Dynamic Effects Increasing Network Vulnerability to Cascading Failures

We study cascading failures in networks using a dynamical flow model based on simple conservation and distribution laws to investigate the impact of transient dynamics caused by the rebalancing of loads after an initial network failure (triggering event). It is found that considering the flow dynamics may imply reduced network robustness compared to previous static overload failure models. This is due to the transient oscillations or overshooting in the loads, when the flow dynamics adjusts to the new (remaining) network structure. We obtain {\em upper} and {\em lower} limits to network robustness, and it is shown that {\it two} time scales $\tau$ and $\tau_0$, defined by the network dynamics, are important to consider prior to accurately addressing network robustness or vulnerability. The robustness of networks showing cascading failures is generally determined by a complex interplay between the network topology and flow dynamics, where the ratio $\chi=\tau/\tau_0$ determines the relative role of the two of them.Comment: 4 pages Latex, 4 figure

Day-ahead allocation of operation reserve in composite power systems with large-scale centralized wind farms

This paper focuses on the day-ahead allocation of operation reserve considering wind power prediction error and network transmission constraints in a composite power system. A two-level model that solves the allocation problem is presented. The upper model allocates operation reserve among subsystems from the economic point of view. In the upper model, transmission constraints of tielines are formulated to represent limited reserve support from the neighboring system due to wind power fluctuation. The lower model evaluates the system on the reserve schedule from the reliability point of view. In the lower model, the reliability evaluation of composite power system is performed by using Monte Carlo simulation in a multi-area system. Wind power prediction errors and tieline constraints are incorporated. The reserve requirements in the upper model are iteratively adjusted by the resulting reliability indices from the lower model. Thus, the reserve allocation is gradually optimized until the system achieves the balance between reliability and economy. A modified two-area reliability test system (RTS) is analyzed to demonstrate the validity of the method.This work was supported by National Natural Science Foundation of China (No. 51277141) and National High Technology Research and Development Program of China (863 Program) (No. 2011AA05A103)

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

PrP is a central player in toxicity mediated by soluble aggregates of neurodegeneration-causing proteins

Neurodegenerative diseases are an enormous public health problem, affecting tens of millions of people worldwide. Nearly all of these diseases are characterized by oligomerization and fibrillization of neuronal proteins, and there is great interest in therapeutic targeting of these aggregates. Here, we show that soluble aggregates of α-synuclein and tau bind to plate-immobilized PrP in vitro and on mouse cortical neurons, and that this binding requires at least one of the same N-terminal sites at which soluble Aβ aggregates bind. Moreover, soluble aggregates of tau, α-synuclein and Aβ cause both functional (impairment of LTP) and structural (neuritic dystrophy) compromise and these deficits are absent when PrP is ablated, knocked-down, or when neurons are pre-treated with anti-PrP blocking antibodies. Using an all-human experimental paradigm involving: (1) isogenic iPSC-derived neurons expressing or lacking PRNP, and (2) aqueous extracts from brains of individuals who died with Alzheimer's disease, dementia with Lewy bodies, and Pick's disease, we demonstrate that Aβ, α-synuclein and tau are toxic to neurons in a manner that requires PrPC. These results indicate that PrP is likely to play an important role in a variety of late-life neurodegenerative diseases and that therapeutic targeting of PrP, rather than individual disease proteins, may have more benefit for conditions which involve the aggregation of more than one protein