David Lingfors

The interest for co-located wind and solar photovoltaic (PV) parks, also known as hybrid power parks (HPPs), is increasing both in industry and in the scientific community. Co-locating wind and PV can lead to synergies in power production, infrastructure, and land usage, which may lower the overall plant cost compared to single technology systems. This review paper summarizes the existing research on power output modeling related to utility-scale HPPs and identifies knowledge-gaps. The main literature shows that there is a need for improved modeling methodologies accounting for the variability of the combined power production. There is potential for immediate improvement by combining state-of-the-art models that have been developed in separate fields and harmonizing the vocabulary across the different research fields. The study also shows that the total number of peer reviewed studies on utility-scale HPPs is limited and further research, in particular comparative studies, is needed to give a comprehensive view of the benefits and challenges of combining technologies. Other areas such as physical design, control strategies, market participation, and quantification of the possible synergies for physical implementation of HPPs also need to be studied further.

The penetration of electric vehicles (EVs) and photovoltaic (PV) systems has increased globally in the last decade. For planning purposes, the spatiotemporal variability of distributed PV power generation and EV charging needs to be quantified for urban and rural areas. This study introduces a state-of-the-art, open, and generally applicable model framework for assessing the spatiotemporal mismatch between EV load and PV generation for urban and rural areas. The model is applied to a rural and an urban area, both 16 km × 16 km and located in Sweden, and is evaluated for the extreme months of January and July. The results show that an energy deficit of, at most, 86% and an up to ten times surplus took place in January and July, respectively. A high self-consumption (SC) of 77% was observed for January and a high self-sufficiency (SS) of 69% for July. This is to say that during July, PV can fulfill 69% of the EV charging load. Moreover, there were no observed correlations between the PV-EV temporal matching scores (the SS and the SC) and the dominant type of charging, e.g., workplace charging in each grid cell (1 km × 1 km) of the areas. This can be partially attributed to the wide distribution of the rooftop orientations in both areas. This challenges the assumption of low PV-EV temporal matching in residential parts of the city. Applying the proposed methodology to other regions is incentivized.