Library Header Image
LSE Research Online LSE Library Services

Comparing district heating options under uncertainty using stochastic ordering

Volodina, Victoria, Wheatcroft, Edward ORCID: 0000-0002-7301-0889 and Wynn, Henry ORCID: 0000-0002-6448-1080 (2022) Comparing district heating options under uncertainty using stochastic ordering. Sustainable Energy, Grids and Networks, 30. ISSN 2352-4677

[img] Text (Wheatcroft_comparing-district-heating-options--published) - Published Version
Available under License Creative Commons Attribution.

Download (996kB)

Identification Number: 10.1016/j.segan.2022.100634


District heating is expected to play an important role in the decarbonisation of the energy sector in the coming years since low carbon sources such as waste heat and biomass are increasingly being used to generate heat. The design of district heating often has competing objectives: the need for inexpensive energy and meeting low carbon targets. In addition, the planning of district heating schemes is subject to multiple sources of uncertainty, such as variability in heat demand and energy prices. This paper proposes a decision support tool to analyse and compare system designs for district heating under uncertainty using stochastic ordering (dominance) so that decision-makers can make robust decisions. The uncertainty in input parameters of the energy system model together with general scenarios are introduced to generate distributions of net present costs and emissions for each design. To perform inference about the induced distributions of outputs, we apply the orderings in the mean and dispersion. The proposed approach is demonstrated in an application to the waste heat recovery problem in district heating in Brunswick, Germany. The results obtained show that heat pump, a low carbon design option, is more robust in comparison to combined heat and power (CHP) and a mix of CHP and heat pump under all three scenarios, highlighting that robustness is an attractive feature of low-temperature waste heat recovery.

Item Type: Article
Official URL:
Additional Information: © 2022 The Authors
Divisions: Centre for Analysis of Time Series
Subjects: Q Science > Q Science (General)
H Social Sciences > HA Statistics
G Geography. Anthropology. Recreation > GE Environmental Sciences
Date Deposited: 09 Mar 2022 14:42
Last Modified: 17 Jun 2022 23:13

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics