Thermal load prediction in district heating systems

被引:48
作者
Guelpa, Elisa [1 ]
Marincioni, Ludovica [1 ]
Capone, Martina [1 ]
Deputato, Stefania [1 ]
Verda, Vittorio [1 ]
机构
[1] Politecn Torino, Energy Dept, Corso Duca Abruzzi 24, I-10129 Turin, Italy
关键词
Load forecast; Demand prediction; Multi-level approach; Thermal network model; Thermal fluid dynamic; District heating network; ENERGY PERFORMANCE; SEASONAL STORAGE; OPTIMIZATION; MODEL; PUMPS; POWER; NETWORKS;
D O I
10.1016/j.energy.2019.04.021
中图分类号
O414.1 [热力学];
学科分类号
摘要
Optimal operation of district heating (DH) systems usually relies on the forecast of thermal demand profiles of the connected buildings. Depending on the purpose of the analysis, thermal request can be required at various levels, from building level to thermal plant level. In the case of demand response for example, thermal request is necessary at a building level to evaluate its applicability and at a plant level to determine the effects. Thermal request profiles are quite different, depending on the observation point. Total requests are not just the summation of the downstream requests, mainly because of the thermal transients. The heat losses also contributes to modify the curves, although generally in a smaller way. In this work, a multi-level thermal request prediction is proposed. This approach has the aim of evaluating the thermal request in the various sections of DH network with reduced computational resources. This includes a compact model for the prediction of building demand and a network model in order to compose together the requests at the various levels. The application to a portion of the Turin district heating network is proposed. This shows that the network dynamics significantly affects the evolution, especially at peak load. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页码:693 / 703
页数:11
相关论文
共 34 条
[1]  
[Anonymous], 2013, EUROHEAT POWER DISTR
[2]   Cooling load prediction for buildings using general regression neural networks [J].
Ben-Nakhi, AE ;
Mahmoud, MA .
ENERGY CONVERSION AND MANAGEMENT, 2004, 45 (13-14) :2127-2141
[3]   Tetradecane and hexadecane binary mixtures as phase change materials (PCMs) for cool storage in district cooling systems [J].
Bo, H ;
Gustafsson, EM ;
Setterwall, F .
ENERGY, 1999, 24 (12) :1015-1028
[4]   An inverse gray-box model for transient building load prediction [J].
Braun, JE ;
Chaturvedi, N .
HVAC&R RESEARCH, 2002, 8 (01) :73-99
[5]   Optimal lay-out and operation of combined heat & power (CHP) distributed generation systems [J].
Casisi, M. ;
Pinamonti, P. ;
Reini, M. .
ENERGY, 2009, 34 (12) :2175-2183
[6]  
Clarke JA, 2017, ENERGY SIMULATION BU
[7]   Key issues and solutions in a district heating system using low-grade industrial waste heat [J].
Fang, Hao ;
Xia, Jianjun ;
Jiang, Yi .
ENERGY, 2015, 86 :589-602
[8]   Compact physical model for simulation of thermal networks [J].
Guelpa, Elisa ;
Verda, Vittorio .
ENERGY, 2019, 175 :998-1008
[9]   Towards 4th generation district heating: Prediction of building thermal load for optimal management [J].
Guelpa, Elisa ;
Marincioni, Ludovica ;
Verda, Vittorio .
ENERGY, 2019, 171 :510-522
[10]   Model for optimal malfunction management in extended district heating networks [J].
Guelpa, Elisa ;
Verda, Vittorio .
APPLIED ENERGY, 2018, 230 :519-530