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Flexibility services enable distribution system operators to actively manage the grid for accommodating demand and generation growth while potentially reducing or delaying investments in grid reinforcements. This paper proposes a novel hybrid particle swarm optimization and linear programming methodology that analyzes explicit flexibility procurement as an alternative to conventional network reinforcements in electricity distribution network planning. The distribution system planning problem is decomposed into a master problem and an inner problem. Binary particle swarm optimization (BPSO) is used to determine the optimal investment decisions, binary variables, from a set of candidate grid reinforcements in the master problem. At the inner linear programming optimization problem, a market-based procurement of flexibility services is performed. The inner optimization problem obtains the total cost of flexibility and the volume of flexibility at each network bus required to defer or avoid part of the grid reinforcements. A real 500-bus medium voltage network is used to validate the proposed methodology. Results illustrate cost-effective network plans that combine flexibility procurement with network reinforcements. A sensitivity to the cost and availability of flexibility services is also conducted to calculate the thresholds where flexibility becomes an efficient alternative to reinforcing the network.

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Abstract The incorporation of natural fibers into polymer matrices poses challenges due to physicochemical incompatibility, which is typically addressed through precursor modification or the use of compatibilizers. Here, we introduce a novel type of composite that overcomes this challenge by utilizing a network of fine, porous cellulosic sheets inter-diffused with a commercial paraffin films. This approach physically adheres the fiber network to the matrix, preserving its structure. Microscopy images confirm the formation of the proposed microstructure, and mechanical testing reveals a gradual increase in modulus and strength with the incorporation of cellulose. The maximum incorporation achieved was 7.6% (w/w) of cellulosic fibers, resulting in a 167% increase (1.67 times improved) in composite stiffness. Moreover, these composites exhibit ductility, with an average deformation of 410 ± 38%, corresponding to 20% reduction in relation to pure matrix. Our findings demonstrate the potential of this approach for developing sustainable materials with improved mechanical properties.

The installed capacity of PVs in the distribution grid is affected not only by network constraints, but also by the economic viability of the related investments. Depending on the market participation models, this is determined critically by the Day Ahead Market (DAM) prices. Increasing RES installations in a country usually results in a long term drop in the market prices and, as a consequence, a reduction in the income of the PVs investors and possible market cannibalization. This paper models the effect of large-scale penetration of PVs on the market prices and identifies the optimal penetration level for the viability of PV projects. The optimal penetration is highly related to the installation of new PVs and this is a parameter for the analysis. Therefore, the paper identifies different penetration costs for the different installation cost. Furthermore, the PV network hosing capacity can be increased by distribution network reinforcements. Therefore, in the paper, the investments for enhancement of the distribution grid are assessed with respect to market prices and are analyzed at the macroscopic level. Again, the analysis considers different costs for network reinforcements. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of the Chinese Ceramic Society is the property of Bulletin of the Chinese Ceramic Society Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We are motivated by the observation that for problems where inputs and outputs are in the same form such as in image enhancement, deep neural networks can be reinforced by retraining the network using a new target set to the output for the original target. As an example, we introduce a new learning strategy for super-resolution by recurrently training the same simple network. Unlike the existing self-trained SR, which involves a single stage of learning with multiple runs at test time, our method trains the same SR network multiple times with increasingly better targets requiring only a single inference at test time. At each stage of the proposed learning scheme, a new target for training is obtained by applying the most recently trained SR network to the original image and downscaling the resultant SR image to normalize the size. Even if downscaling is involved, we argue that the downscaled SR image acts as a better target compared to the old target. We could mathematically demonstrate that this process is similar to unsharp masking when it is linearly approximated and that this process makes the image sharper. However, unlike unsharp masking, the proposed recurrent learning tends to converge to a specific target. By retraining the existing network aiming at a more enhanced target, the proposed method can achieve a similar effect of applying SR multiple times without increasing implementation cost and inference time. To objectively verify the supremacy of our approach by experiments, we propose to use VIQET MOS, which does not require a reference image as a measure of image quality. As far as we know, our work of using an objective quality measure in image enhancement is the first one whose validity was verified by showing similar results to the actual user's subjective evaluation. The proposed recurrent learning scheme makes existing SR algorithms more useful by clearly improving the effect of SR. Codes are available at https://github.com/rtsr82/rtsr.git.

Abstract Background Drug repurposing has been motivated to ameliorate low probability of success in drug discovery. For the recent decade, many in silico attempts have received primary attention as a first step to alleviate the high cost and longevity. Such study has taken benefits of abundance, variety, and easy accessibility of pharmaceutical and biomedical data. Utilizing the research friendly environment, in this study, we propose a network-based machine learning algorithm for drug repurposing. Particularly, we show a framework on how to construct a drug network, and how to strengthen the network by employing multiple/heterogeneous types of data. Results The proposed method consists of three steps. First, we construct a drug network from drug-target protein information. Then, the drug network is reinforced by utilizing drug-drug interaction knowledge on bioactivity and/or medication from literature databases. Through the enhancement, the number of connected nodes and the number of edges between them become more abundant and informative, which can lead to a higher probability of success of in silico drug repurposing. The enhanced network recommends candidate drugs for repurposing through drug scoring. The scoring process utilizes graph-based semi-supervised learning to determine the priority of recommendations. Conclusions The drug network is reinforced in terms of the coverage and connections of drugs: the drug coverage increases from 4738 to 5442, and the drug-drug associations as well from 808,752 to 982,361. Along with the network enhancement, drug recommendation becomes more reliable: AUC of 0.89 was achieved lifted from 0.79. For typical cases, 11 recommended drugs were shown for vascular dementia: amantadine, conotoxin GV, tenocyclidine, cycloeucine, etc.

Since distributed energy resources (DERs) can provide capacity in distribution system, distribution network reinforcement interacts with DER planning (DERP) for meeting load growth. This article proposes the coordination of distribution network reinforcement and DER planning in competitive market. The model is formulated as a multi-stage programming. In first stage, distribution companies (DISCOs) and DER aggregators (DERAs) make bidding plans for network reinforcements and DERP. In second stage, DSO checks the network security. If the check fails, DSO sends capacity signals to DISCOs and DERAs for more capacities. In third stage, the distributed market is cleared by DSO’s optimal operation problem and distribution locational marginal price (DLMP) is returned to DERAs as price signal. An iterative algorithm is proposed to solve the multi-stage model. The algorithm embodies the process of information interaction among DISCOs, DERAs, and DSO, where DISCOs and DERAs compete in capacity market under DSO’s supervision. Numerical results on IEEE 33-system and an actual 141-bus system demonstrate that the proposed model coordinates distribution network reinforcement and DERP in competitive market. [ABSTRACT FROM AUTHOR]

In masonry buildings, walls are the main structural members that deal with lateral forces. The important point in seismic retrofitting projects is identify the most effective method of improving in various levels of displacement. Therefore, in this study based on laboratory results, 12 reinforced and unreinforced walls are subjected to cyclic load, and based on the optimum values for the calibration parameters modeling results with available experimental results, in first step, with change in the specification of reinforced and unreinforced walls, An estimate of the contribution of each component in the improvement of three displacement levels, displacement in the yield point, displacement corresponding to the maximum resistance and the final displacement is made. In the second step, with change in the thickness of the layer masonry materials in unreinforced brick walls and, with change in the thickness of the masonry, concrete, diameter rebar and the distance between the bars, in reinforced brick wall, analytical values are determined. Finally, in the third step the amounts proposed in first step is compared with the amounts extracted in step two that have been derived analytically. In unreinforced walls computing values for the share of masonry materials resistance in three levels of displacement is estimated at around 97%. In reinforced and unreinforced walls the most share in resistance in displacement of the yield point and displacement corresponding to the maximum resistance was for concrete. In the final displacement limit in one way reinforced walls, the most share is for resistance has for masonry material and in two way reinforced walls, the most share is for resistance has for reinforcement. Average computing error of the analytical values obtained from step two in the walls was, about 7.5% is obtained.

The integration of new types of consumers and prosumers into distribution networks presents significant challenges for network planners, especially given the uncertainties of future expansions. This paper introduces a method for distribution network planning that integrates a Long-Short Term Memory (LSTM) model with a confidence interval threshold for long-term scenario predictions. The proposed forecasting model considers datasets collected from monitoring systems which include aggregated demand and self-consumption from photovoltaic sources. The use of the LSTM model enables a more accurate prediction of expansions, as it captures nonlinear patterns in time series data while taking into account the inherent characteristics of non-stationary time series data. The proposed method was tested on an existing radial medium voltage distribution network in Spain, taking into account the operational thermal limits for lines and substation time series measurements. The proposed planning approach also considers asset costs, active planning solutions, and time-series load flow analysis. The results obtained indicate that the use of time-based forecasts from aggregated generation and demand is a more realistic solution for flexible planning solutions when they are compared to traditional strategies. • Planning methodology integrates LSTM model for long-term demand forecasting. • Assessment of the line and transformer loadings based on time series predictions. • Active and passive planning strategies are tested in a real MV network. • Economic assessment considering OPEX and CAPEX values. [ABSTRACT FROM AUTHOR]

Wear is the major mode of failure in machine components made out of polymer and its composites. The low thermal conductivity of the polymer causes heat accumulation near the contact surface and the resulting thermal softening accelerates the wear. In this research work, an attempt is made to enhance the thermal conductivity and wear resistance of the polymer composite using the network reinforcement. Two different composite specimens were prepared with an equal quantity of reinforcement with different geometric form - one with discrete particles and another with the interconnected network. The thermal conductivity and tribological properties of the composites were measured. In particle reinforced composite, the presence of low thermal conductive matrix between the discrete particles act as a barrier for heat conduction and does not enhance the thermal conductivity considerably. In the case of network reinforced composite, the three-dimensional continuity provides an effective heat transfer path and increases the thermal conductivity significantly. The reinforcement in the form of network reduces the wear significantly compared to discrete particle form. The struts in the network reinforcements enhance the strength and arrest the removal of material from the surface.

Traditional expansion planning models reflect usage‐based practices and do not consider the complete utilisation of existing resources. This problem arises due to non‐inclusion of price‐based incentives provided to the consumers who incur additional network reinforcement or expansion cost. The paper proposes a generation and transmission expansion planning framework that includes price‐based incentives to define the future nodal load growth. The framework implements long‐run incremental cost (LRIC)‐based pricing signals for economically distributed demand to minimise consumer usage charges as well as network investment. Peak demand conditions by consumers are further minimised by the time‐of‐use‐based demand response mechanism. The combined effects of price‐based response and demand response are reflected in the future demand which serves as the basis for combined generation and transmission expansion planning. The case studies highlight the drawbacks of the conventional approaches which does not maximise the utilisation of existing grid resources while the proposed framework results in delayed investment strategies as a collective effort of the users and planner to optimise grid resources and the cost of assessing the electricity. [ABSTRACT FROM AUTHOR]

The decoration of sacrificial units (SUs) into rubber network has gained momentum as a non-filling alternative toward efficient network reinforcement. However, most established methods, including the copolymerization with SUs-containing monomers or the post-modification of rubber backbone, still suffer from the multi-step and time-consuming backbone preparation with poor designability. In this work, we proposed a facile and practical strategy to achieve efficient network reinforcement through in - situ decorating isoprene rubber (IR) network with N -carbamyl maleamic acid (NCMA) as SUs. Model compound vulcanization experiment demonstrated that during sulfur vulcanization, the poly/disulfides in active sulfurating agents or crosslinking precursors can cleave into reactive sulfur radicals, which subsequently proceeded radical addition reaction with the unsaturated NCMA. The decorated carbamyl maleamic handles can spontaneously form multiple hydrogen-bonding linkages to serve as SUs. Upon deformation, the reversible detachment/reattachment of the SUs will smooth out the local stress concentration, but also facilitate the orientation of network chains. Therefore, as the NCMA loading increases, the onset of strain-induced crystallization and the overall crystallinity of the obtained IR network are simultaneously promoted, giving rise to a gradual increase in the tensile modulus (∼80 %) of the system. By virtue of the rich variety, commercial availability and facile structural tunability of maleamic acid-derivatives, we envisage that the present study can open up a novel strategy to achieve in - situ decoration of rubber with various functional handles toward efficient network reinforcement and functionalization. [Display omitted] • Poly/disulfide radicals can undergo addition reaction with NCMA during vulcanization. • The decorated NCMA handles can form hydrogen-bonding linkages in the network. • The obtained network exhibit superior crystallization and mechanical performance. [ABSTRACT FROM AUTHOR]

In power systems today, considerable developments are being made in the energy transition from centralized fossil fuels to renewable distributed generation (DG) sources. However, this ongoing progress has presented several challenges to the operation and planning of distribution systems due to the variability of intermittent renewable generation. Demand response (DR) is widely regarded as a feasible tool to provide a seamless transition by altering the load profiles according to the intermittent generation profile. However, the resultant volatile power flows can be taxing for network capacity. This paper offers a framework from the distribution system operator's perspective for the optimal utilization of DR between DG curtailment mitigation and network management. The application of the developed framework to a generic Finnish distribution system demonstrates that the benefits of DR should be envisioned for network management as long as the wind curtailment rate is below a certain level. This means that, beyond the threshold energy curtailment rate, the distribution system operator would be more economically efficient by making network reinforcements. [ABSTRACT FROM PUBLISHER]

Nowadays, there is a wide consensus about integrating more renewable energy sources-RESs to solve a multitude of global concerns such as meeting an increasing demand for electricity, reducing energy security and heavy dependence on fossil fuels for energy production, and reducing the overall carbon footprint of power production. Framed in this context, the coordination of RES integration with energy storage systems (ESSs), along with the network’s switching capability and/or reinforcement, is expected to significantly improve system flexibility, thereby increasing the capability of the system in accommodating large-scale RES power. Hence, this paper presents a novel mechanism to quantify the impacts of network switching and/or reinforcement as well as deployment of ESSs on the level of renewable power integrated in the system. To carry out this analysis, a dynamic and multi-objective stochastic mixed integer linear programming (S-MILP) model is developed, which jointly takes the optimal deployment of RES-based DGs and ESSs into account in coordination with distribution network reinforcement and/or reconfiguration. The IEEE 119-bus test system is used as a case study. Numerical results clearly show the capability of ESS deployment in dramatically increasing the level of renewable DGs integrated in the system. Although case-dependent, the impact of network reconfiguration on RES power integration is not significant. [ABSTRACT FROM AUTHOR]

Hypergraphic matroids were studied first by Lorea [23] and later by Frank et al. [11]. They can be seen as generalizations of graphic matroids. Here we show that several algorithms developed for the graphic case can be extended to hypergraphic matroids. We treat the following: the separation problem for the associated polytope, testing independence, separation of partition inequalities, computing the rank of a set, computing the strength, computing the arboricity and network reinforcement. [ABSTRACT FROM AUTHOR]

With anticipated proliferation of electric vehicles and distributed generations in near future, dynamic thermal rating (DTR) as a tool for unlocking network capacities, is becoming critical for distribution network operators. DTR is gaining a great and still growing focus of attention in today’s power industries. However, potential benefits of DTR, although have been envisioned to be significant, have not yet been studied quantitatively. This paper intends to comprehensively assess the potential impacts of DTR on the performance of a realistic Finnish distribution network. For doing so, first a step-by-step procedure is devised. Then, weather data and loading information of circuits in the network are gathered by advanced metering infrastructure available in the network. Thereafter, the gathered data are fed to mathematical models provided by the IEEE Standard 738-2006 and component dynamic thermal models to give thermal rating of circuits. The ratings are then used in reliability analysis where likely system states are simulated. This paper is performed for several cases and the results are discussed. [ABSTRACT FROM PUBLISHER]

• Advantages of smart grids to flatten the demand curves through control of DSM programmes, EV changing or optimization of other energy resources. • Smart grids increase the potential of investment postponement in grid reinforcement, but with the side effect of increasing losses because of exploiting the grid closer to its maximum capacity. • The lowest global (long-term) cost depends not only on the ability to control the available energy resources (Smart Grid Environment, Business as Usual), but also on the discount rate, energy cost and upgrade trigger. • Investment postponement might give the illusion of savings in the short-term, but the long-term cost should not be underrated. The last decade has witnessed a growing tendency to promote deeper exploitation of power systems infrastructure, postponing investments in networks reinforcement. In particular, the literature on smart grids research often emphasizes their potential to defer investments. The study reported in this paper analyses the impact of reinforcement decisions, comparing the long-term costs associated with different network conditions and economic analysis parameters. The results support the conclusion that network reinforcement deferral is not a panacea, as it often generates costly situations in the long-term. The challenge is not to find new ways to postpone investments, but to find the most beneficial criterion to trigger the grid reinforcements actions. Another contribution of the present work is a decision support system to identify the most economical network reinforcement criterion in terms of the peak to capacity ratio. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a strategy is proposed in order to introduce in a realistic way wind generation into a transmission power system non sequential Monte Carlo adequacy study with economic dispatch. Thanks to the implemented solution, wind generation is consequently confronted to operational constraints related to high powered thermal units, nuclear parks or thermal machines with technical minimum value. Moreover, during each simulated system state, a DC load flow is also calculated in order to evaluate reinforcements optimizing the large scale integration of wind power production. The simulation tool modified during the present work is called Scanner© and is the property of Tractebel Engineering (Gaz de France – Suez) company. It has been here applied to an academic test system: the Roy Billinton Test System (RBTS). [ABSTRACT FROM AUTHOR]

Fuel cells are static energy conversion devices that convert the chemical energy of fuel into electrical energy directly. Compared with conventional power generation systems, they have many advantages, such as high efficiency, zero or low emission (of pollutant gases), and flexible modular structure. Fuel cells are expected to play an important role in future distributed generation (DG) applications. Fuel cell DGs (FCDGs) can either be connected to a utility grid for network reinforcement or installed in a remote area to supply stand-alone power. In this article, a load transient mitigation controller is described for hybrid PEMFC-battery power generation systems. The hybrid FCDG is controlled in a way that during a load transient, the fuel cell provides the steady-state part of the load, and the battery will supply the transient part of the load. The proposed technique can be especially useful in fuel cell vehicles (motor starting and accelerating) as well as in stationary applications, where load transients are unavoidable. The load transient mitigation controller is applicable to other types of FCDGs [ABSTRACT FROM PUBLISHER]

We give an algorithm for the following problem: given a graph G=( V, E) with edge-weights and a nonnegative integer k, find a minimum cost set of edges that contains k disjoint spanning trees. This also solves the following reinforcement problem: given a network, a number k and a set of candidate edges, each of them with an associated cost, find a minimum cost set of candidate edges to be added to the network so it contains k disjoint spanning trees. The number k is seen as a measure of the invulnerability of a network. Our algorithm has the same asymptotic complexity as | V| applications of the minimum cut algorithm of Goldberg & Tarjan. [ABSTRACT FROM AUTHOR]

Silica aerogel is fabricated through replacement of solvent by the air, so that this stage is of high relevance to achieve appropriate physical properties. Although, evaporation under ambient pressure is considered as simplest drying technique, but leads to irreversible shrinkage and collapse of gel network. Here, we improve the efficiency of ambient pressure drying via increasing the strength of gel network through physically reinforcement of the gel network using recycled polyethylene terephthalate (rPET) fibers. The polymeric phase of rPET was then removed from structure using thermal treatment. Also, the effect of using four different sonication time, from 5 to 35 min, on enhancement of aerogel properties was also investigated. the prepared silica aerogels exhibit a high surface area and porosity and using more sonication time was led to better properties. According to results, surface area and porosity were increased from 593 m2/g and 93% up to 691 m2/g and 96% by enhancing sonication time from 5 min to 35 min, respectively. The reason is thought to be related to create further dispersion among silanol groups and fibers surface. As well, the fabricated silica aerogels showed mesopores structure along with a narrow and uniform pore size. These results indicate the high efficiency of proposed method for synthesize of silica aerogel. Improvement of the ambient pressure drying of wet gel to produce the silica aerogel via increasing the strength of gel network by recycled PET fibers reinforcement. [Display omitted] • Development of a cost-effective and eco-friendly approach to silica aerogel preparation based on ambient pressure drying. • Improve the efficiency of drying through reinforcing the gel network using embedding the PET fibers into gel matrix. • Investigate the effect of ultrasonic and dispersion degree on aerogel properties using two different sonication time. • Obtaining the high specific surface area, low density and high porosity based on the proposed approach. [ABSTRACT FROM AUTHOR]

Enhancement on the mechanical properties of hydrogels leads to a wider range of their applications in various fields. Therefore, there has been a great interest recently for developing new strategies to reinforce hydrogels. Moreover, food gels must be edible in terms of both raw materials and production. This paper reviews innovative techniques such as particle/fiber-reinforced hydrogel, double network, dual crosslinking, freeze-thaw cycles, physical conditioning and soaking methods to improve the mechanical properties of hydrogels. Additionally, their fundamental mechanisms, advantages and disadvantages have been discussed. Important biopolymers that have been employed for these strategies and also their potentials in food applications have been summarized. The general mechanism of these strategies is based on increasing the degree of crosslinking between interacting polymers in hydrogels. These links can be formed by adding fillers (oil droplets or fibers in filled gels) or cross-linkers (regarding double network and soaking method) and also by condensation or alignment of the biopolymers (freeze-thaw cycle and physical conditioning) in the gel network. The properties of particle/fiber-reinforced hydrogels extremely depend on the filler, gel matrix and the interaction between them. In freeze-thaw cycles and physical conditioning methods, it is possible to form new links in the gel network without adding any cross-linkers or fillers. It is expected that the utilization of gels will get broader and more varied in food industries by using these strategies. Unlabelled Image • Creative strategies have been developed to enhance the mechanical properties of gels. • Discussed the mechanism of suggested methods for reinforcing viscoelasticity. • Overviewed the pros and cons of techniques and their practical examples for food applications. • Summarized biopolymers that have been used in these methods. [ABSTRACT FROM AUTHOR]

Background: Drug repurposing has been motivated to ameliorate low probability of success in drug discovery. For the recent decade, many in silico attempts have received primary attention as a first step to alleviate the high cost and longevity. Such study has taken benefits of abundance, variety, and easy accessibility of pharmaceutical and biomedical data. Utilizing the research friendly environment, in this study, we propose a network-based machine learning algorithm for drug repurposing. Particularly, we show a framework on how to construct a drug network, and how to strengthen the network by employing multiple/heterogeneous types of data. Results: The proposed method consists of three steps. First, we construct a drug network from drug-target protein information. Then, the drug network is reinforced by utilizing drug-drug interaction knowledge on bioactivity and/or medication from literature databases. Through the enhancement, the number of connected nodes and the number of edges between them become more abundant and informative, which can lead to a higher probability of success of in silico drug repurposing. The enhanced network recommends candidate drugs for repurposing through drug scoring. The scoring process utilizes graph-based semi-supervised learning to determine the priority of recommendations. Conclusions: The drug network is reinforced in terms of the coverage and connections of drugs: the drug coverage increases from 4738 to 5442, and the drug-drug associations as well from 808,752 to 982,361. Along with the network enhancement, drug recommendation becomes more reliable: AUC of 0.89 was achieved lifted from 0.79. For typical cases, 11 recommended drugs were shown for vascular dementia: amantadine, conotoxin GV, tenocyclidine, cycloeucine, etc. [ABSTRACT FROM AUTHOR]

This paper presents a time sequential simulation technique incorporating the effects of weather conditions and restoration resources in reliability cost/worth evaluation of distribution systems. Time varying weight factors (TVWF) are introduced to represent the effects on component failure rates and restoration times of weather and available restoration resources. The average failure rate is combined with the TVWF to create time varying failure rates (TVFR) for each component. The average restoration time is combined with the TVWF to create time varying restoration times (TVRT). Studies conducted in a test distribution system show that the TVFR have large impacts on the interruption costs of frequency sensitive customers and slight effects on others. The TVRT have significant effects on the indices for all the customers. It is therefore important to consider TVRT in evaluating reliability cost/worth of network reinforcement. [ABSTRACT FROM PUBLISHER]