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  • 1.
    Brodén, Daniel
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Paridari, Kaveh
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Nordström, Lars
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    MATLAB Applications to Generate Synthetic Electricity Load Profiles of Office Buildings and Detached Houses2017In: 2017 IEEE Innovative Smart Grid Technologies - Asia: Smart Grid for Smart Community, ISGT-Asia 2017, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 1-6, 2017Conference paper (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.

  • 2.
    Brodén, Daniel A.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Modeling and Simulations of Demand Response in Sweden2017Licentiate 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.

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  • 3.
    Armendariz, Mikel
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Babazadeh, Davood
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Brodén, Daniel
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Nordström, Lars
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Strategies to improve the voltage quality in active low-voltage distribution networks using DSO's assets2017In: IET Generation, Transmission & Distribution, ISSN 1751-8687, E-ISSN 1751-8695, Vol. 11, no 1, p. 73-81Article in journal (Refereed)
    Abstract [en]

    This study addresses the problem of voltage variations in active low-voltage distribution networks caused by distributed photovoltaic (PV) generation. Three strategies based on model predictive control (MPC) are introduced to flatten the voltage profile in a cost-optimal way. The compared strategies are the business as usual approach that manipulates a controllable on-load tap changer at the primary substation, the problematic feeder control strategy (CS) that adds an additional degree of freedom by controlling the critical secondary substations (SSs), and finally the compensation strategy, which controls the primary substation and compensates the non-critical SSs. A sensitivity analysis on the CSs has been conducted comparing the voltage variation reduction and the asset utilization with regard to the accuracy of the prediction models and the forecasted disturbance data. The results show that better (and more costly) characterisation of these parameters only provide a marginal improvement in the reduction of the voltage variations due to the restriction caused by the heavy tap change penalisation. Moreover, the tested case-study shows that the problematic feeder CS outperforms the compensation strategy in terms of larger voltage variation reduction for similar asset utilisation.

  • 4.
    Stifter, Matthias
    et al.
    Austrian Institute of Technology.
    Kamphuis, René
    Toegepast Natuurwetenschappelijk Onderzoek.
    Galus, Matthias
    Renting, Marijn
    Rijneveld, Arnoud
    Targosz, Roman
    Widergren, Steve
    Nordström, Lars
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Brodén, Daniel
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Esterl, Tara
    Kaser, Stephanie
    Galsworthy, Stephen
    Doolla, Suryanarayana
    Conclusions and Recommendations: Demand Flexibility in Households and Buildings2016Report (Other (popular science, discussion, etc.))
  • 5.
    Gliniewicz, Vincent
    et al.
    Vattenfall R&D.
    Ryckebusch, Gaëlle
    Vattenfall R&D.
    Brodén, Daniel
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. Royal Institute of Technology.
    Economic Impact Assessment of using Congestion Management Methods to enable increased Wind Power Integration on Gotland, Sweden2016Conference paper (Refereed)
    Abstract [en]

    Congestion management methods are useful regulatory mechanisms to prevent transmission capacity problems.This paper intends to assess whether congestion management can be cost-efficiently used to postpone or even avoid network capacity reinforcements while increasing the hosting capacity of wind power on the island of Gotland in Sweden. Two methods,re-dispatch and market splitting, are studied in detail and applied to the Gotland case. A simplified electricity market model using historical data from Gotland was designed to perform the simulations. These methods pass on the cost of lack of transmission capacity to different actors in the electricity market (mainly grid owner for re-dispatch and consumers and producers for market splitting). Simulations indicate however that both methods could be employed to raise the installed production capacity of wind power on Gotland by at least 26 MW above the stated limit of 195 MW without negatively impacting the income of any actor. Moreover, market splitting efficiently reflects the transmission problems on the energy price of Gotland, thus giving economic incentive for flexible power consumption to alleviate the problems.

  • 6.
    Bourdette, Romain
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Brodén, Daniel
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Economic Simulations of the Participation of Virtual Power Plants on the Swiss Balancing Market2016In: PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2016 IEEE, IEEE Computer Society, 2016, article id 7856242Conference paper (Refereed)
    Abstract [en]

    In this paper, economic simulations of virtual power plants participating on the Swiss balancing market are performed and discussed. The simulations are performed by creating a generic simulation model for balancing markets which account for functions such as bidding, market clearing, delivery of control energy, and others. Several simulations allowed to estimate the opportunity from both the capacity and the energy market products. The simulations indicated that an example company could hope a decrease in its energy cost between 3 and 4% by participating in a virtual power plant providing frequency control over a year assuming the aggregator gets 50% of the market income. Other simulations demonstrated the greater profit expected from secondary reserves compared to tertiary reserves for a virtual power plant made of controllable loads. Moreover, the paper also gives an insight into balancing markets role and designs, and presents the virtual power plant concept and the potential participants for the provision of frequency control.

  • 7.
    Stifter, Matthias
    et al.
    Austrian Institute of Technology.
    Kamphuis, René
    Toegepast Natuurwetenschappelijk Onderzoek.
    Galus, Matthias
    Renting, Marijn
    Rijneveld, Arnoud
    Targosz, Roman
    Widergren, Steve
    Nordström, Lars
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Brodén, Daniel
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Esterl, Tara
    Galsworthy, Stephen
    Koponen, Pekka
    Doolla, Suryanarayana
    Pilot Studies and Best Practices: Demand Flexibility in Households and Buildings2016Report (Other (popular science, discussion, etc.))
  • 8.
    Stifter, Matthias
    et al.
    Austrian Institute of Technology.
    Kamphuis, René
    Toegepast Natuurwetenschappelijk Onderzoek.
    Galus, Matthias
    Renting, Marijn
    Rijneveld, Arnoud
    Targosz, Roman
    Widergren, Steve
    Nordström, Lars
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Brodén, Daniel
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Esterl, Tara
    Kaser, Stephanie
    Koponen, Pekka
    Galsworthy, Stephen
    Friedl, Werner
    Doolla, Suryanarayana
    Roles and Potentials of Flexible Consumers and Prosumers: Demand Flexibility in Households and Buildings2016Report (Other (popular science, discussion, etc.))
  • 9. Esterl, Tara
    et al.
    Kaser, Stefanie
    Stifter, Matthias
    Austrian Institute of Technology.
    Kamphuis, René
    Toegepast Natuurwetenschappelijk Onderzoek.
    Galus, Matthias
    Renting, Marijn
    Rijneveld, Arnoud
    Targosz, Roman
    Widergren, Steve
    Nordström, Lars
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Brodén, Daniel
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Galsworthy, Stephen
    Valuation Analysis of Residential Demand Side Flexibility: Analysis of business cases and existing valuation frameworks2016Report (Other (popular science, discussion, etc.))
  • 10.
    Armendariz, Mikel
    et al.
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Brodén, Daniel
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Honeth, Nicholas
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Nordström, Lars
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    A Method to Identify Exposed Nodes in Low Voltage Distribution Grids with High PV Penetration2015In: IEEE Power and Energy Society General Meeting 2015, Denver, CO. July 26-30, 2015. / [ed] IEEE, IEEE Press, 2015Conference paper (Refereed)
    Abstract [en]

    The impact of introducing distributed energyresources at the low voltage side of the distribution grid iscurrently raising new challenges for utilities. In particular, thehigh penetration of photovoltaic panels (PVs) in radial grids isincreasing the active power losses in the branches and thevoltage level at some of the nodes. Principally nodes next to PVarray installations. This paper presents a methodology based ondesign of experiments (DOE) to detect such exposed nodes andbranches, together with the identification of the main scenariosthat cause such problems, characterized by: season, type of day,solar radiation and outdoor temperature levels. Themethodology is simulated on a LV network based on the Cigrebenchmark Grid with real utility data. The exposed nodes areclassified for each feeder from most to least problematic andshowed (as expected) sensitivity to seasonality (summertime),characterized by high solar radiation and outdoor temperatures.

  • 11.
    Ryckebusch, Gaëlle
    et al.
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Brodén, Daniel
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Lidström, Erica
    Vattenfall AB.
    Nordström, Lars
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Analysis of demand-response participation strategies for congestion management in an island distribution network2015In: 23rd International Conference on Electricity Distribution, Cired , 2015, p. 1-5, article id Paper:1130Conference paper (Refereed)
    Abstract [en]

    A novel approach compared to traditional grid expansion consists of using flexibility from demand-response resources to manage anticipated congestions. However, such an approach presents challenges as it requires both technical and economic considerations. This paper proposes and analyses two market-based strategies applied to detached houses for day-ahead congestion management. The strategies are implemented in an Ancillary Service Toolbox environment developed in previous work, and is applied to a real use case on Gotland, Sweden. The first strategy involves using a dynamic network tariff while the second uses spot price optimization. Simulations are performed for seasonal worst-case congestion scenarios while satisfying comfort and economic constraints of the DR participants.Results show that while congestions are managed with a feasible number of participants, their savings are negligible for both strategies. The savings are 0.2-4.6 EUR/participant for the dynamic network tariff compared to 0-2.2 EUR/participant for the spot price optimization strategy. Moreover, results show that using a dynamic network tariff strategy implies a DSO cost in the range of 200-10200 EUR.

  • 12.
    Brodén, Daniel
    et al.
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Sandels, Claes
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Nordström, Lars
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Assessment of Congestion Management Potential in Distribution Networks using Demand-Response and Battery Energy Storage2015In: Innovative Smart Grid Technologies Conference (ISGT), 2015 IEEE Power & Energy Society, IEEE conference proceedings, 2015Conference 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.

  • 13.
    Sandels, Claes
    et al.
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Brodén, Daniel
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Widén, Joakim
    Nordström, Lars
    KTH, School of Electrical Engineering (EES), Industrial Information and Control Systems.
    Andersson, Enar
    SP Technical Research Institute.
    Modeling Office Building Consumer Load with a Combined Physical and Behavioral Approach: Simulation and Validation2015In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 162, p. 472-485Article in journal (Refereed)
    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.

  • 14.
    Brodén, Daniel
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Armendariz, Mikel
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Nordström, Lars
    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.
    Anticipating Overrides of Schedulable Space Heating Systems in Detached Houses for Demand ResponseIn: Article in journal (Other academic)
    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.

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