Regularised primal–dual interior‐point method for dynamic optimal power flow with block‐angular structures

Zhang, Chen; Jian, Jinbao; Yang, Linfeng

doi:10.1049/iet-gtd.2019.1528

Cited by 3 publications

(1 citation statement)

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“…Traditional power systems mainly rely on thermal power generation to meet electricity demand, with no renewable energy generation units in the system. Therefore, traditional optimal power flow models proposed in (Bacher and Van Meeteren, 1988;Hua et al, 1998;Zhang et al, 2020;Kardoš et al, 2022) are not suitable for scenarios considering the uncertainty of new energy output. With the gradual development of low-carbon power construction, the significant injection of new energy has huge impacts on the safe and economic operation of the power system, demanding a reform in the power system's operational control mode.…”

Section: Introductionmentioning

confidence: 99%

Robust optimal power flow considering uncertainty in wind power probability distribution

Dai,

Xiao,

Yang

2024

Front. Energy Res.

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This paper proposes an optimal power flow model that takes into account the uncertainty in the probability distribution of wind power. The model can schedule controllable generators under any possible distribution of wind power to ensure the safe and economic operation of the system. Firstly, considering the incompleteness of historical wind power data, the paper models the uncertainty of wind power using second-order moments of probability distribution and their fluctuation intervals. Subsequently, a robust optimal power flow model based on probability distribution model and joint chance constraints is established. The Lagrangian duality theorem is then employed to eliminate random variables from the optimization model, transforming the uncertainty model into a deterministic linear matrix inequality problem. Finally, a convex optimization algorithm is used to solve the deterministic problem, and the results are compared with traditional chance-constrained optimal power flow model. The feasibility and effectiveness of the proposed method are validated through case study simulations.

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Section: Introductionmentioning

confidence: 99%