Large district heating networks greatly benefit from topological changes brought by the construction of loops. The overall effects of malfunctions is smoothed, making existing networks intrinsically robust. In this paper, we demonstrate the use of topology optimization to find the network layout that maximizes robustness under an investment constraint. The optimized design stems from a large ground structure, that includes all the possible looping elements. The objective is an original robustness measure, that does not require any probabilistic analysis of the input uncertainty nor the identification of bounds on stochastic variables. Our case study on the Turin district heating network confirms that robustness and cost are antagonist objectives: the optimized designs obtained by systematically relaxing the investment constraint lay on a smooth Pareto front. A sudden steepness variation divides the front in two different regions. For small investments topological modifications are observed, i.e. new branches appear continuously in the optimized layout as the investment increases. Here, large robustness improvements are possible. However, at high investments no topological modifications are visible and only limited robustness gains are obtained.
Topology optimization of robust district heating networks / Pizzolato, Alberto; Sciacovelli, Adriano; Verda, Vittorio. - In: JOURNAL OF ENERGY RESOURCES TECHNOLOGY. - ISSN 0195-0738. - 140:2(2018). [10.1115/1.4038312]
Topology optimization of robust district heating networks
Pizzolato, Alberto;Sciacovelli, Adriano;Verda, Vittorio
2018
Abstract
Large district heating networks greatly benefit from topological changes brought by the construction of loops. The overall effects of malfunctions is smoothed, making existing networks intrinsically robust. In this paper, we demonstrate the use of topology optimization to find the network layout that maximizes robustness under an investment constraint. The optimized design stems from a large ground structure, that includes all the possible looping elements. The objective is an original robustness measure, that does not require any probabilistic analysis of the input uncertainty nor the identification of bounds on stochastic variables. Our case study on the Turin district heating network confirms that robustness and cost are antagonist objectives: the optimized designs obtained by systematically relaxing the investment constraint lay on a smooth Pareto front. A sudden steepness variation divides the front in two different regions. For small investments topological modifications are observed, i.e. new branches appear continuously in the optimized layout as the investment increases. Here, large robustness improvements are possible. However, at high investments no topological modifications are visible and only limited robustness gains are obtained.Pubblicazioni consigliate
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https://hdl.handle.net/11583/2689479
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