Affiliation:
1. 1 Marketing Service Center (Metrology Center), State Grid Shandong Electric Power Company , Jinan , Shandong , , China .
Abstract
Abstract
With the increasing scale and complexity of power systems, it is gradually difficult for the traditional central control system to meet the control and management needs of the new power systems. In this paper, a control strategy based on multi-terminal information fusion is proposed to fuse and integrate the information from different control nodes to form a global new power system control strategy. A hierarchical cluster-based control node organization is also proposed to organize the control nodes according to function and hierarchy to realize the cooperative control and maximum power point tracking of the new power system. Finally, the effectiveness of the effect of the cooperative control strategy is verified by modeling and simulating the cooperative control network of the new power system. The simulation results show that the charge frequency fluctuation range is stable in [46.38Hz, 55.15Hz] for all three scenarios, which is only 0.02Hz away from the lower limit of the set allowable frequency fluctuation range [46.35Hz, 56.38Hz], indicating that the cooperative control strategy stabilizes the charge frequency fluctuation within the effective range. Therefore, this paper achieves further exploration of the optimization and improvement of the new power system cooperative control network, which achieves more efficient, stable, and reliable power system control and management.
Subject
Applied Mathematics,Engineering (miscellaneous),Modeling and Simulation,General Computer Science
Reference22 articles.
1. Eicke, A., Khanna, T., & Hirth, L. (2020). Locational investment signals: how to steer the siting of new generation capacity in power systems?. The Energy Journal, 41(1).
2. Paganini, F., & Mallada, E. (2019). Global analysis of synchronization performance for power systems: bridging the theory-practice gap. IEEE Transactions on Automatic Control, PP(99), 1-1.
3. Pidaparthy, S. K., Choi, B., & Kim, Y. (2019). A load impedance specification of dc power systems for desired dc-link dynamics and reduced conservativeness. IEEE Transactions on Power Electronics.
4. Ibrahim, M. S., Dong, W., & Yang, Q. (2020). Machine learning driven smart electric power systems: current trends and new perspectives. Applied Energy, 272, 115237.
5. Rahmani, S., & Amjady, N. (2017). A new optimal power flow approach for wind energy integrated power systems. Energy, 134, 349-359.