Value analysis of district heating system with gas-fired peak load boiler in secondary network

In district heating(DH) system with gas-fired peak load regulating boiler in the secondary network,by prolonging run time of base load plants under rated condition,the mean energy efficiency could be increased. The fuels of the system,including coal and gas,would cause different environmental impacts. Meanwhile,the reliability of the heating networks would be changed because the peak load regulating boiler could work as a standby heat source. A model for assessment of heating system was established by value analysis to optimize this kind of system. Energy consumption,greenhouse gas emission,pollution emission and system reliability were selected as functional assessment indexes in the model. Weights of each function were determined by analytical hierarchy process (AHP) and experts consultation. Life cycle cost was used as the cost in the model. A real case as an example was discussed to obtain the optimal base load ratio. The result shows that the optimal base load ratio of the case is 0.77.

The Preliminary Research of Sea Water District Heating and Cooling for Tallinn Coastal Area

This paper describes possibilities to utilize sea water for district heating and cooling purposes in Tallinn costal area. The sea water temperature profiles and suitability of heating and cooling generation are studied for continental climatic conditions. The district network study bases on 21 buildings located near to the Gulf of Finland. Industrial reversible heat pump technology is selected to cover heating and cooling loads for the new buildings. Combination of existing district heating and heat pump technology is considered for existing buildings. The results show possibilities, threats and need for further research of the sea water based heat pump district network implementation.

Wireless Sensor Networks for Optimisation of District Heating

The upgrading of the DH （district heating） system through installing WSN （wireless sensor networks）--a technology by which to monitor and control quality operation of the DH system will lead to more effective use of thermal energy, enabling also the provision of quality customer services, as the data concerning the status of the existing networks is available in a timely manner, and in the stated amounts. Over the last decades, the use of WSN systems in enabling quality monitoring of heat production and supply process has been widely discussed among various researchers and industry experts, but has been little deployed in practice. These researchers and industry experts have analysed the advantages and constraints related to the use of the WSN in district heating. A pilot project conducted by Riga Heat （the main heating supplier in Riga, Latvia） has allowed to gain a real life experience as to the use of the WSN system in district in-house heating substations, and is deemed to be a major step towards future development of WSN technologies.

Test and Analysis of Indoor Thermal Environment of Urban Heating Residential Building in Hanzhong Area

By testing indoor and outdoor thermal environment of residential buildings that apply 4 mostused heating ways in Hantai District,Hanzhong City,this paper explored the indoor thermal environment conditions of different heating ways,to provide references for choosing a suitable heating way in the local area.

Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest energy.Therefore,effective design and planning for estimating heating load(HL)and cooling load(CL)for energy saving have become paramount.In this vein,efforts have been made to predict the HL and CL using a univariate approach.However,this approach necessitates two models for learning HL and CL,requiring more computational time.Moreover,the one-dimensional(1D)convolutional neural network(CNN)has gained popularity due to its nominal computa-tional complexity,high performance,and low-cost hardware requirement.In this paper,we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D CNN.Considering the building shape characteristics,one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps,a dense layer to interpret the maps,and an output layer with two neurons to predict the two real-valued responses,HL and CL.As the 1D data are not affected by excessive parameters,the pooling layer is not applied in this implementation.Besides,the use of pooling has been questioned by recent studies.The performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error(MSE).Thus,the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE.

Topology reduction through machine learning to accelerate dynamic simulation of district heating

District heating networks (DHNs) provide an efficient heat distribution solution in urban areas, accomplished through interconnected and insulated pipes linking local heat sources to local consumers. This efficiency is further enhanced by the capacity of these networks to integrate renewable heat sources and thermal storage systems. However, integration of these systems adds complexity to the physical dynamics of the network, necessitating complex dynamic simulation models. These dynamic physical simulations are computationally expensive, limiting their adoption, particularly in large-scale networks. To address this challenge, we propose a methodology utilizing Artificial Neural Networks (ANNs) to reduce the computational time associated with the DHNs dynamic simulations. Our approach consists in replacing predefined clusters of substations within the DHNs with trained surrogate ANNs models, effectively transforming these clusters into single nodes. This creates a hybrid simulation framework combining the predictions of the ANNs models with the accurate physical simulations of remaining substation nodes and pipes. We evaluate different architectures of Artificial Neural Network on diverse clusters from four synthetic DHNs with realistic heating demands. Results demonstrate that ANNs effectively learn cluster dynamics irrespective of topology or heating demand levels. Through our experiments, we achieved a 27% reduction in simulation time by replacing 39% of consumer nodes while maintaining acceptable accuracy in preserving the generated heat powers by sources.

Economic Model Predictive Control for Hot Water Based Heating Systems in Smart Buildings

This paper presents a study to optimize the heating energy costs in a residential building with varying electricity price signals based on an Economic Model Predictive Controller (EMPC). The investigated heating system consists of an air source heat pump (ASHP) incorporated with a hot water tank as active Thermal Energy Storage (TES), where two optimization problems are integrated together to optimize both the ASHP electricity consumption and the building heating consumption utilizing a heat dynamic model of the building. The results show that the proposed EMPC can save the energy cost by load shifting compared with some reference cases.

Neural Network Based Feasible Region Approximation Model for Optimal Operation of Integrated Electricity and Heating System

This paper proposes a neural network based feasible region approximation model of a district heating system(DHS),and it is intended to be used for optimal operation of integrated electricity and heating system(IEHS)considering privacy protection.In this model,a neural network is trained to approximate the feasible region of the DHS operation and then is reformulated as a set of mixed-integer linear constraints.Based on the received approximation models of DHSs and detailed electricity system model,the electricity operator conducts centralized optimization,and then sends specific heating generation plans back to corresponding heating operators.Furthermore,subsequent optimization is formulated for each DHS to obtain detailed operation strategy based on received heating generation plan.In this scheme,optimization of the IEHS could be achieved and privacy protection requirement is satisfied since the feasible region approximation model does not contain detailed system parameters.Case studies conducted on a small-scale system demonstrate accuracy of the proposed strategy and a large-scale system verify its application possibility.

Enhancing hourly heat demand prediction through artificial neural networks:A national level case study

Meeting the goal of zero emissions in the energy sector by 2050 requires accurate prediction of energy consumption,which is increasingly important.However,conventional bottom-up model-based heat demand forecasting methods are not suitable for large-scale,high-resolution,and fast forecasting due to their complexity and the difficulty in obtaining model parameters.This paper presents an artificial neural network(ANN)model to predict hourly heat demand on a national level,which replaces the traditional bottom-up model based on extensive building simulations and computation.The ANN model significantly reduces prediction time and complexity by reducing the number of model input types through feature selection,making the model more realistic by removing non-essential inputs.The improved model can be trained using fewer meteorological data types and insufficient data,while accurately forecasting the hourly heat demand throughout the year within an acceptable error range.The model provides a framework to obtain accurate heat demand predictions for large-scale areas,which can be used as a reference for stakeholders,especially policymakers,to make informed decisions.

Experimental and Numerical Analysis of a PCM-Integrated Roof for Higher Thermal Performance of Buildings

Phase change materials(PCMs) designate materials able to store latent heat.PCMs change state from solid to liquid over a defined temperature range.This process is reversible and can be used for thermo-technical purposes.The present paper aims to study the thermal performance of an inorganic eutectic PCM integrated into the rooftop slab of a test room and analyze its potential for building thermal management.The experiment is conducted in two test rooms in Antofagasta(Chile) during summer,fall,and winter.The PCM is integrated into the rooftop of the first test room,while the roof panel of the second room is a sealed air cavity.The work introduces a numerical model,which is built using the finite difference method and used to simulate the rooms' thermal behavior.Several thermal simulations of the PCM room are performed for other Chilean locations to evaluate and compare the capability of the PCM panel to store latent heat thermal energy in different climates.Results show that the indoor temperature of the PCM room in Antofagasta varies only 21.1℃±10.6℃,while the one of the air-panel room varies 28.3℃±18.5℃.Under the experiment's conditions,the PCM room's indoor temperature observes smoother diurnal fluctuations,with lower maximum and higher minimum indoor temperatures than that of the air-panel room.Thermal simulations in other cities show that the PCM panel has a better thermal performance during winter,as it helps to maintain or increase the room temperature by some degrees to reach comfort temperatures.This demonstrates that the implementation of such PCM in the building envelope can effectively reduce space heating and cooling needs,and improve indoor thermal comfort in different climates of Chile.

Optimized design and integration of energy storage in Solar-Assisted Ground-Source Heat Pump systems

The integrated use of multiple renewable energy sources to increase the efficiency of heat pump systems,such as in Solar Assisted Geothermal Heat Pumps(SAGHP),may lead to significant benefits in terms of increased efficiency and overall system performance especially in extreme climate contexts,but requires careful integrated optimization of the different system components.In particular,thermal storages take a fundamental role in optimizing the integration of renewable energy sources and the system operation.This work investigates the potential design optimization of a SAGHP system in a mountain site by exploring many different alternatives to optimize the mutual relationship between the solar field,the geothermal field and the water thermal storages.This is done through an original simulation-based multi-objective optimization framework considering energy efficiency and economic feasibility,which allows appraising the impact of the different design alternatives on the overall system performance and on the dynamics of the different system components.Results identify a set of optimized system configurations that optimize the integrated exploitation of the different thermal sources showing a potential increase of the overall system performance leading to 34%lower global cost compared to the initial design.High robustness of the optimal design solutions is reported with respect to the current context of high economic uncertainty.

一种墙体蓄热量的评价方法研究

针对目前围护结构蓄热性能评价方法和指标不能有效评价蓄热量的问题,该文基于“日间热容”概念和有限差分法,提出一种新的围护结构蓄热性能评价方法和相关指标。该方法可获得围护结构逐时动态蓄热量、不同构造层次的蓄热情况以及材料层和围护结构在一定时间段和一定面积内的累积蓄热量。以一个简化的建筑模型为例,运用该方法得出建筑不同朝向、不同部位围护结构各材料层的蓄热量情况。该文的方法可为建筑围护结构实际蓄热效果提供系统全面的定量评价,为太阳能建筑分朝向、分部位的蓄热设计提供方法和依据。

石膏基复合相变储能材料的研究进展

建筑能耗、工业能耗和交通能耗是能源消耗的主要方式。其中,建筑能耗约占能源消耗的40%,建筑能耗的持续上升会增加碳排放和加速化石能源的消耗,因此,如何提升建筑材料的保温节能性能逐渐成为建筑材料领域的研究热点。储热技术不仅可以降低建筑能耗,还可以减少环境污染。相变储能材料具有优异的储放热能力,是实现热能储存以及温度控制的重要技术手段,在建筑节能领域具有广阔的应用前景。该文主要综述了石膏基相变储能材料的研究进展;根据石膏基相变材料的不同,分析归纳了石膏基有机相变材料和石膏基复合相变材料;介绍了浸渍法、多孔材料吸附法、微胶囊法等制备石膏基复合相变储能材料的方法和机理及影响石膏基相变储能材料的因素;展望了石膏基相变储能材料的研究方向。

储热技术研究展望

近年来,储热技术被广泛认为是实现碳中和、碳达峰的一项关键技术备受关注。通过从CNKI、Web of Science等数据库中筛选储热技术相关文献,运用CiteSpace软件进行知识映射,展开系统分析、统计及可视化,绘制出储热技术研究力量合作网络图谱,展示该技术研究力量的分布与科研合作情况。同时针对关键词进行分析,总结储热技术的研究热点、研究前沿及发展趋势,指出相变储热和混合储热模式是未来研究的重点。针对储热材料稳定性差、使用寿命短,有机相变材料成本高、安全性低,系统设备初始造价高、成本回收期长等储热技术现存问题,从政策干预和市场需求角度提出了改进建议。

Dynamic modeling of thermal conditions for hot-water district-heating networks

To investigate the dynamic characteristics of the thermal conditions of hot-water district-heating networks, a dynamic modeling method is proposed with consideration of the heat dissipations in pipes and the characteristic line method is adopted to solve it. Besides, the influences of different errors, space steps and initial values on the convergence of the dynamic model results are analyzed for a model network. Finally, a part of a certain city district-heating system is simulated and the results are compared with the actual operation data in half an hour from 6 secondary heat stations. The results indicate that the relative errors for the supply pressure and temperature in 5 stations are all within 2%, except in one station, where the relative error approaches 4%. So the proposed model and algorithm are validated.

Biomass-fuelled combined heat and power: integration in district heating and thermal-energy storage

Conventional approaches towards energy-system modelling and operation are based upon the system design and performance optimization.In system-design optimization,the thermal or mechanical characteristics of the systems providing for the heat or electricity demands were derived separately without integration with the energy source and without interaction with demand,which results in low-efficiency energy performance.This paper presents a key review on the integration of biomass-powered combined heat and power(BCHP)systems in district-heating systems as well as coupling with thermal-energy storage.In BCHP design,the appropriate sizing of the associated components as part of the district-heating system is very important to provide the optimal dispatch strategy as well as minimized cost and environmental impact while it co-operates with thermal-energy storage.Future strategies for the feasibility,evaluation and integration of biomass-powered energy systems in the context of district systems are also studied.

矩形翅片管糖醇相变储热器传热性能分析

对糖醇基矩形翅片管相变储热器传热性能进行数值模拟与实验研究,利用数值模型研究传热流体入口温度、流量以及储热器翅片间距、管间距等参数对充放热性能的影响。研究表明:从运行参数看,由于相变材料导热系数的影响,翅片强化区外流体入口温度与材料相变温度的温差对充放热速率呈正相关;在管内湍流条件下,管内流速在高于0.53 m/s时,不能显著提升储热器的充放热速率;从结构参数看,增加翅片可显著提升翅片区内充放热速率,翅片间距小于10 mm和管间距小于52.5 mm对充放热速率影响不明显。

利用数据中心余热供热的系统设计与分析

数据中心余热供热是提高数据中心能源利用效率和实现冬季清洁供暖的重要途径。本文提出了一种适用于数据中心的长距离供热系统,在热源和热汇处设置多级热泵以增大供热管网供回水温差,并使用储热装置保障数据中心和供热系统的安全性,提高数据中心余热利用率。以张家口某数据中心为例进行了分析,该系统初投资为50.21元/m^(2),运行费用为26.37元/GJ,投资回收期约为5.31 a,具有较好的经济性和节能减排效果。

某调度通信楼空调系统节能减碳改造设计实践

重点介绍了某调度通信楼空调系统的节能减碳改造设计,通过综合运用余热回收、可再生能源、固体蓄热、高效制冷机房等多项技术,构建了安全可靠、绿色低碳、高效节能的空调系统。采用HDY-SMAD空调负荷计算及分析软件模拟了建筑物全年的空调能耗,并对优化前后空调系统的运行能耗和碳排放进行了测算。结果显示,优化方案空调系统全年节约电量100万kW·h,全年碳减排573 t,节能减碳效果显著。

热网蓄热特性及其对机组调峰能力的影响研究

为了提高热电联产机组调峰能力,利用热网蓄热实现机组的“热电解耦”。建立了某350 MW热电联产机组及其热网系统的动态仿真模型,研究了热网及其热用户在蓄/放热阶段的动态特性及环境温度对机组调峰能力的影响规律。结果表明:热用户滞后时间随着热用户与换热首站的距离的增加而增加,同时也受自身热负荷的直接影响和其余热用户热负荷的间接影响;热网相对蓄/放热功率在改变抽汽质量流量后先迅速变化再缓慢变化;热用户温度近似呈线性变化,其变化速率随着抽汽质量流量变化幅度的增大而增大,且开始变化时间也随之提前;机组最大调峰能力随着环境温度降低而略有增加,环境温度从0℃下降至-15℃时,机组调峰能力从23.8 MW增加至29.7 MW;增加蓄热时长,提高蓄热温度,降低放热温度均有利于提高机组调峰能力。