An adjoint optimization approach for the topological design of large-scale district heating networks based on nonlinear models

TL;DR

This paper introduces an adjoint-based optimization method for large-scale district heating networks, enabling simultaneous topology and pipe size design with reduced computational costs, demonstrated on a network with 160 consumers.

Contribution

It presents a novel adjoint optimization approach with constraint aggregation and numerical continuation to efficiently design large-scale thermal networks.

Findings

Reduced piping investment by 23%

Pump costs decreased by a factor of 14

Optimization completed in less than an hour

Abstract

This article deals with the problem of finding the best topology, pipe diameter choices, and operation parameters for realistic district heating networks. Present design tools that employ non-linear flow and heat transport models for topological design are limited to small heating networks with up to 20 potential consumers. We introduce an alternative adjoint-based numerical optimization strategy to enable large-scale nonlinear thermal network optimization. In order to avoid a strong computational cost scaling with the network size, we aggregate consumer constraints with a constraint aggregation strategy. Moreover, to align this continuous optimization strategy with the discrete nature of topology optimization and pipe size choices, we present a numerical continuation strategy that gradually forces the design variables towards discrete design choices. As such, optimal network topology and pipe sizes are determined simultaneously. Finally, we demonstrate the scalability of the algorithm by designing a fictitious district heating network with 160 consumers. As a proof-of-concept, the network is optimized for minimal investment cost and pumping power, while keeping the heat supplied to the consumers within a thermal comfort range of 5 %. Starting from a uniform distribution of 15 cm wide piping throughout the network, the novel algorithm finds a network lay-out that reduces piping investment by 23 % and pump-related costs by a factor of 14 in less than an hour on a standard laptop. Moreover, the importance of embedding the non-linear transport model is clear from a temperature-induced variation in the consumer flow rates of 72 %.