The synergistic installation of energy storage (ES) and photovoltaic (PV) systems in industrial microgrids plays an irreplaceable role in improving energy efficiency, curbing carbon emission growth, and promoting sustainable economic development. However, the high initial investment costs of ES, coupled with the inherent uncertainty and volatility of PV, have restricted the large-scale application of ES. To address these challenges, this paper proposes an enhanced bi-layer iterative stochastic robust planning method for shared rental ES (SRES) in industrial microgrids. The upper layer develops a multi-objective probabilistic information gap decision theory (IGDT) method to investigate the optimal capacity and power of SRES among industrial microgrids under PV uncertainty. The lower layer proposes a demand power defense-driven distributed model predictive control (DMPC) method to manage the leased power from SRES to industrial microgrid. Numerical results demonstrate that compared with the self-built ES and shared ES, SRES achieves economic benefit improvements of 2.37% and 2.42%, respectively. The proposed planning method effectively mitigates the impact of PV uncertainty, enhances demand power defense capability, and significantly improves overall economic efficiency.