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Anton Friedl, Jiří J. Klemeš, Stefan Radl, Petar S. Varbanov, Thomas Wallek (Eds.) Proceedings of the 28th European Symposium on Computer Aided Process Engineering June 10th to 13th, 2018, Graz, Austria. © 2018 Elsevier B.V. All rights reserved.

Optimisation of Integrated Bioenergy and Concentrated Supply Chains in Massimo Liua, Koen H. van Damb, Antonio M. Pantaleob,c, Miao Guob* aImperial College London, Futures Lab, Department of Mechanical Engineering, South Kensington Campus, London SW7 2AZ, United Kingdom bImperial College London, Department of Chemical Engineering, South Kensington Campus, London SW7 2AZ, United Kingdom cUniversity of Bari Aldo Moro, Department of agro-environmental sciences, Via Amendola 165/A 70125 Bari, Italy *[email protected]

Abstract Climate change and are complex challenges whose solutions depend on multi-faceted interactions between different actors and socio-economic contexts. Energy innovation through integration of renewable in existing systems offers a partial solution, with high potential identified for bioenergy and . In South Africa there is potential to further integrate renewable energies to meet local demands and conditions. Various concentrated solar power (CSP) projects are in place, but there is still land available to generate from the . In combination with sustainable biomass resources these can offer synergetic benefits in improving the power generation’s flexibility. While thermodynamic and thermo-economic modelling for hybrid CSP-Biomass technology have been proposed, energy modelling in the realm of supply chains and demand/supply dynamics has not been studied sufficiently. We present a spatially and temporally Mixed Integer Linear Programming (MILP) model, to optimize the choice and location of technologies in terms of economic cost while being characterised by realistic supply/demand constraints as well as spatially- explicit environmental constraints. The model is driven by electricity demand, resource availability and technology costs as it aspires to emulate key energy and sustainability issues. A case study in the South African province of Gauteng was implemented over 2015-2050 to highlight the potential and challenges for hybrid CSP-Biomass and integrated systems assessment and the applicability of the modelling approach. From the range of hybrid CSP-Biomass technologies considered, based on detailed techno-economic characteristics from the literature, the Biomass only EFGT plant is identified as the cost optimal. When distributed generation (DG) technologies, small- scale Solar PV and Wind Turbines were introduced to the model as a competing alternative, they were demonstrated to be more economically optimal (€65 mil against €85 mil with CSP-Biomass Industrial scale), driven by technology learning cost reductions, evidencing the case for DG technologies to gain momentum. Together these scenarios highlight the possible carbon savings from integrating multiple technologies. Keywords: Solar, Bioenergy, Renewables, South Africa. 2 M. Liu et al.

1. Introduction Driven by the growing global population and economic development, is expected to increase by 48% by 2040 (EIA, 2016), which will exert unprecedented stress on environmental degradation and resource depletion. To combat climate change, the renewed commitments under the 2015 Paris agreement to maintain global average temperature are a testament to the decarbonisation acceleration (UNFCCC, 2015). Global and national policies have been addressing energy demand stress in conjunction with gas (GHG) reduction, by actively promoting the low-carbon renewable energy sources (RES) technologies, which are projected to represent 50% the global energy generation by 2030 (Goldman Sachs, 2017). The integration of renewable energy in existing energy infrastructure offers one of the solutions to low-carbon economy transition, with high potential identified in the use of bioenergy and solar energy (IEA, 2015). Dispatchable renewable energy plants such as reservoir hydro, biomass and CSP with storage have been installed with more frequency, contributing to energy flexibility in the respective systems (REN21, 2016). Biomass and CSP in particular offer strong potential as they have the capability to store solar energy in addition to being well distributed globally and thus being a relatively accessible source of energy (Rosillo-Calle, et al., 2015). These technologies have contributed to grid flexibility and grown distributed generation, which has gained significant momentum in developing countries (REN21, 2016). Together they offer a technology solution with the potential of achieving environmental sustainability and economic viability. This paper focuses on the Hybrid CSP-Biomass integration in the context of South Africa. South Africa has been swiftly implementing policies towards a low carbon economy that diversifies its energy mix by moving away from the current coal-dominant (over 90%) (IEA, 2017). Despite the significantly increased electricity access (35% to 86% of population) in South Africa in the last two decades, over 60% of population in African continent still has no access to modern energy and infrastructure (projected to decrease to 35% by 2040) (AFDB, 2013), which constrains the regional economy and livelihood improvement; whereas two third of the Africa’s electricity is supplied by South Africa (AFDB, 2013). The renewable energy is expected to play significant roles within the overall African energy portfolio to tackle the energy poverty and insecurity (IEA, 2017) as well as achieve the South African national targets on universal electricity access by 2025 (GNESD, 2017). Despite the modelling advances in thermodynamic and thermo-economic aspects, a gap emerged on the renewable integration considering demand/supply dynamics in Africa. By applying MILP optimization approach, this study aims to advance the understanding of the hybrid CSP-Biomass system design at the spatial and temporal scales under the supply-demand as well as environmental constraints in South Africa.

2. Technology Description CSP-Biomass Hybrid technology is still at early-development stage with significant data gaps emerged. In this study, the proposed technologies and the respective techno- economic characteristics were based on a combination of case studies (based on ENEA Archimede project, Griffith, Thermosolar Borges), and performances of operating plants (Thermosolar Borges). The range of technologies possesses different levels of biomass-

Optimisation of Integrated Bioenergy and Concentrated Solar Power Supply Chains 3 solar hybridisation and as such different techno-economic characteristics with varying outputs and resource requirements. Specific CSP cost studies and analysis by IEA (for technology learning), NREL and IRENA were also used to gather more detailed costing data on the different components and resources required (i.e. water, land). These enabled to introduce variations to the representative technologies. An example of such variation is using dry cooling technology instead of the conventional wet cooling, which in turn has an impact on the capital input and operational cost, energy generation and efficiency. In addition to these technologies, data for distributed renewable energy generation technologies in the form of small scale Solar PV and Wind turbines were collected to compare the industrial scale CSP-Biomass plants with DG technologies on a ‘per year’ basis. Table 1 shows the technologies included in the model. Table 1 – Technologies modelled (NREL, 2013; Peterseim et al., 2014; Pantaleo et al., 2017) Technology DNI Water Rated MWh/Tonne (m3/yr) Output Biomass (MW) 100% Biomass NA 15,000 1.4 0.612 Pantaleo D (Wet Cooling) TES 2,256 32,283 1.4 0.769 (1.3h) Pantaleo E (Wet Cooling) (TES 5h) 2,256 41,997 1.4 1 B50 (Griffith) (Wet) 2,100 480,900 30 1.866 Borges Thermosolar (Wet) – peak 1,800 294,000 22.5 1.96 Borges Thermosolar (Wet) – night 2,130 159,000 12 1 (no solar, biomass only) Pantaleo D (Dry Cooling) TES 2,256 2,001 1.4 0.715 (1.3h) Pantaleo E (Dry Cooling) (TES 5h) 2,256 2,603 1.4 0.93 B50 (Griffith) (Dry) 2,100 29,815 30 1.735 Borges Thermosolar (Dry) – peak 1,800 18,228 22.5 1.823 Borges Thermosolar (Dry) – night 2,130 9,858 12 0.93 (no solar, biomass only)

3. Methodology The fundamental concept is to investigate the potential of the novel Hybrid CSP- Biomass renewable power generation in South Africa under a context with strong policy support for RES and abundance in biomass and solar resources. In addition to the insights into the economic feasibility of the technologies, the model aims to reflect the 4 M. Liu et al. underpinning sustainability challenges which are characterised by the supply chain, supply/demand constraints capturing the variability in electricity demands and resources. A spatial-temporal MILP model was formulated and solved in AIMMS using the CPLEX solver.

Table 2 Nomenclature for optimisation model Sets Description 푔, 푘 Set of Cell locations 푔 휖 퐺 푛 Set of biomass residue (source) locations 푐 Set of candidate location cells for plant installation (demand location) 푡 Time 휖 {푌푒푎푟} 푏 Set of biomass residues= {maize residue, wheat residue, etc.} 푚 Transport mode = {푡푟푢푐푘, 푟푎푖푙} 푘푝푖 Set of KPI = {푐표푠푡, 푒푚푖푠푠푖표푛푠} 푝 Set of plant technology candidates Binary Variables

퐴푐,푝 Existence of technology location cell. 퐹푙표푤푛.푐 presence of a link between a technology with a biomass field Continuous Variables

퐸푃푟표푐,푡,푝 Energy production of a technology in a period (non-linear) (MWh) 푇푓푙표푤푛,푐,푏,푚,푡 Transport flow of biomass between a biomass field and a technology location in a given period. (Tonne) 푇표푡푎푙퐶표푠푡 Total economics costs (Euro)

As presented in Eq.(1), the objective function is to minimise the total costs which account for the costs incurred by -1) the infrastructure (퐶퐼푛푓푘푝푖,푡, 퐸푞. (2)) dependent on the annuity factor (퐴푓푝) and related CAPEX cost (퐼퐶표푛푘푝푖,푡,푝); 2) cost of operation (퐶푂푝푘푝푖,푡, 퐸푞. (3)) of plant technology determined by the energy production (퐸푃푟표푐,푡,푝), the variable (퐼푂푝푉푎푟푘푝푖,푡,푝) and fixed (퐼푂푝퐹푖푥푘푝푖,푡,푝) cost of operation; 3) and the supply chain costs of the biomass(퐶퐵푃푟표푘푝푖,푡, 퐸푞. (4)) which are depend on direct (machinery, labour, costs) and indirect costs (compensation in soil for loss of nutrients from removal of biomass residues) in the harvesting, collection, and treatment (퐼퐶푢푙푡푘푝푖,푏,푡); 4) the cost related to the transport of the biomass (퐶푇푛푘푝푖,푡, 퐸푞. (5)), which depends on unit transport cost (퐼푇푛푘푝푖,푚,푡) and distance (퐷푛푛,푐) is also included but is expected to represent a small share of the total costs.

퐾푃퐼,푇 푇표푡푎푙퐶표푠푡 = ∑푘푝푖,푡 (퐶퐼푛푓푘푝푖,푡 + 퐶푂푝푘푝푖,푡 + 퐶퐵푃푟표푘푝푖,푡 + 퐶푇푛푘푝푖,푡) (1) 퐶,푃 퐶퐼푛푓푘푝푖,푡 = ∑푐,푝 퐼퐶표푛푘푝푖,푡,푝 × 퐴푓푝 ∗ 퐴푐,푝 (2) 퐶,푃 퐶,푃 퐶푂푝푘푝푖,푡 = ∑푐,푝 퐼푂푝푉푎푟푘푝푖,푡,푝 × 퐸푃푟표푐,푡,푝 + ∑푐,푝 퐼푂푝퐹푖푥푘푝푖,푡,푝 × 퐴푐,푝 (3) 푁,퐶,푃,푀 퐶퐵푃푟표푘푝푖,푡 = ∑푛,푐,푝,푚 퐼퐶푢푙푡푘푝푖,푏,푡 × 푇푓푙표푤푛,푐,푏,푚,푡 (4) 푁,퐶,푃,푀 퐶푇푛푘푝푖,푡 = ∑푛,푐,푝,푚 퐼푇푛푘푝푖,푚,푡 × 퐷푛푛,푐 × 푇푓푙표푤푛,푐,푏,푚,푡 (5)

The model takes into account a range of constraints that ensure the method feasibility and applicability, which can be summarised as follows (formulation not included here):

Optimisation of Integrated Bioenergy and Concentrated Solar Power Supply Chains 5

. Total number of technology plants in operation per location is upper bounded by user-defined parameters; . Logic constraints on the existence and number of links between biomass source n and technology plant location c; . Transport distance is constrained by user-defined upper bound; . Equality and inequality constraints on the biomass supply-demand balances and flows; . Inequality constraints on water and land resource availability and direct normal irradiance (DNI); . Constraints on plant capacity and operational time.

4. Case Study: Gauteng Province (South Africa) Table 3 - Data Inputs Solar DNI 2,266 kWh/m2 Biomass Availability 24,500Tonne Unmet Electricity Demand to Satisfy 11.07 TWh/annum

Table 4 --Scenarios' key cost results in 2015 Scenario 1 (1 Technology Only) 223,428.00 kEuro Scenario 2 (Multiple Technologies) 85,191.00 kEuro Scenario 3 (Multiple Techs with DG) 65,162.00 kEuro

The modelled results indicated that among the technologies considered, the biomass externally-fired remained the most cost optimal option, which can be explained by the CSP’s high CAPEX intensity and comparatively lower load factors. The cost optimisation scenario runs showed that DG technologies provide more economically competitive solutions. Throughout the time periods (2010s-2050s) modelled, the scenarios executed with DG technologies presented lower cost solutions in comparison with the scenarios without DG technologies (Figure 1). Technology learning effects represented as pivotal for DG technologies selected by the model.

Figure 1. Cost profiling for scenarios with/without DG technologies

6 M. Liu et al.

While centralised generation of energy has demonstrated better economic performances due to the scaling-up effects, densification of generation and higher efficiencies, the advent of small scale generation technologies has been able to satisfy energy needs due to better geographical and operational flexibility compared with centralised generation. The input data used to characterise these DG technologies reflected their advantages, which were confirmed by the projected technology learning, in particular in attracting investments which in turn is expected to drive the economic profiles of the technology.

5. Conclusion The optimisation model presented in this study generated insights into the economically feasible technology solutions under a given context. Such modelling approach could be widely applicable to inform decision-makers in terms of renewable energy integration system design. The model is capable of executing scenarios with a range of technologies considering their economic profiles incurred by capital inputs, technology operation, and transportation and sourcing of biomass feedstock. This model can be further expanded and adapted to a wider range of renewable energy systems. This study revealed that the small scale solar PV and wind turbines are more cost effective to satisfy the specified electricity demands than the large scale hybrid CSP-Biomass plants modelled. Such research findings are driven by a range of modelling factors including the low operation of technologies caused by the difference between demand and rated output of the technologies, the uneven use of resources (biomass residues), negligible resource footprint (water, land) assumed for the DG technologies. Thus it is worth implementing more small scale systems in the region to exploit the abundant solar and biomass residue resource potential. The South African region modelled in the case study showed great potential with abundant solar and biomass resources, and the urgent needs in African continent to increase the electricity access suggest the opportunity for RES generation systems in South Africa which is in line with the national/regional strategy.

References AFDB, 2013, African Development Report 2012 EIA, 2016. World Energy demand and economic outlook 2016 with projections to 2040, Washington, DC: U.S. Energy Information Administration. Goldman Sachs, 2017. The Low Carbon Economy. UNFCCC, 2015. UN Climate Change Newsroom. GNESD & UNEP, 2015. Biomass Residues as Energy Source to improve energy access and local economic activity in low HDI regions of brazil and colombia (BREA), s.l.: Global Network on energy for (GNESD). IEA, 2017. Global Renewable Energy. IEA, World Energy Outlook 2015. 2015 NREL. (2016, February). Distributed Generation Renewable Energy Estimate of Costs. Retrieved August 1, 2017, from NREL: https://www.nrel.gov/analysis/tech_lcoe_re_cost_est.html Pantaleo, A., et al. (2017). Novel Hybrid CSP-Biomass CHP for flexible generation: thermo-economic analysis and profitability assessment. Applied Energy 204, Oct 2017 Rosillo-Calle, F., de Groot, P., Hemstock, S. L., & Woods, J. (2015). The Biomass Assessment Handbook (second edition ed.). New York: Routledge, Taylor & Francis Peterseim, J., White, S., Tadros, A., & al., e. (2014). Concentrating solar power hybrid plants - enabling cost effective synergies. Renewable Energy, 67, 178-185.