Detailed Network Information Research Articles

Optimal aggregation and disaggregation for coordinated operation of virtual power plant with distribution network operator

Virtual power plants (VPPs) offer an effective approach for managing distributed energy resources (DERs), including microturbines, distributed generators, demand response aggregators, and energy storage systems. This technology significantly enhances the economic efficiency and flexibility of distribution network systems. This study aims to facilitate a flexible power exchange between the distribution network system and the upper-level grid by aggregating the power flexibility of heterogeneous DERs via a VPP. Additionally, it introduces a methodology for optimal aggregation and disaggregation within a coordinated operation framework between the VPP and distribution network operator (DNO). On one hand, the VPP can determine its day-ahead feasible region and real-time flexible regulation power based on operational constraints of DERs and representative data provided by the DNO, circumventing the need for detailed network information. On the other hand, day-ahead and real-time correction procedures for the DER cost functions are proposed, effectively neutralizing the impact of network operational constraints on these cost functions. Consequently, precise cost functions for both the active power and the flexible regulation power aggregated by the VPP are derived. Employing this aggregated cost function enables the determination of a cost-minimized optimal scheduling solution in real-time by solving a fundamental economic dispatch problem, significantly alleviating computational demands. Finally, case studies demonstrate that the proposed method achieves an error in aggregated power of only 0.77%, compared to the precise computation method that requires comprehensive system information. The proposed optimal VPP disaggregation scheme exhibits power discrepancies of 0.63% and cost discrepancies of 0.91% relative to the precise method. Additionally, when implementing the most cost-effective demand response plan based on the proposed cost function, the average costs for aggregators are reduced by 19.7%.

Calibration and analysis of the potable water network in the Al-Yarmouk region employing WaterGEMS and GIS

Abstract Water supply and distribution networks play an important role in our daily activities. They make a substantial contribution to public health by providing potable water for public consumption and non-potable applications such as firefighters and other purposes such as irrigation. This study used ArcMap 10.8 and WaterGEMS CONNECT Edition update 1 version to create a hydraulic network model to simulate the pipes’ network. Detailed network information, including pipe lengths, layouts, and diameters, was given by the Baghdad Water Department. The TUF-2000H Handheld digital ultrasonic flow meter has been used to measure the water flows in the network’s source nodes. In eight junctions, the model was calibrated by measuring the pressures using the Bourdon gauge at selected junctions. The analysis was based on the steady-state time at the average demand. The analysis results showed that the pressures in the network ranged from 8 to 21 m H2O with a correlation coefficient of 0.988, with a noticeable decrease in pressures in the distant pipes from the sources supplying water to the network. The velocity of the main pipes was within acceptable limits 0.5–2 m/s. While for the internal distribution network, it was noticed that there is an increase in velocity for the main pipes due to low consumption in the lateral pipes.

Optimal valid path prediction method for inter-domain networks considering commercial relationships*

In the security research of large-scale inter-domain networks, it is often necessary to consider network state changes under the condition of failure or attack. In this case, the connection between ASes often switches between disconnection and recovery frequently, requiring frequent prediction of the AS-level path. Previous AS-level path prediction methods generally require detailed network information data and are complicated to calculate, which is difficult to meet the needs of such large-scale dynamic computation. In this paper, an optimal valid path prediction method for inter-domain networks considering commercial relations is proposed. This method predicts the optimal valid path according to the commercial relationships between ASes, which requires less network information data and low computational complexity. Through experiments, it is found that compared with the shortest path method commonly used in the security simulation of large-scale inter-domain networks, the prediction results are closer to the actual path of the network and can better reflect the actual path situation of the inter-domain network with lower computational complexity.

PSMART: A lightweight, privacy-aware service function chain orchestration in multi-domain NFV/SDN

Service Function Chaining (SFC) has become an integral part of every emerging application to satisfy its specific demand. Due to the massive increase in such diverse applications, there is a need for an efficient solution for flexible SFC orchestration. In this context, deploying SFC across domains can be a promising way to provide the required network services through cost-effective resource utilization. However, the operation of Multi-Domain SFC (MD-SFC) mainly has a trade-off between privacy and performance. While sharing the detailed network information, such as topology and resource availability, is important for the efficient MD-SFC orchestration, the network operators are not willing to disclose such private and detailed network information about the domain network. As a result, there is always a higher demand for privacy preservation while employing efficient MD-SFC orchestration. In this paper, we present pSMART, a lightweight, privacy-aware service function chain orchestration in a multi-domain NFV/SDN scenario. The main purpose of pSMART is to utilize less sensitive information, to reduce privacy and security risks, and to employ learning based decision making for efficient MD-SFC mapping. We quantify and analyse the gain of the proposed pSMART approach in terms of privacy-preservation and response time. The simulation results show that the proposed pSMART approach achieves a higher level of privacy and optimizes the response time of MD-SFC orchestration.

Optimality of Double Intervals in Persuasion: A Convex Programming Framework

We study information design problems, where the payoff of the information designer is an increasing step function of the posterior mean of the state her signals induce. Settings where the designer employs public signaling mechanisms to persuade multiple receivers (who have (i) finitely many actions that exhibit strategic complementarities, and (ii) best responses that are increasing in the state), and where her payoff is increasing in the receivers’ actions, are special cases. We provide a novel and tractable convex optimization framework for obtaining an optimal signaling mechanism. This mechanism is deterministic and admits a double-interval structure: it pools some “good” and “bad” states, and with each signal realization it associates at most two subintervals of the set of states. Our framework is broadly applicable, and we illustrate this through two very different applications. In the first application, we focus on receivers in a network who take binary actions (that can represent, e.g., adoption of a product of unknown quality), and characterize the optimal mechanism in terms of the network’s cores. Interestingly, as a consequence of the double-interval structure, the set of states for which a receiver takes action 1 is in general nonconvex. We show that in large random networks the designer can obtain asymptotically optimal mechanisms even in the absence of detailed network information, by using only the degree distribution. In our second application, we focus on persuading a single receiver whose type is private. Using a slight modification of our framework, we establish that the optimal mechanism is based on a laminar interval structure – a generalization of the double-interval structure – where the number of subintervals associated with each signal realization increases with the number of types.

On An Equivalent Representation of the Dynamics in District Heating Networks for Combined Electricity-Heat Operation

Improved coordination between power and district heating systems (DHSs) can play a vital role in accommodating more wind power. However, the dynamics of district heating networks (DHNs) have not yet been fully described for large transfer delays, which remains an obstacle for optimal electricity-heat coordination. This paper solves this problem by proposing a uniform framework in the Laplace domain by modeling heat losses and transfer delays from an electrical-analog perspective. Based on this framework, an equivalent representation of DHNs is proposed, showing the explicit “end-to-end” relationship between heat power generation and demand. The model is applied in combined electricity and heat operation, in which the black-box model is provided by DHS operators for power system operators without revealing the detailed network information. A case study of an integrated electricity-heat system demonstrates the effectiveness and simplicity of the proposed model.

Aggregate keyword nearest neighbor queries on road networks

Given a group $\mathcal {Q}$ of query points and a set $\mathcal {P}$ of points of interest (POIs), aggregate nearest neighbor (ANN) queries find a POI p from $\mathcal {P}$ that achieves the smallest aggregate distance. Specifically, the aggregate distance is defined over the set of distances between p and all query points in $\mathcal {Q}$ . Existing studies on ANN query mainly consider the spatial proximity, whereas the textual similarity has received considerable attention recently. In this work, we utilize user-specified query keywords to capture textual similarity. We study the aggregate keyword nearest neighbor (AKNN) queries, finding the POI that has the smallest aggregate distance and covers all query keywords. Nevertheless, existing methods on ANN query are either inapplicable or inefficient when applied to the AKNN query. To answer our query efficiently, we first develop a dual-granularity (DG) indexing schema. It preserves abstracts of the road network by a tree structure, and preserves detailed network information by an extended adjacency list. Then, we propose a minimal first search (MFS) algorithm. It traverses the tree and explores the node with the minimal aggregate distance iteratively. This method suffers from false hits arising from keyword tests. Thus, we propose the collaborative filtering technique, which performs keywords test by multiple keyword bitmaps collectively rather than by only one. Extensive experiments on both real and synthetic datasets demonstrate the superiority of our algorithms and optimizing strategies.

A Simple and Speedy Routing with Reduced Resource Information in Large-Capacity Optical WDM Networks

This paper discusses a simple and speedy routing method in large-capacity optical Wavelength Division Multiplexing (WDM) networks. The large-capacity WDM network is necessary to accommodate increasing traffic load in future. In this large-capacity WDM network, each link has many fibers and a huge amount of optical data can be transmitted through these fibers simultaneously. Optical path is configured for transmitting optical data by wavelength reservation including routing and wavelength assignment (RWA). Since traditional RWA methods have to treat much information about available wavelengths in each fiber, it is difficult to resolve RWA problem on time. In other words, the electrical processing becomes the bottleneck in the large-capacity WDM network. Therefore, a simple and speedy RWA method is necessary for the large-capacity WDM network. In this paper, we propose the simple and effective RWA method which considers reduced information as Network Map. The objective is to improve the network performance by using multiple fibers effectively. The complex processing is not suitable for data transmission because the switching operation must be done in very short time for one request. In addition to this, it is not practical to collect detailed network information frequently. The proposed wavelength assignment method assigns wavelength more uniformly than traditional method, and therefore, the proposed routing method can select routes without considering detailed information about each wavelength state. The proposed routing method needs only local information and reduced network information. This paper shows that the proposed routing method can get suitable solution for large-capacity optical WDM networks through computer simulations. The proposed RWA method drastically improves the loss probability against other simple RWA methods. This paper also describes two types of optical switches with tunable or fixed wavelength conversions. The wavelength converters with relatively low technology becomes effective with the proposed RWA method in the large-capacity WDM network. This paper reveals that complex routing methods are not necessary for large-capacity optical WDM networks.

Network-Based Marketing: Identifying Likely Adopters via Consumer Networks

Network-based marketing refers to a collection of marketing techniques that take advantage of links between consumers to increase sales. We concentrate on the consumer networks formed using direct interactions (e.g., communications) between consumers. We survey the diverse literature on such marketing with an emphasis on the statistical methods used and the data to which these methods have been applied. We also provide a discussion of challenges and opportunities for this burgeoning research topic. Our survey highlights a gap in the literature. Because of inadequate data, prior studies have not been able to provide direct, statistical support for the hypothesis that network linkage can directly affect product/service adoption. Using a new data set that represents the adoption of a new telecommunications service, we show very strong support for the hypothesis. Specifically, we show three main results: (1) “Network neighbors”—those consumers linked to a prior customer—adopt the service at a rate 3–5 times greater than baseline groups selected by the best practices of the firm’s marketing team. In addition, analyzing the network allows the firm to acquire new customers who otherwise would have fallen through the cracks, because they would not have been identified based on traditional attributes. (2) Statistical models, built with a very large amount of geographic, demographic and prior purchase data, are significantly and substantially improved by including network information. (3) More detailed network information allows the ranking of the network neighbors so as to permit the selection of small sets of individuals with very high probabilities of adoption.

Axonal growth in evolutionary neurogenesis

Creating ways for neural networks to evolve is an important goal in the field of artificial evolution. The main problem is how to encode the structure and properties of the neural network in the genome. If one overloads the genome with detailed network information, the evolutionary time increases prohibitively. If the genome is too simple, only simple problems can be solved. Since nature has found an efficient evolutionary solution to this problem, it is worth imitating the mechanisms by which biological neural nets are generated. In this article, a model is proposed in which artificial genomes increase the ability of axons to find, deteet, and connect to specific targets. Some initial simulation results for simple tasks are evolved, and the genetic tuning of the developmental processes of artificial evolution is discussed.