High-renewable power systems require planning methodologies that integrate long-term investment optimisation with resource adequacy, storage sizing, and chronological operational validation. This paper presents a novel scalable multi-stage techno-economic planning framework implemented in PLEXOS, linking capacity expansion (CE), resource adequacy assessment (RAA), storage volume optimisation (SVO), and chronological production cost modelling (PCM). The framework is demonstrated across four power systems representing benchmark, wind-dominated, solar-dominated, interconnected, and high-demand-growth renewable transition pathways. Capacity expansion alone produced annual Loss of Load Probability (LOLP) values of up to 50, 49, 245 and 376 h/year across the four case studies. Following the RAA stage, LOLP was reduced to zero in all systems. Chronological production cost simulations confirmed that all principal planning pathways satisfied the adopted 0.05% unserved-energy criterion. For the representative GCC-type system, mean unserved energy remained at 0.015% under fuel-blend operation but increased to 0.183% under hydrogen-only operation. The supporting flexibility portfolio varied substantially across renewable transition pathways, requiring approximately 24–40 MW of 10-h battery energy storage and 12–56 MW of 4-h battery energy storage per 100 MW of installed renewable capacity. Across the validated transition pathways, the resulting levelized cost of energy (LCOE) ranged from 7.97 to 32.48 $/MWh, reflecting differences in renewable-resource mix, storage and flexibility requirements, retained conventional capacity, and interconnection support. The results demonstrate that high-renewable power systems do not converge towards a universal storage or flexibility solution. Instead, storage and balancing requirements are system-specific, with solar-dominated systems requiring substantially greater short- and long-duration storage deployment, while interconnected wind-dominated systems rely more heavily on system flexibility and cross-border balancing. The proposed framework provides a scalable methodology for identifying system-specific storage and flexibility requirements for long-term renewable transition planning.
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Varhegyi et al. (2026) studied this question.
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