EP4014295A1

EP4014295A1 - Method for modelling one or more energy conversion installations in an energy management system

Info

Publication number: EP4014295A1
Application number: EP20761140.1A
Authority: EP
Inventors: Jens Werner; Joachim Seifert; Peter Schegner; Paul Seidel; Tobias Hess
Original assignee: Dieenergiekoppler GmbH
Current assignee: Dieenergiekoppler GmbH
Priority date: 2019-08-15
Filing date: 2020-08-13
Publication date: 2022-06-22
Also published as: DE102019121990A1; WO2021027994A1

Abstract

The invention relates to a method for modelling one or more energy conversion installations (2) in an energy management system (1) and addresses the problem of specifying a solution allowing an improved, more efficient and simple control of an energy management system (1). This problem is solved in that a technology-independent abstract model (26) is generated for each energy conversion installation (2) with its particular static parameters (17) and its particular dynamic parameters (18), wherein the parameters (17, 18) are uniform and independent of the type of energy conversion installation (2), wherein dynamic parameters (18) are formed by processing (63) by means of mathematical algorithms (33) and optimisations (34) on the basis of a demand forecast (29) and restrictions for the installation operation (32), and wherein static parameters (17) are formed by inputting technology-independent installation parameters.

Description

Method for modeling one or more energy conversion systems in an energy management system

The invention relates to a method for modeling one or more energy conversion systems in an energy management system, at least one energy conversion system and a control and regulation unit being provided in the energy management system.

The politically wanted and driven energy turnaround is leading to a decentralization of energy supply in the electricity, heating / cooling and mobility sectors. The sectors are increasingly being networked or coupled with one another. This is done, for example, with small, controllable energy conversion systems such as heat pumps, combined heat and power plants or electric vehicles. The decentralization means that large conventional power plants can be switched off. There is also the objective of increasing the efficiency of energy generation.

These developments mean that so-called energy management systems (EMS) are increasingly being installed. These energy management systems can be used locally on one or more energy conversion systems or at a higher level via a network of energy conversion systems. Such an energy management system can include the following components:

• one or more energy conversion systems, such as combined heat and power systems, heat pumps, fuel cells, heating rods, electric vehicles, photovoltaic systems (PV systems), connected to one or more supply networks

• Requirements (consumers) in one or more sectors (electricity / heating / cooling / mobility)

• Storage units such as thermal, chemical or electrical storage units

• Sensors / meters for recording relevant energetic interfaces or parameters of energy conversion systems and storage • a local control / regulating unit which is / are assigned to individual or a group of energy conversion systems and which controls / regulates them and collects measurement data from the sensors

In a so-called local energy management system, one or more energy conversion systems are assigned a control / regulating unit on which the sub-functionalities of the energy management system are implemented.

In the case of a so-called superordinate energy management system, the energy management system is supplemented by a component of an energy control center for controlling the subordinate units. For this purpose, either the control / regulation unit also has the functionality of a gateway or communication takes place via a separate gateway.

Such energy management systems in various applications are known from the prior art.

Example 1: Virtual power plants for marketing the energy fed in by renewable energies on the electricity exchange

Example 2: Control of a battery storage system in a system consisting of a PV system, battery storage and electrical consumers to increase the proportion of the energy produced by the PV system in relation to the energy consumed in the system (covering internal requirements)

Example 3: Connection and pooling (grouping) of combined heat and power plants (combined heat and power plants) to guarantee the necessary minimum power for the provision of control power as a system service in the context of a virtual power plant

The documents named below are specified as the state of the art:

• DE 102009 044 161 A1

• DE 102005 056 084 A1

• US 2009/0088907 A1 • Hess, T .; Werner, J .; Schegner, P .: Concepts for cross-sectoral energy management, ETG Congress 2019

• Seifert, J .; Werner, J .; Seidel, P .; inter alia: RVK II - practical testing of the regional virtual power plant based on micro-CHP technology, ISBN 978-3-8007-4630-9

• BTC AG: Brochure BTC Virtual Power Plant - Consumers and producers bundle and market, https://www.btc-ag.com/Angebote/BTC-VPP-Virtual- Power-Plant (accessed on July 3, 2019)

A disadvantage of such known systems is that optimizations, for example with regard to economic efficiency, are only carried out to a limited extent. In addition, restrictions for the operation of an energy conversion system, such as requirements, are only insufficiently taken into account in deployment planning.

According to the state of the art, system models must be generated for individual energy conversion systems and incorporated into the deployment planning and control. In addition, the function of the energy management system is not independent of the technology of the energy conversion systems. The flexibility that can be used to control an energy conversion system is therefore insufficient.

Thus, there is a need for a solution which overcomes the disadvantages of the prior art and provides an improved method for controlling an energy management system.

The object of the invention is now to provide a method for modeling one or more energy conversion systems in an energy management system (EMS) which enables an improved, more efficient and simple control of one or more energy conversion systems in an energy management system.

A model generated during such modeling, which depicts one or more energy conversion systems, can be used, for example, to generate a timetable for managing an energy conversion system. The object is achieved by a method with the features according to claim 1 of the independent claims. Developments are given in the dependent claims.

The method for controlling an energy management system provides at least one energy conversion system and one control and regulation unit in one of these types of system.

It is also provided that in a preceding process step an abstract model is generated for each energy conversion system, in which the energy conversion system is mapped with its respective static parameters and its respective dynamic parameters. In this abstract model, the static parameters and dynamic parameters are uniform and independent of the type of energy conversion system. Thus, the number of parameters used does not change from energy conversion system to energy conversion system, but only the content or value of a parameter differs from energy conversion system to energy conversion system.

The abstract model contains all the information that an energy management system needs for individual sub-functionalities. This abstract model is always independent of the type of energy conversion system with its possible special features and is therefore generally applicable. The disadvantage of the prior art, which requires different models specially adapted to a certain energy conversion system, is overcome by the technology-independent abstract model according to the invention.

The abstract model does not define the operation of the energy conversion system, but is used by an energy management system to determine optimized operation. The abstract model defines the limits within which an optimized operation must move through the energy management system so that it can be fulfilled. Using sensors, measurement data are recorded within the energy management system and transmitted to the control and regulation unit. On the basis of this measurement data, a prognosis of an expected energy demand is made.

This energy requirement is mapped in connection with the storage potential (e.g. capacity of an electrical, thermal, chemical storage) in its time course in a so-called energy band, which has a lower energetic limit curve and an upper energetic limit curve. The energy conversion system is controlled within these energetic limit curves.

It is also planned that the abstract models of several energy conversion systems are combined in an abstract model of several energy conversion systems by means of an aggregation and that the lower energetic limit curve and the upper energetic limit curve are determined on the basis of the abstract model of several energy conversion systems. For example, in the case of an aggregation of static parameters of several abstract models, the scope of the parameters does not change in an abstract and aggregated model that is then formed, since these are uniform and independent of the type of energy conversion system. Only the values of the parameters are different after aggregation. This leads to an inventive standardization and unification of the method for modeling one or more energy conversion systems in an energy management system.

It is also envisaged that the abstract model is generated taking common energy requirements into account. Such common energy needs exist when two or more energy conversion systems jointly cover the needs of a consumer. For example, if a photovoltaic system and a combined heat and power system cover the needs of an electrical consumer together, there is the possibility that the photovoltaic system can cover a greater or all of the demand during the day, while the photovoltaic system does not switch on at night is involved in meeting the needs of the consumer.

The foregoing features and advantages of this invention are not after carefully reading the following detailed description of those preferred herein restrictive example embodiments of the invention with the associated drawings to better understand and evaluate, which show:

Fig. 1: an exemplary energy management system with various components,

Fig. 2: an abstract and aggregated model with further functional interrelationships in a schematic diagram,

Fig. 3: an abstract system model of an energy conversion system,

Fig. 4: a representation of basic procedural relationships in an aggregation,

5: an exemplary energy management system using the example of a house with various components,

6: a schematic diagram of an energy conversion system for an electric vehicle,

Fig. 7: a schematic diagram of an energy conversion system Batteriespei cher,

Fig. 8: a representation of performance forecasts of an energy conversion system electric vehicle,

Fig. 9: an illustration of charging and discharging processes of an electric vehicle,

10: an illustration of a forecast mobility requirement of an electric vehicle,

11: a representation of a forecasted charging power of an electric vehicle,

12: an illustration of energetic limit curves generated according to the method,

13: an illustration of energetic limit curves generated according to the method in an overview with possible charging areas of an electric vehicle,

14: an illustration of energetic limit curves generated and optimized according to the method,

15: a representation of the result of the optimization of the energetic

Limit curves,

Fig. 16: a representation of energetic limit curves for a Batteriespei cher, 17: an illustration of power limit curves of a predicted power for a battery storage device,

Fig. 18: an illustration of energetic limit curves of two energy conversion systems with resulting aggregated energetic limit curves,

19: a further representation of energetic limit curves of two energy conversion systems with resulting aggregated energetic limit curves,

20: an illustration of a demand and generation forecast with aggregated power limit curves and

21: a representation of aggregated power limit curves adapted according to the method.

An exemplary energy management system 1 with various components is shown in FIG.

The energy management system 1 comprises one or more energy conversion systems 2. Such energy conversion systems 2 can be, for example, a combined heat and power plant, a heat pump, a fuel cell, a heating element, an electric vehicle or a photovoltaic system. It is provided here that the energy conversion systems 2, which can generate or consume energy, can be connected to a supply network 3 or to a plurality of supply networks 3.

Furthermore, there are energy requirements 4 or, for short, requirements 4, which can occur in the power supply, heating, cooling or mobility sectors.

So-called storage units 5 or energy storage units 5, which can be designed as thermal, chemical or electrical storage units 5, for example, are also provided. The energy management system 1 also includes sensors 6 or counters, wel che at various relevant energetic interfaces, detect parameters or parameters of energy conversion systems 2, supply networks 3, requirements 4 and / or energy stores 5.

Furthermore, a local control and regulation unit 7 is provided, which is assigned to individual or to a group of energy conversion systems 2 and takes over the control or regulation thereof. Such a control or regulation takes place, for example, by means of corresponding control commands 8 generated by the control and regulation unit 7, which in the example of FIG. 1 are transmitted to an energy conversion system 2. In addition, the control and regulation unit 7 receives and processes measurement data 9 from the sensors 6 or counters 6.

These described components are included in what is known as a local energy management system 1a.

In the case of a higher-level energy management system 1 b, the energy management system 1 is supplemented by a component of the energy control center 10, as shown in the upper part of FIG.

For such a supplement or expansion of a local energy management system 1 a with a higher-level energy management system 1 b, the higher-level energy management system 1 b is equipped with an interface 11.

The higher-level energy management system 1 b can be coupled via this interface 11 directly or via a gateway 12, which can also be referred to as a connection unit, to the local energy management system 1 a to form an energy management system 1 containing both components. Such a gateway 12 or a connection unit is usually regarded as a component which establishes a connection such as a data connection between two systems. In the example in FIG. 1, the gateway 12 establishes a data connection between the energy control center 10 and the control and regulation unit 7. The energy flows 13 are shown by means of respective dash-dash lines.

It can be provided that, for example, the control and regulating unit 7 data of an external forecast 14 are transmitted. Such data of an external forecast 14 include information on, for example, the temperature or global radiation at the location of the local energy management system 1a over a forecast period of, for example, one day in a time-resolved form of, for example, 15 minutes.

In a local energy management system 1 a, one or more energy conversion systems 2 are each assigned a control and regulation unit 7 on which the required sub-functionalities of the energy management system 1 a are implemented. This control and regulation unit 7 has at least one computing unit and units for storing data. Such data can, for example, also be stored in a database system.

The local energy management system 1a optimizes the operation of all connected energy conversion systems 2 according to various aspects, such as economic viability, energy efficiency or others, taking into account the energy demand 4. Such an energy demand 4 is either known or can be used in such a system on the Control and regulation unit 7 can be predicted in a suitable manner.

Demand forecasts 29 of this type can be made, for example, on the basis of the measurement data 9 obtained in the energy management system 1, which are recorded by means of various sensors 6 or counters 6. Furthermore, external forecasts 14 such as weather data can be used for such demand forecasts 29, which can be received, for example, via the Internet 15 via radio network or DSL connection or an energy control center 10.

To connect the sensors 6 or counters 6, the energy management system 1 has an associated control and regulation unit 7 with corresponding interfaces for connecting the transmitted measurement data 9. The control and regulation unit 7 is furthermore equipped with corresponding interfaces for connection equipped with one or more energy conversion systems 2. In the case of a superordinate energy management system 1b, the control and regulation unit 7 also has an interface to the superordinate energy management point 10. For the exchange of data between the control and regulation unit 7 and the superordinate energy management system 1b, a connection unit 12 such as a gateway can be used are provided. Alternatively, the functionality of the connection unit 12 can also be integrated into the control and regulation unit 7.

In the case of a higher-level energy management system 1 b, the higher-level energy management system 1 b coordinates several subordinate local energy management systems 1 a, which in turn can consist of one or more energy conversion systems 2 and an associated control and regulation unit 7.

The coordination of such an energy management system 1, comprising a local and a superordinate energy management system 1a, 1b, takes place on the basis of various target variables, such as economic viability, energy efficiency, peak load capping and others. The function of the energy management system 1 is based on the recorded measurement data 9, which are forwarded to the higher-level energy management system 1b by the connection unit 12 (gateway) or the control and regulation unit 7 with connection unit functionality.

Information on the coordination of the individual energy conversion systems 2 is transmitted from the energy control center 10 of the higher-level energy management system 1b to the control and regulation unit 7, which transfers it to the subordinate energy conversion systems 2. In the simplest case, this information for coordination can be direct control commands such as switching on (ON) or switching off (OFF) of a target output specification (Psoii) for the corresponding energy conversion system 2.

For the functioning of such an energy management system 1, at least one model representing the energy management system 1 is stored. Several models with different scenarios can also be stored. The energy management system 1 can use such a model to plan the use of the energy conversion systems 2. Alternatively, the operation of the energy management system 1 can be monitored by means of the control and regulation units 7 in real-time operation.

Such models must therefore contain all relevant properties of the energy conversion systems 2.

This includes data such as the generation output or consumption of each energy conversion system 2 at all energy interfaces of the respective system or, for example, start-up and shutdown time constants of an energy conversion system 2 or power noise.

In addition, within the energy management system 1, relevant parameters for the energetic shift potential, for the demand forecasts 29 and for the current system status must be available. The energetic shift potential indicates how long an energy conversion system 2 can be switched off without violating restrictions such as a heat requirement or how long an energy conversion system 2 can remain switched on until the storage or energy storage 5 is filled to the maximum.

The sub-functionalities of the energy management system 1, which are usually developed application-specific, usually combine a static model with unchangeable information or parameters about at least one energy conversion system 2 to be controlled with the additional information required, such as forecasts based on external forecasts 14 or more recent technical and / or energetic system state of the energy conversion system 2. Thus, an application planning, that is to say a prognosis of the future, desired operation of the controllable energy conversion systems 2 can be undertaken.

The partial functionalities that an energy management system 1 must have can be broken down into operational planning, control / regulation and operational monitoring. For the implementation of an application, for example reducing the peak zenload in an electrical supply network 3, the logical links resulting from the application must be taken into account and are therefore mapped in the model. A logical connection is how, for example, a specific energy conversion system 2, for example an electric vehicle 42, can contribute to reducing the peak load in an electrical supply network 3, taking into account the availability of the electric vehicle 42 and at the same time ensuring that the Demand 4 for mobility is always sufficient energy in the storage of the electric vehicle 42 is available.

For the forecasts, demand forecasts 29 for energy demand 4 as well as generation forecasts 39 for feeding in non-controllable energy conversion systems 2 are used as parameters for the energy management system 1. Such a non-controllable energy conversion system 2 is, for example, a photovoltaic system.

In addition, the energy management system 1 uses the models to implement the planned operation for the energy conversion systems 2 in real time. Here at must be controlled or regulated and monitored. Failures and / or deviations must be recognized promptly and compensated for.

Energy management systems according to the known prior art are based on the fact that the models of the energy conversion systems 2 are configured manually as individual models with their static parameters. Each energy conversion system 2 is always described using its own manufacturer or technology-specific model.

The energy management system with its sub-functionalities of deployment planning, control / regulation and operational monitoring must therefore be specifically adapted to the respective application, i.e. the various manufacturer-specific or technology-specific models must be integrated into the process. At the same time, different goals for the operation or limits that restrict the operation of the energy conversion systems 2 must be taken into account separately in the energy management system and in the procedures for deployment planning, control / regulation and operational monitoring. Examples of operating limits of such systems are, for example:

• The heat demand if two energy conversion systems 2 are involved in the provision of heat

• the availability of an electric vehicle 42 (electric vehicle is connected to the socket)

• the storage charge status of a thermal or electrical storage unit 5

• maximum possible or minimum necessary feed-in into electrical supply networks 3

Implementing an energy management system according to the state of the art becomes difficult, in particular when mapping several energy conversion systems 2 that are logically related.

For example, two or more energy conversion systems 2, which cover a common energy requirement 4 or are connected to a control and regulation unit 7 and are functionally related, are in a logical connection or a logical connection. Such a functional relationship exists, for example, in a system consisting of two combined heat and power plants 2, which together cover the thermal requirements 4 in a building.

The fixed, logical integration of manufacturer or technology-specific models and limits in the procedures for deployment planning, control / regulation and operational monitoring makes the development of an energy management system more difficult. What is needed is an abstraction level upstream of the energy management system, through which various energy conversion systems 2 and various limits can be covered and which standardize and unify the application in the energy management system. The invention is based on the fact that all information that an energy management system 1 needs for the individual sub-functionalities is mapped with an abstract and aggregated model 16. This model 16 is shown in FIG. 2 with further functional relationships.

An energy management system 1 is superordinate to model 16 with its partial functionalities such as deployment planning with regard to the available resources of energy conversion systems 2, energy storage 5 and energy requirements 4. Further partial functionalities are the control and / or regulation as well as the operational monitoring of the energy conversion systems 2 belonging to the energy management system 1 .

Such an abstract, aggregated model 16 describes the combination of several individual abstract models 26 each of a real energy conversion system 2. It thus represents a model 16 of an overall system in an energy management system 1 and includes all associated subordinate or subordinate systems or systems. Components such as energy conversion systems 2. By abstracting both individual models and the overall system (several systems), the partial functionalities of the energy management system 1 can be described independently of the actual type of energy conversion system 2. Here, an energy conversion system 2 can be both a fuel cell and a heat pump or another system.

In addition, a further advantage of this abstraction can be seen in the fact that the implementation of an energy management system 1 is independent of the amount of energy conversion systems 2 in the overall system, since it allows the aggregation of a wide variety of system types. The simple mapping of several energy conversion systems 2 with their stores and / or requirements, which are logically related, that is, cover a common requirement or are assigned to a control and regulation unit 7 and are functionally related, is thus possible. Implementation of energy management systems 1 is significantly simplified in this way. The abstract model 16 is part of the energy management system 1, but upstream of the individual sub-functionalities. It is implemented on the control and regulation unit 7 in a local energy management system 1 a or in a higher-level energy management system 1 b. In the case of a superordinate energy management system 1 b, implementation in the energy control point 10 is also possible.

As shown in FIG. 2, the abstract model 16 consists of static parameters 17 and dynamic parameters 18.

It is provided that the abstract model 16 is used on two levels or that the abstract model 16 extends over two levels. On the one hand on the second level 23 of the individual energy conversion systems 2, on the other hand on a superimposed first level 22.

FIG. 2 also shows the process of disaggregation 21, in which control commands 25 for controlling the individual energy conversion systems 2 are generated from target commands 24 and data from the available model 16.

From each energy conversion system 2, which is assigned to a control and regulating unit 7, the static parameters 17a for each energy conversion system 2 are first configured once in a general model 26a that depicts this energy conversion system 2. An energy conversion system 2a, an energy store 5a and an energy requirement 4a to be covered can be assigned to such a model 26a, as is shown in FIG. The static parameters 17a thus include the parameters of the energy conversion system 2a, the energy storage device 5a and the energy demand 4a to be covered.

The parameters 17 and 18 are uniform and independent of the type of energy conversion system 2. The static parameters 17a, 17b, ..., 17n of several abstract models at the level of an individual system are then fed to an aggregation 19, as shown in FIG Figures 2 and 4 is shown. For this purpose, a suitable reference system 38 is first established, for example the primary, most important energy conversion system 2. This reference system 38 is used in all aggregation steps 19 and disaggregation steps 21 up to the higher-level abstract model. The aggregation 19, ie the linking of models at the system level, takes place on the basis of mathematical algorithms which are dependent on the respective static parameters to be aggregated. In this case, joint energy requirements 4 that must be covered by the systems and the energy interfaces must also be taken into account.

Various auxiliary variables 20 are then derived using mathematical methods and aggregated again, as is shown by way of example in FIG. These auxiliary variables 20 are used in combination with the dynamic parameters 18 of the system models for deployment planning by the energy management system 1. The result is the static parameters 17 of a group of systems that contain the same parameters as the model of a single system and are expanded by the auxiliary variables.

In addition to the static parameters 17, the dynamic parameters 18 have a special rank. The dynamic parameters 18 essentially serve the energy management system 1 for deployment planning. These are continuously changing values. They describe the energetic limits within which a system or a combination of systems can be controlled. For an energy conversion system 2, this is indicated by two energetic limit curves 27,

28 defined. Within these energetic limit curves 27, 28 it is possible through the energy management system 1 to organize an operation for the system or systems without energy requirements 4 not being met or restrictions for free system operation being ignored. For several energy conversion systems 2, so-called aggregated limit curves 59, 60 are used accordingly.

Restrictions for free plant operation can be:

Schedules (operation of the system at the desired times desired), Blocking times (system operation prohibited due to e.g. noise emissions),

• Availability times (electric vehicle is connected to the power grid),

• Maximum possible and minimum necessary feed into the electrical network or

• Storage states of charge of electrical and thermal storage systems

The first and the second energetic limit curve 27 and 28 thus describe the future energetic so-called shift potential, which is given by an energy conversion system 2. The dynamic parameters 18 are supplemented by a so-called control potential, which describes the controllability of the energy conversion system 2 with regard to its performance.

The application of dynamic parameters 18 or 18a, 18b, ..., 18n takes place on two levels. On the one hand at the level of the model 26 or 26a, 26b,..., 26n of an individual energy conversion system 2, as shown in FIG.

These dynamic parameters 18 or 18a, 18b,..., 18n are determined based on demand forecasts 29, which are to be predicted for the energy demands 4 to be covered by the respective energy conversion system 2. The demand forecasts 29 are created on the basis of external forecasts 14, such as weather forecasts and historical measured values 31, which have been recorded and temporarily stored by the connection unit 12 (gateway).

The demand forecasts 29 are processed in a processing step 63 using mathematical algorithms 33 taking into account the restrictions 32 for the free operation of the energy conversion system 2 and the static parameters 17 as well as by means of measurement data 9.

In addition, a so-called control potential, which defines so-called power limit curves 52 and 53 or energetic limit curves 27 and 28, within which regulation can take place, is created for the energy conversion system 2. Therefor the static parameters 17 and the restrictions 32 for the free operation of the energy conversion system 2 are used. These restrictions 32 can be generated by means of a configuration 35, for example.

In this way, the first and the second energetic limit curves 27 and 28 as well as the control potential or the power limit curves 52 and 53 for the operation of an individual energy conversion system 2 are provided.

It is also intended that first optimizations 34 can be carried out directly on the energetic limit curves 27 and 28. Such a first optimization 34 can relate, for example, to desired times of the operation of an energy conversion system 2, the energetically efficient operation of the energy conversion system 2 or also a storage charge management e.g. of a storage unit 5 connected to the energy conversion system 2. For example, it could be provided that an energy conversion system 2 should not be operated at night.

As a result of such a non-mandatory first optimization 34, the dynamic parameters 18a of a first energy conversion system 2 are provided, for example, as shown in FIG.

The dynamic parameters 18 or 18a, 18b,..., 18n are created for each individual energy conversion system 2. One advantage here is that these parameters are based on mathematical methods or a mathematical algorithm 33 in one step of the aggregation 19 can be provided. This step of the aggregation 19 is shown in FIG. 4 both for static parameters 17 and for dynamic parameters 18.

In the case of the aggregation 19, however, it must be taken into account in particular if ver different energy conversion systems 2 jointly cover an energy requirement 4 or jointly cover several energy requirements 4. Therefore, in the aggregation 19, the energetic interfaces 36 or common requirements 37 are taken into account.

The reference system 38 is also used again. In aggregation 19, auxiliary variables 20 are derived again. The dynamic parameters 18 can then be supplemented by second optimizations 64. In contrast to the second level 23 of an individual energy conversion system 2, comprehensive optimizations between different energy conversion systems 2 and taking into account non-controllable energy requirements 4 and / or energy generation 39 are of particular interest. Just as the energy requirements 4 can be calculated in advance by means of a demand forecast 29, the energy generation 39 can be calculated using a generation forecast 39.

In a further step, it becomes possible to modify the limits in terms of the upstream second optimizations 64 of the operation of the energy conversion systems 2. Second optimizations 64 can be, for example, the energy efficiency of an overall system or the optimization of internal requirements, i.e. the maximum use of the generation by energy conversion systems 2 for requirements and avoidance of feeding into supply networks 3. This is particularly relevant in higher-level energy management systems 1b to various to take local targets into account in operational planning. For this purpose, desired operating times can be determined by an optimization algorithm, whereby the dynamic parameters 18a, 18b, ..., 18n are modified. This leads to the limitation of the energetic limit curves 27 and 28 and thus a limitation of the usable energetic shift potential. For operational planning, however, this means that various goals can be met in parallel. This increases the profitability of a higher-level energy management system 1 b.

All the mathematical methods used are designed to be reversible, so that the disaggregation 21 in FIG. 2 is made possible, in which the control commands 25 for controlling the individual energy conversion systems 2 are generated. In particular, the static and dynamic parameters 17 and 18 of the energy conversion systems 2 are used here. The derived auxiliary variables 20 are particularly relevant, since only they make it possible to compensate for the loss of information that occurred during the data aggregation 19 and to implement a reliable disaggregation 21. When a schedule or schedule is determined by the deployment planning, this is translated into a schedule or schedule for each individual energy conversion system 2 using the abstract model 16. This has the advantage that not every energy conversion system 2 has to be integrated into the energy management system 1 for communication purposes, but the integration of the aggregated model 16 (system) is sufficient. The processing of all limits in the models of the energy conversion system 2 makes it possible to achieve a reliable distribution. Such limits can be, for example, requirements 4 to be covered (heat requirements), availability of an energy conversion system 2, time schedules, maximum possible or minimum necessary feed into the supply network 3 or storage charge states.

The abstract model 16 enables a uniform interface to the sub-functionalities of the energy management system 1 such as deployment planning, control, regulation to be generated, after which these do not have to be adapted to the application.

Another advantage is that the dynamic parameters 18, 18a, 18b, ..., 18n over different levels with different limits, such as schedules, blocking times, availability times or requirements, or optimization goals such as optimizations with regard to the energy efficiency of an individual energy wall treatment system 2, with regard to the energy efficiency of several energy conversion systems 2 or an optimization of internal requirements, can be processed. Thus, the abstraction also allows restrictions in the electrical networks to be easily taken into account without further increasing the complexity of the energy management system 1. For example, the maximum allowable feed-in by the energy conversion system 2 and the network of energy conversion systems 2 can be taken into account as a function of the predicted feed-in behavior of photovoltaic systems in a local network.

The dynamic parameters 18, 18a, 18b, ..., 18n are of particular relevance in the method, since this enables the energy management system 1 to deliver valid solutions regardless of the procedure, which system parameters Meter 17 and 18, energy requirement 4, restrictions 32 for local system operation and local optimizations. With the optimization in the sense of the energy management system 1, parallel use cases can be fulfilled, ie various optimization goals can be implemented without great effort.

The use of the abstract model 16 is not limited to a control and regulation unit 7. It can also be used in the energy control center of a superordinate energy management system 1b.

The energy management system 1 shown in FIG. 5 is used to describe the method for controlling an energy management system in an exemplary embodiment.

In this energy management system 1 there is an electrical general consumption 40, a photovoltaic system 41 (PV system), an energy storage device 5 in the form of a battery storage device and an electric vehicle 42 which is connected to the charging box 43 via a connection socket 45 and charged can be.

The battery storage 5 and the charging box 43 are controllable systems, that is to say the charging or discharging can be specified by the control and regulation unit 7 of the energy management system 1 by means of control commands 25. The PV system 41 is viewed as a non-controllable system, ie the provision of power cannot or should not be influenced by the control and regulation unit 7. The control and regulation unit 7 records the energy flows 13 for both energy conversion systems 2, i.e. the electrical consumer 40 and the charging box 43, as well as the PV system 41 via the sensors 6a, 6b, 6c and 6d, which can also be part of the energy management system 1 . The first sensor 6a determines the power PSP from or to the energy store 5. The second sensor 6b determines the power PPV of the PV system 41, while the third sensor 6c determines the power PE-FZG to the charging box 43 or to the electric vehicle 42. In addition, a sensor 6d is provided for detecting the power PHaus to the house or from the house, the energy management system 1 being on in this example System for a house with its components 40, 41, 42 and 5 is. The sensor 6d is, for example, the electrical energy meter or main meter in the house, which is connected to the control and regulation unit 7 and which is relevant to accounting. The sensor 6d or main meter in the house is connected to a general supply network 44. The illustration in FIG. 5 shows that the sensors 6a to 6d transmit their measurement data 9 to the control and regulation unit 7 and the control and regulation unit 7 can send control commands 25 to the energy store 5 and the charging box 43.

The structure for the unit charging box 43 and electric vehicle 42 is shown in greater detail in FIG. In this case, the controllable system is the charging box 43, which is controlled by means of the control commands 25 transmitted by the control and regulation unit 7. Alternatively, there could also be a possibility of activating and deactivating the charging process via the electric vehicle 42 itself.

In the context of the exemplary method, the battery 46 with its capacity is to be understood as the energy storage device in the electric vehicle 42. The need is the mobility of the electric vehicle 42, which, however, cannot be recorded for the energy management 1. The requirement thus arises from the necessary energy that has to be recharged when the electric vehicle 42 is connected to the charging box 43, for example via the connection socket 45. FIG. 6 also shows a battery inverter 47, which is directly connected to the rechargeable battery 46, as well as the motor 48 of the electric vehicle 42.

The structure for the battery storage 5 is shown in FIG. The Batteriespei cher 5 is special in structure because it has no need. In the context of the description of the method, the battery storage 5 is to be understood as a combination of a controllable generator 49, a second accumulator 50 and a controllable consumer 51.

Within this system, there are two individual systems, which are characterized by energy conversion systems 2, storage capacities and 4 energy requirements. An energy conversion system 2 in this context means that two forms of energy are coupled to one another using an internal process. One of the two forms of energy (can also be identical) is to be regarded as the primary form of energy (form of energy 1). This essentially serves as a variable for marketing / optimization in the energy management system 1.

The other form of energy, on whose side the energy demand 4 associated with the energy conversion system 2 is usually located, is to be seen as a restriction for the operation of the system (form of energy 2). Optimization is only subordinate here.

Below are a few examples to illustrate the relationships between the two forms of energy in an energy conversion system 2:

Example charging box for an electric vehicle:

• Energy form 1: electrical energy (on the side of the electrical supply network 3)

• Energy form 2: Electric energy (on the side of the electric vehicle 42) Example of battery storage:

• Energy form 2: electrical energy (on the battery 46 side)

Example of a combined heat and power plant:

• Energy form 1: electrical energy

• Energy form 2: thermal energy

Example heat pump:

Energy form 1: electrical energy Energy form 2: thermal energy

The following is an exemplary description of the process for creating the abstract, aggregated system model. On the basis of the static parameters 17 of the abstract model 26 of an energy conversion system 2, forecasts of requirements 29 and restrictions for the system operation 32, the procedural steps are first of all

Processing 63 and first optimization 34 for the electric vehicle 42 (E-FZG) for the abstract system model 26 of an individual energy conversion system 2 and then the method steps

Aggregation 19 and 19a, derivation of auxiliary variables 20 and 20a and the second optimizations 64 for the abstract, aggregated system model 16 of all associated energy conversion systems 2 are explained. The formation of the abstract system model 16 of an energy conversion system 2 should take place using the example of the electric vehicle 42. The main output variables are the system parameters, i.e. the static parameters 17 and the dynamic parameters 18. For an electric vehicle 42, these can be, for example:

• Power levels that can be set for charging the electric vehicle 42

• Storage capacity of the battery 46

• Power curve for a typical charging process

Battery losses 46 In the following process step, the static parameters 17 of the energy conversion system 2, in this case the electric vehicle 42, are formed using this information. Some of the static parameters 17 are shown below by way of example and their meaning is explained using the example of the electric vehicle 42:

• System performance levels P _{LS An |} : o Corresponds to the performance levels of the system on the side of primary energy form 1 (electrical energy)

° ^ LS Anl = [^ E-FZG LS0 <<^ E-FZG LSmax] ^ E-FZG LSmax> ^ E-FZG LSO> 0

• Power control mode Mr _Ahί o Corresponds to the power control mode (discrete control, modulating power control) o Here: discrete

• Power state OFF P _{off Ani} o Power of the system in the off state

° ^ from Anl = -PE-FZG LSO

• Switch-off time constant: TAbsAni o Corresponds to a period of time for the switch-off of a system (important for control) o Taken from the performance curve for the electric vehicle o E.g. TAbsAnF 30 min

• Energy conversion index: o corresponds to a universal conversion code between the power in energy form 1 and the power in energy form 2 in depen dependence of the power levels o Since, in the electric vehicle 42, the primary and secondary energy form is identical, the factor takes into account only the losses of the Encrypt averaging unit and is approximately a _{to | «} [1, ... 1] • Storage capacity: üsp _k ap _Ani EFI _. - ^a sp _k ap _Ani EF2 or üsp _k ap _Ani EFI - ^a

(? Sp kap Anl EF 2 o The storage capacity corresponds to a storable energy in the storage, which is assigned to the energy conversion system and is based on the energy conversion number on the primary energy form 1. o For the electric vehicle: £ _S p _k ap E -FZG = 1 S _{P k} ap A _kk u

• Power _noise s _RAh1 o The power _noise corresponds to a percentage value that describes how great the undesired fluctuation in power is during operation. The value is particularly necessary for the regulation. o As an example, let s _R = 0 be assumed here.

The dynamic parameters 18 of an energy conversion system 2, in this case the electric vehicle 42, essentially consist of:

• Lower and upper power limit curves 52 and 53

• Lower and upper energetic limit curves 27 and 28

First, the creation of the lower power limit curve 52 Pprog min and the upper power limit curve 53 Pprog max is described. These describe what power an energy conversion system 2 can be specified at a point in time. For this purpose, the static parameters 17 of the energy conversion system 2 and the restrictions 32 for the system operation are used. The first-mentioned essentially includes the Pi_s Ani performance levels. In terms of the electric vehicle 42, the possible charging periods, ie the periods of time in which the electric vehicle 42 is connected to the charging box 43, should be mentioned as a restriction 32 for the plant operation. The information is combined during processing. During the charging periods, the power limit curves 52 and 53 result from the minimum and maximum power level. Outside this time, Pprog max = Pprog min = PE-FZG LSO The power limit curves 52 and 53 are shown in FIG. FIG. 8 shows a predicted power P 54 over time. The areas B2, B3 and B4 shown represent time periods in which the electric vehicle 42 is connected to the charging box 43 via the connection socket 45 and can be charged. The dash-dot line represents the lower power limit curve 52 Pprog min, while the dash-dash line shows the upper power limit curve 53 Pprog max. In area B3, for example, it is possible to charge the electric vehicle 42 between 6 p.m. and 6 a.m. the following day.

In addition to the power limit curves 52 and 53, the lower energetic limit curve 27 Eprog min and the upper energetic limit curve 28 Eprog max are created.The starting point for the creation of the dynamic parameters 18 is the creation of a demand forecast for a period in the future and for which the energy management system is to carry out an optimization with regard to various possible optimization goals. The demand prognosis 29 is created for each energy conversion system 2 with an energy demand 4 and indicates the future demand over time. It is created from the measured values on demand / storage behavior or from derived variables.

In the case of an electric vehicle 42, the prognosis can be obtained from the power curve of the charging process and the storage states, as is shown by way of example in FIG. FIG. 9 shows the course of the storage charge state 58 Esp act over time. The actual charging power Pist is shown in the direction of the left Y-axis in FIG. For example, in a time range between 6 p.m. and 6 a.m. the following day, a reload dependent on the current charge of the battery 46 of the electric vehicle 42 takes place. The charging time is dependent on the energy consumption from the battery 46 between 6 a.m. and 6 p.m., this being linearly interpolated.

It is important to note that the recharging of an electric vehicle 42 does not correspond to the actual need (need = “driving the electric vehicle”). This means that the demand must be derived from the power curve of the charging process or the storage charge states in order to be able to carry out a forecast. For the derivation, the absence times in which the Electric vehicle 42 is not connected to charging box 43, and the assumption of a medium power is used. A forecast mobility requirement 55 that arises in this way is shown in FIG. 10 in its time course. In the direction of the Y axis of FIG. 10, the forecast mobility requirement Pprog is shown.

The following methods can be used for the prognosis:

• Artificial Neural Networks,

• The naive prognosis method,

• Regression method

Based on the demand forecast 29, processing is carried out using mathematical algorithms. For this purpose, the cumulative sum 56 Esum prog for the forecasted charging capacity (mobility requirement) is first formed.

For the following considerations, it is assumed that the forecasted performance curves are available discretely with a time interval of, for example, At = 15 min. The performances thus correspond to mean values over the period At. So with applies

E sum prog for all ie [0 ... t], where i ... corresponds to a counting index for the performance values.

The result is shown in FIG. FIG. 11 shows the course of the forecast charging power 56 in its course over time. In a next step, using the static parameters 17 or system parameters by means of the storage capacity Es _P ka _P , the storage state of charge at the time Es _P (t _a w) and the lower and upper power limit curves 52 and 53, the lower and upper energetic limit curves are ven 27 and 28 determined (Eprog min, E _P rog max). The aim here is also to rule out impermissible conditions. The points mentioned above can be described in terms of a formula for the maximum energy band of energy conversion system 2 as follows:

FIG. 12 shows the time course of the charging power Esum prog 56, which runs between the lower energetic limit curve 27 Eprog min and the upper energetic limit curve 28 Eprog max. In the direction of the Y axis in FIG. 9, the cumulative sum 57 Esum is shown.

Four areas B1, B2, B3 and B4 are shown in FIG. B1 represents an area with an impermissible state. B2, B3 and B4 are areas in which charging is possible, so-called possible charging periods. All 4 areas are taken into account in that the lower and upper power limit curves 52 and 53 are taken into account.

Further processing or optimization may be necessary, for example, if it is required that the battery 46 of the electric vehicle 42 must be charged to at least 50% at the end of the charging cycle. This requirement in connection with the possible charging periods has a direct influence on the lower limit. After such an optimization of the lower energetic limit curve 27, a processed lower energetic limit curve 27a is generated.

The lower energetic limit curve 27 is processed on the basis of the restrictions 32 for free system operation.

The processing of the lower energetic limit curve can be seen in FIG. This processing must be carried out taking into account the static parameters 17 or the system parameters. The numbers 1 to 7 in brackets below correspond to the numbers shown in FIG.

(1) Negative values for the cumulative energetic limit curve are not permitted due to positive values of the power levels. (2), (3) It is required that a storage capacity of 50% must be reached at the end of the possible charging period. This requires that the lower limit curve intersect this point (3). The rise in the lower limit curve (2) must correspond to the maximum output of the system.

(4), (5) At the end of the possible charging period B3, a storage charge state must be sufficient so that the subsequent, forecast demand can be met. In the case of the electric vehicle, this means that the minimum storage charge state is present at the beginning of the next recharging period B4. In order to achieve the necessary storage charge status in B3, charging must be carried out in good time. The increase (4) in turn corresponds to the maximum output of the system.

(6), (7) analogous to (2), (3), corresponds to the requirement for a storage battery charge of 50% at the end of a possible recharging period.

In the next step, system optimizations can be taken into account. This could, for example, be the requirement that charging always takes place at the end of the possible reloading period. However, such a system optimization is not necessary for the electric vehicle 42 due to the low standstill losses.

Taking into account all the processing steps described, the lower and upper energetic limit curves 27a and 28a are finally obtained, as shown in FIG.

The formation of an aggregated, abstract system model 16 is described below using an example of a battery store 5 known from FIG.

The following are the static and dynamic parameters of the battery storage model.

Static parameters 17

• Performance levels of the system P \ _s An \ -

° ^ LS Anl = [PBSP LS0 <<^ BSP LSmax] ^ BSP LS0 <0, Rb SP LSmax> 0

• Power control mode M _PAnl o discreet

• Power state OFF P _{off Ani} o Power of the system in the off state

° Paus Anl ⁼ 0VK

• Switch-off time constant: TAbsAni o TAbs Ani = 0 min

• Energy conversion index: oa _An * [1,

• Storage capacity: Psp _k ap _Ani EFI. = ^a sp _k ap _Ani EF2 or Psp _k ap _Ani EFI = ^a

Q Sp cap Anl EF 2

° ^ SP cap BSP ⁼ 1 ^' ^ Sp cap battery

• Power _noise s _RAh1 = 0

Dynamic parameters 18

The individual system of the battery storage 5 has no need to cover the secondary form of energy. Therefore, the rise in the lower and upper energetic limit curve 27 and 28 is zero. Since the battery storage 5 can also assume negative power values, in the case of a feed, the result with the lower and upper energetic limit curves 27 and 28 is an axis-parallel band to the abscissa, as shown in FIG. The lower power limit curve 52 associated with this battery storage device 5 and the upper power limit curve 53 are shown in FIG.

The static parameters 17 and 17a of the energy conversion systems 2, that is to say the battery storage unit 5 and the electric vehicle 42, are then superimposed.

The static parameters 17 and 17a of the two energy conversion systems 2 must be superimposed in the next step. The following procedures / measures are used for this purpose:

• mathematical algorithms,

• Knowledge of requirements that can be met equally by both individual systems,

Definition of a reference system In the example shown, the two energy conversion systems 2 have no jointly covered needs. An example for this case would be, for example, if two CHP systems covered the heating demand of a building via the same heating circuit.

Since there are no common requirements in the example, no reference system needs to be defined. A reference system is only necessary if two or more energy conversion systems 2 jointly cover an energy demand 4. A reference system must then be specified for the aggregation 19, which is usually the prioritized system and is primarily used.

The individual static parameters 17 and 17a can be superimposed, for example, using the algorithms mentioned below:

• Power levels P _LS1 : o Superimposition depending on the power control mode of the systems: o For example, with discrete power levels: aggregation by forming all possible combinations without repetition, addition of values and ascending sorting o Possible further processing is, among other things, the reduction of power levels

• Power control mode M _P o This results from the method of aggregating the power levels o Eg when discrete power levels are superimposed -> discrete, as well

• Power state OFF P _off o Addition of the power in the OFF state of all individual systems

• Switch-off time constant: TAbs o Switch-off time constant results from the maximum of all switch-off time constants

• Energy ^{conversion index} : a = [ ^{? EF 1 LS 0} , ^{Pef 1 LS max} ]

^P EF 2 LS 0 ^P EF 2 LS max o Superimposition depending on the power control mode of the systems o For example, with discrete power levels: aggregation by forming all possible combinations without repetition, multiplication of values and Sorting according to the assignments to the aggregated performance levels

• Storage capacity: E _{SP kap} o Addition of the storage capacities of all individual systems

• Power noise s _R = 0 o Addition of the power noise of all individual systems

Following the superposition of the static parameters 17 and 17a, an auxiliary variable 20 is derived. This auxiliary variable 20 is a performance vector, which results from the performance levels, the energy conversion indicators, the knowledge of joint needs 37 to be covered and the specified reference system. It is shown below as P _LS and supplements the static parameters 17.

In addition to the static parameters 17 and 17a of all energy conversion systems 2, the dynamic parameters 18 and 18a of the two energy conversion systems 2 must also be aggregated. First, the lower and upper limit curves 27, 27 ^' and 28, 28 ^{' of} the two energy conversion systems 2 are added. For this purpose, analogously to the aggregation 19 of the static parameters 17 and 17a, the knowledge of the energy requirements 37 to be covered jointly and the reference system must also be considered in the aggregation 19. The result of the aggregation 19 is shown in FIGS. 18 and 19.

FIG. 18 shows in a common representation the lower and upper energetic limit curves 27 and 28 for the electric vehicle 42 as well as the lower and upper energetic limit curves 27 ^' and 28 ^' for the battery storage 5. The result of the processing according to the method results in the aggregated lower energetic limit curve 59 and the aggregated upper energetic limit curve 60, as shown in FIG. The Y-axis corresponds to a cumulative sum.

19 shows a common representation of the lower and upper power limit curves 52 and 53 for the electric vehicle 42 and the lower and upper power limit curves 52 ^' and 53 ^' for the battery storage device 5. The result of the processing according to the method is the aggregated lower one Power limit curve 65 and the aggregated upper limit curve 66, as shown in FIG. The Y-axis shows the predicted power P. In the example in FIG. 19, the aggregated lower power limit curve 65 covers the lower power limit curve 52 ^'of the battery store.

In a second step, an auxiliary variable 20a is derived again as part of the aggregation process, which describes how long an energy conversion system 2 or a combination of energy conversion system 2n must be in operation at maximum power in order to meet the lower energetic limit curve 27. This auxiliary variable supplements the dynamic parameters 18.

In a further step, the systems that cannot be influenced, such as a photovoltaic system 42 and the electrical consumer 40, can still be taken into account. An optimization is possible here both in the energetic limit curves 59 and 60 according to FIG. 18 and in the power limit curves 65 and 66 according to FIG. 19.

In the example shown, it makes sense to take the load profiles into account in the power limit curves. The power limit curves with the prognoses in FIG. 20 can be seen as an example.

FIG. 20 shows, between the aggregated lower power limit curve 65 and the aggregated upper power limit curve 66, a demand prognosis 29 of the energy demand and a generation prognosis 39.

As part of the optimization, it can be specified with which power the controllable energy conversion systems 2 should be operated at a minimum and maximum, taking into account the non-controllable energy conversion systems 2. For example, in the case of non-controllable consumption, the maximum permissible power, i.e. the aggregated to reduce upper power limit curve 66 at least partially. The reduction can be different, depending on how high the "level" of optimization should be. Such an optimization is shown as an example in FIG. In FIG. 21, the power limit curves optimized in this way, that is to say the adapted aggregated lower power limit curve 61 and the adapted aggregated upper power limit curve 62 are shown. The space between the lower power limit curve 61 and the upper power limit curve 62 is the area available at the time in which the energy management system 1 can control individual energy conversion systems 2 using the corresponding control and regulation unit 7.

LIST OF REFERENCES

1 energy management system

1a local energy management system

1b superordinate energy management system

2 energy conversion system

3 supply network

4 energy demand / demand

5 energy storage

6, 6a, 6b, ..., 6n sensor / counter

7 Control and regulation unit

8 control command

9 measurement data

10 Energy control center 11 Interface 12 Connection unit / gateway

13 Energy Flow

14 external forecast

15 Internet

16 abstract and aggregated model

17, 17a, 17b, ..., 17n static parameters

18, 18a, 18b, ..., 18n dynamic parameters

19, 19a, 19b aggregation

20, 20a, 20b auxiliary variables 21 disaggregation

22 first level

23 second level

24 Setpoint command

25 control command abstract model of an energy conversion system, 27a first (lower) energetic limit curve, 28a second (upper) energetic limit curve

Demand forecast

Historical readings

Plant operation restrictions

Mathematical algorithms first optimizations

configuration

Energetic interface of common energy requirements

Reference system

Generation forecast of electrical consumers

Photovoltaic system (PV system)

Electric vehicle

Charging box

Connection socket for the first battery, battery inverter, motor

Controllable energy generator further battery controllable consumer lower power limit curve upper power limit curve predicted power P predicted mobility demand curve predicted charging power Esum prog cumulative total Esum storage state of charge Esp act aggregated lower energetic limit curve aggregated upper energetic limit curve adapted aggregated lower power limit curve adapted aggregated upper power limit curve processing second optimization aggregated lower power limit curve aggregated upper power limit curve

Formula symbols meanings

PAV Electrical power of general consumption (electrical consumers) (40)

^ E-FZG Electrical power that is consumed or output by an electric vehicle (42)

P LS Anl Services for the output levels of an energy conversion system (2) on the side of the primary energy form 1 (e.g. electrical energy)

^ E-FZG LSO performance at performance level 0 of an electric vehicle (42) on the side of primary energy form 1 (e.g. electrical energy)

^ E-FZG LSmax power at the maximum power level of an electric vehicle (42) on the side of primary energy form 1 (e.g. electrical energy)

Mp Anl Mode of the power control of an energy conversion system (2), for example continuous power control or discrete power control in stages p r from Anl Electrical power in the state from the energy conversion system (2)

^ Abs Anl switch-off time constant, currently corresponds to the duration required for the process of switching off an energy conversion system (2) -Anl Energy conversion index of an energy conversion system (2), corresponds to the ratio between the power on the side of energy form 1 to the power on the side of energy form 2

PA _H I EF I LS O Power of the energy conversion system (2) at power level 0 on the energy form 1 side

P _{Ani EF} 2 _L so power of the energy conversion system (2) at power level 0 on the side of energy form 2

P _{Ani EF i LSmax} Power of the energy _{conversion system} (2) at maximum power level on the energy form 1 side

^ Anl EF 2 LS max power of the energy conversion system (2) at maximum power level on the energy form 2 side

P _Sp ka _P Ani EFi Electrical storage capacity related to energy form 1, corresponds to the storable energy in the memory (5), which is assigned to the energy conversion system (2), related to the side of energy form 1

P _{Sp kaP Ani} EF2 Electrical storage capacity related to energy form 2, corresponds to the storable energy in the storage unit (5), which is assigned to the energy conversion system (2), related to the side of energy form 2

C? Sp kap Anl EF2 Thermal storage capacity related to energy form 2, corresponds to the thermally storable energy in the storage (5), which is assigned to the energy conversion system (2), based on the energy form 2 side

Esp kap accumulator Storage capacity of the accumulator of an electric vehicle (42) for energy form 2

The E-FZG storage capacity of an electric vehicle (42) based on the energy form 1 sR Ah1 power noise of an energy conversion system (2) corresponds to a percentage value that describes how great the undesired fluctuation in the electrical power of the energy conversion system (2) is in operation , related to energy form 1

^ _progmin Performance curve according to the lower performance limit curve (52), corresponds to forecast values

^ _progmax power curve according to the upper power limit curve (53), corresponds to forecast values p _rogmin cumulative energy curve of the lower energetic limit curve (27), corresponds to forecast values p _rogmax cumulative energy curve of the upper energetic limit curve (28), corresponds to forecast values

£ _{Sp is the} curve of the energy stored at the current point in time in a memory (5) that is assigned to an energy conversion system (2), here for an electric vehicle (42) P _{is the} curve of the electrical power for an energy conversion system (2), here the charging power for an electric vehicle (42)

P _prog Predicted output for an energy conversion system (2), which is to be covered as demand by the energy conversion system (2), here the forecasted charging capacity of an electric vehicle (42)

^ _sumprog Cumulative sum of the energy that results from the predicted power for an energy conversion system (2) P _prog , here the cumulative sum of the predicted charging power of an electric vehicle (42) i counting step t time interval between two counting steps it number of maximum counting steps taking into account a time to be considered (forecast duration) and a time interval between the counting steps

P _LSmax power at maximum power level

P _{B SP LSO} power at power level 0 of a battery _storage (5) on the side of primary energy form 1 ( _e.g. electrical energy) i _ß sp _LSmax performance at maximum power level of a battery storage (5) on the side of primary energy form 1 (e.g. electrical energy) Psp kap E-FZG Storage capacity of a battery storage system (5) based on energy form 1

P JS Services for the performance levels of several energy conversion systems (2) on the side of primary energy form 1 (e.g. electrical energy)

M _P Power control mode for several energy conversion systems (2), for example continuous power control or discrete power control in stages

P _from electrical power in the state of several energy conversion systems (2)

TA _t bs switch-off time constant, corresponds to the maximum time that the process of switching off several energy conversion systems (2) requires a Energy conversion index for a combination of several energy conversion systems (2), corresponds to the ratio between the power on the energy form 1 side and the power on the energy form side 2

PEF I LS O Performance of several energy conversion systems (2) at performance level 0 on the energy form 1 side

PEF2 LS O Performance of several energy conversion systems (2) at performance level 0 on the energy form 2 side ^ EF 1 LS max power of several energy conversion systems (2) at maximum power level on the energy form 1 side

P _pp 2 LS max power of the energy conversion system (2) at maximum power level on the energy form 2 side

^ s _{p kap} Electrical storage capacity related to energy form 1, corresponds to the storable energy of several storage units (5), which is assigned to several energy conversion systems (2), related to the side of energy form 1 s _R power noise of several energy conversion systems (2), corresponds to a percentage value , which describes how great the undesired fluctuation of the electrical power of the energy conversion system (2) is in operation, based on energy form 1

P _LS Auxiliary variable as a derivation from the performance levels, the energy conversion indicators, the knowledge of the needs to be covered (4) and the reference system to be determined

Claims

PATENT CLAIMS

1. Method for modeling one or more

Energy conversion systems (2) in an energy management system (1), at least one energy conversion system (2) and a control unit (7) being provided in the energy management system (1), characterized in that a technology-independent abstract model (26) for each energy conversion system (2) with their respective static

Parameters (17) and their respective dynamic parameters (18) is generated, the parameters (17, 18) being uniform and independent of the type of the respective energy conversion system (2), with dynamic parameters (18) being processed (63) using mathematical Algorithms (33) and optimizations (34) are formed on the basis of a demand forecast (29) and restrictions for the system operation (32) and static parameters (17) are formed by entering technology-independent system parameters.

2. The method according to claim 1, characterized in that an abstract and aggregated model (16) is formed from two or more abstract models (26) each of an energy conversion system (2) by means of an aggregation (19), which at least the static parameters (17a , 17b, ..., 17n) and the dynamic parameters (18a, 18b, ..., 18n) of the two or more energy conversion systems (2).

3. The method according to claim 1 or 2, characterized in that an aggregation (19) with the help of energetic interfaces (36), a reference system (38), mathematical algorithms (33) and common energy requirements (37) takes place.

4. The method according to any one of claims 1 to 3, characterized in that during the aggregation (19) auxiliary variables (20) are formed, the static parameters (17a) and (17b) of the abstract models (26) during the aggregation (19) formed auxiliary variables (20) are stored in the static parameters (17) of the abstract and aggregated model (16) and the from the dynamic parameters (18a) and (18b) of the abstract models (26) during the aggregation (19 ) formed auxiliary variables (20) are stored in the dynamic parameters (18) of the abstract and aggregated model (16).

5. The method according to any one of claims 2 to 4, characterized in that the dynamic parameters (18) of the abstract and aggregated model (16) are formed by second optimizations (64), static parameters (17) of the abstract and aggregated system model ( 16) as well as the output variables of the aggregation (19b), the demand forecast (29) and the generation forecast (39) can be used.

6. The method according to any one of claims 1 to 5, characterized in that control commands (25) for energy conversion systems (2) by means of a disaggregation (21) using set commands (24) from the energy management system (1) and the static parameters (17) and the dynamic parameters (18) of the abstract and aggregated model (16) are formed.

7. The method according to any one of claims 1 to 6, characterized in that the first optimization (34) takes place after predetermined times of the operation of an energy conversion system (2) or an energetically efficient operation of the energy conversion system (2).

8. The method according to any one of claims 1 to 7, characterized in that the second optimization (64) takes place according to economy, energy efficiency or peak load capping.

9. The method according to any one of claims 1 to 8, characterized in that operating limits such as a heat demand to be met by the energy conversion system (2), an availability of an electric vehicle (42), a storage status of a thermal or electrical storage (5) and a maximum possible or the minimum necessary feed into electrical supply networks (3).

10. The method according to any one of claims 1 to 9, characterized in that the energy management system (1) is a local energy management system (1a) or a superordinate energy management system (1b).