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Load transfer without power interruption in the loop closing of an active distribution network is the core technology for improving the reliability of power supply. A single rotary phase shifting transformer (RPST) is used in the loop closing scenario with a 30° phase angle difference. However, its regulation mode is single and cannot realize flexible loop closing under any voltage deviation. In this study, a dual rotary phase shifting transformer (DRPST) is proposed to achieve the function of continuously regulating voltage amplitude and phase angle at the loop closing point through the cooperation of dual rotor angles. This DRPST exhibits the advantages of a flexible control mode, high control precision, and high reliability. First, the working principle and mathematical model of the proposed DRPST are analyzed in accordance with its topology. Second, the voltage phase difference feedback method at both sides of the loop closing point based on virtual power is adopted, and the reference value of the DRPST synthesis vector of voltage is obtained by combining with voltage phase and amplitude deviations. Then, the reference value is converted and decomposed into the reference values for dual rotary phase shifting angles, realizing the non‐differential regulation of voltage at both sides of the loop closing point through the closed loop control of double rotor angles and the coordinated control of the servo motors’ speed. Finally, a DRPST device with a capacity of 40 kVA is developed and a loop closing experiment is performed, verifying the feasibility and effectiveness of the proposed DRPST in realizing the loop closing of an active distribution network.

This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management is first described using a modified prediction interval scheme. Forecasting results are then achieved every 10 min using the modified fuzzy prediction interval model, which is trained by particle swarm optimization. A scenario set is also generated using an unserved power profile and coverage grades of forecasting to compare the feasibility of the proposed method with that of the deterministic approach. The worst case operating points are achieved by the scenario with the maximum transaction cost. In summary, selection of the maximum transaction operating point from all the scenarios provides a cushion against uncertainties in renewable generation and load demand.

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