## 1 Introduction  
In the current era of rapid technological and economic growth, there is an escalating demand for an improved quality of life that emphasizes sustainability, efficiency, and resilience [1]. As cities expand and urban populations grow, managing the increasing number of connected devices and services has become increasingly complex [2]. This complexity necessitates the development of smart cities--urban environments that leverage advanced technologies, including renewable energy sources, to enhance the quality of urban life. These technologies encompass a wide range of applications, including infrastructure monitoring, intelligent traffic management, and strategies aimed at mitigating greenhouse gas emissions [3]. A smart city integrates numerous systems and applications within its infrastructure, creating a complex network that requires robust management to remain sustainable. Effective data handling and integration are critical to the sustainable development of urban areas. New technologies, particularly the Internet of Things (IoT), provide extensive resources for managing the vast amounts of data generated by cities [4]. Ensuring the interoperability and reusability of this data involves sophisticated data management techniques, including data acquisition, processing, and dissemination. High data quality is essential for accurate data fusion and informed decision-making processes, which ultimately improve the performance of applications and services within the city [5]. These interconnected applications and services transform traditional urban centers into intelligent, smart cities [6]. Mobility is a central component of smart cities, playing a crucial role in supporting daily urban routines [7]. Innovative traffic and transportation services, such as smart traffic management (STM), empower users with data to make safer and more informed decisions [8]. Fully electric vehicles (FEVs) are integral to STM, as they require connection to smart grids for efficient power supply management and problem forecasting [9]. The integration of FEVs into smart cities is not just a technical challenge but a critical step towards achieving sustainable urban mobility. The transition towards electric vehicles (EVs) represents a significant milestone in the global effort to reduce greenhouse gas emissions and combat climate change. However, the rapid adoption of EVs presents new challenges for the stability and efficiency of electrical grids. The integration of a large number of EVs, particularly during peak demand periods, has the potential to cause significant disruptions in grid stability [10]. Traditional power systems, designed for a one-way flow of electricity from centralized power plants to consumers, are increasingly under pressure to adapt to the bidirectional flow of electricity associated with EV charging and renewable energy sources. As the energy landscape evolves, it is imperative to develop advanced management systems that can effectively balance the supply and demand of electricity while maintaining grid stability [11; 12]. Demand-side management (DSM) has emerged as a critical strategy in addressing the challenges posed by the integration of EVs into the power grid. DSM involves the planning and implementation of measures to influence consumer electricity demand in ways that benefit both the utility and the end-user. This can be achieved through various mechanisms, including time-of-use pricing, load shifting, and real-time demand response. Additionally, the integration of renewable energy sources, such as solar and wind power, into DSM frameworks can further optimize energy consumption patterns, allowing the system to better respond to fluctuations in energy supply and demand. The objective of DSM is to optimize energy consumption patterns, thereby reducing peak demand, lowering electricity costs, and enhancing grid stability [1]. One of the most promising approaches to managing the complex interactions between EV charging and grid stability is model predictive control (MPC).