High penetration rates of novel building energy technologies has prompted a growing concern about their microeconomic effect and grid influence. Deployment of photovoltaic (PV), solar water heating (SWH) systems and energy storage solutions, in addition to the growth of electric vehicles (EV) fleet, are reshaping the structure of built stock and lead to the changes in its electric energy demand profile. Long-term forecasting of such structural changes is necessary to guide the decision-making process that would satisfy the needs of both, energy consumers and the suppliers. Whereas electric energy price model is one of the key influencing factors of technologies acceptance for households, peak loads and grid feed-in determine the needed capacity of power grids. The objective of this study was to assess both, the aggregated cost of energy and the changes in cumulative load profiles that are excepted by 2050 for one of residential building typologies in Norway. Methodologically, it was achieved with descriptive statistics, stochastic forecasting and detailed energy performance simulation. Annual electric energy cost for consumers were evaluated under six pricing models. The results suggested that time-of-use and variable maximum power extraction models represent the lowest and the highest extremes in energy cost. At the aggregated level, peak load will decrease in range 1% to 13% compared to current level. Peak PV feed-in will reach up to 40% of peak load by 2050.
The use of photovoltaic (PV) technologies is one of the key means for achieving the balance between operational power demand and generation in net Zero Energy Buildings (nZEBs). However, direct use of PV power on-site is limited due to wide variability and uncertainty of PV output, the temporal mismatch between PV generation and load and other factors. Consequently, in addition to low self-consumption rates, the problem of peak grid load and peak PV feed into the grid persists. Batteries that are coupled to PV units may partially offer the solution to these problems, if operated under an intelligent control strategy. In this paper we proposed a forecast-based control strategy for battery-to-grid interaction aimed at enhancing selfconsumption and at reducing peak load. Python programming environment was used for data processing and algorithm development. Exemplification was made based on the reported hourly energy demand in one office building of 3000 m2 heated floor area located in Trondheim, Norway. Forecasting of electricity load profiles was based on the seasonal autoregressive integral moving average (SARIMA) model. For PV power forecasting, the algorithm communicated with external service – Solcast API. The search method for optimal scheduling of operational time and the extent of charging/discharging was proposed. The results showed that as opposed to conventional battery use, this control strategy allowed to achieve significantly more consistent grid interaction. It offered highly accurate battery scheduling on a day-ahead basis while utilising minimum historical data and computational resources. The algorithm may be beneficial for end-users and grid operators, and thus, it has a high potential for future integration into building energy supply systems.
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