying and size reduction technologies utilising EFB cab be 651 considered as the “must invest” technology. This is because of the huge amount of EFB that 652 emerged from the palm oil mills and its relatively high calorific value as shown in Table 3 and 653 Table 5 respectively. Aside from this, Figure 14 shows that the technology chosen increases with 654 the targeted electricity produced. Besides, PKS and POME utilisation technologies are also 655 considered among the primary technologies used. The technology of OPT and OPF are least 656 encouraged as the volume produced increases the difficulty in transportation and the necessity to 657 invest in more equipment. In fact, PP systems were prioritised over of CHP systems since the 658 intended output of this particular model is electricity rather than steam. However, when CHP 659 systems are selected, PKS was prioritised as fuel due to its highest calorific value and light weight. 660 This is evident when the electricity produced exceeds the 2160 TJ interval shown in Figure 15.

661

5040

) TJ

( 4320

2880 - 3600

2160

Electricity Output ElectricityOutput 1440

720

Configuration of Chosen Technologies Legend  EFB routed to Size Reduction and Power Plant  EFB routed to Superheated Steam Drying and Power Plant  OPT routed to Power Plant  POME routed to Power Plant  PKS routed to Power Plant  PMF routed to Power Plant

 PKS routed to CHP  OPF routed to CHP  OPF routed to Power Plant  OPT routed to CHP

 PMF routed to CHP 662

663 Figure 15 Analysis of Technologies Chosen based on Different Electricity Output.

664

665 Summary of Insights:

666  With output of 5040 TJ, the BSCN is able to reduce carbon emission intensity of the state of 667 Selangor by 9.71%, given no export of biomass and a stagnant growth factor for plantations. 668  All plantations are able to cope with reduced growth factors up to 0.8, without experiencing a 669 significant drop in electricity output. In other words, 0.8 is the minimum growth factor in 670 which all plantations can afford to operate at.

671  BSCN is able to manage up to 30% increase in yield for all plantations starting from growth 672 factor of 1.1 without steep increase in cost.

673  CO2 reductions experience a steep drop between export factors 0.3–0.5. This the range of 674 biomass export that Selangor should consider avoiding if the aim is to generate large amounts 675 of electricity.

676  When export for OPT and OPF increases, the effect on electricity output is minimal. It would 677 be logical to direct OPT and OPF residues for other uses.

678  When amount of exported PKS increases, the electricity output will decrease. The calorific 679 value for both PKS is the highest among all biomass available. However, the difference in 680 electricity output between both cases are minimal because the huge amount of EFB and PMF 681 available compensates for the loss in calorific value caused by high PKS export.

682  OPT and OPF technologies were the least encouraged as the volume produced increases the 683 difficulty in transportation and requires high investment costs.

684  PP technologies are prioritised over CHP technologies as the main output is electricity instead 685 of steam. However, if the CHP system is utilised, PKS is prioritised as fuel due to its highest 686 calorific value and light weight. This is evident when the electricity produced exceeds 2160 TJ.

687

688 5 Implications 689 As a whole, the methodology presented in this work provides policy-makers a systematic tool

690 to analyse potential CO2 intensity reduction strategies and policies to meet commitments made in 691 the Paris Agreement. Fundamentally, the presented methodology provides tailored approaches for

692 both developed and developing countries to analyse CO2 intensity reduction and BSCN planning.

693 Key results from both scenarios could provide several policy implications. For instance, 694 results from Scenario 1 suggests that oil palm plantations in the state of Selangor could provide a

695 CO2 intensity reduction of 9.71%, given that that growth factors are stagnant at 1 and export 696 factors are kept to 0. It is worth noting that Selangor is just one of 14 federal states in Malaysia. 697 This 9.71% provides policy-makers insight on the potential carbon reduction that could be 698 achieved when a BSCN is implemented in a state rather than exporting its biomass to other 699 countries. Aside from this, Scenario 2 provided further insights on lowest possible growth factors

700 that each plantation can tolerate to achieve acceptable CO2 intensity reductions. Essentially, such 701 insight allows policy-makers to determine the likelihood of poor growth rates in oil palm 702 plantations by comparing to historical data that is readily available.

703 Alongside this, the technology analysis provided information on technologies frequently 704 selected for a given range of electricity output. Such insight allows policy-makers to determine the 705 promising technologies worth investing in either via investment and/or research. On top of this, 706 policy-makers can make more informed decisions on formulating incentive packages, subsidies 707 and grants to encourage local investment in such technologies. 708 6 Conclusion

709 In conclusion, this work presented a hybrid planning methodology to consider CO2 emission 710 reduction target while simultaneously optimising a bioenergy supply chain network (BSCN). This 711 methodology made a combined use of carbon emission pinch analysis (CEPA) and superstructural 712 optimisation to analyse two distinctive policy scenarios. The first scenario is the output-driven 713 approach, which prioritised the output of the bioenergy supply chain network (BSCN) and 714 followed by its corresponding emission reduction. The second approach, which emission-driven 715 targeted emission reductions first and subsequently determined the BSCN with minimum cost. 716 Sensitivity analyses were also conducted in the second scenario, where the impact of both growth 717 factors of agricultural plantations and export factors of the biomass were analysed. Finally, 718 technology analysis was performed to analyse the range of technologies chosen based on the 719 electricity output. Results from the two scenario solved and analyses conducted, provided several

720 key findings and insights. Firstly, an optimised BSCN with 5,040 TJ output could reduce CO2 721 emission intensity by 9.71%. Secondly, it was found that all oil palm plantations could afford to

722 operate on low growth factors of up to 0.8 to avoid steep drops in CO2 reduction performance in 723 the BSCN. In addition, it was found that the optimised BSCN experiences minimal change in costs 724 when plantations have growth factors beyond 1.1. Lastly, it is worth noting that power plant 725 technologies were favoured more as compared to combined heat and power systems. The 726 proposed methodology can be improved further to tackle biomass aggregators in the supply chain 727 and uncertainties in biomass feedstock collection via Monte Carlo simulations. Moreover, 728 proposed methodology can be directed towards additional tactical and operational supply chain 729 planning decisions such as transport mode selection, optimal biomass delivery schedules and 730 vehicle planning.

731

732 Acknowledgement

733 The authors would like to acknowledge LINDO Systems for providing academic licenses to 734 conduct this research.

735

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