EP4176504A1

EP4176504A1 - Network for distributing electrical energy

Info

Publication number: EP4176504A1
Application number: EP21720781.0A
Authority: EP
Inventors: Monika FREUNEK
Original assignee: BKW Energie AG
Current assignee: BKW Energie AG
Priority date: 2020-07-03
Filing date: 2021-04-27
Publication date: 2023-05-10
Also published as: JP2023531576A; CA3160582A1; CH717615A1; WO2022002458A1; US20230155414A1; AU2021302159A1; CN115136441A

Abstract

The invention relates to a network (1) for distributing electrical energy, said network comprising: a first network area (10) consisting of a plurality of local, self-regulating functional groups (11.1...8) having first sources, loads, lines and/or sensor components, switching components or converter components, wherein each of the functional groups (11.1...8) is designed to satisfy assigned control limits for voltage quality variables in the network (1), and wherein the first network area (10) has a first size; and a second network area (20) having second sources, loads, lines and/or sensor components, switching components or converter components, wherein an estimated total variance of the voltage quality variables is assigned to the second network area (20), and wherein the second network area (20) has a second size. The control limits of the functional groups (11.1...8) and the first size are selected in such a way that, taking into account the second size and the estimated total variance, predefined target operating range limits are satisfied for the entire network (1).

Description

Network for the distribution of electrical energy

Technical area

The invention relates to a network for the distribution of electrical energy. It also relates to a computer-implemented method for structuring an existing network for the distribution of electrical energy, comprising as network components at least sources, loads, lines, sensor, switching and converter components that are interconnected in an output topology, a method for operating a network for Distribution of electrical energy and computer programs for implementing the structuring method and the operating method.

State of the art

Networks for the distribution of electrical energy (power networks) comprise a network of electrical lines (namely overhead lines and underground cables) and other network components that are interconnected with the lines in a specific topology. The other network components include sources, e.g. B. the generators of power plants or intermediate storage such. B. Batteries, loads (consumers), sensor components for detecting operating parameters of the network (voltages, frequency, currents, powers, temperatures, etc.), switching components for connecting and disconnecting components or network sections and converter components, e.g. B. Transformers, for example to change the voltage.

The topology is divided into several network levels. Starting from a generator such as a power plant, the large-scale distribution initially takes place via a transmission network with maximum voltage (e.g. 380 or 220 kV). National distribution networks with high voltage (e.g. 36-150 kV) are connected to these via substations with transformers regional distribution networks with medium voltage (e.g. 1-36 kV) via further transformers. The local distribution network with low voltage (e.g. 400 V - 1 kV) is then connected via further transformers, which (if necessary via transformer stations) to the house connections and thus to the end consumer (including private households, industrial, commercial and agricultural operations) leads.

The specific topology with the components present in the network has grown historically, depending on the locations and services of the producers (power plants) and consumers. Changes to the topology usually require additional or differently running or dimensioned electrical lines and are therefore complex.

In recent years - especially due to the emergence of local producers such. B. Photovoltaic systems - the requirements for the power grid changed. It is no longer only used for the hierarchical distribution of electrical energy "from above" (ie from the power plant) "downwards" (ie to the consumers), but the electricity flows can vary depending on production conditions (e.g. solar radiation) and consumption patterns . In general, the production patterns of many renewable electricity producers are stochastic and associated with uncertainties. So are z. B. the production output of photovoltaic or wind turbines strongly dependent on the weather. The future, short, medium and long-term development of the corresponding production capacities is not known and is difficult to forecast because many of the corresponding systems are built by private and commercial manufacturers who are independent of the previous electricity producers or network operators.

There are also decisive changes on the consumer side. Electric vehicles in particular lead to an increase in the power required at times, and their charging behavior is also stochastic and difficult to predict.

Ultimately, there is an operating state of the power grid with chaotic behavior.

Furthermore, due to the climatic development there is an increased risk of damage to exposed pipe sections, e.g. B. due to forest or bush fires, storms, heavy precipitation events or landslides. All of this brings challenges in the planning and operation of fail-safe power grids. In addition, today's power supply networks of different operators are closely linked, so that problems in the network of a first network operator can cascade-like problems in the networks of other operators within a short period of time. This can range from problems with frequency compliance to power outages (blackouts).

The control or regulation of the network, which aims to ensure safe operation and specifically to ensure that specified control limits (e.g. with regard to frequency, voltage, current) are adhered to, is usually organized hierarchically, which means that the Requirements have risen sharply and more frequent interventions are necessary to maintain operational reliability. In order to obtain further information on the consumer side, in particular, which can flow into the control or regulation, so-called "smart meters" are increasingly used today, which record information, namely consumption information, directly from the consumers and via a communication network to higher-level facilities of the network, z. B. a central collection point transferred.

If control commands are to be generated based on simulation and optimization, high-performance computers must be used at this superordinate point in order to process the most comprehensive information possible at short notice. This is particularly due to the enormous amounts of data that arise that have to be processed within a short period of time.

In addition to the enormous effort involved in these calculations, such a centralized system also harbors various sources of error. The selection of the measures to be taken in the subordinate network section is complex, and errors in the transmission of the measurement signals from the smart meters (and other sensor components) to the higher-level unit or the control signals back to the components in the network can cause operational disruptions. In addition, all potentially relevant information is hardly ever available, because this z. B. concern networks of neighboring network operators or privately operated power generation plants. The same is true for many consumers. On the other hand, in the case of central data processing, data are processed that contain a lot of redundant information, so that ultimately an unnecessarily high effort with corresponding energy consumption is carried out for data processing.

EP 3 323 183 B1 (Siemens Aktiengesellschaft) relates to a method for computer-aided control of the power in an electrical power network with several interconnected nodes, each of which contains a first energy generator and / or a second energy generator and / or an energy consumer. A power estimate is specified for each node, which is composed of an estimate of the future load of the consumer or an estimate of the future output of the second, regenerative energy producer in the node. Furthermore, fluctuations of the first type and the second type of the power estimates are permitted in predetermined tolerance ranges, the fluctuations of the first type being compensated for by primary control power and the fluctuations of the second type by secondary control power in the power grid. In the described method, an optimization problem is solved for the assignment of the control power, in the context of which a steady state of the power grid is modeled with a steady grid frequency and whose boundary conditions include compliance with the grid frequency within specified tolerances and maximum power on the power lines of the power grid.

The method described requires a central control for a number of nodes in order to create sufficient degrees of freedom for the optimization. It is assumed that the estimate includes all nodes and has some reliability. In practice, this results in problems because - as mentioned above - often not all of the necessary information is available and because the stochastic behavior of many producers and consumers results in dynamic changes.

WO 2018/1 1 404 A1 (BKW Energie AG) describes a method for structuring an existing network for the distribution of electrical energy, the network comprising at least sources, loads, lines, sensor, switching and converter components as network components, which are included in an output topology are interconnected, the network components in a plurality of local, self-regulating ones are based on property variables of the network components and specifiable control limits Function groups summarized. Control processes are assigned to each local function group, which include actions that are carried out when trigger criteria are reached in order to comply with the control limits. Starting from an existing network for the distribution of electrical energy, the method leads to a network that is restructured with regard to the regulation, which as far as possible dispenses with a hierarchical structure with regard to regulation and is instead made up of local, self-regulating function groups in normal operation . This results in, among other things, a reduction in the susceptibility to errors and thus an increase in operational and supply security. This approach avoids the disadvantages of the centralized approaches of the prior art. The structuring of an overall network by providing appropriate functional groups is complex, however, and additional measures must be taken to limit influences from neighboring networks.

DISCLOSURE OF THE INVENTION The object of the invention is to create a power network belonging to the technical field mentioned at the beginning, which enables simple structuring with local functional groups and the systematic consideration of the influences of neighboring networks and network sections.

The solution to the problem is defined by the features of claim 1. According to the invention, the network comprises a) a first network area, consisting of a plurality of local, self-regulating function groups with first sources, loads, lines and / or sensor, switching or converter components, each of the function groups for maintaining assigned control limits for Voltage quality variables are formed in the network and wherein the first network area has a first variable; b) a second network area with second sources, loads, lines and / or sensor, switching or converter components, the second network area being one the estimated total variance of the voltage quality variables is assigned and wherein the second network area has a second variable; The control limits of the function groups and the first variable are selected in such a way that, taking into account the second variable and the estimated total variance, predetermined target operating range limits are maintained for the entire network.

A local function group in the sense of the method according to the invention is formed by components interconnected with one another in accordance with a topology, with an individual network component also being able to form a function group in the extreme case. In this context, “local” does not necessarily mean that all components of a functional group must be located within a certain spatial area. However, if the latency of the information transmission and the distance over which information has to be transmitted are taken into account when grouping network components into function groups, this should generally mean that all local function groups are limited to relatively small geographical areas. As a rule, a functional group will not contain any "holes" or any areas that are isolated from the rest of the network components it comprises.

Function groups can basically be nested within one another, whereby an inner function group can be viewed as a network component of the outer function group.

The local function groups regulate themselves in normal operation. They can be formed and operated, for example, in accordance with WO 2018/1 14404 A1 (BKW Energie AG). Thus, through respective actions by the control processes assigned to the function groups, measures outside the respective function group can be triggered if trigger criteria are met. The rule processes can provide for further actions which only work within the function group. Basically, the term "control process" refers to both interventions in the operation of network components and the sending of certain information from one network component to certain other network components (the same function group, another function group or a higher or secondary position). For self-regulation, the local functional groups include sensors (e.g. current or voltage sensors), actuators (e.g. switching or regulating devices for generators and / or loads) and control means (computers or controls). With the help of the sensors it is checked in particular whether the assigned control limits are adhered to. The control means trigger actions as a function of the data recorded by the sensors. These can in particular include rule actions by controlling the aforementioned actuators as well as communication actions to secondary or superordinate function groups or entities with the help of suitable communication means.

The function groups enable a quick and local reaction. Due to the decentralized arrangement of the computing means, the amounts of data to be transmitted to other functional groups or a higher-level logic are minimized, and costly central calculations are avoided. In addition, there is a reduction in communication times, including latency times, which enables faster reactions. The risk of a failure of a central control with far-reaching consequences is avoided. In the network according to the invention, the failure of a computer unit or a communication channel generally has no, but at most a small influence on the overall stability of the network.

The sizes of the network areas can be characterized in different ways. A suitable measure is, for example, the average total electricity volume in the corresponding network area. Other quantities, which characterize, for example, a total output or total capacity of the facilities in a network area, are also suitable. It can be assumed that the first network area and the second network area are constructed similarly, e.g. B. with regard to the type and distribution of consumers and producers, the number of the respective network components can also simply be used. In the case of a more or less homogeneous density of the network, the area covered in each case may also suffice.

The second network area should not be empty. In addition, it is not structured like the first network area, ie it is not made up of local function groups that regulate themselves to comply with assigned control limits. The second network area acts In particular, it is an existing, hierarchically controlled network with a network topology that has evolved over time, or a sub-area thereof.

In the context of the network according to the invention, the first network area comprises, in particular, several functional groups, and the size of the second network area is at least one third, in particular at least half of the first network area.

Voltage quality variables include, for example, the frequency, the network voltage (voltage level or effective value) or statistical and / or dynamic parameters in relation to such parameters; current-related variables can also be used as voltage quality variables.

The target operating range limits can be defined with the aid of such voltage quality variables, with target ranges being specified for several such variables as a rule. Alternatively or additionally, other criteria can be used, e.g. B. Maximum failure rates.

The uncertainty of the overall network is thus distributed between the controlled first network area and the uncontrolled second network area. If the sizes of the first network area and the second network area (or a ratio between these parameters) and the control limits for the first network area are known, a statement about the behavior of the corresponding voltage quality parameters for the entire network is possible. The topology and network capacities between the function groups can be specifically taken into account or included in the calculation of the uncertainties as a lump sum.

Based on the information available on the first network area, which consists of self-regulating function groups, the uncertainty with regard to the second network area can be partially compensated. According to a simplified example, a voltage of at least 222 V should be guaranteed in the network. In the first network area, a voltage of at least 224 V is guaranteed due to the self-regulating function groups, in particular because the minimum voltage is specified as the control limit. The voltage quality in the first network area is therefore always better than the specification for the entire network. If now the ratio between the second size and the first Size does not exceed a certain ratio, due to the guaranteed voltage quality in the first network area, the target value for the entire network, including the non-specifically regulated second network area, can be achieved. The relationship between the variables that must be observed results from the estimated total variance of the voltage quality variable assigned to the second network area and the

Difference between the voltage quality guaranteed in the first network area and the specification for the entire network.

Worst-case values are assumed for the estimation of the total variance of the voltage quality variable in the second network area. The estimation can be based on measured values, models and / or simulations. With an improved

Estimation results in a lower overall variance, which enables the following within the framework of the network according to the invention: a relaxation of the control limits in the first network area,

(theoretically) a reduction in the size of the first network area and / or - an enlargement of the second network area by expanding the system boundaries.

For example, machine learning approaches can be used for the modeling.

In addition to the local regulation of the functional groups in the first network area, the network according to the invention is distinguished by the fact that target operating area limits can be adhered to for the entire network, including a second network area without self-regulating function groups. Accordingly, it is not necessary to restructure the entire network. It can be more cost-effective to structure only part of the network with self-regulating function groups and to assign stricter control limits to them than to structure the entire network with somewhat less strict control limits. It can thus initially z. B. structure those areas of a network in which this process is associated with the lowest costs, e.g. B. new network areas, network areas that are being refurbished anyway or network areas that are particularly suitable for structuring due to their existing structure. Also the Availability of information can be relevant when selecting the network area to be structured.

With the help of a network according to the invention, strategically important network sections can be secured, e.g. B. by the network according to the invention is designed so that particularly strict target operating range limits are met.

The estimated total variance advantageously covers an expected network operation for a period of at least one year. Seasonal fluctuations are thus taken into account. The configuration of the network according to the invention is therefore suitable for continuous operation and, as a rule, has to be adapted above all in the following cases: if relevant properties change in the second network area which result in a different estimated total variance; when the second size changes.

Of course, there is also a need for change if new function groups are deliberately created or function groups are canceled, if the system limits are changed or if the control limits for function groups or the target operating range limits for the entire network are changed.

In principle, it is possible to estimate the total variance in the second network area for a shorter period of time, e.g. For example, if a network structure is only supposed to exist for a limited period of time anyway or if the structure of the network is updated at regular intervals (e.g. every six months).

The network preferably comprises at least one switching device in order to decouple the network from higher-level and / or secondary networks for distributing electrical energy. Networks for the distribution of electrical energy, e.g. B. a network of a certain network operator or electricity supplier, are usually not isolated, but connected to other networks. With the help of the switching device you can now excessively If necessary, disruptive influences from neighboring networks can be avoided by temporarily decoupling them.

A subordinate further network can be a defined part of the distribution network of the operator who operates the network according to the invention. In this case, in addition to the first network area with self-regulating function groups and the second network area, the total variance of which is included in the dimensioning of the network according to the invention, there is also a third area that can be decoupled from the first and second network area if necessary. This network is therefore outside the system limits of the network according to the invention, but cannot destabilize it despite its connection to the two network areas because it can be decoupled if necessary.

With the help of the switching device it can be ensured that the system limits taken into account for the definition of the function groups, the control limits and the scope of the first and second network area can always be adhered to. A maximum expansion of the functional groups is preferably selected such that a maximum signal propagation time is maintained within the functional groups. In real-time-critical applications, switching times in the ms or even ps range should be possible, e. B. for switching operations in emergencies or for trading. In practice, such switching times can only be reliably achieved with a decentralized control or regulation, as it takes place within the framework of the network according to the invention in the first network area.

Starting from an existing network for the distribution of electrical energy, which comprises at least sources, loads, lines, sensor, switching and converter components as network components, which are interconnected in an initial topology, a network according to the invention can be created by means of a computer-implemented method for structuring which comprises the following steps: a) recording the existing network within specified system boundaries; b) Detection of control limits for local, self-regulating function groups; c) Detection of target operating range limits for the structured network to be created; d) performing an optimization of an objective function by varying

Network properties, where e) the variable network properties have at least one assignment of

Network components to one of several local functional groups of a first network area or an assignment of network components to a second

Include network area, f) a total variance of voltage quality variables is estimated for the second network area; g) whereby the boundary condition for the optimization is compliance with

Target operating area limits are specified, which are checked taking into account the control limits of the functional groups, a first size of the first network area and a second size of the second network area and the total variance of the second network area.

An "existing network" can be a section of a larger network. In principle, the user can specify the scope of the procedure, i. H. determine which network components are to be taken into account at all.

A “source” in the sense of the method according to the invention can be a generator, a battery (which delivers current) or another energy storage device or simply an “input” of the network or network section under consideration. "Loads" in the sense of the method are consumers, batteries or other energy storage devices in charging mode or simply an "output" of the network or network section under consideration. Depending on the operating status of the network, certain network components can temporarily represent sources or loads. There are also network components that combine several functions (e.g. load and sensor components, source and converter components, etc.).

The existing network can be represented by means of a topology with additional information, information on the geographical location of the network components and / or a network plan are also information on the existing network that is included in the process can be recorded. By recording the existing network, the system boundaries are also initialized. If necessary, they can still be adjusted later - as described below.

The recorded control limits relate both to the current control limits of already existing function groups and to control limits that are to be adhered to by function groups to be created. When the existing network is recorded, any function groups that have already been defined, including current control limits and other parameters, are also recorded. However, the procedure can also be used if no function groups have yet been defined within the system boundaries.

The network components can be assigned to an existing function group as well as to a newly formed function group within the framework of the variation of the network properties. The number of functional groups is therefore variable. This also applies to the size of the first network area and the size of the second network area, which change when a network component of the second network area is assigned to a function group, i.e. when a network component is transferred from the second network area to the first network area.

The variable network properties can also include the control limits for one, several or all of the function groups, so that a comprehensive optimization of the entire network is made possible within the system limits, taking into account the given boundary conditions. The presence and / or placement of (additional) switching and control devices for existing consumers and / or generators can also be part of the variable network properties.

When estimating the total variance in the second network area, individual sub-areas of the second network area can be treated in a special way, e.g. B. those for which more detailed information is available or which are known to be characterized by a comparatively low variance. This also includes transition zones, which have already been partially adapted as part of a restructuring of the network.

The acquisition steps a) -c) do not have to be carried out in the specified order. Several of the information to be recorded can be obtained from the same data source It is also possible to generate individual pieces of information to be recorded by combining data from several data sources.

The optimization is, in particular, an optimization with the aid of a numerical optimization method, e.g. B. a method of linear optimization. Suitable algorithms include e.g. B. Simplex method or interior point method. Due to the complexity of a distribution network and the many degrees of freedom, optimization cannot be carried out without using computer-aided numerics.

In the context of numerical optimization, raw data sets are advantageously only used where this is unavoidable. Otherwise, the optimization is preferably based on data sets that have been obtained through machine learning on the basis of high-quality historical data.

In a preferred embodiment of the method, the total variance of the voltage quality variables for the second network area is estimated based on historical operating data.

The historical operating data can in particular include the temporal course of the current (balanced or over three phases), the voltage (balanced or over three phases) and / or the electrical power (balanced or over three phases).

If the estimated total variance is to cover the expected network operation in the long term, the historical operating data relate to a period of at least one year. In this way, seasonal fluctuations can be taken into account. The fluctuations from year to year can also be taken into account by using longer time series and / or through estimates, preferably with the help of corresponding data-supported computer-implemented simulation and measurement methods.

In addition to the historical operating data, further information can flow into the estimation, for example information about the network topology and the network components and / or results of model calculations or simulations. So is For example, it is possible to assign reference profiles to the network components, with worst-case estimates being used in case of doubt.

In an alternative embodiment, historical operating data is not used. In this case, the estimate is based on simulations and / or model calculations.

The variable network properties advantageously include the presence and / or placement of an additional switching device for the selective decoupling of a part of the second network area and / or an additional device for power and / or voltage limitation. With the help of such switching devices, the network to be structured can also be automatically optimized with regard to its system limits. The switching devices can also be used to decouple higher-level networks or third-party networks. The facilities for performance and / or

Voltage limitation can also protect the network to be structured or parts of it from external influences. With the help of the switching devices and / or the devices for power or voltage limitation, it can be ensured that the system limits defined or obtained in the course of the optimization can also always be adhered to.

The variable network properties advantageously include the presence and / or placement of an additional storage facility and / or an additional production facility. In particular, the first network area, which is made up of self-regulating function groups, can be expanded automatically. By taking into account the costs of additional storage or production systems, it is ensured that the solution found in the course of the optimization is also advantageous from an economic point of view - additional such systems are only proposed if the structuring cannot easily be implemented in another way.

When placing the storage or production systems and assigning them to function groups, signal transit times and the capacities of the lines are also taken into account. The variable network properties advantageously include an expansion of the specified system limits. For example, both initial system limits and maximum system limits are specified when the method according to the invention is initialized, the maximum system limits e.g. B. include all networks that are in the sphere of influence of the network operator. If the target size can now be better achieved in the course of optimization by expanding the system limits, the system limits are expanded within the framework of the maximum system limits. For example, network components can be integrated into existing or newly created functional groups outside the initial system limits. The specified

System boundaries are chosen in such a way that the network encompasses the

Already complies with the target operating range limits, after which the system limits are iteratively expanded until compliance is no longer possible or other boundary conditions are violated. The optimization is carried out in each iteration step, with further

Network components are assigned. Existing or possible additional switching devices are also taken into account.

Even if the existing network within the system limits the

Does not yet comply with the target operating range limits, the iterative expansion can still take place in a later phase, after an optimization within the system limits.

Alternatively, the system limits are fixed. They can be changed by the user when the method is initialized in order to check different scenarios.

Maximum communication times between several function groups can advantageously be specified as a further boundary condition for the optimization. Compliance with maximum communication times ensures that the control limits are adhered to again within the necessary period. Furthermore, local regulation of the network as possible is promoted. A maximum communication time within a function group can also advantageously be specified as a boundary condition. As a result, as part of the optimization, functional groups that are as local as possible are formed, which can react quickly to changing requirements.

When assigning function groups, the number of these, their geographical location, the number of neighbors and other parameters can also be taken into account.

The target function is advantageously dependent on an amount of data transmitted between the network components for regulating the network, and the optimization favors a minimization of this amount of data.

This criterion also leads to a network that is regulated as locally as possible. In addition, a reduction in the amount of data transferred leads to a lower absolute number of errors for a given error rate. The disruption rate in the overall network is thus reduced.

The objective function is advantageously dependent on the costs of an adaptation between the existing network and the structured network to be created, and the numerical optimization favors a minimization of these costs. The costs of the adaptation include costs for additional network components.

The objective function can depend on further criteria, e.g. B. Local prices for the local functional groups (nodal pricing). Another optimization criterion can be the C0 ₂ saving, it should be noted that additional actuators, sensors, computing systems, etc. represent an additional C0 ₂ load. In this regard, the method according to the invention is in any case advantageous over conventional centralized approaches due to the local processing of sensor data and the reduction of data transmitted over long distances. The invention can thus also be used to achieve _{CO 2 targets through optimal use of resources.}

With the aid of the method according to the invention, the number of function groups required according to WO 2018/1 14404 A1 (BKW Energie AG) can also be directly minimized within specified system limits, if required. A computer-implemented method for operating a network for distributing electrical energy comprises the following steps: a) in a first network area, operating a plurality of local, self-regulating function groups with first sources, loads, lines and / or sensor, switching or converter components, so that each of the function groups complies with the control limits for voltage quality variables in the network; b) Operating second sources, loads, lines and / or sensor, switching or

Converter components of a second network area, so that an overall variance of voltage quality variables is maintained in the second network area; wherein c) the first network area has a first size and the second network area has a second size; and e) the control limits of the function groups and the first variable are selected so that, taking into account the second variable and the total variance, predetermined target operating range limits for the entire network of the first and second

Network area are complied with.

For self-regulation, the local functional groups include sensors (e.g. current or voltage sensors), actuators (e.g. switching or regulating devices for generators and / or loads) and control means (computers or controls). With the help of the sensors it is checked in particular whether the assigned control limits are adhered to. the

Control means solve depending on the data recorded by the sensors

Actions assigned to a function group to ensure compliance with the control limits. These can in particular include rule actions by controlling the aforementioned actuators as well as communication actions to secondary or superordinate function groups or entities with the help of suitable communication means.

The sizes of the network areas can be characterized in different ways. A suitable measure is, for example, the average total electricity volume in the corresponding network area. The second network area should not be empty. In addition, it is not structured like the first network area, ie it is not made up of local function groups that regulate themselves to comply with assigned control limits. The second network area is, in particular, an existing network with a network topology that has evolved over time.

Voltage quality variables include, for example, the frequency, the network voltage (voltage level or effective value) or curve-related variables; current-related variables can also be used as voltage quality variables.

In principle, the current structure of the network in the first network area and the second network area and the structure of the first network area in local functional groups can be checked periodically or continuously during operation. In this way, it is immediately recognized whether a change in the division into network areas or the assignment to function groups and / or an adjustment of the control processes would be useful due to changed framework conditions. Such a change can then be implemented at an appropriate time.

Compliance with the specified target operating range limits is advantageously monitored, and if the target operating range limits are not complied with, at least one device for limiting one of the power supplied to the functional groups is actuated. The facility can form part of a functional group and limit the external power supplied to this functional group. It can also be superordinate to function groups and limit the power supplied to several function groups up to the entire first network area.

In the short term, excess power can be dissipated using components such as resistance heating. In somewhat longer time scales, storage devices (including charging devices, supercaps and batteries) can also be used. If the target operating range limits are not adhered to, at least one switching device for decoupling the network from higher-level and / or secondary networks for distributing electrical energy and / or at least one switching device for decoupling part of the second network area is advantageously actuated. The decoupling takes place in particular when the measures to limit power reach their limits and even with such measures, the compliant operation of the network can no longer be guaranteed.

A decoupling (island operation) can also be useful in other situations, e.g. B. when a dissipation of energy to the outside can be prevented. With the help of the switching devices, it can be ensured that the system limits defined or maintained as part of the optimization can always be adhered to.

A computer program according to the invention for carrying out the method according to the invention for structuring an existing network for distributing electrical energy or for operating the network according to the invention is adapted in such a way that it executes a corresponding method when it is run on a computer. As a rule, the computer program will comprise a number of components which, under certain circumstances, run on different processors of a distributed computer system.

Further advantageous embodiments and combinations of features of the invention emerge from the following detailed description and the entirety of the patent claims.

Brief description of the drawings

The drawings used to explain the exemplary embodiment show:

1 shows a schematic representation of a network according to the invention for distributing electrical energy; 2A shows the course of a voltage quality variable in a period in the first

Network area and in the second network area; and

2B shows the course of the voltage quality variable over the period in the entire network.

In principle, the same parts are provided with the same reference symbols in the figures.

Ways of Carrying Out the Invention

FIG. 1 is a schematic representation of a network 1 according to the invention for distributing electrical energy. It comprises a first network area 10, which is structured according to the teaching of WO 2018/1 14404 A1 (BKW Energie AG) in eight largely self-regulating function groups 1 1.1 ... 8, and a second network area 20 without such a structure. The network has four connecting lines 2.1 ... 4 to adjacent, superordinate or subordinate further networks. A connecting line 2.1 opens out from the second function group 1 1.2, another connecting line 2.2 opens out from the seventh function group 1 1.7, and two further connecting lines 2.3, 2.4 open out from the second network area 20.

As known from WO 2018/1 14404 A1, the function groups 11.1 ... 8 each comprise several elements of the network and components connected to them, namely sources, loads, lines, sensor, switching and converter components. Each of the function groups 1 1. 1 ... 8 comprises a computer unit 12.1 (symbolized by a rectangle). . .8th. This can be an independent unit, a dedicated microprocessor attached to a component, or an existing element of a component. At least one sensor unit (not shown here) which measures one or more relevant variables and transmits them to the corresponding computer unit 12.1. In some of the function groups 11.1... 8 there are also actuators, by means of which, triggered by the respective computer unit 12.1. In the example shown, five function groups 1 1.4 ... 8 are combined to form a cluster. This means that in addition to the local computer units 12... 8 there is also a cluster computer unit 13 which is connected to the local computer units 12. The computer units 12.1 ... 8 of adjacent function groups 1 1. 1 ... 8 are also connected to one another for the exchange of signals and can exchange information when corresponding actions are triggered. In the example shown, the following connections exist: Both the computer units 12.1 ... 3 of the function groups 1 1.1 ... 3 not connected to the cluster and the cluster computer unit 13 are also connected to a central computer 3. This forms a control center, but in contrast to conventional networks, this is only required in exceptional cases in relation to the first network area if the function groups cannot resolve an event themselves. The connections shown are to be understood as examples. The representation does not mean that (direct) physical connections between the named components must exist, the data exchange can take place via any network topology between the components.

As shown in detail in WO 2018/1 14404 A1, functional groups can extend over several network levels and Include transducers.

In order to be able to decouple individual function groups or the entire network from other networks if necessary, a switching device 14.2, 14.7, 24.1, 24.2 is arranged in each of the connecting lines 2.1 ... 4. It can be used to temporarily disconnect the connection. Two switching devices 14.2, 14.7 are each assigned to the corresponding function group 1 1.2, 1 1.7 and are controlled by the corresponding

Computer unit 12.2, 12.7 controlled. Two further switching devices 24.1, 24.2 in the second network area are controlled directly by the central computer 3.

Each of the function groups 11.1. Each function group 1 1. 1 ... 8 is assigned control limits, i.e. target ranges of the variables to be controlled.

For the target operation, each of the function groups 1 1. 1 ... 8 are assigned rules, possible actions and required information in order to be able to check whether trigger criteria for the actions are met. To define the control limits, an orientation is based on existing components and / or standards (e.g. maximum permissible current for a cable) or - in the case of a new building - on the connection system and a requested maximum output.

The projections of the future services are carried out with the usual methods of network planning, but in particular with the use of simulations and modeling and machine learning.

Each action comprises one or more measures, in particular activating an actuator and / or sending a message to other components. The actions are assigned to the individual function groups. If actions are defined, which affect several function groups, actions can also be assigned to specific combinations of (interconnected) function groups.

In the following table, for example, parameters for target operation in a local distribution network are listed. The action listed in the last column is carried out if the operating range is not maintained, i.e. a corresponding triggering criterion is met:

Unit parameter lower upper action

Operating area

Lower counter PV with frequency 49.5 Hz 50.5 Hz P _active , from 52 Hz from

Disconnect control output from mains and breaker

Draw PV meter with voltage 207 V 253 V reactive power, if

Control output that is not enough, lower power and breaker

Meter PV with current 0 A 100 A Disconnect from the grid / change tariff control output / send message and breaker

PV counter with Harmonisc 0 20 Save number of control output he exceedances and interrupters, if more than

10, message to

Send network operator / switch on short-circuit current amplifier or filter / contact customer and change tariff

Unit parameter lower upper rule action

Operating area Counter with voltage, EN 50160 EN 50160 action with voltage

Customers with electricity 0 x receive the lowest moderate, time informa- tion according to time-limited mation EN50160 lower, if

Load limit Exceeding the load limit

Counter for current 0 x upper current limit to customers with upper operating load limit range comply with operating range

Further possible actions include, for example, the time shifting of the operation of consumers or the loading of stores or the time control of the production output of producers or the unloading of stores. Communication takes place in first priority within a given function group, in second priority between function groups or in the cluster and only in third priority to the central computer, i.e. to the control center.

FIG. 2A shows the course of a voltage quality variable over a period of time in the first network area and in the second network area. FIG. 2B shows the course of the voltage quality variable over the period in the entire network.

The state of a network for the distribution of electrical energy is determined by the temporal progression of voltage quality variables, e.g. B. the phased voltages, phased currents and phases defined. These time courses can be mapped by a time-dependent vector-valued function F (t) with components F, (t). In existing networks, both the function F (t) and the variances of the individual component functions are largely unknown. Because the function F ultimately results from a large number of sub-functions for individual components of the distribution network, for which no complete information is available, it is also difficult in practice to reproduce the function F (t). Mathematically, therefore, the system described cannot be fully grasped. Approaches to make the stochastic behavior more predictable can only partially solve this basic problem, among other things because the system is not completely closed and thus the number and characteristics of all partial functions of F (t) are not known.

In the context of the invention, it is accordingly proposed to carry out the following steps:

1. A maximum permitted variance s (F (t)) is assigned to the distribution network characterized by the function F (t), within which the security of supply and / or other optimization parameters are ensured within a given confidence range. The parameters to be observed accordingly can result from a legal requirement, e.g. B. for the permissible voltage and / or frequency ranges. The corresponding setpoint range 35 for a component F is shown in FIGS. 2A, 2B. It should be noted that the target size and / or the width of the target range can vary over time depending on the cutting quality size.

2. Let F (t) = k (t) + m (t), where k (t) includes all facilities in a first network area that is structured by self-regulating function groups according to WO 2018/1 14404 A1 (BKW Energie AG). Because control limits are assigned to these function groups, reliable statements about the variance can be made for k (t). m (t) covers a second network area that is not structured by self-regulating function groups with specified control limits. An expected maximum variance can be assigned to m (t) on the basis of historical data and / or simulations or model calculations. The total variance s (F (t)) then results from the variances s (k (t)) and s (m (t)). FIG. 2A shows the curve 31 for the voltage quality variable F in the first network area and the curve 32 for the voltage quality variable F in the second network area, these curves being based on the assumption that the individual network areas are operated independently of one another (i.e. not with one another are coupled). Are also shown the corresponding fluctuation bands 33, 34. It can be seen that in this case the specifications (target area 35) are not complied with in the second network area.

3. Because the requirements in the first network area are exceeded, this results in one

Coupling the two network areas together a course 36 of the

Voltage quality variable F, in the overall network, which complies with the specifications according to target area 35 (see FIG. 2B).

4. As part of an optimization, the factors on which k (t) and m (t) are based can now be varied. B. the maximum tolerable fluctuation of the frequency and / or (if known) of the power and / or voltage tolerance bands per network level are set. The factors include, in particular, the assignment of network components to function groups: If further network components are assigned to a function group, the size of the second network area becomes smaller, and the estimated variance (s (m (t)) decreases accordingly. In addition, the contribution to the variance s Reliably calculate (k (t)) of the first network area. Other variables relate to the control limits assigned to the function groups, the addition of additional components (sources, loads, switching devices, etc.), the expansion or restriction of the system limits, etc. Certain production or consumption outputs are optional (e.g. from storage power plants, heat storage systems or batteries) a time flexibility is assigned as an optimization parameter.

The optimization can be used to build the network, i. H. starting from an existing network in which no local self-regulating function groups have yet been defined, or for its further development, i.e. starting from a network that is already (partially) structured accordingly. It can be proceeded in an iterative manner: It is started with a core cell. If the result is satisfactory and leaves room for maneuver, the area can be expanded in a further optimization step.

In an extended version, simulations and models of technological developments such as efficiency increases or cost reductions can also be built into an optimization run. In this case, a run would not include one reference year, but several. For the (numerical) optimization in the 4th step, an objective function is defined. It contains the desired optimization parameters for the overall system. The optimization can be carried out with a view to the following optimization goals: a) Minimizing the number of required functional groups; b) Proximity of the position of the functional groups to predetermined positions or areas; c) minimizing the costs for stable operation; d) Minimizing the control limits of existing function groups.

The corresponding parameters can be optimized against each other. The weighting depends on the goals of the user, usually an energy supplier, its regulatory options, the importance of economic factors and geographical limitations, if any.

In addition to the above-mentioned boundary conditions for network stability, the following boundary conditions can be included in the optimization: a) Limitations on the transferable power, e.g. B. due to cable cross-sections; b) the maximum permitted signal transmission time and the resulting maximum possible distance between functional groups in order to be able to communicate with one another and, if necessary, to carry out switching operations, control interventions or trading transactions; c) maximum permitted signal transmission time, resulting from this the maximum possible distance between one, several, or all function groups and another unit, such as the central computer, in order to be able to communicate with one another and, if necessary, to carry out switching operations, control interventions or trading transactions; d) Time restrictions, e.g. for postponements or limitations of services; e) geographical / topological conditions (exclusion of certain areas or definition of certain areas as functional groups); f) economic criteria; g) regulatory criteria.

Control processes ultimately include the determination of one or more measured variables, the processing to determine the action (s) to be taken and the implementation of the action up to influencing the controlled variable. Depending on the complexity of the control process, the distribution of the components involved in the network and the time required for processing the measured variables, there is a certain signal transmission time. The maximum signal transmission times do not have to be the same for all control processes, because certain controls have to take place faster than others if the operation of the network is not to be negatively affected. However, by comparing with the physically possible smallest information latency times, certain scenarios can be eliminated immediately that are not compatible with the required communication times (taking into account the latency times), e.g. For example, real-time control of a smart grid using smart meters when "real-time" is in the seconds range or when data is only transmitted once a day (e.g. from the household meter) and "real-time" means a maximum of 10 minutes.

With the help of suitable boundary conditions, inter alia ensure that the network found as part of the optimization can also function physically, in that the services to be compensated can be transmitted in the required time frame and without overloading lines (and possibly other components). Strategically positioned functional groups can be essential when it comes to maintaining tension. It may therefore not be sufficient to just keep the total variance within a given range. With the help of the technical boundary conditions mentioned, areas in the system in which at least one self-regulating function group should be arranged arise in the course of optimization in such cases.

So that the optimization can take place, the following information is provided: a) Topological information of the network within the initial or maximum system limits, for example in the form of a network plan, including network components and possibly existing switching devices; such information can e.g. B. obtained from a network-related geographical information system (GIS); b) Information on the system limits - the corresponding selection can be made in a manner known per se via a graphical interface, e.g. B. by selecting the parts of the network to be taken into account or those parts not to be taken into account

Parts are deselected; A restriction to certain network levels is also possible; c) the number and characteristics of the self-regulating function groups already present in the network (including size information, e.g. a temporal balance sheet total of the service in a reference period as well as control limits); d) per function group: temporal course of the current (on balance or over three phases) over a selected reference time, e.g. one year, voltage (on balance or over three phases) over a selected reference time, e.g. one year; alternatively electrical power (on balance or over three phases) over a selected reference time, e.g. one year; e) maximum permitted tolerances, e.g. B. in relation to the frequency and / or voltage, in general or at certain network positions; f) Environment information and weighting factors: technical factors, costs for technologies, energy prices, electricity tariffs, other economic factors.

Historical data from production and consumption or data from models and simulations that model, for example, a generator type and the locally typical course of an environmental variable or a consumer type can be used to generate the time courses. Physical limitations, e.g. B. due to installed transformers or production facilities can also be included in the estimate. In a preferred embodiment, models with historical data and machine learning are linked to form reference profiles and, if necessary, adjusted more precisely, for example by means of a production or consumption profile that is adapted to regional conditions and habits. For consumption, this can include holidays or working hours and break habits, as well as a maximum possible for production Photovoltaic production based on global radiation data and available areas as well as their orientation.

With the help of statistical methods such as the use of sample theory, the quality of these estimates can be further refined and implemented in terms of their reliability as a "historical tolerance band".

Common numerical optimization methods are suitable for optimization, e.g. B. Simplex or interior point method. Due to the many degrees of freedom, numerical optimization is computationally expensive. Because it does not determine the ongoing operation of the network, but its structure, the optimization step is not time-critical. The computational effort can be limited by reducing the observed or maximum system limits or by foregoing certain degrees of freedom (e.g. with regard to the existing functional groups or with regard to measures that are per se associated with high implementation costs).

From the optimization go, inter alia. the following variables stand out: a) Number of self-regulating function groups, information on the corresponding assignment of network components; b) costs associated with structuring and / or operating the network; c) the permitted tolerance bands to be specified for the functional groups; d) necessary communication, control and regulation units in function groups, central control units and at the system boundaries.

Depending on the objective, the method according to the invention for structuring can be used in different ways:

1. If an energy supplier wants to protect itself, for example, from the chain reactions of unforeseen major events in the network, it will strive for a system that allows isolated operation in an emergency. At the same time, however, the cost of the adjustments should be minimized. Starting from a network that already has some function groups, strategically important function groups that are built on are localized as part of the optimization. Particularly narrow tolerance bands are assigned to these functional groups in order to keep the number of functional groups low. The control center will be equipped with a communication interface to selected function groups. The lines of the system boundaries are retrofitted with communication and control technology. Some functional groups are equipped with communication, control and regulation technology.

2. If an energy supplier primarily wants to optimize its trading, especially with renewable energy, and make it more predictable, it will strive for trading quotas to be known at an early stage and reliably available.

Starting from a network that already has a number of functional groups, the number and nature of the functional groups still required to obtain stable trade forecasts are identified as part of the optimization. A central or decentralized control device (e.g. the control center or a comparable device) is equipped with a communication interface to selected function groups. The trade will be equipped with a communication interface to selected function groups and / or the control device. Some or all of the functional groups are equipped with communication, control and regulation technology.

When operating the network according to the invention, the individual function groups of the first network area regulate themselves as far as possible. If this is no longer possible within a function group without violating the control limits, communication with other function groups and / or higher-level bodies takes place starting from the function group according to a given scheme with several escalation levels. Different schemes can be specified for different function groups. In practice, the physical limits in relation to the signal propagation times must be taken into account. When several function groups are grouped together to form a cluster (virtual function group), the regulation takes place in first priority within the individual function groups, in second priority within the cluster and only in third priority if mutual compensation among the cluster function groups is no longer possible under Involvement of other functional groups or components.

In its simplest form, a trigger criterion is formed by a specified value of a variable and by specifying whether the criterion is met when the value of an input variable (e.g. a measured variable) is exceeded or not reached. A trigger criterion can, however, also be defined by specifying a range or be based on a more complex function, which in particular also includes logical (Boolean) operators. A trigger criterion can relate to a current value of the input variable or several input variables, or a certain past time interval is taken into account. Trigger criteria can also be dependent not only on the variables assigned to the respective control limit, but also on a rate of change of such variables (i.e. the time derivative). A rapid increase or rapid decrease in a variable can indicate that there is a need for action before the control limits are reached.

If, for example, function group A has too little power due to an unusually high volume of electric cars and below-average PV production, a local computer unit of function group A sends a request signal to the local computer unit of neighboring function group B. Function group B transmits the short and medium-term availability Power. At the request of function group A, function group B then releases the service required at short notice. Function group A decreases the performance. Since the service is not sufficient in the medium term, function group B sends a signal to a communication interface of a virtual function group C. This is formed by the network of function groups DG, whose function group E includes a larger hydropower plant. The communication interface of the virtual function group C sends a signal to the function group E containing, among other things, the required production output and the expected time period. Function group E confirmed to the communication interface, this to function group A and / or B. Function group A ultimately decreases the performance.

In another scenario, a line mast was destroyed in the area of function group A by a storm. Function group A recognizes this as a malfunction and sends an emergency call to a higher-level control center so that a fitter can dispose of it. At the same time, function group A requests the lost power from neighboring function group B at the highest priority level. Function group B extends its tolerance range up to a maximum permissible value and regulates its controllable loads, storage units and production systems in such a way that the required power can be delivered. Because ultimately not all of the requested power can be provided within function groups A and B or individual (non-critical) consumers have to be switched off or regulated back, both the local computer unit of function group A and the local computer unit of function group B send a signal to a communication center or to a locally stored list so that customers are informed of a malfunction with minor impairments.

Based on the method according to the invention for structuring the network, an operation can also take place, the system boundaries of which vary depending on the operating situation. If, for example, it is too expensive for an energy supplier to plan and operate the entire network immediately according to the present patent, a core area can be started which consists of self-regulating functional groups and can be physically separated from the rest of the area if necessary.

In addition to the core area, there may be transition zones, some of which have already been optimized for stability, but are not yet able to be operated completely independently. For such transition zones, proportions of m (t) or s (m (t)) can be estimated more precisely, if necessary, so that the estimated variance s (m (t)) is reduced.

As a rule, it will be necessary to decouple the optimized and operated area from adjacent, conventionally operated networks if they meet the defined target operation endangered and stabilizing measures within the system boundaries considered in the context of optimization are not sufficient. The switching devices 14.2, 14.7, 24.1, 24.2 (see FIG. 1), which are automatically or, if necessary, manually actuated after receiving a corresponding recommendation from the system, and / or control and regulating devices, are used for this purpose. If these are already available, a check is made as part of the optimization to determine whether additions are necessary, for example through communication connections. Otherwise the type, number and dimensioning of the existing disconnectors, control and regulating devices are the starting parameters.

In a corresponding scenario, interference occurs in a network due to influences outside the system limits of the optimized system, so that the necessary tolerances in phase, frequency, voltage or power can no longer be adhered to. There is a risk of massive equipment damage, production losses, failure of critical infrastructures or a blackout. Several function groups transmit signals to the control center and / or to each other about violations of the tolerance limits. As soon as a function group or the control center has received or calculated a certain critical value, a control or regulation command for power regulation or decoupling is sent to some or all of the system limits and executed.

Similarly, in the context of the operation of the network according to the invention, with a view to optimizing costs or trade, a price-controlled release of services as well as production and charging control can also take place.

In summary, it can be stated that the invention creates a systematically feasible method for structuring a network for distributing electrical energy, which can be individually adapted to the given framework conditions, and also a distribution network with high security of supply and a method for operating the same.

Claims

1. Network for the distribution of electrical energy, comprising a) a first network area, consisting of a plurality of local, self-regulating function groups with first sources, loads, lines and / or sensor, switching or converter components, each of the function groups for compliance associated control limits for voltage quality variables is formed in the network and wherein the first network area has a first variable; b) a second network area with second sources, loads, lines and / or sensor, switching or converter components, the second network area being assigned an estimated total variance of the voltage quality parameters and the second network area having a second size; The control limits of the function groups and the first variable are selected in such a way that, taking into account the second variable and the estimated total variance, predetermined target operating range limits are maintained for the entire network.

2. Network according to claim 1, characterized in that the estimated total variance covers an expected network operation for a period of at least one year.

3. Network according to claim 1 or 2, characterized by at least one switching device to decouple the network from higher-level and / or secondary networks for distributing electrical energy.

4. Network according to one of claims 1 to 3, characterized in that a maximum extent of the functional groups is selected so that a maximum signal propagation time is maintained within the functional groups.

5. Computer-implemented method for structuring an existing network for

Distribution of electrical energy, comprising as network components at least sources, loads, lines, sensor, switching and converter components that are in a Output topology are interconnected to create a network according to one of claims 1 to 4, comprising the following steps: a) detecting the existing network within predetermined system limits; b) Detection of control limits for local, self-regulating function groups; c) Detection of target operating range limits for the structured network to be created; d) performing an optimization of an objective function by varying

Comprise network components to one of several local functional groups of a first network area or an assignment of network components to a second network area, f) an overall variance of voltage quality parameters is estimated for the second network area; g) whereby the boundary condition for the optimization is compliance with

6. The method according to claim 5, characterized in that the total variance of the voltage quality variables for the second network area is estimated based on historical operating data.

7. The method according to claim 5 or 6, characterized in that the variable network properties a presence and / or a placement of an additional

Switching device for selectively decoupling a part of the second network area and / or an additional device for power and / or voltage limitation.

8. The method according to any one of claims 5 to 7, characterized in that the variable network properties include the presence and / or placement of an additional storage facility and / or an additional production facility.

9. The method according to any one of claims 5 to 8, characterized in that the variable network properties include an expansion of the specified system limits.

10. The method according to claim 9, characterized in that when the existing network is recorded, the specified system limits are selected so that the network included already complies with the target operating range limits, after which the system limits are iteratively expanded until compliance is no longer possible or other boundary conditions are violated will.

1 1. The method according to any one of claims 5 to 10, characterized in that maximum communication times between several function groups are specified as a further boundary condition for the optimization.

12. The method according to any one of claims 5 to 1 1, characterized in that the

The objective function is dependent on a data volume transmitted between the network components for regulating the network and that the optimization favors a minimization of this data volume.

13. The method according to any one of claims 5 to 12, characterized in that the objective function is dependent on the costs of an adaptation between the existing network and the structured network to be created and that the numerical optimization favors a minimization of these costs.

14. Computer-implemented method for operating a network for distributing electrical energy, in particular a network according to one of claims 1 to 4, comprising the following steps: a) in a first network area, operating a plurality of local, self-regulating function groups with first sources, loads , Lines and / or Sensor, switching or converter components, so that each of the function groups complies with the control limits assigned to the voltage quality variables in the network; b) operating second sources, loads, lines and / or sensor, switching or converter components of a second network area, so that an overall variance of voltage quality parameters in the second network area is maintained; wherein c) the first network area has a first size and the second network area has a second size; and e) the control limits of the functional groups and the first variable are selected so that, taking into account the second variable and the total variance, predetermined target operating range limits are maintained for the entire network comprising the first and second network areas.

15. The method according to claim 14, characterized in that compliance with the specified target operating range limits is monitored and, in the event of non-compliance with the target operating range limits, at least one device for

Limitation of one of the function groups supplied power is operated.

16. The method according to claim 15, characterized in that if the target operating range limits are not adhered to, at least one switching device for decoupling the network from higher-level and / or secondary networks for distributing electrical energy and / or actuating at least one switching device for decoupling part of the second network area will.

17. Computer program for performing the method according to one of claims 5 to 16.