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The growing adoption of electric vehicles (EVs) and the expansion of their charging infrastructure offer substantial potential to advance sustainable mobility in smart cities. Nevertheless, the highly interconnected design of EV charging stations (EVCSs) renders this infrastructure vulnerable to multiple cybersecurity threats, such as false data injection, man-in-the-middle attacks, malware infections, and denial-of-service attacks. Moreover, financial threats—including fraudulent billing and credit card data theft—pose significant risks to EV users. In this study, we introduce a blockchain network built on Hyperledger Fabric tailored for EVCSs to address these vulnerabilities. The proposed network employs smart contracts to securely and transparently handle essential operations, including authentication, charging session oversight, and payment confirmation. By identifying and preventing unauthorized data alterations or access attempts, the blockchain framework enhances the robustness of EVCS networks. A before-and-after comparative evaluation of the implemented blockchain network illustrates its effectiveness in countering prevalent cyberattacks targeting EVCS infrastructure. The evaluation encompasses performance indicators derived from the Hyperledger Caliper benchmarking tool, revealing the solution’s minimal latency suitable for real-time applications and its capacity to scale in line with expanding EV infrastructure demands. Implementing this blockchain-augmented security solution is anticipated to boost user confidence and dependability in EVCS systems. 

This study explores the consequences of synchronized false data injection attacks (FDIAs) on voltage behavior in smart microgrids that rely on renewable-based distributed energy resources (DERs)—a cornerstone of modern smart city energy infrastructure. Employing both simulation-based and real-world experimental techniques, the work introduces a unified framework for analyzing and defending against coordinated threats, thereby safeguarding the reliable operation of renewable-integrated DERs, which are central to today’s urban power ecosystems. In the proposed scenario, multiple attackers independently select the most effective moments to execute two complementary cyberattacks, one producing undervoltage and the other causing overvoltage within the same smart microgrid. Each attack model is driven by an objective function that maximizes the severity of voltage deviations—either excessive undervoltage or harmful overvoltage—induced by its corresponding FDIA. Under this coordinated scheme, the adversaries construct a multi-objective optimization problem (MOOP) that simultaneously provokes significant voltage violations across the microgrid’s most exposed zones. Due to the competing objectives in this MOOP, a Pareto-front approach is applied to generate a set of non-dominated optimal solutions, along with a single best compromise solution (BCS). The potency of this combined FDIA strategy is substantiated through detailed simulation studies and laboratory-based experiments. Specifically, the well-known IEEE 13-node test feeder was reconfigured to represent a microgrid environment for the numerical simulations, while the physical validation utilized a small-scale hybrid photovoltaic/wind microgrid testbed featuring actual renewable generation sources.