Solar heating forecast in heat system optimal control

Riku Pasonen, Atte Löf, Göran Koreneff

    Research output: Book/ReportReport

    Abstract

    Use of solar radiation forecasting in heating system optimization simulation was carried out in this study. Open data from Finnish Meteorological Institute (FMI) data base was used to generate solar and temperature forecast. Temperature forecast was used to estimate the coefficient of performance (COP) for heat pump and generate heat load forecast with heat model. Temperature data from previous year was used to build heat model of case building (district heating connected building in Espoo). The solar forecast calculation was built using Python language, the heat forecast model with APL and the optimization code using Julia language. The aim of the optimization was to minimize operational costs and investigate results using different storage and collector sizes. It was possible to get savings in heating costs with solar collectors and heat pump. Storage can also yield savings both with heat pump and solar collectors. Excess energy from solar collectors can be stored to storage and used later and also heat pump usage shifted to different hours to take advantage on hourly electricity prices. The results indicate that with low electricity price that we have now in Nordic markets it is possible have cheaper heating energy than district heating. However due to the limited scope of the work here, maintenance fees are not investigated. The operation cost can however be up to 50% less without taking account the maintenance. For total profitability analysis, investment cost and longer time period performance simulation would be needed. Target of the study was to investigate integration of solar radiation and heat load forecast models to optimization code. Two models were combined via a bit non-optimal way because both had been previously built on different platforms. It was discussed during the project that combined system could be rewritten with Python for more elegant approach.
    Original languageEnglish
    Place of PublicationEspoo
    PublisherVTT Technical Research Centre of Finland
    Number of pages16
    Publication statusPublished - 2016
    MoE publication typeD4 Published development or research report or study

    Publication series

    SeriesVTT Tutkimusraportti
    NumberVTT-R-04946-16

    Fingerprint

    Optimal control systems
    Solar heating
    Solar collectors
    Pumps
    District heating
    Thermal load
    Solar radiation
    Heating
    Costs
    Electricity
    Hot Temperature
    Temperature
    Profitability

    Keywords

    • solar heat
    • forecasting
    • optimization

    Cite this

    Pasonen, R., Löf, A., & Koreneff, G. (2016). Solar heating forecast in heat system optimal control. Espoo: VTT Technical Research Centre of Finland. VTT Tutkimusraportti, No. VTT-R-04946-16
    Pasonen, Riku ; Löf, Atte ; Koreneff, Göran. / Solar heating forecast in heat system optimal control. Espoo : VTT Technical Research Centre of Finland, 2016. 16 p. (VTT Tutkimusraportti; No. VTT-R-04946-16).
    @book{c68922dea77146b08e53b32cb29c41c0,
    title = "Solar heating forecast in heat system optimal control",
    abstract = "Use of solar radiation forecasting in heating system optimization simulation was carried out in this study. Open data from Finnish Meteorological Institute (FMI) data base was used to generate solar and temperature forecast. Temperature forecast was used to estimate the coefficient of performance (COP) for heat pump and generate heat load forecast with heat model. Temperature data from previous year was used to build heat model of case building (district heating connected building in Espoo). The solar forecast calculation was built using Python language, the heat forecast model with APL and the optimization code using Julia language. The aim of the optimization was to minimize operational costs and investigate results using different storage and collector sizes. It was possible to get savings in heating costs with solar collectors and heat pump. Storage can also yield savings both with heat pump and solar collectors. Excess energy from solar collectors can be stored to storage and used later and also heat pump usage shifted to different hours to take advantage on hourly electricity prices. The results indicate that with low electricity price that we have now in Nordic markets it is possible have cheaper heating energy than district heating. However due to the limited scope of the work here, maintenance fees are not investigated. The operation cost can however be up to 50{\%} less without taking account the maintenance. For total profitability analysis, investment cost and longer time period performance simulation would be needed. Target of the study was to investigate integration of solar radiation and heat load forecast models to optimization code. Two models were combined via a bit non-optimal way because both had been previously built on different platforms. It was discussed during the project that combined system could be rewritten with Python for more elegant approach.",
    keywords = "solar heat, forecasting, optimization",
    author = "Riku Pasonen and Atte L{\"o}f and G{\"o}ran Koreneff",
    year = "2016",
    language = "English",
    series = "VTT Tutkimusraportti",
    publisher = "VTT Technical Research Centre of Finland",
    number = "VTT-R-04946-16",
    address = "Finland",

    }

    Pasonen, R, Löf, A & Koreneff, G 2016, Solar heating forecast in heat system optimal control. VTT Tutkimusraportti, no. VTT-R-04946-16, VTT Technical Research Centre of Finland, Espoo.

    Solar heating forecast in heat system optimal control. / Pasonen, Riku; Löf, Atte; Koreneff, Göran.

    Espoo : VTT Technical Research Centre of Finland, 2016. 16 p. (VTT Tutkimusraportti; No. VTT-R-04946-16).

    Research output: Book/ReportReport

    TY - BOOK

    T1 - Solar heating forecast in heat system optimal control

    AU - Pasonen, Riku

    AU - Löf, Atte

    AU - Koreneff, Göran

    PY - 2016

    Y1 - 2016

    N2 - Use of solar radiation forecasting in heating system optimization simulation was carried out in this study. Open data from Finnish Meteorological Institute (FMI) data base was used to generate solar and temperature forecast. Temperature forecast was used to estimate the coefficient of performance (COP) for heat pump and generate heat load forecast with heat model. Temperature data from previous year was used to build heat model of case building (district heating connected building in Espoo). The solar forecast calculation was built using Python language, the heat forecast model with APL and the optimization code using Julia language. The aim of the optimization was to minimize operational costs and investigate results using different storage and collector sizes. It was possible to get savings in heating costs with solar collectors and heat pump. Storage can also yield savings both with heat pump and solar collectors. Excess energy from solar collectors can be stored to storage and used later and also heat pump usage shifted to different hours to take advantage on hourly electricity prices. The results indicate that with low electricity price that we have now in Nordic markets it is possible have cheaper heating energy than district heating. However due to the limited scope of the work here, maintenance fees are not investigated. The operation cost can however be up to 50% less without taking account the maintenance. For total profitability analysis, investment cost and longer time period performance simulation would be needed. Target of the study was to investigate integration of solar radiation and heat load forecast models to optimization code. Two models were combined via a bit non-optimal way because both had been previously built on different platforms. It was discussed during the project that combined system could be rewritten with Python for more elegant approach.

    AB - Use of solar radiation forecasting in heating system optimization simulation was carried out in this study. Open data from Finnish Meteorological Institute (FMI) data base was used to generate solar and temperature forecast. Temperature forecast was used to estimate the coefficient of performance (COP) for heat pump and generate heat load forecast with heat model. Temperature data from previous year was used to build heat model of case building (district heating connected building in Espoo). The solar forecast calculation was built using Python language, the heat forecast model with APL and the optimization code using Julia language. The aim of the optimization was to minimize operational costs and investigate results using different storage and collector sizes. It was possible to get savings in heating costs with solar collectors and heat pump. Storage can also yield savings both with heat pump and solar collectors. Excess energy from solar collectors can be stored to storage and used later and also heat pump usage shifted to different hours to take advantage on hourly electricity prices. The results indicate that with low electricity price that we have now in Nordic markets it is possible have cheaper heating energy than district heating. However due to the limited scope of the work here, maintenance fees are not investigated. The operation cost can however be up to 50% less without taking account the maintenance. For total profitability analysis, investment cost and longer time period performance simulation would be needed. Target of the study was to investigate integration of solar radiation and heat load forecast models to optimization code. Two models were combined via a bit non-optimal way because both had been previously built on different platforms. It was discussed during the project that combined system could be rewritten with Python for more elegant approach.

    KW - solar heat

    KW - forecasting

    KW - optimization

    M3 - Report

    T3 - VTT Tutkimusraportti

    BT - Solar heating forecast in heat system optimal control

    PB - VTT Technical Research Centre of Finland

    CY - Espoo

    ER -

    Pasonen R, Löf A, Koreneff G. Solar heating forecast in heat system optimal control. Espoo: VTT Technical Research Centre of Finland, 2016. 16 p. (VTT Tutkimusraportti; No. VTT-R-04946-16).