Optimization of Grid Expansion

The tasks of distribution grids are increasing with the transformation of the energy system. The electrification of the heating and transport sectors and the integration of distributed decentralized generation are massively increasing the need for grid expansion.

With our tools, global scenarios can be broken down to the grid area under consideration with grid connection point accuracy. In order to estimate the future supply task as precisely as possible, the grid operators data is blended with publicly available geoinformation.

Load and generation time series are generated on the basis of local weather conditions so that weather-related correlations, e.g. in the operation of heat pumps, are realistically modeled. This allows the challenges facing grid operators to be precisely quantified. Grid expansion decisions can be made taking future operational management options into account. 

The Intelligent Distribution Grid Optimization Framework (InDiGO) can be used to accelerate the process of finding optimal grid expansion measures. InDiGO calculates cost-optimized grid expansion measures for given grid congestion, taking smart grid technologies into account. InDiGO can also determine target grids for long-term scenarios and the associated major changes to the supply task.

• Grid Expansion Planning and Target Grid Planning
• Automated Grid Expansion Planning
• Synthetic Power Grids
• Network Analysis for Distribution Network Optimization

In addition to the expansion of renewable energies, the energy transition will increasingly affect the electricity grids due to the mobility and heating transition. With the InDiGO tool, we can automatically calculate proposals for solving grid overloads. In addition to conventional grid expansion, new smart grid technologies and operational management strategies for reducing grid expansion costs are also taken into account. We can also carry out target grid planning across all voltage levels. The tool was developed in the BKM 2.0, DiGO, MAPSEN, WP-QS im Bestand and straZNP projects, among others.

We use automated processes to find the optimal grid expansion solution for specific overloads. This includes smart grid technologies as well as reactive power control, controllable local grid transformers and the optimization of variable grid charges for targeted grid relief. If the supply task cannot be solved with “smart” technologies, we create automated proposals for equipment replacement or new routes.

Geodata analyses are used to identify local actors in order to model their electricity and heat consumption. This data is then linked to road routes to create realistic synthetic electricity grids. These grids are based on current geodata. In the synthesis process, transformers and lines are integrated and dimensioned in such a way that they comply with the limit values in the supply areas. The synthetic grids created in this way can then be used for a wide range of analyses and modeling.

Das InDiGO Framework ermöglicht die Erstellung von Verbrauchs- und Erzeugungszeitreihen für jeden Anschlussnehmer im Netzgebiet, wofür der Lastprofilgenerator synPro eingebunden wird. Dadurch lässt sich der zeitliche Verlauf der Netzbelastung präzise modellieren. Auf dieser Basis können gemäß §14a EnWG variable Netzentgelte festgelegt werden, welche Anschlussnehmer dazu motivieren, ihre Verbräuche zu verschieben und somit die Netzbelastung in kritischen Zeiten zu reduzieren.