Data processing system for cloud-based monitoring and control of resilient energy supply chains

Abstract

Data processing system for cloud-based monitoring and control of resilient energy supply chains, comprehensive a data acquisition unit for collecting and standardizing data from a plurality of distributed supply-relevant sources, a resilience analysis unit for determining at least one resilience value for at least one chain segment, an infrastructure component, a supply path or a supply network based on the collected data, a dependency mapping unit for generating a machine-readable linking structure between energy infrastructure, supply sources, transport paths, maintenance resources and operational control variables, a simulation unit for simulating at least one disturbance scenario based on the linkage structure and the resilience value, and a control unit for generating, selecting or triggering at least one countermeasure depending on a detected or simulated resilience state.

Description

Technical field

[0001] The invention relates to a data processing system for monitoring, evaluating, and controlling energy supply chains. In particular, the invention relates to a cloud-based system for processing and linking operational, logistical, infrastructural, and supply-related data in order to assess the resilience of energy supply chains in large-scale energy supply environments and to provide control-related measures. Furthermore, the invention relates to a system with modules for data acquisition, resilience analysis, dependency mapping, fault simulation, and action orchestration. State of the art

[0002] In energy supply systems with large-scale generation, storage, transmission, and distribution structures, security of supply depends to a considerable extent on the stability of the underlying energy supply chain. This includes, among other things, generation units, grid components, storage facilities, spare parts supply, supplier relationships, transmission routes, maintenance procedures, and external factors such as weather events or infrastructure disruptions.

[0003] Common monitoring and control solutions typically record individual operating states or display isolated supply and plant data in the form of dashboards or management interfaces. While such systems provide a status overview, they are generally not designed to handle complex dependencies between supply sources, infrastructure, logistics paths, and operational recovery capabilities within a coherent system logic.

[0004] Another disadvantage of known solutions is that disruptions or bottlenecks are often only addressed reactively. In particular, there is frequently a lack of a system that derives a robust resilience state of individual chain segments or the entire network from distributed input data and automatically or semi-automatically generates control-relevant countermeasures from this.

[0005] Furthermore, existing systems are often unable to identify cascading dependencies between infrastructure components, suppliers, transport routes, and time-critical maintenance processes at an early stage. Likewise, simulation-based prediction of future disruption consequences, taking into account alternative procurement, maintenance, or logistics measures, is frequently lacking.

[0006] Therefore, there is a need for a data processing system that monitors energy supply chains via the cloud, analyzes resilience-relevant relationships, simulates the effects of disruptions, and provides suitable control measures to increase security of supply. Object of the invention

[0007] The invention is based on the objective of providing a data processing system that improves, monitors and controls the resilience of an energy supply chain.

[0008] In particular, a system should be created that combines distributed data sources in a cloud-based processing architecture, determines a resilience state for individual chain segments or interconnected network areas, recognizes mutual dependencies, simulates potential disruption effects, and derives measures to stabilize or restore the supply.

[0009] Another task is to provide a system that not only enables the display of states, but also actively contributes to the operational control of resilient energy supply chains. Summary of the invention

[0010] The task is solved by a data processing system for cloud-based monitoring and control of resilient energy supply chains with a data acquisition unit, a resilience analysis unit, a dependency mapping unit, a simulation unit and an action control unit.

[0011] The data acquisition unit is designed to collect and standardize data from a multitude of distributed sources. These data sources include, in particular, generation plants, storage facilities, grid components, supplier systems, maintenance data, transport nodes, inventory levels, weather data, and load-related supply data.

[0012] The resilience analysis unit is designed to determine at least one resilience score for a chain segment, infrastructure component, supply path, or supply network based on the collected data. The resilience score can represent vulnerability to disruption, recoverability, degree of redundancy, time-critical dependency, or supply risk.

[0013] The dependency mapping unit is designed to create a machine-readable linking structure between physical energy infrastructures, supply sources, spare parts paths, transport links, maintenance resources, and operational control variables.

[0014] The simulation unit is designed to simulate at least one disruption scenario based on the linkage structure and the resilience value, and to determine a likely impact on the energy supply chain.

[0015] The action control unit is designed to generate, select, or initiate at least one countermeasure depending on a detected or simulated resilience state. The countermeasure may include, in particular, rerouting a procurement path, prioritizing a maintenance process, redistributing inventory, activating a substitute supplier, or issuing an operational recommendation.

[0016] The combination of these units creates an integrated cloud-based data processing system for proactively ensuring the stability and recoverability of energy supply chains. Detailed description of the invention

[0017] The invention relates to a data processing system for cloud-based monitoring and control of resilient energy supply chains. The system comprises a data acquisition unit for recording and standardizing supply-relevant data, a resilience analysis unit for determining a resilience value, a dependency mapping unit for generating a link structure between infrastructure, supply sources, and transport paths, a simulation unit for simulating disruption scenarios, and a control unit for selecting or triggering countermeasures. This enables the early detection of resilience-relevant weaknesses, the prediction of disruption impacts, and the initiation of appropriate measures to stabilize the energy supply chain.

[0018] In one embodiment, the data processing system comprises a data acquisition unit that receives operational data, delivery data, infrastructure states, and environmental data from a variety of distributed sources. These data sources can include sensors, network monitoring systems, warehouse management systems, supplier interfaces, maintenance databases, transportation management systems, and weather data sources. The data acquisition unit is preferably configured to convert the received data into a uniform data format and to provide it with time and location references.

[0019] The resilience analysis unit processes the standardized data and determines at least one resilience value. This resilience value can be generated for individual components, sub-chains, or the entire system. In one embodiment, the resilience analysis unit considers, among other things, the probability of failure, restart time, availability of alternative sources of supply, inventory range, transport delays, degree of redundancy, and critical network load conditions.

[0020] The dependency mapping unit generates a structured relationship representation from the data, in which physical energy facilities, storage facilities, suppliers, spare parts relationships, transport corridors, maintenance units, and operational control variables are linked to one another. This relationship representation can be implemented as a graph structure, a node-edge model, or another machine-readable topology. This allows for the identification of individual risks, bottleneck concentrations, and cascading fault paths.

[0021] The simulation unit is designed to calculate the potential development of the energy supply chain based on a predefined or detected disruption event. Such a disruption event could be, for example, a supplier failure, a failure of a network component, an interruption of a transport route, a weather event, delayed maintenance, or a storage bottleneck. The simulation unit preferably determines which chain segments are affected, what temporal effects are to be expected, and how the disruption impacts supply capability.

[0022] The action control unit is designed to determine suitable countermeasures based on simulation or a currently detected resilience state. In one embodiment, this includes considering alternative supply routes, substitute suppliers, prioritized maintenance windows, redistributed inventory, or operational load shifts. The action control unit can issue a recommendation for action to a user interface or provide a control instruction to a downstream system.

[0023] In a preferred embodiment, the data acquisition unit, the resilience analysis unit, the dependency mapping unit, the simulation unit, and the action control unit are coupled together in a common cloud-based data processing architecture. This allows changes to be processed in real time or near real time and used for continuous resilience assessment.

[0024] In another embodiment, the system includes a learning unit that evaluates historical fault data, response histories, and the effects of interventions in order to adjust weightings within the resilience analysis or priorities within the intervention management. This allows the system to continuously improve its evaluation and control logic.

Claims

[1] Data processing system for cloud-based monitoring and control of resilient energy supply chains, comprising a data acquisition unit for collecting and standardizing data from a plurality of distributed supply-relevant sources, a resilience analysis unit for determining at least one resilience value for at least one chain segment, an infrastructure component, a supply path or a supply network based on the collected data, a dependency mapping unit for generating a machine-readable linking structure between energy infrastructure, supply sources, transport paths, maintenance resources and operational control variables, a simulation unit for simulating at least one disturbance scenario based on the linkage structure and the resilience value, and a control unit for generating, selecting or triggering at least one countermeasure depending on a detected or simulated resilience state. [2] Data processing system according to claim 1, characterized by that the data acquisition unit processes data from generation plants, storage facilities, network components, supplier systems, maintenance data, inventory levels, transport systems, weather data sources or load-related supply data. [3] Data processing system according to claim 1, characterized by that the resilience value includes at least one parameter from a probability of failure, a recovery time, a degree of redundancy, a storage range, a delivery delay or a time-critical dependency. [4] Data processing system according to claim 1, characterized by, that the linking structure is designed as a graph structure for the detection of cascading fault paths and individual vulnerability concentrations. [5] Data processing system according to claim 1, characterized by that the action control unit selects a countermeasure from a rerouting of a procurement path, an activation of a substitute supplier, a prioritization of a maintenance operation, a redistribution of inventory, or an operational load shift. [6] Data processing system according to claim 1, characterized by , that a learning unit is provided which evaluates historical disruption data and the effects of measures in order to adjust weightings of the resilience analysis unit or priorities of the action control unit.