A method and system for testing advanced applications of active distribution networks based on dynamic simulation

Abstract

The application belongs to the field of power distribution automation, and the method comprises the following steps: constructing a main active power distribution network model by using a dynamic simulation platform to perform advanced application testing of the main active power distribution network, collecting dynamic network frame data, and forming a network topology file and transmitting the network topology file to an advanced application model library; setting the power grid operation environment, source and load output curve, and forming the main active power distribution network operation scene by using the dynamic simulation platform; setting network operation parameters and algorithm control parameters through an algorithm service interface and inputting the network operation parameters and the algorithm control parameters to the advanced application module; starting the advanced application module by using a one-key start-stop control button, performing advanced application power flow calculation on the network, and verifying the feasibility of the advanced application testing; and realizing advanced application testing of a multi-source ring-shaped main active power distribution network, providing flexible and diverse active power distribution network operation scene simulation, realizing superposition of different scenes, automatically identifying and automatically generating the network topology of the main active power distribution network, and providing a one-key advanced application testing function.

Description

Technical Field

[0001] This invention belongs to the field of power distribution automation, and in particular to an advanced application testing method for active power distribution networks based on dynamic simulation. Background Technology

[0002] Currently, with the widespread integration of distributed power sources into distribution networks, traditional unidirectional power supply distribution networks are gradually transforming into flexible and variable bidirectional active power supply distribution networks. This has led to the emergence of the concept of active distribution networks, characterized by high distributed power source penetration and stringent control requirements. Active distribution networks can comprehensively utilize flexible network structures (control switches), various distributed power sources (such as photovoltaics, wind turbines, and energy storage devices), and voltage regulation equipment (such as reactive power compensation devices, active power phase shifters, and on-load tap-changing transformers). Through the scheduling and management of the management system, the distribution network can achieve safe and stable operation under normal conditions and control and recovery under fault conditions. With the continuous advancement of active distribution network construction, new theories and practices regarding active distribution networks are flourishing, urgently requiring verification through reasonable experimental methods to ensure the feasibility and implementability of the technology.

[0003] Due to safety and reliability requirements, advanced application experiments related to active distribution networks, such as power control, reactive power optimization, and source-load forecasting, are often subject to numerous limitations, making it impossible to conduct sufficient and extensive testing. Some of these experiments are even unusable in actual systems. However, current research on simulation test systems for active distribution networks largely focuses on verifying the grid connection function of single resources, failing to provide reasonable and efficient testing for different types of advanced applications of active distribution networks to verify related technical solutions.

[0004] Active distribution networks are characterized by high flexibility and uncertainty. Currently, there is a lack of a platform capable of connecting various known and unknown distributed power systems and conducting research on forward-looking topics and new technologies related to active distribution networks. This platform should be able to construct multiple operating scenarios and flexibly conduct advanced application testing and verification for different scenarios. This invention discloses an advanced application testing method for active distribution networks based on dynamic simulation, relating to the field of distribution automation. It solves the problem of the lack of a flexible platform for advanced application testing of active distribution networks, enabling effective testing and verification of new technologies and theories in active distribution networks. Summary of the Invention

[0005] In view of the aforementioned existing problems, this invention is proposed. This invention optimizes the operating parameters of the distribution network, reduces energy loss, and improves the economy and reliability of the power system by simulating and testing advanced application functions; it simulates the operating state of the active distribution network under various emergency conditions, evaluates its stability, and provides safety assurance for actual operation; by analyzing test results, it identifies problems in the active distribution network, proposes optimization schemes, and improves the power grid structure; and it uses dynamic simulation technology to test and verify new active distribution network technologies, providing technical support for the future development of active distribution networks.

[0006] Therefore, an advanced application testing method for active distribution networks based on dynamic simulation is provided.

[0007] To address the aforementioned technical issues, a dynamic simulation-based advanced application testing method for active distribution networks is proposed, including:

[0008] An active distribution network model was constructed using a dynamic simulation platform to conduct advanced application testing of the active distribution network. Dynamic model network data was collected, and a network topology file was generated and transferred to the advanced application model library. The dynamic simulation platform was used to set the power grid operating environment and source-load output curves with one click, forming an active distribution network operating scenario. Network operating parameters and algorithm control parameters were set through the algorithm service interface and input into the advanced application module. The advanced application module was started using a one-click start / stop control button to perform advanced application power flow calculations on the network, verifying the feasibility of the advanced application testing.

[0009] As a preferred embodiment of the advanced application testing method for active distribution networks based on dynamic simulation described in this invention, the advanced application testing of active distribution networks includes: constructing an active distribution network topology using collected data and operational information of the active distribution network through a dynamic simulation platform; setting initial values ​​for equipment output; updating the historical and real-time databases of the advanced application model through protocol conversion; forming various basic operation scenarios of the distribution network such as economic dispatch operation, active power coordination control, and reactive power and voltage optimization control; performing global pre-optimization of the entire network by advanced application algorithms; distributing the optimization results to various regions within the active distribution network; and completing the target control by the advanced application module of the energy management system in the dynamic simulation platform using feeder control errors.

[0010] Advanced application functions for active distribution networks include optimal power flow calculation, state estimation, operation mode optimization, global optimization, and active and reactive power optimization and coordinated control for the operation and control of active distribution networks. Measured values ​​include node voltage, active power, reactive power, and line current. Element types include bus nodes, lines, and transformers.

[0011] As a preferred embodiment of the advanced application testing method for active distribution networks based on dynamic simulation described in this invention, the state estimation includes voltage state estimation, current state estimation, and power state estimation.

[0012] The operation mode optimization includes optimizing the operation mode of devices in the network. System operation mode optimization includes optimizing the on / off states of switches and the operation mode of devices. When optimizing the operation mode, constraints are added to the advanced application module, including the selection of device operation modes and the combination scheme of switch on / off states, expressed as follows:

[0013]

[0014] S = {s1, s2, ..., s} n},s n ={a1,a2,...,a n}

[0015] Initial switch combinations and equipment operation mode combinations are selected to form a set of equipment operation and switch state combination schemes, which are stored in an advanced application database. The scheme set is traversed sequentially, and calculations are performed using economic objectives and optimal power flow calculation methods. The equipment operation mode and switch on / off state with the best economic efficiency within the period are selected as the pre-optimization result of the operation mode. The result is distributed to each device and switch state in the network through a hierarchical distributed controller and local controller via a dynamic simulation platform. Here, s is the switch state combination, a is a 0-1 variable, 0 is closed and 1 is open, and S is the set of switch combination schemes.

[0016] As a preferred embodiment of the advanced application testing method for active distribution networks based on dynamic simulation described in this invention, the construction of the active distribution network model includes designing an advanced application testing model architecture for active distribution networks. The architecture includes an active distribution network simulation grid, a local controller, a hierarchical distributed controller, an active distribution network dynamic simulation platform, and an advanced application module for an active distribution network energy management system.

[0017] The dynamic simulation platform constructs the actual operating environment of the power grid based on grid parameters and operating conditions, simulating the grid operating status under various conditions. During this process, the local controller collects real-time data on power sources and switch opening / closing status in the active distribution network and uploads the data to the hierarchical distributed controller. The hierarchical distributed controller receives the real-time data from the grid equipment uploaded by the local controller, summarizes and processes it, and forwards the data to the energy management system of the dynamic simulation platform via a protocol interface. Simultaneously, the FTU and DTU devices in the dynamic simulation network also upload data not covered by the local controller via the protocol interface as test data input for advanced applications. The dynamic simulation platform performs one-click network setup as needed, monitors the operation of the active distribution network, and provides historical and real-time data simulation cases to construct different advanced application test scenarios: power control, reactive power and voltage control, and voltage over-limit scenarios. The advanced application module tests and verifies the accuracy of algorithms and other performance characteristics according to different scenarios. The advanced application test results, i.e., the target value, are transmitted to the local controller via the hierarchical distributed controller through the data transmission interface. The local controller then issues target commands to various source and load devices and switches in the grid for testing and verification.

[0018] The feasibility verification of advanced application testing includes the following steps: When the power grid operation status reaches the expected target, the local controller feeds back the execution result to the hierarchical distributed controller, which then feeds it back to the dynamic simulation platform, and finally the dynamic simulation platform feeds it back to the advanced application module. At this time, the modules at each level evaluate that the actual operation status is consistent with the expected status. When the expected target is not reached or an abnormal situation occurs in the power grid, the local controller feeds back real-time data and operation status to the hierarchical distributed controller, the dynamic simulation platform, and the advanced application module. At this time, the modules at each level evaluate that the actual operation status is inconsistent with the expected status. The modules at each level perform internal cause analysis, adjust algorithms and strategies, and readjust the control.

[0019] As a preferred embodiment of the advanced application testing method for active distribution networks based on dynamic simulation described in this invention, the advanced application testing method for active distribution networks based on dynamic simulation includes: collecting distributed power source data of the active distribution network dynamic model grid using a local controller, and uploading it to a hierarchical distributed controller through a protocol interface, including historical and real-time data of devices such as network switches, distributed power sources, controllable loads, and energy storage.

[0020] The system utilizes a dynamic simulation platform to communicate and interact with the hierarchical distributed controller and terminal equipment within the network structure. It acquires controllable equipment and network structure data for the entire area through a protocol interface, and automatically generates an active distribution network simulation network structure using one-click network setup. This data is then output to the active distribution network operation monitoring module. The dynamic simulation platform models the active distribution network topology based on the simulation network structure and generates a corresponding network topology file, which is input into the advanced application model history library. Initial values ​​for the power and voltage of the simulated equipment in the simulation network are set through a parameter setting interface. Load forecast data and power output forecast data are generated through case studies and input to the advanced application module via a data processing service interface. AND model parameters, acquisition parameters, and algorithm control parameters are transmitted to the advanced application module through an algorithm service interface for advanced application start-up and shutdown control operations.

[0021] The advanced application module adopts a one-click start / stop control method. Based on the network data provided by the simulation platform, it verifies the accuracy of the algorithm and outputs the results to a specified path through the algorithm service interface, thus completing the accuracy verification of the advanced application test algorithm.

[0022] The dynamic simulation platform reads the target value of the output result and sends it to the hierarchical distributed controller and terminal equipment through the protocol conversion interface. The hierarchical distributed controller forwards the target value instruction to the local controller through the protocol forwarding. The local controller verifies the effectiveness of the target value for the real-time control of the distributed power supply and verifies the closed-loop capability of advanced application testing based on dynamic simulation.

[0023] As a preferred embodiment of the advanced application testing method for active distribution networks based on dynamic simulation described in this invention, the advanced application power flow calculation includes combining the power and voltage of distribution network nodes, and the node power model is as follows:

[0024]

[0025] Further transformation yields:

[0026]

[0027] The power balance equations for each node are as follows:

[0028]

[0029] The corrections for node voltage, phase angle, and power are obtained using the Jacobian matrix, and the power, voltage, and phase angle of each node in the network are updated. Then, the optimal solution is obtained through iterative analysis using the interior-point method, followed by a second-order Taylor expansion using Newton's method, and finally fed into power flow calculations to obtain the optimal power flow optimization result within the cycle. Where, Y ij U is the admittance matrix of the line impedance. i Let P be the voltage at node i. i and Qi These represent the active and reactive power injected into node i, respectively.

[0030] As a preferred embodiment of the advanced application testing method for active distribution networks based on dynamic simulation described in this invention, the data communication and algorithm module includes a data transmission protocol interface and an algorithm parameter interface.

[0031] The data transmission protocol interface includes a conversion tool between the MODBUS protocol and the IEC104 protocol, enabling data interaction between the controller and the simulation platform. The simulation platform converts the simulated power grid operating status into physical signals, which are then transmitted to the hierarchical distributed controller, local controller, and grid terminal equipment via the IEC104 protocol. Simultaneously, the physical information of the local controller is converted into simulation signals through the interface and transmitted to the controllable distributed power source simulation controller in the simulation platform for the control process of the controlled unit.

[0032] The algorithm parameter interface transmits model parameters, acquisition parameters, algorithm control parameters, algorithm startup, and calculation and analysis results to the advanced application module as data input for advanced application testing of active distribution networks.

[0033] The local control module includes a hierarchical distributed controller. When communicating with the upper-level controller, the local controller receives commands from the upper-level controller, forwards them through protocols, and makes an appropriate response based on the type of command: if it is a data call command, it retrieves the required data from the real-time database and sends it to the hierarchical distributed controller.

[0034] If it is a remote control command, the active power control target is parsed from the message according to the format of the 104 protocol, and a message with a specific ID is sent to the area. Then, the control strategy plugin reads the required information from the real-time database of the scheduling layer according to the active power and the control target, and performs corresponding control on the distributed power source. The control target of each distributed power source is sent to the scheduling layer in the form of a command. Finally, the master station protocol reads the command in the scheduling layer and sends the command to each distributed power source in the form of the 104 protocol according to the specific information of the command.

[0035] Another objective of this invention is to provide a system for testing advanced applications of active distribution networks based on dynamic simulation. This invention aims to test and verify the advanced application functions and performance of active distribution networks by simulating the operating conditions of distribution networks in a real-world environment.

[0036] An advanced application test system for an active distribution network based on dynamic simulation is characterized by comprising an active distribution network dynamic model grid module, a local control module, a hierarchical distributed control module, a dynamic simulation module, an advanced application module, and a data communication and algorithm module.

[0037] The active distribution network dynamic model grid module includes a photovoltaic power simulator, a wind turbine simulator, an energy storage simulator, and a programmable RLC load, among other distributed source and load devices. It also connects to terminal devices such as FTU and DTU for data acquisition and network operation monitoring. The distributed power sources in the dynamic model grid interact with the local controller through a communication protocol interface, uploading historical and real-time network data. The simulation devices are all initialized through the dynamic model platform to simulate the operating states of different source and loads in the grid.

[0038] The local control module is responsible for the local control of the active distribution network. It coordinates the power output of the distributed power sources it controls according to the instructions from the superior, and obtains the real-time operation data of the distributed power sources in the dynamic grid and uploads it to the hierarchical distributed controller.

[0039] The hierarchical distributed control module is responsible for communicating and controlling with the local controllers within its jurisdiction. It collects the exchange power between the area and the feeder in real time and converts it according to the protocol. It coordinates the input of the local controllers in the area and summarizes the distributed power information collected by the local controllers in the entire area and uploads it to the dynamic simulation platform through the protocol interface.

[0040] The dynamic simulation module provides one-click network setup and generation, enabling automatic identification and generation of the active distribution network dynamic model structure, and outputting the corresponding network topology file to the advanced application module. Simultaneously, the dynamic simulation platform can process and modify the simulation equipment through a data interaction interface, and provides information exchange on active distribution network model parameters, acquisition parameters, real-time data information, algorithm control parameters, algorithm startup, and calculation and analysis results through an algorithm interface and the active distribution network operation monitoring module. The platform also supports the creation and generation of historical and real-time data case studies, and can construct different power grid operating conditions through one-click environment and curve settings as advanced application test scenarios.

[0041] The advanced application module receives network parameters transmitted from the algorithm interface, as well as historical and real-time data from the model library. It performs various advanced application calculations, including power flow calculation, state estimation, and operation optimization. Its functions include verifying algorithm accuracy and outputting network control target values. The control target values ​​are saved to the algorithm output path set on the dynamic simulation platform and then sent to the hierarchical distributed controller and various terminal devices in the dynamic model network through the protocol interface. The hierarchical distributed controller sends the target values ​​to the local controllers, and together with the target values ​​obtained by the FTU and DTU terminals, they optimize and coordinate the control of the active distribution network to complete the closed-loop verification of the advanced application test functions.

[0042] The data communication and algorithm module, through the data transmission protocol interface and algorithm parameter interface, realizes data interaction and information transmission between the controller and the dynamic simulation platform and advanced application module, so as to simulate and test the operation and control process of the active distribution network.

[0043] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program as steps of a method for advanced application testing of an active distribution network based on dynamic simulation.

[0044] A computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, is a method for a method of advanced application testing of an active distribution network based on dynamic simulation.

[0045] The beneficial effects of this invention are as follows: This invention can realize advanced application testing of multi-source ring active distribution networks; it can provide flexible and diverse simulation of active distribution network operation scenarios and realize the superposition of different scenarios; it can automatically identify and generate the network topology of active distribution networks, quickly form an experimental environment and controllable operation mode; it can provide one-click advanced application testing function. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0047] Figure 1 The present invention provides an overall flowchart of an advanced application testing method for active distribution networks based on dynamic simulation, as an embodiment of the present invention.

[0048] Figure 2 This invention provides an advanced application testing process for an active distribution network advanced application testing method based on dynamic simulation, as one embodiment of the present invention.

[0049] Figure 3 The present invention provides a flowchart of an advanced application testing system architecture for an active distribution network based on dynamic simulation, which is an embodiment of the present invention.

[0050] Figure 4 This invention provides a power grid model topology diagram for an advanced application testing method for active distribution networks based on dynamic simulation, as an embodiment of the present invention.

[0051] Figure 5This is a network topology data diagram of an advanced application testing method for active distribution networks based on dynamic simulation, provided as an embodiment of the present invention.

[0052] Figure 6 This is a real-time measurement data graph of an advanced application testing method for active distribution networks based on dynamic simulation, provided as an embodiment of the present invention.

[0053] Figure 7 This invention provides a method for setting up a power grid operating environment diagram with one click, based on an advanced application testing method for active distribution networks using dynamic simulation, as an embodiment of the present invention.

[0054] Figure 8 This is a system functional architecture diagram of an advanced application testing system for active distribution networks based on dynamic simulation, provided as an embodiment of the present invention. Detailed Implementation

[0055] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0056] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive, either alone or selectively, with other embodiments.

[0058] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0059] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0060] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0061] Example 1

[0062] Reference Figures 1-3 This is the first embodiment of the present invention, which provides an advanced application testing method for active distribution networks based on dynamic simulation, including:

[0063] S1: Utilize the dynamic simulation platform to build an active distribution network model for advanced application testing of the active distribution network, collect dynamic model network data, and generate network topology files that are then transferred to the advanced application model library.

[0064] Furthermore, by collecting data and operational information from the active distribution network, an active distribution network topology is constructed using a dynamic model platform. Initial values ​​for equipment output are set, and the acquired real-time measurement data is converted through a protocol to update the historical and real-time databases of the advanced application model, forming various basic operational scenarios for the distribution network. Finally, advanced application algorithms are used to perform global pre-optimization of the entire network.

[0065] PandaPower constructs the network topology from the input network structure information and obtains source-load prediction data within a certain period T. This data, input from the platform's case system, represents the periodic prediction data for the area to be controlled and serves as the initial input for global network optimization. The objectives differ for different distribution network infrastructure operation scenarios. Specifically, for the active distribution network economic dispatch operation scenario, the pre-optimization objective is to minimize the electricity purchase cost within the period, with the following optimization objectives:

[0066]

[0067] In the formula, P gen,n,t ρ gen,n,t P represents the generator's output and its corresponding unit operating cost.sgen,m,t ρ sgen,m,t P represents the output of a static generator and its corresponding unit operating cost. extgrid,t ρ extgrid,t Power output to the external power grid and the corresponding on-grid electricity price.

[0068] In addition to the power balance constraints mentioned above, the corresponding constraints also include the upper and lower limits of output of each generator set, ramping limits, and capacity constraints.

[0069] For the active power optimization control scenario of active distribution network, the pre-optimization objective is to minimize line active power loss and achieve optimal economic efficiency. The optimization objectives are as follows:

[0070]

[0071] Where α and β are the weight coefficients of the target, respectively, and g ij For the susceptance of lines i and j, U i U j θ ij Let represent the voltage and phase angle at nodes i and j.

[0072] In addition to the constraints included in the economic dispatch operation scenario, the constraints also include voltage constraints and phase angle constraints.

[0073] For reactive power voltage optimization control scenarios, the global pre-optimization objective can be to minimize network loss, as shown below:

[0074]

[0075]

[0076] In the formula, the voltage constraint and reactive power constraint are respectively shown in the following formulas:

[0077]

[0078]

[0079] The power flow calculation application module sets different target values ​​according to different power distribution network operation scenarios. Through optimal power flow calculation, for the periodic operation iteration of power flow calculation, the optimal set of optimization results in the iteration is selected, including node active power, reactive power, equipment planned output and node voltage, as the target values ​​for control issuance.

[0080] The optimization results are distributed to various regions within the active distribution network, which is accomplished by the advanced application module of the energy management system in the dynamic model platform. Based on the optimization results provided by the advanced application module, each region issues local optimization control commands to its distributed power sources, energy storage, and controllable equipment according to the optimization target value. This is accomplished by the hierarchical distributed controller, using feeder control errors to achieve target control.

[0081] Target control is mainly divided into active power optimization control and reactive power voltage optimization control. For active power optimization control, if the output power of the grid-connected distributed generation (DG) and the load are determined, the exchange power between the ADN and the external power grid can be uniquely determined based on power flow calculations (hereinafter referred to as P). E (Indicated). When the load changes unexpectedly, or when the DG or energy storage system deviates from its optimal operating state, P E It also changes accordingly. Therefore, P E It can reflect the overall operating status of the ADN. The feeder control error is defined as the difference between the actual PE value and the optimal value or the globally optimized planned value:

[0082] FCE=P E,C -P E,S

[0083] Among them, P E,S The optimal value, i.e., the optimal external grid feed-in power under the above active power pre-optimization scenario, is P. E,C The PE value returned by the controller during actual operation, P E,C P E,S All are positive when injected into the active distribution network.

[0084] In actual operation, if the load power and the actual power of the DG and energy storage are the same as the global optimization, then FCE = 0; FCE > 0 indicates that the load power has increased or the output power of the DG and energy storage has decreased; conversely, FCE < 0 indicates that the load power has decreased or the output power of the DG and energy storage has increased. Under different control modes, based on the sign and absolute value of FCE or the integral value of FCE over time, the DG and energy storage systems are coordinated and controlled to make the actual operating state approach the operating state in the global optimization.

[0085] For active distribution network reactive voltage optimization control, the platform uses the voltage fluctuation range index as the control target, namely the equivalent voltage upper and lower limits (EVL). This index is calculated using the power flow iteration method. The voltage value of each node is obtained by power flow calculation, and nodes that exceed the voltage limit are screened out.

[0086] In the calculation of the equivalent voltage upper and lower limits (EVL), the control node voltage is defined as the voltage of the first node in the control area where the controller is located. When calculating the EVL of a certain control area, the control node voltage of that area will be affected by the switching of reactive power control equipment in other surrounding control areas. Therefore, the impact of reactive power control equipment in other areas on the current control area's EVL index needs to be comprehensively considered in the calculation. Under the two extreme cases of full activation and full deactivation, the power flow is iteratively calculated to obtain the area control node voltage range [U1, U3] when all reactive power equipment in other areas is activated and the area node voltage range [U2, U4] when all reactive power equipment in other areas is deactivated. The final calculation yields:

[0087] EVL=[min{U1,U2,U3,U4},max{U1,U2,U3,U4}]

[0088] Based on local optimization control commands, the distributed power sources on the same distribution node are optimized and controlled, and the power point tracking speed of the distributed power sources is accelerated through the local controller.

[0089] Advanced application functions for active distribution networks include optimal power flow calculation, state estimation, operation mode optimization, global optimization, and active and reactive power optimization and coordinated control for the operation and control of active distribution networks. The state estimation includes voltage state estimation, current state estimation, and power state estimation.

[0090] The state estimation includes voltage state estimation, current state estimation and power state estimation. The measured values ​​include node voltage, active power, reactive power and line current. The element types include bus nodes, lines and transformers.

[0091] Operation mode optimization is mainly divided into several categories. The first is to optimize the operation mode of equipment in the network, including the operation mode and power control of distributed power sources such as photovoltaic, wind power and energy storage. The second is to optimize the system operation mode, mainly based on the closing and closing of switches and the switching on and off of equipment. By optimizing the operation mode by optimizing the opening and closing status of switches and the operation of equipment, the active distribution network can be divided into regions and the equipment can be selected within the region, thus completing the optimized operation of the active distribution network.

[0092] When optimizing the operation mode, corresponding optimization objectives need to be set. The optimal operation mode of the equipment and the optimal operation mode of the system are obtained through optimal power flow calculations by advanced applications. The optimization objective is mainly based on the economic efficiency of system operation, which is consistent with the objective under the economic operation scenario, and will not be elaborated here.

[0093] When optimizing operation modes, unlike the constraints of the economic operation scheduling scenario mentioned above, operation mode optimization also needs to consider the equipment operation mode and the combination of switches. In the advanced application module, constraints need to be added, including the selection of equipment operation modes and the combination scheme of switch opening and closing, as shown in the following formula:

[0094]

[0095] S = {s1, s2, ..., s} n},s n ={a1,a2,...,a n}

[0096] Where s represents the switch state combination, a is a 0-1 variable, 0 represents closed and 1 represents open; S represents the set of switch combination schemes.

[0097] First, select the initial combination of switches and the combination of equipment operation modes to form a set of equipment operation and switch state combination schemes, which are stored in the advanced application database. Iterate through the scheme set in turn, and use economic objectives and optimal power flow calculation methods to calculate and solve the problem. Select the equipment operation mode and switch on / off state with the best economic efficiency within the period as the pre-optimization result of the operation mode. Then, through the dynamic simulation platform, distribute the results to each device and switch state in the network via the hierarchical distributed controller and local controller.

[0098] By constructing an active distribution network model through a dynamic modeling platform, global pre-optimization of the active distribution network can be performed, improving grid operation efficiency and stability. Real-time measurement data is collected and, through protocol conversion, the historical and real-time databases of advanced application models are updated, providing real-time and accurate data support for grid operation control. Hierarchical distributed controllers enable local optimization control of distributed power sources, energy storage, and controllable devices, improving grid operation efficiency. For the operation control of the active distribution network, optimal power flow calculations and state estimations are performed, providing a basis for grid operation mode optimization and global optimization. Stable and efficient grid operation is achieved through operation mode optimization and coordinated control of active and reactive power optimization. This technology can adapt to various operating scenarios of active distribution networks, improving the grid's adaptability and resilience to complex environments. By optimizing grid operation, efficient energy utilization is achieved, energy consumption is reduced, carbon emissions are reduced, and green and low-carbon development is promoted.

[0099] The advanced application testing method for active distribution networks described in this invention is mainly aimed at the testing and verification of advanced applications for active distribution networks with a dynamic simulation platform as the core. It verifies the convenience and effectiveness of the dynamic simulation platform in the advanced application testing of active distribution networks, and verifies the open-loop and closed-loop capabilities of active distribution networks to carry out advanced applications on the dynamic simulation platform.

[0100] The advanced application testing method for active distribution networks needs to meet the following conditions: 1. Provide simulations of the operation status of the active distribution network and possible operation control scenarios, and obtain real-time data such as the distribution network, source and load equipment, and switch status within the region.

[0101] 2. Provides interactive interfaces for real-time measurement data, network topology data, and advanced application modules of the active distribution network, ensuring that the data is consistent with the actual operating conditions.

[0102] 3. It has the capability to verify the open-loop and closed-loop results of advanced active distribution network applications, and the active distribution network can be actually operated and optimized based on the target value results.

[0103] S2: The dynamic simulation platform allows for one-click setup of the power grid operating environment and source-load output curves, forming an active distribution network operating scenario. Network operating parameters and algorithm control parameters are set through the algorithm service interface and input to the advanced application module.

[0104] Furthermore, an advanced application test model architecture for active distribution networks is designed. This architecture includes an active distribution network simulation grid, local controllers, hierarchical distributed controllers, an active distribution network dynamic simulation platform, and advanced application modules for an active distribution network energy management system. This highly integrated approach facilitates smoother collaboration among various components, improves overall operational efficiency, enables precise monitoring of the grid's operating status, and helps in the early detection and resolution of potential problems.

[0105] The dynamic simulation platform constructs the actual operating environment of the power grid based on grid parameters and operating conditions, simulating the grid's operating status under various conditions. During this process, local controllers collect real-time data on power sources and switch opening / closing status in the active distribution network and upload this data to the hierarchical distributed controller. The hierarchical distributed controller receives the real-time data from the grid equipment uploaded by the local controller, aggregates and processes it, and forwards the data to the platform's energy management system via a protocol interface. Simultaneously, FTUs and DTUs in the dynamic simulation network also upload data not covered by the local controller via the protocol interface, serving as test data input for advanced applications. The dynamic simulation platform performs one-click network setup as needed, monitors the active distribution network, and provides historical and real-time data simulation cases to construct different advanced application test scenarios: power control, reactive power and voltage control, and voltage limit exceedance scenarios. The advanced application module performs algorithm accuracy and other performance tests based on different scenarios. Testing and verification: The advanced application test results, i.e., the target values, are transmitted to the local controller via the data transmission interface through the hierarchical distributed controller. The local controller then issues target commands to various source-load devices and switches in the power grid for testing and verification. The verification of the feasibility of the advanced application test includes the following steps: When the power grid operation status reaches the expected target, the local controller feeds back the execution results to the hierarchical distributed controller, which then feeds them back to the dynamic simulation platform. Finally, the dynamic simulation platform feeds them back to the advanced application module. At this time, each module evaluates that the actual operating status is consistent with the expected status. When the expected target is not reached or an abnormal situation occurs in the power grid, the local controller feeds back real-time data and operating status to the hierarchical distributed controller, the dynamic simulation platform, and the advanced application module. At this time, each module evaluates that the actual operating status is inconsistent with the expected status. Each module performs internal cause analysis, adjusts algorithms and strategies, and re-regulates to improve the system's self-adjustment and response capabilities.

[0106] S3: Use the one-click start / stop control button to start the advanced application module, perform advanced application power flow calculations on the network, and verify the feasibility of advanced application testing.

[0107] Furthermore, the local controller collects distributed power source data from the active distribution network's dynamic grid structure and uploads it to the hierarchical distributed controller via a protocol interface. This includes historical and real-time data from network-internal switches, distributed power sources, controllable loads, energy storage, and other devices.

[0108] The system utilizes a dynamic simulation platform to communicate and interact with the hierarchical distributed controller and terminal equipment within the network structure. It acquires controllable equipment and network structure data for the entire area through a protocol interface, and automatically generates an active distribution network simulation network structure using one-click network setup. This data is then output to the active distribution network operation monitoring module. The dynamic simulation platform models the active distribution network topology based on the simulation network structure and generates a corresponding network topology file, which is input into the advanced application model history library. Initial values ​​for the power and voltage of the simulated equipment in the simulation network are set through a parameter setting interface. Load forecast data and power output forecast data are generated through case studies and input to the advanced application module via a data processing service interface. AND model parameters, acquisition parameters, and algorithm control parameters are transmitted to the advanced application module through an algorithm service interface for advanced application start-up and shutdown control operations.

[0109] The advanced application module adopts a one-click start / stop control method. Based on the network data provided by the simulation platform, it verifies the accuracy of the algorithm and outputs the results to a specified path through the algorithm service interface, thus completing the accuracy verification of the advanced application test algorithm.

[0110] The dynamic simulation platform reads the target value of the output result and sends it to the hierarchical distributed controller and terminal equipment through the protocol conversion interface. The hierarchical distributed controller forwards the target value instruction to the local controller through the protocol forwarding. The local controller verifies the effectiveness of the target value for the real-time control of the distributed power supply and verifies the closed-loop capability of advanced application testing based on dynamic simulation.

[0111] It should be noted that the advanced application power flow calculation uses the Newton-Raphson method, the principle of which is as follows: Setting the initial value X0 and the iteration termination precision, we obtain the second-order Taylor expansion of the multivariate nonlinear function f(X) at the initial point, that is:

[0112]

[0113] when Reversible, can be obtained And an approximate solution X is derived. k The general form is:

[0114]

[0115] If k satisfies X k ≈X k-1 And ||g k If ||≤ε, immediately stop the iteration and take Xk as the final solution of the multivariate nonlinear equation f(X)=0.

[0116] Combining the power and voltage of distribution network nodes, the node power model is as follows:

[0117]

[0118] In the formula, Y ij U is the admittance matrix of the line impedance. i Let P be the voltage at node i. i and Q i Let be the active and reactive power injected at node i, respectively. Further conversion yields:

[0119]

[0120] The power balance equations for each node are as follows:

[0121]

[0122] As can be seen from the above equation, the power flow calculation equation is a set of nonlinear equations with multiple variables, and its solution process is as follows:

[0123]

[0124] Assume the initial values ​​of the variables are respectively If the adjustment amount is for each variable, then:

[0125]

[0126] Expanding the above equation using Taylor series according to the aforementioned principle, and neglecting higher-order terms, results in the following:

[0127]

[0128] Represented in matrix form as shown in the following equation:

[0129]

[0130] Using vector representation, the above equation can be simplified to:

[0131] f(x (0) )=-J (0) Δx (0)

[0132] The matrix J is the Jacobian matrix. The Jacobian matrix can be used to obtain the corrections for node voltage, phase angle, and power, and to update the power, voltage, and phase angle of each node in the network.

[0133] Furthermore, for the optimal power flow calculation method, the interior point method is adopted to transform the constrained problem into an unconstrained problem, and the gradient descent of the unconstrained function is used for iteration until the optimal solution is obtained.

[0134] First, the constrained problem is transformed into an unconstrained problem. For the minimization linear programming problem, the objective function is set as follows:

[0135] minF = c T x

[0136] stAx≤x

[0137] Similar to the Lagrange relaxation method, the above planning problem can be expressed as follows:

[0138]

[0139] Where m is the number of constraint equations, and I is the indicator function, generally defined as follows:

[0140]

[0141] Based on this, a second-order Taylor expansion is performed using Newton's method and then incorporated into the power flow calculation to obtain the optimal power flow optimization result within the cycle.

[0142] This invention describes an advanced application testing method for active distribution networks based on a dynamic model platform. It can also provide a verification platform and various testing schemes for the functional verification of equipment, the feasibility verification of algorithm results, system training, and forward-looking research in active distribution networks.

[0143] It should also be noted that the data communication and algorithm module is divided into a data transmission protocol interface and an algorithm parameter interface.

[0144] The data transmission protocol interface includes a conversion tool between the MODBUS protocol and the IEC104 protocol, enabling data interaction between the controller and the simulation platform. The simulation platform converts the simulated power grid operating status into physical signals, which are then transmitted to the hierarchical distributed controller, local controller, and grid terminal equipment via the IEC104 protocol. Simultaneously, the physical information of the local controller is converted into simulation signals through the interface and transmitted to the controllable distributed power source simulation controller in the simulation platform for the control process of the controlled unit.

[0145] The algorithm parameter interface transmits model parameters, acquisition parameters, algorithm control parameters, algorithm startup, and calculation and analysis results to the advanced application module as data input for advanced application testing of active distribution networks.

[0146] The local control module includes communication with the upper-level hierarchical distributed controller. When the local controller receives commands from the upper-level controller, it forwards them through protocols and makes appropriate responses based on the type of command: if it is a data call command, it retrieves the required data from the real-time database and sends it to the hierarchical distributed controller.

[0147] If it is a remote control command, the active power control target is parsed from the message according to the format of the 104 protocol, and a message with a specific ID is sent to the area. Then, the control strategy plugin reads the required information from the real-time database of the scheduling layer according to the active power and the control target, and performs corresponding control on the distributed power source. The control target of each distributed power source is sent to the scheduling layer in the form of a command. Finally, the master station protocol reads the command in the scheduling layer and sends the command to each distributed power source in the form of the 104 protocol according to the specific information of the command.

[0148] Example 2

[0149] Reference Figures 4-7 As an embodiment of the present invention, an advanced application testing method for active distribution networks based on dynamic simulation is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0150] The present invention discloses an advanced application testing method for active distribution networks based on dynamic simulation, wherein the steps of the implementation case testing method are as follows:

[0151] S1: Construct an active distribution network model using a dynamic simulation platform. The network topology is as follows. Figure 4 As shown, the power distribution network includes one infinite power source with a capacity of 210kVA, one simulated thermal power source with a capacity of 75kVA, six distributed power sources (four wind turbines and two simulated photovoltaics), and four loads with a capacity of 12kVA. The lines are divided into three categories.

[0152] S2: Collect dynamic model network structure data using the dynamic simulation platform and generate a network topology file, which is then transferred to the advanced application model library. The network topology file contains network topology data and real-time measurement data, as follows: Figure 5 , Figure 6 As shown.

[0153] S3: As Figure 7 As shown, the dynamic simulation platform can set up the power grid operating environment and source-load output curves with one click, forming an active distribution network operating scenario. Network operating parameters and algorithm control parameters can be set through the algorithm service interface and input to the advanced application module.

[0154] S4: Use the one-click start / stop control button to start the advanced application module, perform advanced application power flow calculations on the network, and verify the feasibility of advanced application testing. The results are shown in the table below.

[0155] Table 1. Power Flow Calculation and Test Results

[0156]

[0157] Table 2 Power Flow Calculation Test Results

[0158] Serial Number type p_mw q_mvar 0 Ext_grid 2.247659 5.396258 1 Gen 0.0 1.809535 2 Sgen1 0.005 0 3 Sgen2 0.0 0.005

[0159] Table 3 Load flow calculation test results

[0160] Serial Number p_mw q_mvar 0 0.0036 0 1 0 0.0036 2 0.0036 0 3 0 0.0036 4 0.0036 0 5 0 0.0036 6 0.0036 0 7 0 0.0036 8 0.0036 0

[0161] The data in the table are the results of load flow calculations, which provide the pre-optimized output of each device. As a reference for the target feeder power value, the data is transmitted to each device via the controller to provide a control value reference for the device output.

[0162] Furthermore, the following methods exist for advanced application power flow calculation: The advanced application power flow calculation module utilizes the Pandapower toolbox to develop OPF and other advanced application functions. First, the advanced application module reads the input CIME model file and uses Pandapower elements to create and generate the network topology and corresponding devices. The main devices created are as follows:

[0163] (1) Blank network creation

[0164] net=pandapower.create_empty_network(name=", f_hz=50.0, sn_mva=1, add_stdtypes=True)

[0165] In the formula, f_hz represents the frequency of the power system; name is the network name; sn_mva represents the reference apparent power of the unit system; and add_stdtypes represents the standard types contained in the network.

[0166] (2) Bus node creation

[0167] Bus=pandapower.create_bus(net,vn_kv,name=None,index=None,geodata=None,type='b',zone=None,in_service=True,max_vm_pu=nan,min_vm_pu=nan,coords=None,**kwargs)

[0168] In the formula, vn_kv represents the network voltage level; index indicates that the ID must be specified, and if it is None, the index number higher than the existing highest index will be selected; geodata represents the geographic coordinates used to draw the network; type indicates zongxia by type, where n is the node and b is the bus; in_service indicates whether it is in the running state; max_vm_pu represents the maximum bus voltage; min_vm_pu represents the minimum bus voltage; and coords represents the bus coordinates with multiple points.

[0169] (3) Line creation

[0170] Line=pandapower.create_line(net,from_bus,to_bus,length_km,std_type,name=None,index=None,geodata=None,df=1.0,parallel=1,in_service=True,max_loading_percent=nan,alpha=nan,temperature_degree_celsius=nan,**kwargs)

[0171] In the formula, from_bus and to_bus represent line connection nodes; length_km represents line length; std_type represents line type; parallel represents the number of parallel lines; and max_loading_percent represents the maximum allowable load ratio of the line.

[0172] (4) Switch creation

[0173] Switch=pandapower.create.create_switch(net,bus,element,et,closed=True,type=None,name=None,index=None,z_ohm=0,in_ka=nan,**kwargs)

[0174] In the formula, element represents the component number; closed represents the switch's open / closed state; type represents the switch type; z_ohm represents the switch resistance; and in_ka represents the maximum current the switch can withstand.

[0175] (5) Load creation

[0176] Load=pandapower.create_load(net,bus,p_mw,q_mvar=0,const_z_percent=0,const_i_percent=0,sn_mva=nan,name=None,scaling=1.0, index=None, in_service=True, type='wye', max_p_mw=nan, min_p_mw=nan, max_q_mvar=nan, min_q_mvar=nan, controllable=nan, **kwargs)

[0177] In the formula, bus represents the load access node; p_mw represents the load active power demand; q_mvar represents the load reactive power demand; const_z_percent represents the percentage of active and reactive power under rated voltage and constant load; const_i_percent represents the percentage of active and reactive power under rated voltage and actual load; and controllable represents whether the load is controllable.

[0178] (6) Generator Creation

[0179] pandapower.create.create_ext_grid(net,bus,vm_pu=1.0,va_degree=0.0,name=None,in_service=True,s_sc_max_mva=nan,s_sc_min_mva=nan,rx_max=nan,rx_m in=nan, max_p_mw=nan, min_p_mw=nan, max_q_mvar=nan, min_q_mvar=nan, index=None, r0x0_max=nan, x0x_max=nan, controllable=nan, slack_weight=1.0, **kwargs)

[0180] In the formula, vm_pu represents the voltage of the slack node; va_degree represents the voltage phase angle of the slack node; s_sc_max_mva represents the maximum allowable short-circuit current of the external power grid when calculating the short-circuit current; s_sc_min_mva represents the minimum allowable short-circuit current of the external power grid when calculating the short-circuit current; rx_max represents the maximum R / X ratio, used to calculate the internal impedance of the external power grid for short-circuit calculation; rx_min represents the minimum R / X ratio, used to calculate the internal impedance of the external power grid for short-circuit calculation; max_p_mw and min_p_mw represent the maximum and minimum active power injection when solving for optimal power flow; max_q_mvar and min_q_mvar represent the maximum and minimum reactive power injection when solving for optimal power flow.

[0181] In addition to the aforementioned equipment, specific equipment such as transformers, photovoltaics, energy storage, and capacitors in the network can also be modeled to form a network model.

[0182] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0183] Example 3

[0184] The third embodiment of the present invention differs from the first two embodiments in that:

[0185] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0186] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0187] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0188] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0189] Example 4

[0190] Reference Figure 8 This is the fourth embodiment of the present invention. This embodiment provides an advanced application test system for active distribution networks based on dynamic simulation, including an active distribution network dynamic simulation grid module, a local control module, a hierarchical distributed control module, a dynamic simulation module, an advanced application module, and a data communication and algorithm module.

[0191] The active distribution network dynamic model grid module includes a variety of distributed source and load devices such as photovoltaic power simulator, wind turbine simulator, energy storage simulator, and programmable RLC load. It also connects to terminal devices such as FTU and DTU for data acquisition and network operation monitoring. The distributed power sources in the dynamic model grid interact with the local controller through the communication protocol interface, uploading historical and real-time network data. The simulation devices are all initialized through the dynamic model platform to simulate the operating status of different source and load in the grid.

[0192] The local control module is responsible for the local control of the active distribution network. It coordinates the power output of the distributed power sources it controls according to the instructions from the superior, and obtains the real-time operation data of the distributed power sources in the dynamic grid and uploads it to the hierarchical distributed controller.

[0193] The hierarchical distributed control module is responsible for communicating and controlling with the local controllers within its jurisdiction. It collects the exchange power between the area and the feeder in real time and converts it through the protocol. It coordinates the input of the local controllers in the area and summarizes the distributed power information collected by the local controllers in the entire area and uploads it to the dynamic simulation platform through the protocol interface.

[0194] The dynamic simulation module provides one-click network setup and generation, enabling automatic identification and generation of active distribution network dynamic model structures, and outputting the corresponding network topology file to the advanced application module. Simultaneously, the dynamic simulation platform can process and modify simulation equipment through a data interaction interface, and provides information exchange on active distribution network model parameters, acquisition parameters, real-time data, algorithm control parameters, algorithm startup, and calculation and analysis results through the algorithm interface and the active distribution network operation monitoring module. The platform also supports the creation and generation of historical and real-time data case studies, and can construct different power grid operating conditions through one-click environment and curve settings, serving as advanced application test scenarios.

[0195] The advanced application module receives network parameters from the algorithm interface, as well as historical and real-time data from the model library. It performs various advanced application calculations, including power flow calculation, state estimation, and operation optimization. Its functions include verifying algorithm accuracy and outputting network control target values. The control target values ​​are saved to the algorithm output path set on the dynamic simulation platform and then sent to the hierarchical distributed controller and various terminal devices in the dynamic model network through the protocol interface. The hierarchical distributed controller sends the target values ​​to the local controllers, and together with the target values ​​obtained by the FTU and DTU terminals, they optimize and coordinate the control of the active distribution network to complete the closed-loop verification of the advanced application test functions.

[0196] The data communication and algorithm module realizes data interaction and information transmission between the controller and the dynamic simulation platform and advanced application modules through the data transmission protocol interface and algorithm parameter interface, so as to simulate and test the operation and control process of the active distribution network.

[0197] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for advanced application testing of active distribution networks based on dynamic simulation, characterized in that: The application relates to a method for testing advanced distribution network functions. The method comprises the following steps: collecting data of a dynamic simulation platform of a distribution network, and forming a network topology file which is transmitted to a model library of advanced distribution network functions; The dynamic simulation platform sets the power grid operation environment and source and load output curves by one key, forms an operation scenario of the advanced distribution network, sets network operation parameters and algorithm control parameters through an algorithm service interface, and inputs the parameters into the advanced application module; The advanced application module is started by one key start-stop control buttons, advanced application power flow calculation is performed on the network, and the feasibility of the advanced application test is verified; The advanced distribution network function test comprises the following steps: collecting data and operation information of the advanced distribution network, constructing a network topology of the advanced distribution network by using the dynamic simulation platform, setting initial values of device output, converting real-time measurement data through a protocol, updating a historical library and a real-time library of the advanced application model, forming multiple basic operation scenarios of the distribution network such as economic dispatching operation, active coordinated control and reactive voltage optimization control, performing global pre-optimization on the overall network by using an advanced application algorithm, and feeding the optimization results to each region in the advanced distribution network, so that the energy management system advanced application module in the dynamic simulation platform is completed and target control is realized by using feeder control error; The advanced distribution network function comprises optimal power flow calculation, state estimation, operation mode optimization, global optimization and active and reactive optimization coordinated control for operation control of the advanced distribution network, measurement values comprise node voltage, active power, reactive power and line current, and element types comprise bus nodes, lines and transformers.

2. The method of claim 1, wherein the method is based on a dynamic simulation of the active distribution grid. The state estimation comprises voltage state estimation, current state estimation and power state estimation; The operation mode optimization comprises optimization of operation modes of devices in the network, and the optimization of the system operation mode comprises optimization of the opening and closing states of switches and the input of devices, wherein constraints are added in the advanced application module during the operation mode optimization, and the constraints comprise selection of device operation modes and expression of switch opening and closing combination schemes as follows: S = {s1, s2,..., s n}, s n = {a1, a2,..., a n} Switch initial combinations and device operation mode combinations are selected, a device operation and switch state combination scheme set is formed, the scheme set is stored in an advanced application database, the scheme set is iterated in sequence, economic targets and optimal power flow calculation methods are used for calculation and solution, the device operation mode and switch opening and closing state which are optimal in economy in a period are selected as the operation mode pre-optimization result, and the result is fed to each device and switch state in the network through hierarchical distributed controllers and local controllers in the dynamic simulation platform, wherein s is a switch state combination, a is a 0-1 variable, 0 is closed, 1 is opened, and S is a switch combination scheme set.

3. The method of claim 2, wherein the method is based on a dynamic simulation of the active distribution grid. The method for testing advanced distribution network functions comprises the following steps: designing a model architecture of advanced distribution network functions, the architecture comprising an advanced distribution network simulation power grid, a local controller, a hierarchical distributed controller, an advanced distribution network dynamic simulation platform and an advanced distribution network energy management system advanced application module. The dynamic simulation platform constructs the operation environment of the actual power grid according to the grid parameters and operation conditions, simulates the operation state of the power grid under various working conditions, at this time, the local controller collects the real-time data of the power source in the active distribution network and the opening and closing state of the switch, and uploads the data to the hierarchical distributed controller, the hierarchical distributed controller collects and processes the real-time data of the grid equipment uploaded by the local controller, and transmits the data to the energy management system of the dynamic simulation platform through the protocol interface, at the same time, the FTU and DTU devices in the dynamic simulation network also upload the data that cannot be covered by the local controller through the protocol interface as the test data input of the advanced application, the dynamic simulation platform sets up the network according to the demand, monitors the operation of the active distribution network, and provides historical data and real-time data simulation cases to construct different test scenes of the advanced application: power control, reactive power and voltage control and voltage out-of-limit scene, the advanced application module tests and verifies the accuracy of the algorithm and other performances according to different scenes: the test results of the advanced application, i.e. target value, are transmitted to the local controller through the data transmission interface via the hierarchical distributed controller, the local controller issues target instructions to each source and load device and switch in the grid for test and verification: The feasibility of the advanced application test includes that when the operation state of the power grid reaches the expected target, the local controller feeds back the execution result to the hierarchical distributed controller, then the hierarchical distributed controller feeds back to the dynamic simulation platform, and finally the dynamic simulation platform feeds back to the advanced application module, at this time, each module evaluates that the actual operation state is consistent with the expected state, when the expected target is not reached and the power grid has an abnormal situation, the local controller feeds back the real-time data and operation state to the hierarchical distributed controller, the dynamic simulation platform and the advanced application module, at this time, each module evaluates that the actual operation state is inconsistent with the expected state, each module analyzes the internal reason, adjusts the algorithm and strategy, and re-regulates and controls.

4. The method of claim 3, wherein the method further comprises: The active distribution network advanced application test method based on dynamic simulation includes collecting the distributed power supply data of the dynamic simulation network of the active distribution network by the local controller, and uploading the data to the hierarchical distributed controller through the protocol interface, including the historical data and real-time data of the network internal switch, distributed power supply, controllable load and energy storage device; The dynamic simulation platform, the hierarchical distributed controller and the terminal device in the network are communicated and interacted, the controllable devices and network data of the whole region are obtained through the protocol interface, and the one-key network setting is adopted to automatically generate the simulation network of the active distribution network, which is output to the active distribution network operation monitoring module, the dynamic simulation platform models the topology of the active distribution network according to the simulation network, and generates the corresponding network topology file, which is input to the advanced application model historical library, the initial value of the simulation device power and voltage in the simulation network is set through the parameter setting interface, and the load prediction data and power output prediction data are generated through the case making, which are input to the advanced application module through the data processing service interface, the ADN model parameters, collection parameters and algorithm control parameters are transmitted to the advanced application module through the algorithm service interface for start-stop control of the advanced application; The high-level application module adopts one-key start-stop control mode, verifies the accuracy of the algorithm according to the network data provided by the dynamic simulation platform, and outputs the result to the specified path through the algorithm service interface, thereby completing the accuracy verification of the high-level application test algorithm part; The dynamic simulation platform reads the output result target value, and sends the target value to the hierarchical distributed controller and the terminal equipment through the protocol conversion interface, the hierarchical distributed controller sends the target value instruction to the local controller through the protocol forwarding, and the local controller verifies the effectiveness of the target value for the real-time control of the distributed power supply, and verifies the closed-loop capability of the high-level application test based on the dynamic simulation.

5. The method of claim 4, wherein the method further comprises: The high-level application power flow calculation includes the node power and voltage of the distribution network, and the node power model is as follows: Further conversion can be obtained: The node power balance equation is as follows: The correction amount of node voltage, phase angle and power is obtained by using Jacobian matrix, and the network node power, voltage and phase angle are updated, then the optimal solution is obtained by using interior point method iteration, and the second order Taylor expansion is carried out by using Newton method and entering power flow calculation to obtain the optimal power flow optimization result in the period, wherein, Y ij is the admittance matrix of line impedance, U i is the voltage of node i, P i and Q i are the active and reactive power injected into node i respectively, θ ij is the phase angle of node i, j.

6. An active distribution grid advanced application testing system based on dynamic simulation emulation, characterized by: The active distribution network dynamic network architecture module, the local control module, the hierarchical distributed control module, the dynamic simulation module, the high-level application module and the data communication and algorithm module are included. The active distribution network dynamic network architecture module includes a photovoltaic power supply simulator, a fan simulator, an energy storage simulator, a programmable RLC load distributed source and load equipment, and also accesses FTU and DTU terminal equipment for data collection and network operation monitoring. The distributed power supply in the dynamic network architecture interacts with the local controller through the communication protocol interface and uploads the network historical data and real-time data. The simulation equipment in the dynamic network architecture is set with initial values through the dynamic simulation platform to simulate the operating state of different sources and loads in the network architecture. The local control module is responsible for the local control of the active distribution network, coordinates the power output of the distributed power supply under the control of the superior instruction, and uploads the real-time operating data of the distributed power supply in the dynamic network architecture to the hierarchical distributed controller. The hierarchical distributed control module is responsible for communication control with the local controller in the jurisdiction area, collects the exchange power of the area and feeder in real time, and coordinates the input of the local controller in the area through protocol conversion, and uploads the distributed power supply information collected by the local controller in the whole area to the dynamic simulation platform through the protocol interface. The dynamic simulation module provides one-key network setting and generation, can realize automatic identification and generation of the active distribution network dynamic network architecture, and outputs the corresponding network topology file to the high-level application module. At the same time, the dynamic simulation platform can realize the processing change of the simulation equipment through the data interaction interface, and provide the information interaction of the active distribution network model parameters, collected parameters, real-time data information, algorithm control parameters, algorithm start, calculation analysis result through the algorithm interface and the active distribution network operation monitoring module. At the same time, the dynamic simulation platform also supports case making and generation of historical data and real-time data, can construct different power grid operating conditions through one-key environment and curve setting, and serves as a high-level application test scene. The senior application module receives network parameters transmitted by the algorithm interface and historical data and real-time data in the model library, carries out various senior application calculations such as power flow calculation, state estimation and operation optimization, and can realize functions including algorithm accuracy verification and output of network control target values, the control target values are saved to the algorithm output path set by the dynamic simulation platform, and are issued to the hierarchical distributed controller and various terminal devices in the dynamic network frame through the protocol interface, the hierarchical distributed controller issues the target values to the local controller, and together with the target values obtained by the FTU and DTU terminal, optimizes and coordinates the control of the active distribution network to complete the closed-loop verification of the senior application test function; The data communication and algorithm module realizes data interaction and information transmission between the controller and the dynamic simulation platform and the senior application module through the data transmission protocol interface and the algorithm parameter interface, so as to simulate and test the operation and control process of the active distribution network.

7. The active distribution network advanced application testing system based on dynamic simulation of claim 6, wherein: The data communication and algorithm module includes a data transmission protocol interface and an algorithm parameter interface; The data transmission protocol interface includes a conversion tool of MODBUS protocol and IEC104 protocol, realizes data interaction between the controller and the dynamic simulation platform, the dynamic simulation platform converts the simulation power grid operation state into a physical signal, and transmits the physical signal to the hierarchical distributed controller, the local controller and the network frame terminal device through the IEC104 protocol, at the same time, the physical information of the local controller is converted into a simulation signal through the interface, and is transmitted to the controllable distributed power supply simulation controller in the dynamic simulation platform to control the controlled unit; The algorithm parameter interface transmits model parameters, acquisition parameters, algorithm control parameters, algorithm start, calculation analysis result information to the senior application module as data input of the active distribution network senior application test; When communicating with the upper layer hierarchical distributed controller, the local control module receives the command of the upper layer controller, transmits the command through the protocol, and makes appropriate response according to the type of the command: If it is a data calling command, the required data is obtained from the real-time database and sent to the hierarchical distributed controller; If it is a remote control command, the active control target is parsed from the message according to the format of the 104 protocol, a message with a specific ID is sent to the region, then the control strategy plug-in reads the required information from the real-time database of the dispatch layer according to the active and control target, controls the distributed power supply correspondingly, sends the control target of each distributed power supply to the dispatch layer in the form of a command, finally, the master station protocol reads the command in the dispatch layer, and sends the command to each distributed power supply in the form of the 104 protocol according to the specific information of the command.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to realize the steps of the method in any one of claims 1 to 5.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method in any one of claims 1 to 5.

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