Abstract

AbstractThis paper presents a study on the synthesis and optimisation of a renewable energy and resource supply chain network (SCN) to satisfy agricultural greenhouse resource and energy demands. This is motivated by the high costs and emissions to run greenhouses due to the resources and energy required for optimal growth conditions. The investigation aims to contribute to the growing area of research on circular practices in the agricultural sector. The SCN includes biomass from agricultural waste transported to a utility hub, consisting of water and carbon dioxide (CO2) as resource supplies, as well as a boiler and steam turbine-generator and solar photovoltaic (PV), as energy conversion technologies. The resources and energy are distributed to greenhouses. The objectives are to minimise both the total annualised costs (TAC) and environmental impact (EI), calculated as CO2 emissions of the integrated network. Multi-periodicity accounts for the monthly and daily variation in constraints on the system, such as feedstock and solar radiation availability and energy limitations. The model is applied to a case study of an agro-industrial zone in Kwa-Zulu Natal (KZN) province, South Africa. The network meets greenhouse demands at a minimum TAC and EI of 8.052 $$\times {10}^{6}$$ × 10 6 R/y and 14,370 tCO2e/y, respectively. The biomass supply chain (BSC) contributes most significantly to the TAC at 43.7% while biomass combustion makes up 99.8% of the EI. Additional key results include bagasse and corn stover being selected as the only feedstocks and rail as the transport mode. The 26,560 MWh/y of electricity supplied to the greenhouses is made up by 71.4% from the boiler and turbine-generator with the remainder from solar PV. The potential of solar PV as a clean and low-cost energy source is demonstrated by the model selection at its full capacity. The study confirms that holistic modelling and optimisation of renewable energy SCNs for greenhouses is necessary to meet a trade-off between the network costs and CO2 emissions.

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