article · Ain Shams Engineering Journal
Rising adoption of electric vehicles places considerable pressure on power distribution networks. To address this challenge, an optimisation approach has been developed to determine the ideal placement of electric vehicle charging stations while accounting for both electrical grid limits and road network conditions. The method operates in two distinct stages. First, it simulates unpredictable charging demand by clustering driver behaviour according to travel distances, departure times, and arrival times. Second, it clusters the road network and applies a multi-objective algorithm, known as Galaxy Gravity Optimisation, to identify station locations that cut power losses, reduce voltage fluctuations, and enhance electrical grid reliability. The approach was evaluated on a coupled model consisting of a twenty-five node transportation system and an IEEE 69-bus power distribution network, demonstrating how targeted station siting directly improves network performance and service reliability.
Unplanned placement of electric vehicle chargers can overload local power grids, causing power outages and voltage instability. By modelling actual driving habits alongside power network constraints, grid operators and city planners can strategically place charging infrastructure. This ensures reliable electricity delivery, prevents costly network strain, and supports the broader transition to cleaner transport.
This research provides a computational optimisation tool suitable for electrical distribution utilities, transport planners, and municipal infrastructure developers planning charging networks. The work represents early-stage simulation research, tested on an academic benchmark system comprising a twenty-five-node road network and a standard IEEE 69-bus grid, and it would require adaptation and validation with real-world geographical and grid data before operational deployment.
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This paper presents a new and effective approach for optimally allocating Electric Vehicle (EV) stations in a distribution network by integrating electrical and road constraints. The proposed methodology, a two-stage optimization technology, addresses the challenges posed by the increasing energy demand caused by the adoption of EVs. In the first stage, the stochastic demand load of EVs is simulated considering the probability and hierarchic clustering of EV load based on the travelling distance of users. This stage focuses on emulating the uncertainty of EV user travel patterns, time of arrival and departure, and their impact on the EV load profile. In the second stage, a multi-objective problem is formulated to minimize power losses, voltage deviation, and enhance system reliability. To achieve this, charging station zones are determined by clustering the transportation network into groups, and statistical simulations of EV user behaviour are conducted numerous times based on probability. To obtain the optimal solution for the investigated system, which consists of a 25-node transportation network interconnected with the IEEE 69 bus distribution network, the Galaxy Gravity Optimization technique is applied. The results obtained from this study illustrate how the placement of charging stations influences reliability indices, power loss, and voltage deviation.
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DOI: 10.1016/j.asej.2024.102763
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