14,082 research outputs found
Chance Constrained Optimal Power Flow Using the Inner-Outer Approximation Approach
In recent years, there has been a huge trend to penetrate renewable energy
sources into energy networks. However, these sources introduce uncertain power
generation depending on environmental conditions. Therefore, finding 'optimal'
and 'feasible' operation strategies is still a big challenge for network
operators and thus, an appropriate optimization approach is of utmost
importance. In this paper, we formulate the optimal power flow (OPF) with
uncertainties as a chance constrained optimization problem. Since uncertainties
in the network are usually 'non-Gaussian' distributed random variables, the
chance constraints cannot be directly converted to deterministic constraints.
Therefore, in this paper we use the recently-developed approach of inner-outer
approximation to approximately solve the chance constrained OPF. The
effectiveness of the approach is shown using DC OPF incorporating uncertain
non-Gaussian distributed wind power
Two-Stage Subspace Constrained Precoding in Massive MIMO Cellular Systems
We propose a subspace constrained precoding scheme that exploits the spatial
channel correlation structure in massive MIMO cellular systems to fully unleash
the tremendous gain provided by massive antenna array with reduced channel
state information (CSI) signaling overhead. The MIMO precoder at each base
station (BS) is partitioned into an inner precoder and a Transmit (Tx) subspace
control matrix. The inner precoder is adaptive to the local CSI at each BS for
spatial multiplexing gain. The Tx subspace control is adaptive to the channel
statistics for inter-cell interference mitigation and Quality of Service (QoS)
optimization. Specifically, the Tx subspace control is formulated as a QoS
optimization problem which involves an SINR chance constraint where the
probability of each user's SINR not satisfying a service requirement must not
exceed a given outage probability. Such chance constraint cannot be handled by
the existing methods due to the two stage precoding structure. To tackle this,
we propose a bi-convex approximation approach, which consists of three key
ingredients: random matrix theory, chance constrained optimization and
semidefinite relaxation. Then we propose an efficient algorithm to find the
optimal solution of the resulting bi-convex approximation problem. Simulations
show that the proposed design has significant gain over various baselines.Comment: 13 pages, accepted by IEEE Transactions on Wireless Communication
Convex Relaxations and Approximations of Chance-Constrained AC-OPF Problems
This paper deals with the impact of linear approximations for the unknown
nonconvex confidence region of chance-constrained AC optimal power flow
problems. Such approximations are required for the formulation of tractable
chance constraints. In this context, we introduce the first formulation of a
chance-constrained second-order cone (SOC) OPF. The proposed formulation
provides convergence guarantees due to its convexity, while it demonstrates
high computational efficiency. Combined with an AC feasibility recovery, it is
able to identify better solutions than chance-constrained nonconvex AC-OPF
formulations. To the best of our knowledge, this paper is the first to perform
a rigorous analysis of the AC feasibility recovery procedures for robust
SOC-OPF problems. We identify the issues that arise from the linear
approximations, and by using a reformulation of the quadratic chance
constraints, we introduce new parameters able to reshape the approximation of
the confidence region. We demonstrate our method on the IEEE 118-bus system
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