@Article{kuester2013distributed,
author = {Tobias K"{u}ster and Marco L"{u}tzenberger and Daniel Freund and Sahin Albayrak},
title = {Distributed Evolutionary Optimisation for Electricity Price Responsive Manufacturing using Multi-Agent System Technology},
year = {2013},
journal = {International Journal On Advances in Intelligent Systems},
volume = {7},
number = {1&{}2},
pages = {27--40},
issn = {1942-2679},
abstract = {With the recent uptake in renewable energies, such as wind and solar, often comes the apprehension of unreliable energy supply due to variations in the availability of those energy sources, also resulting in severe fluctuations in the price of electricity at energy exchange spot markets. However, those fluctuations in energy costs can also be used to stimulate industry players to shift energy intense processes to times when renewable energies are abundant, not only saving money but at the same time also stabilising the power grid. In previous work, we presented a software framework that can be used to simulate and optimise industrial production processes with respect to energy price forecasts, using a highly generic meta-model and making use of evolutionary algorithms for finding the best process plan, and multi-agent technology for distributing and parallelising the optimisation. In this paper, we want to wrap up our work and to aggregate the results and insights drawn from the EnEffCo project, in which the system has been developed.}
}