Open AccessMILP‐based technique for smart self‐healing grids
Author(s) -
Al Owaifeer Maad,
AlMuhaini Mohammad
Publication year - 2018
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2017.1844
Subject(s) - smart grid , control reconfiguration , computer science , linear programming , mathematical optimization , integer programming , distributed computing , electric power system , grid , reliability engineering , power (physics) , engineering , embedded system , algorithm , mathematics , electrical engineering , physics , geometry , quantum mechanics
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.