Miroslav Prýmek

Multi-agent approach to power distribution network modelling

Power supply reliability has become a question of ever growing importance. Due to its geographical distribution and the high costs of maintaining electric power systems (EPS), the necessary quality cannot be achieved simply by redundancy. It is necessary to search for new and inexpensive yet effective means of ensuring the required reliability. n nEmergent systems and intelligent agents are in the focus of great attention in many areas of computer science and artificial intelligence. Intelligent autonomous systems are seen as systems that can react intelligently and flexibly upon changes in operating conditions and demands from the surrounding processes. n nThe Rice multi-agent system, which is presented in this paper, is designed as a distributed environment with elements that use communication standards for local interactions. The task of maintenance, control and simulation of EPS is handled by modelling the network with agents that resemble the behaviour of real EPS components. The overall design and main architecture goals of Rice are described here and interesting Rice simulation use-cases and acquired outputs are demonstrated.

Economic aspects of multi-source demand-side consumption optimization in the smart home concept

Current energy consumption trends lead to rapidly growingnconsumption of local renewable energy sources. Suchninstallations bring new requirements on energy consumptionnprofiles. Due to the massive multiplication of the results, onenof the most interesting elements of the power grid, in thisnrespect, is formed by households. Smart profiling of householdnenergy consumption may be crucial for the adaptability of thenglobal grid. In this article, we present the design and usagenof a demand-side, consumption profiling system named thenPriority-driven Appliance Control System (PAX). We describe thenmain features of the PAX system and show its application usingnreal-world data. The main benefits are presented as directneconomic assets in connection with various household energynsources (energy grid and photovoltaic panels) and efficientnusage with regard to government energy grants.

Smart Home Modeling with Real Appliances

Renewable energy sources bring new challenges to power consumption planning on all levels (household, city or region). In this paper, we present a smart household appliance-level scheduling system, called PAX. We describe the details of the PAX scheduling core and its usage for optimizing an appliance power consumption profile according to actual power source regime.

Power network reliability computations using multi-agent simulation

The reliability of a power network as a whole is limited by the reliability of the weakest element in the network. Ensuring the reliability just with nothing more than a readiness to repair or replace broken parts is not feasible economically, and planned element replacements in the middle of their working life is often a waste of working resources. n nThis article presents a multi-agent simulation system designed for modelling all aspects of power network elements and power network processes. The modelling of this system is able to predict the weak points of a simulated power network over an extended time span (decades). This mode of prediction takes into account the network topology, which means that each element is modelled in a context that is close to the real one. n nTo show the system capabilities, we present two models of computations based on real power networks of 10 kV. Both models make use of the main feature of the system, which is the ability to use local interactions even for global analytic computations, such as the computation of a power network steady state or locating the least reliable point in the network.

Modelling three-phase power network with a multi-agent system

Multi-agent systems have already proven to be useful for modelling processes in distributed environments, such as an electricity distribution network. The autonomous design of each network element and its capabilities allows to adapt the model to any situation and the results can be used for monitoring as well as real time control. n nOne disadvantage of such models lies in a seeming inability to evaluate global analytical computations. In this paper, we show how such computations can be performed within a power network simulation system by means of purely local interactions and an iterative communication flow. The results are then compared to a standard analytical approach.

Multi-agent System for Blackout Prevention by Means of Computer Simulations

The paper presents a dynamic simulation model of power flows in a power distribution network using communication in multi-agent systems. The model is based on local interchange of knowledge between autonomous agents representing the network elements. The agents use KQML as an inter-agent knowledge interchange language. The main purpose of the work is to develop a scalable distributed simulation model, flexible enough for incorporation of intelligent control and condition-based management of a power distribution network. This model is designed to optimize the electric distribution networks and prevent failures in the power grid (blackout). The network’s reliability is affected by the parameters of connected power sources (both traditional and renewable sources), but also by the unexpected failures. The simulation is using the parameters calculated from the real database of electric distribution network failures.

Inter-Agent Communication Adaptations for Power Network Processes Simulation

In the paper, we present the agent communication standards that are implemented in the Rice simulation system for management and control of processes conducted in an electrical power network. The system models the power network using the multi-agent approach, with the agent communication flowing through a 4-layer communication protocol. We summarize the main features of the Knowledge Query and Manipulation Language (KQML) which is used as the system’ s content language. These features are then confronted with specific requirements of the area of electrical power network simulation and specific adaptations directed to system optimizations are described.