Smart grids, due to their complexity and reliance on digital infrastructures, are vulnerable to unexpected events such as natural disasters, infrastructure failures, and cyberattacks. Resilience—defined as the ability to prevent, withstand, respond to, and rapidly recover from such disruptions—is therefore of critical importance. This study aims to enhance smart grid resilience by presenting a comprehensive approach for optimizing the siting and sizing of Energy Storage Systems (ESS) in the IEEE 33-bus standard network. Utilizing an advanced genetic algorithm, an optimal configuration was achieved, comprising two battery units (at buses 24 and 25) and one pumped-hydro storage unit (at bus 21) with a total capacity of 29.9 MWh. Results demonstrate a 21.4% reduction in Energy Not Supplied (ENS), a 27% decrease in Loss of Load Probability (LOLP), and a 38.6% improvement in the System Average Interruption Duration Index (SAIDI). Analysis of failure scenarios (mainline outage, feeder disconnection, storm, generator trip, and cyberattack) confirmed high ESS effectiveness in localized scenarios but revealed limitations in broader disruptions. Despite significant resilience enhancements, the economic analysis indicated a long payback period and a negative Net Present Value (NPV), highlighting the need for financial support or innovative revenue models. Recommendations include integrating ESS into ancillary service markets, conducting advanced sensitivity analysis, and developing supportive policies for practical implementation. • Hybrid ESS Optimization: Proposed a Genetic Algorithm framework for siting and sizing Battery and Pumped-Hydro systems. • Multi-Hazard Resilience: Integrated physical storm damage and coordinated cyber-physical attacks into a unified model. • Quantified Impact: Achieved 21.4% reduction in Energy Not Supplied (ENS) and 38.6% improvement in SAIDI. • Economic Viability: Demonstrated a reduced payback period of 6.5 years using FERC Order 841-aligned resilience tariffs. • Policy Framework: Recommended “Dual-Use” asset classification to enable rate-basing and market participation for storage.
Naghavi et al. (Tue,) studied this question.