Method for operating an energy management system for an associated region by means of an electronic computing device, computer program product, computer-readable storage medium and electronic computing device

Abstract

The invention relates to a method for operating an energy management system (10) for an associated region (12) by means of an electronic computing device (14), the method comprising the following steps: operating the energy management system (10) in a normal mode with a specified first target criterion (30) by means of the electronic computing device (14); receiving a criticality event (28) by means of the electronic computing device (14); switching the energy management system (10) from the normal mode to a criticality mode, wherein a second target criterion (32) independent of the first target criterion (30) is specified in the criticality mode; and operating the energy management system (10) in the criticality mode by means of the electronic computing device (14). The invention also relates to a computer program product, a computer-readable storage medium, and an energy management system (10).

Description

[0001] Description

[0002] Method for operating an energy management system for a related region using an electronic computing device, computer program product, computer-readable storage medium and electronic computing device

[0003] The following invention relates to a method for operating an energy management system for a related region by means of an electronic computing device according to claim 1. The invention further relates to a computer program product, a computer-readable storage medium and an electronic computing device.

[0004] Modern, distributed energy systems consist primarily of multiple generators (e.g., renewable and non-renewable), corresponding storage facilities, and loads, resulting in a complex energy system. Controlling such a system, especially considering external input signals such as dynamic tariffs or CC>2 signals, requires an energy management system (EMS). These systems typically forecast upcoming loads and non-controllable generation units and optimize the setpoints for all controllable assets accordingly.

[0005] Especially during severe weather conditions such as blizzards or hurricanes, the focus of such an energy management system should be on maximizing autonomy rather than minimizing operating costs or CO2 emissions.

[0006] The object of the present invention is to provide a method, a computer program product, a computer-readable storage medium and an electronic computing device by means of which improved operation of an energy management system for a related region is made possible.

[0007] This problem is solved by a method, a computer program product, a computer-readable storage medium, and an electronic computing device according to the independent claims. Advantageous embodiments are specified in the dependent claims.

[0008] One aspect of the invention relates to a method for operating a

[0009] An energy management system for a region is operated by an electronic computer. The system operates in normal mode with a predefined primary objective. A criticality event is then received by the computer. The energy management system switches from normal mode to criticality mode, in which a second objective, independent of the primary objective, is defined and prioritized. The energy management system then operates in criticality mode.

[0010] In particular, this allows for two different modes of the energy management system. During normal operation, for example, to optimize the first objective, such as reducing operating costs, reducing CO2 emissions, or maximizing self-consumption of generated energy, the energy management system can be operated in this way. If a criticality event, such as an extreme weather event, is announced or imminent, the system can switch to criticality mode. In criticality mode, a second objective is then defined, which is independent of the first objective.For example, a second objective could be to specify that maximum energy storage capacity should be achieved within the region, so that in the event of criticality, the maximum amount of energy can be made available for self-consumption. This has the advantage, for instance, that a sufficient amount of energy can be provided during the event, preventing a collapse of the energy grid within the region.

[0011] A region is defined as one that is interconnected, for example, via an electrical or thermal network, or similar infrastructure. The region is therefore not limited to local conditions, but specifically refers to corresponding topologies of thermal lines, electrical lines, fluid lines, or the like.

[0012] In particular, the following invention describes the energy management system, which can be specifically geared towards increasing autonomy while simultaneously minimizing deviations from, for example, original primary objectives. In other words, increasing autonomy is specified as a second objective criterion, which is prioritized in criticality mode. In particular, severe weather forecasts from, for example, the World Meteorological Organization, which are generally forwarded by weather service providers, can be used. These forecasts are standardized within the framework of the so-called Common Alarm Protocol and can be provided, for example, from corresponding alerts for wind, snow, thunderstorms, heat waves, cold waves, fires, avalanches, and floods.

[0013] This information can then be retrieved by the electronic computing unit, typically via an interface to a weather service provider, allowing weather forecast data to be queried regularly during operation. Based on a configurable threshold that assesses the severity of the warning, the electronic computing unit can decide to switch from normal operation to criticality mode.

[0014] In particular, it can be stipulated that, in criticality mode, dependencies on external energy suppliers are minimized. This is achieved by maximizing the so-called autonomy of the electronic computing device or the energy management system. Maximizing autonomy in distributed energy systems can be achieved, for example, by maximizing the storage capacity of all available storage devices within the region as quickly as possible.

[0015] In particular, it is intended that, depending on the selected mode, the electronic computing device generates corresponding control signals to control, for example, electrical energy consumers, storage devices, or other devices within the defined region. For instance, depending on the selected operating mode, an electrical storage device can be charged or discharged. Depending on the specified target criterion, the control signals for the relevant devices can thus be generated.

[0016] In particular, this introduces a simple way of modeling autonomy as, for example, maximizing storage capacity and the interplay of multiple optimization goals within the energy management system. The described approach can easily be integrated into existing energy management systems. Furthermore, this approach also enables optimal preparation for severe weather events by maximizing the capacity of internal storage and thus minimizing dependence on external infrastructure, such as external power grids.

[0017] This can, for example, prevent power outages or failures of parts of the energy system infrastructure, such as production machinery, thus saving on production losses that would otherwise occur. It could also protect the systems from hard shutdowns during power outages.

[0018] In other words, it is intended that in criticality mode the electronic computing device will perform the task of maximizing z autonomy (1) optimized. In this context, autonomy is defined in particular as the sum of the storage contents of all storage devices over the entire observation period:

[0019] In solving this optimization problem, all technical and legal constraints are taken into account. The solution, for example, is an operating plan that maximizes storage capacity without considering the original objectives, such as monetary or environmental goals. However, to consider the original goal of the energy management system, a second, subsequent optimization step can be proposed. In this step, the optimal target value from the first optimization is determined, and the objective function is modified to reflect the original, primary goal, specifically the first target criterion.

[0020] According to an advantageous embodiment, a weather event is received as a criticality event. Specifically, the weather event can be received, for example, from a corresponding weather station, such as weather station servers or similar devices. In other words, the electronic computing device is connected to this server and can receive the relevant weather data, particularly in real time, and corresponding weather forecasts. Based on this, the current and future weather conditions can be predicted within specific time windows, and the system can then be switched to criticality mode. Examples of such weather conditions include blizzards, winds, floods, and similar events.This makes it possible to forecast or predict relevant weather events and, based on this, to switch to criticality mode.

[0021] It is also advantageous if a future and / or current weather event is recognized as a criticality event. In particular, this allows for a response even to ongoing weather events, enabling an increase in autonomy where possible. Especially with future weather events, it is then possible to determine the optimal time to switch from normal mode to criticality mode. This takes into account relevant consumption and generation within the region, allowing for a prediction of when to switch from normal mode to criticality mode to achieve maximum autonomy at the time of the event. This enables improved operation of the energy management system.

[0022] Another advantageous design involves setting maximum energy utilization as the primary objective. In other words, the shared region could also be referred to as a smart grid. Specifically, this approach aims to optimize maximum energy utilization within the smart grid. In other words, before excess energy from the shared region is fed into, for example, a higher-level grid, every consumer within this smart grid should be served. Furthermore, corresponding storage systems can be optimally filled. This allows for improved operation of the energy management system during normal operation.

[0023] It is also advantageous to specify maximum CO2 reduction as the primary objective criterion. In other words, the aim is to minimize CO2 emissions into the environment during normal operation. This optimization problem thus enables improved environmental protection, which in turn allows for improved operation of the energy management system. Another advantageous design involves specifying maximum energy independence for the region from at least one energy source as the secondary objective criterion. Specifically, a corresponding degree of independence from an external power grid can be specified as an energy source. For example, it can be stipulated that, essentially, 100% autonomy from the external energy source is provided at the time of the event.In other words, it can be stipulated that essentially all storage devices within the network should be filled at the time the event occurs. This allows for a correspondingly increased level of autonomy in the event of a criticality situation.

[0024] In a further advantageous embodiment, a second target criterion is defined: a predetermined degree of energy independence for the region from at least one energy generation source. For example, it could be stipulated that the region must be independent of external energy sources for at least 80 percent of the time, particularly at the time of an event. Thus, it could be stipulated, for instance, that the storage facilities located within the region are filled to 80 percent capacity. It could then be stipulated that, for example, after reaching the 80 percent threshold, optimization is then essentially reverted to the first target criterion, thereby ensuring a balance between the second and, subsequently, the first target criterion. This allows for improved operation of the energy management system.

[0025] It is also advantageous to consider energy storage facilities in the region within the criticality mode. This allows for the integration of relevant energy storage systems into the criticality mode. Specifically, both energy supply and energy absorption capacities can be taken into account. This allows for the advantageous determination of the degree of autonomy and, for example, the provision of a time for switching from normal mode to criticality mode. Furthermore, a switchover point from criticality mode to normal mode can also be determined.

[0026] It is also advantageous to consider electrical, mechanical, chemical, and / or thermal energy storage systems in the region. In particular, this allows for the consideration of various energy storage options. Buildings and their thermal absorption capacity can also be taken into account. Furthermore, it can be considered that turbines, for example, require start-up time to reach their operating mode. This allows them to be started up early, or buildings to be preheated, so that operation can be maintained with minimal energy consumption in criticality mode.

[0027] Furthermore, it has proven advantageous to charge the energy storage devices in criticality mode. For example, the energy storage devices can be electrical. In criticality mode, the energy storage device is then charged accordingly, so that electrical energy is available during the event, thus increasing the degree of autonomy.

[0028] Another advantageous design involves considering the first target criterion as an additional dependency in criticality mode. Specifically, this allows the first target criterion to be disregarded in a first step until, for example, a predefined value for the second target criterion is reached. In a subsequent step, once the predefined value for the second target criterion has been reached, optimization can then be performed primarily on the first target criterion, taking the second target criterion into account. This allows for the provision of different optimization options, enabling highly efficient operation of the energy management system.

[0029] Another advantageous design feature involves weighting the first and second target criteria against each other in criticality mode. The respective target functions can be scaled accordingly to prevent unintended trade-offs. This can also be done dynamically, depending on the event occurring. For example, if a time value for the event is recorded, weighting can be applied based on the event's duration. This allows for highly efficient operation of the energy management system.

[0030] The presented method is essentially a computer-implemented method. Therefore, a further aspect of the invention relates to a computer program product with program code means which, when executed by the electronic computing device, cause the electronic computing device to carry out a method according to the preceding aspect. Furthermore, the invention also relates to a computer-readable storage medium containing the computer program product according to the preceding aspect.

[0031] A further aspect of the invention relates to an electronic computing device for operating an energy management system for a related region, wherein the electronic computing device is configured to carry out a method according to the preceding aspect. In particular, the method is carried out by means of the electronic computing device.

[0032] Furthermore, the invention also relates to an energy management system with at least one electronic computing device for a related region.

[0033] Advantageous embodiments of the process are to be regarded as advantageous embodiments of the computer program product, the computer-readable storage medium, the electronic computing device, and the energy management system. The electronic computing device and the energy management system possess tangible features to enable the execution of the corresponding process steps.

[0034] A computing unit / electronic computing device can be understood, in particular, as a data processing device containing a processing circuit. The computing unit can therefore process data to perform arithmetic operations. This may also include operations to perform indexed accesses to a data structure, such as a load profile table (LUT).

[0035] The computing unit may, in particular, contain one or more computers, one or more microcontrollers, and / or one or more integrated circuits, for example, one or more application-specific integrated circuits (ASICs), one or more field-programmable gate arrays (FPGAs), and / or one or more systems on a chip. The computing unit may also contain one or more processors, for example, one or more microprocessors, one or more central processing units (CPUs), one or more graphics processing units (GPUs), and / or one or more signal processors, in particular one or more digital signal processors (DSPs). The computing unit may also include a physical or virtual cluster of computers or other units of the aforementioned type.In various embodiments, the computing unit includes one or more hardware and / or software interfaces and / or one or more storage units.

[0036] A storage unit can be volatile data storage, for example as dynamic random access memory (DRAM) or static random access memory (SRAM), or as non-volatile data storage, for example as read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory or flash EEPROM, ferroelectric random access memory (FRAM), or magnetoresistive random access memory.It can be designed as MRAM (magnetoresistive random access memory) or as phase-change random access memory, PCRAM (phase-change random access memory).

[0037] For use cases or application situations that may arise in a method according to the invention and that are not explicitly described herein, it may be provided that, according to the method, an error message and / or a request for user feedback is issued and / or a default setting and / or a predetermined initial state is set.

[0038] Regardless of the grammatical gender of a particular term, persons with male, female or other gender identities are included.

[0039] Further features and combinations of features of the invention will become apparent from the figures and their description, as well as from the claims. In particular, further embodiments of the invention need not necessarily include all features of any one of the claims. Further embodiments of the invention may have features or combinations of features not mentioned in the claims.

[0040] Figure 1 shows a schematic perspective view of an embodiment of an energy management system with an embodiment of an electronic computing device; and

[0041] FIG 2 shows a schematic flowchart according to one embodiment of the method.

[0042] The invention is explained in more detail below with reference to specific embodiments and associated schematic drawings. In the figures, identical or functionally equivalent elements may be designated with the same reference numerals. The description of identical or functionally equivalent elements is not necessarily repeated with respect to different figures.

[0043] FIG 1 shows a schematic block diagram according to an embodiment of an energy management system 10 for a contiguous region 12 with at least one electronic computing unit 14. The electronic computing unit 14 is in turn coupled to a weather service 16. For example, a corresponding communication link 18 between the electronic computing unit 14 and the weather service 16 can be established. Furthermore, the electronic computing unit is configured to generate corresponding control signals 20 for components 22, 24, 26 within the region 12 and to transmit them to these components 22, 24, 26. The transmission can take place via cable, mobile communication, or local area networks.

[0044] For example, a component 22, 24, 26 can be configured as an electrical load 22. Furthermore, a component 22, 24, 26 can be configured as an energy generator 24. Furthermore, a component 22, 24, 26 can be configured as an energy storage device 26.

[0045] FIG. 1 further shows that, for example, a criticality event 28 can be determined by the weather service 16 and transmitted to the electronic computing device 14. In the present embodiment, a future weather event and / or a current weather event for the associated region 12 is shown in particular.

[0046] Furthermore, FIG. 1 shows that the electronic computing device 14 can operate the energy management system 10 based on at least a first target criterion 30 and a second target criterion 32. In particular, FIG. 1 shows the energy management system 10 with the electronic computing device 14. The energy management system 10 is operated in a normal mode with the first target criterion 30 by means of the electronic computing device 14. The criticality event 28 is then received by the electronic computing device 14. The energy management system 10 is then switched from the normal mode to a criticality mode, in which the second target criterion 32 is specified independently of the first target criterion 30. The energy management system 10 is then operated in criticality mode by means of the electronic computing device 14.

[0047] FIG. 2 shows a schematic flowchart according to one embodiment of the method. In FIG. 2, in particular, the energy management system 10 is operated in normal mode in a first step S1. In the second step S2, the criticality event 28 is received by the electronic computing unit 14. In the third step S3, a time is determined at which the system will / should switch from normal mode to criticality mode. This occurs automatically and depends on the duration of the criticality event 28 as well as the time of occurrence of the criticality event 28. In the fourth step S4, the system then switches to criticality mode. In the fifth step S5, the electronic computing unit 14 or the energy management system 10 is operated in criticality mode.In the sixth step, S6, a decision can then be made as to when to switch from criticality mode to normal mode. This decision depends, in particular, on the duration of criticality event 28 and the time of its end. From step six, S6, the system can then switch back to the first step, S1, and thus to normal operation.

[0048] In particular, it may be stipulated that a future weather event and / or a current weather event is received as the criticality event 28. Furthermore, a maximum energy utilization can be specified as the first target criterion. Additionally, a maximum CO2 reduction can be specified as the first target criterion 30. Furthermore, a maximum energy independence degree of region 12 from at least one external energy generation source can be specified as the second target criterion 32. Additionally, a predetermined energy independence value of region 12 from at least energy generation source 24 can be specified as the second target criterion 32. Furthermore, it may be stipulated that energy storage facilities 26 in region 12 are taken into account in criticality mode.In particular, electrical energy storage devices 26 and / or mechanical energy storage devices 26 and / or chemical energy storage devices 26 and / or thermal energy storage devices 26 in region 12 can be taken into account. Furthermore, the energy storage devices 26 can be filled with energy in criticality mode.

[0049] Furthermore, it can be provided that the first target criterion 30 is considered as an additional dependency in criticality mode. In this criticality mode, the first target criterion 30 and the second target criterion 32 can be weighted accordingly.

[0050] In particular, the invention provides that, for example, severe weather forecasts, especially the forecast of criticality events 28, are published, for example, by the World Meteorological Organization and are generally forwarded by the weather service 16. These are standardized within the framework of the so-called Common Alarm Protocol and include, in particular, alerts for, among other things, wind, snow, thunderstorms, heat waves, cold waves, fires, avalanches, and floods.

[0051] This information can now be retrieved by the electronic computing unit 14, which typically has an interface to the weather service 16 and regularly queries weather forecast data from it during operation. Based on a configurable threshold that assesses the severity of the warning, the energy management system 10 or the electronic computing unit 14 can decide to switch from normal operation to criticality mode, particularly for severe weather operations.

[0052] In the present application, a novel approach is used to minimize the dependence of the underlying energy system on an external power supply. This is achieved by maximizing the autonomy of the energy management system 10 and can be accomplished by maximizing the corresponding storage contents of all available storage devices as quickly as possible. The electronic computing device 14 therefore changes the objective to: maximize z autonomy (1)

[0053] Autonomy is defined as the sum of the storage contents of all energy storage devices 26 over the entire observation period:

[0054] In solving this optimization model, all technical and legal constraints are taken into account. The solution is, in particular, an operating plan that maximizes the storage capacity of the energy storage system 26 without preferentially considering initial objectives, such as monetary or environmental goals. To consider the initial objective, in other words, the first objective criterion 30, a second optimization step can be proposed in which the optimal target value from the first optimization is determined and the objective function is changed to the original, primary objective of the electronic computing device 14: maximize z

[0055] As an example, it can be suggested that an initial optimization in a z autonom y was determined to be 1,000 megawatt hours, and the second optimization was: maximize z subject to z autonom y > i000

[0056] Zautonomy can also be chosen to be slightly smaller, for example at 999. The result of the second optimization run is the final result of the calculation of the energy management system 10. It primarily maximizes autonomy and the original goal with regard to the predicted criticality conditions.

[0057] Furthermore, a minor deviation may be permitted in the second optimization step for numerical reasons and to ensure rapid convergence. Additionally, the objective functions described in Formula 1 and Formula 2 can be modified to incorporate further objectives by introducing weighting factors for each objective. For example, another objective could be the minimization of gradients, particularly those of assets or at the point of joint coupling.

[0058] Furthermore, maximizing autonomy can also lead to significant limitations with respect to the first objective criterion 30. Therefore, corresponding penalty costs can be added to the second objective to balance autonomy and the original objective. The optimization model according to the third formula would then be modified according to the following formula. It is permissible to slightly reduce autonomy, but this is penalized. However, finding a suitable value depends on the application, as it involves trade-offs between multiple objectives.

[0059] Furthermore, a percentage could also be set at which the maximum possible

[0060] The goal is to achieve autonomy, which in this case corresponds to the degree of energy independence. The problem here is:

[0061] Alternatively, it is also possible to reverse the order of the optimization problems and define a specific threshold value that is used to handle the criticality event 28. This means that the energy management system 10 first optimizes the first target criterion 10, for example, low operating costs. In a second step, the energy management system 10 then maximizes autonomy and incorporates the optimal target value from the original optimization as a bound, similar to Formula 3, to obtain a predefined relative or absolute value. An example would be a relaxation of the minimum operating costs by ten percent, or for example, €1,000.00, which could then be spent to maximize autonomy in order to cope with the predicted severe weather conditions.

[0062] Furthermore, the energy storage systems 26 are not limited to physical storage systems such as electrical storage systems, batteries or active thermal storage systems such as hot water storage systems, but can also be passive thermal storage systems, such as the thermal inertia of buildings.

[0063] Furthermore, if region 12 contains turbines with significant start-up or shutdown times, such as specialized production machines in industrial plants, the electronic computing device 14 can also force the turbine to go online and into standby mode. Additionally, loads, for example from critical production processes, can be shut down slowly and, in particular, safely if the energy management system 10 is not designed for completely autonomous operation.

[0064] Reference symbol list

[0065] 10 Energy Management System

[0066] 12 Region 14 electronic computing facility

[0067] 16 Weather service

[0068] 18 connection

[0069] 20 Control signal

[0070] 22 Consumers 24 Energy source

[0071] 26 Energy storage

[0072] 28 Criticality event

[0073] 30 first target criterion

[0074] 32 Second target criterion S1 to S6 Steps of the procedure

Claims

1. Patent claims 1. Method for operating an energy management system (10) for a related region (12) using an electronic computing device (14), comprising the steps: - Operating the energy management system (10) in a normal mode with a predefined first target criterion (30) using the electronic computing device (14); - Receiving a criticality event (28) using the electronic computing device (14); - Switching the energy management system (10) from normal mode to criticality mode, in which a second target criterion (32) independent of the first target criterion (30) is specified; and - Operating the energy management system (10) in criticality mode using the electronic computing device (14).

2. Method according to claim 1, characterized in that a weather event is received as a criticality event (28).

3. Method according to claim 2, characterized in that a future weather event and / or a current weather event is received as a criticality event (28).

4. Method according to one of the preceding claims, characterized in that a maximum self-consumption of energy is specified as the first target criterion (30).

5. Method according to one of the preceding claims, characterized in that a maximum CO2 reduction is specified as the first target criterion (30).

6. Method according to one of the preceding claims, characterized in that a maximum degree of energy independence of the region (12) from at least one external energy generation source is specified as a second target criterion (32).

7. Method according to one of claims 1 to 5, characterized in that a predetermined energy independence value of the region (12) from at least one external energy generation source is specified as the second target criterion (32).

8. Method according to one of the preceding claims, characterized in that at least one energy storage device (26) in the region (12) is taken into account in the criticality mode.

9. Method according to claim 8, characterized in that electrical energy storage devices (26) and / or mechanical energy storage devices (26) and / or chemical energy storage devices (26) and / or thermal energy storage devices (26) are taken into account in the region (12).

10. Method according to claim 8 or 9, characterized in that the energy storage devices (26) are filled with energy in criticality mode.

11. Method according to one of the preceding claims, characterized in that the first target criterion (30) is taken into account as an additional dependency in the criticality mode.

12. Method according to claim 11, characterized in that in criticality mode the first target criterion (30) and the second target criterion (32) are weighted relative to each other.

13. Computer program product comprising program code means which cause an electronic computing device (14) to perform a method according to one of claims 1 to 12 when the program code means are executed by the electronic computing device (14).

14. Computer-readable storage medium comprising at least one computer program product according to claim 13.

15. Electronic computing device (14) for operating an energy management system (10) for a related region (12), wherein the electronic computing device (14) is configured to carry out a method according to any one of claims 1 to 12.

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