Grobler, Jan-HendrikKaramanski, StefanZandamela, Frank2025-03-192025-03-192024-11http://hdl.handle.net/10204/14182As a result of persistent loadshedding since 2008, numerous industries have increased their efforts to adopt alternative energy technologies to lessen their reliance on the national grid. Industrial spaces are no exception, particularly with the emergence of eco-industrial parks - a group of manufacturing and service businesses situated on a shared property. Questions arise regarding how much an industrial park can decrease its reliance on the national grid and how much emissions can be reduced by utilizing an optimal combination of available energy technologies. These questions are addressed through a least-cost optimisation case study of a medium-sized eco-industrial park. A customised capacity expansion planning tool built on the opensource platform Python for Power System Analysis (PyPSA) is employed in the study. Renewable energy is sourced from rooftop solar PV and an offsite wind installation. Energy storage is provided by commercially available LiFePO4 containerised lithium-ion batteries. The model assumes a multiyear simulation horizon, with learning rate assumptions for the renewables based on the NREL Annual Technology Baseline report. Several scenarios are modelled, and the outcomes are compared to a "business as usual" case, where reliance is solely on the national grid and no embedded renewable energy is employed. The study shows that the employment of currently available renewable energy solutions offers industrial spaces a significant cost saving and a reduced carbon footprint while simultaneously reducing their dependence on the grid.FulltextModelling and decision support toolsPyPSADecarbonizationLeast-cost analysisConference PresentationN/A