To address the challenges of increased grid volatility due to the high penetration of renewable energy integration and the difficulty of traditional reactive power reserve planning methods in ensuring reserve effectiveness
a two-stage distributed robust optimization model is proposed. This model holistically considers the uncertainty of renewable energy output and the effectiveness of the reactive power reserve it provides in actual operation. By integrating the strong second-order cone relaxation method and the column-and-constraint generation (C&CG) algorithm
the model achieves the economic optimality of reactive power reserve optimization configuration while ensuring efficient solution. Simulation verification based on an actual grid case demonstrates that the proposed model effectively tackles the issues of volatility and reactive power reserve effectiveness brought by high-proportion renewable energy integration. It significantly reduces ineffective reserves and configuration costs while enhancing system voltage stability
thereby offering a new approach for reactive power reserve planning in new-type power systems.
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