6,385 research outputs found
Transitions from trees to cycles in adaptive flow networks
Transport networks are crucial to the functioning of natural and
technological systems. Nature features transport networks that are adaptive
over a vast range of parameters, thus providing an impressive level of
robustness in supply. Theoretical and experimental studies have found that
real-world transport networks exhibit both tree-like motifs and cycles. When
the network is subject to load fluctuations, the presence of cyclic motifs may
help to reduce flow fluctuations and, thus, render supply in the network more
robust. While previous studies considered network topology via optimization
principles, here, we take a dynamical systems approach and study a simple model
of a flow network with dynamically adapting weights (conductances). We assume a
spatially non-uniform distribution of rapidly fluctuating loads in the sinks
and investigate what network configurations are dynamically stable. The network
converges to a spatially non-uniform stable configuration composed of both
cyclic and tree-like structures. Cyclic structures emerge locally in a
transcritical bifurcation as the amplitude of the load fluctuations is
increased. The resulting adaptive dynamics thus partitions the network into two
distinct regions with cyclic and tree-like structures. The location of the
boundary between these two regions is determined by the amplitude of the
fluctuations. These findings may explain why natural transport networks display
cyclic structures in the micro-vascular regions near terminal nodes, but
tree-like features in the regions with larger veins
Distributed Stochastic Market Clearing with High-Penetration Wind Power
Integrating renewable energy into the modern power grid requires
risk-cognizant dispatch of resources to account for the stochastic availability
of renewables. Toward this goal, day-ahead stochastic market clearing with
high-penetration wind energy is pursued in this paper based on the DC optimal
power flow (OPF). The objective is to minimize the social cost which consists
of conventional generation costs, end-user disutility, as well as a risk
measure of the system re-dispatching cost. Capitalizing on the conditional
value-at-risk (CVaR), the novel model is able to mitigate the potentially high
risk of the recourse actions to compensate wind forecast errors. The resulting
convex optimization task is tackled via a distribution-free sample average
based approximation to bypass the prohibitively complex high-dimensional
integration. Furthermore, to cope with possibly large-scale dispatchable loads,
a fast distributed solver is developed with guaranteed convergence using the
alternating direction method of multipliers (ADMM). Numerical results tested on
a modified benchmark system are reported to corroborate the merits of the novel
framework and proposed approaches.Comment: To appear in IEEE Transactions on Power Systems; 12 pages and 9
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Robust Matrix Completion State Estimation in Distribution Systems
Due to the insufficient measurements in the distribution system state
estimation (DSSE), full observability and redundant measurements are difficult
to achieve without using the pseudo measurements. The matrix completion state
estimation (MCSE) combines the matrix completion and power system model to
estimate voltage by exploring the low-rank characteristics of the matrix. This
paper proposes a robust matrix completion state estimation (RMCSE) to estimate
the voltage in a distribution system under a low-observability condition.
Tradition state estimation weighted least squares (WLS) method requires full
observability to calculate the states and needs redundant measurements to
proceed a bad data detection. The proposed method improves the robustness of
the MCSE to bad data by minimizing the rank of the matrix and measurements
residual with different weights. It can estimate the system state in a
low-observability system and has robust estimates without the bad data
detection process in the face of multiple bad data. The method is numerically
evaluated on the IEEE 33-node radial distribution system. The estimation
performance and robustness of RMCSE are compared with the WLS with the largest
normalized residual bad data identification (WLS-LNR), and the MCSE
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