What can be extracted from literature?

Although not all the distribution networks available in literature have been taken into account, the previous review is considered as a coherent and sufficient picture of the current status. Several models have been developed to analyze the electric distribution grid behaviour under different scenarios. These studies are subject to many assumptions, either simple, or more complex in modelling buses, branches and loads. The desired goals of each research activities is different and goes from:

control algorithm for electric vehicles charging, extreme distributed renewable gen-eration, voltage and transformer stress monitoring, demand response programs to network reinforcement cost.

Historically, in order to perform those analysis representative reference network models were developed for both transmission and distribution with standardized structure and known parameters. These developed networks are reasonable as a benchmark, and therefore researches have used them for decades to test different scenarios and algorithms.

Two major distribution grid networks classes pop out from the scientific works: real network grids and synthetic ones (IEEE and not-IEEE). The real network grids are less common in academic community due to their intrinsic confidential nature, in fact are often subjected to non disclosure agreement signed with the DSO. Further-more, depending on the agreement often these network are incomplete and therefore manually designed. The second category of networks, thus the synthetic ones built upon real network data, are the most known and used in the research community.

For this reason, they are more suitable for testing algorithm or operation scenar-ios. This category are represented mainly by IEEE, EPRI, TAMU and CIGRE networks. Among them, for the IEEE networks there are already made files in Matlab (MATPOWER) with all the information concerning the network features which made them, for this reason, the most adopted in research projects [67].

More in detail, the literature indicates, excluding those networks related to trans-mission, that there are 8 IEEE grids with respectively 13, 15, 32, 33, 34, 37, 69, 123 buses. Moreover, the literature shows that IEEE networks are modified, based on user’s knowledge, in terms of transformer capacity, power lines lengths and au-tomation devices, to perform the specific analysis. This clearly indicates that more

flexible solutions are required in both layout and network characteristics. This occurs because different goals are applied to the networks and in particular the most frequent optimization are: minimizing overall costs, minimize transformer degradation and maximize electric vehicles penetration. In addition, all these op-timization algorithms are then subjected to very different initial assumptions, like households load balanced between phases, aggregators revenues, loading capacity of distribution lines (thus no overloading), EVs number per node, V2G constraints, EVs initial state of charge, EVs charging power, which are often based on the infor-mation available by researchers. Moreover, some of these assumptions are manually implemented and applied on the identified feeders, according merely to users knowl-edge, by aggregating or clustering different loads, modifying lines capacity, networks layout, etc. [68].

Besides the IEEE there are some examples of actual distribution grids, which are directly provided from the local DSOs, such as: the Island of Bornholm in Denmark [63], a semi-urban system in Dublin [57], a fraction of the city of Borup in Den-mark [62], a distribution system of Kaili city in China [58], a small neighbourhood in China has been used by [30], the urban distribution network of Manjil city in Iran [64], a real Danish distribution grid in [59], a small area of Katerini in Greece [66], a realistic Flemish distribution grid [65]. These actual distribution grids are case specific, and among them they greatly differ in terms of geographical area, number of consumers, transformer capacity and many more aspects.

However, a well defined picture arises from literature which sees, as a common element, the relatively limited number of available distribution network layouts.

Moreover, another clear outcome has emerged, indeed network parameters are not well documented or even known in certain cases, therefore calculations of several operation scenarios are complicated and often time consuming. To summarize and provide a better view of the current situation, among all the articles analyzed, some common weaknesses concerning network grids and EVs charging profile have been detected and listed hereby:

• The relatively limited number of buses, branches and therefore consumers;

• The distribution network layout is rarely geo-referenced to real city areas;

• The electric vehicle impact is analyzed almost only at the substation level, and few times at buses level;

• The electricity network is the only layer used in the studies leaving several open questions.

Another criticism that arises is if these networks, developed in the 90’s, are still valid for the current distribution grid due to the drastic changes occurred (and still occurring) at power system level. Nevertheless, none of those networks were originally designed neither for distribution network analysis either for large-scale representative network for European cases. To complete this aspect, the IEEE networks, which are the most utilized in the scientific community, were designed for United States distribution network grids which have significant differences com-pared to European ones especially in terms of unbalances and three-phase MV/LV transformers [19]. More specific regarding unbalances, if in Europe they occur due to the existence of single-phase loads which may be unevenly distributed, in US it happens because of how the grid is designed (three-phase feeders with single-phase laterals). On top of all this, another relevant element observed is the lack of real electric vehicles demand profile that can be partially attributed to the fact that monitoring system are not yet standardized and big data is still missing.

In order to overcome this lack of knowledge on distribution networks, part of the activity of this thesis has been carried out to assist the development of a distri-bution network platform named DiNeMo aiming at reproducing the representative distribution grid of a given area of interest. The networks characteristics, gathered through the DSO survey and described in Chapter2.3, serve as input for the design of synthetic networks. This platform combined with the work carried on EVs and charging columns dataset (Chapter4), acquired from ElaadNL³ for the Netherlands pave a new frontier for the real case studies on the impacts of EVs on distribution networks.

3As stated in their website ”...Through their mutual involvement via ElaadNL, the grid opera-tors prepare for a future with electric mobility and sustainable charging. It is our mission to make sure that everyone can charge smart. We monitor the EV-charging infrastructure and coordinate the connections between public charging stations and the electricity grid...”