Self‐healing optimization in active distribution network to improve reliability, and reduction losses, switching cost and load shedding

Summary Self‐healing refers to the specific ability of the smart grid that takes precautionary actions before the fault. It also performs fault location, isolation, and service restoration to minimize network damage after the fault. This process is performed with the help of communications infrastru...

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Vydáno v:International transactions on electrical energy systems Ročník 30; číslo 5
Hlavní autoři: Choopani, Keyvan, Hedayati, Mahdi, Effatnejad, Reza
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken John Wiley & Sons, Inc 01.05.2020
Témata:
Algorithms
Distributed generation
Electrical loads
Fault location
Graph theory
Healing
improve reliability
Integer programming
Linear programming
Load distribution
Load shedding
loss reduction
Mixed integer
Network reliability
Nonlinear programming
Nonlinearity
Optimization algorithms
Particle swarm optimization
Power loss
Remote control
Rescheduling
restoration
self‐healing
Service restoration
Smart grid
Solvers
Stress concentration
Switching
ISSN:2050-7038, 2050-7038
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Shrnutí:Summary Self‐healing refers to the specific ability of the smart grid that takes precautionary actions before the fault. It also performs fault location, isolation, and service restoration to minimize network damage after the fault. This process is performed with the help of communications infrastructure and remote control. The main components of this article include the use of graph theory and binary particle swarm optimization algorithm for optimal switching, the use of particle swarm optimization algorithm and forward‐backward sweep load flow for load shedding and distributed generation rescheduling, investigating the effect of self‐healing on improved reliability, power losses reduction, and load shedding reduction and the present a new approach for self‐healing optimization and a new mathematical model. The problem of self‐healing due to nonlinear and unbounded constraints is one of the nonconvex mixed integer nonlinear programming optimization problems. The optimization problem becomes a convex mixed integer linear programming optimization problem using the proposed scheme. Due to the nonlinearity and the nonconvexity of the problem, commercial solvers are not able to solve this problem. Even the Baron solver, which is used to solve nonconvex nonlinear problems, cannot achieve the optimal global solution. However, the proposed method is easily able to achieve the optimal solution by eliminating the nonlinear and nonconvex element. A modified RBTS‐4 distribution system is used as a test system. The IEEE 33‐bus distribution system is used to validate the proposed method. The results of case studies demonstrate the effectiveness of the proposed methodology.
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ISSN:2050-7038
2050-7038
DOI:10.1002/2050-7038.12348