Applied Sciences · 2019 · 34 citations · 13 references
EngineeringArchitectural EngineeringMechanical EngineeringEnergy ManagementEnergy SystemsHeatingBuilding Energy SystemsDemand ResponseDemand-side ManagementDistrict CoolingDistrict HeatingRenewable EnergyCoolingDemand Response OperationDemand-side Heating ManagementDistributed Energy Resources IntegrationDistrict Energy IntegrationDistrict Heating NetworksIntegration With Renewable EnergyLow-temperature District Heating
District heating and cooling networks can pose the possibility of including a variety of renewable energy sources as well as waste heat into a district’s heat supply concept. Unfortunately, low demand densities as they increasingly occur through higher building energy standards and in rural areas render conventional heating and cooling networks inefficient. At the same time, power-to-heat is becoming more and more important to make use of a larger amount of renewable energy sources on the electrical side by providing more flexibility by means of demand response and demand-side management. Within this work, a rural Plus-Energy settlement is presented addressing those topics by a low-temperature district heating and cooling network connected to a novel agrothermal collector supplying 23 residential buildings with decentralized heat pumps and PV systems. The collector, the network, and six of the buildings are equipped with comprehensive monitoring equipment. Within those buildings, forecast and optimization algorithms are implemented to adapt their heat pump operation to enable an increase of self-consumption, to include flexible electricity tariffs, and also to participate in power markets. Thereby, for the low-temperature district heating and cooling network, it has been shown that the concept can operate in the future at competitive heat costs. On the building level, up to 50% of cost savings could be achieved under ideal conditions with the optimization of the self-consumption of PV electricity. However, to ensure optimal results, the individual system components have to be dimensioned for this task.
13
Scikit-learn: Machine Learning in Python
Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · arXiv (Cornell University) · 2012 · 63.7K citations · Full text
4th Generation District Heating (4GDH)
Henrik Lund, S. Werner, Robin Wiltshire et al. · Energy · 2014 · 2.1K citations
Engineering, Architectural Engineering, Mechanical Engineering +12
Rishee K. Jain, Kevin M. Smith, Patricia J. Culligan et al. · Applied Energy · 2014 · 592 citations
Architecture, Architectural Engineering, Building Science +17
Time-based pricing and electricity demand response: Existing barriers and next steps
Cherrelle Eid, Elta Koliou, Mercedes Vallés et al. · Utilities Policy · 2016 · 267 citations · Full text