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Modeling Office Building Consumer Load with a Combined Physical and Behavioral Approach: Simulation and Validation
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems. (Power System Management with related Information Exchange (PSMIX))ORCID iD: 0000-0002-9860-4472
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems. (Power System Management with related Information Exchange (PSMIX))ORCID iD: 0000-0002-4140-4736
(Built Environment Energy Systems Group (BEESG))
KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems. (Power System Management with related Information Exchange (PSMIX))ORCID iD: 0000-0003-3014-5609
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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. Vol. 162, 472-485 p.
Keyword [en]
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: urn:nbn:se:kth:diva-175815DOI: 10.1016/j.apenergy.2015.10.141ISI: 000367631000043Scopus ID: 2-s2.0-84945571135OAI: oai:DiVA.org:kth-175815DiVA: diva2:862479
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
In thesis
1. Modeling and Simulation of Electricity Consumption Profiles in the Northern European Building Stock
Open this publication in new window or tab >>Modeling and Simulation of Electricity Consumption Profiles in the Northern European Building Stock
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
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., 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:nbn:se:kth:diva-184093 (URN)978-91-7595-886-6 (ISBN)
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
2. 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. 55 p.
Series
TRITA-EE, ISSN 1653-5146 ; 2017:148
Keyword
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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Sandels, ClaesBrodén, DanielNordström, Lars

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