Distributed and Decentralized Control of the Power Grid
The introduction of remotely controlled network devices is transforming the way the power system is operated and studied. The ability to provide real and reactive power support can be achieved at the end-user level. In this dissertation, a framework and algorithm to coordinate this type of end-user control are presented. The algorithm is based on a layered architecture that would follow a chain of command from the top layer (transmission grid) to the bottom layer (distribution grid). At the distribution grid layer, certain local problems can be solved without the intervention of the top layers. A reactive load control optimization algorithm to improve the voltage profile in the distribution grid is presented. The framework integrates agent-based technologies to manage the data and control actions required to operate this type of architecture. In the distribution network, action can be initiated locally to find solutions to certain problems. That is the reason that in this dissertation decentralized optimization problems are studied to find a solution to control reactive power resources. Four decentralized optimization techniques are studied in two different distribution networks. From the analysis, the Lagrangian relaxation algorithms show the best results to implement a decentralized scheme to control reactive resources. Since capacitors are another reactive power resource to be controlled, the dissertation also presents a decentralized optimization algorithm to minimize losses in the distribution network. The decentralized algorithm results are found to be similar to those using a centralized algorithm. Finally, because the decentralized optimization algorithm needs to iterate among regions to find a solution, another algorithm is introduced to find a local solution to reactive resource problems in the distribution network. The algorithm is based on sensitivities of voltages to reactive resources to estimate the top of a feeder bus voltage of a particular region inside the distribution network. The algorithm is shown to effectively find a solution to a local problem, and the results are similar to a centralized optimization problem. The framework and the algorithms presented in this dissertation integrate agent-based technologies to manage the data and control actions required to operate this type of architecture.
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