EP-4736093-A1 - COMPUTER-IMPLEMENTED METHOD FOR DETERMINING AN INSTALLATION LOCATION FOR AT LEAST ONE CHARGING COLUMN FOR ELECTRIC VEHICLES, METHOD FOR INSTALLING CHARGING COLUMNS AT AT LEAST ONE INSTALLATION LOCATION, COMPUTING DEVICE, COMPUTER PROGRAM AND ELECTRONICALLY READABLE DATA MEDIUM

Abstract

The invention relates to a computer-implemented method for determining an installation location for at least one charging column for electric vehicles in a covered geographical area (3), in particular an urban area, wherein an optimization problem (16) comprising a cost function to be optimized is formulated and is solved in an optimization process in order to determine the installation location, comprising the following steps: - providing geodetic data (12) that describe the positions of charging columns that have already been installed, the positions of locations of interest, the positions (1, 2, 10a, 10b, 10c) of end-user transformers of a power grid and land use, in particular with regard to the presence of parking areas, in the area (3), - determining candidate positions (6, 7, 9a, 9b, 9c) for the installation location from the geodetic data (12), - in a preparation process, deriving deterministic parameters from the geodetic data (12) that are incorporated into the cost function, alongside at least one unknown parameter not able to be deduced from the geodetic data (12), for the candidate positions (6, 7, 9a, 9b, 9c), and - defining the optimization problem (16) from the deterministic parameters and the at least one unknown parameter, - wherein a stochastic optimization method is applied in order to take the at least one unknown parameter into consideration.

Inventors

• HUSAREK, Dominik
• LI, Gen
• Tomaselli, Domenico

Assignees

• Siemens Aktiengesellschaft

Dates

Publication Date

20260506

Application Date

20240515

Claims (15)

1. 1. Computer-implemented method for determining a location for at least one charging station for electric vehicles in a covered spatial area (3), in particular an urban area, wherein an optimization problem (16) is formulated with a cost function to be optimized and solved in an optimization process for determining the location, comprising the following steps: - providing geodetic data (12) describing the positions of charging stations already installed, the positions of places of interest, the positions (1, 2, 10a, 10b, 10c) of end-user transformers of a power grid and the land use, in particular with regard to the presence of parking areas, in the area (3), - Determination of candidate positions (6, 7, 9a, 9b, 9c) for the installation site from the geodetic data (12), - in a preparatory process, deriving deterministic parameters from the geodetic data (12) which, in addition to at least one unknown parameter which cannot be derived from the geodetic data (12), are included in the cost function for the candidate positions (6, 7, 9a, 9b, 9c), and - defining the optimization problem (16) from the deterministic parameters and the at least one unknown parameter, - wherein a stochastic optimization method is applied to take into account the at least one unknown parameter.
2. 2. Method according to claim 1, characterized in that a two-stage stochastic optimization method is used, in which in the first stage at least one candidate position (6, 7, 9a, 9b, 9c) is selected and in the second stage, if requirements by the power grid and/or requirements with regard to vehicles to be charged cannot be fulfilled, a Backstop technology based penalty term is used in the cost function.
3. 3. Method according to claim 2, characterized in that in addition to the candidate position (6, 7, 9a, 9b, 9c) at least one further variable which describes the charging power that can be provided by the at least one charging station is selected in the first stage, wherein the further variable relates in particular to a number of charging stations.
4. 4. Method according to claim 3, characterized in that in the context of the determination of the candidate positions (6, 7, 9a, 9b, 9c) and/or the preparation process, at least one restriction for at least one of the at least one further variable is determined and taken into account in the definition of the solution space and/or the optimization problem (16).
5. 5. Method according to one of the preceding claims, characterized in that at least one background information, in particular concerning the use of electric motor vehicles or concerning the use of the power grid, is used to determine the candidate positions (6, 7, 9a, 9b, 9c) and/or the deterministic parameters and/or that at least one simulation based on the geodetic data (12) is carried out to determine at least some of the deterministic parameters.
6. 6. Method according to claim 5, characterized in that at least one simulation is selected from the group comprising - a simulation of the traffic involving electric vehicles and/or their charging states and/or their residence times, in particular in relation to the places of interest, and - a simulation of the utilisation of the electricity grid is carried out, in particular with regard to the time-dependent power available for charging electric vehicles. 7. Method according to one of the preceding claims, characterized in that the at least one unknown parameter is a parameter related to the power supply from the power grid, in particular a parameter which relates to an end-user transformer to which the candidate position (6,
7. 7, 9a, 9b, 9c).
8. 8. Method according to one of the preceding claims, characterized in that the optimization problem (16) is defined on the basis of an optimization model which comprises a non-stochastic model component (14) which can be determined directly from the deterministic parameters and a stochastic model component (15) which is determined in a modeling process.
9. 9. The method according to claim 8, characterized in that in the modeling process, in particular based on a multinomial distribution, submodel scenarios (8) for different values of the at least one unknown parameter, all candidate positions (6, 7, 9a, 9b, 9c) and the entire spatial area (3) are generated and combined in a weighted manner to form the stochastic component (15).
10. 10. Method according to claim 9, characterized in that when using parameters which define an end-user transformer to which a respective candidate position (6, 7, 9a, 9b, 9c) as unknown parameters for generating the submodel scenarios (8), in particular as distribution parameters of the multinomial distribution, - distances from candidate positions (6, 7, 9a, 9b, 9c) to end-user transformers, and - Connection information regarding the end-user transformations for further, neighboring candidate positions (6, 7, 9a, 9b, 9c) are used.
11. 11. Method according to one of the preceding claims, characterized in that the cost function is formulated to maximize a monetary return and/or to minimize the monetary costs and/or a sample average approximation method is used to solve the optimization problem (16) and/or the optimization problem (16) is formulated or defined comprising at least one boundary condition, in particular at least partially defined by deterministic and/or unknown parameters and/or determined as a component of the model components (14, 15).
12. 12. Method for setting up charging stations at at least one installation location within a spatial area (3), in particular an urban area, characterized in that the at least one installation location is determined by a method according to one of the preceding claims.
13. 13. Computing device (19) comprising at least one processor and at least one storage means (20) and designed to carry out a method according to one of claims 1 to 11.
14. 14. A computer program which, when executed on a computing device (19), causes the computing device (19) to carry out the steps of a method according to one of claims 1 to 11.
15. 15. Electronically readable data carrier on which a computer program according to claim 14 is stored.

Description

Description Computer-implemented method for determining a location for at least one charging station for electric vehicles, method for setting up charging stations at at least one location, computing device, computer program and electronically readable data carrier The invention relates to a computer-implemented method for determining a location for at least one charging station for electric vehicles in a covered spatial area, in particular an urban area, wherein an optimization problem is formulated with a cost function to be optimized and is solved in an optimization process for determining the location. In addition, the invention relates to a method for setting up charging stations at the at least one location, a computing device, a computer program and an electronically readable data carrier. Purely electric vehicles, for example passenger cars, buses or vans, are increasingly being used as motor vehicles. These vehicles have a battery that can be charged at appropriate charging facilities. Publicly accessible and usable charging facilities are referred to as charging columns or charging stations. They are set up, for example, in public car parks, in particular at or near points of interest (POIs). POIs can include, for example, sights, restaurants, supermarkets and other shops, parks and the like. As the number of electric vehicles increases, so does the number of charging stations that need to be installed. In particular, a sufficient number of charging stations in an area is essential for the progressive electrification of its traffic. Charging stations are provided by profit-oriented companies. In order to find suitable locations, it is not only important to plan the expected use as well as possible. but also to assess the connection to the electricity grid from which the charging stations are fed. Typically, the charging stations are connected to the end-user transformers, which are also known as low-voltage substations or secondary substations. End-user transformers are stations where the medium voltage is transformed to a low voltage for the end users. For the technical problem described here, which is also known as EVCSAP ("electric vehicle charging station allocation planning"), approaches are already known in the state of the art to determine and use technically sensible locations for charging stations, which also represent a profit for the respective companies. Such known methods can, for example, include optimization processes that seek to maximize the respective monetary profits. For these optimization processes, an optimization problem (also: optimization program or optimization model) is put together, which includes a cost function and boundary conditions and can, for example, be passed on to a solution algorithm (solver). EP 4 184 421 A1 discloses a method for determining installation locations for charging stations for electric vehicles. Potential installation locations are first provided, after which the area in which the installation locations are to be determined is divided into several grid elements. Based on potential values of the grid elements, which indicate how advantageous it is to place charging stations in the respective grid element, sensible grid elements are first selected in a first selection step and then the potential installation locations are weighted using a weighting for the grid elements. Installation locations selected in a second selection step can be used as a starting solution for subsequent mathematical optimization. A problem in selecting locations for charging stations is often knowledge of the possible connection to the electricity grid. For example, information regarding the use and connection to end-user transformers, as well as capacities at the potential locations, in particular information on how many charging stations can be connected, is useful. However, this information is usually only available to the operator of the electricity grid. The uncertainty regarding the connection to the power grid can be a hurdle for a rapid additional distribution of charging stations in areas, especially urban areas. The transaction costs are also increased because the operator of the power grid has to be involved at an early planning stage. These additional costs and effort lead to a reduction in the number of installation locations that are actually evaluated, which in turn can lead to suboptimal allocations due to limited solution space. Although optimization methods have already been proposed in the state of the art that do not take the connection to the power grid into account at all, these solution approaches also produce suboptimal allocations. The invention is therefore based on the object of providing an improved possibility for finding suitable installation locations for charging stations in a spatial area, in particular one which reduces the effort and the necessary database. This object is achieved according to the invention by a computer-implemented de