MILP-based technique for smart self-healing grids
The development of smart grids has offered many technical solutions that can increase the reliability and resilience of distribution systems. Self-healing is an important characteristic of smart grids, as it pertains to the capability of the grid to isolate and restore the system, or part of it, to...
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| Vydané v: | IET generation, transmission & distribution Ročník 12; číslo 10; s. 2307 - 2316 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
The Institution of Engineering and Technology
29.05.2018
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| Predmet: | |
| ISSN: | 1751-8687, 1751-8695 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | The development of smart grids has offered many technical solutions that can increase the reliability and resilience of distribution systems. Self-healing is an important characteristic of smart grids, as it pertains to the capability of the grid to isolate and restore the system, or part of it, to its normal operation following interruptions. This is achieved by adopting advanced monitoring and control systems and utilising all local available distributed sources. In this study, a smart self-healing optimisation strategy for smart grids is proposed. The proposed technique considers several factors, including the available power supply, system configuration, and load management. Moreover, a load prioritisation model is presented and incorporated into the proposed technique. The self-healing strategy is formulated as a mixed-integer linear programming problem, which is solved mathematically, ensuring global optimality of the solution. The strategy is tested by applying it to 16-bus and 33-bus smart grid systems. Further, the proposed formulation is utilised to solve the reconfiguration for the loss-minimisation problem for a 69-bus system. The simulation results indicate the capability of the proposed strategy in providing the optimal network configuration, optimal distributed generators output, and optimal load curtailment with remarkable accuracy and computational time. |
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| ISSN: | 1751-8687 1751-8695 |
| DOI: | 10.1049/iet-gtd.2017.1844 |