The Department of Information Science is one of six departments that make up the School of Business at the University of Otago. The department offers courses of study leading to a major in Information Science within the BCom, BA and BSc degrees. In addition to undergraduate teaching, the department is also strongly involved in postgraduate research programmes leading to MCom, MA, MSc and PhD degrees. Research projects in spatial information processing, connectionist-based information systems, software engineering and software development, information engineering and database, software metrics, distributed information systems, multimedia information systems and information systems security are particularly well supported.The views expressed in this paper are not necessarily those of the department as a whole. The accuracy of the information presented in this paper is the sole responsibility of the authors. CopyrightCopyright remains with the authors. Permission to copy for research or teaching purposes is granted on the condition that the authors and the Series are given due acknowledgment. Reproduction in any form for purposes other than research or teaching is forbidden unless prior written permission has been obtained from the authors. CorrespondenceThis paper represents work to date and may not necessarily form the basis for the authors' final conclusions relating to this topic. It is likely, however, that the paper will appear in some form in a journal or in conference proceedings in the near future. The authors would be pleased to receive correspondence in connection with any of the issues raised in this paper, or for subsequent publication details. Please write directly to the authors at the address provided below. (Details of final journal/conference publication venues for these papers are also provided on the Department's publications web pages: http://www.otago.ac.nz/informationscience/pubs/). Any other correspondence concerning the Series should be sent to the DPS Coordinator. Abstract. Increasing complexity of software applications forces researchers to look for automated ways of programming and adapting these systems. Self-adapting, self-organising software system is one of the possible ways to tackle and manage higher complexity. A set of small independent problem solvers, working together in a dynamic environment, solving multiple tasks, and dynamically adapting to changing requirements is one way of achieving true self-adaptation in software systems. Our work presents a dynamic multi-task environment and experiments with a self-adapting software system. The Evolvable Virtual Machine (EVM) architecture is a model for building complex hierarchically organised software systems. The intrinsic properties of EVM allow the independent programs to evolve into higher levels of complexity, in a way analogous to multi-level, or hierarchical evolutionary processes. The EVM is designed to evolve structures of self-maintaining, self-adapting ensembles, that are open-ended and hierarchically organised. This arti...

This paper analyzes the management of a large number of distributed battery energy storage systems (BESSs) by a energy utility in order to provide some market services. A heuristic algorithm based on two parts is proposed for this task. The first part, the aggregation, combines the abilities and behavior of the fleet of BESS into a virtual power plant (VPP) by a concise but flexible model. This VPP can be used by the utility as they are used to with traditional power plants. The second part, the disaggregation, distributes VPP control schedules back to the individual BESS by a greedy first-fit decreasing heuristic. The management of a fleet of BESS can also be modeled as a mathematical linear optimization program. The proposed heuristic is compared to and evaluated against this global optimization regarding computational performance and quality of results. It is shown, that the heuristic provides a remarkable speedup when applied to larger number of units. With it, it is possible to handle a group of at least 100,000 individual BESS. Further, the quality of the results are shown. First, the solution of the heuristic is compared to the optimal one of the mathematical program. Second, the methods are both applied and compared in a realistic case study.

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