EP-4742496-A1 - METHOD FOR DETERMINING A NETWORK TOPOLOGY DISTRIBUTION OF AN ELECTRICAL NETWORK AND USE THEREOF FOR CONTROLLING THE ELECTRICAL NETWORK

Abstract

Method for determining a network topology distribution of a power grid and its use for controlling the power grid A computer-aided method for determining a probability distribution p ( G | X , Y ) (3) of network topologies G of a power grid is proposed, wherein the power grid has several local network substations (O1, O2) and end consumers (E1, E2), where X is associated with power measurements (21) and Y with voltage measurements (22) of the power grid. The method is characterized by the following steps: - (S1) Determining a first a posteriori distribution p ( a | X, Y) (1) where a is an assignment of the end consumers (E1, E2) to the local network stations (O1, O2); - (S2) Determining a second a posteriori distribution p ( G | a, X, Y ) (2), where only assignments with p ( a | X, Y ) > p min are considered, and p min ≥ 0 is a specified minimum threshold; and - (S3) Determining the probability distribution p ( G | X , Y ) (3) of the network topologies G of the power grid using the first and second a posteriori distribution (2, 3) by p ( G | X , Y ) = Σ a p ( G | a, X, Y ) · p ( a | X, Y ) . Furthermore, the invention relates to a method for determining a network topology G* of a power grid, a control unit for controlling a power grid and a computer program product.

Inventors

• METZGER, MICHAEL
• Stursberg, Paul
• Tomaselli, Domenico

Assignees

• Siemens Aktiengesellschaft

Dates

Publication Date

20260513

Application Date

20241107

Claims (15)

1. Computer-aided method for determining a probability distribution p ( G | X , Y ) (3) of network topologies G of a power grid, wherein the power grid has several local network substations (O1, O2) and end consumers (E1, E2), wherein X is associated with power measurements (21) and Y with voltage measurements (22) of the power grid, characterized by the following steps: - (S1) Determining a first a posteriori distribution p ( a | X, Y) (1) where a is an assignment of the end consumers (E1, E2) to the local network stations (O1, O2); - (S2) Determining a second a posteriori distribution p ( G | a, X, Y ) (2), where only assignments with p ( a | X, Y ) > p min are considered, and p min ≥ 0 is a specified minimum threshold; and - (S3) Determining the probability distribution p(G | X,Y ) (3) of the network topologies G of the power grid using the first and second a posteriori distribution (2, 3) by p ( G | X , Y ) = Σ a p ( G | a, X, Y ) · p ( a | X, Y ) .
2. Method according to claim 1, characterized in that the first A posteriori distribution p ( a | X, Y ) (1) is determined by means of p ( a | X, Y ) ∝ p ( X | a, Y ) · p ( Y | a ) · p ( a ) where p ( X | a, Y) and p ( Y | a ) are the respective measurement distributions (21) and p ( a ) is the A priori distribution (11) of the assignments.
3. Method according to claim 2, characterized in that the first A posteriori distribution p ( a | X, Y ) (1) is approximated by p ( a | X, Y ) ∝ p ( a | X ) ∝ p ( X | a ) · p ( a ).
4. Method according to claim 3, characterized in that p ( X | a ) is approximated by a multidimensional normal distribution.
5. Method according to one of the preceding claims, characterized in that the second a posterior distribution p ( G | a, X, Y ) (2) is determined by means of p ( G | a, X, Y ) ∝ p ( X | G, a, Y ) · p ( Y | G, a ). p ( G | a ) where p ( X | G , a,Y ) and p ( Y | G , a ) are the respective measurement distributions (22).
6. Method according to claim 5, characterized in that the second A posteriori distribution p ( G | a, X, Y ) (2) is approximated by p ( G | a, X, Y ) ∝ p ( G | a, Y ) ∝ p ( Y | G , a ) · p ( G | a ).
7. Method according to claim 6, characterized in that p ( Y | G, a ) is approximated by a multidimensional normal distribution.
8. Method according to one of the preceding claims, characterized in that the power measurements X (21) are measurements at the local network stations (O1, O2) and the voltage measurements Y (22) are measurements at the end consumers (E1, E2).
9. Method according to one of the preceding claims, characterized in that the determination of the first A posteriori distribution p ( a | X, Y) (1) is carried out using a Markov chain Monte Carlo method.
10. Method according to one of the preceding claims, characterized in that for determining the second A posteriori distribution p ( G | a, X, Y ) (2) the number of net topologies G is restricted by physical and/or technical boundary conditions.
11. Method for determining a network topology G* of a power grid, wherein the power grid has several local network substations (O1, O2) and end consumers (E1, E2), in which power measurements X (21) and voltage measurements Y (22) associated with the power grid are provided, characterized in that the determination of the network topology G* is carried out by means of a probability distribution p ( G | X , Y ) (3) of network topologies G of the power grid determined according to one of the preceding claims.
12. Method according to claim 11, characterized in that the net topology G* is determined by G* = argmax G [ p ( G | X , Y )].
13. Control unit for controlling a power grid with several local network stations (O1, O2) and end consumers (E1, E2), comprising a computing unit, characterized in that the computing unit is designed and configured to carry out a method according to one of the preceding claims.
14. Control unit according to claim 13, characterized in that the power grid is designed as a medium-voltage network or a low-voltage network.
15. Computer program product comprising instructions which, when the program is executed by a computing unit, in particular a computer, cause it to execute a method and/or steps of the method according to any one of claims 1 to 11.

Description

Method for determining a network topology distribution of a power grid and its use for controlling the power grid The invention relates to a method according to the preamble of claim 1, a method according to the preamble of claim 11, a control unit according to the preamble of claim 13 and a computer program product according to the preamble of claim 14. The increasing spread of decentralized renewable energies, for example charging stations for electric vehicles or heat pumps, typically requires a modernization and expansion of the grid infrastructure at medium voltage and/or low voltage levels. Due to the long history of many power grids or distribution networks, there is typically little to no reliable and directly usable information available about the network topology of the power grids. Therefore, network models are needed to topologically reconstruct the existing infrastructure. These models are often based on limited information regarding the respective power grid. While information about the location of secondary substations and end-consumers can be obtained from asset management and/or billing systems, the network topology, or the topology of existing distribution networks, is typically unknown and therefore uncertain. Furthermore, low-voltage lines are typically buried underground, which makes verifying any available topology information difficult and costly. Determining the network topology of a power grid is therefore a highly underdetermined problem, meaning that a single, reliable network topology or solution cannot be expected. Probabilistic approaches are thus necessary, which capture the aforementioned technical uncertainties using probabilities or probability distributions. In principle, available measurement data can be considered to reduce the uncertainty and ensure that the The determined probability distribution only includes network topologies that respect the aforementioned measured values. It is known to estimate the network topology of an electricity grid using geoinformation, for example, using road layouts and the locations of end consumers. Alternatively, probabilistic methods are known that generate an ensemble of different, non-georeferenced network topologies that correspond to the topological and/or electrical properties of real distribution networks. However, none of the approaches mentioned can satisfactorily account for the uncertainty, apart from the use of a statistical tool, in the case of very limited available information about the topology. The present invention is based on the objective of providing an improved method for determining a probabilistic network topology of a power grid. The problem is solved by a method with the features of independent claim 1, by a method with the features of independent claim 11, by a control unit with the features of independent claim 13, and by a computer program product with the features of independent claim 14. Advantageous embodiments and further developments of the invention are specified in the dependent claims. The computer-aided method according to the invention for determining a probability distribution p(GIX, Y) of network topologies G (network topology distribution) of a power grid, wherein the power grid has several local network substations and end consumers, wherein X is associated with power measurements and Y with voltage measurements of the power grid, is characterized by at least the following steps: Determining a first a posteriori distribution p ( a | X, Y), where α is an assignment of end consumers to the local network stations; Determining a second a posteriori distribution p ( G | a, X, Y ) , where only assignments with p ( a | X, Y ) > p min are considered, and p min ≥ 0 is a fixed minimum threshold; and Determining the probability distribution p ( G | X , Y ) of the network topologies G of the power grid using the first and second a posteriori distributions by p ( G | X , Y ) = Σ a p ( G | a , X , Y ). p ( a | X, Y ). The method according to the invention and/or one or more functions, features and/or steps of the method according to the invention and/or one of its embodiments may be computer-aided. The power grid is an electrical distribution network, specifically a medium-voltage and/or low-voltage network. The power grid has several network nodes, which are primarily associated with local distribution substations and/or end consumers. Furthermore, the power grid typically has several lines extending from one network node to another. The topology of the power grid can include feeders and/or loops. The topology, or grid topology, of the power grid can also be referred to as a grid model. Additionally, the power grid and its topology can be modeled as a mathematical graph. The distributions, or probability distributions, can be provided discretely and/or as probability density functions. In principle, the distributions can be integrated over their corresponding variables. In the discrete case, integration m