Performance assessment of a multi-source heat production system with storage for district heating

被引:5
|
作者
Descamps, M. N. [1 ]
Leoncini, G. [1 ]
Vallee, M. [1 ]
Paulus, C. [1 ]
机构
[1] CEA, LITEN, 17 Rue Martyrs, F-38054 Grenoble, France
基金
欧盟地平线“2020”;
关键词
Hybrid District Heating; Power to Heat; Dynamic Simulation; ENERGY;
D O I
10.1016/j.egypro.2018.08.203
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This work contributes to the development of a multi-vector flexibility management platform, combining electric, heat and gas optimization at district level. The multi-vector flexibility management platform will be validated both experimentally and by simulation, on a set of demonstration scenarios. Each scenario refers to an eco-district topology with a given distribution network of energy, a consumer side and a multi-source heat production plant, consisting of a gas boiler, a solar collector, and a heat pump. In addition, a thermal storage in the form of a water tank is connected to the network. A key aspect is the ability to optimize such a system at district level, with the performance of the individual components depending on operating temperatures and environmental conditions, and varying primary energy prices. By simulating the distribution network with a dynamic model, the non-linear influence of various parameters on the system can be investigated. In particular, the current study focuses on the operation of the multi-source heat production system and thermal storage. A 1D model of the multi-source heat production (gas, solar and heat pump), the thermal storage, and a global consumer is performed using the equation-based object-oriented language Modelica along with the simulation platform Dymola. The model is run with standard controls from district heating provider, i.e. constant or linear controls. For a given consumer load, a set of key performance indicators (KPI) are used to assess the performance of the system, e.g. energy share from renewables and storage utilization rate. The sensitivity to the model's input is analyzed as well. The results can be used as reference to apply optimal control schemes and study the influence on the corresponding KPIs. (C) 2018 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:390 / 399
页数:10
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