Computational intelligence-based energy management for a large-scale PHEV/PEV enabled municipal parking deck

► Describe the mathematical framework for large-scale PHEV/PEV charging control. ► Manage the highly concentrated PHEV chargers considering real-world constraints. ► Develop and implement a suite of computational intelligence-based algorithms. ► Evaluate a variety of charging scenarios and the corre...

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Vydáno v:Applied energy Ročník 96; s. 171 - 182
Hlavní autoři: Su, Wencong, Chow, Mo-Yuen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.08.2012
Témata:
algorithms
artificial intelligence
Electric Vehicle (EV)
electricity
energy
energy resources
Estimation of Distribution Algorithm (EDA)
infrastructure
markets
Particle Swarm Optimization (PSO)
Plug-in Electric Vehicle (PEV)
Plug-in Hybrid Electric Vehicle (PHEV)
Smart Grid
society
vehicles (equipment)
Estimation of Distribution Algorithm (EDA)
Plug-in Hybrid Electric Vehicle (PHEV)
Electric Vehicle (EV)
Particle Swarm Optimization (PSO)
Plug-in Electric Vehicle (PEV)
Smart Grid
ISSN:0306-2619, 1872-9118
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Shrnutí:► Describe the mathematical framework for large-scale PHEV/PEV charging control. ► Manage the highly concentrated PHEV chargers considering real-world constraints. ► Develop and implement a suite of computational intelligence-based algorithms. ► Evaluate a variety of charging scenarios and the corresponding control strategies. ► Demonstrate the effectiveness of the proposed computational intelligence approaches. There is a growing need to address the potential problems caused by the emergence of Plug-in Hybrid Electric Vehicles (PHEVs) and Plug-in Electric Vehicles (PEVs) within the next 10years. In the near future, a large number of PHEVs/PEVs in our society will add a large-scale energy load to our power grids, as well as add substantial energy resources that can be utilized. The large penetration of these vehicles into the marketplace poses a potential threat to the existing power grid. The existing parking infrastructure is not ready for the large penetration of plug-in vehicles and the high demand of electricity. Nowadays, the advanced computational intelligence methods can be applied to solve large-scale optimization problems in a Smart Grid environment. In this paper, authors propose and implement a suite of computational intelligence-based algorithms (e.g., Estimation of Distribution Algorithm, Particle Swarm Optimization) for optimally managing a large number of PHEVs/PEVs charging at a municipal parking station. Authors characterize the performance of the proposed methods using a Matlab simulation, and compare it with other optimization techniques.
Bibliografie:http://dx.doi.org/10.1016/j.apenergy.2011.11.088
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2011.11.088