Abstract

Sizing distribution equipment of a district heating (DH) network is a complex yet decisive process to target techno-economic optimality for these systems. This paper describes and validates a framework that uses multi-objective optimization to support decisions regarding the sizing of a DH network. A genetic algorithm (NSGA2) is employed to generate optimal pipe diameters with respect to the operational and investment cost. This framework is then validated using an elementary toy problem consisting of a straight horizontal two-tube network, with a set of identical consumers equally distributed in the network and supplied by a unique heat source. We show that the framework, on the aforementioned problem, reaches very good convergence and diversity for the optimal solutions.This sizing method is then compared to typical local sizing method. We demonstrate a significant decrease of global cost by 40% by using DH network system simulation to take into account local interactions inside the DH system.

Highlights

  • District heating (DH) networks are an efficient way of producing and distribute heat on a territory and are widely used in Europe (Werner, 2017)

  • This paper reports on the first steps towards the design and validation of an optimization framework dedicated to the sizing of distribution pipes in a DH network

  • In order to have a better understanding of the reference Pareto front, we propose to compare it to a solution elaborated with a local sizing method that is a solution widely employed in engineering offices

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Summary

Introduction

District heating (DH) networks are an efficient way of producing and distribute heat on a territory and are widely used in Europe (Werner, 2017). Oversizing the distribution pipes can lead to useless investment cost and under sizing may prevent some customer’s demand to be fulfilled. In this domain, engineering guidelines generally rely on a pipe by pipe approach using threshold on either the fluid velocity, the regular pressure loss or both (Frederiksen & Werner, 2013). Recommendations may vary depending on the local standards but the calculations are often performed on a static sizing point

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