Computer-implemented method for determining energy ratio for consumed energy

Abstract

A computer-implemented method for determining an energy ratio for consumed energy includes providing an energy network model configured to describe energy flows between a plurality of assets in an energy network, where the energy flows include a first energy flow from a first energy source and a second energy flow from a second energy source; receiving load demand data of the energy network and power supply data of the energy network generated by the first energy source; controlling and tracking a first energy flow from the first energy source to the plurality of assets in the energy network and a second energy flow from the second energy source to the plurality of assets in the energy network based on an energy network model and the received load demand data and power supply data; tracking one or more consumption times of the at least one energy consumption asset; determining an energy ratio of the at least one energy consuming asset based on the tracked first and second energy flows and a consumption time of the at least one energy consuming asset; the determined energy ratio is provided for further processing.

Description

Technical Field

[0001] The invention relates to a computer-implemented method for determining an energy ratio of consumed energy, a corresponding data processing device, a computer program and a computer-readable medium. Background Art

[0002] As we all know, energy consuming assets in energy networks are state of the art. There are different strategies to provide energy to these energy consuming assets. The energy in such networks can come from different energy sources. The methods to control the corresponding energy flows are challenging tasks.

[0003] It is now clear that a method of determining the energy ratio of the energy consumed is also needed. Summary of the invention

[0004] In view of the above, the object of the present invention is to provide a method for determining the energy ratio of consumed energy. In particular, the object of the present invention is to provide an improved method for determining the energy ratio of consumed energy in an energy network. These and other objects that become apparent on reading the following description are solved by the subject matter of the independent claims. The dependent claims refer to preferred embodiments of the invention.

[0005] In one aspect, a computer-implemented method for determining an energy ratio of consumed energy is provided, comprising: providing an energy network model configured to describe energy flow between multiple assets in an energy network, wherein at least one of the multiple assets consumes energy, wherein the energy network also includes a first energy source and a second energy source as assets, wherein the energy flow includes a first energy flow from the first energy source and a second energy flow from the second energy source; receiving load demand data for the energy network and power supply data for the energy network generated by the first energy source; controlling and tracking a first energy flow from the first energy source to multiple assets in the energy network and a second energy flow from the second energy source to multiple assets in the energy network based on the energy network model and the received load demand data and the received power supply data; tracking one or more consumption times of at least one energy consuming asset; determining an energy ratio for at least one energy consuming asset based on the tracked first energy flow and second energy flow and the tracked consumption time consumed by at least one energy consuming asset; and providing the determined energy for further processing.

[0006] As used herein, the term energy ratio should be understood broadly and may relate to a ratio described by the share of the consumed energy (i.e., the amount of energy) provided by one asset (e.g., a PV system) relative to the total consumed energy provided by multiple assets (i.e., the PV system and the grid). The energy ratio may be formed by a first amount of energy from a first energy source and a second amount of energy from a second energy source. One or more energies come from one or more energy sources. For example, an asset is a machine tool that produces a product, and the consumed energy comes from a first energy source and a second energy source, wherein the first energy source provides 50% of the consumed energy and the second energy source provides 50% of the consumed energy. In this example, the first energy source is a green energy source, such as a photovoltaic system, and the second energy source is a grid. In this example, the corresponding energy ratio may be an energy ratio of a 50% green energy share. The energy ratio may relate to the mix of energy used for a product (e.g., for producing a product) or alternatively for a service. Services may include charging an electric vehicle.

[0007] As used herein, the term consumed energy should be understood broadly and may relate to the amount of energy consumed by an asset.Consumed energy may include one or more amounts of energy provided by one or more energy sources.

[0008] As used herein, the term energy network model should be understood broadly and may relate to any model configured to describe the energy flow between multiple assets in an energy network. The energy flow may also include a direction, such as an inflow or outflow of an asset. The energy network model may be a deterministic model. The energy network model may be a network graph model. The energy network model may be a time-dependent model. The energy network model may include at least one asset that consumes energy and at least two assets that act as energy sources for the energy-consuming asset. The energy network may preferably be an electrical energy network.

[0009] As used herein, the term energy network should be understood broadly and may preferably relate to a physical network between multiple assets that provide, store and / or consume energy. The assets are connected together, for example, by power lines for transmitting electrical energy / electricity. The energy network may preferably be an electrical energy network.

[0010] The energy network, in particular one or more assets (i.e. energy sources, battery energy storage, energy consumption assets) may include one or more controls configured to receive control commands from a data processing device configured to perform the above method. The respective controls may also be configured to exchange data with the data processing device.

[0011] As used herein, the term energy flow should be understood broadly and preferably relates to the amount of energy provided each time. Energy flow can be expressed in units of W. Energy flow can also include the direction of inflow or outflow involving assets, energy sources and / or battery energy storage. The energy flow can be controlled by the controls of the respective assets (i.e., energy sources). The energy flow can then also be tracked and used in the method to perform further calculations, specifically the calculation of energy ratios.

[0012] As used herein, the term asset should be understood broadly and may refer to any physical entity that consumes energy, provides energy and / or stores energy. This asset may be a machine tool, a charger, a battery storage system, a power grid, an energy source.

[0013] As used herein, the term energy source should be understood broadly and may relate to any physical entity configured to provide energy to an asset. The energy source may be one of the following: a PV system, a wind turbine, a hydro turbine, a diesel generator set, a coal-fired power plant, a nuclear power plant, a gas-fired power plant, and a hydrogen fuel cell. The second energy source may preferably be an electrical grid. The first energy source may preferably be any energy source other than an electrical grid.

[0014] As used herein, the term first energy flow should be understood broadly and may relate to a flow of energy from a first energy source to an asset. For example, the first energy flow may be energy from a wind turbine.

[0015] As used herein, the term second energy flow should be understood broadly and may relate to an energy flow from a second energy source to an asset. For example, the second energy flow may be energy from a power grid. Preferably, the second energy flow is from a power grid.

[0016] As used herein, the term load demand should be understood broadly and may relate to any load demand for the energy network. The load demand data may preferably include the total amount of energy for the energy network. The load demand data may relate to a time period. The time period may include 30 minutes, 20 minutes or 15 minutes. It should be noted that smaller or larger time periods may also be used in the method. The load demand data may come from a forecast.

[0017] As used herein, the term power data should be understood broadly and may relate to the flow of energy from a power source. The power data may also include the time period during which the energy flow is available. The power data may come from a forecast.

[0018] As used herein, the term consumption time should be understood broadly and may preferably relate to the time that the asset consumes energy from the first energy source and / or the second energy source or any additional energy source. For example, the consumption time of the first energy source includes 30 minutes and the consumption time of the second energy source includes 20 minutes. The consumption time may be received from a control of the asset that records the consumption time of the asset.

[0019] In other words, the basic concept of the present invention may require the use of an energy network model and load requirements for the energy network and available power sources in order to control the energy flow in the energy network and determine the energy ratio of the product or service provided in the energy network. The second energy source may preferably be a power grid. In the case where the first energy source (e.g., a wind turbine) does not meet the energy load requirements, the necessary energy difference may preferably be filled by energy from the power grid. The fluctuation of the available power from the first power source is considered by uncertainty measurement and / or limits. It should be noted that if the second energy source is not a power grid, but another energy source (e.g., a coal-fired power plant) that can compensate for such fluctuations, the method is also valid. It should be noted that the energy network model is used to describe the real physical energy network and is also used in the method to track and control the energy flow. The connection between the energy network model and the real physical energy network is established by a data processing device that executes the method. The method can advantageously be scalable, which means that it is applicable to multiple assets, particularly energy consumption assets, energy sources, and battery energy storage. This may be beneficial to provide products or services with specific energy ratios. This may be important for consumers who are trying to save the earth. This can also track the sustainability of products or services.

[0020] In an embodiment of the method, the determination of the energy ratio may be based on the integration of the first energy flow and the second energy flow over the consumption time being tracked. The integration of the energy flow over a period of time may result in the corresponding energy. By integrating the first energy flow and the second energy flow over their respective consumption times, the respective energies may be calculated. The energy ratio may then be calculated by dividing the first energy by the sum of the first energy and the second energy. This may advantageously result in an accurate determination of the energy ratio.

[0021] In an embodiment of the method, load demand data for the energy network and power supply data for the energy network generated by the first energy source may be based on a forecast. The forecast may be based on historical data. The forecast may be received by an interface (e.g., an HMI or an API). The forecast may include different time periods (minutes, hours, days). In other words, the forecast may be used as an input to an energy network model in order to control energy flows in the energy network. This may enable the use of the method even when no real-time data for the asset is available. This may result in a flexible use of the method. It should be noted that this also applies to situations where multiple energy sources and / or energy consuming assets are considered by the method.

[0022] In an embodiment, power data for an energy network generated by a first energy source can be received in real time. The power data can be received directly from a control of the first energy source and transmitted to a device that performs the method through an interface, an API, or an online portal. Real-time power data can be more accurate than forecast data. This can lead to more accurate control of energy flow and more accurate determination of energy ratios. As used herein, the term real-time should be understood in a broad sense and can preferably be a relative term involving a time step of an energy network model. Therefore, in the case where the time step of the energy network model is large (e.g., 5 minutes), each power data received every minute can also be real-time data. It should be noted that this also applies to the case where multiple energy sources are considered by the method.

[0023] In an embodiment of the method, load demand data for an energy network and / or power supply data for an energy network generated by a first energy source may be associated with respective uncertainty measurements. As used herein, the term uncertainty measurement should be understood broadly and may relate to any parameter that takes into account the uncertainty of power supply data. Uncertainty measurements may be applied to power supply data. For example, uncertainty measurements of power supply data may be multiplied with power supply data. For example, uncertainty measurements may include a value of 95%. This may advantageously result in a conservative estimate of power supply data. For example, in a case where a PV system is used as an energy source and the uncertainty measurement is 95%, the other 5% comes from a second source, which in this example may be a power grid. This may be advantageous for the stability of the determined energy ratio. Uncertainty measurements may be applied to load demand data. For example, uncertainty measurements of load demand may include a value of 105%. Uncertainty measurements may be multiplied with load demand data. This may advantageously result in a safe estimate of load demand data. It should be noted that in the case of multiple energy sources (e.g., more than two energy sources), uncertainty measurements of a third energy source and another energy source are also considered by the method. The same applies to multiple energy consumption assets in an energy network.

[0024] In an embodiment of the method, the load demand data for the energy network may include an upper limit, and the power supply data for the energy network generated by the first energy source include a lower limit. As used herein, the term upper limit should be understood broadly and may relate to an upper threshold value. As used herein, the term lower limit should be understood broadly and may relate to a lower threshold value. The upper limit limits the value of the load demand data. The upper limit can be derived from a forecast. This can improve the reliability of the energy network model. The lower limit limits the value of the power supply data. This can improve the reliability of the energy network model, in particular the determination of the energy ratio.

[0025] In an embodiment of the method, the energy source may be one of: a photovoltaic system, a wind turbine, a hydro turbine, an electrical grid, a diesel generator, and a hydrogen fuel cell.The second energy source may preferably be an electrical grid.

[0026] In an embodiment of the method, the energy source model may be a time-dependent network graph model. The time-dependent energy network model may include a time step of, for example, 5 minutes or 10 minutes.

[0027] In an embodiment of the method, the energy network may include a battery energy storage for storing energy as an asset, and may also include controlling and tracking a first energy flow from a first energy source to the battery energy storage and a second energy flow from a second energy source to the battery energy storage based on the energy network model and the received load demand data and the received power supply data, and tracking one or more charging times of the battery energy storage, and determining the energy storage energy ratio based on the tracked first energy flow and the second energy flow and the tracked charging times. In other words, the method controls and tracks the inflow of energy sources to the battery energy storage, so that each time it is known how much energy from the first energy source or the second energy source is currently stored in the battery energy storage. This information is also used to determine the battery energy ratio. As used herein, the battery energy ratio should be understood broadly and may preferably involve the ratio of the first energy from the first energy source to the sum of the first energy from the first energy source and the second energy from the second energy source. Energy can be calculated by integrating the energy flow over the number of charging times. This information can be used to determine the battery energy ratio. This can advantageously increase the level of detail of the energy network model. The same applies to multiple energy sources in the energy network.

[0028] In an embodiment of the method, the method may further include controlling and tracking a first battery energy flow and a second battery energy flow from a battery energy storage to a plurality of assets in an energy network, wherein the first battery energy flow is based on a first stored energy related to the first energy flow, and the second battery energy flow is based on a second stored energy related to the second energy flow, wherein the tracked first battery energy flow and the second battery energy flow are used to determine an energy ratio for at least one energy consuming asset, and one or more discharge times of the battery energy storage are also tracked, and the energy storage energy ratio is determined based on the tracked first energy flow, the second energy flow, the first battery energy flow, the second battery energy flow, the number of charges, and the number of discharges. In other words, the battery energy storage is treated as a special energy source that includes energy from different energy sources. The inflow and outflow of energy sources to the battery energy storage are controlled and tracked, and are used to determine the energy ratio of the energy consuming asset. This can advantageously increase the level of detail of the energy network model, thereby improving the quality of control when determining the energy ratio in the energy network. The same applies to multiple energy sources in the energy network.

[0029] In an embodiment of the method, controlling and tracking a first energy flow from a first energy source to a plurality of assets in an energy network and a second energy flow from a second energy source to a plurality of assets in an energy network can also be based on allocation logic, wherein the allocation logic is preferably dynamic. As used herein, the term allocation logic should be understood broadly and preferably relates to a strategy for setting a predefined target value for an energy ratio of an energy consuming asset. For example, the energy ratio should be 50% of the first energy from the first energy source. The allocation logic can then control the energy flows from the first energy source and the second energy source so that the respective energy consuming assets each obtain a portion of their energy from the first energy source and the second energy source. The same applies to multiple energy sources in an energy network.

[0030] In an embodiment of the method, more than one of the plurality of assets may consume energy. It should also be noted that the method may also take into account more than two energy sources. This may advantageously result in scalability of the use of the method.

[0031] Another aspect of the present disclosure relates to a data processing device, comprising means for executing the above method.

[0032] Another aspect of the present disclosure relates to a computer program, comprising instructions, which, when the program is executed by a computer, cause the computer to perform the above-mentioned method.

[0033] Another aspect of the present disclosure relates to a computer-readable medium comprising instructions which, when executed by a computer, cause the computer to perform the above-described method.

[0034] The device or apparatus according to one or more example embodiments may be implemented using hardware, software and / or a combination thereof. For example, a hardware device may be implemented using a processing circuit system such as, but not limited to, a processor, a central processing unit (CPU), a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a system on a chip (SoC), a programmable logic unit, a microprocessor, or any other device capable of responding to and executing instructions in a defined manner.

[0035] Its equipment or device may include one or more interface circuits. In some examples, the interface circuit may include a wired or wireless interface connected to a local area network (LAN), the Internet, a wide area network (WAN), or a combination thereof. The functionality of any given equipment or unit of the present disclosure may be distributed among multiple units or equipment connected by interface circuits.

[0036] The device or apparatus according to one or more example embodiments may also include one or more storage devices. The one or more storage devices may be tangible or non-temporary computer-readable storage media, such as random access memory (RAM), read-only memory (ROM), permanent mass storage devices (such as disk drives), solid-state (e.g., NAND flash memory) devices, and / or any other similar data storage mechanism capable of storing and recording data. The one or more storage devices may be configured to store computer programs, program codes, instructions, or some combination thereof.

[0037] Any disclosure and embodiments described herein are related to the above-listed methods, devices, computer programs, computer-readable media, and vice versa. Advantageously, the benefits provided by any embodiment and example are also applicable to all other embodiments and examples, and vice versa. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Exemplary embodiments of the present invention will be described in the following figures.

[0039] Figure 1 A flow chart showing a computer implemented method of determining an energy ratio of energy consumed;

[0040] Figure 2 An energy network is shown;

[0041] Figure 3 The corresponding energy network model is shown;

[0042] Figure 4 The corresponding energy network model is shown;

[0043] Figure 5 shows a forecast of the energy measured with the applied uncertainty;

[0044] Figure 6 shows the possible evolution of the energy content of a battery store over time; and

[0045] Figure 7 An energy network model for the production of a single commodity is shown. DETAILED DESCRIPTION

[0046] Figure 1 A flow chart of a computer implemented method of determining an energy ratio of energy consumed is shown.

[0047] Step S10 includes providing an energy network model, which is configured to describe the energy flow between multiple assets in an energy network, wherein at least one of the multiple assets consumes energy, wherein the energy network also includes a first energy source and a second energy source as assets, wherein the energy flow includes a first energy flow from the first energy source and a second energy flow from the second energy source.

[0048] Step S20 receives load demand data for the energy network and power supply data for the energy network generated by the first energy source.

[0049] Step S30 includes controlling and tracking a first energy flow from a first energy source to multiple assets in the energy network and a second energy flow from a second energy source to multiple assets in the energy network based on the energy network model and the received low demand data in the received power supply data.

[0050] Step S40 includes tracking one or more consumption times of at least one energy consuming asset.

[0051] Step S50 includes determining an energy ratio for at least one energy consuming asset based on the tracked first energy flow and the second energy flow and the tracked consumption time of the at least one energy consuming asset.

[0052] Step S60 comprises providing the determined energy ratio for further processing.

[0053] Optionally, the energy ratio can be used to identify the goods produced or to classify the goods according to their energy ratio. The first energy source can be a PV energy source. The second energy source can be a power grid. In this example, the energy consuming asset can be a machine tool. The energy network model can be a time-dependent network graph model. In this example, the energy network model can include only the above two energy sources and one energy consuming asset. Alternatively, the energy network model can include multiple additional assets. The assets can include photovoltaic systems, wind turbines, hydroelectric turbines, power grids or diesel generators and hydrogen fuel cells as energy sources. The assets of the energy network are interconnected by power lines. It should be noted that not every asset must be connected to every other asset. For example, it is sufficient that the first energy consuming asset is connected to the first energy source and the second energy source. However, the second energy consuming asset does not have to be connected to the first energy consuming asset. It is sufficient that the second energy consuming asset is connected to the first energy source and / or the second energy source. One or more power lines can converge at a node or a node can allocate itself to one or more power lines. The energy network model can allow the energy flow between the first asset and the second asset to be described. The energy network model can allow such energy flow to be described in two directions. The energy network model can allow uncertainty measurement to be applied to the energy flow of the energy source. It should be noted that preferably, one energy source is always the power grid. In the case where other energy sources (such as PV systems, diesel generators, etc.) cannot provide the required energy flow, the power grid will provide the energy flow that is missing in reality. The uncertainty measurement of the power supply data takes this aspect into account. This can be advantageous because it can improve the reliability of determining the energy ratio. Optionally, the load demand data for the energy network and the power supply data for the energy network generated by the first power supply can be based on forecasts. Additionally or alternatively, the power supply data for the energy network generated by the first energy source can be received in real time. Optionally, the energy load demand data for the energy network can include an upper limit, and the power supply data for the energy network generated by the first energy source can include a lower limit. It should be noted that this can also be applied to multiple energy sources (such as a second energy source, a third energy source, a fourth energy source, etc.). The determination of the energy ratio can be based on the integral of the first energy flow and the second energy flow during the consumption time tracked. The energy source can be one of the following: a PV system, a wind turbine, a hydro turbine, a diesel generator and / or a power grid.

[0054] Optionally, the energy network may include a battery energy storage for storing energy as an asset, and also include controlling and tracking a first energy flow from a first energy source to the battery energy storage and a second energy flow from a second energy source to the battery energy storage based on an energy network model and received load demand data and received power supply data, and tracking one or more charging times of the battery energy storage, and determining an energy storage energy ratio based on the tracked first energy flow and second energy flow and the tracked charging times.

[0055] Optionally, the method may also include controlling and tracking a first battery energy flow and a second battery energy flow from a battery energy storage to multiple assets in an energy network, wherein the first battery energy flow is based on a first stored energy related to the first energy flow, and the second battery energy flow is based on a second stored energy related to the second energy flow, wherein the tracked first battery energy flow and second battery energy flow are used to determine an energy ratio for at least one energy consuming asset, and also track one or more discharge times of the battery energy storage, and determine the energy storage energy ratio based on the tracked first energy flow, second energy flow, first battery energy flow, second battery energy flow, charging times, and discharging times.

[0056] Optionally, controlling and tracking the first energy flow from the first energy source to the plurality of assets in the energy network and the second energy flow from the second energy source to the plurality of assets in the energy network may also be based on a distribution logic, wherein the distribution logic is preferably dynamic.

[0057] Figure 2 An exemplary energy network 100 is shown.

[0058] In this example, the energy network 100 includes six assets, namely a first energy source 101, a second energy source 102, a third energy source 103, a first energy consumption asset 104, a second energy consumption asset 105, and a battery energy storage 106. In this example, the first energy source 101 is a PV system, the second energy source 102 is a power grid, the third energy source 103 is a PV system, and the first energy consumption asset 104 is a production machine, and the second energy consumption asset 105 is an electric vehicle charger. The assets in the energy network 100 are connected by power lines 107 for transmitting electrical energy. The power lines 107 run together at nodes 108 and are distributed from nodes 108. It should be noted that as depicted here, the energy network 100 shows a physical reality. The energy network 100 also includes controls (not shown) for controlling the flow of energy from energy sources to other assets. The energy consumption assets and battery energy storage also include control units for controlling their respective assets. The assets may also include an interface for exchanging data with a data processing device configured to perform the above method.

[0059] Figure 3An energy grid model 200 is shown.

[0060] The energy network model 200 corresponds to the above energy network 100. The energy network model 200 includes the same assets as the energy network, namely a first energy source 201, a second energy source 202, a third energy source 203, a first energy consumption asset 204, a second energy consumption asset 205 and a battery energy storage 206. Figure 2 , the assets are connected by power line 207 and power line 208. Power line 207 allows power transfer from the first energy source 203 to the energy consuming asset 205, which is represented by the single arrow at the end of power line 207. Power line 208 allows power transfer between the second energy source 202 and the battery energy storage 206 in both directions, which is represented by each arrow at the end of line 208. The same applies to the other power lines connecting the respective assets.

[0061] Figure 4 An energy grid model 300 is shown.

[0062] Energy Network Model 300 and Figure 3 The energy network model shown differs in the power lines. The energy network model 300 includes the same assets as the energy network model 200, namely a first energy source 301, a second energy source 302, a third energy source 303, a first energy consumption asset 304, a second energy consumption asset 305 and a battery energy storage 306. Figure 3Instead, there are now two power lines 307 and 308 connecting the third energy source 303 with the second energy consuming asset 305. The first power line 307 relates to a power line that provides an energy flow from the third energy source 303 with a high probability. The second power line 308 relates to a power line that provides an energy flow from the third energy source 303 with a low probability due to uncertainty (e.g., random effects in energy generation), and is therefore considered to be an energy flow from the second energy source 302. The energy network model 300 takes into account this distinction, i.e., the uncertainty measurement applied to the respective energy sources. The same applies to the power line 309 and the power line 310 between the grid 302 and the battery energy storage 306. The power line 309 relates to a power line that provides an energy flow from the battery energy storage 306 with a high probability. The second power line 310 relates to a power line that provides an energy flow from the battery energy storage 306 with a low probability due to uncertainty, and is therefore considered to be an energy flow from the second energy source 302. The energy network model 300 also includes time-dependent variables, which describe the energy flow from one asset to another asset at time t. The energy network model 300 may also include a battery storage variable describing the energy stored in the battery at time t and an additional variable, which refers to the amount of stable stored energy from the first energy source and the possible stored energy (e.g., electricity) from the second energy source. The energy network model may also include a set of constraints, such as statutory grid limits of the power grid. Additionally, the charging efficiency and discharging efficiency of the battery energy storage may be considered. The energy network model allows tracking the energy flow from each energy source to each energy consuming asset and to each battery energy storage.

[0063] Figure 5 A forecast diagram 400 of power (energy flow) of an energy source with applied uncertainty measurements is shown.

[0064] The time is marked on the horizontal axis 401. The power of the energy source (e.g., a PV system) is marked on the vertical axis 402. The course 403 of the power of the energy source is depicted in two areas. The lower area 404 shows the power share to which an uncertainty measure (e.g., 95%) is applied. For example, this means that 95% of the power is generated by the energy source (e.g., a PV system) with a high probability. The upper area 405 shows the power share that is generated with a low probability of only 5% due to uncertainty (e.g., random effects in energy generation). The forecast can be generated using a machine learning model. Based on the forecast and the applied uncertainty measure, a lower bound can be derived. In an energy network, this information can be used to describe the energy flow from the energy source. The same aspect also applies to the load demand of an asset (e.g., a machine tool). This may be useful for reliably forecasting the amount of energy generated and consumed. The forecast can have a multi-day range. The time step of such a forecast may include 1 minute, 5 minutes, or 10 minutes. However, other values ​​may also be suitable.

[0065] Figure 6 A possible evolution of the energy content of a battery storage over time is shown. Battery energy storage 500 to 505 show the same battery energy storage at different times 506 with a time step of 15 minutes. In this example, the battery energy storage includes three different energies 507, 508 and 509 from a first energy source (e.g. a PV system), a second energy source (e.g. a grid) and a third energy source (e.g. a wind turbine). The respective energies 507, 508 and 509 change their amount over time due to inflows and outflows.

[0066] Figure 7 A diagrammatic energy network 600 is shown for the production of a single commodity.

[0067] In this example, the energy network model 600 includes a PV system as a first energy source 601, a grid as a second energy source 602, a battery energy storage 604 and a machine tool as an energy consuming asset 603. By applying the above method, it is possible to control the energy flows from the energy sources 601, 602 and the battery energy system 604 and allow to produce products with certain energy ratios (e.g. milled parts) and determine the energy ratios.

[0068] In the following, a summary of the main effects and main advantages of the present disclosure is provided:

[0069] The invention may describe new mathematical modeling methods for electrical networks, for example as they occur in energy management systems, and may allow the power consumption of any load and the energy stored in a battery system to be decomposed into different categories, such as green and non-green. Traditionally, electrical networks can be modeled according to their physical topology. While one can easily measure the total power through a given line, the information about which part of the power is generated by which asset, and in particular which part of the consumed energy is green and which is not green, is not directly available.

[0070] The invention may provide for a virtual tracking of the share of green energy consumed and stored at each point in time. In a further step, this may allow the generation of energy consumption certificates for sites and continuously for any kind of product, indicating how green the production process is (in terms of the energy consumed). The ability to generate these certificates may be very important if one day green products will benefit from tax incentives.

[0071] The local energy network can include energy generating assets (e.g. photovoltaic systems, wind turbines, diesel generators) and energy consuming assets, battery energy storage systems and connections to a larger grid that acts as an unlimited source of electricity and energy. As an example, one can imagine a factory that needs energy to generate a certain product, but on the other hand has a photovoltaic system on its roof and includes an energy storage system.

[0072] Traditionally, electrical networks can be modeled according to their physical topology. While one can easily measure the total power passing through a given power line, information about which part of the power is generated by which asset, and in particular which part of the consumed energy is green and which is not, is not directly available. This may be particularly true for battery energy systems, which do not have the ability to decompose the stored energy into different origin components (or remember the shares corresponding to them) according to the generation type.

[0073] In contrast, interest in the green and zero-emission production of commodities is likely to grow. Therefore, it could be beneficial for any company to track the amount of green energy, which was used to produce its (single) commodity. Furthermore, trustworthy labels or certificates that could state the share of green energy used in the production process or the amount of CO2 emissions saved could generate high interest.

[0074] However, the generation of such tags can be trivial, especially if battery energy storage is involved, as this requires detailed tracking of green and non-green power and energy in different parts of the energy network at any point in time.

[0075] In order to solve the above problems, a new approach to modeling a given energy network is proposed. Instead of converting the physical power lines 1:1 into a mathematical model, the following items can be implemented:

[0076] For each generating asset (i.e. energy source) (renewable energy sources, ...), except the grid, and for battery energy storage, two virtual power lines can be added to any possible power receiver: one power line refers to the (definitely) green energy and the other power line refers to the (possibly) non-green energy that may be produced and released by the power receiver. Here, for example, for renewable energy sources, due to their stochastic nature, the 'non-green' part may correspond to an uncertain production. If information about the share of green or non-green power transferred from the grid to the asset is available, the method can perform a similar treatment for the grid. Otherwise, for the grid, only one virtual power line can be added to each possible receiver.

[0077] This may allow a way to virtually track green and non-green power through the system "end-to-end". Specifically, the energy stored in the battery energy storage depending on its energy source (green or non-green) can also be decomposed, and the power or energy consumed at any point in time can be decomposed. Furthermore, modeling may make it possible to virtually select the share of green energy released from the battery energy storage at any point in time depending on the share of stored energy (e.g. if the corresponding share is zero, no more green energy can be released). In extreme cases, this enables two options:

[0078] 1) Use 50% of green energy to produce all goods.

[0079] 2) Use pure green and pure non-green energy alternately to produce goods.

[0080] In practice, power / energy flows can be somewhat stochastic and can include uncertainty measurements, giving conservative statements about the green power / energy consumed or stored, which are likely to underestimate the green share. In this way, trustworthy statements can be made about (at least) the amount of green energy employed. This can be used to generate certificates of origin for any kind of product in terms of energy.

[0081] The benefits of the proposed solution can be:

[0082] • For any energy consuming asset / battery energy storage, it may be feasible to virtually decompose the consumed power / energy into green and non-green shares at any point in time.

[0083] This can help track how much green power is used directly by the site, stored and reused, or pushed into the grid.

[0084] In more detail, this information can be used to generate an energy certificate of origin for any kind of product, indicating a lower limit on the share of green energy used in the production process.

[0085] Furthermore, virtual discharges of single shares in the battery energy storage can be modeled while respecting the energy balance of each share.

[0086] Furthermore, real batteries can be divided into two dynamic parts. One part stores local green power for further use, while the other part helps to stabilize the grid.

[0087] Furthermore, this information can be used by energy management systems, for example to ensure that a certain share of green energy is used for production at any point in time or to optimize the use of green energy.

[0088] The concept can include four steps:

[0089] 1. Generate a suitable energy network model.

[0090] 2. Incorporating uncertainty measurements into the control unit that controls the virtual power flow / energy flow at any point in time, allowing conservative tracking of, for example, the green energy share to a defined certainty.

[0091] 3. Calculate the resulting green share in terms of power / energy consumption and energy storage at any point in time.

[0092] 4. Calculate the origin label / certificate.

[0093] Step 1: Model Generation

[0094] Given a physical energy network, we consider the corresponding network graph. We generate a new graph to which we transfer all assets as nodes. For each asset A_1, we find all components A_2 that the network allows it to transfer power from A_1 to A_2, and add a virtual power line A_1→A_2, directly connecting A_1 and A_2.

[0095] In the second step, we reconsider these virtual power lines A_1→A_2, where A_1 refers to a generation asset (other than the grid) or battery energy storage. For these, we replace all single power lines A_1→A_2 with two power lines A_1→_g A_2 and A_1→_b A_2, where A_1→_g A_2 is a virtual power line that transfers (definitely) green energy from A_1 to A_2, and A_1→_g A_2 is a power line that transfers (possibly) non-green ("brown") energy.

[0096] This could be done in a similar way to grid connection, as the share of green power would be available in real time via an online portal / API in the future, so the algorithm could handle this split as well.

[0097] Now, for each virtual connection We add the time-dependent variable π_(A_1→_x A_2)(t)≥0 to our mathematical model, which describes the corresponding virtual power flow from asset A_1 to asset A_2 at time t. If both variables π_(A_1→_x A_2)(t) and π_(A_2→_x A_1)(t) exist, they can also be combined into real-valued variables, also allowing negative values, thus allowing reverse energy flow in the physical system. Possible constraints on the physical power lines are transferred to the virtual power lines, so that any set of feasible virtual power flows satisfies the physical laws and constraints.

[0098] Furthermore, for each battery storage system B, we add the variable Σ_B(t), which describes the energy stored in battery B at time t, and the variables Σ_(B,g)(t) and Σ_(B,b)(t), which refer to the amount of (definitely) green and (possibly) non-green energy stored.

[0099] For simplification reasons, in this case we only use green and brown virtual arrows to identify the energy, but there could be more virtual arrows, e.g. for onsite diesel generators or even to differentiate between wind and PV.

[0100] The power flow and state of charge need to satisfy a set of constraints that depend on accurate modeling to respect, for example, technical grid limits, technical limits on maximum power flow through physical power lines, conservation of power and energy, and additionally, the efficiency of charging and discharging the battery when calculating the state of charge due to the incoming / outgoing power flow and the fact that power cannot flow in and out at the same time, etc.

[0101] The energy network model can allow direct tracking of power flow from each energy emitting asset (i.e., energy source) to each energy receiving asset (i.e., energy consuming asset), and finally even if battery energy storage is used.

[0102] Step 2: Control unit and incorporation of uncertainty measurements

[0103] The power flows in the system will proceed according to the given (configurable) controller logic. The controller can calculate the virtual power flows / energy flows according to the energy network model generated in step 1. For each asset, a set of virtual flows can be incorporated with the total inflow and outflow information. Therefore, the physical power flows can be easily calculated from the virtual power flows.

[0104] For power outflows from battery energy storage and, in principle, also from the grid, the energy network model can allow the share of green and non-green power to be selected. This can be constrained by the share of stored energy. Thus, the modeling can allow, e.g.

[0105] a) Satisfy all load requirements using 50% green power at all times

[0106] b) Alternate in time to meet certain load demands using pure green and pure non-green power

[0107] This may allow a virtual distinction between, for example, purely green and purely non-green production.

[0108] The virtual power flow may depend on the load demand and the power generated by the renewable energy sources, both of which are uncontrollable and usually have a stochastic nature. However, for both, it may be possible to reliably predict the amount of consumed / generated power over a (short) considered time interval (e.g. using machine learning methods) and, more importantly, provide reliable upper bounds for load and reliable lower bounds for generation, which will be met with a very high probability.

[0109] These conservative measures can serve as inputs to the controllers employed and the resulting power flows are calculated and the grid compensates for any deviations that occur in reality (e.g. if more green energy is generated, less energy is drawn from the grid).

[0110] Step 3: Calculation of Green Share

[0111] For example, at time point t_0 and at t_0, the share of green and non-green energy may be known (e.g. the algorithm may be from the start of work, production, ... of the site). Then, the time range [t_0, t_1] may be considered, where t_1>t_0. According to step 2, a forecast may be provided or received for the lower bound of the green energy generated by all assets, and the upper bound of the renewable energy generated by all assets for all load demands

[0112] And the resulting virtual power flow (encoded in the variable π_(A_1→_x A_2)(t) , see above) can be calculated in the interval [t_0, t_1] according to the controller logic.

[0113] Step 3a: Battery Share

[0114] For a given battery energy store B, this may mean that the different energy contents at t_0 are known; here, for simplicity, only green and non-green energy are considered. Now, let η_(B,Charge) describe the charging efficiency of the battery, and π_(→_g B)(t) may describe the total inflow of definitely green power at time t, and π_(→B)(t) describes the total inflow of all power at time t. The variables Σ_B(t) (energy stored at time t), Σ_(B,g)(t) (definite green energy stored at time t), and Σ_(B,b)(t) (possible non-green energy stored at time t) may be updated as follows:

[0115]

[0116] Note: Depending on the definition of the problem, all flow variables can be either zero or positive. Additionally, mathematical constraints can exclude having both inflow and outflow at the same time, which does not make any physical sense in reality.

[0117] Therefore, at t_1, for each battery energy storage, the share of green and non-green energy stored in the battery energy storage may be known, and we may proceed to the next time frame [t_1, t_2] according to the same logic described previously.

[0118] During discharge, although the efficiency, more specifically the discharge efficiency - which can usually be different from the charging efficiency, can be in the denominator, and there may be two possibilities for extracting energy from the battery energy storage, as described above may also apply. Now, the arrow at π can be behind the asset, so it can describe the outflow. It can distinguish between two extraction methods:

[0119] 1) Each energy content Σ_(B_g) and Σ_(B_b) can be accessed individually at each extraction:

[0120]

[0121] Therefore, since Σ_B=Σ_(B_g)+Σ_(B_b), the total energy content can also be changed:

[0122]

[0123] 2) The extraction π_(B_g→)(t_E) at t_E depends on the energy content Σ_(B_g)(t_E) which is linearly related to the total energy content Σ_B(t_E) of the battery, and both equations can be obeyed simultaneously. This may mean that individual categories cannot be accessed without the other, although individual categories can be routed to different locations:

[0124]

[0125] The self-discharge of a battery may not be given, but it can contribute to the total energy Σ_B stored in the battery over time.

[0126] As shown above, from a mathematical point of view both versions are possible, and the choice can only depend on the possible rules.

[0127] Step 3b: Product Green Share

[0128] From time t1 to time t2, products can be produced on machine A2, so the green energy used for load / product on machine A2 can be calculated by

[0129]

[0130] Sum all machines used for the product.

[0131] ∑ Product，green =sum x (∑ProductonMachinex，green )

[0132] ∑ Product，green =sumx(∑ ProductonMachinex，green )

[0133] This can be concluded into the total energy of the product:

[0134] ∑ Product =∑ Product，green +∑ Product，none-green

[0135] Step 4: Calculate the origin tag / certificate.

[0136] During the production of any single commodity, different equipment (i.e. energy consuming assets) can be involved at each point in time to produce the commodity. The energy network model can calculate the virtual power flows at each point in time and the amount of (definitely) green and (possibly) non-green power consumed by each equipment at each point in time can be determined. Since the adoption duration of all single equipment used to produce a single commodity can be tracked, the amount of (definitely) green and (possibly) non-green energy can be easily calculated (the sum of the integrals of the corresponding power flows over the corresponding durations), see also step 3b. In this way, the energy ratio (e.g. green and non-green energy used for the production process) can be determined. This energy ratio can then become part of the origin tag.

[0137] Additional Benefits

[0138] By adding additional constraints to the model, the described mechanism can be used not only to produce a single commodity and track its individual energy contribution, but also to more advanced settings where two (or more) commodities can be produced, but all green energy can be used for product 1 and all potentially non-green energy can be used for product 2.

[0139] This way, it is possible to target different markets. Note that the solution can use green energy from own PV production and from the grid, the device where the code may be running (or the solution can be calculated) can use the API to read the current green energy share and can calculate positive green share and non-green share as described above. Thus, it can allow a virtual separation of energy and power origins.

[0140] Reference numerals list

[0141] S10 provides energy network model

[0142] S20 receives load demand data and power supply data

[0143] S30 controls and tracks energy flow

[0144] S40 Tracking Elapsed Time

[0145] S50 Determine the energy ratio

[0146] S60 provides energy ratio

[0147] 100 Energy Network

[0148] 101 First Energy Source

[0149] 102 Second Energy Source

[0150] 103 The Third Energy Source

[0151] 104 First Energy Consumption Asset

[0152] 105 Second Energy Consumption Asset

[0153] 106 Battery Energy Storage

[0154] 107 Power Lines

[0155] 108 nodes

[0156] 200, 300 energy network model

[0157] 201, 301 First Energy Source

[0158] 202, 302 Second Energy Source

[0159] 203, 303 The third energy source

[0160] 204, 304 First Energy Consumption Assets

[0161] 205, 305 Second energy consumption asset

[0162] 206, 306 battery energy storage

[0163] 207, 208, 307, 308, 309, 310 power lines

[0164] 400 Forecast

[0165] 401 Horizontal Axis

[0166] 402 Vertical Axis

[0167] 403 The Process of Electricity

[0168] 404 Higher Area

[0169] 405 Lower Area

[0170] 500, 501, 502, 503, 504, 505 battery energy storage

[0171] 506 time steps

[0172] The energy of 507, 508, and 509

[0173] 600 Energy Network Model

[0174] 601 The First Energy Source

[0175] 602 Second Energy Source

[0176] 603 Energy consuming assets

[0177] 604 Battery Energy Storage

Claims

1. A computer-implemented method for determining an energy ratio for energy consumed, comprising: providing an energy network model, the energy network model being configured to describe energy flows between a plurality of assets in an energy network, wherein at least one asset of the plurality of assets consumes energy, wherein the energy network further comprises a first energy source and a second energy source as assets, wherein the energy flows comprise a first energy flow from the first energy source and a second energy flow from the second energy source; receiving load demand data for the energy network and power supply data for the energy network generated by the first energy source; controlling and tracking the first energy flow from the first energy source to the plurality of assets in the energy network and the second energy flow from the second energy source to the plurality of assets in the energy network based on the energy network model and the received load demand data and the received power supply data; tracking one or more consumption times of the at least one energy consuming asset, determining an energy ratio for the at least one energy consuming asset based on the tracked first energy flow and the second energy flow and the tracked consumption time of the at least one energy consuming asset; The determined energy ratio is provided for further processing. 2 . The method of claim 1 , wherein determining the energy ratio is based on an integral of the first energy flow and the second energy flow over the tracked elapsed time. 3 . The method according to claim 1 , wherein the load demand data for the energy network and the power supply data for the energy network generated by the first energy source are based on forecasts. 4 . 4 . The method according to claim 1 , wherein the power supply data for the energy network generated by the first energy source is received in real time. 5 . The method according to claim 1 , wherein the load demand data for the energy network and the power supply data for the energy network generated by the first energy source are associated with respective uncertainty measures. 6 . The method according to claim 1 , wherein the load demand data for the energy network comprises an upper limit and the power supply data for the energy network generated by the first energy source comprises a lower limit.

7. The method according to any of the preceding claims, wherein the energy source is one of the following: a photovoltaic system, a wind turbine, a hydro turbine, an electrical grid, a diesel generator, and / or wherein the second energy source is an electrical grid.

8. The method according to any of the preceding claims, wherein the energy network model is a time-dependent network graph model.

9. A method according to any one of the preceding claims, wherein the energy network includes a battery energy storage for storing energy as an asset, and further includes controlling and tracking the first energy flow from the first energy source to the battery energy storage and the second energy flow from the second energy source to the battery energy storage based on the energy network model and the received load demand data and the received power supply data, and tracking one or more charging times of the battery energy storage, and determining an energy storage energy ratio based on the tracked first energy flow and the second energy flow and the tracked charging times.

10. The method according to claim 9 further includes controlling and tracking a first battery energy flow and a second battery energy flow from the battery energy storage to the multiple assets in the energy network, wherein the first battery energy flow is based on a first stored energy related to the first energy flow, and the second battery energy flow is based on a second stored energy related to the second energy flow, wherein the tracked first battery energy flow and the second battery energy flow are used to determine the energy ratio for the at least one energy consuming asset, and also track one or more discharge times of the battery energy storage, and determine the energy storage energy ratio based on the tracked first energy flow, the second energy flow, the first battery energy flow, the second battery energy flow, the charging times and the discharging times.

11. A method according to any of the preceding claims, wherein controlling and tracking the first energy flow from the first energy source to the multiple assets in the energy network and the second energy flow from the second energy source to the multiple assets in the energy network are also based on allocation logic, wherein the allocation logic is preferably dynamic.

12. The method of any preceding claim, wherein more than one of the plurality of assets consumes energy.

13. A data processing device comprising means for executing the method according to any one of claims 1 to 12.

14. A computer program comprising instructions which, when executed by a computer, cause the computer to perform the steps of the method according to any one of claims 1 to 12.

15. A computer readable medium comprising instructions which, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 12.

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