UC Berkeley

There has been multiple national directives to enhance the economic operations of the power transmission system and promote the efficient use of the current grid configuration and resources. Transmission switching has been proposed as a new control method for various benefits, including improving the economic efficiency and meeting the reliability requirements. Optimal Transmission Switching model has been introduced to find an optimal generation dispatch and network topology to minimize the dispatch cost. Binary decision variables are used to denote the control of the transmission lines, which makes the model a nonlinear program. It suffers from curse of dimensionality and faces serious computational challenges.

To tackle the computational challenge of the Optimal Transmission Switching model, we propose three greedy algorithms in which only one line is switched at an iteration. In each iteration we solve a series of linear programs or smaller MIP programs, which can be implemented in parallel with the aid of high performance computing. The first algorithm enumerates all the possible line switching actions. The second algorithm produces a priority list ranking lines by a sensitivity factor based on dual criterion, and evaluate lines starting from the top of the list. The last one divides lines into small groups and consider each group at one time. We test the algorithms on the IEEE 118-Bus network and the FERC 13,867-Bus network which is representative of PJM Regional Transmission Organization. The results show that all three proposed algorithms result in cost reduction close to the best known optimal within a reasonable timeframe for IEEE network. For the FERC network which can not be solved directly by the OTS, the first two greedy algorithms are able to produce switching sequences which result in considerable cost reduction.

Furthermore we propose three machine learning based methods to produce priority lists for ranking possible line switching actions to facilitate faster searching. The algorithms take in the parameters from network status and network configuration to produce a standardized score representing the possible cost reduction of the line switching action. The numerical results on IEEE network and FERC network are presented. We evaluate the effectiveness of the priority lists based on dual criterion and machine learning methods by both regression analysis and comparison with random lists.

Based on the algorithms in literature and those we develop, we propose an algorithm selection method which selects the algorithm to optimize the cost improvement at each iteration. They show improvement in the cost reduction compared to the individual algorithms, especially for the FERC network.