Optimize heat prosumers' economic performance under current heating price models by using water tank thermal energy storage

• Published in: Energy (Oxford) Vol. 239; p. 122103
• Main Authors: Li, Haoran, Hou, Juan, Tian, Zhiyong, Hong, Tianzhen, Nord, Natasa, Rohde, Daniel
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15-01-2022; Elsevier BV
• Subjects: 4th generation district heating; Distributed heat sources; District heating; Economic models; Energy storage; Heating price model; Mismatch problem; Optimization; Payback periods; Peak load; Thermal energy; Thermal energy storage; Water tanks; Distributed heat sources; Thermal energy storage; 4th generation district heating; Peak load; Mismatch problem; Heating price model
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2021.122103

Due to heat prosumers' dual roles of heat producer and heat consumer, the future district heating (DH) systems will become more flexible and competitive. However, the current heating price models have not yet supported the reverse heat supply from prosumers to the central DH system, which means the prosumers would gain no economic benefit from supplying heat to the central DH system. These unidirectional heating price models will reduce interest in prosumers, and thus hinder the promotion of prosumers in DH systems. This study aimed to optimize prosumers' economic performance under the current heating price models by introducing water tank thermal energy storage (WTTES). A dynamic optimization problem was formulated to explore prosumers' economic potentials. The size parameter of WTTESs was swept in prosumers to obtain the optimal storage size considering the trade-off between the payback period and the heating cost saving. The proposed method was tested on a campus DH system in Norway. The results showed that the prosumer's annual heating cost was saved up to 9%, and the investment of WTTES could be recovered in less than ten years. This study could provide guidelines on improving prosumers' economic performance and promote the development of prosumers during the transformation period of DH systems.
•Explore heat prosumers' economic potential using a dynamic optimization approach.•Optimize TESs' storage size using parameter sweeping technique.•Study the impacts of peak load definition on prosumers by sensitivity analysis.•Improve heat prosumers' performances by peak load shaving and heat use saving.•Make a trade-off between TESs' payback period and heating cost saving.