Optimizing Renewable Energy Capacity Development Using Standard-Pseudo Multi-Mode Gradient Descent Algorithm for Australia’s 2050 Net-Zero Transition

Australia’s transition toward net-zero emissions (NZE) requires the integration of renewable energy (RE) sources with substantially different capacity requirements and operational characteristics. However, existing transition assessments provide limited quantitative evaluation of how “Proposed”, “Anticipated”, and “Committed” renewable capacity additions may influence the required capacity pathway. This study develops a Multi-Mode Gradient Descent (MMGD) framework combining Standard Gradient Descent with an adopted Pseudo-Gradient approach to assess annual capacity additions for five energy sources: solar, wind, hydro, biomass, and battery storage. Australian Energy Market Operator (AEMO) capacity data are used as the reference, with “Proposed”, “Anticipated”, and “Committed” additions assessed under “Low”-, “Medium”-, and “High”-influence conditions, respectively. The MMGD results show substantial variation across these conditions. Relative to the AEMO reference (1.0 = 100%), the Low-influence results range from 0.22 to 1.43, Medium-influence from 1.10 to 7.16, and High-influence from 2.19 to 14.33 across the five energy sources. The MMGD results are benchmarked against the corresponding AEMO reference capacities to evaluate the sensitivity of the required renewable direction and magnitude of capacity adjustment under different influence conditions. The study contributes a gradient-based decision-support framework for quantitatively assessing renewable energy capacity pathways toward Australia’s 2050 NZE target.