Preprint Article Version 1 This version is not peer-reviewed

Development of a Decision-Making Framework for Distributed Energy Systems in a German District

Version 1 : Received: 23 December 2019 / Approved: 24 December 2019 / Online: 24 December 2019 (14:44:57 CET)
Version 2 : Received: 9 January 2020 / Approved: 10 January 2020 / Online: 10 January 2020 (04:32:59 CET)

How to cite: Schmeling, L.; Schönfeldt, P.; Klement, P.; Wehkamp, S.; Hanke, B.; Agert, C. Development of a Decision-Making Framework for Distributed Energy Systems in a German District. Preprints 2019, 2019120324 (doi: 10.20944/preprints201912.0324.v1). Schmeling, L.; Schönfeldt, P.; Klement, P.; Wehkamp, S.; Hanke, B.; Agert, C. Development of a Decision-Making Framework for Distributed Energy Systems in a German District. Preprints 2019, 2019120324 (doi: 10.20944/preprints201912.0324.v1).

Abstract

The planning and decision-making for a distributed energy supply concept in complex actor structures like in districts calls for the approach to be highly structured. An strategy with strong use of energetic simulations is developed here and the core elements shall be presented. The exemplary implementation is shown using the case study of a new district on the former Oldenburg airbase in northwestern Germany. The process is divided into four consecutive phases, which are carried out with different stakeholder participation and use of different simulation-tools. Based on a common objective, a superstructure of the applicable technologies is developed. Detailed planning is then carried out with the help of an optimal sizing algorithm and Monte Carlo based risk assessment. The process ends with the operating phase, which is to guarantee a further optimal and dynamic mode of operation. The main objective of this publication is to give a brought introduction to the intended planning processes and decision-making framework and to find and identify research gaps that will have to be addressed in the future.

Subject Areas

energy system planning; energy system simulation; optimal sizing; risk analysis; monte carlo simulation; distributed energy systems; local energy markets

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