Journal
ENERGY
Volume 232, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2021.120987
Keywords
Global sensitivity analysis; Parameter analysis; Machine learning; Energy system planning model; District heating
Categories
Ask authors/readers for more resources
Thermal networks have gained attention as a means for the European Union to reach climate targets. This study aims to describe the decision boundary between thermal networks and competing alternatives using an energy system planning model. Through parameter analysis and machine learning, new situations are generated to improve understanding of the decision boundary.
ABSTR A C T Thermal networks have gained attention in recent research as a means for the European Union to reach its climate targets. The potential has been well established for a multitude of regions. However, there is more to learn regarding the quantity and behaviour of the decision boundary between the use of thermal networks and competing alternatives. This study aims to describe that decision boundary with a parameter analysis of an energy system planning model. First, parameters of the energy system planning model (e.g. the linear heat density) describe a situation. Second, an energy system planning model finds the optimal (economic) energy supply mix for a given situation. Third, a sampling method with machine learning manipulates the values of the parameters to generate new situations, which improves the in-formation on the as of yet unknown decision boundary. And fourth, the final location of the decision boundary is then predicted with machine learning from the optimised set of situations. Illustration of the concept is provided by means of a simplified case study. For that case study it is found that the decision for district heating is bound by a minimum value of several parameters instead of a single parameter. (c) 2021 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available