Forecasted output of wind electric generators (WEGs) in a 24-h horizon has large uncertainties. These uncertainties pose a challenge while computing optimal bids necessary for participating in the day-ahead unit commi...
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Forecasted output of wind electric generators (WEGs) in a 24-h horizon has large uncertainties. These uncertainties pose a challenge while computing optimal bids necessary for participating in the day-ahead unit commitment process (DACP) thus limiting their integration and success. This study proposes a new optimal participation strategy for a WEG that employs an energy storage device (ESD) for participating in the DACP. The WEG is modelled to function as a price-taker. The proposed formulation has two objectives: (a) maximise returns from the market considering the best forecast;and (b) minimise risks considering the forecast uncertainties. Risk in the participation strategy is quantified by computing expected energy not served (EENS). The multiobjective mixed integer linear programming formulation is transformed into a fuzzy optimisation problem and solved. Through suitable examples, the ESD is shown to play two important roles: (a) it helps to shift wind energy produced during hours with low marginal prices to those hours with higher marginal prices by appropriately storing and releasing it. This shift can be forward or backward in time. (b) The second crucial role played by ESD, upon minimising EENS, is to maintain an energy reserve akin to spinning reserve such that the risk of the optimal participation schedule is the least.
Construction of signaling pathway maps and identification of drug effects are major challenge for pharmaceutical industries. Signaling maps are usually obtained from manual literature search, automated text mining alg...
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ISBN:
(纸本)9781424441242
Construction of signaling pathway maps and identification of drug effects are major challenge for pharmaceutical industries. Signaling maps are usually obtained from manual literature search, automated text mining algorithms, or canonical pathway databases (i.e. Reactome, KEGG, STKE, Pathway Studio, Ingenuity etc.) and in some cases they are used in combination with gene expression or mass spec data in an effort to create pathways specific to cell types or diseases. Our approach combines computational models with novel multicombinatorial high-throughput phosphoproteomic data for the functional analysis of signalling networks in mammalian cells. On the experimental front, we subject the cells with hundreds of co-treatment with a diverse set of ligands and inhibitors and we measure phosphorylation events on key signaling proteins using the xMAP technology. On the computational front, we create pathway maps that are cell type specific by fitting our phosphoprotein dataset into generic signaling maps via an integer linear programming formulation. To identify drug effects, we monitor the differences of topologies created with and without the presence of drug. In the present work, we use this approach to identify the effects of Nilotinib, a well known anti-cancer drug.
Background: In addition to component-based comparative approaches, network alignments provide the means to study conserved network topology such as common pathways and more complex network motifs. Yet, unlike in class...
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Background: In addition to component-based comparative approaches, network alignments provide the means to study conserved network topology such as common pathways and more complex network motifs. Yet, unlike in classical sequence alignment, the comparison of networks becomes computationally more challenging, as most meaningful assumptions instantly lead to NP-hard problems. Most previous algorithmic work on network alignments is heuristic in nature. Results: We introduce the graph-based maximum structural matching formulation for pairwise global network alignment. We relate the formulation to previous work and prove NP-hardness of the problem. Based on the new formulation we build upon recent results in computational structural biology and present a novel Lagrangian relaxation approach that, in combination with a branch-and-bound method, computes provably optimal network alignments. The Lagrangian algorithm alone is a powerful heuristic method, which produces solutions that are often near-optimal and - unlike those computed by pure heuristics - come with a quality guarantee. Conclusion: Computational experiments on the alignment of protein-protein interaction networks and on the classification of metabolic subnetworks demonstrate that the new method is reasonably fast and has advantages over pure heuristics. Our software tool is freely available as part of the LISA library.
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