Reprint

District Heating and Cooling Networks

Edited by
May 2020
270 pages
  • ISBN978-3-03928-839-7 (Paperback)
  • ISBN978-3-03928-840-3 (PDF)

This book is a reprint of the Special Issue District Heating and Cooling Networks that was published in

Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary
Conventional thermal power generating plants reject a large amount of energy every year. If this rejected heat were to be used through district heating networks, given prior energy valorisation, there would be a noticeable decrease in the amount of fossil fuels imported for heating. As a consequence, benefits would be experienced in the form of an increase in energy efficiency, an improvement in energy security, and a minimisation of emitted greenhouse gases. Given that heat demand is not expected to decrease significantly in the medium term, district heating networks show the greatest potential for the development of cogeneration. Due to their cost competitiveness, flexibility in terms of the ability to use renewable energy resources (such as geothermal or solar thermal) and fossil fuels (more specifically the residual heat from combustion), and the fact that, in some cases, losses to a country/region’s energy balance can be easily integrated into district heating networks (which would not be the case in a “fully electric” future), district heating (and cooling) networks and cogeneration could become a key element for a future with greater energy security, while being more sustainable, if appropriate measures were implemented. This book therefore seeks to propose an energy strategy for a number of cities/regions/countries by proposing appropriate measures supported by detailed case studies.
Format
  • Paperback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
district heating; energy efficiency; baseline model; energy prediction; verification; low temperature district heating system; biomass district heating for rural locations; 4th generation district heating; CO2 emissions abatement; district heating; biomass; energy management in renovated building; nZEB; district cooling; space cooling; air-conditioning; hot climate; thermally activated cooling; sustainable energy; Gulf Cooperation Council; district heating; energy efficiency; optimization; heat pumps; low temperature networks; data center; heat reuse; Computational Fluid Dynamics; prediction algorithm; neural networks; district heating (DH) network; CFD model; optimal control; time delay; parameter analysis; low-temperature district heating; ultralow-temperature district heating; variable-temperature district heating; twin-pipe; thermal-hydraulic performance; thermal inertia; hydronic pavement system; district heating; primary energy use; energy system modeling; greenhouse gas emissions; district heating; residential; domestic; Scotland; TRNSYS; retrofit; big data frameworks; data mining algorithms; machine learning; energy consumption forecast; data streams analysis