A Distributionally robust AC Network-constrained unit commitment
- Authors: Dehghan, Shahab , Aristidou, Petros , Amjady, Nima , Conejo, Antonio
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE transactions on power systems Vol. 36, no. 6 (2021), p. 5258-5270
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- Description: This paper presents a distributionally robust network-constrained unit commitment (DR-NCUC) model considering AC network modeling and uncertainties of demands and renewable productions. The proposed model characterizes uncertain parameters using a data-driven ambiguity set constructed by training samples. The non-convex AC power flow equations are approximated by convex quadratic and McCormick relaxations. Since the proposed min-max-min DR-NCUC problem cannot be solved directly by available solvers, a new decomposition algorithm with proof of convergence is reported in this paper. The master problem of this algorithm is solved using both primal and dual cuts, while the max-min sub-problem is solved using the primal-dual hybrid gradient method, obviating the need for using duality theory. Also, an active set strategy is proposed to enhance the tractability of the decomposition algorithm by ignoring the subset of inactive constraints. The proposed model is applied to a 6-bus test system and the IEEE 118-bus test system under different conditions. These case studies illustrate the performance of the proposed DR-NCUC model to characterize uncertainties and the superiority of the proposed decomposition algorithm over other decomposition approaches using either primal or dual cuts.
Contrasting insect activity and decomposition of pigs and humans in an Australian environment : a preliminary study
- Authors: Dawson, Blake , Barton, Philip , Wallman, James
- Date: 2020
- Type: Text , Journal article
- Relation: Forensic Science International Vol. 316, no. (2020), p.
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- Description: Non-human vertebrate animals, primarily domestic pigs, have been widely used in forensic science research as analogues for humans due to ethical and logistical constraints. Yet the suitability of pigs to mimic human decomposition and entomological patterns remains largely untested, and explicit comparative research in this area is lacking. We compared the decomposition rates and insect communities found at pig and human remains during summer and winter at the Australian Facility for Taphonomic Experimental Research (AFTER). Pigs decomposed faster than humans, with pigs entering active decay earlier in both summer and winter, and humans undergoing desiccation rather than skeletonisation. There was also a delay in the colonisation of humans by both flies and beetles. Species richness of these necrophagous taxa was between two and five times higher during the first two weeks of decomposition on pigs compared to humans during both summer and winter. Insect species composition was also significantly different between pigs and humans in each season. We interpret our findings to mean that the difference between humans and pigs, such as their mass, diet, medical history, or their microbiomes, might be causing different decomposition processes and altered timing or production of chemical cues for insect colonisation. Although preliminary, our results suggest that pigs might not be accurate substitutes for humans in particular fields of taphonomy and forensic entomology. Our findings also have broader implications for the reliability of forensic studies using pigs as models for humans, and highlight the need to recognise intrinsic differences between animal models and humans. © 2020 Elsevier B.V.
- Description: This work was supported in part by the Australian Research Council ( LE150100015 ), as well as by a SMAH Small Project Grant ( University of Wollongong ).
Incorporating energy storage and demand response into intentional controlled islanding using time decomposition
- Authors: Ghamsari‐Yazdel, Mohammad , Najafi, Hamid , Amjady, Nima
- Date: 2020
- Type: Text , Journal article
- Relation: International transactions on electrical energy systems Vol. 30, no. 10 (2020), p. n/a
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- Description: Summary To improve electrical energy system resilience under catastrophic events, an efficient intentional controlled islanding (ICI) model is proposed in this article. The proposed remedial action relies on a new mixed integer linear programming (MILP) model which aims at minimizing the overall energy curtailment, power flow disruption, and generation and demand re‐dispatches through a cost‐based objective function. Another innovative characteristic of this model is demand response (DR) inclusion in the proposed ICI. To improve the balance between demand and supply of electricity, DR can be employed as an effective strategy in the ICI problem. In addition, another main original feature of the proposed model is considering energy storage units (ESUs) in each resulted island after the splitting process. To provide enough time for the system operator to re‐dispatch the islands and to improve frequency stability of islands, a charging/discharging scheme is proposed for ESUs during ICI. Moreover, a new time decomposition is proposed to accurately model the fast and slow corrective actions considering their interactions. Using this time decomposition, energy curtailments, considering their period durations, are treated as decision variables in the ICI problem to minimize involuntary load shedding as the most expensive corrective action. The results of scrutinizing the proposed ICI framework on the IEEE 118‐bus test system illustrate its performance. In addition, the results of the proposed ICI approach are compared with the results of other ICI models to illustrate the effectiveness of the new features of the proposed approach. To improve electrical energy system resilience under catastrophic events, an efficient intentional controlled islanding (ICI) model is proposed in this article. The proposed remedial action relies on a new mixed integer linear programming (MILP) model which aims at minimizing the overall energy curtailment, power flow disruption, and generation and demand re‐dispatches through a cost‐based objective function.
Dynamic soil nutrient and moisture changes under decomposing vertebrate carcasses
- Authors: Quaggiotto, Maria , Evans, Maldwyn , Higgins, Andrew , Strong, Craig , Barton, Philip
- Date: 2019
- Type: Text , Journal article
- Relation: Biogeochemistry Vol. 146, no. 1 (2019), p. 71-82
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- Description: The decomposition of animal carcasses contributes to nutrient recycling in ecosystems worldwide, including by delivering nutrients to soil. Although several studies have characterised changes in soil chemistry occurring under carcasses, many ecological studies have occurred over extended post-mortem intervals and fine-scale temporal changes in physicochemical conditions are poorly understood. We examined changes in a suite of soil physicochemical properties occurring under decomposing rabbit carcasses during summer in a grassland ecosystem. We found that carcasses lost over 90% of their starting mass and reached dry decay and skeletonization after 20 days of decomposition. Carcass temperatures were up to 15 °C higher than ambient temperatures during the active decay stage (days 3 and 5) of decomposition. Soil moisture also increased by day 4, and this was matched with a simultaneous increase in total nitrogen and ammonium, as well increases in pH and electrical conductivity. Whereas these measures remained relatively stable as decay progressed, we found total phosphorus and phosphate continued to increase to day 20. The contrasting dynamics of N and P reflect the initial nutrient and fluid input during the rapid decay of soft tissues and intense activity of fly larvae, and the subsequent dry decay and exposure of skeletal components. Our study provides new information about the fine-scale timing of nutrient inputs and moisture and temperature changes occurring at the carcass/soil interface. © 2019, Springer Nature Switzerland AG.
Network decomposition based large-scale reverse engineering of gene regulatory network
- Authors: Chowdhury, Ahsan , Chetty, Madhu
- Date: 2015
- Type: Text , Journal article
- Relation: Neurocomputing Vol. 160, no. (2015), p. 213-227
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- Description: A Gene Regulatory Network (GRN) is the functional circuitry of a living organism that exhibits the regulatory relationships among genes of a cellular system at the gene level. In real-life biological networks, the number of genes present are very large exhibiting both, the instantaneous and time-delayed regulations. While our recent technique [1] addresses the modeling of time-delays occurring in genetic interactions, the issue of large-scale GRN modeling still remains. In this paper, we propose a novel methodology for large-scale modeling of GRNs by decomposing the GRN into two independent sub-networks utilizing its biological traits. Using the time-delayed S-system model [1], these two sub-networks are learnt separately and then combined to get the entire GRN. To speed up the inference mechanism, a cardinality-based fitness function, especially developed for inferring large-scale GRNs is proposed to allow incorporation of knowledge of maximum in-degree. A novel local-search method is also proposed to further facilitate the incorporation of biological knowledge by gene clustering and gene ranking. Experimental studies demonstrate that the proposed approach is successful in learning large genetic networks, currently not achievable with existing S-system based modeling approaches.
Thermal decarboxylation of 2-furoic acid and its implication for the formation of furan in foods
- Authors: Varelis, Peter , Hucker, Barry
- Date: 2011
- Type: Text , Journal article
- Relation: Food Chemistry Vol. 126, no. 3 (2011), p. 1512-1513
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- Description: Letter to the editor