Direct quantification of multiple-source energy flexibility in a residential building using a new model predictive high-level controller

Reino Ruusu, Sunliang Cao, Benjamin Manrique Delgado, Ala Hasan

    Research output: Contribution to journalArticleResearchpeer-review

    Abstract

    This article presents a new energy management system (EMS) for a variety of energy flexibility conversion, routing and storage options in buildings. The EMS uses an efficient nonlinear optimization-based model-predictive control (MPC) method, which achieves low computational complexity by utilizing successive linear programming (SLP) for continuous approximations of discrete (two-level) control problems. Whole-year simulation runs demonstrate that the method is applicable to a residential building system that has multiple energy generation, conversion and storage units with significant nonlinear interactions. Both qualitative and quantitative comparison of the simulation results with a rule-based reference control showed strong dependencies between cost and CO2 emission flexibility goals, energy selling prices and forecasting accuracy. This study shows that significant cost savings can be obtained by taking advantage of energy price fluctuations, increasing the average coefficient of performance (COP) of the heating system, and reducing passive losses in heat storage. In the simulated case study the EMS was able to improve the average COP of a heating system from 2.20 to 2.43–2.74, depending on energy cost assumptions, when compared against a rule-based control (RBC). With a performance bound of perfect forecasting the EMS was able to improve net economic outcome by 38–168%, or by 21–75% of the cost of imported electricity.

    LanguageEnglish
    Pages1109-1128
    Number of pages20
    JournalEnergy Conversion and Management
    Volume180
    DOIs
    Publication statusPublished - 15 Jan 2019
    MoE publication typeNot Eligible

    Fingerprint

    Energy management systems
    Controllers
    Costs
    Heating
    Heat storage
    Level control
    Model predictive control
    Energy conversion
    Linear programming
    Computational complexity
    Sales
    Electricity
    Economics

    Keywords

    • Energy cost minimization
    • Energy flexibility
    • Energy management
    • Model predictive control
    • Nonlinear optimization
    • Smart buildings

    OKM Publication Types

    • A1 Refereed journal article

    OKM Open Access Status

    • 0 Not Open Access

    ASJC Scopus subject areas

    • Renewable Energy, Sustainability and the Environment
    • Nuclear Energy and Engineering
    • Fuel Technology
    • Energy Engineering and Power Technology

    Cite this

    @article{2d6fc89a8ee7461085929f86421ae70e,
    title = "Direct quantification of multiple-source energy flexibility in a residential building using a new model predictive high-level controller",
    abstract = "This article presents a new energy management system (EMS) for a variety of energy flexibility conversion, routing and storage options in buildings. The EMS uses an efficient nonlinear optimization-based model-predictive control (MPC) method, which achieves low computational complexity by utilizing successive linear programming (SLP) for continuous approximations of discrete (two-level) control problems. Whole-year simulation runs demonstrate that the method is applicable to a residential building system that has multiple energy generation, conversion and storage units with significant nonlinear interactions. Both qualitative and quantitative comparison of the simulation results with a rule-based reference control showed strong dependencies between cost and CO2 emission flexibility goals, energy selling prices and forecasting accuracy. This study shows that significant cost savings can be obtained by taking advantage of energy price fluctuations, increasing the average coefficient of performance (COP) of the heating system, and reducing passive losses in heat storage. In the simulated case study the EMS was able to improve the average COP of a heating system from 2.20 to 2.43–2.74, depending on energy cost assumptions, when compared against a rule-based control (RBC). With a performance bound of perfect forecasting the EMS was able to improve net economic outcome by 38–168{\%}, or by 21–75{\%} of the cost of imported electricity.",
    keywords = "Energy cost minimization, Energy flexibility, Energy management, Model predictive control, Nonlinear optimization, Smart buildings",
    author = "Reino Ruusu and Sunliang Cao and {Manrique Delgado}, Benjamin and Ala Hasan",
    year = "2019",
    month = "1",
    day = "15",
    doi = "10.1016/j.enconman.2018.11.026",
    language = "English",
    volume = "180",
    pages = "1109--1128",
    journal = "Energy Conversion and Management",
    issn = "0196-8904",
    publisher = "Elsevier",

    }

    Direct quantification of multiple-source energy flexibility in a residential building using a new model predictive high-level controller. / Ruusu, Reino; Cao, Sunliang; Manrique Delgado, Benjamin; Hasan, Ala.

    In: Energy Conversion and Management, Vol. 180, 15.01.2019, p. 1109-1128.

    Research output: Contribution to journalArticleResearchpeer-review

    TY - JOUR

    T1 - Direct quantification of multiple-source energy flexibility in a residential building using a new model predictive high-level controller

    AU - Ruusu, Reino

    AU - Cao, Sunliang

    AU - Manrique Delgado, Benjamin

    AU - Hasan, Ala

    PY - 2019/1/15

    Y1 - 2019/1/15

    N2 - This article presents a new energy management system (EMS) for a variety of energy flexibility conversion, routing and storage options in buildings. The EMS uses an efficient nonlinear optimization-based model-predictive control (MPC) method, which achieves low computational complexity by utilizing successive linear programming (SLP) for continuous approximations of discrete (two-level) control problems. Whole-year simulation runs demonstrate that the method is applicable to a residential building system that has multiple energy generation, conversion and storage units with significant nonlinear interactions. Both qualitative and quantitative comparison of the simulation results with a rule-based reference control showed strong dependencies between cost and CO2 emission flexibility goals, energy selling prices and forecasting accuracy. This study shows that significant cost savings can be obtained by taking advantage of energy price fluctuations, increasing the average coefficient of performance (COP) of the heating system, and reducing passive losses in heat storage. In the simulated case study the EMS was able to improve the average COP of a heating system from 2.20 to 2.43–2.74, depending on energy cost assumptions, when compared against a rule-based control (RBC). With a performance bound of perfect forecasting the EMS was able to improve net economic outcome by 38–168%, or by 21–75% of the cost of imported electricity.

    AB - This article presents a new energy management system (EMS) for a variety of energy flexibility conversion, routing and storage options in buildings. The EMS uses an efficient nonlinear optimization-based model-predictive control (MPC) method, which achieves low computational complexity by utilizing successive linear programming (SLP) for continuous approximations of discrete (two-level) control problems. Whole-year simulation runs demonstrate that the method is applicable to a residential building system that has multiple energy generation, conversion and storage units with significant nonlinear interactions. Both qualitative and quantitative comparison of the simulation results with a rule-based reference control showed strong dependencies between cost and CO2 emission flexibility goals, energy selling prices and forecasting accuracy. This study shows that significant cost savings can be obtained by taking advantage of energy price fluctuations, increasing the average coefficient of performance (COP) of the heating system, and reducing passive losses in heat storage. In the simulated case study the EMS was able to improve the average COP of a heating system from 2.20 to 2.43–2.74, depending on energy cost assumptions, when compared against a rule-based control (RBC). With a performance bound of perfect forecasting the EMS was able to improve net economic outcome by 38–168%, or by 21–75% of the cost of imported electricity.

    KW - Energy cost minimization

    KW - Energy flexibility

    KW - Energy management

    KW - Model predictive control

    KW - Nonlinear optimization

    KW - Smart buildings

    UR - http://www.scopus.com/inward/record.url?scp=85057251250&partnerID=8YFLogxK

    U2 - 10.1016/j.enconman.2018.11.026

    DO - 10.1016/j.enconman.2018.11.026

    M3 - Article

    VL - 180

    SP - 1109

    EP - 1128

    JO - Energy Conversion and Management

    T2 - Energy Conversion and Management

    JF - Energy Conversion and Management

    SN - 0196-8904

    ER -