Climate change and energy security are complex challenges whose solutions depend on multi-faceted interactions between different actors and socio-economic contexts. Energy innovation through integration of renewable energies in existing systems offers a partial solution, with high potential identified for bioenergy and solar energy. 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 electricity from the sun. 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 renewable energy technologies.