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A stochastic bottom-up model for space heating and domestic hot water load profiles for German households
KTH, School of Industrial Engineering and Management (ITM), Energy Technology. Fraunhofer Inst Solar Energy Syst, Germany.
2016 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 124, 120-128 p.Article in journal (Refereed) Published
Resource type
Text
Abstract [en]

In 2013 83% of energy in-the German residential sector is used for the preparation of domestic hot water (13%) and space heating (70%). Thermal demand profiles are essential to correctly determine operation and sizing of heating technologies. In this work, the stochastic bottom-up approach for electric loads is extended to cover domestic hot water (DHW) and space heating demands. The approach is presented for individual buildings and residential areas, validated and compared to currently used approaches. A behavioural model is used to determine DHW tappings, electric appliance use and temperature settings of the building. Building heat load is calculated using a simplified physical model, to allow for realistic energy demand profiles, efficient model parametrisation and fast computation. A randomisation approach for building heat load based on a clustered building typology, a variation of building parameters and heating settings is presented which allows the simulation of larger quantities of similar buildings. Validation against measured data for German single family houses shows a correlation of the typical daily load profile for DHW consumption of 0.92 and a mean relative error of 3% and for space heating 0.89 and 9% respectively.

Place, publisher, year, edition, pages
2016. Vol. 124, 120-128 p.
Keyword [en]
Household energy model, Residential areas, Load profiles, Space heating model, DHW modelling, Behavioural modelling
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-189790DOI: 10.1016/j.enbuild.2016.04.069ISI: 000378179400012Scopus ID: 2-s2.0-84966293333OAI: oai:DiVA.org:kth-189790DiVA: diva2:949610
Note

QC 20160721

Available from: 2016-07-21 Created: 2016-07-15 Last updated: 2017-09-15Bibliographically approved
In thesis
1. Integrating Heat Pumps into Smart Grids
Open this publication in new window or tab >>Integrating Heat Pumps into Smart Grids
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Transforming our energy system towards 100% renewable energy sources requires radical changes across all energy sectors. Heat pumps as efficient heat generation technology link the electricity and heat sector. From 2010 to 2015 between 750,000 and 800,000 heat pumps have been sold every year across Europe. Those heat pumps, when connected to thermal storage or using the buildings’ thermal inertia, have the potential to offer demand flexibility to the power system. In a renewable, interconnected and to a large extend decentralised energy system - the smart grid - heat pumps can be operated according to the need of the electric power system. This might impact heat pump system design, controls and operation, which is investigated in this thesis. The main objective of this thesis is to add knowledge and suggest methods to facilitate the transition towards a renewable and smart energy system, in which heat pump systems and their flexibility are used and designed in an optimal way. Therefore this thesis investigates the integration of heat pumps in a smart grid on three different system boundary levels. On each boundary level the focus is on different aspects and different methods are used. On the highest boundary level, the integration of heat pumps into a smart grid and the resulting requirements for heat pump system design are studied. Results of a literature study show, that currently discussed applications of heat pumps in a smart grid focus on the provision of ancillary services, the integration of renewable energy sources, and the operation under time variable electricity prices. Integrating heat pumps into the power system can be achieved by direct, indirect and agent based control strategies. The next level of investigation covers the aggregation of heat pumps into pools. For this purpose a stochastic bottom-up model for heat pump pools has been developed. This model accounts for the diversity of buildings, heat pump systems and occupants. Results of a simulation study of a heat pump pool highlight the fact that flexibility of heat pumps is not constant and is changing during the course of the day and year. A characteristic response of a heat pump pool towards direct load control signals is identified and shows three characteristic phases 1) charging/ activation phase, 2) steady state phase, 3) discharging/regeneration phase. It is found that the duration of the control signal and the load shift strategy implemented in the heat pump systems are decisive for flexibility. Further it is shown that flexibility might come at the cost of efficiency of the local heat pump systems. On the level of individual buildings this thesis explores to which extent the sizing of heat pumps, storage and back-up heater as well as system controls have to be adjusted when integrating heat pumps into a smart grid. Results of a structural optimisation study, targeting to minimise total cost of ownership, show that sizing of the heat pump unit and the electric back-up heater remain almost unchanged when PV and time variable electricity prices are introduced. However an increase in storage capacity is beneficial to profit from time variable prices or onsite photovoltaic (PV). It showed that the ways heat pumps and storages are sized in Germany today provided sufficient storage capacity for most of the investigated scenarios. Furthermore increasing storage leads to diminishing returns as investment costs and system losses increase with increasing storage size. This leads to the conclusion that local heat pump system efficiency as well as flexibility requirements of the power system should be considered, when designing heat pump systems. Improving the controls shows great potential for increasing heat pump system efficiency, reducing operation cost and scheduling heat pump operation along to match the requirements from the power system. A dynamic building simulation study, where rule-based, predictive rule-based and model predictive control approaches were compared, reveals that the use of model predictive controls can reduce annual electricity cost and increase PV self-consumption significantly, compared to tailored rule-based and predictive rule-based control approaches. When deciding upon a control strategy the following should be taken into account: complexity of design, robustness against changes in external conditions and computational resources. It is shown that operating heat pumps in a smart grid changes operating hours, temperatures, on/off cycles and seasonal performance compared to today’s heatdriven operation. It is shown that the goals to reduce operating cost, maximise system efficiency or increase PV self-consumption can be conflicting and are often impossible to achieve simultaneously. Not necessarily will operation in a smart grid increase the efficiency of individual systems, rather offers the possibility to increase efficiency of the overall energy system. It is found that sizing, controls and use-case are interconnected and should be considered simultaneously in the design process of heat pump systems. A goal for future research should be the design of optimum flexible heat pump systems, where the heat pump unit, the building, the hydraulic system, heat distribution, storage and controls are designed optimally for the flexibility requirements of both the end-users and the power system.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2017. 96 p.
Series
TRITA-REFR, ISSN 1102-0245 ; 17/03
National Category
Energy Engineering
Research subject
Energy Technology
Identifiers
urn:nbn:se:kth:diva-214444 (URN)978-91-7729-503-7 (ISBN)
Public defence
2017-09-29, Kollegiesalen, Brinellvägen, 68, Stockholm, 10:00 (English)
Opponent
Supervisors
Available from: 2017-09-15 Created: 2017-09-14 Last updated: 2017-09-18Bibliographically approved

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