以隨機協商的方式解決智慧電網中的復電問題

A Stochastic Negotiation Approach to Power Restoration Problems in a Smart Grid

彭彥博(Yan-Bo Peng)

指導教授 ： 蘇豐文

國立清華大學/電機資訊學院/資訊工程學系/碩士(2011年)

https://doi.org/10.6843/NTHU.2011.00520

摘要

因為天災或人為疏失所造成的意外，停電是無法避免的。如何能在意外發生之後，盡可能的恢復停電區域的電力，稱之為復電問題。在本論文中，我們將傳統的電力輸配系統視為一個智慧電網，在智慧電網中的每一個元件亦皆可視為一個智慧代理人。我們以決策理論為基礎建立了一個隨機協商的機制，透過這個機制，我們可以在連續的決策中避免陷入區域最佳化的情形，在智慧電網中的代理人會利用此機制彼此協議來解決復電問題。我們會證明我們的系統可以正常運作，並且可以解決許多特殊的復電問題。我們也會證明我們的系統效能比使用委員會導向的多代理人系統更好。在某些情況下，復電問題是非常急迫且有時間限制的，因此，我們最後會分析回復電力的區域比率和復電作業執行時間之間的關係，以期找出一個較適當的時間限制。

關鍵字

隨機協商 ； 復電問題 ； 多代理人系統

並列摘要

Because of the accidents, the outages of electrical power service occur inevitably. How to restore the out-of-service zones after a fault occurs is called the power restoration problem. In this thesis, we treat the electrical power grid as a smart grid so that devices in the smart grid can be treated as agents. We build a stochastic negotiation protocol based on the decision theory. This stochastic negotiation protocol can avoid trapping into local optimal in sequential decision making. Agents in the smart grid can negotiate with each other using the stochastic negotiation protocol to solve the power restoration problem. We show that our system is workable and capable to solve subtle cases. We also show that the performance of our system is better than a committee-based negotiation multi-agent system. Under some conditions, the power restoration problem is emergent and has a time limit. For this reason, we finally analyze the relationship between the restoration rate and the execution time so that we may find a more proper time limit.

參考文獻

1. Adibi, M.M. and R.J. Kafka, Power system restoration issues. Computer Applications in Power, IEEE, 4(2): p. 19-24, 1991.

2. Hsiao, Y.T. and C.Y. Chien, Enhancement of restoration service in distribution systems using a combination fuzzy-GA method. IEEE Transactions on Power Systems, 15(4): p. 1394-1400, 2000.

3. Lambert-Torres, G., et al., Particle Swarm Optimization Applied to System Restoration. 2009 IEEE Bucharest Powertech, Vols 1-5, 1992-1997, 2009.

4. Luan, W.P., M.R. Irving, and J.S. Daniel, Genetic algorithm for supply restoration and optimal load shedding in power system distribution networks. IEE Proceedings-Generation Transmission and Distribution, 149(2): p. 145-151, 2002.

5. Shin, D.J., et al., Optimal service restoration and reconfiguration of network using Genetic-Tabu algorithm. Electric Power Systems Research, 71(2): p. 145-152, 2004.