Optimization-based planning of local energy systems -bridging the research-practice gap

SUSTAINABLE BUILT ENVIRONMENT D-A-CH CONFERENCE 2019

生态环境科学

Andrew Bollinger, L.^1 ; Marquant, J.^1 ; Sulzer, M.^1

Empa - Swiss Federal Laboratories for Materials Science and Technology, Urban Energy Systems Laboratory, Uberlandstrasse 129, Dübendorf

8600, Switzerland^1

关键词: Complex technical systems;    Computational tractability;    Iterative methodology;    Local energy systems;    Optimization methodology;    Optimization modeling;    Technology options;    Temporal decomposition;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/323/1/012077/pdf
DOI  :  10.1088/1755-1315/323/1/012077

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Optimization-based planning of local energy systems - though increasingly mature from a methodological perspective - is not commonly applied in practice. This paper synthesizes learnings from 4 case studies - focused on 4 different sites - in which optimization-based methodologies have been applied to the planning of local energy systems. The aim is to generate insights to facilitate the more effective application of optimization-based methodologies to local energy systems planning in practice. The results indicate that an intensively iterative methodology is critical not only as a basis for adapting the analysis based on stakeholder input, but also to facilitate learning on the part of stakeholders with regard to the value and limitations of the approach and the results. With regard to optimization methodologies, in particular temporal decomposition methodologies are identified as critical to preserving computational tractability in the optimization of complex technical systems, especially those featuring networks and those with many energy carriers or technology options. It is suggested that the methodological tailoring of an optimization model to a specific case and the calculation/visualization of key indicators can be largely automated, which could significantly accelerate future studies and reduce the knowledge required for their execution.