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High resolution stochastic generator of European household specific electricity demand load curves for decentralized power self-production applications
Highlights A new low time step stochastic generator of electricity demand data is developed. Hourly time steps are not suitable for PV or µCHP coupled to buildings simulations. The tool has been validated thanks to in situ measurements and aggregated curves. Energy gas Stirling engine µCHP coupled to buildings simulations has been conducted.
Abstract This work relates to a new stochastic and probabilistic tool allowing to model dwellings specific electricity load profiles (in the European context) for 39 different electrical appliances with a high temporal resolution (2-min time step). This tool was developed by using probabilistic (Monte Carlo) and statistic (Time Of Use curves) methods as well as transition probabilities (Markov chains), seasonal, daily and hourly effects coefficients to obtain the more realistic as possible electricity load curves. The tool distinguishes the flexible part which can be shifted during power self-production operation (micro combined heat and power µCHP or photovoltaic panels PV) periods and the no flexible part (lighting for example). A sensitivity analysis shows the impact of the time step and the repeatability level of the tool on a µCHP system coupled with a building to prove the need to use high resolution electricity demand load curves at low scale (a building) in comparison with hourly data which can overestimate µCHP electrical generation self-consumption rate up to 20%. Time step of maximum 2 min is required for the electricity demand profiles to obtain reliable results on energetic indicators. Finally, a validation procedure based on literature review profiles, high resolution in situ measurements and expansion national load curves is carried out.
High resolution stochastic generator of European household specific electricity demand load curves for decentralized power self-production applications
Highlights A new low time step stochastic generator of electricity demand data is developed. Hourly time steps are not suitable for PV or µCHP coupled to buildings simulations. The tool has been validated thanks to in situ measurements and aggregated curves. Energy gas Stirling engine µCHP coupled to buildings simulations has been conducted.
Abstract This work relates to a new stochastic and probabilistic tool allowing to model dwellings specific electricity load profiles (in the European context) for 39 different electrical appliances with a high temporal resolution (2-min time step). This tool was developed by using probabilistic (Monte Carlo) and statistic (Time Of Use curves) methods as well as transition probabilities (Markov chains), seasonal, daily and hourly effects coefficients to obtain the more realistic as possible electricity load curves. The tool distinguishes the flexible part which can be shifted during power self-production operation (micro combined heat and power µCHP or photovoltaic panels PV) periods and the no flexible part (lighting for example). A sensitivity analysis shows the impact of the time step and the repeatability level of the tool on a µCHP system coupled with a building to prove the need to use high resolution electricity demand load curves at low scale (a building) in comparison with hourly data which can overestimate µCHP electrical generation self-consumption rate up to 20%. Time step of maximum 2 min is required for the electricity demand profiles to obtain reliable results on energetic indicators. Finally, a validation procedure based on literature review profiles, high resolution in situ measurements and expansion national load curves is carried out.
High resolution stochastic generator of European household specific electricity demand load curves for decentralized power self-production applications
Bouvenot, Jean-Baptiste (author) / Latour, Benjamin (author) / Flament, Bernard (author) / Siroux, Monica (author)
Energy and Buildings ; 229
2020-09-10
Article (Journal)
Electronic Resource
English
The specific contributions of activities to household electricity demand
| Elsevier | 2019
| Elsevier | 2025