Economic and sizing impact of PV forecast inaccuracies on charging infrastructure depending on its use case

Motivated by climate change, electricity production and mobility are to be restructured worldwide. Increasing sales of electric vehicles (EV) require the expansion of charging infrastructure, which must not burden the power grid. Decentralized energy supply systems with photovoltaic (PV) systems and stationary battery storage can solve this problem and ensure emission-free electricity production. In combination with a scalable charging station, the demand for charging infrastructure can be met and the expansion of renewable energies can be supported.
However, the use of PV poses challenges due to fluctuating power production and requires planning to meet the EV demand. In order to operate self-sufficiently in the future and thus sustainably relieve the power grid, the system components must be coordinated with each other. Dimensioning and operational optimization are thus the most important aspects for setting up and operating a system economically. An increased self-consumption of the energy produced can be achieved with an intelligent charging strategy by means of PV forecasts and load shifts. Due to the inaccuracy of the PV forecast, there is an economic disadvantage because the energy management cannot cover the load in an economically optimal way. ... mehrThis can influence investment decisions. Furthermore, use cases have a significant influence on its sizing.
Taking the above considerations into account, in this work, a DC coupled charging infrastructure for electric vehicles is considered. The charging infrastructure is connected to the power grid, uses PV and stationary storage as energy sources, and has both DC and AC charging points. The objective of the considered topology is to avoid efficiency losses due to DC coupling and to achieve economic benefits by considering the overall concept. To illustrate the incomplete knowledge about the expected PV power, PV forecasting models are compared. Apart from the influence of PV forecasting inaccuracies, the system sizing due to the use cases of workplace, residential complex, mall and highway service station is compared.
The presentation addresses an overview of the current state of research in the field of charging infrastructure sizing, energy management and the impact of PV forecasts on economic efficiency. Subsequently, the optimal configuration of the charging infrastructure described above is analysed in terms of economic efficiency and self-sufficiency depending on the use case and PV forecasting model. The operation and role of self-consumption-maximizing energy management is discussed. The factors of net present value, charging tariff, payback and covered charging power are addressed. Maximizing self-consumption has advantages for both the economic efficiency and the self-sufficiency of the system.