Due to the rising of both economic and environmental concerns in the energy sector, each subdivision of the community is investigating new solutions to overcome this critical issue. For this reason, electric vehicles ...
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Due to the rising of both economic and environmental concerns in the energy sector, each subdivision of the community is investigating new solutions to overcome this critical issue. For this reason, electric vehicles (EVs) have gained more significance in the transportation sector owing to their efficient and clean operation chance. These improvements, however, bring new challenges such as installation costs, infrastructure renovation, and loading of the existing power system. Here, optimal sizing and siting of EV charging stations (CSs) are examined in a mixed-integer linear programming framework with the aim of minimizing the number of EVCSs in the distribution system (which in turn means to minimize CS-related investment while satisfying EV owners' needs) while satisfying constraints. The proposed optimization model considers EVCS types with different charging rate capabilities to provide opportunities for demand-side management. Moreover, the model takes the actual behaviour of the battery charging pattern into account by using real measured EV charging data together with the consideration of an actual distribution system belonging to a region in Turkey. Lastly, a bunch of case studies is conducted in order to validate the accuracy and effectiveness of the devised model.
As urban traffic congestion is on the increase worldwide, many cities are increasingly looking to inexpensive public transit options such as light rail that operate at street-level and require coordination with conven...
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As urban traffic congestion is on the increase worldwide, many cities are increasingly looking to inexpensive public transit options such as light rail that operate at street-level and require coordination with conventional traffic networks and signal control. A major concern in light rail installation is whether enough commuters will switch to it to offset the additional constraints it places on traffic signal control and the resulting decrease in conventional vehicle traffic capacity. In this study, the authors study this problem and ways to mitigate it through a novel model of optimised traffic signal control subject to light rail schedule constraints solved in a mixed-integerlinearprogramming (MILP) framework. The authors' key results show that while this MILP approach provides a novel way to optimise fixed-time control schedules subject to light rail constraints, it also enables a novel optimised adaptive signal control method that virtually nullifies the impact of the light rail presence, reducing average delay times in microsimulations by up to 58.7% versus optimal fixed-time control.
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