Performance analysis of a semi-virtual renewable energy system and building operation

This paper describes the composition and operation of a semi-virtual energy system and building emulator. The research is part of a joint project between VTT Technical Research Centre of Finland and Aalto University funded by the Academy of Finland. The system consists of a real part of energy production (PV panels, micro-wind turbine and solar thermal collectors), storage (battery pack and hot water storage tanks) and conversion (ground-source heat pump GSHP and electric heater) combined with a virtual part that models a single-family house in the computer simulation program TRNSYS. A Labview program is used for communication and control between the real and the virtual parts. In this paper, measurement data from six selected studied periods in each month in September 2015 to February 2016 are analysed. The data indicates that during the studied period, the electricity consumption was 1290 kWh, divided to 44/38/18 % between household appliances, GSHP and electric heater, respectively. The local net production was 250 kWh, or 19 % of the demand. 54% of the production came from PV, which was heavily concentrated in September and October. Wind production was winter-centred. Two indices are used to represent the electrical energy matching aspects of the system: OEFe (on-site electrical energy fraction), the fraction of the building electrical demand covered by local production, and OEMe (on-site electrical energy matching), the ratio of the building's self-consumption to the total local production. It is found that the matching capability of the energy system is poor, which is due to daily and seasonal mismatches between energy production and demand. The small net production compared to demand leads to small OEFe and large OEMe values overall. OEFe exhibits strong daily/nightly trends in September and October due to the PV production. OEME is mostly unity except for short times when it drops around noon when PV production is at its highest. In wind-dominated winter months, the day/night trends of the indices are not so visible.