DE102019121990A1 - Method for modeling one or more energy conversion systems in an energy management system - Google Patents

Abstract

The invention, which relates to a method for modeling one or more energy conversion systems (2) in an energy management system (1), is based on the object of specifying a solution with which an improved, more efficient and simple control of an energy management system (1) is made possible. This object is achieved in that a technology-independent abstract model (26) is generated for each energy conversion system (2) with its respective static parameters (17) and their respective dynamic parameters (18), with dynamic parameters (18) being processed by processing (63 ) by means of mathematical algorithms (33) and optimizations (34) based on a demand forecast (29) and restrictions for the system operation (32) and with static parameters (17) being formed by entering technology-independent system parameters.

Description

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, through 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.

• • ein oder mehrere Energiewandlungsanlagen, wie beispielsweise Kraft-Wärme-Kopplungsanlagen, Wärmepumpen, Brennstoffzellen, Heizstäbe, Elektrofahrzeug, Photovoltaik-Anlagen (PV-Anlage), angeschlossen an ein oder mehrere Versorgungsnetze
• • Bedarfe (Verbraucher) in einem oder mehreren Sektoren (Strom / Wärme / Kälte / Mobilität)
• • Speichereinheiten, wie thermisch, chemisch oder elektrisch arbeitende Speicher
• • Sensorik / Zähler zur Erfassung relevanter energetischer Schnittstellen oder Kenngrößen von Energiewandlungsanlagen und Speichern
• • eine lokale Steuerungs- / Regelungseinheit, welche einzelnen oder einer Gruppe an Energiewandlungsanlagen zugeordnet ist / sind und deren Steuerung / Regelung übernehmen sowie Messdaten der Sensorik erfassen

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 components listed below:

• • 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 / regulation unit, which is / are assigned to individual or a group of energy conversion systems and take over their control / regulation as well as record measurement data from the sensors

In a so-called local energy management system, one or more energy conversion systems are assigned a control / regulation unit on which the partial 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 are known in various applications from the prior art.

Example 1: Virtual power plants for marketing the energy fed in from 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 (coverage of 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 within the framework of a virtual power plant

• • DE 10 2009 044 161 A1
• • DE 10 2005 056 084 A1
• • US 2009 / 0088907 A1
• • Hess, T.; Werner, J.; Schegner, P.: Konzepte für ein sektorenübergreifendes Energiemanagement, ETG Kongress 2019
• • Seifert, J.; Werner, J.; Seidel, P.; u. a.: RVK II - Praxiserprobung des Regionalen Virtuellen Kraftwerks auf Basis der Mikro-KWK-Technologie, ISBN 978-3-8007-4630-9
• • BTC AG: Broschüre BTC Virtual Power Plant - Verbraucher und Erzeuger bündeln und vermarkten, https://www.btc-ag.com/Angebote/BTC-VPP-Virtual-Power-Plant (Abrufdatum 03.07.2019)

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

• • DE 10 2009 044 161 A1
• • DE 10 2005 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 .; including: 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 bundling and marketing, https://www.btc-ag.com/Angebote/BTC-VPP-Virtual-Power-Plant (retrieved on 03.07.2019)

A disadvantage of such known systems is that optimizations, for example with regard to economic efficiency, are only made 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 for individual energy conversion systems have to be generated and integrated into the operational 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 now consists in specifying 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.

The object is achieved by a method with the features according to claim 1 of the independent claims. Further 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 such a 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.

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 intended 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.

It is also envisaged that the abstract model is generated while 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.

• 1: ein beispielhaftes Energiemanagementsystem mit verschiedenen Komponenten,
• 2: ein abstraktes und aggregiertes Modell mit weiteren funktionalen Zusammenhängen in einer Prinzipdarstellung,
• 3: ein abstraktes Anlagenmodell einer Energiewandlungsanlage,
• 4: eine Darstellung prinzipieller verfahrenstechnischer Zusammenhänge bei einer Aggregation,
• 5: ein beispielhaftes Energiemanagementsystem am Beispiel eines Hauses mit verschiedenen Komponenten,
• 6: eine Prinzipdarstellung einer Energiewandlungsanlage Elektrofahrzeug,
• 7: eine Prinzipdarstellung einer Energiewandlungsanlage Batteriespeicher,
• 8: eine Darstellung von Leistungsprognosen einer Energiewandlungsanlage Elektrofahrzeug,
• 9: eine Darstellung von Lade- und Entladevorgängen eines Elektrofahrzeugs,
• 10: eine Darstellung eines prognostizierten Mobilitäts-Bedarfs eines Elektrofahrzeugs,
• 11: eine Darstellung einer prognostizierten Ladeleistung eines Elektrofahrzeugs,
• 12: eine Darstellung von verfahrensgemäß erzeugten energetischen Grenzkurven,
• 13: eine Darstellung von verfahrensgemäß erzeugten energetischen Grenzkurven in einer Zusammenschau mit möglichen Ladebereichen eines Elektrofahrzeuges,
• 14: eine Darstellung von verfahrensgemäß erzeugten und optimierten energetischen Grenzkurven,
• 15: eine Darstellung des Resultats der Optimierung der energetischen Grenzkurven,
• 16: eine Darstellung von energetischen Grenzkurven für einen Batteriespeicher,
• 17: eine Darstellung von Leistungsgrenzkurven einer prognostizierten Leistung für einen Batteriespeicher,
• 18: eine Darstellung von energetischen Grenzkurven zweier Energiewandlungsanlagen mit resultierenden aggregierten energetischen Grenzkurven,
• 19: eine weitere Darstellung von energetischen Grenzkurven zweier Energiewandlungsanlagen mit resultierenden aggregierten energetischen Grenzkurven,
• 20: eine Darstellung einer Bedarfs- und Erzeugungsprognose mit aggregierten Leistungsgrenzkurven und
• 21: eine Darstellung von verfahrensgemäß angepasster aggregierter Leistungsgrenzkurven.

The previously explained features and advantages of this invention can be better understood and assessed after careful study of the following detailed description of the preferred, non-limiting example embodiments of the invention with the accompanying drawings, which show:

• 1 : an exemplary energy management system with various components,
• 2 : an abstract and aggregated model with further functional relationships in a schematic diagram,
• 3 : an abstract system model of an energy conversion system,
• 4th : a representation of basic procedural relationships in an aggregation,
• 5 : an exemplary energy management system using the example of a house with various components,
• 6th : a schematic diagram of an energy conversion system for an electric vehicle,
• 7th : a schematic diagram of an energy conversion system battery storage,
• 8th : a representation of performance forecasts of an energy conversion system electric vehicle,
• 9 : a representation of charging and discharging processes of an electric vehicle,
• 10 : a representation of a forecast mobility demand of an electric vehicle,
• 11 : a representation of a predicted charging power of an electric vehicle,
• 12 : a representation of process-generated energetic limit curves,
• 13 : a representation of energetic limit curves generated according to the method in a synopsis with possible charging areas of an electric vehicle,
• 14th : a representation of process generated and optimized energetic limit curves,
• 15th : a representation of the result of the optimization of the energetic limit curves,
• 16 : a representation of energetic limit curves for a battery storage,
• 17th : a representation of power limit curves of a predicted power for a battery storage,
• 18th : a representation of energetic limit curves of two energy conversion plants with resulting aggregated energetic limit curves,
• 19th : Another representation of energetic limit curves of two energy conversion plants with the resulting aggregated energetic limit curves,
• 20th : a representation of a demand and generation forecast with aggregated power limit curves and
• 21st : a representation of process-adapted aggregated power limit curves.

In the 1 is an exemplary energy management system 1 shown with different components.

The energy management system 1 includes 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 that the energy conversion systems 2 which can generate or consume energy to a supply network 3 or to several supply networks 3 can be connected.

There are also energy requirements 4th or briefly needs 4th which can occur in the power supply, heating, cooling or mobility sectors.

So-called storage units are also provided 5 or energy storage 5 which, for example, as thermal, chemical or electrical storage units 5 can be designed.

The energy management system 1 furthermore includes sensors 6th or meters, which at various relevant energy interfaces parameters or parameters of energy conversion systems 2 , Utility networks 3 , Needs 4th and / or energy storage 5 capture.

There is also a local control and regulation unit 7th provided which individual or a group of energy conversion systems 2 is assigned and takes control or regulation. A Such control or regulation takes place, for example, by means of a corresponding control and regulation unit 7th generated control commands 8th , which in the example of the 1 to an energy conversion system 2 be transmitted. In addition, the control and regulation unit 7th Measurement data 9 from the sensors 6th or counters 6th received and processed.

These components are described by what is known as a local energy management system 1a includes.

In the case of a higher-level energy management system 1b becomes the energy management system 1 through part of the energy control center 10 complements as it is in the upper part of the 1 is shown.

For such a supplement or expansion of a local energy management system 1a with a higher-level energy management system 1b is the higher-level energy management system 1b with an interface 11 fitted. Via this interface 11 the higher-level energy management system can 1b directly or via a gateway 12 , which can also be referred to as a connection unit, with the local energy management system 1a to an energy management system containing both components 1 be coupled. As such a gateway 12 or a connection unit is usually viewed as a component that establishes a connection such as a data connection between two systems. In the example of 1 provides the gateway 12 a data link between the energy control center 10 and the control and regulation unit 7th here.

The energy flows 13 are shown by means of the respective dash-dash lines.

It can be provided that, for example, the control and regulation unit 7th External forecast data 14th be transmitted. Such data from an external forecast 14th include information about, 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.

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

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 4th . Such an energy requirement 4th is either known or can be used in such a system on the control and regulation unit 7th be forecast in an appropriate manner.

Such demand forecasts 29 can for example based on the energy management system 1 obtained measurement data 9 take place, which by means of various sensors 6th or counter 6th are recorded. Furthermore, for such demand forecasts 29 external forecasts 14th how, for example, weather data is used, which for example via the Internet 15th via radio network or DSL connection or an energy control center 10 can be received.

For connecting the sensors 6th or counter 6th owns the energy management system 1 an assigned control and regulation unit 7th with appropriate interfaces for connecting the transmitted measurement data 9 . The control and regulation unit 7th is also with appropriate interfaces for connection to one or more energy conversion systems 2 fitted. In the case of a higher-level energy management system 1b owns the control and regulation unit 7th in addition, an interface to the higher-level energy control center 10 . For data exchange between the control and regulation unit 7th and the higher-level energy management system 1b can be a connection unit 12 like a gateway. Alternatively, the functionality of the connection unit 12 also in the control and regulation unit 7th to get integrated.

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

The coordination of such an energy management system 1 , comprising a local and a higher-level energy management system 1a , 1b takes place on the basis of various target parameters, such as profitability, energy efficiency, peak load capping and more. The function of the energy management system 1 is based on the recorded measurement data 9 , which through the connection unit 12 (Gateway) or the control and regulation unit 7th with connection unit functionality to the higher-level energy management system 1b to get redirected.

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

For the functioning of such an energy management system 1 is at least one the energy management system 1 depicted model deposited. Several models with different scenarios can also be stored.

Using such a model, the energy management system 1 an application planning for the energy conversion systems 2 make. Alternatively, operational monitoring of the energy management system 1 by means of the control and regulation units 7th can be carried out in real time.

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

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

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

The partial functionalities of the energy management system 1 , which are usually developed for a specific application, usually combine a static model with unchangeable information or parameters about at least one energy conversion system to be controlled 2 with the additional information required, such as forecasts based on external forecasts 14th or current technical and / or energetic system status of the energy conversion system 2 . This enables deployment planning, i.e. a forecast of the future, desired operation of the controllable energy conversion systems 2 be made.

The partial functionalities that make up an energy management system 1 can be broken down into operational planning, control / regulation and operational monitoring. For the implementation of an application, for example the reduction of the peak load 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 represented here, such as a specific energy conversion system 2 , for example an electric vehicle 42 to reduce the peak load in an electrical supply network 3 can contribute to the availability of the electric vehicle 42 is taken into account while ensuring that for the need 4th In terms of mobility, there is always enough energy in the storage of the electric vehicle 42 is available.

Demand forecasts are usually used for the forecasts 29 to energy requirements 4th as well as generation forecasts 39 for feeding non-controllable energy conversion systems 2 as a parameter for the energy management system 1 used. Such a non-controllable energy conversion system 2 is for example a photovoltaic system.

In addition, the energy management system uses 1 the models to the planned operation for the energy conversion systems 2 to be implemented in real-time operation. This 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 state of the art are based on the models of the energy conversion systems 2 be configured manually as individual models with their static parameters. Any 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 and technology-specific models must be integrated into the process. At the same time there must be different goals for the operation or limits that the operation of the energy conversion systems 2 be considered separately in the energy management system and in the procedures for deployment planning, control / regulation and operational monitoring.

• • der Wärmebedarf, wenn zwei Energiewandlungsanlagen 2 an der Wärmebereitstellung beteiligt sind
• • die Verfügbarkeit eines Elektrofahrzeugs 42 (E-Fahrzeug ist an Steckdose angeschlossen)
• • der Speicherladezustand eines thermischen oder elektrischen Speichers 5
• • maximale mögliche oder minimal notwendige Einspeisung in elektrische Versorgungsnetze 3

Examples of operating limits of such systems are, for example:

• • the heat demand if there are two energy conversion systems 2 are involved in the provision of heat
• • the availability of an electric vehicle 42 (E-vehicle is connected to the socket)
• • the storage charge status of a thermal or electrical storage unit 5
• • maximum possible or minimum necessary feed into electrical supply networks 3

Especially when mapping several energy conversion systems 2 , which are logically related, the implementation of an energy management system according to the prior art becomes difficult.

For example, two or more energy conversion systems are in a logical connection or a logical connection 2 which have a common energy requirement 4th cover or on a control and regulation unit 7th are connected and functionally related. Such a functional relationship exists, for example, in a system consisting of two combined heat and power plants 2 which jointly meet the thermal demand 4th cover 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 the various energy conversion systems 2 and different 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 required for the individual sub-functionalities, with an abstract and aggregated model 16 be mapped. In the 2 is this model 16 shown with further functional contexts.

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

Such an abstract, aggregated model 16 describes the combination of several individual abstract models 26th one real energy conversion system each 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 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 regardless of the actual type of energy conversion system 2 to be discribed. Here an energy conversion system 2 be both a fuel cell and a heat pump or other system.

Another advantage of this abstraction can be seen in the fact that the implementation of an energy management system 1 regardless of the amount of energy conversion systems 2 is in the overall system, as it allows the aggregation of the most diverse types of systems. The simple illustration of several energy conversion systems 2 with their memories and / or requirements, which are logically related, i.e. cover a common requirement or to a control and regulation unit 7th are assigned and are functionally related, becomes possible. An implementation of energy management systems 1 is so much simplified.

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 7th with a local energy management system 1a or with a higher-level energy management system 1b . With a higher-level energy management system 1b is the implementation in the energy control center 10 possible.

Like in the 2 shown, the abstract model exists 16 from static parameters 17th and dynamic parameters 18th .

It is intended 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 22nd .

In the 2 is also the process of disaggregation 21st shown in which from target commands 24 and data from the available model 16 Control commands 25th to control the individual energy conversion systems 2 be generated.

From every energy conversion system 2 , which is a control and regulation unit 7th is assigned, first the static parameters 17a per energy conversion system 2 in a general, this energy conversion system 2 Imaging model 26a configured once. Such a model 26a can be assigned an energy conversion system 2a, an energy store 5a and an energy requirement 4a to be covered, as shown in FIG 3 is shown. Thus, the static parameters include 17a the parameters of the energy conversion system 2a, the energy store 5a and the energy demand 4a to be covered.

The parameters 17th and 18th are uniform and independent of the type of energy conversion system 2 . The static parameters 17a , 17b , ..., 17n several abstract models at the level of an individual plant are followed by an aggregation 19th fed as it is in the 2 and 4th is shown.

For this purpose, a suitable reference system is first created 38 defined, for example the primary, most important energy conversion system 2 . This frame of reference 38 is used in all aggregation steps 19th and disaggregation steps 21st used up to the higher-level abstract model. The aggregation takes place on the basis of mathematical algorithms, which are dependent on the respective static parameters to be aggregated 19th , ie the linking of models at the plant level. In addition, there must be common energy requirements 4th that must be covered by the systems and the energetic interfaces are taken into account.

Following are various auxiliary variables 20th derived using mathematical procedures and aggregated again, as exemplified in the 4th is shown. These auxiliary variables 20th are combined with the dynamic parameters 18th of the system models for resource planning by the energy management system 1 used. The result is the static parameters 17th a group of systems that contains the same parameters as the model of a single system and is expanded by the auxiliary variables.

In addition to the static parameters 17th own the dynamic parameters 18th a special rank. The dynamic parameters 18th 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. This will be for an energy conversion system 2 through two energetic limit curves 27 , 28 Are defined. Within these energetic limit curves 27 , 28 it is through the energy management system 1 possible to organize an operation for the plant or plants without energy requirements 4th are not met or restrictions for free system operation are disregarded. For several energy conversion systems 2 so-called aggregated limit curves are generated accordingly 59 , 60 used.

• • Zeitpläne (Betrieb der Anlage zu gewünschten Zeiten gewollt),
• • Sperrzeiten (Anlagenbetrieb aufgrund von z.B. Geräuschemissionen verboten),
• • Verfügbarkeitszeiten (Elektrofahrzeug ist an das Stromnetz angeschlossen),
• • Maximal mögliche und minimal notwendige Einspeisung in das elektrische Netz oder
• • Speicherladezustände von elektrischen und thermischen Speichern

Restrictions for free plant operation can be:

• • Schedules (operation of the system at the desired times is desired),
• • Blocking times (plant operation is 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 generated by an energy conversion system 2 given is. The dynamic parameters are supplemented 18th by a so-called control potential, which makes the energy conversion system controllable 2 in terms of their performance.

The application of dynamic parameters 18th or. 18a , 18b , ..., 18n takes place on two levels. On the one hand at the model level 26th or 26a, 26b, ..., 26n of a single energy conversion system 2 , like in the 3 shown.

These dynamic parameters 18th or. 18a , 18b , ..., 18n are determined based on demand forecasts 29 , which for the respective energy conversion system 2 energy needs to be covered 4th are to be forecast. The demand forecasts 29 are created on the basis of external forecasts 14th such as weather forecasts and historical readings 31 , which through the connection unit 12 (Gateway) were recorded and cached.

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

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

This is how the first and second energetic limit curves become 27 and 28 as well as the control potential or the power limit curves 52 and 53 for the operation of a single energy conversion system 2 provided.

It is still planned that the first optimizations 34 directly at the energetic limit curves 27 and 28 can be made. Such a first optimization 34 for example, the desired times for the operation of an energy conversion system 2 , the energetically efficient operation of the energy conversion system 2 or a storage charge management, for example one on the energy conversion system 2 connected memory 5 affect. 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 are for example the dynamic parameters 18a a first energy conversion system 2 provided as it is in the 3 is shown.

The creation of the dynamic parameters 18th or. 18a , 18b , ..., 18n takes place 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 aggregation 19th can be provided. This step of aggregation 19th is in the 4th both for static parameters 17th as well as for dynamic parameters 18th shown.

In aggregation 19th however, must be particularly taken into account when using different energy conversion systems 2 together an energy requirement 4th or several energy requirements together 4th cover. Therefore, when aggregating 19th the energetic interfaces 36 or common needs 37 considered.

The frame of reference is likewise again 38 used. In aggregation 19th become auxiliary variables again 20th derived. The dynamic parameters 18th for second optimizations 64 can be added.

In contrast to the second level 23 a single energy conversion system 2 are especially comprehensive optimizations between different energy conversion systems 2 and taking into account non-controllable energy requirements 4th and / or power generation 39 of interest. Just like the energy requirements 4th by means of a demand forecast 29 can be calculated in advance, the energy production 39 by means of a production forecast 39 be calculated.

In a further step it becomes possible to set the limits in terms of upstream second optimizations 64 the operation of the energy conversion systems 2 to modify. Second optimizations 64 can be, for example, the energy efficiency of an overall system or the optimization of internal requirements, ie the maximum use of generation by energy conversion systems 2 for requirements and avoidance of feeding into supply networks 3 . This is particularly important in higher-level energy management systems 1b relevant in order to consider various local goals in the operational planning. For this purpose, desired operating times can be determined by means of an optimization algorithm, whereby the dynamic parameters 18a , 18b , ..., 18n be 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 1b .

All used mathematical procedures are designed reversible, so that the disaggregation 21st in 2 is made possible in which the control commands 25th to control the individual energy conversion systems 2 be generated. In particular, the static and dynamic parameters 17th and 18th of the energy conversion systems 2 used. The derived auxiliary variables are particularly relevant 20th , since only they allow the data aggregation 19th to compensate for any loss of information and reliable disaggregation 21st to realize.

When a schedule or timetable is determined by the deployment planning, this is made using the abstract model 16 into a schedule or timetable for each individual energy conversion system 2 translated. This has the advantage of being in the energy management system 1 not every energy conversion system 2 must be integrated in terms of communication technology, but rather the integration of the aggregated model 16 (Systems) is sufficient. The processing of all limits in the models of the energy conversion system 2 enables a reliable distribution to be achieved. Such limits can be, for example, requirements to be covered 4th (Heat demand), availability of an energy conversion system 2 , Schedules, maximum possible or minimum necessary feed into the supply network 3 or memory charge states.

The abstract model 16 enables a uniform interface to the partial functionalities of the energy management system 1 how an application planning, control, regulation can be generated, after which these do not have to be adapted to the application.

Another advantage is that the dynamic parameters 18th , 18a , 18b , ..., 18n over different levels with different limits, such as schedules, blocking times, availability times or requirements, or optimization goals such as optimizing the energy efficiency of an individual energy conversion system 2 , regarding the energy efficiency of several energy conversion systems 2 or an optimization of internal requirements, can be processed. The abstraction also allows restrictions in the electrical networks to be easily taken into account, without the complexity of the energy management system 1 to increase further. For example, the maximum permissible feed-in from the energy conversion system 2 and the network of energy conversion systems 2 depending on the predicted feed-in behavior of photovoltaic systems in a local network.

The dynamic parameters 18th , 18a , 18b , ..., 18n are of particular relevance in the process, as this results in the energy management system 1 is able to deliver valid solutions regardless of the procedure, which system parameters 17th and 18th , Energy requirements 4th , Restrictions 32 for local system operation and local optimization. With optimization in terms of the energy management system 1 this enables parallel applications to be fulfilled, ie various optimization goals can be implemented without great effort.

The use of the abstract model 16 is not on a control and regulation unit 7th limited. It is also used in the energy control center of a higher-level energy management system 1b possible.

To describe the method for controlling an energy management system in an exemplary embodiment, this is described in FIG 5 energy management system shown 1 used.

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

The battery storage 5 and the charging box 43 are controllable systems, ie the charging and discharging can be done by means of control commands 25th through the control and regulation unit 7th of the energy management system 1 can be specified. The PV system 41 is viewed as a non-controllable system, ie the provision of power cannot or should not be done by the control and regulation unit 7th to be influenced. The control and regulation unit 7th 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 about the sensors 6a , 6b , 6c and 6d, which are also part of the energy management system 1 could be. The first sensor determines 6a the power P _SP from or to the energy store 5 . The second sensor 6b determines the power P _{PV of} the PV system 41 , while the third sensor 6c the power P _E-FZG to the charging box 43 or to the electric vehicle 42 determined. In addition, a sensor 6d is provided for detecting the power P _house towards the house or from the house, the energy management system in this example 1 a 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 7th is connected and which is relevant for billing. The sensor 6d or main meter in the house is connected to a general supply network 44. The representation in the 5 shows that the sensors 6a to 6d their measurement data 9 to the control and regulation unit 7th transmit and the control and regulation unit 7th Control commands 25th to the energy storage 5 and the charging box 43 can send.

The structure for the unit charging box 43 and electric vehicle 42 is in the 6th shown in more detail. In this case, the controllable system is the charging box 43 , which by means of the control and regulation unit 7th transmitted control commands 25th is controlled. Alternatively, there could also be a possibility of activating and deactivating the charging process via the electric vehicle 42 exist themselves.

As energy storage in electric vehicles 42 is the battery in the context of the process example 46 with its capacity to understand. The need is the mobility of the electric vehicle 42 which, however, is used for energy management 1 cannot be captured. The need therefore arises from the energy required that must be recharged when the electric vehicle 42 at the charging box 43 for example via the connection socket 45 connected. The 6th also shows a battery inverter 47 which directly connects to the battery 46 connected as well as the engine 48 of the electric vehicle 42 .

The structure for the battery storage 5 is in the 7th shown. The battery storage 5 is special in its structure because it has no need. The battery storage is part of the process description 5 as a combination of a controllable generator 49 , a second battery 50 and a controllable consumer 51 to understand.

There are two individual systems within this system, which are powered by energy conversion systems 2 , Storage capacities and energy requirements 4th are shaped. An energy conversion system 2 in this context means that two forms of energy are coupled with 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 parameter for marketing / optimization in the energy management system 1 .

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

• Beispiel Ladebox für Elektrofahrzeug:
  • • Energieform 1: Elektroenergie (auf Seite des elektrischen Versorgungsnetzes 3)
  • • Energieform 2: Elektroenergie (auf Seite des Elektrofahrzeugs 42)
• Beispiel Batteriespeicher:
  • • Energieform 1: Elektroenergie (auf Seiten des elektrischen Versorgungsnetzes 3)
  • • Energieform 2: Elektroenergie (auf Seite des Akkus 46)
• Beispiel Kraft-Wärme-Kopplungsanlage:
  • • Energieform 1: Elektroenergie
  • • Energieform 2: Wärmenergie
• Beispiel Wärmepumpe:
  • • Energieform 1: Elektroenergie
  • • Energieform 2: Wärmeenergie

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:
  • • Form of energy 1 : Electric energy (on the side of the electrical supply network 3 )
  • • Form of energy 2 : Electric energy (on the side of the electric vehicle 42 )
• Example battery storage:
  • • Form of energy 1 : Electric energy (on the part of the electrical supply network 3 )
  • • Form of energy 2 : Electric energy (on the battery side 46 )
• Example of a combined heat and power plant:
  • • Form of energy 1 : Electrical energy
  • • Form of energy 2 : Thermal energy
• Example heat pump:
  • • Form of energy 1 : Electrical energy
  • • Form of energy 2 : Thermal energy

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

processing 63 and first optimization 34 for the electric vehicle 42 (E-FZG) for the abstract system model 26th a single energy conversion system 2
and then the process steps
Aggregation 19th and 19a , Derivation of auxiliary quantities 20th and 20a and the second optimizations 64 for the abstract, aggregated system model 16 all associated energy conversion systems 2
explained.

• • Leistungsstufen, welche eingestellt werden können zur Ladung des Elektrofahrzeugs 42
• • Speicherkapazität des Akkus 46
• • Leistungsverlauf für typischen Ladevorgang
• • Verluste des Akkus 46
• • ...

The formation of the abstract plant model 16 an energy conversion system 2 should using the example of the electric vehicle 42 respectively. The system parameters, i.e. the static parameters, are essential output variables 17th and the dynamic parameters 18th . For an electric vehicle 42 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
• • Loss of battery 46
• • ...

• • Leistungsstufen der Anlage P_{LS Anl}:
  • ◯ Entspricht den Leistungsstufen der Anlage auf der Seite der primären Energieform 1 (Elektroenergie)
  • ◯ P_{LS Anl} = [P_{E-FZG LS0}, ..., P_{E-FZG LSmax}] P_{E-FZG LSmax} > P_{E-FZG LS0} > 0
• • Modus Leistungsregelung M_{P Anl}
  • ◯ Entspricht dem Modus der Leistungsregelung (diskrete Regelung, modulierende Leistungsregelung)
  • ◯ Hier: diskret
• • Leistung Zustand AUS P_{aus Anl}
  • ◯ Leistung der Anlage im Zustand aus
  • ◯ Paus Anl = P_{E-FZG LS0}
• • Abschaltzeitkonstante: T_{Abs Anl}
  • o Entspricht einer Zeitdauer für die Abschaltung einer Anlage (für Regelung wichtig)
  • o Für das Elektrofahrzeug aus der Leistungskurve entnommen
  • o Z.B. T_{Abs Anl}= 30 min
• • Energiewandlungskennzahl: ${\mathrm{\alpha }}_{\text{Anl}}=\left[\frac{{\mathrm{<figure-callout\; id="1"\; label="P\; Anl\; EF"\; filenames="DE102019121990A1\_0011.png,DE102019121990A1\_0013.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{Anl EF}}}{{\mathrm{<figure-callout\; id="2"\; label="P\; Anl\; EF"\; filenames="DE102019121990A1\_0009.png,DE102019121990A1\_0015.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{Anl EF}}},\dots ,\frac{{\mathrm{<figure-callout\; id="1"\; label="P\; Anl\; EF"\; filenames="DE102019121990A1\_0011.png,DE102019121990A1\_0013.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{Anl EF}}}{{\mathrm{<figure-callout\; id="2"\; label="P\; Anl\; EF"\; filenames="DE102019121990A1\_0009.png,DE102019121990A1\_0015.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{Anl EF}}}\right]$
  
  • o Entspricht einer allgemeingültigen Wandlungskennzahl zwischen der Leistung in Energieform 1 und der Leistung in Energieform 2 in Abhängigkeit der Leistungsstufen
  • o Da beim Elektrofahrzeug 42 die primäre und sekundäre Energieform identisch ist, berücksichtigt der Faktor nur die Verluste der Umwandlungseinheit und beträgt ca. α_Anl ≈ [1,...1]
• • Speicherkapazität: E_{Sp kap Anl EF1}. = α · E_{Sp kap Anl EF2} bzw. E_{Sp kap Anl EF1} = α · Q_{Sp kap Anl EF 2}
  • o Die Speicherkapazität entspricht einer speicherbaren Energie im Speicher, welcher der Energiewandlungsanlage zugeordnet ist und wird anhand der Energiewandlungszahl auf die primäre Energieform 1 bezogen.
  • ◯ Für das Elektrofahrzeug ergibt sich: E_{SP kap E-FZG} = 1 · E_{Sp kap Akku}
• • Leistungsrauschen σ_{P Anl}
  • o Das Leistungsrauschen entspricht einem prozentualen Wert, welcher beschreibt, wie groß die ungewünschte Fluktuation der Leistung im laufenden Betrieb ist. Der Wert ist insbesondere für die Regelung notwendig.
  • o Beispielhaft sei hier σ_P = 0 angenommen.

In the following process step, the static parameters are determined using this information 17th the energy conversion system 2 , in this case the electric vehicle 42 , educated. The following are some of the static parameters 17th shown by way of example and their significance using the example of the electric vehicle 42 explained:

• • Performance levels of the system P _{LS Anl} :
  • ◯ Corresponds to the performance levels of the system on the side of the primary form of energy 1 (Electrical energy)
  • ◯ P _{LS Anl} = [P _{E-FZG LS0} , ..., P _{E-FZG LSmax} ] P _{E-FZG LSmax} > P _{E-FZG LS0} > 0
• • Power control mode M _{P Anl}
  • ◯ Corresponds to the power control mode (discrete control, modulating power control)
  • ◯ Here: discreet
• • Power state OFF P _{from Anl}
  • ◯ System performance in the off state
  • ◯ Paus Anl = P _{E-FZG LS0}
• • Switch-off time constant: T _{Abs Anl}
  • o Corresponds to a period of time for the shutdown of a system (important for control)
  • o Taken from the performance curve for the electric vehicle
  • o E.g. T _{Abs Anl} = 30 min
• • Energy conversion index: ${\mathrm{\alpha }}_{\text{Appendix}}=\left[\frac{{\mathrm{<figure-callout\; id="1"\; label="P\; Anl\; EF"\; filenames="DE102019121990A1\_0011.png,DE102019121990A1\_0013.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{Anl EF}}}{{\mathrm{<figure-callout\; id="2"\; label="P\; Anl\; EF"\; filenames="DE102019121990A1\_0009.png,DE102019121990A1\_0015.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{Anl EF}}},...,\frac{{\mathrm{<figure-callout\; id="1"\; label="P\; Anl\; EF"\; filenames="DE102019121990A1\_0011.png,DE102019121990A1\_0013.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{Anl EF}}}{{\mathrm{<figure-callout\; id="2"\; label="P\; Anl\; EF"\; filenames="DE102019121990A1\_0009.png,DE102019121990A1\_0015.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{Anl EF}}}\right]$
  
  • o Corresponds to a generally applicable conversion ratio between the power in the form of energy 1 and performance in energy form 2 depending on the performance levels
  • o As with the electric vehicle 42 the primary and secondary forms of energy are identical, the factor only takes into account the losses of the conversion unit and is approx. α _Anl ≈ [1, ... 1]
• • Storage capacity: E _{Sp kap Anl EF1} . = α · E _{Sp kap Anl EF2} or E _{Sp kap Anl EF1} = α · Q _{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 form of energy 1 based.
  • ◯ For the electric vehicle: E _{SP cap E-FZG} = 1 · E _{Sp cap battery}
• • Power noise σ _{P Anl}
  • o The power noise corresponds to a percentage value which describes how great the undesired fluctuation in power is during operation. The value is particularly necessary for regulation.
  • o As an example, σ _P = 0 is assumed here.

• • Untere und obere Leistungsgrenzkurve 52 und 53
• • Untere und obere energetische Grenzkurve 27 und 28

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

• • Lower and upper power limit curve 52 and 53
• • Lower and upper energetic limit curve 27 and 28

First, the creation of the lower power limit curve 52 P _{prog min} and the upper power _limit curve 53 P _{prog max} described. These describe the performance of an energy conversion system 2 can be specified at a time. The static parameters 17th the energy conversion system 2 as well as the restrictions 32 used for system operation. The first-mentioned essentially includes the performance levels P _{LS Anl} . As a caveat 32 for system operation are in the sense of the electric vehicle 42 to mention in particular the possible charging periods, ie the periods in which the electric vehicle 42 to the charging box 43 connected. The information is combined during processing. The power limit curves arise during the charging periods 52 and 53 from the minimum and maximum power level. Outside of this time P _{prog max} = P _{prog min} = P _{E-FZG LS0} . The power limit curve 52 and 53 are exemplary of 8th shown.

The 8th shows a predicted performance P 54 over time. The areas B2, B3 and B4 shown represent time periods in which the electric vehicle 42 via the connection socket 45 with the charging box 43 is connected and can be charged. The dash-dot line represents the lower power limit curve 52 P _{prog min} , while the dash-dash line represents the upper power limit curve 53 P _prog max is shown. For example, the electric vehicle can be used in area B3 between 6 p.m. and 6 a.m. the following day 42 to charge.

In addition to the power limit curves 52 and 53 the lower energetic limit curve is created 27 E _{prog min} and the upper energetic limit curve 28 E _{prog max.starting} point for the creation of the dynamic parameters 18th 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 forecast 29 becomes for every energy conversion system 2 With Energy demand 4th creates and specifies future needs 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 performance curve of the charging process and the storage states, as exemplified in the 9 is shown. 9 shows the progression of the storage charge status over time 58 E _{Sp is} . In the direction of the left Y-axis of the 9 is the actual charging power P _is shown. For example, in a time range between 6 p.m. and 6 a.m. the following day, the battery is currently charged 46 of the electric vehicle 42 dependent reloading. The charging time depends on the energy consumption from the battery 46 between 6 a.m. and 6 p.m., with linear interpolation.

It is important to note that recharging an electric vehicle 42 does not correspond to the actual need (need = "driving the e-vehicle"). This means that the demand must be derived from the performance 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 not on charging box 43 is connected, and the assumption of a medium power is used. A forecast mobility demand that arises in this way 55 is in the 10 shown in its course over time. In the direction of the Y-axis of the 10 the forecast mobility requirement P _{prog is} shown.

• • Künstliche Neuronale Netze,
• • Das Naive Prognoseverfahren,
• • Regressionsverfahren

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 total 56 Esum prog formed for the forecast charging capacity (mobility requirement).

für alle i ∈:[0... T], wobei i ... einem Zählindex zu den Leistungswerten entspricht.For the following considerations, it is assumed that the predicted performance curves are available discretely with a time interval of, for example, Δt = 15 min. The performances thus correspond to mean values over the period of time Δt. Thus applies $${\mathrm{E.}}_{\text{sum prog}}\left(i\right)={\displaystyle \sum _0^i{P}_{\text{prog}}\left(i\right)}\cdot \Delta t$$

for all i ∈: [0 ... T], where i ... corresponds to a counting index for the power values.

The result is in the 11 shown. 11 shows the course of the predicted charging power 56 in their course over time. The next step is based on the static parameters 17th or system parameters by means of the storage capacity E _{Sp kap} , the storage charge state at the time E _Sp (t _act ) and the lower and upper power limit curve 52 and 53 the lower and upper energetic limit curves 27 and 28 determined (E _{prog min} , E _{prog max} ). The aim here is also to rule out inadmissible conditions.

The points mentioned above can be used for the maximum energy band of the energy conversion system 2 can be described technically as follows: $${\mathrm{E.}}_{\text{prog max}}\left(i\right)=\text{min}\left(P_{\text{LS max}}\left(i\right)\cdot \Delta t,{\mathrm{E.}}_{\text{sum prog}}\left(t\right)-{\mathrm{E.}}_{\text{sum prog}}\left(i-1\right)\right)+{\mathrm{E.}}_{\text{Sp}}\left(t_{\text{act}}\right)$$

The 12 shows the time curve of the charging power Esum prog 56, which is between the lower energetic limit curve 27 E _{prog min} and the upper energetic limit curve 28 E _{prog max} runs. In the direction of the Y-axis of the 9 is the cumulative total 57 Esum depicted.

In the 13 four areas B1, B2, B3 and B4 are shown. B1 represents an area with an inadmissible state. B2, B3 and B4 are areas in which charging is possible, so-called possible charging periods. All 4th Areas are taken into account by the lower and upper power limit curve 52 and 53 is taken into account.

Further processing or optimization may be necessary, for example, if the battery is required at the end of the charging cycle 46 of the electric vehicle 42 must be charged to at least 50%. 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 is processed 27a generated.

Processing of the lower energetic limit curve 27 is based on the restrictions 32 for free system operation.

• (1) Negative Werte für die kumulierte energetische Grenzkurve sind aufgrund positiver Werte der Leistungsstufen unzulässig.
• (2), (3) Es ist gefordert, dass ein Speicherladezustand von 50% am Ende des möglichen Ladezeitraums erreicht sein muss. Dies erfordert, dass die untere Grenzkurve diesen Punkt (3) schneidet. Der Anstieg der unteren Grenzkurve (2) muss der maximalen Leistung der Anlage entsprechen.
• (4), (5) Am Ende des mögliche Ladezeitraums B3 muss ein Speicherladezustand erreicht sein, dass der darauffolgende, prognostizierte Bedarf erfüllt werden kann. Im Falle des Elektrofahrzeugs bedeutet dies, dass zum Beginn des nächsten Nachladezeitraums B4 der minimale Speicherladezustand vorhanden ist. Um den notwendigen Speicherladezustand in B3 zu erreichen, muss rechtzeitig geladen werden. Der Anstieg (4) entspricht wiederum die maximale Leistung der Anlage.
• (6), (7) analog zu (2), (3), entspricht der Forderung nach einem Speicherladezustand von 50% am Ende eines möglichen Nachladezeitraums.

The processing of the lower energetic limit curve is in 14th evident. This processing must take into account the static parameters 17th or system parameters. The numbers in brackets below 1 to 7th correspond to those in the 14th digits shown.

• (1) Negative values for the cumulative energetic limit curve are not permitted due to positive values for the performance 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 this point ( 3 ) cuts. The rise of 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, the storage tank must be charged 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 rise ( 4th ) 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. For the electric vehicle 42 However, such a system optimization is not necessary due to low downtime losses.

Taking into account all the processing steps described, the final result is the lower and upper energetic limit curve 27a and 28a , like in the 15th is shown.

The following is the formation of an aggregated, abstract plant model 16 using an example from the 7th known battery storage 5 described.

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

Static parameters 17

• • Performance levels of the system P _{LS Anl} :
  • ◯ P _{LS Anl} = [P _{BSP LS0} , ..., P _{BSP LSmax} ] P _{BSP LS0} <0, P _{BSP LSmax} > 0
• • Power control mode M _{P Anl}
  • ◯ discreet
• • Power state OFF P _{from Anl}
  • ◯ System performance in the off state
  • ◯ P _{from Anl} = 0W
• • Switch-off time constant: T _{Abs Anl}
  • ◯ T _{Abs Anl} = 0 min
• • Energy conversion index: ${\mathrm{\alpha }}_{\text{Appendix}}=\left[\frac{{\mathrm{<figure-callout\; id="1"\; label="P\; Anl\; EF"\; filenames="DE102019121990A1\_0011.png,DE102019121990A1\_0013.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{Anl EF}}}{{\mathrm{<figure-callout\; id="2"\; label="P\; Anl\; EF"\; filenames="DE102019121990A1\_0009.png,DE102019121990A1\_0015.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{Anl EF}}},...,\frac{{\mathrm{<figure-callout\; id="1"\; label="P\; Anl\; EF"\; filenames="DE102019121990A1\_0011.png,DE102019121990A1\_0013.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{Anl EF}}}{{\mathrm{<figure-callout\; id="2"\; label="P\; Anl\; EF"\; filenames="DE102019121990A1\_0009.png,DE102019121990A1\_0015.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{Anl EF}}}\right]$
  
  • ◯ α _Anl ≈ [1, ..., 1]
• • Storage capacity: ${\mathrm{E.}}_{\text{Sp cap Anl EF1}\text{.}}\text{=}\mathrm{\alpha }\cdot {\mathrm{E.}}_{\text{Sp <figure-callout id="2" label="cap Anl EF" filenames="DE102019121990A1\_0009.png,DE102019121990A1\_0015.png" state="{{state}}">cap Anl EF</figure-callout>}2}$
  
  or. ${\mathrm{E.}}_{\text{Sp cap Anl EF1}}\text{=}\mathrm{\alpha }\cdot Q_{\text{Sp <figure-callout id="2" label="cap Anl EF" filenames="DE102019121990A1\_0009.png,DE102019121990A1\_0015.png" state="{{state}}">cap Anl EF</figure-callout>}2}$
  
  • ◯ ${\mathrm{E.}}_{\text{Sp cap BSP}}\text{= 1}\cdot {\mathrm{E.}}_{\text{Sp kap AKKU}}$
    
• • Power noise σ _{P Anl} = 0

Dynamic parameters 18

The single system of the battery storage 5 has no need to cover the secondary form of energy. Hence the rise of the lower and upper energetic limit curve 27 and 28 equals zero. As the battery storage 5 can also assume negative power values, in the case of a feed-in, results from the lower and upper energetic limit curve 27 and 28 an axially parallel band to the abscissa, as shown in the 16 is shown. The to this battery storage 5 associated lower power limit curve 52 and the upper power limit curve 53 are in the 17th shown.

The static parameters are then superimposed 17th and 17a of the two energy conversion systems 2 , so the battery storage 5 and the electric vehicle 42 .

• • mathematische Algorithmen,
• • Kenntnisse zu Bedarfen, die durch beide Einzelanlagen gleichermaßen gedeckt werden können,
• • Definition eines Bezugssystems

The static parameters 17th and 17a of the two energy conversion systems 2 must be overlaid 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, both have energy conversion systems 2 no jointly covered needs. An example for this case would be if, for example, 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 together an energy requirement 4th cover. Then it must for aggregation 19th a reference system can be determined, which is usually the prioritized system and is primarily used.

• • Leistungsstufen P_LSI :
  • o Überlagerung abhängig von dem Modus der Leistungsregelung der Anlagen:
  • o Z.B. bei diskreten Leistungsstufen: Aggregation durch Bildung aller möglichen Kombinationen ohne Wiederholung, Addition der Werte und aufsteigende Sortierung
  • o Mögliche Weiterverarbeitung ist u.a. die Reduktion der Leistungsstufen
• • Modus Leistungsregelung M_P
  • ◯ Ergibt sich aus der Methodik der Aggregation der Leistungsstufen
  • ◯ Z.B. bei Überlagerung diskreter Leistungsstufen -> diskret, ebenso
• • Leistung Zustand AUS P_aus
  • o Addition der Leistung im Zustand AUS aller Einzelanlagen
• • Abschaltzeitkonstante: T_Abs
  • ◯ Abschaltzeitkonstante ergibt sich aus dem Maximum aller Abschaltzeitkonstanten
• • Energiewandlungskennzahl: ${\mathrm{\alpha }}_{\text{Anl}}=\left[\frac{{\mathrm{<figure-callout\; id="1"\; label="P\; EF"\; filenames="DE102019121990A1\_0011.png,DE102019121990A1\_0013.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{ EF}}}{{\mathrm{<figure-callout\; id="2"\; label="P\; EF"\; filenames="DE102019121990A1\_0009.png,DE102019121990A1\_0015.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{EF}}},\dots ,\frac{{\mathrm{<figure-callout\; id="1"\; label="P\; EF"\; filenames="DE102019121990A1\_0011.png,DE102019121990A1\_0013.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{EF}}}{P_{\text{EF}}}\right]$
  
  • ◯ Überlagerung abhängig vom Modus der Leistungsregelung der Anlagen
  • ◯ Z.B. bei diskreten Leistungsstufen: Aggregation durch Bildung aller möglichen Kombinationen ohne Wiederholung, Multiplikation Werte und Sortierung gemäß den Zuordnungen zu den aggregierten Leistungsstufen
• • Speicherkapazität: E_{SP kap}
  • ◯ Addition der Speicherkapazitäten aller Einzelanlagen
• • Leistungsrauschen σ_P = 0
  • ◯ Addition des Leistungsrauschen aller Einzelanlagen

The superposition of the individual static parameters 17th and 17a can be done using the algorithms mentioned below:

• • Performance levels P _LSI :
  • o Overlay depending on the power control mode of the systems:
  • o For example, with discrete performance levels: aggregation by forming all possible combinations without repetition, adding the values and sorting in ascending order
  • o Possible further processing is, among other things, the reduction of the performance levels
• • Power control mode M _P
  • ◯ Result from the method of aggregating the performance levels
  • ◯ E.g. if discrete power levels are superimposed -> discrete, likewise
• • Power state OFF P _off
  • o Addition of the power in the OFF state of all individual systems
• • Switch-off time constant: T _Abs
  • ◯ Switch-off time constant results from the maximum of all switch-off time constants
• • Energy conversion index: ${\mathrm{\alpha }}_{\text{Appendix}}=\left[\frac{{\mathrm{<figure-callout\; id="1"\; label="P\; EF"\; filenames="DE102019121990A1\_0011.png,DE102019121990A1\_0013.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{EF}}}{{\mathrm{<figure-callout\; id="2"\; label="P\; EF"\; filenames="DE102019121990A1\_0009.png,DE102019121990A1\_0015.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{EF}}},...,\frac{{\mathrm{<figure-callout\; id="1"\; label="P\; EF"\; filenames="DE102019121990A1\_0011.png,DE102019121990A1\_0013.png"\; state="\{\{state\}\}">P</figure-callout>}}_{\text{EF}}}{P_{\text{EF}}}\right]$
  
  • ◯ Overlay depending on the system capacity control mode
  • ◯ Eg with discrete performance 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 cap}
  • ◯ Addition of the storage capacities of all individual systems
• • Power noise σ _P = 0
  • ◯ Addition of the power noise of all individual systems

Following the superposition of the static parameters 17th and 17a becomes an auxiliary variable 20th derived. This auxiliary variable 20th is a performance vector that differs from the performance levels, the energy conversion indicators, the knowledge of the needs to be met together 37 and the specified reference system. It is shown below as P̃ _LS and supplements the static parameters 17th .

In addition to the static parameters 17th and 17a of all energy conversion systems 2 also need the dynamic parameters 18th and 18a of the two energy conversion systems 2 be aggregated. The lower and upper limit curves are initially used 27 , 27 'and 28, 28' of the two energy conversion systems 2 added. For this purpose, analogous to the aggregation 19th the static parameters 17th and 17a knowledge of the energy needs to be covered jointly 37 and the reference system in the aggregation 19th to be viewed as. The result of the aggregation 19th is in the 18th and 19th shown.

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

The 19th shows the lower and upper power limit curve in a common representation 52 and 53 for the electric vehicle 42 as well as the lower and upper power limit curves 52 'and 53' for the battery storage 5 . The result of the processing according to the method results in the aggregated lower power limit curve 65 and the aggregated upper limit curve 66 , like in the 19th shown. The Y-axis shows the predicted power P. The aggregated lower power limit curve 65 covered in the example of 19th the lower power limit curve 52 'of the battery storage.

In a second step, an auxiliary variable is again used as part of the aggregation process 20a derived, 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 reach the lower energetic limit curve 27 to meet. This auxiliary variable supplements the dynamic parameters 18th .

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

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 forecasts in 20th evident.

20th shows between the aggregated lower power limit curve 65 and the aggregated upper power limit curve 66 a demand forecast 29 of the energy demand as well as a generation forecast 39 .

As part of the optimization, it can be specified with what power the controllable energy conversion systems 2 minimum and maximum should be operated taking into account the non-controllable energy conversion systems 2 . For example, in the case of non-controllable consumption, it is possible to use the maximum permissible power, i.e. the aggregated upper power limit curve 66 to reduce at least partially. The reduction can be different, depending on how high the "level" of optimization should be. Such an optimization is exemplified in 21st shown.

In the 21st are the power limit curves optimized in this way, i.e. the adjusted aggregated lower power limit curve 61 as well as the adjusted aggregated upper power limit curve 62 shown. The space between the lower power limit curve 61 and the upper power limit curve 62 is the area in which the energy management system is available at the time 1 by means of the corresponding control and regulation unit 7th a control or regulation of individual energy conversion systems 2 can make.

List of reference symbols

1

Energy management system

1a

local energy management system

1b

higher-level energy management system

2

Energy conversion system

3

Supply network

4th

Energy demand / demand

5

Energy storage

6, 6a, 6b, ..., 6n

Sensor / counter

7th

Control and regulation unit

8th

Control command

9

Measurement data

10

Energy control center

11

interface

12th

Link unit / gateway

13

Energy flow

14th

external forecast

15th

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

21st

Disaggregation

22nd

first floor

23

second level

24

Target command

25th

Control command

26th

abstract model of an energy conversion system

27, 27a

first (lower) energetic limit curve

28, 28a

second (upper) energetic limit curve

29

Demand forecast

31

Historical readings

32

Plant operation restrictions

33

Mathematical Algorithms

34

first optimizations

35

configuration

36

Energetic interface

37

common energy demand

38

Reference system

39

Generation forecast

40

electrical consumer

41

Photovoltaic system (PV system)

42

Electric vehicle

43

Charging box

45

Connection socket

46

first battery

47

Battery inverter

48

engine

49

Controllable energy generator

50

further battery

51

controllable consumer

52

lower power limit curve

53

upper power limit curve

54

forecast performance P

55

forecast mobility needs

56

Course of the forecasted charging capacity Esum prog

57

cumulative total Esum

58

The storage _{charge level is} E _sp

59

aggregated lower energetic limit curve

60

aggregated upper energetic limit curve

61

adjusted aggregated lower power limit curve

62

adjusted aggregated upper power limit curve

63

processing

64

second optimization

65

aggregated lower power limit curve

66

aggregated upper power limit curve

Formula symbol Meanings P _AV Electrical power of general consumption (electrical consumer) (40) P _E-FZG Electrical power that is consumed or output by an electric vehicle (42) P _{LS Anl} Services for the performance levels of an energy conversion system (2) on the side of the primary energy form 1 (e.g. electrical energy) P _{E-FZG LS0} Power at power level 0 of an electric vehicle (42) on the side of primary energy form 1 (e.g. electrical energy) P _{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) M _{P Anl} Power control mode of an energy conversion system (2), for example continuous power control or discrete power control in stages P _{from appendix} Electrical power in the state from the energy conversion system (2) T _{Abs Anl} Switch-off time constant, corresponds to the time required for the process of switching off an energy conversion system (2) α _Appendix 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 P _{Anl EF 1 LS 0} Power of the energy conversion system (2) at power level 0 on the energy form 1 side P _{Anl EF 2 LS 0} Power of the energy conversion system (2) at power level 0 on the energy form 2 side P _{Anl EF 1 LS max} Power of the energy conversion system (2) at maximum power level on the energy form 1 side P _{Anl EF 2 LS max} Power of the energy conversion system (2) at the maximum power level on the energy form 2 side E _{Sp cap Anl EF1} 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 E _{Sp cap Anl EF2} Electrical storage capacity based on energy form 2 corresponds to the storable energy in the storage device (5), which is assigned to the energy conversion system (2), based on the side of energy form 2 Q _{Sp cap Anl EF2} Thermal storage capacity related to energy form 2, corresponds to the thermally storable energy in the memory (5), which is assigned to the energy conversion system (2), related to the side of energy form 2 E _{Sp kap battery} Storage capacity of the battery of an electric vehicle (42) for energy form 2 E _{Sp kap E-FZG} Storage capacity of an electric vehicle (42) based on the energy form 1 σ _{P Anl} Power noise of an energy conversion system (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 _{prog min} Power curve according to the lower power limit curve (52), corresponds to forecast values P _{prog max} Power curve according to the upper power limit curve (53), corresponds to forecast values E _{prog min} Cumulative energy curve of the lower energetic limit curve (27), corresponds to forecast values E _{prog max} Cumulative energy curve of the upper energetic limit curve (28), corresponds to forecast values E _{Sp is} Course 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 Electric power curve for an energy conversion system (2), here the charging power for an electric vehicle (42) P _prog Predicted power for an energy conversion system (2), which is to be covered as demand by the energy conversion system (2), here the predicted charging power of an electric vehicle (42) E _{sum prog} 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 Δt Time interval between two counting steps i τ Number of maximum counting steps taking into account a period to be considered (forecast duration) and a time interval between the counting steps P _{LS max} Performance at maximum performance level P _{BSP LS0} Power at power level 0 of a battery storage (5) on the side of primary energy form 1 (e.g. electrical energy) P _{BSP LSmax} Power at the maximum power level of a battery storage (5) on the side of the primary energy form 1 (e.g. electrical energy) E _{Sp kap E-FZG} Storage capacity of a battery store (5) based on the energy form 1 P _LS Services for the performance levels of several energy conversion systems (2) on the side of the 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 _off Electrical power in the state of several energy conversion systems (2) T _Abs Switch-off time constant, corresponds to the maximum time that the process of switching off several energy conversion systems (2) requires α Energy conversion index of a combination of several energy conversion systems (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 P _{EF 1 LS 0} Performance of several energy conversion systems (2) at performance level 0 on the energy form 1 side P _{EF 2 LS 0} Performance of several energy conversion systems (2) at performance level 0 on the energy form 2 side P _{EF 1LS max} Power of several energy conversion systems (2) at maximum power level on the energy form 1 side P _{EF 2 LS max} Power of the energy conversion system (2) at the maximum power level on the energy form 2 side E _{Sp cap} 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 σ _P Power noise from 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 P̃ _LS Auxiliary variable as a derivation from the performance levels, the energy conversion indicators, knowledge of the needs to be covered (4) and the reference system to be defined

QUOTES INCLUDED IN THE DESCRIPTION

This list of the documents listed by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent literature cited

• DE 102009044161 A1 [0010]
• DE 102005056084 A1 [0010]

Claims (10)

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 and regulation unit (7) being provided in the energy management system (1), characterized in that a technology-independent abstract model (26) is generated for each energy conversion system (2) with its respective static parameters (17) and its respective dynamic parameters (18), 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. Procedure according to Claim 1 , characterized in that an abstract and aggregated model (16) is formed from two or more abstract models (26) each from 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). Procedure according to Claim 1 or 2 , characterized in that an aggregation (19) takes place with the aid of energetic interfaces (36), a reference system (38), mathematical algorithms (33) and common energy requirements (37). Method according to one of the Claims 1 to 3 , characterized in that during the aggregation (19) auxiliary variables (20) are formed, the auxiliary variables (20) formed from the static parameters (17a) and (17b) of the abstract models (26) during the aggregation (19) in the static parameters (17) of the abstract and aggregated model (16) are stored and the auxiliary variables (20) formed from the dynamic parameters (18a) and (18b) of the abstract models (26) during the aggregation (19) in the dynamic parameters (18) of the abstract and aggregated model (16) are stored. Method according to one of the Claims 2 to 4th , 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) and output variables of the aggregation (19b), the demand forecast (29) and the generation forecast (39) can be used. Method according to one of the Claims 1 to 5 , characterized in that control commands (25) for energy conversion systems (2) by means of a disaggregation (21) using target 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. Method according to one of the Claims 1 to 6th , 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). Method according to one of the Claims 1 to 7th , characterized in that the second optimization (64) takes place according to economy, energy efficiency or peak load capping. Method according to one of the Claims 1 to 8th , characterized in that operating limits such as a heat demand to be met by the energy conversion system (2), the availability of an electric vehicle (42), a storage status of a thermal or electrical storage device (5) and a maximum possible or minimum necessary feed into electrical supply networks (3) be taken into account. Method according to one of the 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).

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