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A hybrid genetic algorithm for optimal active power curtailment considering renewable energy generation
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Advisor(s)
Abstract(s)
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.
Description
Keywords
Energy curtailment Optimal power flow Genetic algorithm Interior point Active-set
Pedagogical Context
Citation
Pedroso, André Felipe Pereira; Amoura, Yahia; Pereira, Ana I.; Ferreira, Ângela P. (2023). A hybrid genetic algorithm for optimal active power curtailment considering renewable energy generation. In 23rd International Conference on Computational Science and Its Applications (ICCSA). Cham: Springer, p. 479-494. ISBN 978-303137107-3
Publisher
Springer