News

18.02.2012

SeMa² - a Hybrid Semantic Service Matching Approach

Book Chapter Release Announcement

18.01.2012

Strategic Behavior in a Living Environment

Best Paper Nomination - Winter Simulation Conference (WSC)

18.01.2012

Integrating Process Modelling Into Multi-Agent System Engineering

Article released - MAGS Journal

16.01.2012

W2V2G Algorithms for sustainable EV Energy Management Systems

Paper accepted - IEEE International Electric Vehicle Conference

12.01.2012

An Agent-based Augmented Reality Demonstrator in the Domestic Energy Domain

Paper Accepted ­- PAAMS 2012

 
 
 
18.01.2012 15:23 Age: 36 days

Distributed Optimization of Energy Costs in Manufacturing using Multi-Agent System Technology

Upcoming Talk - 2nd Intl. Conference on Smart Grids, Green Communications and IT Energy-aware Technologies (Energy 2012)

Late this march we will present our paper "Distributed Optimization of Energy Costs in Manufacturing using Multi-Agent System Technology" at the Second International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies (Energy 2012). The Energy 2012 belongs to InfoSys 2012, a joint conference, consisting of ICNS, INTENSIVE, ICAS, LMPCNA. The conference is being held between the 25.03. and the 30.03. in St. Maarten. In our presentation we will present our so far experiences in using the JIAC V framework to solve problems from the energy domain. 

 Paper Authors: Tobias Küster, Marco Lützenberger, Daniel Freund, and Sahin Albayrak 

Abstract: While widely endorsed, the increased provision of electricity from renewable sources comes with the concern that energy supply will not be as reliable in the future as it is today, due to variations in the availability of wind and solar power. However, fluctuations in energy supply also give rise to volatility of the price for short-term energy procurement, and therefore bear the opportunity to save costs through shifting energy consumption to periods of low market prices. In a previous work, we presented an evolution-strategy-based optimization of production schedules with respect to day-ahead energy price predictions, yielding good results, but ­ being a stochastic optimization ­ not always arriving at the best solution. In this paper, we extend our framework by agent-based mechanisms for distribution and parallelization of the optimization, to increase scalability and reliability of the approach.