Network Partitioning and Self-sizing Methods for QoS Management with Autonomic Characteristics

With the increasing integration of distribution generations (DGs), active distribution networks (ADNs) need to address the new challenges in voltage control. Meanwhile, the traditional centralized method in reactive power optimization usually leads to a heavy computational burden for large-scale distribution networks. A distributed voltage control model with a novel network partitioning method is proposed in this study, where the capacity curve of DGs and the regulation of electrical storage systems (ESS) are specially addressed. According to the number of decision variables and the number of constraints in the optimization model, the computational complexity can be utilized in the network partitioning method with the power balance and the electrical distance to automatically partition a distribution network in an optimal manner. Taking the coordination between two boundary sub-areas into consideration, a distributed voltage control strategy is developed and solved by a distributed solution method based on Lagrangian dual relaxation. Finally, numerical studies based on two IEEE test systems and a practical 152-bus system are performed to verify the effectiveness of the proposed method.

Spatially partitioning heterogeneous traffic networks into multiple subnetworks is crucial for practical tasks, such as distributed signal control and model parallel processing. Existing partitioning methods that account for traffic characteristics over a certain period typically require calculating similarities between the time series of all sensors. Due to the quadratic increase in complexity with network size, these methods are inefficient for large-scale networks and extended time periods. Additionally, calculating similarities requires complete data, making such methods highly sensitive to missing data and lacking robustness. To address these issues, this article proposes a four-step fundamental-diagram-informed traffic network partitioning method. First, spatially adjacent sensors are clustered into subclusters. Next, the S3 speed-occupancy function is used to fit the aggregated data of each subcluster to extract fundamental diagram information. Then, this information is used to perform secondary clustering to form clusters. Finally, the cluster boundaries are fine-tuned to produce subnetworks with smooth boundaries. The proposed method calculates the parameter similarity between subclusters instead of the time series similarity between all sensors. This reduces computational costs and effectively handles data missing. A case study using real-world data verifies the effectiveness of the proposed method and its stability in the presence of missing data. Compared to spectral clustering, the total within-cluster variance and NcutSilhouette metric decrease by 5.7% and 17.8%, respectively. The proposed method enhances distributed or parallel tasks on traffic networks by providing stable and meaningful partitioning results. This method is beneficial for the analysis and effective management of complex heterogeneous traffic networks.

Hierarchical network partition of DC power systems containing a large number of switches

Uniform network partition is a type of network partitioning problem that consists of dividing a network into components, such that the components are of about the same size and there are few connections between the components. This paper presents a network partition method using dichotomy based on a criterion called sub network exchange gain. We put forward the network fragmentation degree to evaluate the efficiency of a network. Using dichotomy with the criterion of sub network exchange gain, we proved that a network can be split into two communities totally by cutting the least connecting lines.

Service providers rely on the management systems housed in their Network Operations Centers (NOCs) to remotely operate, monitor and provision their data networks. Lately there has been a tremendous increase in management traffic due to the growing complexity and size of the data networks and the services provisioned on them. Traffic engineering for management flows is essential for the smooth functioning of these networks to avoid congestion, which can result in loss of critical data such as billing records, network alarms, etc. As is the case with most intra-domain routing protocols, the management flows in many of these networks are routed on shortest paths connecting the NOC with the service provider's POPs (points of presence). This collection of paths thus forms a "confluent" tree rooted at the gateway router connected to the NOC. The links close to the gateway router may form a bottleneck in this tree resulting in congestion. Typically this congestion is alleviated by adding layer two tunnels (virtual links) that offload the traffic from some links of this tree by routing it directly to the gateway router. The traffic engineering problem is then to minimize the number of virtual links needed for alleviating congestion. The traffic engineering problem described above also has applications to alleviating congestion resulting from focused overloads in VoIP networks and for dealing with congesting resulting from flash crowds in the world wide web.In this paper we formulate a traffic engineering problem motivated by the above mentioned applications. We show that the general versions of this problem are hard to solve. However, for some simpler cases in which the underlying network is a tree, we design efficient algorithms. We use these algorithms as the basis for designing efficient heuristics for alleviating congestion in general (non-tree) service provider network topologies.

Optimizing the cellular network’s cell locations is one of the most fundamental problems of network design. The general objective is to provide the desired Quality-of-Service (QoS) with the minimum system cost. In order to meet a growing appetite for mobile data services, heterogeneous networks have been proposed as a cost- and energy-efficient method of improving local spectral efficiency. Whilst unarticulated cell deployments can lead to localized improvements, there is a significant risk posed to network-wide performance due to the additional interference. The first part of the paper focuses on state-of-the-art modelling and radio-planning methods based on stochastic geometry and Monte-Carlo simulations, and the emerging automatic deployment prediction technique for low-power nodes (LPNs) in heterogeneous networks. The technique advises a LPN where it should be deployed, given certain knowledge of the network. The second part of the paper focuses on algorithms that utilize interference and physical environment knowledge to assist LPN deployment. The proposed techniques can not only improve network performance, but also reduce radio-planning complexity, capital expenditure, and energy consumption of the cellular network. The theoretical work ar X iv

Stochastic network link transmission model

Heterogeneous wireless networks are considered as promising technologies to improve energy efficiency. In heterogeneous networks, interference management is very important since the interference due to spectrum sharing can significantly degrade overall performance. In the existing work, various resource allocation methods are proposed to either improve energy efficiency or mitigate interference in orthogonal frequency-division multiple access (OFDMA)-based multicell networks. To the best of our knowledge, no research on resource allocation has jointly considered improving energy efficiency and performing interference control, especially using interference power constraint strategies. Furthermore, most existing work assumes that all of the channel state information (CSI) is known completely, which might not be realistic in heterogeneous networks due to the limited capacity of the backhaul links and varied ownership of network devices. In this paper, we propose a game-theoretical scheme using energy-efficient resource allocation and interference pricing for an interference-limited environment in heterogeneous networks. We formulate the problems of resource allocation and interference management as a Stackelberg game with incomplete CSI. A backward induction method is used to analyze the proposed game. A closed-form expression of the Stackelberg equilibrium (SE) is obtained for the proposed game with various interference power constraints. Simulation results are presented to show the effectiveness of the proposed scheme.

The electromagnetic transient simulation of large-scale power system is often accelerated by parallel simulation with sub-networks. However, the general network partitioning method based on geographical location information is not self-adaptive, resulting in seriously constrained sub-network numbers, uneven scales, and many associated lines. This paper proposes a self-adaptive network partitioning algorithm based on the k-way thought, making use of the high sparsity of power system. This algorithm combines the mapping relationship between the original data and the graph structure of power system, the preprocessing skills of simplifying complex graph structure and the linear deterministic greedy algorithm to overcome the shortcomings of general network partitioning method and further improve the speed of electromagnetic transient parallel simulation of power system. The algorithm's results are applied to the electromagnetic transient parallel simulation, verifying its reliability and acceleration effect.

Distributed operation optimization of active distribution network with P2P electricity trading in blockchain environment

In order to analyze the DC integrated power systems containing a large number of switches accurately and quantitatively, this paper constructs a real-time simulation hardware-in-the-loop test platform. The platform integrates three key technologies, the first one is the real-time simulation method of large-scale power systems. This method is composed of modeling method of crucial equipment, network partitioning method, and electrical decoupling method. The second one is the construction of asynchronous communication interface. This platform realizes the function of uploading the system data to the database by UDP protocol and sending the control instructions to the system model by TCP protocol. The last one is the joint debugging of physical equipment and real-time simulator. Taking the integrated power system (IPS) as an example, the power system model is simulated by the real-time simulator, the network and energy management system are actual physical equipment, constructing the real-time simulation hardware-in-the-loop experimental platform. The real-time simulation performance and dynamic calculation results of the system verify the effectiveness of the real-time simulation method of large-scale power systems and the superiority of the platform.

Recent methods in the literature to parallelize the traffic assignment problem consider partitioning a network into subnetworks to reduce the computation time. In this article, a partitioning method is sought that generates subnetworks minimizing the computation time of a decomposition approach for solving the traffic assignment (DSTAP). The aim is to minimize the number of boundary nodes, the interflow between subnetworks, and the computation time when the traffic assignment problem is solved in parallel on the subnetworks. Two different methods for partitioning are tested. The first is an agglomerative clustering heuristic that reduces the subnetwork boundary nodes. The second is a flow weighted spectral partitioning algorithm that uses the normalized graph Laplacian to partition the network. The performance of both algorithms is assessed on different test networks. The results indicate that the agglomerative heuristic generates subnetworks with a lower number of boundary nodes, which reduces the per iteration computation time of DSTAP. However, the partitions generated may be heavily imbalanced leading to a higher computation time when the subnetworks are solved in parallel separately at a particular DSTAP iteration. For the Austin network partitioned into four subnetworks, the agglomerative heuristic requires 3.5 times more computation time to solve the subnetworks in parallel. The results also show that the spectral partitioning method is superior for minimizing the interflow between subnetworks. This leads to a faster convergence rate of the DSTAP algorithm.

The impact of globalization on maritime transportation has led to its enormous growth over the last decade. Due to the rapid increase in sea-borne demand, large emphasis is placed on making ports more efficient, by promoting the effective utilization of available resources. Therefore, the role of optimization becomes crucial, as port operators aim for the cost-effective option of maximizing port efficiency, rather than the costly alternative of expanding existing capacity. One of the most important seaside planning problems that has received a great deal of attention in research streams is the assignment of quay space to incoming vessels; it is known as the Berth Allocation Problem (BAP). Even though it has been studied extensively, there remain certain unaddressed gaps. Relatively little attention has been focused on the operation of bulk ports, in which terminal operators are concerned with integrating and managing the sea-side area (wharf) and the buffer area for storage. The cargo type must be explicitly known to the bulk port operator, who in turn assigns to it the best storage area and the use of appropriate specialized equipment for loading and discharging. It is evident that the integration of the BAP with yard assignment is necessary, in order to maximize efficiency and obtain the optimal berthing plan in bulk ports. Thus, the current paper studies the integrated dynamic hybrid berth allocation and yard assignment problem (BYAP) in the context of bulk ports. Important assumptions are taken into consideration in order to produce a realistic and practical model. Finally, a relevant case study is presented for the case of Mina Zayed Port in Abu Dhabi.

In this paper we consider a dual-gradient optimization flow control scheme. In an earlier work it was shown that such algorithms converge in a delay free case. We present the sufficient condition under which the stability can be global focusing on the scenario of a single flow and bottleneck link with delay. We first show that this synchronous algorithm is convergent in general network topology without delay. Then we provide a result that even with delays, the queue length increasing at the router is bounded. The upper bound grows with increase in the number of flows as well as the maximum source sending rate and the maximum round trip delay. The upper bound decreases as the link departing rate and stepsize increase.

This article studies a distributed consensus law to achieve a coordinated trochoidal formation among a team of mobile agents. The mobile agents are modeled as double integrators. We generalize an existing consensus algorithm for double-integrator dynamics (1) to achieve trochoidal patterns under a general network topology and (2) to extend the pattern formation in a 3-D plane. Since the trochoidal patterns are 2-D curves, the proposed control law allows us to specify the 2-D plane in the 3-D space on which the patterns will be formed. We modify the standard consensus protocol and manipulate the gains to create trochoidal motion through local information exchange by appropriately placing the eigenvalues of the system. Trochoids have been used in multifaceted robotic applications such as area coverage, hurricane sampling, and orbit designs for satellites, apart from their aesthetic appeal. Computer simulations and multi-robot experiments are presented to validate the theoretical findings.