Source network load storage micro energy network control system

Abstract

The invention discloses a source network load storage micro-energy network control system, which comprises a data cloud platform and a plurality of control scheduling systems, the control scheduling system comprises a control module and a plurality of micro-energy networks. The control module is used for collecting micro-energy network state data and environment data in the control scheduling system and sending the micro-energy network state data and the environment data to the data cloud platform; the data cloud platform is used for collecting, processing and storing micro-energy network state data and environment data in each control scheduling system, obtaining a control command corresponding to each control scheduling system according to the micro-energy network state data and the environment data, and sending the control command to the control module; and the control module is also used for controlling and scheduling the micro-energy network according to the control command. Different micro-energy networks of different control scheduling systems are scheduled and managed through the data cloud platform, and the control integration level of the micro-energy networks is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of micro energy network control systems, and in particular to a source-grid-load-storage micro energy network control system. Background Art

[0002] The source-grid-load-storage micro-energy grid control system refers to a technology that uses a variety of interactive means among the four parts of power supply, grid, load and energy storage to more economically, efficiently and safely improve the power dynamic balance capability of the power system, thereby maximizing the utilization of energy resources.

[0003] In the prior art, the patent publication number is CN109768571A, "A Grid-connected Microgrid Control System and Method". The microgrid control system has a low construction cost, selects efficient and reliable sensors to detect the voltage and current information of each unit, and the grid coordination controller determines the operating status of the microgrid in real time based on the sensor data; delayed batch switching is used to achieve switching between grid-connected and off-grid, which reduces the impact of switching between grid-connected and off-grid on the microgrid system and load, ensures the stability and reliability of the microgrid system, and ensures the safe use of the load. Priority is used to close and power the load in batches to ensure the stability of electricity consumption in key areas, so that the microgrid can achieve better economic benefits in actual use.

[0004] With the integration and development of large power grids and micro-energy grids, several distributed micro-energy grids are usually set up in different locations in an area. Therefore, it is necessary to coordinate the management of several micro-energy grids in the entire area. However, the above-mentioned prior art only studies the control system and method of a single micro-energy grid, but does not consider the coordinated management between different micro-energy grids with different control and scheduling systems. The control integration of the micro-energy grid is relatively low. Summary of the invention

[0005] The present invention provides a source-grid-load-storage micro-energy network control system to solve the problem in the prior art that there is no relatively reliable coordinated management between different micro-energy networks without considering different control and scheduling systems, and the control integration of the micro-energy network is low.

[0006] On the one hand, the present invention provides a source-grid-load-storage micro-energy network control system, including: a data cloud platform and several control and scheduling systems; the control and scheduling system includes: a control module and several micro-energy networks.

[0007] The data cloud platform is communicatively connected with the control module, the control module in each control and dispatching system is electrically connected with the micro energy grid, the micro energy grids in each control and dispatching system are electrically connected with each other in an on-off manner, and the micro energy grid is electrically connected with the power grid in an on-off manner.

[0008] The control module is used to collect micro-energy network status data and environmental data in the control and scheduling system, and send the micro-energy network status data and the environmental data to the data cloud platform.

[0009] The data cloud platform is used to collect, process and store the micro-energy network status data and environmental data in each control and scheduling system, obtain the control command corresponding to each control and scheduling system according to the micro-energy network status data and the environmental data, and send the control command to the control module.

[0010] The control module is also used to control and schedule the micro energy network according to the control command.

[0011] In a possible implementation, the data cloud platform includes: a data production layer, a data processing layer, a data storage layer, and a cloud application layer.

[0012] The data production layer is used to receive the micro energy network status data and the environmental data in real time.

[0013] The data processing layer is used to process the micro energy network status data and the environmental data.

[0014] The data storage layer is used to store the micro energy network state data and the environmental data.

[0015] The cloud application layer is used to obtain the control command corresponding to each control and scheduling system according to the micro-energy network status data and the environmental data, and send the control command to the control module.

[0016] In a possible implementation, the cloud application layer is also used to predict the micro-energy network status data and the environmental data to obtain predicted micro-energy network status data and predicted environmental data, and obtain control commands corresponding to each control and scheduling system based on the predicted micro-energy network status data and the predicted environmental data.

[0017] In a possible implementation, the control module includes two modes: manual control and automatic control.

[0018] When in manual control mode, the micro energy network is manually controlled through the access switch of the micro energy network.

[0019] When in automatic control mode, the control module controls and dispatches the micro energy network according to the control command.

[0020] In a possible implementation, the micro energy network status data includes: the load capacity of each power supply point of the micro energy network.

[0021] The control command includes: splitting and restoring power supply to the area where power supply needs to be restored.

[0022] In a possible implementation, the data cloud platform is also used to evaluate the impact of the micro energy network on the power grid in which it is located, and to obtain the optimal location and optimal scale of the micro energy network.

[0023] The data cloud platform is also used to adjust and optimize the micro energy network according to the optimal location and optimal scale of the micro energy network.

[0024] In a possible implementation, the micro-power grid status data includes: voltage deviation, voltage and current effective value, frequency deviation, three-phase imbalance, flicker and power factor.

[0025] The control command includes: switching the micro energy grid to grid-connected operation or off-grid operation.

[0026] In a possible implementation, when the micro energy grid is in off-grid operation, the control command further includes: switching the micro energy grid to off-grid distributed operation or off-grid independent operation.

[0027] A source-grid-load-storage micro-energy grid control system in the present invention has the following advantages: Different micro-energy networks with different control and dispatching systems are dispatched and managed through the data cloud platform, which improves the control integration of the micro-energy network.

[0028] The micro energy network status data and the environmental data are predicted through the cloud application layer to obtain predicted micro energy network status data and predicted environmental data, and the control commands corresponding to each control and scheduling system are obtained according to the predicted micro energy network status data and predicted environmental data, thereby improving the control foresight of the micro energy network.

[0029] The proposed control module includes two modes: manual control and automatic control, which improves the control flexibility of the micro-energy grid.

[0030] The proposed micro-energy network status data includes the load capacity of each power supply point in the micro-energy network, and the control commands include splitting and restoring power supply to areas that need power supply restoration, thereby improving power supply stability and safety.

[0031] The proposed data cloud platform is also used to evaluate the impact of the micro-energy grid on the power grid where it is located, to obtain the optimal location and scale of the micro-energy grid, and to adjust and optimize the micro-energy grid based on its optimal location and scale, thereby improving the optimizability and scalability of the micro-energy grid.

[0032] The proposed micro-energy grid status data includes voltage deviation, voltage and current effective value, frequency deviation, three-phase imbalance, flicker and power factor. The control commands include switching the micro-energy grid to grid-connected operation or off-grid operation. The control commands also include switching the micro-energy grid to off-grid distributed operation or off-grid independent operation, which improves the control flexibility and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 A schematic diagram of the modules of a source-grid-load-storage micro-energy grid control system and a power grid provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] like Figure 1 As shown, an embodiment of the present invention provides a source-grid-load-storage micro-energy network control system, including: a data cloud platform and several control and scheduling systems; the control and scheduling system includes: a control module and several micro-energy networks.

[0037] The data cloud platform is communicatively connected with the control module, the control module in each control and dispatching system is electrically connected with the micro energy grid, the micro energy grids in each control and dispatching system are electrically connected with each other in an on-off manner, and the micro energy grid is electrically connected with the power grid in an on-off manner.

[0038] The control module is used to collect micro-energy network status data and environmental data in the control and scheduling system, and send the micro-energy network status data and the environmental data to the data cloud platform.

[0039] The data cloud platform is used to collect, process and store the micro-energy network status data and environmental data in each control and scheduling system, obtain the control command corresponding to each control and scheduling system according to the micro-energy network status data and the environmental data, and send the control command to the control module.

[0040] The control module is also used to control and schedule the micro energy network according to the control command.

[0041] Specifically, in this embodiment, three control and scheduling systems are provided, and two micro-energy grids are provided in each control and scheduling system. In other possible embodiments, other numbers of control and scheduling systems or other numbers of micro-energy grids may be provided.

[0042] Specifically, when the micro-energy grids within each control and dispatching system are connected and disconnected from the power grid, it is off-grid distributed operation; when the micro-energy grids within each control and dispatching system are disconnected and disconnected from the power grid, it is off-grid independent operation; when the micro-energy grids within each control and dispatching system are connected to the power grid, it is grid-connected operation.

[0043] Exemplarily, the data cloud platform includes: a data production layer, a data processing layer, a data storage layer, and a cloud application layer.

[0044] The data production layer is used to receive the micro energy network status data and the environmental data in real time.

[0045] The data processing layer is used to process the micro energy network status data and the environmental data.

[0046] The data storage layer is used to store the micro energy network state data and the environmental data.

[0047] The cloud application layer is used to obtain the control command corresponding to each control and scheduling system according to the micro-energy network status data and the environmental data, and send the control command to the control module.

[0048] Specifically, the data production layer receives in real time the micro-energy network status data and environmental data in the control and scheduling system collected by the control module; the data processing layer performs data cleaning on the micro-energy network status data and environmental data; and the data storage layer stores the micro-energy network status data and environmental data.

[0049] Exemplarily, the cloud application layer is also used to predict the micro-energy network status data and the environmental data to obtain predicted micro-energy network status data and predicted environmental data, and obtain control commands corresponding to each control and scheduling system based on the predicted micro-energy network status data and the predicted environmental data.

[0050] Specifically, the cloud application layer uses statistical prediction algorithms or time series prediction algorithms to obtain predicted micro-energy network state data and predicted environmental data based on real-time micro-energy network state data and environmental data, as well as historical micro-energy network state data and environmental data. It also uses optimization algorithms such as linear programming, genetic algorithms, or deep learning to obtain the control commands corresponding to each control and dispatching system based on the predicted micro-energy network state data and predicted environmental data.

[0051] Exemplarily, the control module includes two modes: manual control and automatic control.

[0052] When in manual control mode, the micro energy network is manually controlled through the access switch of the micro energy network.

[0053] When in automatic control mode, the control module controls and dispatches the micro energy network according to the control command.

[0054] Exemplarily, the micro energy grid status data includes: the load capacity of each power supply point of the micro energy grid.

[0055] The control command includes: splitting and restoring power supply to the area where power supply needs to be restored.

[0056] Specifically, first, analyze the load capacity of each power source point in the micro-energy network, including: technical specifications, installed capacity, actual operating conditions, potential maximum output capacity, operating efficiency, maintenance status and expected life of each power source point, so as to obtain the load capacity of each power source point; secondly, analyze the total power load, load distribution and load priority of the area where power supply needs to be restored, so as to obtain the power supply demand of the area where power supply needs to be restored; finally, formulate a split power supply strategy based on the load capacity of each power source point and the power supply demand of the area where power supply needs to be restored, and split and restore power supply to the area where power supply needs to be restored according to the split power supply strategy.

[0057] Exemplarily, the data cloud platform is also used to evaluate the impact of the micro-energy network on the power grid in which it is located, and to obtain the optimal location and optimal scale of the micro-energy network.

[0058] The data cloud platform is also used to adjust and optimize the micro energy network according to the optimal location and optimal scale of the micro energy network.

[0059] Specifically, first, use power system simulation software to establish a power grid model including the micro energy grid, and simulate the impact of micro energy grids at different locations and different scales on the local power grid; second, through simulation analysis, evaluate the impact of the micro energy grid access on the grid's electrical parameters such as voltage, current, and power factor to ensure that the electrical performance of the grid after access meets the requirements; third, analyze the stability and reliability of the grid after the micro energy grid access, including the grid recovery capability and power supply reliability in the event of a fault; fourth, conduct cost-benefit analysis, environmental benefit assessment, and social benefit analysis; finally, based on the simulation analysis results of the micro energy grid (i.e., electrical performance, grid recovery capability, and power supply reliability) and the benefit analysis results (i.e., cost-benefit, environmental benefit, and social benefit), obtain the optimal location and optimal scale of the micro energy grid, and adjust and optimize the micro energy grid based on the optimal location and optimal scale of the micro energy grid.

[0060] Exemplarily, the micro-energy grid status data includes: voltage deviation, voltage and current effective value, frequency deviation, three-phase imbalance, flicker and power factor.

[0061] The control command includes: switching the micro energy grid to grid-connected operation or off-grid operation.

[0062] Specifically, the voltage deviation collection process includes: using a voltage monitoring device to collect the real-time voltage, and comparing it with the rated voltage to obtain the voltage deviation.

[0063] The process of collecting the effective values ​​of voltage and current includes: measuring the effective values ​​of voltage and current using a multimeter method, an oscilloscope method or an average value method, or directly sampling the AC signal and performing analog-to-digital conversion to obtain the effective values ​​of voltage and current.

[0064] The frequency deviation acquisition process includes: using the Fourier transform phase difference frequency measurement method, calculating the measured signal through Fourier transform, and thus obtaining the frequency deviation.

[0065] The process of collecting the three-phase unbalance includes: using a three-phase meter or a vector analyzer to test and obtain the three-phase unbalance.

[0066] The flicker collection process includes: using the amplitude modulation wave detection method to detect flicker.

[0067] The power factor acquisition process includes: directly measuring the instantaneous power factor value using the power factor meter method, or measuring the active power and reactive power of the load using the power method, and obtaining the power factor through trigonometric calculation, or recording the active power and reactive power over a period of time using the power method, and obtaining the power factor through trigonometric calculation.

[0068] In this embodiment, by setting the weighted thresholds of voltage deviation, voltage and current effective value, frequency deviation, three-phase unbalance, flicker and power factor, the micro-energy network is switched to grid-connected operation or off-grid operation according to the size relationship between the real-time micro-energy network state data and the weighted threshold (for example, when the real-time micro-energy network state data is greater than the weighted threshold, or when the real-time micro-energy network state data is less than the weighted threshold). In a possible embodiment, the micro-energy network can also be switched to grid-connected operation or off-grid operation according to the size relationship between the predicted micro-energy network state data and the weighted threshold.

[0069] Exemplarily, when the micro energy grid is in off-grid operation, the control command further includes: switching the micro energy grid to off-grid distributed operation or off-grid independent operation.

[0070] Specifically, the switching between off-grid distributed operation and off-grid independent operation of the micro energy grid is similar to the switching between grid-connected operation and off-grid operation of the micro energy grid. And the weighted threshold corresponding to the switching between off-grid distributed operation and off-grid independent operation of the micro energy grid is set to be within the range of off-grid operation of the micro energy grid.

[0071] The embodiment of the present invention performs scheduling management on different micro-energy networks of different control and scheduling systems through a data cloud platform, thereby improving the control integration of the micro-energy network.

[0072] The micro energy network status data and the environmental data are predicted through the cloud application layer to obtain predicted micro energy network status data and predicted environmental data, and the control commands corresponding to each control and scheduling system are obtained according to the predicted micro energy network status data and predicted environmental data, thereby improving the control foresight of the micro energy network.

[0073] The proposed control module includes two modes: manual control and automatic control, which improves the control flexibility of the micro-energy grid.

[0074] The proposed micro-energy network status data includes the load capacity of each power supply point in the micro-energy network, and the control commands include splitting and restoring power supply to areas that need power supply restoration, thereby improving power supply stability and safety.

[0075] The proposed data cloud platform is also used to evaluate the impact of the micro-energy grid on the power grid where it is located, to obtain the optimal location and scale of the micro-energy grid, and to adjust and optimize the micro-energy grid based on its optimal location and scale, thereby improving the optimizability and scalability of the micro-energy grid.

[0076] The proposed micro-energy grid status data includes voltage deviation, voltage and current effective value, frequency deviation, three-phase imbalance, flicker and power factor. The control commands include switching the micro-energy grid to grid-connected operation or off-grid operation. The control commands also include switching the micro-energy grid to off-grid distributed operation or off-grid independent operation, which improves the control flexibility and stability.

[0077] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0078] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A source-grid-load-storage micro-energy grid control system, characterized in that: include: Data cloud platform and several control and dispatch systems; The control and dispatching system comprises: a control module and a plurality of micro-energy networks; The data cloud platform is communicatively connected with the control module, the control module in each control and dispatching system is electrically connected with the micro energy network, the micro energy networks in each control and dispatching system can be electrically connected with each other, and the micro energy network can be electrically connected with the power grid; The control module is used to collect micro-energy network status data and environmental data in the control and scheduling system, and send the micro-energy network status data and the environmental data to the data cloud platform; The data cloud platform is used to collect, process and store micro-energy network status data and environmental data in each control and dispatch system, obtain the control command corresponding to each control and dispatch system according to the micro-energy network status data and the environmental data, and send the control command to the control module; The control module is also used to control and schedule the micro energy network according to the control command.

2. A source-grid-load-storage micro-energy grid control system according to claim 1, characterized in that: The data cloud platform includes: data production layer, data processing layer, data storage layer, and cloud application layer; The data production layer is used to receive the micro energy network status data and the environmental data in real time; The data processing layer is used to process the micro energy network status data and the environmental data; The data storage layer is used to store the micro energy network state data and the environmental data; The cloud application layer is used to obtain the control command corresponding to each control and scheduling system according to the micro-energy network status data and the environmental data, and send the control command to the control module.

3. A source-grid-load-storage micro-energy grid control system according to claim 2, characterized in that: The cloud application layer is also used to predict the micro-energy network status data and the environmental data to obtain predicted micro-energy network status data and predicted environmental data, and obtain control commands corresponding to each control and scheduling system based on the predicted micro-energy network status data and the predicted environmental data.

4. A source-grid-load-storage micro-energy grid control system according to claim 1, characterized in that: The control module includes two modes: manual control and automatic control; When in manual control mode, the micro energy network is manually controlled through the access switch of the micro energy network; When in automatic control mode, the control module controls and dispatches the micro energy network according to the control command.

5. A source-grid-load-storage micro-energy grid control system according to claim 1, characterized in that: The micro energy network status data includes: the load capacity of each power supply point of the micro energy network; The control command includes: splitting and restoring power supply to the area where power supply needs to be restored.

6. A source-grid-load-storage micro-energy grid control system according to claim 1, characterized in that: The data cloud platform is also used to evaluate the impact of the micro-energy network on the power grid where it is located, and to obtain the optimal location and optimal scale of the micro-energy network; The data cloud platform is also used to adjust and optimize the micro energy network according to the optimal location and optimal scale of the micro energy network.

7. A source-grid-load-storage micro-energy grid control system according to claim 1, characterized in that: The micro-energy network status data includes: voltage deviation, voltage and current effective value, frequency deviation, three-phase imbalance, flicker and power factor; The control command includes: switching the micro energy grid to grid-connected operation or off-grid operation.

8. A source-grid-load-storage micro-energy grid control system according to claim 7, characterized in that: When the micro energy grid is in off-grid operation, the control command further includes: switching the micro energy grid to off-grid distributed operation or off-grid independent operation.

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