The number of photovoltaic (PV) systems installed worldwide is steadily increasing, along with their share in the energy produced in the power grid. Getting the maximum benefit from these PV systems involves two primary considerations: maximizing power generation over the entire lifetime of the PV modules and optimally integrating them into the power grid.
Getting the maximum output from a given PV installation requires continuous monitoring to detect underperformance and failures as early as possible. However, this type of monitoring is seldom implemented, particularly outside of utility-scale photovoltaic plants or research-focused installations. These facilities are equipped with all the instruments required for accurate monitoring, in contrast to residential and commercial installations.
The capabilities of modern data analytic methods can help raise the proportion of PV installations that can be monitored. In addition, data-driven modeling can make monitoring a PV system simpler: data-driven models do not require detailed information about the system, and they can also combine data from sources that are available outside of plants and research centers, but which standard, physics-based modeling approaches can't take advantage of.
One thing that could hinder the integration of PV systems into the power grid is the stochastic nature of solar energy. This stochasticity could lead to adverse effects such as over- and under-production and challenge the stability of power grids. This means that in addition to monitoring, the integration will also require the most accurate possible forecasting of expected power to control the grid optimally and avoid unnecessary stresses on it. Data-driven methodologies can help make the implementation of such best practices more accessible and more widely available, benefiting both PV system owners and power grid managers.
In this project, we investigate data-driven monitoring and forecasting systems for photovoltaic installations. As an application, we study the potential and benefits of PV power monitoring and forecasting for load management in support of electric mobility integration in the public transport system. We focus specifically on electric buses and the load generated from charging their batteries. Suppose the charging tasks are performed in an uncontrolled way. In that case, the large electric loads of the batteries might cause instability of the power grid and reduce the environmental benefits of such electric vehicles. Contrary to this, load management coordinates the charging processes to benefit as much as possible from renewable energy sources while preserving the power grid stability.
The coordination of energy generation from PV systems and energy consumption by electric vehicles is a key to a green transition in the transport sector.
- P. Graniero, D. Rößler, C. Ulbrich, and N. Kliewer. Potentials and Challenges for Integration of Electric Bus Fleets and PV-Systems. (Oral presentation), 31st European Conference on Operational Research (EURO 2021) in Athens, Greece / online, 11-14 July 2021.
- P. Graniero, A. Louwen, R. Schlatmann, and C. Ulbrich. Comparison of different data sources for Machine Learning algorithms in photovoltaic output power estimation. (Poster presentation), 37th EU PVSEC, Online, 7-11 September 2020.