Voltage Control in Active Distribution Networks Under Uncertainty in the System Model: A Robust Optimization Approach
| Autor: | Ali Abur, Mario Paolone, Konstantina Christakou |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Engineering
Mathematical optimization General Computer Science business.industry 020209 energy Robust optimization 02 engineering and technology AC power Optimal control Network topology Grid Admittance parameters System model Ancillary services Control theory Voltage control 0202 electrical engineering electronic engineering information engineering epfl-smartgrids Active distribution networks Distributed generation Intelligent control business |
| Zdroj: | IEEE Transactions on Smart Grid. 9:5631-5642 |
| ISSN: | 1949-3061 1949-3053 |
| DOI: | 10.1109/tsg.2017.2693212 |
| Popis: | Within the context of ancillary services for Active Distribution Networks (ADNs), application of intelligent control techniques is required in order to achieve specific operation objectives. Despite their differences, most control mechanisms proposed in the literature rely on the assumption that the Distribution Network Operator (DNO) has an accurate and upto- date model of the network topology and a complete knowledge of the line parameters, i.e., a correct network admittance matrix Y. However, this assumption does not always hold in reality due to both an incomplete knowledge of the grid asset and /or a physical change of the line parameters. In this work, we consider the problem of optimal voltage control in ADNs under uncertain, but bounded, line parameters with no assumptions on the parameters’ uncertainty distribution. In particular, availability of a monitoring infrastructure is assumed and the goal is to control the active and reactive power injections of a number of distributed generators connected to the network buses in coordination with the transformers on-load tap changers (OLTC). The optimal control problem is formulated as a mixedinteger linear problem by means of sensitivity coefficients and a robust optimization framework is used in order to account for the uncertainties in the network admittance matrix. In order to estimate the benefits of the proposed method, the evaluation of the algorithm is carried out by using both the IEEE 13-and the IEEE 34-nodes test feeder. |
| Databáze: | OpenAIRE |
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