A Stochastic Resource-Sharing Network for Electric Vehicle Charging

We consider a distribution grid used to charge electric vehicles such that voltage drops stay bounded. We model this as a class of resource-sharing networks, known as bandwidth-sharing networks in the communication network literature. We focus on resource-sharing networks that are driven by a class of greedy control rules that can be implemented in a decentralized fashion. For a large number of such control rules, we can characterize the performance of the system by a fluid approximation. This leads to a set of dynamic equations that take into account the stochastic behavior of EVs. We show that the invariant point of these equations is unique and can be computed by solving a specific ACOPF problem, which admits an exact convex relaxation. We illustrate our findings with a case study using the SCE 47-bus network and several special cases that allow for explicit computations.Comment: 13 pages, 8 figure

A Computer Modeling Approach Using Critical Resource Diagramming Network Analysis in Project Scheduling

The problem of resource constrained project scheduling (RCPSP) continues to be an important topic in project management. Different scheduling processes have been introduced to solve cases of RCPSP. Most of the developed methods are based on a network analysis approach. The two main technique of project network analysis used for planning, scheduling, and control are PERT and CPM. These approaches assume unlimited resource availability in project network analysis. In realistic projects, both the time and resource requirements of activities should be considered in developing network schedules. Another particularity of the methods created, so far, is the focus on activities during the scheduling process. Therefore, from a resource point of view, the current procedures do not allow the project manager to incorporate information concerning each resource unit under supervision in the scheduling process of a project. There is a need for simple tools for resource planning, scheduling, tracking, and control. Critical resource diagramming (CRD) is a relatively new resource management tool. CRD is a simple extension to the CPM technique developed for resource management purposes. Unlike activity networks, CRD uses nodes to represent each resource unit. Also, contrasting with activities, a resource unit may appear more than once in a CRD network, specifying all different tasks to which a particular unit is assigned. Similar to CPM, the same backward and forward computations may be performed to CRD. The CRD method can also be used in solving RCPSP problems. This present study explores that advantage of CRD. The purposes are to develop a methodology, based on CRD, which allows its use in specific case of RCPSP, to implement the developed technique using a computer model, to conduct test to validate the CRD computer model, and then to investigate the advantages and disadvantages of the introduced method. The CRD computer model will be implemented using the Visual Basic 6.0 language

Modeling social information skills

In a modern economy, the most important resource consists in\ud human talent: competent, knowledgeable people. Locating the right person for\ud the task is often a prerequisite to complex problem-solving, and experienced\ud professionals possess the social skills required to find appropriate human\ud expertise. These skills can be reproduced more and more with specific\ud computer software, an approach defining the new field of social information\ud retrieval. We will analyze the social skills involved and show how to model\ud them on computer. Current methods will be described, notably information\ud retrieval techniques and social network theory. A generic architecture and its\ud functions will be outlined and compared with recent work. We will try in this\ud way to estimate the perspectives of this recent domain

Learning a Scalable Algorithm for Improving Betweenness in the Lightning Network

This paper presents a scalable algorithm for solving the Maximum Betweenness Improvement Problem as it occurs in the Bitcoin Lightning Network. In this approach, each node is embedded with a feature vector whereby an Advantage Actor-Critic model identifies key nodes in the network that a joining node should open channels with to maximize its own expected routing opportunities. This model is trained using a custom built environment, lightning-gym, which can randomly generate small scale-free networks or import snapshots of the Lightning Network. After 100 training episodes on networks with 128 nodes, this A2C agent can recommend channels in the Lightning Network that perform competitively with recommendations from centrality based heuristics and in less time. This approach provides a fast, low resource, algorithm for nodes to increase their expected routing opportunities in the Lightning Network