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This chapter delves into how smart city innovations positively affect workforce efficiency, residents’ quality of life (QoL), and the delivery of services, particularly within the dynamic context of smart cities: innovation, development, transformation, and prosperity. It discusses the role of technologies like cyber-physical systems, the Internet of Things, and intelligent transport systems in creating efficient, sustainable urban spaces that benefit the workforce and the broader community. The chapter highlights strategies for improving urban environments, ensuring workforce well-being, and fostering sustainable growth by examining the interplay between these technologies and urban living. The narrative emphasizes the necessity of ongoing innovation, policy support, and workforce adaptation, underscoring the importance of tailoring smart city initiatives to regional needs for maximal impact on employee performance, QoL, and service delivery. Additionally, it introduces a comprehensive framework designed to guide the development of next-generation smart cities. This framework integrates advanced technologies for optimized urban management and service provision, directly linking to enhanced employee performance through improved urban infrastructure and services. The strategic application of this framework aims to elevate economic prosperity and societal well-being, ensuring workforce efficiency is central to the urban development agenda. The enhanced employee performance, catalyzed by smart city innovations, is pivotal in driving economic vibrancy, social inclusivity, and environmental sustainability, shaping the future of urban development. This analysis will offer valuable insights for smart cities research and development in the Gulf Region, suggesting pathways for implementing these concepts to address the region’s urbanization and development challenges.

Reactive power in radial distribution networks (RDN) leads to detrimental effects like power factor degradation, voltage profile alterations and increased power losses, ultimately impacting network stability. This paper aims to present a novel two-phase optimization approach to address the challenging task of locating, sizing and determining the optimal number of capacitors in RDNs.

The first step of the proposed methodology is using a hybrid technique that combines the loss sensitivity factors (LSF) with voltage sensitivity factors (VSF) to identify network nodes requiring capacitor installation efficiently. The second step uses an external approximation technique to optimize the size and number of capacitors for each identified node, achieving significant power loss reductions.

The effectiveness of this new approach is evaluated on two RDNs: 33- and 69-bus. Simulations on these test systems demonstrate the effectiveness of the proposed approach, reducing total power loss by 34.7% in the first case and 35.3% in the second. The method’s robustness compared to other approaches further highlights its potential for practical implementation in RDNs, contributing to improved network stability and efficient power distribution.

This paper presents a novel, efficient and robust approach to determining the optimal number, location and size of an RDN capacitor. The problem is addressed through a new formulation with modified constraints. The method consists of two stages: initially, a hybrid LSF–VSF method identifies potential capacitor locations, followed by an external approximation-based mixed-integer nonlinear programming (MINLP) solver to optimize capacitor numbers and sizes. The proposed methodology is applied to the widely used 33-bus and 69-bus RDN test systems. Comparative analysis with existing methods highlights the proposed approach’s effectiveness. Key contributions of this study include the following: Proposes a new problem formulation with modified constraints. Proposes a novel two-stage framework for optimally locating and sizing capacitors in RDNs. Introduces a hybrid LSF–VSF algorithm to identify promising capacitor locations efficiently. Using an external approximation-based MINLP for optimal sizing. Demonstrates the effectiveness of the proposed approach through rigorous testing on standard benchmark systems. Provides a comprehensive comparative analysis against state-of-the-art methods, highlighting the proposed approach’s superior performance.