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District Heating and Cooling Networks
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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.
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Keywords
- 4th generation district heating
- air-conditioning
- baseline model
- big data frameworks
- Biomass
- biomass district heating for rural locations
- CFD model
- CO2 emissions abatement
- computational fluid dynamics
- data center
- data mining algorithms
- data streams analysis
- district cooling
- district heating
- district heating (DH) network
- domestic
- energy consumption forecast
- Energy Efficiency
- energy management in renovated building
- energy prediction
- energy system modeling
- greenhouse gas emissions
- Gulf Cooperation Council
- heat pumps
- heat reuse
- hot climate
- hydronic pavement system
- low temperature district heating system
- low temperature networks
- low-temperature district heating
- Machine learning
- neural networks
- nZEB
- Optimal Control
- optimization
- parameter analysis
- prediction algorithm
- primary energy use
- residential
- retrofit
- Scotland
- space cooling
- Sustainable Energy
- thermal inertia
- thermal-hydraulic performance
- thermally activated cooling
- time delay
- TRNSYS
- twin-pipe
- ultralow-temperature district heating
- variable-temperature district heating
- Verification
Links
DOI: 10.3390/books978-3-03928-840-3Editions
