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Office of Scientific and Technical Information
 

Optimization of Distribution Feeder Topology: A Differential Programming Learning Approach

Conference ·
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This paper presents a gradient based method for optimizing distribution feeder network topology under load un- certainty. We recast the optimal network reconfiguration problem as a learning problem where edge weights of a graph are learned to produce an optimized spanning tree for a distribution network. Using recent methods published on differentiable programming, we provide a data driven method for learning these weights. We test our method on 100 variations of an IEEE 15-bus test system. Our results show that our method outperforms more traditional mathematical programming-based approaches.

Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program
DOE Contract Number:
AC36-08GO28308
OSTI ID:
2229088
Report Number(s):
NREL/CP-2C00-88233; MainId:89008; UUID:58de6295-c57f-4501-809c-db88beace34f; MainAdminID:71245
Resource Relation:
Conference: Presented at the the 2023 IEEE Power & Energy Society General Meeting (PESGM), 16-20 July 2023, Orlando, Florida
Country of Publication:
United States
Language:
English

References (11)

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On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem February 1956
Variable Scaling Hybrid Differential Evolution for Solving Network Reconfiguration of Distribution Systems May 2005
Efficient Network Reconfiguration Using Minimum Cost Maximum Flow-Based Branch Exchanges and Random Walks-Based Loss Estimations February 2011
An Efficient Mathematical Model for Distribution System Reconfiguration Using AMPL January 2021
Convex Models of Distribution System Reconfiguration February 2012

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Related Subjects

MATHEMATICS AND COMPUTING
differentiable programming
optimal network reconfiguration
stochastic programming