Srdjan Vukmirovic

Algorithms in electric power system one-line diagram creation

This paper analyzes various algorithms for the automatic generation of electric power system one-line diagrams. Historically these one-line diagrams were created manually. As these diagrams can be large, their manual creation takes a lot of time and resources, and it is also prone to errors. One possible solution to this problem is the automatic generation of one-line diagrams. Automatic visualization can be performed by existing rule-based algorithms, or if the network is modeled by a mathematical graph, then the problem can also be solved by graph drawing algorithms. Initial results achieved with soft computing algorithms will also be shown. We discuss the similarities, differences and applicability of these different algorithms in automatic one-line diagram generation. The final goal is to find the most suitable algorithm for the automatic generation of visually pleasing one-line diagrams, which allow dispatchers and engineers working in control centers a higher level of efficiency in performing their everyday tasks.

Role-Based Access Control Model Supporting Regional Division in Smart Grid System

Smart Grid is a modern electric power infrastructure that incorporates elements of traditional power systems and information and communication technology (ICT) with the aim to improve the reliability, efficiency and safety as crucial requirements of critical infrastructure systems. Relying on the ICT, Smart Grid exposes electric power systems to new security issues and vulnerabilities over the network. Therefore, the security is becoming an increasing concern at both physical and information levels. Access controls are one of the most important aspects of information security. Role-based access control (RBAC) model is widely used in complex systems that are characterized by many participants. However, the existing models are not suitable for critical infrastructure systems with specialized features, such as a high number of devices dispersed over the vast geographical regions in “an Internetlike distributed environment”. In this paper, access control mechanism optimized for Smart Grid systems is developed considering the regional division and concept of areas of responsibility (AOR). To this end, the standardized RBAC model will be extended to provide an efficient and consistent policy with greater level of granularity from the aspect of resource protection. The proposed RBACAOR model is formally described and then applied to the smart grid environment.

A HYBRID GENETIC ALGORITHM FOR PARTITIONING OF DATA MODEL IN DISTRIBUTION MANAGEMENT SYSTEMS

In this paper, we propose a Hybrid Genetic Algorithm for data model partitioning of power distributionnetwork. Analytical functions are the core of Distribution Management Systems (DMSs). Efficient calculation of thefunctions is of the utmost importance for the DMS users; the necessary preconditions for the efficient calculation areoptimal load balancing of processors and data model partitioning among processors. The proposed algorithm is appliedto different real models of power distribution systems. It obtains better results than classical evolutionary algorithms(Genetic Algorithm and Particle Swarm Optimization). The Hybrid Genetic Algorithm also achieves better results thanmultilevel algorithm (METIS) in cases of small graphs. http://dx.doi.org/10.5755/j01.itc.40.4.981

Architecture of Real-Time Database in Cloud Environment for Distributed Systems

In every distributed system there is a need for a real-time database when working with a large amount of data and when quick response from a service is expected on the client side. Cloud companies do not offer real-time databases, therefore, in this paper we present the architecture of a real time database in cloud environment, as well as reviews of other papers dealing with this topic, analyzing their advantages and disadvantages. The above-mentioned real-time database attempts to store different kind of data, and our proposed solution is implemented in each distributed system service. Unlike other solutions, this real-time database will not occupy main memory-base because it is running in cloud environment. Also, we are proposing which cloud environment services can be used to implement this real time database. As an example, we are modeling one distributed system using Petri nets where solution can be applied.

Enabling the IoT paradigm through multi-tenancy supported by scalable data acquisition layer

The Internet of things (IoT) is composed of a large number of end systems. Data gathered from these end systems are processed in the cloud environment. This paper delineates multi-tenancy as a prerequisite for enabling cost effective IoT solutions using any cloud platform. In the IoT world, a large number of end systems imply a large number of users who should have open access to specific subsets of data. As multi-tenancy includes sharing the same application instance, security is a key requirement when engineering new software as a service (SaaS) applications. This paper describes multi-tenant implementation approaches and inspects security aspects and data isolation. It proposes a solution for data layer scalability in order to achieve the desired performances. Furthermore, it proposes an SOA solution to provide a secure multi-tenant web service with scalable data layers. We demonstrate that the proposed approach results in some performance loss, which is not acceptable for performance critical applications but is acceptable for services developed for tenants themselves. We experimentally confirm our data scalability theory based on an actor model and developed a cost estimator. Finally, we conclude that application level multi-tenancy is necessary from a cost efficiency point of view in a scenario involving a large number of users. The solution was tested in a private cloud environment.

Publisher/Subscriber Implementation in Cloud Environment

In modern-day systems there is a need for a system which allows indirect asynchronous communication during message exchange between clients and servers. This is known as Publisher/Subscriber system. Such system is important for transmitting data in distributed systems. This paper describes Publisher/Subscriber implementation in Microsoft Windows Azure environment for working services in distributed systems. This is useful for the large scale and big data processing because Microsoft Windows Azure is motivated by it. Pub/Sub system is implemented in every service/client in real-time distributed systems, tested with different distributed networks and the results of this type of implementation are shown in this paper.

Providing flexible software as a service for smart grid by relying on big data platforms

When migrating existing software solutions to cloud platforms, the design should be modified in order to fully utilize the elasticity that cloud platforms provide. Emerging Internet of Things (IoT) technologies will intersect with the smart grid of the future, while big data platforms are a perfect match for storing massive amounts of data in cloud environments. Cloud based services are yet to be adopted in the smart grid, but features that require big data solutions like demand response (DR) will accelerate the shift to cloud services. Among the competition, only effective services will prevail. Consequently, the purpose of this study is to examine approaches to using the elasticity of the cloud in order to create a flexible service, meaning the service consumers can choose between cost and comfort. Hence, the paper proposes a general approach to service design considerations based on big data platforms. The proposed method is applied to the case of demand response feature along with distributed management system (DMS) applications for managing smart grids and is implemented using Apache HBase, the Hadoop database. Since the experiments were set up to give necessary inputs for a design discussion, we tested the solution in the cloud environment. The results were able to match requirements, but more importantly we could draw conclusions as to how we could design the proposed service. Moreover, the same principles could be applied to any service that relies on a big data platform.

Combining real-time processing streams to enable demand response in smart grids

Modern smart grids are trending towards direct involvement of end customers in reaching the optimum in balancing energy consumption and generation. In the traditional model, supply follows demand while the modern grids are shifting to the opposite where the load follows supply. Since smart grid systems process a lot of data in real-time, we have researched how we could use the data in order to save energy and reduce peaks. Reducing peaks in energy consumption can save investments on utility side by supplying the same number of customers with less power generation units. The paper presents a possible solution that gives insight into the amount of energy that could potentially be saved at any time by turning off particular devices in the Demand Response (DR) program. Moreover, the proposed solution allows utility to easily and effectively manage network. The solution relies on real-time big data processing and is implemented as Apache Storm topology. Storm processes the gathered data in two data streams - location of customers and devices measurements. By combining two data streams, we check whether a household is empty and how much energy could be saved in every moment. We measured throughput for three distinct loads which were used to simulate three different city sizes. By increasing parallelism and the number of nodes we have noticed that those two factors have a significant influence on the obtained results. What is more, these results provide us with a valuable insight into the overall and complete state of the network in real time.