4.7 Article

Multi-objective optimization of district heating network model and assessment of demand side measures using the load deviation index

期刊

JOURNAL OF CLEANER PRODUCTION
卷 182, 期 -, 页码 338-351

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2018.02.083

关键词

Load deviation index; District heating network; Sustainable energy service; Multi-objective optimization; Demand side measures

资金

  1. Education, Audiovisual, and Culture Executive Agency (EACEA) of the European Commission [2012-0034]

向作者/读者索取更多资源

The future design of small-to-medium scale district heating networks must integrate policy-related targets while being able to provide a certain energy service to its consumers with profitable business models. Policy targets emphasize cross-cutting developments, like the increase of biomass share and the implementation of demand side measures. This implies the need for multi-goal, optimum design of heat production, whose performance is highly dependent on the yearly heat load profile. In this work, a holistic energy service model is proposed for design optimization of energy supply from a biomass-based heating plant for small-to-medium scale applications. Furthermore a new indicator called load deviation index is proposed, which is used (a) for characterization of heat load profiles and (b) for the assessment of the impact of different demand side measures onto optimum design. A review of the actual policy framework and the business implications reveal the need for a holistic future district heating network design, where both demand-side and supply-side services are included. A multi objective optimization is applied for calculating Pareto-optimum solutions of the heating plant design while the demand side is modelled using the load deviation index. The model is applied to a typical case study, whereas its results are not restricted to it. Generic results are obtained through normalization and provide evidence that demand side measures do not always contribute to higher performance of such systems. (C) 2018 Elsevier Ltd. All rights reserved.

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