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Modeling and Simulation of Electricity Consumption Profiles in the Northern European Building Stock
KTH, School of Electrical Engineering (EES), Electric power and energy systems.ORCID iD: 0000-0002-9860-4472
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The electric power systems are currently being transformed through the integration of intermittent renewable energy resources and new types of electric loads. These developments run the risk of increasing mismatches between electricity supply and demand, and may cause non-favorable utilization rates of some power system components. Using Demand Response (DR) from flexible loads in the building stock is a promising solution to overcome these challenges for electricity market actors. However, as DR is not used at a large scale today, there are validity concerns regarding its cost-benefit and reliability when compared to traditional investment options in the power sector, e.g. network refurbishment. To analyze the potential in DR solutions, bottom-up simulation models which capture consumption processes in buildings is an alternative. These models must be simple enough to allow aggregations of buildings to be instantiated and at the same time intricate enough to include variations in individual behaviors of end-users. This is done so the electricity market actor can analyze how large volumes of flexibility acts in various market and power system operation contexts, but also can appreciate how individual end-users are affected by DR actions in terms of cost and comfort.

The contribution of this thesis is bottom-up simulation models for generating load profiles in detached houses and office buildings. The models connect end-user behavior with the usage of appliances and hot water loads through non-homogenous Markov chains, along with physical modeling of the indoor environment and consumption of heating and cooling loads through lumped capacitance models. The modeling is based on a simplified approach where openly available data and statistics are used, i.e. data that is subject to privacy limitations, such as smart meter measurements are excluded. The models have been validated using real load data from detached houses and office buildings, related models in literature, along with energy-use statistics from national databases. The validation shows that the modeling approach is sound and can provide reasonably accurate load profiles as the error results are in alignment with related models from other research groups.

This thesis is a composite thesis of five papers. Paper 1 presents a bottom-up simulation model to generate load profiles from space heating, hot water and appliances in detached houses. Paper 2 presents a data analytic framework for analyzing electricity-use from heating ventilation and air conditioning (HVAC) loads and appliance loads in an office building. Paper 3 presents a non-homogeneous Markov chain model to simulate representative occupancy profiles in single office rooms. Paper 4 utilizes the results in paper 2 and 3 to describe a bottom-up simulation model that generates load profiles in office buildings including HVAC loads and appliances. Paper 5 uses the model in paper 1 to analyze the technical feasibility of using DR to solve congestion problems in a distribution grid.

Abstract [sv]

Integrering av förnybara energikällor och nya typer av laster i de elektriska energisystemen är möjliga svar till klimatförändringar och uttömning av ändliga naturresurser. Denna integration kan dock öka obalanserna mellan utbud och efterfrågan av elektricitet, och orsaka en ogynnsam utnyttjandegrad av vissa kraftsystemkomponenter. Att använda efterfrågeflexibilitet (Demand Response) i byggnadsbeståndet är en möjlig lösning till dessa problem för olika elmarknadsaktörer. Men eftersom efterfrågeflexibilitet inte används i stor skala idag finns det obesvarade frågor gällande lösningens kostnadsnytta och tillförlitlighet jämfört med traditionella investeringsalternativ i kraftsektorn. För att analysera efterfrågeflexibilitetslösningar är botten-upp-simuleringsmodeller som fångar elförbrukningsprocesser i byggnaderna ett alternativ. Dessa modeller måste vara enkla nog för att kunna representera aggregeringar av många byggnader men samtidigt tillräckligt komplicerade för att kunna inkludera unika slutanvändarbeteenden. Detta är nödvändigt när elmarknadsaktören vill analysera hur stora volymer efterfrågeflexibilitet påverkar elmarknaden och kraftsystemen, men samtidigt förstå hur styrningen inverkar på den enskilda slutanvändaren. 

Bidraget från denna avhandling är botten-upp-simuleringsmodeller för generering av elförbrukningsprofiler i småhus och kontorsbyggnader. Modellerna kopplar slutanvändarbeteende med elförbrukning från apparater och varmvattenanvändning tillsammans med fysikaliska modeller av värmedynamiken i byggnaderna. Modellerna är byggda på en förenklad approach som använder öppen data och statistisk, där data som har integritetsproblem har exkluderats. Simuleringsresultat har validerats mot elförbrukningsdata från småhus och kontorsbyggnader,  relaterade modeller från andra forskargrupper samt energistatistik från nationella databaser. Valideringen visar att modellerna kan generera elförbrukningsprofiler med rimlig noggrannhet.

Denna avhandling är en sammanläggningsavhandling bestående av fem artiklar. Artikel 1 presenterar botten-upp-simuleringsmodellen för genereringen av elförbrukningsprofiler från uppvärmning, varmvatten och apparater i småhus. Artikel 2 presenterar ett dataanalytiskt ramverk för analys av elanvändningen från uppvärmning, ventilation, och luftkonditioneringslaster (HVAC) och apparatlaster i en kontorsbyggnad. Artikel 3 presenterar en icke-homogen Markovkedjemodell för simulering av representativa närvaroprofiler i enskilda kontorsrum. Artikel  4 använder resultaten i artiklarna  2 och 3 för att beskriva en botten-upp-simuleringsmodell för generering av elförbrukningsprofiler från HVAC-laster och apparater i kontorsbyggnader. Artikel  5 använder modellen i artikel 1 för att analysera den tekniska möjligheten att använda efterfrågeflexibilitet för att lösa överbelastningsproblem i ett eldistributionsnät.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. , xiv, 73 p.
Series
TRITA-EE, ISSN 1653-5146 ; 2016:038
Keyword [en]
Demand Response, Flexible loads, Building stock energy-use, Bottom-up simulation models, Load profiles, Non-homogeneous Markov-chains, End-user behavior, Lumped capacitance models, HVAC system control, Smart Grid.
Keyword [sv]
Efterfrågeflexibilitet, Flexibla laster, Energianvändning i byggnadsstocken, Botten-upp-simuleringsmodeller, Elförbrukningsprofiler, Icke-homogena Markovkedjor, Slutanvändarbeteenden, Värmedynamikmodellering, Styrning av HVAC-laster, Smarta elnät.
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-184093ISBN: 978-91-7595-886-6 (print)OAI: oai:DiVA.org:kth-184093DiVA: diva2:914035
Public defence
2016-04-22, L1, Drottning Kristinas väg 30, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20160329

Available from: 2016-03-29 Created: 2016-03-23 Last updated: 2016-04-22Bibliographically approved
List of papers
1. Forecasting household consumer electricity load profiles with a combined physical and behavioral approach
Open this publication in new window or tab >>Forecasting household consumer electricity load profiles with a combined physical and behavioral approach
2014 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 131, 267-278 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, a simulation model that forecasts electricity load profiles for a population of Swedish households living in detached houses is presented. The model is constructed of three separate modules, namely appliance usage, Domestic Hot Water (DHW) consumption and space heating. The appliance and DHW modules are based on non-homogenous Markov chains, where household members move between dierent states with certain probabilities over the days. The behavior of individuals is linked to various energy demanding activities at home. The space heating module is built on thermodynamical aspects of the buildings, weather dynamics, and the heat loss output from the aforementioned modules. Subsequently, a use case for a neighborhood of detached houses in Sweden is simulated using a Monte Carlo approach. For the use case, a number of justified assumptions and parameter estimations are made. The simulations results for the Swedish use case show that the model can produce realistic power demand profiles. The simulated profile coincides especially well with the measured consumption during the summer time, which confirms that the appliance and DHW modules are reliable. The deviations increase for some periods in the winter period due to, e.g. unforeseen end-user behavior during occasions of extreme electricity prices.

Place, publisher, year, edition, pages
Elsevier, 2014
Keyword
Markov-chain models, Domestic electricity demand, Detached house architecture, Stochastic, Holistic
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-146969 (URN)10.1016/j.apenergy.2014.06.048 (DOI)000341335500026 ()2-s2.0-84904121881 (Scopus ID)
Note

QC 20141003

Available from: 2014-06-18 Created: 2014-06-18 Last updated: 2017-12-05Bibliographically approved
2. Day-Ahead Predictions of Electricity Consumption in a Swedish Office Building from Weather, Occupancy, and Temporal data
Open this publication in new window or tab >>Day-Ahead Predictions of Electricity Consumption in a Swedish Office Building from Weather, Occupancy, and Temporal data
2015 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 108Article in journal (Refereed) Published
Abstract [en]

An important aspect of Demand Response (DR) is to make accurate predictions for the consumption in the short term, in order to have a benchmark load profile which can be compared with the load profile influenced by DR signals. In this paper, a data analysis approach to predict electricity consumption on load level in office buildings on a day-ahead basis is presented. The methodology is: (i) exploratory data analysis, (ii) produce linear models between the predictors (weather and occupancies) and the outcomes (appliance, ventilation, and cooling loads) in a step wise function, and (iii) use the models from (ii) to predict the consumption levels with day-ahead prognosis data on the predictors. The data has been collected from a Swedish office building floor. The results from (ii) show that occupancy is correlated with appliance load, and outdoor temperature and a temporal variable defining work hours are connected with ventilation and cooling load. It is concluded from the results in (iii) that the error rate decreases if fewer predictors are included in the predictions. This is because of the inherent forecast errors in the day-ahead prognosis data. The achieved error rates are comparable with similar prediction studies in related work.

Place, publisher, year, edition, pages
Elsevier, 2015
Keyword
Office Building Electricity Consumption, Load Level, Building Energy Management System, HVAC, Exploratory Data Analysis, Prediction, Regression, Demand Response.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-172802 (URN)10.1016/j.enbuild.2015.08.052 (DOI)000365364500028 ()2-s2.0-84943265845 (Scopus ID)
Funder
StandUp
Note

QC 20160114

Available from: 2015-08-29 Created: 2015-08-29 Last updated: 2017-12-04Bibliographically approved
3. Simulating Occupancy in Office Buildings with Non-Homogeneous Markov Chains for Demand Response Analysis
Open this publication in new window or tab >>Simulating Occupancy in Office Buildings with Non-Homogeneous Markov Chains for Demand Response Analysis
2015 (English)In: Power & Energy Society General Meeting, 2015 IEEE, IEEE conference proceedings, 2015, 1-5 p.Conference paper, Published paper (Refereed)
Abstract [en]

Demand Response (DR) is a promising solution todeal with supply/demand imbalances in the power systems. To appreciate the availability of DR, it is important to include enduser behavior in the analysis. In this paper, a model that can generate representative occupancy profiles in single office rooms is presented. The used method is non-homogeneous Markov chain modeling, along with exploratory data analysis of occupancy data, and an estimation of occupancy levels for different months and weekdays. The Markov chain model is calibrated with occupancy sensor data from 24 single rooms collected from an office building floor in Sweden. The simulation results have been statistically compared with a test data set consisting of occupancy data in 23 other rooms on the same floor. It was shown that the model could reproduce key occupancy properties of the test data.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015
Keyword
Occupancy patterns, Office buildings, Demand Response, Non-homogeneous Markov chains
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-170865 (URN)10.1109/PESGM.2015.7285865 (DOI)000371397501008 ()2-s2.0-84956853150 (Scopus ID)
Conference
The IEEE Power & Energy Society General Meeting, July 26-30, 2015 2015
Funder
StandUp
Note

QC 20151015

Available from: 2015-07-09 Created: 2015-07-09 Last updated: 2016-04-04Bibliographically approved
4. 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, 472-485 p.Article 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
5. 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

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