This paper presents a novel approach to addressing the challenges associated with energy storage capacity allocation in high-permeability wind and solar distribution networks. . Distributed energy resources —technologies used to generate, store, and manage energy consumption for nearby energy customers—can help increase power system reliability while providing energy locally. The Wind Energy Technologies Office's (WETO) distributed wind research program is advancing wind. . Distributed wind assets are often installed to offset retail power costs or secure long term power cost certainty, support grid operations and local loads, and electrify remote locations not connected to a centralized grid. However, there are technical barriers to fully realizing these benefits. . Firstly, we introduce a meticulously designed uncertainty modeling technique aimed at optimizing wind power forecasting deviations, thus augmenting the controllability of distributed wind power variations. Subsequently, we establish a cutting-edge real-time dynamic optimization model for state of. . Methods: This article proposes a two-stage wind-storage coordination planning method that considers source-load uncertainty. The approach is based on an improved antlion algorithm and incorporates distributed energy storage charging and discharging strategies. The proposed method is a two-phase distributed robust energy storage capacity allocation method, which aims to regulate the. . Wind turbines used as distributed energy resources—also called distributed wind—produce electricity that is consumed on-site or locally, as opposed to large, centralized wind farms that generate bulk electricity for distant end users. Distributed wind is a valuable tool in meeting local energy. .
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