As cities worldwide accelerate toward electrification and decarbonization, the convergence of distributed energy resources and electric mobility is reshaping the architecture of modern power systems. Distributed Generation (DG), encompassing technologies such as solar photovoltaics, wind turbines, and fuel cells, has emerged as a critical enabler for reducing transmission losses and enhancing energy resilience. At the same time, the rapid adoption of electric vehicles (EVs) is introducing unprecedented demand patterns, placing new stress on existing distribution networks. The challenge lies not merely in deploying these technologies, but in orchestrating their integration in a way that ensures grid stability, efficiency, and sustainability.
This study addresses a pivotal question at the heart of smart city development: how can DG units and fast Electric Vehicle Charging Stations (EVCS) be optimally allocated within a power distribution network to maximize performance while minimizing adverse impacts? Rather than treating these components independently, the research advances a unified optimization framework that simultaneously determines the placement and sizing of both DG units and fast EVCS. By doing so, it directly tackles the complex interplay between generation and load introduced by high EV penetration, which is often associated with increased power losses, voltage instability, and infrastructure strain.
The proposed approach leverages two bio-inspired swarm intelligence algorithms—Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC)—to solve this high-dimensional, nonlinear optimization problem. These algorithms emulate collective behaviors observed in nature, enabling efficient exploration of large solution spaces. Their performance is rigorously evaluated on both the IEEE 69-bus test system and a real-world 33 kV distribution network in Ghana's Ashanti region, offering both theoretical validation and practical relevance.
The results demonstrate a compelling enhancement in network performance when DG units and fast EVCS are optimally co-located. Under high penetration scenarios—up to 40% integration—the system achieves a remarkable reduction in active power losses of up to 68%. This improvement significantly surpasses many conventional planning approaches, highlighting the value of coordinated allocation strategies. Moreover, voltage profiles across the network are substantially stabilized, with deviations maintained within the stringent ±5% limits defined by international standards. Notably, PSO consistently outperforms ABC in minimizing voltage deviation indices, indicating superior convergence behavior and solution quality in this application context.
Beyond numerical gains, the implications of these findings extend to broader societal benefits. Reduced power losses translate directly into improved energy efficiency and lower operational costs, while enhanced voltage stability contributes to more reliable electricity delivery. In the context of rapidly urbanizing regions—particularly in emerging economies—such improvements are essential for supporting both residential and commercial electrification. Furthermore, by facilitating higher penetration of renewable DG sources alongside EV infrastructure, the proposed framework contributes to significant reductions in greenhouse gas emissions, aligning with global climate targets.
Looking ahead, the integration strategy outlined in this study opens new pathways for intelligent energy planning in future smart cities. The simultaneous optimization of DG and EVCS can be further extended to incorporate dynamic factors such as real-time load variability, renewable generation intermittency, and vehicle-to-grid (V2G) interactions. Incorporating advanced forecasting techniques and adaptive control mechanisms could further enhance system responsiveness and resilience. Additionally, the application of hybrid or next-generation metaheuristic algorithms may yield even greater optimization performance, particularly in large-scale, heterogeneous networks.
In practical terms, this research provides a scalable and adaptable framework for utilities, policymakers, and urban planners seeking to design energy systems that are both efficient and future-ready. By demonstrating that coordinated deployment strategies can unlock substantial technical and economic benefits, it challenges conventional siloed approaches to infrastructure planning.
Ultimately, this work underscores a fundamental shift in how energy systems are conceived: not as static networks, but as dynamic ecosystems where generation, consumption, and mobility are deeply interconnected. Through the intelligent application of swarm-based optimization, it offers a forward-looking solution to one of the most pressing challenges in sustainable urban development, paving the way for cleaner, smarter, and more resilient cities.
Reference
Author: Isaac Prempeh a, Albert K. Awopone a, Patrick N. Ayambire a, Ragab A. El-Sehiemy b
Title of original paper: Optimal allocation of distributed generation units and fast electric vehicle charging stations for sustainable cities
Article link: https://www.sciencedirect.com/science/article/pii/S2773153725000313
Journal: Green Energy and Intelligent Transportation
DOI: 10.1016/j.geits.2025.100281
Affiliations:
a Department of Electrical and Electronics Engineering, Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development, Kumasi, Ghana
b Electrical Engineering Department, Kafrelsheikh University 33516, Kafrelsheikh, Egypt
