850 research outputs found
Multi-objective optimal battery placement in distribution networks
Due to high penetration of renewable energy resources in today\u27s electricity generation, considerable voltage fluctuations are witnessed in power systems. As an attempt to solve this issue, in this study, multi-objective optimal placement and sizing of distribution-level battery storage system is performed using semidefinite programing. Placement of one or multiple battery system is studied under various objectives including the cost, voltage regulation, reactive power dispatch, renewable resource curtailment, and minimum network power losses. Power flow equations are solved in the form of semidefinite constraints and the rank constraint is ignored. Additionally, combination of these objectives to form a multi-objective problem and regularization of the number of battery sites are studied. Finally, simulation results are provided to analyze the proposed formulation --Abstract, page iii
Exact Convex Relaxation of Optimal Power Flow in Radial Networks
The optimal power flow (OPF) problem determines power generation/demand that
minimize a certain objective such as generation cost or power loss. It is
nonconvex. We prove that, for radial networks, after shrinking its feasible set
slightly, the global optimum of OPF can be recovered via a second-order cone
programming (SOCP) relaxation under a condition that can be checked a priori.
The condition holds for the IEEE 13-, 34-, 37-, 123-bus networks and two
real-world networks, and has a physical interpretation.Comment: 32 pages, 10 figures, submitted to IEEE Transaction on Automatic
Control. arXiv admin note: text overlap with arXiv:1208.407
Convex Relaxation of Optimal Power Flow, Part I: Formulations and Equivalence
This tutorial summarizes recent advances in the convex relaxation of the
optimal power flow (OPF) problem, focusing on structural properties rather than
algorithms. Part I presents two power flow models, formulates OPF and their
relaxations in each model, and proves equivalence relations among them. Part II
presents sufficient conditions under which the convex relaxations are exact.Comment: Citation: IEEE Transactions on Control of Network Systems,
15(1):15-27, March 2014. This is an extended version with Appendices VIII and
IX that provide some mathematical preliminaries and proofs of the main
result
Deployment Strategies of Multiple Aerial BSs for User Coverage and Power Efficiency Maximization
Unmanned aerial vehicle (UAV) based aerial base stations (BSs) can provide
rapid communication services to ground users and are thus promising for future
communication systems. In this paper, we consider a scenario where no
functional terrestrial BSs are available and the aim is deploying multiple
aerial BSs to cover a maximum number of users within a certain target area. To
this end, we first propose a naive successive deployment method, which converts
the non-convex constraints in the involved optimization into a combination of
linear constraints through geometrical relaxation. Then we investigate a
deployment method based on K-means clustering. The method divides the target
area into K convex subareas, where within each subarea, a mixed integer
non-linear problem (MINLP) is solved. An iterative power efficient technique is
further proposed to improve coverage probability with reduced power. Finally,
we propose a robust technique for compensating the loss of coverage probability
in the existence of inaccurate user location information (ULI). Our simulation
results show that, the proposed techniques achieve an up to 30% higher coverage
probability when users are not distributed uniformly. In addition, the proposed
simultaneous deployment techniques, especially the one using iterative
algorithm improve power-efficiency by up to 15% compared to the benchmark
circle packing theory
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