has increased the observability of distribution networks. A high share of smart meters is a prerequisite for the total transition towards smart, active distribution networks. Also, it is needed for the exploitation of the end-users flexibility in the mitigation of different voltage and current issues.

The databases with the network data are often large and they contain a lot of data that cannot be used in the initial form. Before using this data in any of the analyses, it requires preprocessing and editing, so all the errors and unnecessary information are removed. Network topology and other network data are analyzed and edited using tools based on Geographic Information Systems (GIS). Since the network data besides the technical attribute often contains geographical, locational attributes, GIS-based tools are suitable for running the topological queries and other geospatial operations needed for editing the network data, which then can be used in power flow, harmonic, and other relevant analyses. The data valid for the end-users, e.g., consumption or voltages, often cannot be used in the primary form due to outliers or missing values. Also, this data is large and complex and requires the use of tools that are suitable to work with such datasets. Recently, machine learning (ML) algorithms have been widely used in the operation of smart distribution networks. These algorithms show great capability in working with large and complex datasets but require precise data. The data without any missing or outlier values and other potential errors are ensured by analysis made in the preprocessing.  Despite the increased number of installed smart meters, their full potential remains unexploited since they are used only for collecting the basic billing and processing data such as end-users active consumption and phase voltages. Since the analyses require more detailed data, ML-based algorithms are used for different purposes, e.g., distribution of the consumption among the phases. ML algorithms can be used in forecasting the consumption of end-users and a load of substations, forecasting network losses, or the RESs production.

End-users are becoming the central figure in the green energy transition. Their flexibility potential is more often used in enabling the integration of LC units and the mitigation of technical challenges in the operation of smart distribution networks. The operation of smart distribution networks also requires the development of the tools and implementation of the advanced models that will help in all necessary analyses and in solving voltage, current, power quality, and other problems related to the integration of LC units. ML, optimization, and algorithms based on the other systems such as GIS are widely used in the aspect of the network’s operation and their functionalities are used to overcome all data-related issues and consequentially to exploit the potential of end-users and maximize the DER’s hosting capacity.

• SERVIFLEX tool (Integrated flexibility management tool): It is the tool for flexibility managers to take advantage of the value of energy storage along with other demand flexibility resources towards the establishment of a holistic framework for flexibility extraction, profiling, forecasting, classification, clustering and management to serve different market and grid needs.

• GRIDFLEX tool (Advanced tool for automatic control and observability): It is the tool for distribution grid and microgrid operators in order to prevent congestion and power quality problems with the increasing share of intermittent RES, giving special attention to the potential grid problems due to extreme climate events.

• MARKETFLEX tool (Market platform and new market mechanisms): It is the tool for flexibility owners to access the market through an aggregator or through a Balancing Responsible Party (BSP) to participate on different markets: the existing wholesale markets and balancing markets, but also local electricity markets and local flexibility markets for DSO.

• X-FLEX platform (Flexible and scalable integrated platform): The X-FLEX platform integrates all the X-FLEX solutions in order to provide services for all the energy actors and ensure more secure, stable and clean energy supply.
The expected results of the X-FLEX project include socio-economic benefits by the creation of jobs related to distributed flexibility technologies after the commercialization, deployment and implementation of the X-FLEX solutions. Environmentally, X-FLEX predicts the increase of RES production by shifting the demand toward high-generation periods thus avoiding curtailment of DRES. Besides, this project aims at a market transformation at the distribution level and a large-scale deployment of local markets.

In the panel, Mislav Kirac from Zelena Energetska Zadruga (ZEZ) will present REDREAM project, one of the “sister projects” on topic of flexibility and results from the Croatian pilot site.