Interval power flow calculation algorithm for multi‐terminal dc distribution networks considering distributed generation output uncertainties
An interval power flow calculation (PFC) algorithm for multi‐terminal DC distribution networks is proposed to handle the uncertainties of distributed generation output powers and loads. Firstly, an equivalent resistance is introduced to represent the effect of droop control in DC distribution networ...
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Published in | IET generation, transmission & distribution Vol. 15; no. 5; pp. 986 - 996 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Wiley
01.03.2021
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Subjects | |
Online Access | Get full text |
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Summary: | An interval power flow calculation (PFC) algorithm for multi‐terminal DC distribution networks is proposed to handle the uncertainties of distributed generation output powers and loads. Firstly, an equivalent resistance is introduced to represent the effect of droop control in DC distribution networks. The power losses of the equivalent resistance are offset by adding the injection powers to droop nodes, which guarantees the precision of the equivalent resistance model. The Newton–Raphson method is used for deterministic PFC based on the equivalent resistance, and it is applicable to DC distribution networks with different kinds of network topologies and voltage source converter control modes. Secondly, based on the deterministic PFC algorithm and affine arithmetic, an interval PFC algorithm is presented to handle the uncertainties of distributed generation output powers and loads. The affine arithmetic based interval PFC algorithm not only can determine power flow intervals quickly and accurately, but also can analyse the influences of distributed generation outputs to nodal voltages. Finally, a series of test systems are used to validate the proposed algorithms. Simulation results illustrate the accuracy and efficiency of the deterministic PFC algorithm and the interval PFC algorithm for multi‐terminal DC distribution networks. |
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ISSN: | 1751-8687 1751-8695 |
DOI: | 10.1049/gtd2.12074 |