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  • 1.
    Averfalk, Helge
    et al.
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).
    Werner, Sven
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).
    Essential improvements in future district heating systems2017In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 116, p. 217-225Article in journal (Refereed)
    Abstract [en]

    The major common denominator for future efficient fourth generation district heating systems is lower temperature levels in the distribution networks. Higher efficiencies are then obtained in both heat supply and heat distribution. Heat supply becomes more efficient with respect to combined heat and power, flue gas condensation, heat pumps, geothermal extraction, low temperature excess heat, and heat storage. Heat distribution becomes more efficient from lower distribution losses, less pipe expansion, lower scalding risks, and plastic pipes. The lower temperature levels will be possible since future buildings will have lower temperature demands when requiring lower heat demands. This paper aims at providing seven essential recommendations concerning design and construction strategies for future fourth generation systems. The method used is based on a critical examination of the barriers for lower temperature levels and the origins of high return temperatures in contemporary third generation systems. The two main research questions applied are: Which parts of contemporary system design are undesirable? Which possible improvements are desirable? Key results and the corresponding recommendations include temperature levels for heat distribution, recirculation, metering, supervision, thermal lengths for heat exchangers and heat sinks, hydronic balancing, and legionella. The main conclusion is that it should be possible to construct new fourth generation district heating networks according to these seven essential recommendations presented in this paper. © 2017 The Authors. Published by Elsevier

  • 2.
    Calikus, Ece
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pinheiro Sant'Anna, Anita
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Ranking Abnormal Substations by Power Signature Dispersion2018In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 149, p. 345-353Article in journal (Refereed)
    Abstract [en]

    The relation between heat demand and outdoor temperature (heat power signature) is a typical feature used to diagnose abnormal heat demand. Prior work is mainly based on setting thresholds, either statistically or manually, in order to identify outliers in the power signature. However, setting the correct threshold is a difficult task since heat demand is unique for each building. Too loose thresholds may allow outliers to go unspotted, while too tight thresholds can cause too many false alarms.

    Moreover, just the number of outliers does not reflect the dispersion level in the power signature. However, high dispersion is often caused by fault or configuration problems and should be considered while modeling abnormal heat demand.

    In this work, we present a novel method for ranking substations by measuring both dispersion and outliers in the power signature. We use robust regression to estimate a linear regression model. Observations that fall outside of the threshold in this model are considered outliers. Dispersion is measured using coefficient of determination R2 which is a statistical measure of how close the data are to the fitted regression line.

    Our method first produces two different lists by ranking substations using number of outliers and dispersion separately. Then, we merge the two lists into one using the Borda Count method. Substations appearing on the top of the list should indicate higher abnormality in heat demand compared to the ones on the bottom. We have applied our model on data from substations connected to two district heating networks in the south of Sweden. Three different approaches i.e. outlier-based, dispersion-based and aggregated methods are compared against the rankings based on return temperatures. The results show that our method significantly outperforms the state-of-the-art outlier-based method. © 2018 The Authors. Published by Elsevier Ltd.

  • 3.
    Gong, Mei
    et al.
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).
    Werner, Sven
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).
    Mapping energy and exergy flows of district heating in Sweden2017In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 116, p. 119-127Article in journal (Refereed)
    Abstract [en]

    District heating has been available in Sweden since the 1950s and used more than half of the total energy use in dwelling and no-residential premises in 2013. Energy and exergy efficient conversion and energy resources are key factors to reduce the environmental impact. It is important to understand energy and exergy flows from both the supply and demand sides. The exergy method is also a useful tool for exploring the goal of more efficient energy-resource use. Sankey diagrams together with energy and exergy analyses are presented to help policy/decision makers and others to better understand energy and exergy flows from primary energy resource to end use. The results show the most efficient heating method in current district heating systems, and the use of renewable energy resources in Sweden. It is exergy inefficient to use fossil fuels to generate low quality heat. However, renewable energies, such as geothermal and solar heating with relative low quality, make it more exergy efficient. Currently, about 90% of the energy sources in the Swedish district heating sector have an origin from non-fossil fuels. Combined heat and power is an efficient simultaneous generator of electricity and heat as well as heat pump with considering electricity production. Higher temperature distribution networks give more distribution losses, especially in exergy content. An outlook for future efficient district heating systems is also presented.

  • 4.
    Lygnerud, Kristina
    et al.
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).
    Werner, Sven
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).
    Risk of industrial heat recovery in district heating systems2017In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 116, p. 152-157Article in journal (Refereed)
    Abstract [en]

    Industrial heat recovery can be used in district heating systems. It is a possibility to make use of heat that is otherwise lost. Increased usage of industrial heat recovery reduces the need for fuel combustion lowering green-house gas (GHG) emissions, such as CO2. Industrial companies can, however, move or close down industrial activities. This is apprehended as a risk and lowers the interest of district heating companies to invest in industrial heat recovery.

    In Swedish district heating systems, industrial heat recoveries have been undertaken since 1974. Today, the heat recovery is active in about seventy systems. This leads to the question of how risky it is, for district heating companies, to engage in industrial heat recovery.

    Over forty years of operation statistics have been collected and analyzed in order to estimate the risk of industrial heat recovery to district heating companies. Key results show that the risk is not linked to different industrial branches. Recommendations include suggestions to management on how to consider risk and consequence when assessing potential industrial heat recovery investments. © 2017 The Authors. Published by Elsevier Ltd.

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