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Anticipating Overrides of Schedulable Space Heating Systems in Detached Houses for Demand Response
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. (Power system operation and control)
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. (Power system operation and control)ORCID iD: 0000-0002-2014-0444
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. KTH, Superseded Departments (pre-2005), Electrical Systems. KTH, School of Industrial Engineering and Management (ITM), Production Engineering. (Power system operation and control)ORCID iD: 0000-0003-3014-5609
(English)In: Article in journal (Other academic) Submitted
Abstract [en]

In this paper we propose and evaluate two cases of a model predictive scheduling approach to anticipate overrides of schedulable electric space heating systems in detached houses. We assume a demand-response set-up where the space heating systems of a population of heterogeneous detached houses are scheduled over a finite horizon with the objective of having their aggregated space heating load follow a desired load profile. We envision that the desired load profile provides hourly to sub-hourly ancillary services to electricity market actors and define schedule overrides as the interruption of demand response following a violation of the indoor temperature comfort in a house. We use a model to represent the indoor temperature change in detached houses on minute resolution which considers, among other variables, weather- and individual behavioral-related heat gains and losses in the building. The model predictive scheduling approach is evaluated on a use-case consisting of a balance responsible player looking to minimize its daily expected power imbalances on the intraday market. The scheduling is performed on 100 detached houses participating in demand-response for two predictive cases and a non-predictive case for comparison. The predictive cases differ in the level of information known about thebuilding attributes of the population. Simulations are performed for 90 consecutive days corresponding to a Swedish winter period where results indicate power imbalance reductions of up to 30% and notable differences between predictive cases.

Keywords [en]
demand response, model predictive scheduling, ancillary services
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-215624OAI: oai:DiVA.org:kth-215624DiVA, id: diva2:1148331
Note

QC 20171011

Available from: 2017-10-10 Created: 2017-10-10 Last updated: 2017-10-11Bibliographically approved
In thesis
1. Modeling and Simulations of Demand Response in Sweden
Open this publication in new window or tab >>Modeling and Simulations of Demand Response in Sweden
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Electric power systems are undergoing a paradigm shift where an increasing number of variable renewable energy resources such as wind and solar power are being introduced to all levels of existing power grids. At the same time consumers are gaining a more active role where self energy production and home automation solutions are no longer uncommon. This challenges traditional power systems which were designed to serve as a centralized top-down solution for providing electricity to consumers. Demand response has risen as a promising solution to cope with some of the challenges that this shift is creating. In this thesis, control and scheduling studies using demand response, and consumer load models adapted to environments similar to Sweden are proposed and evaluated. The studies use model predictive control approaches for the purpose of providing ancillary and financial services to electricity market actors using thermal flexibility from detached houses. The approaches are evaluated on use-cases using data from Sweden for the purpose of reducing power imbalances of a balance responsible player and congestion management for a system operator. Simulations show promising results for reducing power imbalances by up to 30% and managing daily congestion of 5-19 MW using demand response. Moreover, a consumer load model of an office building is proposed using a gray-box modeling approach combining physical understanding of buildings with empirical data. Furthermore, the proposed consumer load model along with a similar model for detached houses are packaged and made freely available as MATLAB applications for other researchers and stakeholders working with demand response. The applications allow the user to generate synthetic electricity load profiles for heterogeneous populations of detached houses and office buildings down to 1-min resolution. The aim of this thesis has been to summarize and discuss the main highlights of the included articles. The interested reader is encouraged to investigate further details in the second part of the thesis as they provide a more comprehensive account of the studies and models proposed.

Place, publisher, year, edition, pages
Stockholm, Sweden: KTH Royal Institute of Technology, 2017. p. 55
Series
TRITA-EE, ISSN 1653-5146 ; 2017:148
Keywords
demand response, demand side management, model predictive scheduling
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-215627 (URN)978-91-7729-574-7 (ISBN)
Presentation
2017-11-10, F3, Lindstedtsvägen 26, Stockholm, 10:30 (English)
Opponent
Supervisors
Note

QC 20171011

Available from: 2017-10-11 Created: 2017-10-10 Last updated: 2017-11-13Bibliographically approved

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Armendariz, MikelNordström, Lars

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