Data driven two-tier optimization framework for assessing the optimal penetration of renewable energy based firm capacity systems considering techno economic analysis

• Proposes a novel two-tier data-driven optimization framework for Renewable Energy-based Firm Capacity (REFC) systems. • Validates framework across three diverse Chinese locations with site-specific resource data. • Integrates Binary Search Algorithm (BSA) for optimal renewable penetration level estimation. • Employs Butterfly Particle Swarm Optimization (BPSO) for techno-economic sizing of solar PV with Battery and Hydrogen Storage Systems. • SPV-HSS configurations achieve 1–2 % higher RE penetration than SPV-BSS systems. With the growing population, economic improvement, escalating industrialization, and modern digital lifestyles and electrification improvement, energy demands are increasing on the day-by-day bases. Therefore, to fulfill the energy demand and environmental concern of the fossil fuel-based energy generation, globally shift toward the renewable energy (RE) sources are exhibited in the last few decades. However, the incorporation of RE sources acquaint with substantial challenges to existing grid infrastructure, particularly in maintaining system stability and reliability due to their intermittent nature. Among the different RE source integration strategies the Renewable energy based firm capacity (REFC) systems suggest a capable solution by ensuring round-the-clock (RTC) electricity supply through the integration of energy storage systems (ESSs) and dispatchable energy sources, thereby mitigating the variability of RE sources output. However, the optimal penetration level determination is a key aspect of REFC system for technical feasible and economic grid integration. Therefore, this work presents a novel two-tire data-driven optimization framework for the planning of a solar photovoltaic (SPV) based REFC systems with incorporation of Battery Storage Systems (BSS) and Hydrogen Storage Systems (HSS). The first tire of framework is design to analyze the optimal penetration by utilizing Binary Search Algorithm (BSA) and second tire used to determine optimal component size for particular penetration level using Butterfly PSO (BPSO) by incorporating the techno-economic analysis in both levels. The proposed novel framework is validated across three distinct locations in China (Urumqi, Gonghe County, and Jiuquan). Further, two types of sensitivity analyses are conducted as one assessing REFC system behavior at different penetration levels, and another estimating performance under component variations across all locations. The results reveal that SPV-HSS configurations consistently support higher RE penetration (1–2 %) compared to SPV-BSS systems. These findings highlight the importance of selecting appropriate storage technologies and applying site-specific data driven optimization framework for the development of gainful, resilient energy system. This work offers valuable insights for energy planners and policymakers to facilitate sustainable and enhanced RE integration, mainly for grid expansion and electrification in remote areas.