The recipients of the "EvoAPPLICATIONS Best Paper Awards" will be invited to submit an extended version of their works to a special issue of Memetic Computing.
Along with the worldwide incentive to reduce fossil and nuclear based power generation, the number of distributed generators and other forms of distributed energy resources which are installed in power networks has been steadily increasing over the last years. This increased integration has triggered a transformation of the energy system and challenges the conventional operation of these networks.
On a network level, this transformation requires new control and communication approaches, to guarantee the security of energy supplies as well as an optimal exploitation of available resources. On a generator level, advanced control strategies as well as morphological optimization (e.g., tuning of wind-blade design) can help to assure an optimal performance of the generator.
EvoEnergy is intended as a platform for new, innovative computational intelligence and nature-inspired techniques in the domain of energy-related optimization research. We seek contributions ranging from new control concepts for decentralized generation, strategies for their coordination in the network to the morphological optimization of distributed generators.
Accepted papers will appear in the proceedings of EvoStar, published in a volume of the Springer Lecture Notes in Computer Science, which will be available at the Conference.
Submissions must be original and not published elsewhere. The submissions will be peer reviewed by at least three members of the program committee. The authors of accepted papers will have to improve their paper on the basis of the reviewers comments and will be asked to send a camera ready version of their manuscripts. At least one author of each accepted work has to register for the conference and attend the conference and present the work. The reviewing process will be double-blind, please omit information about the authors in the submitted paper.Submission format: Springer LNCS