期刊
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
卷 45, 期 10, 页码 15049-15084出版社
WILEY
DOI: 10.1002/er.6783
关键词
distributed computing; minimum spanning tree; self‐ healing; smart grids
资金
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq - Brazil)
- FundacAo Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ- Brazil)
This study proposes a distributed implementation of self-healing mechanism in smart grids, which can quickly find satisfactory reconfiguration solutions and enhance network intelligence. The results show that this implementation performs significantly better than the expected upper bounds in terms of reconfiguration time and communication cost, achieving substantial speedup in cases of single and multiple failures.
In the event of a network failure that compromises energy supply, the characteristic of smart grid self-healing consists of finding a proposal for reconfiguration of the grid, aiming at restoring the power, partially or completely to supply all network nodes. The search for a satisfactory reconfiguration is a combinatorial problem whose complexity is proportional to the network size. An exhaustive search-based method is time-consuming and often computationally non-viable. To overcome this difficulty, techniques for generating minimal spanning trees (MSTs) of the graph that represents the smart grid, are exploited. However, existing studies provide centralized implementations. In this work, we propose a distributed implementation, where each of the network switch collaborates in developing of the recovery solution. The proposed decentralized approach seeks a reduction of reconfiguration time requirements, thus increasing network intelligence. For this purpose, a distributed algorithm for building the MST is embedded in the processing elements available at the commutation nodes. The evaluated case studies show that, whenever possible, the proposed solution allows for a successful reconfiguration, regardless of the number of simultaneous failures. Moreover, the network reconfiguration time is not significantly impacted by the number of buses and included lines. The implementation presents results of communication cost and reconfiguration time significantly lower than the expected upper bounds. Notably, for the case studies, the proposed implementation achieves 70% and 69% speedup regarding the reconfiguration time from simple and multiple failures, respectively, when compared to the expected theoretical performance. Furthermore, when compared to existing multiagent-based self-healing systems, the proposed implementation recovers from failures twice as fast, making it more desirable in a smart-grid real implementation.
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