Method for controlling an energy swarm system and computer and database system for carrying out the method

Abstract

Method for controlling an energy swarm system (2) comprising one or more energy clusters (1), each energy cluster (1) comprising one or more thermal, electrical and / or material energy generators and / or energy consumers and / or energy storage devices, the method comprising the following steps, which can be carried out iteratively: a) determining and updating future-oriented energy flow schedules (10) for each individual energy cluster (1) by cluster control units (11) which map the individual energy flows (9) in the energy cluster (1) in a predetermined time grid; b) Determination and updating of future-oriented energy flexibility offers (6) of the individual energy clusters (1) by the cluster control units (11), which, taking into account the associated energy flow schedule (10) and boundary conditions for the energy cluster (1), map future realizable energy potentials of the energy cluster (1) in the specified time frame; c) transmitting the energy flow schedules (10) and the flexibility offers (6) of the individual energy clusters (1) to a central swarm control unit (12) of the energy swarm system (2) after determining or updating according to one of steps a) or b); d) offering flexibility offers (6) broken down according to the specified time grid by the swarm control unit (12) to energy network operators (13) and / or energy suppliers at least one time cycle of the time grid before the energy can be physically provided; e) Determination of a future-oriented energy exchange requirement in the specified time frame between the energy clusters (1) from the energy flow schedules (10), the flexibility offers (6) and external energy demands (8) by the swarm control unit (12); f) determining flexibility requests (7) based on the future energy exchange requirement for using the individual flexibility offers (6) between the energy clusters (1) or for making flexibility offers (6) available to another energy swarm system (2) or to an energy grid (3) or an energy supplier; g) transmitting flexibility requests (7) to the cluster control units (11) and updating the flexibility offer to other swarm control units and / or energy network operators and / or energy suppliers; h) adjusting the energy flow schedules (10) by the cluster control units (11) according to transmitted flexibility requests (7) and controlling the energy flows (9) in the energy clusters (1) according to the adjusted energy flow schedules (10).

Description

[0001] The invention relates to a method for controlling energy grids, energy swarm systems, and / or energy clusters. Furthermore, the invention relates to a computer and database system for automatically executing such a method.

[0002] The increasing need to integrate volatile, renewable energy sources into energy grids is increasing both the requirements for grid stabilization regulation and the requirements for the integration of decentralized energy generation systems, which must be integrated into the energy grids using a standardized process. Currently, these requirements for energy distribution networks are primarily met by reactionary solutions that analyze the current centralized generation of electrical and / or thermal energy, the current energy consumption, and the current distribution of electrical and / or thermal power in the energy grids. Based on these findings, they initiate steps to stabilize the grid and redistribute energy volumes within the energy grid.

[0003] The skilled person recognizes that, according to the first law of thermodynamics, energy can neither be generated nor consumed, but only converted. Whenever reference is made in this document to energy generation, energy consumption, or energy storage, this refers to the conversion of energy into another physical form.

[0004] For example, WO 2018 / 114404 A1 describes a method for structuring an existing energy grid, whereby different grid components are grouped according to their properties and control limits. Each group is assigned control processes that must be executed when trigger criteria are reached to ensure compliance with the control limits. The claimed method thus reacts to trigger criteria and applies certain control processes when the trigger criteria are met. This corresponds to a top-down control approach.

[0005] Bremer, Jörg; Lehnhoff, Sebastian: Assessing the Similarity of Flexibility in Renewable Energy Generation in the Smart Grid. Smart Cities / Smart Regions: Technical, Economic, and Social Innovations. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. pp. 611624. ISBN 9783658252106. https: / / link.springer.com / chapter / 10.1007 / 9783658252106_47 [accessed on March 24, 2025] shows that for the reliable functioning of a future smart grid in which decentralized energy producers coordinate themselves to also responsibly assume the classic control and regulation tasks of operational management, adequate modeling of the flexibilities of the participating plants is essential. Approaches already exist for this. New use cases require a comparison of these flexibilities with one another, and suitable key performance indicators are needed for this.In addition to a review of known metrics for this use case, a new variant based on the Jaccard index for the similarity of sets is proposed. The extended version uses decoders that can be automatically derived from flexibility models. A simulation study evaluates the applicability and accuracy of the assessment using the newly proposed approach in comparison with established metrics.

[0006] Büchner, Peter: From Local Power Generators to Virtual Power Plants: A Strategy for the Expansion of Electrical Energy Systems. In: Scientific Journal of the Technical University of Dresden, 56, 2007, 34, p. 105110. https: / / nbnresolving.org / urn:nbn:de:bsz:14ds120057747557996347 [accessed on March 24, 2025] shows that the constant availability of the "noble energy" electricity is taken for granted by businesses and households. With the integration of more and more decentralized power generators, which generate electricity primarily from renewable energy sources, the operation of the electrical energy system, which is oriented towards large central power plants, must be reconsidered. One possible strategy in this regard could be "virtual power plants" (VPP), which can be integrated into central systems as an intelligent network of smaller units.This gives rise to technical and organizational problems, which are addressed in this article from the perspective of the research results of a DFG graduate school.

[0007] The constant availability of the "precious energy" electricity is taken for granted by businesses and households. With the integration of more and more decentralized power generators, which primarily generate electricity from renewable energy sources, the operation of the electrical energy system, which is oriented towards large central power plants, must be reconsidered. One possible strategy for this could be "virtual power plants" (VPPs), which can be integrated into central systems as an intelligent network of smaller units. This raises technical and organizational problems, which are addressed in this article from the perspective of the research results of a DFG graduate school.

[0008] Mashlakov, Aleksei [et.al.]: Use Case Description of Real-Time Control of Microgrid Flexibility. In: 2018 15th International Conference on the European Energy Market (EEM), September 24, 2018, p. 15. ISSN 21654093. https: / / doi.org / 10.1109 / EEM.2018.8469218 [accessed March 24, 2025] shows that the increasing number of distributed energy resources requires greater flexibility at the distribution grid level. One way to achieve this flexibility is to aggregate these resources into microgrids and further monitor them as part of active grid management. Among other things, these flexibility services lack standardized information and communication technology solutions, which hinders their implementation. This study assesses the required communication, information, and functional competencies for such services and describes them using use case modeling at smart grid architecture model levels.The paper focuses in particular on an information exchange based on a web application programming interface called Smart API. The study's findings present a smart grid architecture that would enable real-time control of microgrid resources in active grid management through flexibility market services.

[0009] Virtual power plant. In: Wikipedia, The Free Encyclopedia. Edited on: March 24, 2025. URL: https: / / de.wikipedia.org / w / index.php?title=Virtuelles_Kraftwerk&stableid=200745946[accessed on March 24, 2025] shows that a virtual power plant is an interconnection of decentralized power generation units, such as For example, photovoltaic systems, hydroelectric power plants, biogas and wind turbines, combined heat and power plants, and battery storage power plants are combined into a network. This network reliably provides electrical power and can thus replace supply-independent power from large power plants. The term "virtual power plant" refers to the combination of several locations—but not to the fact that no electricity is generated at them. Other common terms are combined cycle power plants, swarm power plants and DEA clusters (= clusters of decentralized generation plants). An important aspect of virtual power plants is the marketing of electricity and the provision of system services from a network of small, decentralized plants. Virtual power plants involve costs for communication and the effort of central control. The term "virtual power plant" encompasses both visions of a future power supply and existing business models.

[0010] Zhang, Gao; Jiang, Chuanwen; Wang, Yu: Comprehensive review on structure and operation of virtual power plant in electrical system. In: IET Generation, Transmission & Distribution, 13, 15.11.2018, 2, pp. 145-156. - ISSN 1751-8687. https: / / digital-library.theiet.org / content / journals / 10.1049 / iet-gtd.2018.5880 [accessed on 24.03.2025] shows that the rapid growth of distributed energy resources is slowed by low capacity and volatility, leading to consumption problems and investment barriers. As an effective integration and management technology, the virtual power plant (VPP) becomes a suitable cornerstone for the future development of renewable energy. Based on current scientific research results, this study aims to provide a detailed overview of VPPs from the internal perspective (e.g., integration and operation of energy resources) to the external aspect, including participation in the electricity market.In accordance with market diversity, various problems in VPP bidding strategy optimization are formulated, and their corresponding mathematical solution methods are extensively reviewed. To extract the characteristics of VPP, a comparison between energy management techniques (e.g., VPP, microgrid, active distribution system, and load aggregator) is conducted, and the advantages and shortcomings of VPP are also addressed. At the same time, realistic deployment scenarios of VPP in European electricity markets are elaborated to verify feasibility and applicability. To better accommodate the future development of renewable energy, a flexible structure and effective management mechanism for VPP should be established, leading to technical innovation and management reform.With this triple development orientation, VPP, as a coordinated and operational entity, will undoubtedly improve the use and management of renewable energy sources.

[0011] In Villar, Jose; Bessa, Ricardo; Matos, Manuel: Flexibility products and markets: Literature review. In: Electric Power Systems Research, 154, September 15, 2017, pp. 329-340. - ISSN 0378-7796. https: / / www.sciencedirect.com / science / article / pii / S0378779617303723 [accessed March 24, 2025], flexibility products and flexibility markets are examined that are currently being discussed or developed to support the operation of power systems in their evolving environment. This evolution is characterized by the increase in renewable generation and distributed energy resources (including distributed generation, self-consumption, demand response, and electric vehicles). The article is an attempt to review and classify flexibility products considering their main characteristics such as scope, purpose, location or provider and to summarize some of the main approaches to the design and implementation of flexibility markets.It also highlights the key gaps in the current literature and the most promising research directions for future work. Ali, Syed Saqib; Choi, Bong Jun: State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review. In: Electronics, 9, June 22, 2020, 6, pp. 1–25. - ISSN 2079-9292. https: / / www.mdpi.com / 2079-9292 / 9 / 6 / 1030 [accessed March 24, 2025] shows that the global energy system is currently undergoing a revolutionary transformation due to the integration of various distributed components, including advanced metering infrastructure, communications infrastructure, distributed energy resources, and electric vehicles, to improve the reliability, energy efficiency, management, and security of the future energy system. These components are becoming increasingly closely integrated with the Internet of Things.They are expected to generate a large amount of data to support various applications in the smart grid, such as distributed energy management, generation forecasting, grid health monitoring, fault detection, residential energy management, and more. With these new components and information, artificial intelligence techniques can be used to automate and further improve the performance of the smart grid. In this article, we provide a comprehensive overview of the latest artificial intelligence techniques to support various applications in a decentralized smart grid. In particular, we discuss how artificial intelligence techniques are being used to support the integration of renewable energy sources, the integration of energy storage systems, demand response, grid and home energy management, and security.Since the smart grid encompasses diverse actors such as energy producers, markets, and consumers, we also discuss how artificial intelligence and market liberalization can potentially contribute to increasing the overall social welfare of the grid. Finally, we present further research challenges for the large-scale integration and orchestration of automated distributed devices to realize a truly smart grid.

[0012] Antonopoulos, Loannis [et.al.]: Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. In: Renewable and Sustainable Energy Reviews, 130, 10.06.2020, pp. 109899:1 - 109899:35. - ISSN 1364-0321. https: / / doi.org / 10.1016 / j.rser.2020.109899 [accessed on 24.03.2025] shows that in recent years there has been increasing interest in demand response (DR) as a means of providing flexibility and thus improving the reliability of energy systems in a cost-effective manner. However, the high complexity of the tasks associated with DR, combined with the use of extensive data and the frequent need for near-real-time decisions, has led to Artificial Intelligence (AI) and Machine Learning (ML) - a subfield of AI - recently emerging as key technologies for enabling demand-side response.AI methods can be used to address various challenges, from selecting the optimal group of consumers to respond, learning their characteristics and preferences, dynamic pricing, scheduling and controlling appliances, to learning incentives for participants in DR programs and rewarding them fairly and economically efficiently. This paper provides an overview of AI methods used for DR applications based on a systematic review of over 160 articles, 40 companies and commercial initiatives, and 21 large-scale projects. The articles are classified both with regard to the AI / ML algorithms used and their application area in energy data reduction.It then presents commercial initiatives (from both start-ups and established companies) and large-scale innovation projects that have applied AI methods for energy data reduction. The article concludes with a discussion of the advantages and potential limitations of the investigated AI techniques for various DR tasks and outlines directions for future research in this rapidly growing field.

[0013] WO 2018 / 224 249 A1 relates to a method for operating a plurality of technical units as a network in an electrical distribution network. A central controller is used to receive flexibility data from each technical unit, wherein the flexibility data from the unit is used to specify a power interval within which the electrical power of the unit may or does vary in an expected manner. Depending on a predetermined optimization objective of the network, an individual, non-binding stimulus function is determined for each unit from the flexibility data of each unit and provided to the respective unit.In response to the individual stimulus function, a respective plan is then received from each unit, which describes a time course of the power exchange planned according to a local optimization objective of the unit, and an overall plan for the network is generated from the plans of the units.

[0014] The increasing emergence of decentralized, often renewable energy generation and conversion plants creates the need to utilize even small amounts of decentralized control power to stabilize energy grids. Since this cannot be ensured by a purely reactionary approach, the object of the invention is to provide a method for stabilizing energy grids that is capable of tapping decentralized energy potentials regardless of their size and making them usable for grid stabilization. At the same time, a system is to be specified with which this problem can be solved, preferably in an automated manner.

[0015] The present object is achieved by a method according to the invention for controlling an energy swarm system. According to the invention, an energy swarm system has one or more energy clusters, each energy cluster having one or more thermal, electrical and / or material energy generators and / or energy consumers and / or energy storage devices. An energy swarm system according to the invention can therefore have only one energy cluster, but can also have a plurality of energy clusters. An energy cluster within the scope of the invention represents at least one unit for energy generation, energy consumption and / or energy storage that can no longer be physically separated or split, but can also be a mixed network of several energy generators and / or energy consumers and / or energy storage devices, which, however, forms, for example, an economic unit.According to at least one embodiment, coupled energy generators are also included in an energy cluster, which convert one energy flow into two energy flows, for example a material energy flow into both thermal and electrical energy flows. The method according to the invention is further applicable to energy clusters that are physically connected, but can also be applied to energy clusters and / or energy swarm systems that have physically non-connected, especially spatially separated, subunits. The executability of the method according to the invention is therefore independent of the structure of the energy clusters. According to the invention, the method according to independent claim 1 has the following steps, which are carried out iteratively: First, energy cluster control units, of which each individual energy cluster has one, determine and update, in step a), future-oriented energy flow schedules for each individual energy cluster. These schedules map the individual positive and negative energy flows within the energy cluster in a predefined time frame with constant or variable increments throughout the day. Energy flow is defined as the transfer of a physical amount of energy to, from, or between technical components. In a next step b), the cluster control units determine and update future-oriented energy flexibility offers of the individual energy clusters, which map the future realizable energy potential of the energy cluster in the predefined time frame.The associated energy flow plan of the energy cluster and boundary conditions for the energy cluster, such as storage levels or capacity limits of generation plants, are taken into account. The flexibility offers according to the invention indicate, broken down in a specified time frame, how far the energy flows in the energy cluster can deviate, positively or negatively, from the energy flows in the energy flow schedule. The flexibility offers do not represent actual energy flows, but rather energy potentials that can be accessed as needed. Like the energy flow schedules, the flexibility offers are also determined through forecasts.

[0016] In a next step c), the energy flow schedules and the flexibility offers of the individual energy clusters are transmitted to a central swarm control unit of the energy swarm system. This transmission preferably occurs after determining or updating according to one of steps a) and / or b). Thus, the inventive concept also encompasses executing only one of the two steps a) or b), skipping the other step, and then transmitting the energy flow schedules and flexibility offers in step c).

[0017] In the following step d), the swarm control unit offers flexibility offers broken down according to the specified time frame to energy grid operators and / or energy suppliers, with the offer being made at least one time cycle of the time frame before the offered amount of energy is physically available. It is obvious to a person skilled in the art that at least one physical connection, via which an amount of energy can be transported, must exist between the energy clusters of the energy swarm system and the energy grid operator and / or energy supplier in order to enable the exchange of such an amount of energy. If such a connection exists, the method according to the invention can be used even if the energy swarm system is spatially removed from the energy grid operated by the energy grid operator and / or the supply area of the energy supplier.

[0018] According to at least one embodiment, the flexibility offers of the cluster control units can be aggregated by the swarm control unit before being offered to the energy grid operators and / or energy suppliers, so that the energy grid operators and / or energy suppliers can be offered a larger amount of energy flexibility bundled from several smaller flexibility offers, a so-called package. Such packages typically have a size of 100 kW or more, which is specified by the energy grid operator. From this, the person skilled in the art will recognize in particular the bottom-up approach chosen by the invention, in contrast to the top-down control chosen in the prior art.

[0019] As will be apparent to a person skilled in the art, aggregating flexibility offers is only possible if the energy swarm system has a plurality of energy clusters, each of whose cluster control units has sent a flexibility offer to the control unit of the energy swarm system, or to a certain extent. According to at least one embodiment, energy network operators and energy suppliers can then choose from all non-aggregated and / or aggregated flexibility offers provided to them by the swarm control units of different energy swarm systems and retrieve these through external flexibility requests. It is common knowledge to deduce from the explanations given so far that an energy network managed by an energy network operator oran energy network supplied with energy by an energy supplier may have one or more energy swarm systems, each of which may in turn have one or more energy clusters.

[0020] Within the scope of the invention, the swarm control units of the energy swarm systems, or the swarm control unit of the energy swarm system, can determine optimized energy utilization in the energy swarm system from the transmitted energy flow schedules and flexibility offers before the flexibility offers are offered to energy grid operators and / or energy suppliers in step d). Thus, energy surpluses and energy deficits in the energy clusters can initially be balanced as effectively as possible within the energy swarm system, controlled by the swarm control unit, before the latter forwards the remaining energy quantity, which can be either an energy surplus or an energy deficit, to the energy suppliers and / or energy grid operators as a flexibility request or offer. The swarm control unit can also perform distribution based on aspects relevant to the grid stability of the energy grid.Overall, when determining this optimized energy utilization in the energy swarm system, the existing energy network structure, which can specify physical limits for energy distribution, existing contractual obligations, as well as achievable sales revenues from the sale of flexibility offers can be taken into account.

[0021] According to at least one embodiment, the energy flow schedules and / or flexibility requests can be determined based on data that is specified once by a user at the beginning of the process, for example, upon commissioning of the energy cluster. However, it is also possible to use historical energy generation, energy storage, and energy consumption data to determine and update the energy flow schedules and / or flexibility requests, and to forecast the future using forecasts based on infrastructure data, weather, seasonal, and calendar data, contract data, price trends, consumer behavior, and / or legal requirements. A person skilled in the art will recognize that other data can also be incorporated here, which influences the forecasting of the energy flow schedules and / or flexibility requests or makes the forecast more reliable.The above list is therefore only exemplary and does not claim to be complete. When applying such a method, the energy flow schedules are not calculated independently of the system behavior observed over time, as would be the case with a one-time initialization of the process step for determining and updating the energy flow schedules. Instead, the calculation and updating of the energy flow schedules is continuously improved. This improvement becomes increasingly precise with increasing runtime of the method, as more and more historical energy generation, energy storage, and energy consumption data can be used to improve the quality of the update.

[0022] According to the invention, the determination and updating of the flexibility offers by the cluster control units in step b) can be carried out in such a way that the greatest possible deviation from the calculated energy flow schedule is possible over the longest possible period of the time grid, i.e. over as many cycles of the time grid as possible, but remains constant over the period. Offering energy blocks with constant power as a flexibility offer facilitates further processing at higher levels. Energy blocks that span a long period of time and have a constant value result in lower computational effort than energy blocks that only cover a short period of time, since in the case of long-lasting energy blocks, fewer computing operations per unit of time may be necessary to process the amount of energy. Constant energy or flexibility blocks also support the stability of forecasts, sinceThe exploitation of extreme potentials is avoided in order to maintain a constant energy or flexibility block. Extreme potentials are thus not overinterpreted, whereas extreme potentials that can actually be accessed are available as control reserves to maintain stability while adhering to energy flow schedules. However, experts are also aware of other objectives that can be used to determine flexibility offers.

[0023] When determining and updating energy flow schedules in step a), a similar goal can be pursued: ensuring that the energy flows in the energy flow schedule remain constant over the longest possible period. This also contributes to the stability of the energy supply of the energy cluster.

[0024] The invention also encompasses the possibility that the flexibility offers can be forwarded directly by the swarm control units or can be bundled before being forwarded and sent, for example, to secondary swarm control units, i.e. to swarm control units that are arranged in the same energy grid as the sending swarm control unit, wherein a physical connection exists between the associated energy swarm systems. External energy demands can also be retrieved from these neighboring swarm control units. It is within the scope of the expert's skill to provide a solution for this, which can include an energy grid operator that provides the physical grid that connects the two or the plurality of energy swarm systems.

[0025] According to the invention, the method may further comprise one or more of the following steps after step d) of offering flexibility offers by the swarm control unit to energy network operators and / or energy suppliers: In step e), the swarm control unit of the energy swarm system uses the energy flow schedules, flexibility offers, and external energy demands to determine a future-oriented energy exchange requirement between the energy clusters within a specified time frame. The energy exchange requirement indicates whether and how energy can be redistributed between the energy clusters in the energy swarm system. The calculation can incorporate a wide variety of boundary conditions, such as economic or ecological boundary conditions, etc., which are familiar to the expert.In contrast to the previously described optimization of the energy flow(s) in the energy swarm system prior to transmitting the flexibility offers, which can be either directly transmitted or aggregated, the invention determines the future energy exchange demand between the energy clusters after receiving the external energy demands from energy suppliers and / or energy grid operators. These can thus be taken into account when determining the energy exchange demand.

[0026] In a step f), flexibility requests are determined based on a future-oriented energy exchange requirement for utilizing the individual flexibility offers between the energy clusters or for making flexibility offers available to another energy swarm system, an energy grid, or an energy supplier. In a further step g), flexibility requests are transmitted from the swarm control unit to the cluster control units. At the same time, in this step, the flexibility offer is updated to swarm control units of other energy swarm systems, to energy grid operators, or to energy suppliers.In a further step h), according to the invention, the energy flow schedules in the energy clusters are adapted by the cluster control units in accordance with the transmitted flexibility requests. The cluster control units then control the energy flows in the energy clusters in accordance with the adapted energy flow schedules, for example by means of local energy management systems.

[0027] According to at least one embodiment, the energy flows of an energy cluster can be monitored by a control unit. If a deviation in an energy flow determined by the control unit necessitates an adjustment of the energy flow schedule, or if the cluster control unit or an energy management system of the energy cluster instructs the energy cluster to adjust the energy flow to the corresponding value in the energy flow schedule, an energy flow plan adjusted by the cluster control unit and a flexibility offer adjusted thereto can be transmitted according to step c). The control unit can therefore have a feedback system that feeds the measured values from the energy management systems of the energy cluster back to the control unit, so that the control unit can perform a target-actual value comparison to check whether the specified forecast energy flows actually correspond to the actual energy flows in the energy cluster.If a target value in the energy flow plan deviates from a monitored actual value, the energy flows in the energy cluster can be adjusted back to the energy flow plan value. If a determined deviation of an energy flow from the value in the energy flow schedule requires an adjustment of the energy flow schedule, for example, because the energy flow can no longer be aligned with the energy flow schedule, for example, due to an error in an energy generation forecast, an adjusted energy flow plan and an adjusted flexibility offer can be determined.

[0028] Preferably, such a control is implemented independently of the structure of the energy cluster, i.e. of the existing energy converters and / or storage devices. This enables the platform-independent provision of the same cluster control units and monitoring units, which leads to easier maintenance and implementation for the provider and lower prices for the user. According to at least one embodiment, this can be made possible, for example, by a standardizing protocol interface that is arranged in the information flow between the measured variable detection of the energy management system in the energy cluster and the monitoring unit. This can act as a translator unit between the communication structure orthe fieldbus systems of the energy management systems and the communication structure of the cluster control units and normalize the measured variables in such a way that they can be processed by the cluster control unit independently of the underlying physical measured variable.

[0029] Within the scope of the invention, the external energy requests made by the energy grid operators and / or energy suppliers and / or secondary swarm control units to the swarm control unit that sent the flexibility offers can be configured as contracts. Requested energy quantities and / or services and / or provided energy quantities and / or services can be assigned an economic and / or ecological equivalent. Those skilled in the art will recognize that economic / ecological equivalents can take a variety of forms, for example, as a monetary amount, but also in the form of, for example, pollutant emission certificates or other services that represent some form of value in the energy supply. The provision of a voucher system is also encompassed by the inventive concept.

[0030] According to at least one embodiment, the energy flow schedules cover a period of minutes, hours, days, weeks, or months from the time of calculation, depending on the application. Power and energy values are specified in a predetermined time grid with time increments whose length is minutes, hours, days, or fractions thereof. For example, the time grid, which can be used by energy grid operators and / or energy suppliers as well as swarm control units and cluster control units, is 15 minutes, and the forecast horizon of the energy flow schedules is 48 hours from the time of calculation in order to enable early response to any temporal fluctuations in the energy generation or consumption curve and also to allow the provision of control energy. It is within the scope of expert skill to select other time periods or time increments, which can be adapted depending on the application.

[0031] According to at least one embodiment, the method is repeated at regular intervals. Preferably, the method is repeated in 15-minute increments; however, the inventive concept encompasses the possibility of performing the method according to the invention at other regular or irregular intervals (time intervals) and adapting the time intervals depending on the time of day or day of the week, e.g., at night or on weekends and holidays or other forecasts, such as weather data, stock market prices, changes in legislation, etc.

[0032] The regular time intervals at which the method is carried out can correspond to the time intervals of the specified time grid in which the flexibility offers and energy flow schedules are mapped. However, the inventive concept also encompasses the possibility of the method being carried out at regular or irregular intervals (time intervals) other than the time steps of the specified time grid. In this case, however, the time intervals and intervals should have a common denominator.

[0033] In one possible embodiment of the method according to the invention, a calculation time grid has different time steps than a communication time grid, the time grid in which the energy flow schedules and the flexibility offers for communication between cluster control units, swarm control units, and energy grid operators or suppliers are broken down. Preferably, the energy flow schedules and flexibility offers are calculated at the cluster level according to the calculation time grid and then converted to the temporal resolution of the communication time grid when the energy flow schedules and flexibility offers are communicated to the swarm control unit. Further preferably, the step size of the calculation time grid is selected to be larger than that of the communication time grid or even variable in order to increase the computational efficiency of the method.If the method according to the invention has a computing time grid with variable step size, the step size can be adapted to the complexity of the calculation. This makes it possible to perform computationally intensive or numerically unstable calculation steps with smaller step sizes, thus increasing the accuracy and stability of the method. Less computationally intensive steps can be performed with larger step sizes, which conserves computing resources. It is advantageous if the step sizes of the communication time grid and the computing time grid have a common divisor, which facilitates the conversion from the computing time grid to the communication time grid and vice versa.

[0034] The method according to the invention can further comprise a third time grid, e.g., an execution time grid, which has a step size different from the computing time grid and / or communication time grid. This time grid forms the basis for controlling the energy flows at the cluster level by the cluster control units in step h). Similar to the computing time grid, a narrow-meshed execution time grid can be selected for complex control requirements, whereas simple control signals are executed in a more sparse execution time grid.

[0035] According to at least one embodiment, the execution of the method can also be triggered by certain physical events in addition to or as an alternative to execution at regular intervals. For example, if a measured variable exceeds a limit value, an alarm can be generated, which causes the cluster control unit associated with the measured variable and monitoring it to begin executing the method according to the invention.

[0036] Additionally or alternatively, alarms from other, secondary swarm control units and / or other, secondary cluster control units and / or energy grid operators and / or energy suppliers may also require the implementation of the method according to the invention. This may be necessary, in particular, if an adjustment of flexibility requests, flexibility offers, energy flow schedules, or energy redistribution becomes necessary due to system changes detected at short notice.

[0037] According to the invention, the determination and updating of the energy flow schedules in step a) and / or the adaptation of the energy flow schedules in step h) can be carried out by optimizing data models. Such data models can also be used for optimization when optimizing the energy flows in the energy swarm system before transmitting the flexibility offers in step d) or when determining the energy exchange demand in step e). According to at least one embodiment, the data models can be suitable for predicting the future behavior of the energy cluster(s) or the energy swarm system(s). Energy grid structures, the current state of the energy cluster, contract data and / or weather data and / or supply contracts and / or prices for the energy supply can be taken into account.A variation in the model parameters, for example, the power generated by energy generation plants or the amount of energy absorbed by energy storage systems, affects the resulting energy flow schedule. Characteristic values of the energy flow schedules, such as energy supply costs or primary energy consumption, are summarized in an objective function. From the objective function's arguments, a characteristic target value for the energy flow schedule can then be calculated, which should be minimized or maximized, depending on the application. In other words, energy flow schedules are determined, updated, and / or adapted by optimizing a target value, which is the result of an objective function whose arguments are calculated from data models.

[0038] In one possible embodiment of the objective function, the arguments contained in the objective function can be weakened or strengthened by weighting factors in their influence on the target value, which represents the result of the objective function. According to at least one embodiment, for example, the weather data, etc., used in the objective function and / or the data models can be predicted by the cluster control units or swarm control units. However, the invention also encompasses the inclusion of, for example, external forecasts for weather data, etc., in the calculations. According to at least one embodiment, flexible pricing for the energy supply can also be included in the determination, updating, and / or adaptation of the energy flow schedules.For example, according to at least one embodiment, a flexible pricing system can be applied that is based on energy supply and energy demand and determines a current price for the energy supply from these two variables. In order to be able to react proactively to changes in energy behavior in energy grids, one embodiment of the invention provides for the future price development to be predicted by the cluster control devices, the swarm control devices, or an external forecasting unit.

[0039] Mixed-integer linear data models, for example, can be used to determine and / or update energy flow schedules and flexibility offers. These offer the advantage that the linear mapping of the energy systems enables efficient processing of the model data. Thus, even with limited computing power, the energy behavior of energy clusters and / or energy swarms can be reliably and quickly predicted. In such an inventive, for example mixed-integer, linear model, all variables that predict the future system behavior of the energy system represented by the model can be implemented as time series. For example, consumers or producers whose consumption or generation is based on static variables can be described by predicting their output in the form of a time series.According to at least one embodiment, the problem of the lack of accuracy of predictions determined using mixed-integer linear data models can be addressed by repeatedly comparing the real system behavior with the predictions of the data model. If deviations between the prediction and the real behavior are detected, the parameters of the data model can be adjusted to improve the prediction quality.

[0040] In the case of a linear model, the energy converters can essentially be described by matrices or vectors which convert incoming energy flows into outgoing energy flows for each time step, i.e. for which the outgoing time series can be calculated from the incoming time series by multiplying it with the matrix describing the converter. In the sense of energy conservation, no energy may be lost or added in a model of an energy system according to the invention. In order to avoid unsolvable optimization problems which can be caused by these two conditions in the model, according to at least one embodiment, artificial energy sources and energy sinks can be incorporated into the model, which compensate for physically unreasonable energy flows at very high costs in order to thus prevent the model from becoming unsolvable. According to at least one embodiment, the solving of these linear models orLinear optimization algorithms are used to optimize the resulting linear objective functions. It will be apparent to those skilled in the art that the methods according to the invention can also be implemented with nonlinear data models, even if this requires greater computational effort than linear models. However, the accuracy of the model is increased in the nonlinear case.

[0041] The optimization algorithms according to the invention can, for example, calculate total costs for energy supply, CO2 equivalent emissions for energy supply, total energy demand for energy supply, a key figure for distribution grid stability, or a comparable economic, ecological, or technical key figure for energy supply. These variables can be the result of optimized objective functions. The individual functional arguments of the objective functions can be converted into the unit of the objective value, so that essentially only one physical or economic unit predominates in the objective function. The inventive concept encompasses the possibility of implementing this using weighting factors that assign a constant scaling factor to the respective functional argument.A conversion can also be performed during the calculation of the function arguments themselves, eliminating the need for conversion using weighting factors. Both solutions are encompassed by the inventive concept, as are other solutions within the scope of expert knowledge. It is also within the scope of expert knowledge to select the appropriate optimization algorithm for each objective function. For example, a linear minimization algorithm can be used for an objective function that represents the total costs of energy supply in a linear form.

[0042] In one embodiment of the invention, an analysis of the actual past energy behavior and an analysis of the actual past thermal, electrical, and material energy over time is performed during the creation and / or updating of the data models. This analysis allows the model data used by the data models to depict reality to be continuously updated and compared, resulting in a better representation of the actual system behavior by the model. For such a training process, training algorithms from the field of neural networks or genetic algorithms, for example, which are familiar to those skilled in the art, can be used.

[0043] According to at least one embodiment, the control units of the energy swarm systems and / or the energy grid operators and / or the energy suppliers can, for example, select from several flexibility offers provided by different swarm control units. This enables the development of a market concept based on supply and demand, which can find the best economic and / or ecological solution for energy provision at any time.

[0044] Preferably, the method is carried out automatically by one or more computers connected to databases, wherein the cluster control unit and the swarm control unit can be implemented on different end devices or are implemented on the same end device. In the case of spatially connected swarm energy systems consisting of locally adjacent or closely spaced clusters, it may be expedient to execute the swarm control unit of the swarm energy system and the cluster control units or the one cluster control unit of the energy swarms or the energy swarm on the same computer. However, if a swarm energy system has several cluster energy systems that are spatially separated from one another, the swarm control unit and the subordinate control units are preferably implemented on different computers.

[0045] Also encompassed by the invention is a computer and database system with several software and / or hardware units communicating with each other for the automated execution of the method according to the invention.

[0046] The inventive method for controlling an energy swarm system thus offers a way to integrate decentralized energy producers into an energy grid in a simple and cost-effective manner for energy generation and distribution, and to proactively respond to generation bottlenecks or surpluses in the energy grid. Forecast-based flexibility options that are independent of the structure of the energy clusters enable communication between the lower and higher levels of an energy system, which can be applied regardless of the energy system structure.The possibility of bundling small flexibility offers into large flexibility packages makes it possible to provide control energy on a scale relevant to energy distribution networks, whereby both energy network operators, energy suppliers and decentralised energy producers contribute to energy network stability and a solution is found for the increased control effort resulting from the integration of decentralised, statistical producers into energy networks.

[0047] The invention is presented in further detail below with reference to selected exemplary embodiments, which, however, do not limit the scope of the inventive concept. The detailed application examples can be combined with one another, as well as with features that were only generally described above. Fig. 1 Shows an exemplary embodiment of a method for controlling an energy swarm system. Fig. 2 Shows a further embodiment of a method for controlling an energy swarm system. Fig. 3 Shows another exemplary embodiment of a method for controlling an energy swarm system. Fig. 4 Shows a diagram of an energy network according to the invention. Fig. 5 Shows a diagram of electrical power generation in a combined heat and power plant, which is operated once according to the state of the art and once according to the method according to the invention. Fig. 6 Shows an exemplary flexibility offer according to the invention.

[0048] Fig. 1 shows an embodiment of the method according to the invention according to claim 1, in which the method steps a) to d) are carried out iteratively. Furthermore, the Fig. 1 are divided in such a way that it becomes clear whether they take place at the level of an energy cluster 1 or an energy swarm system 2. According to the invention, an energy swarm system 2 comprises at least one energy cluster 1, whereby an energy swarm system 2 can also have several energy clusters 1. The exact design of an energy cluster 1 or the number of energy clusters 1 that an energy swarm system 2 has are irrelevant for the feasibility of the method according to the invention. It is equally irrelevant whether an energy cluster 1, for example, comprises only one or no energy generation plant or energy conversion plant, or a plurality of energy conversion plants, all of which are subordinate to the same cluster control unit 11.By forwarding the future-oriented energy flow schedules of the energy clusters 1 determined and updated in step a) and transmitting the future-oriented energy flexibility offers 6 of the individual energy clusters 1 determined and updated in step b), a communication structure is created between the cluster control units 11 and a control unit 12 of the energy swarm system 2 that is independent of the structure of the energy clusters 1. Step c), which is executed after step b), represents this transmission of the energy flow schedules and the flexibility offers 6 of the energy cluster 1 to a central swarm control unit 12.The control unit 12 of the energy swarm system 2, also referred to as the swarm control unit 12, offers flexibility offers to the energy grid operator 13 and / or energy supplier in a step d), wherein the energy swarm system 2 is preferably arranged in physical proximity to the grid 3 of the energy grid operator 13 and / or to the energy supplier's power generation plant. Particularly preferably, the swarm energy system 2 is even part of the grid of the energy grid operator 13. However, the invention also encompasses the possibility that the energy swarm system is not part of the energy grid 3 of the energy grid operator 13 and is also not arranged in physical proximity to the same grid 3 and / or to the energy supplier's power generation plant.

[0049] The arrows in Fig. 1 visualizes the flow direction of the method. First, in step a), a future-oriented energy flow schedule is determined, after which future-oriented energy flexibility offers 6 are determined in step b). Subsequently, the energy flow schedules 10 and the flexibility offers 6 are sent to a central swarm control unit 12, which represents step c). In step d), which follows step c), the flexibility offers 6 are offered to energy network operators 13 and / or energy suppliers. A reference arrow from step d) to step a) represents the iterative execution of the method according to the invention, wherein it can be provided according to the invention that the energy flow schedules 10 determined in step a) and the energy flexibility offers 6 determined in step b) are only updated and not redetermined during repeated runs of the method according to the invention.

[0050] Fig. Figure 2 shows an energy swarm system 2 comprising several energy clusters 1. It is also Fig. 2 shows that steps a) to c) of the method according to the invention, which are carried out by cluster control units 11, are carried out separately for each cluster 1, which each has a cluster control unit 11, wherein step d) is carried out once for the entire swarm energy system 2, which can only have one swarm control unit 12. The cluster control units 11 thus determine, for each energy cluster 1, separately in step a), future-oriented energy flow schedules 10, which map the individual energy flows 9 in the energy cluster 1 in a predetermined time grid, or update them. In step b), the cluster control units 11 for each energy cluster 1, i.e. in the case of Fig. 2, three future-oriented energy flexibility offers 6 are determined and updated, which, taking into account the associated energy flow schedule and boundary conditions for the energy cluster, map future realizable energy potentials of the energy cluster 1 in the specified time frame. In step c), each cluster control unit 11 transmits the energy flow schedules 10 and flexibility offers 6 of the individual energy clusters 1 calculated by it to the central swarm control unit 12 of the energy swarm system 2 after determining or updating them according to one of steps a) or b).The energy flow schedules 10 and flexibility offers 6 of the individual energy clusters 1 are combined by the swarm control unit 12 of the energy swarm system 2 and offered as itemized flexibility offers 6 to energy network operators 13 and / or energy suppliers according to the specified time grid, wherein the offering takes place at least one time step in the time grid before the energy can be physically provided.

[0051] According to the invention, the flexibility offers 6 sent by the cluster control units 11 to the swarm control unit 12 can be forwarded directly to energy network operators 13 and / or energy suppliers. However, it is also possible for the flexibility offers 6 of several cluster control units 11 or of all cluster control units 11 to be aggregated by a higher-level swarm control unit 12 before being offered to the energy network operators 13 and / or energy suppliers in step d), i.e. to be bundled into flexibility packages, and subsequently offered. It is further encompassed by the inventive concept that the swarm control unit 12 offers the energy network operators 13 and / or energy suppliers a mixture of aggregated and non-aggregated flexibility offers 6. Regardless of the status of the flexibility offers 6, these can be supplemented by energy network operators 13 and / or energy suppliers through external energy demands 7 orexternal flexibility calls can be made.

[0052] In Fig. 3 shows a further exemplary embodiment of an energy swarm system 2. Fig. Figure 3 shows two energy swarm systems 2, each of which, for the sake of simplicity of illustration and description, has only one energy cluster 1. The two energy swarm systems 2 are part of an energy network 3, to which they are assigned after completing the process steps a) to d), which have already been described in connection with the Fig. 1 and Fig. 2, provide flexibility offers 6 and from which the swarm control units 12 of the energy swarm systems 2 receive external energy demands 6. According to at least one embodiment, an exchange of flexibility offers 6 and external energy demands 7 can also take place between the two energy swarm systems 2.

[0053] In a step e), the swarm control units 12 can determine a future-oriented energy exchange demand between the energy clusters 1. This demand is broken down into the specified time frame and is determined from the energy flow schedules 10 transmitted by the cluster control units 11, the flexibility offers 6, and external energy demands 7. If the energy swarm systems 2 have only one energy cluster 1, this step can be omitted.

[0054] In step f), the swarm control units 12 can determine flexibility requests 7 based on a future energy exchange demand. These can be used to utilize the flexibility offers 6 between the individual energy clusters 1, but can also provide flexibility offers 6 to another energy swarm system 2, an energy grid 3, or an energy supplier. The energy exchange demand is divided into packages, the flexibility requests 7, that can be provided by the cluster control units.

[0055] In step g), flexibility requests 7 can be transmitted from the swarm control units 12 to the cluster control units 11. The flexibility offer 6, which can be made available to other swarm control units 12 and / or energy grid operators 13 and / or energy suppliers, is then updated and adjusted. The flexibilities already requested are eliminated from the offers.

[0056] In a step h), the energy flow schedules 10 at the level of the energy clusters 1 are adjusted by the cluster control units 11 according to the transmitted flexibility requests 7. The energy flows 9 in the energy clusters 1 are controlled by the cluster control units 11 according to the adjusted energy flow schedules.

[0057] This method according to the invention can also be run iteratively, as shown by the feedback arrow.

[0058] Fig. 4 shows the exemplary structure of an energy network 3, which has several energy swarm systems 2, which in turn can have several energy clusters 1. In Fig. 4, the energy clusters 1 and energy swarm systems 2 are shown as spatially connected units, however, this is not absolutely necessary according to the invention. The invention also encompasses the combination of spatially distant units into energy clusters 1 and / or energy swarm systems 2. Furthermore, Fig. 4 different configurations of energy swarm systems 2 are indicated, each characterized by a different interconnection of the energy clusters 1 through energy networks 3. The number of energy clusters 1 in an energy swarm system 2 can also vary.

[0059] So in Fig. 4, for example, shows a swarm system 2 which has only one energy cluster system 1. However, swarm systems 2 are also shown which have three energy cluster systems 1. The person skilled in the art will recognize that an even larger number of energy clusters 1 can be provided in an energy swarm system 2. The energy swarm systems 2 are connected by an energy network 3, wherein the energy network 3 has a plurality of energy lines 4. It is within the scope of the invention to understand the energy lines 4 not only as electrical lines, but also as material and / or thermal energy lines or as communication lines, which can also enable an exchange of energy and power as well as data via the energy network 3. The energy network 3 furthermore has connections to other energy networks 3, which enable an exchange of energy between energy networks 3.

[0060] Fig. Figure 4 further shows that each energy cluster 1 is assigned a cluster control unit 11. The cluster control units 11 communicate, for example, via information connections 5, which can be designed as cables or wirelessly and in Fig. 4 are shown as dash-dot lines, with higher-level swarm control units 12. The inventive concept also encompasses the fact that the cluster control units 11 are interconnected via information links 5. The swarm control units 12 are in contact with an energy network operator 13 via information links 5. The information links 5 can, for example, be used to transmit flexibility offers 6 and / or flexibility requests 7 and / or external energy demands 8. However, other signals can also be exchanged between the control units of the energy system via the information links 5.

[0061] Fig. Figure 5 shows a comparison of two diagrams, the first, upper diagram showing the electrical output curve of an energy cluster 1 using the example of a combined heat and power plant (CHP) controlled according to a prior art method. The lower diagram shows the electrical output of a combined heat and power plant controlled according to the method according to the invention. In both cases, time is plotted on the abscissa of the diagrams, whereas a graph for the electrical output of the CHP in kilowatts and a graph for a day-ahead exchange price in euros per megawatt hour are plotted on the ordinate. The solid lines in both diagrams correlate with the day-ahead exchange price. The dashed lines correspond to the electrical output of the CHP.

[0062] The upper diagram shows that when a CHP plant is operated in a heat-controlled manner - a common practice in the state of the art - high day-ahead exchange prices are not exploited to sell electrical energy, although the CHP plant shown here and the energy system in which the CHP plant is located and which supplies it with heat and electrical energy have a certain potential to shift the generation periods in which the CHP plant generates electrical energy.

[0063] Through the inventive exchange of flexibility offers and flexibility requests between energy clusters 1, energy swarm systems 2 and energy grid operators 13 and / or energy suppliers, it is possible, according to at least one embodiment, to access the potential of the CHP plant precisely when the market price for electrical energy is at its highest. Therefore, the dashed line representing the electrical power generation of the CHP plant in the second diagram differs from the first diagram in that it provides a high electrical power precisely when the day-ahead market price is at its highest. The method according to the invention serves not only to economically optimize electricity procurement orElectricity sales, but also grid stability, since the exchange price is composed of supply and demand and is therefore particularly high when supply and demand threaten to drift apart, which endangers the stability of the frequency in the electrical grid.

[0064] Fig. Figure 6 shows an example of how energy flow schedules and flexibility offers can be communicated from cluster control units 11 to swarm control units 12 or from swarm control units 12 to energy grid operators 13 and / or energy suppliers. According to at least one embodiment, this can be done, for example, in table form, possibly also in matrix form, wherein the exemplary table can have eight columns.

[0065] The first column shows the time cycles of a predefined time grid, which forms the common basis for energy flow schedules and flexibility offers of all control units. The eighth column shows values for an energy flow schedule, such as those that can be provided by a power generation plant. The columns in between show in what form and at what price the energy generation of the power generation plant can deviate from the predefined schedule. The third column provides an overview of the power potential for each time cycle that can be provided in a positive direction relative to the energy flow schedule. Column four shows the corresponding positive energy quantity, column two the corresponding price that is charged for the amount of energy requested. Columns five to seven show the price, power and energy in the event of a negative deviation from the energy flow schedule.

[0066] By relating energy quantities and power to a predetermined time cycle inherent in the entire system, a predefined time frame enables fast and unambiguous communication between higher-level and lower-level units. Furthermore, flexibility and potential requests and queries can be made at the same time, as they always refer to the same time frame.

[0067] The Fig. The communication structure presented in Chapter 6, which is based on flexibility offers and energy flow schedules, is independent of the structure of the control units between which the communication takes place. It can therefore be used for a wide variety of applications and, despite or precisely because of its versatility, makes a relevant contribution to exploiting the economic and ecological potential of decentralized energy generation. LIST OF REFERENCE SYMBOLS 1 energy cluster 2 Energy swarm system 3 Energy network 4 Power line 5 Information connection 6 Flexibility offer 7 Flexibility call 8 external energy demand 9 Energy flow 10 Energy flow plan 11 Cluster control unit 12 Swarm control unit 13 energy network operators

Claims

[1] Method for controlling an energy swarm system (2) comprising one or more energy clusters (1), each energy cluster (1) comprising one or more thermal, electrical and / or material energy generators and / or energy consumers and / or energy storage devices, the method comprising the following steps, which can be carried out iteratively: a) determining and updating future-oriented energy flow schedules (10) for each individual energy cluster (1) by cluster control units (11) which map the individual energy flows (9) in the energy cluster (1) in a predetermined time grid; b) Determination and updating of future-oriented energy flexibility offers (6) of the individual energy clusters (1) by the cluster control units (11), which, taking into account the associated energy flow schedule (10) and boundary conditions for the energy cluster (1), map future realizable energy potentials of the energy cluster (1) in the specified time frame; c) transmitting the energy flow schedules (10) and the flexibility offers (6) of the individual energy clusters (1) to a central swarm control unit (12) of the energy swarm system (2) after determining or updating according to one of steps a) or b); d) offering flexibility offers (6) broken down according to the specified time grid by the swarm control unit (12) to energy network operators (13) and / or energy suppliers at least one time cycle of the time grid before the energy can be physically provided; e) Determination of a future-oriented energy exchange requirement in the specified time frame between the energy clusters (1) from the energy flow schedules (10), the flexibility offers (6) and external energy demands (8) by the swarm control unit (12); f) determining flexibility requests (7) based on the future energy exchange requirement for using the individual flexibility offers (6) between the energy clusters (1) or for making flexibility offers (6) available to another energy swarm system (2) or to an energy grid (3) or an energy supplier; g) transmitting flexibility requests (7) to the cluster control units (11) and updating the flexibility offer to other swarm control units and / or energy network operators and / or energy suppliers; h) adjusting the energy flow schedules (10) by the cluster control units (11) according to transmitted flexibility requests (7) and controlling the energy flows (9) in the energy clusters (1) according to the adjusted energy flow schedules (10). [2] Method according to claim 1, wherein a plurality of flexibility offers (6) of the cluster control units (11) are aggregated by the swarm control unit (12) before being offered to the energy network operators (13) and / or to the energy suppliers in step d) and are subsequently offered and the energy network operators (13) and / or energy suppliers can retrieve the non-aggregated or aggregated flexibility offers (6) through external energy demands (8). [3] Method according to one of claims 1 or 2, wherein, before step d), an optimized energy utilization in the energy swarm system (2) is determined by the swarm control unit (12) from the transmitted energy flow schedules (10) and flexibility offers (6), taking into account an existing energy network structure, contractual obligations of the energy swarm system, achievable sales proceeds from the sale of the flexibility offers, environmental criteria, or the like. [4] Method according to one of the preceding claims, in which historical energy generation, energy storage and energy consumption data are used to determine and update the energy flow schedules (10) and are forecast into the future by means of predictions based on infrastructure data, weather and seasonal data, calendar data, contract data, price developments, consumer behavior and / or legal requirements. [5] Method according to one of the preceding claims, wherein the energy flow schedules (10) in step a) are determined such that they provide constant energy flows (9) over as long a period of the time grid as possible and / or the flexibility offers (6) in step b) are determined such that over as long a period of the time grid as possible, a deviation from the energy flow schedule (10) that is as high as possible but remains constant over the period is possible. [6] Method according to one of the preceding claims, wherein the flexibility offers (6) in step d) are also sent to secondary swarm control units (12) and can be retrieved by them by external energy requests (8). [7] Method according to one of the preceding claims, wherein the energy flows (9) of an energy cluster (1) are monitored by a control unit and a transmission according to step c) of an energy flow schedule (10) adapted by the cluster control unit (1) and a flexibility offer (6) adapted thereto takes place if the deviation of an energy flow (9) determined by the control unit makes an adaptation of the energy flow schedule (10) necessary, or the cluster control unit (11) or an energy management system of the energy cluster (1) instructs the energy cluster (1) to adapt the energy flow (9) to the corresponding value in the energy flow schedule (10). [8] Method according to one of the preceding claims, wherein the external energy demands (8) are implemented as contracts between swarm control units (12) and the energy network operators (13) and / or the energy suppliers and / or secondary swarm control units (12) and an economic and / or ecological equivalent is assigned to the requested energy quantities and / or services. [9] Method according to one of the preceding claims, wherein the energy flow schedules (10) cover a period of minutes and hours, days, weeks or months from the time of calculation and power and energy values are specified in a predetermined time grid with time cycles whose length is minutes, hours or days or fractions thereof. [10] Method according to one of the preceding claims, wherein the method is repeated at regular time intervals. [11] Method according to one of the preceding claims, wherein steps a) and b) are carried out in a calculation time cycle which is different from the time step size of the predetermined time grid in which energy flow schedules (10) and flexibility offers (6) are broken down. [12] Method according to one of the preceding claims, wherein the determination and updating of the energy flow schedules (10) in step a) and / or the determination of the energy exchange requirement in step e) and / or the adaptation of the energy flow schedules (10) in step h) and / or the determination of an optimized energy utilization according to claim 3 are calculated by optimizing data models which are suitable for predicting the future behavior of the energy clusters (1), energy swarm systems (2) taking into account the energy network structures, the current state of the energy cluster (1), contract data and / or weather data and / or energy prices and / or further economic and / or ecological specifications. [13] Method according to claim 12, wherein the data models are mixed-integer linear data models and mixed-integer linear optimization algorithms are used to optimize the energy flow schedules (10) and the flexibility offers (6). [14] Method according to claim 12 or 13, wherein the optimization algorithms calculate the total costs for the energy supply, a CO2 equivalent emission for the energy supply, a total energy demand for the energy supply, a characteristic value for distribution network stability or a comparable economic, ecological or technical characteristic value for the energy supply, always under the condition that existing supply contracts are fulfilled. [15] Method according to one of claims 12 to 14, wherein the data models are created and / or continuously updated by analyzing the past energy behavior and analyzing the past thermal, electrical and material energy flows over time. [16] Method according to one of the preceding claims, wherein the swarm control units (12) and / or the energy network operators (13) and / or the energy suppliers can select from a plurality of flexibility offers (6) provided by different swarm control units (12). [17] Method according to one of the preceding claims, wherein the method is carried out automatically by one or more computers connected to databases which are networked with one another. [18] Computer and database system with several communicating hardware and software units for automatically executing the method according to one of the preceding claims.

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