INTERNATIONAL JOURNAL of RENEWABLE ENERGY RESEARCH A.Allik et al., Vol.9, No.2, June, 2019 Ramp Rates of Building-Integrated Renewable Energy Systems Alo Allik‡, Heiki Lill, Andres Annuk Chair of Energy Application Engineering, Institute of Technology, Estonian University of Life Sciences, Fr. R. Kreutzwaldi 56, 51006 Tartu, Estonia (alo.allik@emu.ee, heiki.lill@emu.ee, andres.annuk@emu.ee) ‡ Corresponding Author; Alo Allik, Fr. R. Kreutzwaldi 56, Tartu, Estonia, Tel: +372 5249237, alo.allik@emu.ee Received: 14.01.2019 Accepted:19.12.2019 Abstract- This article analyses the ramp rates of household electricity consumption and the power production of building- integrated PV panels and wind generators. These aspects are important for the optimization of energy storage and demand-side management in buildings with prosumer status. The output power data from PV panels and a wind generator from the same building were used. It was found that the yearly standard deviation of solar energy output is greater than the standard deviation of output from the analyzed wind generator but the ramp rates are higher for the wind turbine. Ramp rates of the solar energy power plant have a slower rising slope comparatively to the same parameter from the wind generator. This shows higher temporal stability in PV output, which was also validated with autocorrelation functions. Keywords- Electricity consumption, wind generator, photovoltaic array, output fluctuations, ramp rates. NOMENCLATURE l Time lag between values P n nominal power r(l) autocorrelation function x i values in a time series x ̄ mean of the time series CDF cumulative distribution function PV photovoltaic WG wind generator 1. Introduction The stochastic nature of large wind generators (WG) and photovoltaic (PV) power plants is evident from earlier research [1], [2] and changes in output power of building- integrated PV and wind generators are described in [3]–[5]. The stochasticity of WG output is caused by sudden wind gusts and turbulence [6], [7] on the other hand fast changes of out from PV panels are caused mainly by the movement of clouds [8], [9]. This can affect the voltage stability in the distribution grid [10]. One proposed solution to mitigate this problem is the geographic dispersion of generation units [11], another one is storage [12][13]. The stochasticity of those energy sources has been compared on the scale of European countries [11], but even if we assume that the large scale fluctuations could be mitigated by robust interconnections between countries, then the local issues like voltage quality and economic factors related to renewable energy self-consumption remain [14]– [17]. These topics are especially topical because of regulations that incentivize the installation of renewable energy generation devices on all new buildings [18]. Hourly average values are often used for the modelling of small renewable energy systems, which gives the impression that the energy generation and production during an hour are constant, but renewable energy sources have significant intra-hourly fluctuations [19]. The use of hourly average data series may result in models that appear more stable than the reality that they describe [20]. The hourly time resolution is somewhat acceptable in describing processes that occur in the transmission grid, because of the large number of interactors that use the transmission and distribution grids, but a higher time resolution could be beneficial even there [21]. Energy is currently commercially measured as hourly averages (“Nordpool spot Electricity price,”). In the same time, it is foreseeable that data acquisition with higher resolution will be necessary to facilitate the needs of distributed energy generation and use the full potential of remote metering possibilities. The application of real-time tariffs in the future is also a possibility [22]. The novelty of this paper lies in the application of analysis methods on building based energy systems which are until now only used on grid-scale facilities, like wind parks. The aim of this study is to demonstrate the rate of changes in both, the consumption and production output in building integrated energy systems. The results can be used