Reconfiguration and Load Balancing in the LV and MV Distribution Networks for Optimal Performance

• Published in: IEEE transactions on power delivery 2007-10, Vol.22 (4), p.2534-2540
• Main Authors: Siti, M.W., Nicolae, D.V., Jimoh, A.A., Ukil, A.
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Cables; Connection and protection apparatus; Distribution automation; distribution control; Electric potential; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Heuristic; heuristic algorithm; Heuristic algorithms; Heuristic methods; load balancing; Load management; Medium voltage; Minimization methods; Miscellaneous; Networks; neural network; Neural networks; optimal control; Optimization; Performance loss; phase arrangement; phase current imbalance; Phase transformations; phase voltage imbalance; power loss; Power networks and lines; Reconfiguration; Robustness; Studies; Substations; Switches; Switching theory; Transformers; Transformers and inductors
• ISSN: 0885-8977; 1937-4208
• DOI: 10.1109/TPWRD.2007.905581

To get the distribution network to operate at its optimum performance in an automated distribution system reconfiguration was been proposed and researched. Considering, however, that optimum performance implies minimum loss, no overloading of transformers and cables, correct voltage profile, and absence of phase voltage and current imbalances, network reconfiguration alone is insufficient. It has to be complemented with techniques for phase rearrangement between the distribution transformer banks and the specific primary feeder with a radial structure and dynamic phase and load balancing along a feeder with a radial structure. This paper contributes such a technique at the low-voltage and medium-voltage levels of a distribution network simultaneously with reconfiguration at both levels. While the neural network is adopted for the network reconfiguration problem, this paper introduces a heuristic method for the phase balancing/loss minimization problem. A comparison of the heuristic algorithm with that of the neural network shows the former to be more robust. The approach proposed here, therefore for the combined problem, uses the neural network in conjunction with a heuristic method which enables different reconfiguration switches to be turned on/off and connected consumers to be switched between different phases to keep the phases balanced. An application example of the proposed method using real data is presented.