Non-Iterative Enhanced SDP Relaxations for Optimal Scheduling of Distributed Energy Storage in Distribution Systems

Convexification of an optimal scheduling algorithm for distributed energy storage (DES) in radial distribution systems with high penetration of photovoltaic resources is studied. The AC power flow equalities are taken into account as constraints in the optimization model. Different from the typical...

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Bibliographic Details
Published in:IEEE transactions on power systems Vol. 32; no. 3; pp. 1721 - 1732
Main Authors: Qifeng Li, Vittal, Vijay
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Constraints
Convex relaxation
distributed energy storage (DES)
Energy distribution
Energy storage
Engineering
enhanced SDP (ESDP) relaxation
Linear programming
Load flow
Mathematical model
non-iterative
optimal dispatch
Optimal scheduling
Optimization models
Power flow
Radial distribution
Scheduling
Semidefinite programming
Three-dimensional displays
Tightness
ISSN:0885-8950, 1558-0679
Online Access:Get full text
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Summary:Convexification of an optimal scheduling algorithm for distributed energy storage (DES) in radial distribution systems with high penetration of photovoltaic resources is studied. The AC power flow equalities are taken into account as constraints in the optimization model. Different from the typical optimal power flow problem, the objective function of a DES optimal scheduling (DESOS) problem varies with changing operational requirements. In this paper, three frequently-used objective functions are considered for the DESOS problem. Two of them are monotonic over the feasible set while the third is not. An illustrative example elucidates that the descent direction of a chosen objective function significantly impacts the efficiency of the second-order cone programming (SOCP) relaxation for the DESOS problem. To obtain tighter semidefinite programming (SDP) relaxations for the DESOS cases where the SOCP relaxation is not exact, this paper looks for computationally efficient convex constraints that can approximate the rank-1 constraint in the non-iterative framework. The designed non-iterative enhanced SDP relaxations are compared in terms of tightness of convexification for the DESOS problems considering the three objective functions independently. The comparison is performed on several radial IEEE test systems and a real world distribution feeder.
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USDOE Office of Energy Efficiency and Renewable Energy (EERE)
EE0004679
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2016.2594298