| Power networks are one of the most complex systems in the field of electrical and computer engineering. In power networks, power supply-demand balancing can be achieved in a static or a dynamic model. In a static model, the power network cannot be easily adapted to intentional or unintentional network topology changes because the network design is predetermined, whereas in a dynamic model, the power network can be dynamically constructed and reconfigured at run-time, which leads to a more nimble, flexible, and stable system. In this dissertation, a novel Market-based Multi-agent System (MMS) is proposed to solve supply-demand balancing and power restoration problems in a dynamic model. The power network is modeled as a market environment consisting of Belief-Desire-Intention (BDI) agents representing three characters: 1) consumer, 2) supplier, and 3) middleman. The BDI agents are able to negotiate power supply and demand of the power network, with consumers exploring the market and exchanging power information with neighboring middlemen and suppliers. So long as all consumers and suppliers establish supply-demand relationships represented in tree data structures, a qualified minimal access structure is found as the lower bound of the system reliability. When contingencies occur, the agents can quickly respond and restore loads guided by the relationships using minimum computational resource. Based on case studies and simulation results, the proposed approach delivers more effective performance of contingencies response and better computation time efficiency as the scale of the power network expands. The proposed MMS shows promises for solving various real-world power supply-demand and restoration problems, and serves as a solid foundation for future power networks refinement and improvement. |
