Distribution Network Model Platform: A First Case Study

Abstract

Decarbonisation policies have recently seen an uncontrolled increase in local electricity production from renewable energy sources (RES) at distribution level. As a consequence, bidirectional power flows might cause high voltage/ medium voltage (HV/MV) transformers to overload. Additionally, not-well-planned installation of electric vehicle (EV) charging stations could provoke voltage deviations and cables overloading during peak times. To ensure secure and reliable distribution network operations, technology integration requires careful analysis which is based on realistic distribution grid models (DGM). Currently, however, only not geo-referenced synthetic grids are available inliterature. This fact unfortunately represents a big limitation. In order to overcome this knowledge gap, we developed a distribution network model (DiNeMo) web-platform aiming at reproducing the DGM of a given area of interest. DiNeMo is based on metrics and indicators collected from 99 unbundled distribution system operators (DSOs) in Europe. In this work we firstly perform a validation exercise on two DGMs of the city of Varaždin in Croatia. To this aim, a set of indicators from the DGMs and from the real networks are compared. The DGMs are later used for a power flow analysis which focuses on voltage fluctuations, line losses, and lines loading considering different levels of EV charging stations penetration.In literature several works have focused on creating realistic reference networks and validation methodology. A non-exhaustive list follows. A synthetic MV test grid in [2] was modeled to demonstrate the impact of distributed power generation on the grid using the tool called Smart Grid Metric. Urban and rural networks were modeled differently because of insufficient data for rural grids. Two types of rural area were built using Google Earth and statistics report for load density: large area with low population and small area with high population with a remark that no load is located outside the residential area. To investigate the impact of distributed energy resources (DER) penetration on MV network in [3], urban, rural, and industrial area are modeled based on combining a variable number of the typical representative feeders. Additionally, an economic analysis is performed through providing ancillary services at several market models. Three market frameworks were presented: flexibility bids offered directly from DER, or coupled and represented by distribution system operator (DSO) or aggregator. A statistical tool has been developed in [4] for generating representative distribution networks. Firstly, technical and geographical grid data were collected and different metrics have been investigated. Secondly, the purpose of the grid analysis needed to be identified in order to successfully select the best method for network generation. Finally, the validation was performed through comparing the performance of real grids and generated networks. The authors in [5] used metric-based validation process to demonstrate that public test...

A framework for regional smart energy planning using volunteered geographic information

“…Allhamwi et al (2019) use OSM data to estimate the energy demand of urban energy infrastructure of a region under study in Germany. Finally, Grzanic et al (2019) develop an approach for the generation of an electric power distribution system based on OSM and indicators collected from 99 unbundled distribution system operators (DSOs) in Europe (Prettico et al, 2018) that was validated in a Croatian region.…”

“…Nevertheless, as electricity flows do not always take the shortest route, this can be adapted (see Kays et al, 2017 for a discussion). Moreover, other methods such as the uesgraphs library can be used to generate a grid topology (Schiefelbein et al, 2019) or the NetworkX Python library (Hagberg, 2008).…”

“…However, due to the complexity of energy systems, developing a suitable tool for the modeling of electrical systems is not a trivial task. In the literature several works are dedicated to the simulation of realistic electricity systems, focusing on its different subsystems (transmission grid, distribution grids, smart grids), with a local, regional, or national focus (see for instance Grzanic et al, 2019, for a recent review). As a result, different tools of simulation, optimization, and validation methodologies of electrical systems exist and have been developed to cover different needs and levels of analysis (Kriechbaum et al, 2018), with regard to distribution networks.…”

“…By instance, the simulation of new smart energy systems requires considering active and reactive power flows. In the case of distribution networks, as Grzanic et al (2019) highlight, one of the major problems with the simulation of energy models is that a validation process is needed in order to show that the results are a realistic representation of the underlying system. Nevertheless, at the lower voltage grid level, the validation of energy systems requires access to grid topology data including information about feeders, transformers, and energy consumption data, which are mostly not available.…”

The FlexPlan Approach to Include the Contribution of Storage and Flexible Resources in Grid Planning

“…For this reason, a procedure for the automatic development of synthetic (but realistic) distribution grid models will be adopted in order to evaluate the potential of low-power storage/resources for the optimal planning of both distribution and transmission systems. Taking advantage of the availability of the web-platform DiNeMo [12], realistic distribution networks can be built on the basis of the metrics collected from more than one-hundred European DSOs. The tool, in addition to return the model of the distribution network, it also provides realistic coordinates for the construction of a geo-referenced infrastructure.…”