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Impacts of a forecast-based operation strategy for grid-connected PV storage systems on profitability and the energy system

 
: Teichtmann, Lukas; Klingler, Anna-Lena

European Council for an Energy-Efficient Economy -ECEEE-, Paris:
eceee 2017 Summer Study on Energy Efficiency. Consumption, efficiency and limits : 29 May - 3 June 2017, Belambra Les Criques, Toulon/Hyères, France
Paris: ECEEE, 2017
ISBN: 978-91-983878-0-3 (Print)
ISBN: 978-91-983878-1-0 (Online)
pp.1091-1102
European Council for an Energy-Efficient Economy (ECEEE Summer Study) <2017, Toulon>
English
Conference Paper
Fraunhofer ISI ()
Batteries; Photovoltaics; Optimisation; Load management; Neural network; Forecast

Abstract
Integrating photovoltaic (PV) electricity generation into the German energy system is proving to be a growing challenge due to its fluctuating nature. The combination of more rigid regulation for feeding PV power into the grid and steadily rising electricity prices means that energy storage devices are becoming more attractive to private households as a way of upping their energy self-sufficiency. At the same time, storage systems make the household’s power purchasing strategy more complex. For these reasons, control concepts are required for PV and storage systems which ensure system-friendly operation as well as considering the household’s primary objectives. This paper presents a three-part model, which enables forecast-based load management of a battery storage system in combination with a PV system. In the first modelling step, forecasts of hourly electricity demand and solar generation are created using artificial neural networks. In a second step, the model optimizes the energy flows considering a real-time price tariff based on EPEX Spot in addition to its main task of using the forecasts to maximize on-site self-consumption. In the third step, a control algorithm adjusts the actual energy flows if forecast deviations occur. The study shows that the model enables system-friendly operation of the battery storage as well as intensified usage. As an added value, the forecasting approach presented is closer to reality than the otherwise frequently used optimization algorithms that assume perfect foresight of electricity load and generation. It therefore provides a real-world basis for planning, but also shows that the inevitable forecasting errors are reflected in higher electricity bills. Considering the inaccuracy of forecasting, we conclude that if a system-friendly integration of solar power storages is to be promoted in the future, households should be provided with better forecast data or offered other incentives to compensate for their lost profit.

: http://publica.fraunhofer.de/documents/N-453408.html