4.7 Article

A generalised optimal design methodology for distributed energy systems

Journal

RENEWABLE ENERGY
Volume 200, Issue -, Pages 1223-1239

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2022.10.029

Keywords

Multiobjective optimisation; Energy system planning; Energy system simulation; Pareto front; District energy systems

Funding

  1. Federal Ministry for Economic Affairs and Climate Action (BMWK)
  2. Federal Ministry of Education and Research (BMBF) [03SBE111]

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The optimal combination of energy conversion and storage technologies with local energy demand is a key challenge of distributed energy. The choice of technology and operational strategy have a decisive influence on the optimality.
The optimal combination of energy conversion and storage technologies with local energy demand is a key but in its result not obvious challenge of distributed energy. Although a variety of possible approaches to the optimal design of limited technology selections can be found in the literature, the previous design step, the actual technology selection, and the subsequent step, the selection of the optimal operating strategy, are often neglected. We develop and demonstrate a methodology, which can optimise energy systems with arbitrary technology selection and under multi-criteria optimality definitions. The energy system modelled in oemof.solph is optimised using a MOEA/D approach with regard to economic, ecological and technical key performance indicators. The aim is to find trends and tendencies with a methodology that is as generalised as possible in order to integrate it into the decision-making process in energy system planning. We demonstrate the method by means of a German district for which an integrated supply concept is being sought. Different evaluation and visualisation possibilities are presented and the chances and limitations of the developed methodology are identified. We show that not only the choice of technology, but especially its sizing and operational strategy have a decisive influence on the optimality.

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