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Modeling and Simulations of Demand Response in Sweden
KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. (Power system operation and control)ORCID iD: 0000-0002-4140-4736
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
Keyword [en]
demand response, demand side management, model predictive scheduling
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-215627ISBN: 978-91-7729-574-7 (print)OAI: oai:DiVA.org:kth-215627DiVA, id: diva2:1148343
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
List of papers
1. Assessment of Congestion Management Potential in Distribution Networks using Demand-Response and Battery Energy Storage
Open this publication in new window or tab >>Assessment of Congestion Management Potential in Distribution Networks using Demand-Response and Battery Energy Storage
2015 (English)In: Innovative Smart Grid Technologies Conference (ISGT), 2015 IEEE Power & Energy Society, IEEE conference proceedings, 2015Conference paper, Published paper (Refereed)
Abstract [en]

The integration of large shares of renewable energy sources in distribution grids runs the risk of outpacing the capacity of the network. Thus, high investment costs are expected at distribution system level to expand the existing grid to manage, among other challenges, anticipated congestion. This paper involves a study of the technical feasibility of using an ancillary service toolbox including day- and hour-ahead demand-response and battery energy storage as an alternative to grid expansion. The ancillary service toolbox is applied on radial distribution grids having large shares of renewable generation, controllable loads and power export capability to the overlaying power grid. The toolbox is simulated for a real use case presenting results on required demand-response participants and operation of flexibility resources for different congestion scenarios. The study concludes that the ancillary service solution is technically feasible for the use case, which may imply network investment deferral for distribution system operators.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-156854 (URN)10.1109/ISGT.2015.7131793 (DOI)2-s2.0-84939209588 (Scopus ID)
Conference
Innovative Smart Grid Technologies,February 17-20, 2015 Washington D.C., USA
Note

QC 20150304

Available from: 2014-12-03 Created: 2014-12-03 Last updated: 2017-10-10Bibliographically approved
2. Modeling Office Building Consumer Load with a Combined Physical and Behavioral Approach: Simulation and Validation
Open this publication in new window or tab >>Modeling Office Building Consumer Load with a Combined Physical and Behavioral Approach: Simulation and Validation
Show others...
2015 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 162, p. 472-485Article in journal (Refereed) Published
Abstract [en]

Due to an expanding integration of renewable energy resources in the power systems, mismatches between electricity supplyand demand will increase. A promising solution to deal with these issues is Demand Response (DR), which incentives end-users to be flexible in their electricity consumption. This paper presents a bottom up simulation model that generates office building electricity load profiles representative for Northern Europe. The model connects behavioral aspects of office workers with electricity usage from appliances, and physical representation of the building to describe the energy use of the Heating Ventilation and Air Conditioning systems. To validate the model, simulations are performed with respect to two data sets, and compared with real load measurements. The validation shows that the model can reproduce load profiles with reasonable accuracy for both data sets. With the presented model approach, it is possible to define simple portfolio office building models which subsequently can be used for simulation and analysis of DR in the power systems.

Place, publisher, year, edition, pages
Elsevier, 2015
Keyword
Office electricity demand, Office building design and architecture, HVAC system, Markov-chain models, Demand Response, Holistic.
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-175815 (URN)10.1016/j.apenergy.2015.10.141 (DOI)000367631000043 ()2-s2.0-84945571135 (Scopus ID)
Funder
SweGRIDS - Swedish Centre for Smart Grids and Energy Storage, 31
Note

QC 20160205. QC 20160207

Available from: 2015-10-22 Created: 2015-10-22 Last updated: 2017-12-01Bibliographically approved
3. MATLAB Applications to Generate Synthetic Electricity Load Profiles of Office Buildings and Detached Houses
Open this publication in new window or tab >>MATLAB Applications to Generate Synthetic Electricity Load Profiles of Office Buildings and Detached Houses
2017 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

In this paper we present two MATLAB applications that generates synthetic electricity load profiles for office buildings and detached houses down to 1-minute resolution. The applications have been developed using App Designer — a MATLAB environment for application development. The applications are based on consumer load models for office buildings and detached houses published in previous research work. The aim of this paper is to present an overview of the application functionalities, code design, assumptions and limitations, and examples of their potential use in power system education and research. To the author’s knowledge these are the first applications which allow generating synthetic load profiles for office buildings and houses in practical and intuitive manner where building attributes can be easily configured.

Keyword
MATLAB, App designer, Applications, Consumer load models, Synthetic load data
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-215626 (URN)
Conference
Innovative Smart Grid Technologies Asia
Note

QC 20171011

Available from: 2017-10-10 Created: 2017-10-10 Last updated: 2017-11-10Bibliographically approved
4. Anticipating Overrides of Schedulable Space Heating Systems in Detached Houses for Demand Response
Open this publication in new window or tab >>Anticipating Overrides of Schedulable Space Heating Systems in Detached Houses for Demand Response
(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.

Keyword
demand response, model predictive scheduling, ancillary services
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-215624 (URN)
Note

QC 20171011

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

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