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Author: search.filters.author.Marmolejo Saucedo, José AntonioSubject: search.filters.subject.Artificial intelligence

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    Item type:Publication,
    A Genetic Algorithm to Solve Power System Expansion Planning with Renewable Energy
    (2018)
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    Marmolejo Saucedo, José Antonio
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    Ramírez, Juan Manuel
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    Hernández, Agustina
    In this paper, a deterministic dynamic mixed-integer programming model for solving the generation and transmission expansion-planning problem is addressed. The proposed model integrates conventional generation with renewable energy sources and it is based on a centralized planned transmission expansion. Due a growing demand over time, it is necessary to generate expansion plans that can meet the future requirements of energy systems. Nowadays, in most systems a public entity develops both the short and long of electricity-grid expansion planning and mainly deterministic methods are employed. In this study, an heuristic optimization approach based on genetic algorithms is presented. Numerical results show the performance of the proposed algorithm. © 2018, Springer Nature Switzerland AG.
    Scopus© Citations 1  34  1
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    Nature-inspired meta-heuristics approaches for charging plug-in hybrid electric vehicle
    (2019)
    Vasant, Pandian
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    Marmolejo Saucedo, José Antonio
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    Litvinchev, Igor
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    Currently, there is a remarkable focus on green technologies for taking steps towards more use of renewable energy sources within the sector of transportation and also decreasing pollution. At this point, employment of plug-in hybrid electric vehicles (PHEVs) needs sufficient charging allocation strategy, by running smart charging infrastructures and smart grid systems. In order to daily usage of PHEVs, daytime charging stations are required and at this point, only an appropriate charging control and a management of the infrastructure can lead to wider employment of PHEVs. In this study, four swarm intelligence based optimization techniques: particle swarm optimization (PSO), gravitational search algorithm (GSA), accelerated particle swarm optimization, and hybrid version of PSO and GSA (PSOGSA) have been applied for the state-of-charge optimization of PHEVs. In this research, hybrid PSOGSA has performed very well in producing better results than other stand-alone optimization techniques. © 2021 Springer Nature Switzerland AG. Part of Springer Nature.
    Scopus© Citations 43  15  2