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A hybrid genetic algorithm for optimal active power curtailment considering renewable energy generation

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Resumo:This paper analyzes the application of a population-based algorithm and its improvement in solving an optimal power flow problem. Simulations were performed on a 14-bus IEEE network modified to include renewable energy sources-based power plants: a wind park and two photovoltaic solar parks. In this scenario, the high penetration of intermittent energy sources in the grid makes it necessary to curtail active power during peak generation to maintain the balance between load and generation. However, European energy market regulations limit the annual curtailment of RES generators and penalize discriminatory curtailment actions between generators. This work exploits the minimization of transmission active loss while respecting its security constraints. Additionally, constraints were introduced in the optimal power flow problem to mitigate active power curtailment of the renewable source generators and to secure a non-discriminatory characteristic in curtailment decisions. The non-convex nature of the problem, intensified by the introduction of non-linear constraints, suggests the exploitation of heuristic algorithms to locate the optimal global solution. The obtained results demonstrate that a hybrid GA algorithm can improve convergence speed, and it is useful in determining the problem solution in cases where deterministic algorithms are unable to converge.
Autores principais:Pedroso, André Felipe Pereira
Outros Autores:Amoura, Yahia; Pereira, Ana I.; Ferreira, Ângela P.
Assunto:Energy curtailment Optimal power flow Genetic algorithm Interior point Active-set
Ano:2023
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso restrito
Instituição associada:Instituto Politécnico de Bragança
Idioma:inglês
Origem:Biblioteca Digital do IPB

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