TY - GEN
T1 - Robust reconfiguration of a distribution system
AU - Moradzadeh, Benyamin
AU - Liu, Guodong
AU - Tomsovic, Kevin
N1 - Publisher Copyright:
© 2017 Proceedings of the Annual Hawaii International Conference on System Sciences. All rights reserved.
PY - 2017
Y1 - 2017
N2 - In this paper, a robust reconfiguration approach based on Mixed Integer Programming (MIP) is proposed to minimize loss in distribution systems. A Depth-First Search (DFS) algorithm to enumerate possible loops provides radiality constraint. This provides a general solution to the radiality constraint for distribution system reconfiguration/expansion problems. Still, imprecision and ambiguity in net loads, i.e. load minus renewable generation, due to lack of sufficient measurements and high utilization of demand response programs and renewable resources, creates challenges for effective reconfiguration. Deterministic optimization of reconfiguration may no lead to optimal/feasible results. Two methods to address these uncertainties are introduced in this paper: one, based on a stochastic MIP (SMIP) formulation and two, based on a fuzzy MIP (FMIP) formulation. Case studies demonstrate the robustness and efficiency of the proposed reconfiguration methods.
AB - In this paper, a robust reconfiguration approach based on Mixed Integer Programming (MIP) is proposed to minimize loss in distribution systems. A Depth-First Search (DFS) algorithm to enumerate possible loops provides radiality constraint. This provides a general solution to the radiality constraint for distribution system reconfiguration/expansion problems. Still, imprecision and ambiguity in net loads, i.e. load minus renewable generation, due to lack of sufficient measurements and high utilization of demand response programs and renewable resources, creates challenges for effective reconfiguration. Deterministic optimization of reconfiguration may no lead to optimal/feasible results. Two methods to address these uncertainties are introduced in this paper: one, based on a stochastic MIP (SMIP) formulation and two, based on a fuzzy MIP (FMIP) formulation. Case studies demonstrate the robustness and efficiency of the proposed reconfiguration methods.
KW - Depth-First Search (DFS)
KW - Distribution System Reconfiguration (DSR)
KW - Fuzzy Mixed Integer Programming (FMIP)
KW - Stochastic Mixed Integer Programming (SMIP)
UR - http://www.scopus.com/inward/record.url?scp=85075536244&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85075536244
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
SP - 3222
EP - 3230
BT - Proceedings of the 50th Annual Hawaii International Conference on System Sciences, HICSS 2017
A2 - Bui, Tung X.
A2 - Sprague, Ralph
PB - IEEE Computer Society
T2 - 50th Annual Hawaii International Conference on System Sciences, HICSS 2017
Y2 - 3 January 2017 through 7 January 2017
ER -