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Optimizing energy consumption in smart cities’ mobility: electric vehicles, algorithms, and collaborative economy
- Source :
- Energies, Volume 16, Issue 3, Pages: 1268
- Publication Year :
- 2023
-
Abstract
- Mobility and transportation activities in smart cities require an increasing amount of energy. With the frequent energy crises arising worldwide and the need for a more sustainable and environmental friendly economy, optimizing energy consumption in these growing activities becomes a must. This work reviews the latest works in this matter and discusses several challenges that emerge from the aforementioned social and industrial demands. The paper analyzes how collaborative concepts and the increasing use of electric vehicles can contribute to reduce energy consumption practices, as well as intelligent x-heuristic algorithms that can be employed to achieve this fundamental goal. In addition, the paper analyzes computational results from previous works on mobility and transportation in smart cities applying x-heuristics algorithms. Finally, a novel computational experiment, involving a ridesharing example, is carried out to illustrate the benefits that can be obtained by employing these algorithms.
- Subjects :
- Optimization
Control and Optimization
smart cities
Energies [Àrees temàtiques de la UPC]
Energy Engineering and Power Technology
Transportation
X-heuristics
energy consumption
Programació (Matemàtica)
Electrical and Electronic Engineering
Engineering (miscellaneous)
Energia -- Consum -- Aspectes ambientals
transportation
Desenvolupament humà i sostenible [Àrees temàtiques de la UPC]
Mobility
Programming (Mathematics)
Renewable Energy, Sustainability and the Environment
Matemàtiques i estadística [Àrees temàtiques de la UPC]
Algoritmes
Building and Construction
mobility
x-heuristics
Energy consumption
optimization
Algorithms
Energy (miscellaneous)
Smart cities
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Energies, Volume 16, Issue 3, Pages: 1268
- Accession number :
- edsair.doi.dedup.....6cfc86c1fa378306774eef4c4db1ca23