The primary goal of this paper is to design a Multi-Converter Unified Power Quality Conditioner (MC-UPQC). The proposed method is solved based on the Synchronous Reference Frame (SRF) theory. The MC-UPQC involves two series Voltage Source Converters (VSCs) for power transfer between feeders to eliminate voltage sag, swell, interruption, and transient response in the system. The proposed model adopts control strategies based on an optimized Fuzzy Logic Controller (FLC) in SRF, utilizing a hybrid metaheuristic algorithm called Beetle Swarm-based Butterfly Optimization Algorithm (BS-BOA), which combines Beetle Swarm Optimization (BSO) and Butterfly Optimization Algorithm (BOA) for membership limit optimization and control rule generation. The major benefit of the optimized FLC-based MC-UPQC is its quick behavior in minimizing Total Harmonic Distortion (THD) in source and load side voltages and currents. Simulation and MATLAB environment are used for the entire system implementation. The comparative analysis of the proposed controller for MC-UPQC is performed against conventional controllers to validate its effectiveness. Quantitative data related to the main research outcomes, such as THD of source voltage and current, and dynamic behavior of the system, are included. The main benefit of the study is the significant reduction in THD and improved dynamic performance of the system.

Modern smart cities increasingly depend on resilient and intelligent energy infrastructures to maintain critical urban services during large-scale disturbances and multi-fault conditions. Conventional restoration approaches are often limited by centralized operation, delayed response, and inadequate coordination of distributed energy resources (DERs) under emergency conditions. To address these challenges, this paper proposes a Federated AI-Driven Urban Energy Resilience Framework for Smart City Critical Infrastructure Restoration using Virtual Power Plant (VPP) coordination, blockchain-enabled peer-to-peer (P2P) energy trading, and intelligent distributed energy management. The proposed framework is validated on the IEEE 118-bus radial distribution system under severe dual-fault outage conditions, representing urban disaster-induced infrastructure interruptions. Critical urban service zones, including healthcare support systems, emergency loads, smart residential sectors, and EV charging corridors, are considered during the restoration process. The Seagull Optimization Algorithm (SOA) is employed to optimize DER dispatch and improve restoration performance under operational constraints. A progressive restoration strategy comprising conventional outage conditions, VPP-assisted restoration, blockchain-enabled decentralized energy trading, and AI-driven coordinated restoration is analyzed. Simulation results demonstrate that the proposed framework significantly enhances urban energy resilience by increasing load restoration from 55.05% to 94.20%, reducing Energy Not Supplied (ENS), improving voltage stability, and lowering interruption-related economic losses. The minimum bus voltage improves to 0.965 p.u. under the proposed coordinated restoration strategy. The results show that coordinated VPP operation and blockchain-based energy sharing can support reliable restoration of critical urban infrastructure during major outage conditions. The results indicate that integrating AI-assisted VPP coordination with secure decentralized energy trading can effectively support smart city critical infrastructure continuity during extreme outage conditions. The proposed framework provides a scalable and resilient solution for future intelligent urban energy systems and disaster-resilient smart city applications.