Distribution network optimization dispatching method, device and distribution network optimization system

Abstract

The present application provides a distribution network optimization scheduling method, device and distribution network optimization system. The method includes: obtaining the operation scenario of the distribution network; obtaining the initial operation information of the operation scenario; optimizing the initial operation information according to the target optimization algorithm to obtain the target operation information; using the target operation information to optimize the distribution network, at least using the voltage margin to determine the flexibility of the distribution network after optimization scheduling, and continuing to optimize the distribution network when the flexibility of the distribution network does not meet the preset flexibility conditions, until the flexibility of the distribution network meets the preset flexibility conditions. This solution solves the problem that the efficiency of power optimization scheduling of the distribution network in the prior art is low, resulting in poor overall flexibility of the distribution network.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network optimization and dispatching, and in particular, to a distribution network optimization and dispatching method, device, computer program product and distribution network optimization system. Background Art

[0002] In recent years, with the continuous development of new power systems, the penetration rate of distributed power sources, electric vehicles and other flexible controllable loads and distributed energy storage in distribution networks has gradually increased, making the distribution network transformed from a passive unidirectional power supply network to an active network with bidirectional power flow. The application of these new technologies has brought new control methods to the distribution network, but also brought many challenges: the intermittent and volatile output of distributed power sources such as wind and light can easily lead to voltage fluctuations and reduce the quality of power; the increase in new sources and loads such as distributed power sources, distributed energy storage and flexible loads has added more variables to power flow control and increased the complexity of power flow optimization problems; the dual attributes of source and load and energy storage attributes of distributed energy storage require energy management strategies to consider planning in the time dimension.

[0003] Therefore, in response to a series of challenges brought to the distribution network by various new sources and loads with increasing penetration rates, the distribution network needs to take proactive measures to control controllable resources such as distributed power sources, distributed energy storage and flexible loads, and improve the distribution network's ability to coordinate various flexible resources and adjust operating conditions in order to achieve goals such as voltage stability, energy balance and economic optimization control.

[0004] However, the current power optimization scheduling efficiency of the distribution network is low, resulting in poor overall flexibility of the distribution network. Summary of the invention

[0005] The main purpose of the present application is to provide a distribution network optimization scheduling method, device, computer program product and distribution network optimization system, so as to at least solve the problem that the efficiency of power optimization scheduling of distribution networks in the prior art is low, resulting in poor overall flexibility of the distribution network.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for optimizing and dispatching a distribution network is provided, comprising: obtaining an operation scenario of the distribution network, wherein the operation scenario is one or more of a scenario of wind power output generating power, a scenario of photovoltaic output generating power, and a scenario of load output generating power in the distribution network within a target duration; obtaining initial operation information of the operation scenario, wherein the initial operation information at least includes one or more of the voltage of the power router, the current of the power router, the charging and discharging power of the power router, the transmission power of the power router, and the exchange power of the power router; optimizing the operation scenario according to the target optimization algorithm ... The initial operation information is optimized to obtain target operation information, wherein the target optimization algorithm includes one or more of a MATLAB algorithm, a YALMIP algorithm, and a CPLEX algorithm; the distribution network is optimized and scheduled using the target operation information, and the flexibility of the distribution network after the optimized scheduling is determined by at least the voltage margin, and when the flexibility of the distribution network does not meet the preset flexibility condition, the distribution network is continuously optimized and scheduled until the flexibility of the distribution network meets the preset flexibility condition, wherein the voltage margin is positively correlated with the flexibility of the distribution network, and the voltage margin is the voltage fluctuation range of the distribution network.

[0007] Optionally, obtaining an operating scenario of the distribution network includes: obtaining a first operating scenario, wherein the first operating scenario is the operating scenario of a first target time period, and the first target time period is a historical time period; obtaining a second operating scenario, wherein the second operating scenario is the operating scenario of a second target time period, the latest moment in the first target time period is earlier than the earliest moment in the second target time period, the duration of the first target time period is greater than the duration of the second target time period, and the second target time period is a current time period; obtaining a third operating scenario, wherein the third operating scenario is the operating scenario of a third target time period, the latest moment in the second target time period is earlier than the earliest moment in the third target time period, the duration of the second target time period is greater than the duration of the third target time period, and the third target time period is a real-time time period.

[0008] Optionally, when the operating scenario is the first operating scenario, the initial operating information is optimized according to a target optimization algorithm to obtain target operating information, including: obtaining first constraint information, wherein the first constraint information includes one or more of constraint information of node power balance, constraint information of the power router, constraint information of branch flow, constraint information of node voltage, constraint information of branch current, constraint information of branch capacity, constraint information of engine power, and constraint information of energy storage device; using the first constraint information as a constraint, optimizing the first initial operating information according to the target optimization algorithm to obtain first target operating information, wherein the first initial operating information is the initial operating information corresponding to the first operating scenario.

[0009] Optionally, when the operating scenario is the second operating scenario, the initial operating information is optimized according to a target optimization algorithm to obtain target operating information, including: obtaining second constraint information, wherein the second constraint information is constraint information of the state of charge; using the first constraint information and the second constraint information as constraints, optimizing the second initial operating information according to the target optimization algorithm to obtain second target operating information, wherein the second initial operating information is the initial operating information corresponding to the second operating scenario; when the operating scenario is the third operating scenario, the initial operating information is optimized according to a target optimization algorithm to obtain target operating information, including: using the first constraint information and the second constraint information as constraints, optimizing the third initial operating information according to the target optimization algorithm to obtain third target operating information, wherein the third initial operating information is the initial operating information corresponding to the third operating scenario.

[0010] Optionally, when the operation scenario is the second operation scenario, before optimizing the initial operation information according to the target optimization algorithm to obtain the target operation information, the method further includes: calculating the similarity between the second operation scenario and all the first operation scenarios to obtain multiple first similarities; extracting the first target operation information of the first operation scenario with the highest first similarity as reference information in the optimization process of the second operation scenario; when the operation scenario is the third operation scenario, before optimizing the initial operation information according to the target optimization algorithm to obtain the target operation information, the method further includes: calculating the similarity between the third operation scenario and all the second operation scenarios to obtain multiple second similarities; extracting the second target operation information of the second operation scenario with the highest second similarity as reference information in the optimization process of the third operation scenario.

[0011] Optionally, before optimizing the initial operation information according to the target optimization algorithm to obtain the target operation information, the method further includes: obtaining an objective function, wherein the objective function is a function in the optimization process according to the target optimization algorithm, and the objective function is:

[0012]

[0013] f represents the objective function, T represents the total optimization time period, I represents the total number of nodes in the distribution network, and U i,t represents the voltage of the ith node at time t, U cr,i represents the critical stable voltage of the i-th node.

[0014] Optionally, before obtaining the operating scenario of the distribution network, the method also includes: obtaining initial power, wherein the initial power is one or more of the initial power generated by wind power output, the initial power generated by photovoltaic output, and the initial power generated by load output in the distribution network; obtaining an error function, wherein the error function is a function obtained by normal distribution and / or a function obtained by beta distribution; and using the error function to correct the initial power to obtain the target power.

[0015] According to another aspect of the present application, there is provided an optimization dispatching device for a distribution network, comprising: a first acquisition unit, for acquiring an operation scenario of the distribution network, wherein the operation scenario is one or more of a scenario of wind power output generating power, a scenario of photovoltaic output generating power, and a scenario of load output generating power in the distribution network within a target duration; a second acquisition unit, for acquiring initial operation information of the operation scenario, wherein the initial operation information includes at least one or more of a voltage of an electric energy router, a current of the electric energy router, a charge and discharge power of the electric energy router, a transmission power of the electric energy router, and an exchange power of the electric energy router; a first processing unit, for obtaining an operation scenario according to a target optimization. The optimization algorithm is used to optimize the initial operation information to obtain target operation information, wherein the target optimization algorithm includes one or more of a MATLAB algorithm, a YALMIP algorithm, and a CPLEX algorithm; a second processing unit is used to optimize the distribution network using the target operation information, at least using the voltage margin to determine the flexibility of the distribution network after the optimized scheduling, and when the flexibility of the distribution network does not meet the preset flexibility condition, continue to optimize the distribution network until the flexibility of the distribution network meets the preset flexibility condition, wherein the voltage margin is positively correlated with the flexibility of the distribution network, and the voltage margin is the voltage fluctuation range of the distribution network.

[0016] According to another aspect of the present application, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the steps of any one of the methods for optimizing the scheduling of a distribution network.

[0017] According to another aspect of the present application, a distribution network optimization system is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing any one of the distribution network optimization scheduling methods described.

[0018] By applying the technical solution of the present application, a target optimization algorithm is adopted to optimize the power router during the operation of the distribution network. The target optimization algorithm can optimize the load distribution of the power grid, and the power router can control the distribution and flow of electric energy in the distribution network. Therefore, this solution can make full use of the ability of the power router to quickly regulate flexible resources. The voltage margin can reflect the anti-disturbance ability of the distribution network itself. For example, the larger the voltage margin, the stronger the ability to resist power fluctuations, the safer the node, and the higher the overall node flexibility of the distribution network. Therefore, this solution can improve the efficiency of power optimization scheduling of the distribution network, thereby improving the overall flexibility of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 A hardware structure block diagram of a mobile terminal for executing a method for optimizing scheduling of a distribution network provided in an embodiment of the present application is shown;

[0021] Figure 2 A schematic diagram of a flow chart of a method for optimizing scheduling of a distribution network provided according to an embodiment of the present application is shown;

[0022] Figure 3 A schematic diagram of a specific flow chart of an optimization dispatching method for a distribution network is shown;

[0023] Figure 4 The topological structure diagram of the nodes of the power router of this solution is shown;

[0024] Figure 5 A schematic diagram showing a first operating scenario of wind power output generating power;

[0025] Figure 6 A schematic diagram showing a first operating scenario of load output generating power;

[0026] Figure 7 A schematic diagram showing a comparison of the voltage mean values ​​of nodes before and after scheduling of the first operation scenario;

[0027] Figure 8 A schematic diagram showing the comparison of voltage mean values ​​at various moments before and after the first operation scenario scheduling is shown;

[0028] Fig. 9 A schematic diagram showing the scheduling results of energy storage charging and discharging power in the first operating scenario;

[0029] Fig.10 A schematic diagram showing a second operating scenario of wind power output generating power;

[0030] Fig.11 A schematic diagram showing a second operating scenario of load output generating power;

[0031] Fig.12 A schematic diagram showing a comparison of the voltage mean values ​​of nodes before and after the second operation scenario scheduling;

[0032] Fig.13 A schematic diagram showing the comparison of voltage mean values ​​at various moments before and after the second operation scenario scheduling is shown;

[0033] Fig.14 A schematic diagram showing the scheduling results of energy storage charging and discharging power in the second operation scenario;

[0034] Fig.15 A schematic diagram showing the voltage value before the optimized scheduling of the third operation scenario is shown;

[0035] Fig.16 A schematic diagram showing the voltage value after the optimized scheduling of the third operation scenario;

[0036] Fig.17 A schematic diagram showing a comparison of the voltage mean values ​​of nodes before and after scheduling in the third operation scenario;

[0037] Fig.18 A schematic diagram showing the comparison of voltage mean values ​​at various moments before and after the third operation scenario scheduling is shown;

[0038] Fig.19 A schematic diagram showing a comparison of energy storage charging and discharging power dispatch plans;

[0039] Fig. 20 A schematic diagram showing a comparison of power exchange in a power grid is shown;

[0040] Fig.21 A schematic diagram showing the comparison of the power of the tie lines between the power routers is shown;

[0041] Fig. 22A structural block diagram of an optimization scheduling device for a distribution network provided according to an embodiment of the present application is shown.

[0042] The above drawings include the following reference numerals:

[0043] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0044] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0045] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0046] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0047] As introduced in the background technology, the efficiency of power optimization scheduling of distribution networks in the prior art is low, resulting in poor overall flexibility of the distribution network. To solve the above problems, the embodiments of the present application provide a distribution network optimization scheduling method, device, computer program product and distribution network optimization system.

[0048] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0049] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a method for optimizing and dispatching a distribution network according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0050] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the display method of device information in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0051] In this embodiment, a method for optimizing the scheduling of a distribution network running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0052] Figure 2FIG. 1 is a flow chart of a method for optimizing the dispatching of a distribution network according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0053] Step S201, obtaining an operation scenario of the distribution network, wherein the operation scenario is one or more of a scenario of wind power output generating power, a scenario of photovoltaic power output generating power, and a scenario of load output generating power in the distribution network within a target duration;

[0054] Specifically, during the use of the distribution network, power can be generated by wind power output, photovoltaic power output, or load output. Of course, the specific power generating device is selected according to the actual situation. The scenarios in which different devices generate power can be called operation scenarios, and the data obtained can be wind-solar-load scenario data (i.e., data on power generated by wind power output, photovoltaic output, and load output, or predicted power data).

[0055] Step S202, obtaining initial operation information of the operation scenario, wherein the initial operation information includes at least one or more of the voltage of the power router, the current of the power router, the charge and discharge power of the power router, the transmission power of the power router, and the exchange power of the power router;

[0056] Specifically, as the core equipment in the new distribution network, the power router integrates information technology and power electronic conversion technology. The power router can serve as an energy hub to provide plug-and-play interfaces for various renewable energy power generation equipment, energy storage equipment and power consumption equipment, while controlling the energy flow inside the power router and the energy exchange with other power routers or power grids. Therefore, the initial operation information of the distribution network after the power router is connected can be obtained, that is, one or more of the voltage of the power router, the current of the power router, the charging and discharging power of the power router, the transmission power of the power router, and the exchange power of the power router.

[0057] Step S203, optimizing the initial operation information according to a target optimization algorithm to obtain target operation information, wherein the target optimization algorithm includes one or more of a matlab algorithm, a yalmip algorithm, and a cplex algorithm;

[0058] Specifically, the initial operation information can be optimized and can be optimized using a target algorithm. Compared with the initial operation information, the optimized target operation information can make the distribution network more flexible.

[0059] Step S204, using the above-mentioned target operation information to optimize the scheduling of the above-mentioned distribution network, at least using the voltage margin to determine the flexibility of the above-mentioned distribution network after the optimization scheduling, and continuing to optimize the scheduling of the above-mentioned distribution network when the flexibility of the above-mentioned distribution network does not meet the preset flexibility conditions until the flexibility of the above-mentioned distribution network meets the preset flexibility conditions, wherein the above-mentioned voltage margin is positively correlated with the flexibility of the above-mentioned distribution network, and the above-mentioned voltage margin is the voltage fluctuation range of the above-mentioned distribution network.

[0060] Specifically, the voltage margin is used as an indicator to reflect the flexibility of the distribution network. That is, the higher the optimized voltage value, the higher the ability to resist fluctuations caused by the outside world, the stronger the ability of the node to resist power fluctuations, the safer the node, and the higher the overall node flexibility of the distribution network.

[0061] The flexibility conditions of the distribution network include load response capability, energy storage capacity, adjustable load and renewable energy access, etc. The specific numerical examples are as follows:

[0062] Load response capability: The distribution network needs to have a certain load response capability to cope with sudden load changes. For example, when the system load increases rapidly, the distribution network needs to be able to respond quickly and adjust the load to maintain system balance. The preset load response capability can be 100 megawatts per second.

[0063] Energy storage capacity: The distribution network needs to have a certain amount of energy storage capacity to balance the volatility and intermittency of renewable energy. For example, when the supply of renewable energy is insufficient, the energy storage system can release the stored energy to meet the system demand. The preset energy storage capacity can be 100 MWh.

[0064] Adjustable load: The distribution network needs to have a certain amount of adjustable load to flexibly adjust the system power. For example, by adjusting the operating status of industrial production equipment, the system load can be quickly adjusted. The preset adjustable load can be 50 megawatts per second.

[0065] Renewable energy access: The distribution network needs to have a certain amount of renewable energy access to increase the proportion of renewable energy in the system. For example, by connecting renewable energy facilities such as wind power generation and photovoltaic power generation, the renewable energy supply of the system can be increased. The preset renewable energy access can be 200 megawatts per second.

[0066] Through this embodiment, a target optimization algorithm is used to optimize the power router during the operation of the distribution network. The target optimization algorithm can optimize the load distribution of the power grid, and the power router can control the distribution and flow of electric energy in the distribution network. Therefore, this solution can make full use of the ability of the power router to quickly regulate flexible resources. The voltage margin can reflect the anti-disturbance ability of the distribution network itself. For example, the larger the voltage margin, the stronger the ability to resist power fluctuations, the safer the node, and the higher the overall node flexibility of the distribution network. Therefore, this solution can improve the efficiency of power optimization scheduling of the distribution network, thereby improving the overall flexibility of the distribution network.

[0067] In the specific implementation process, obtaining the operation scenario of the distribution network can be achieved through the following steps: obtaining a first operation scenario, wherein the first operation scenario is the operation scenario of the first target time period, and the first target time period is a historical time period; obtaining a second operation scenario, wherein the second operation scenario is the operation scenario of the second target time period, the latest moment in the first target time period is earlier than the earliest moment in the second target time period, the duration of the first target time period is greater than the duration of the second target time period, and the second target time period is the current time period; obtaining a third operation scenario, wherein the third operation scenario is the operation scenario of the third target time period, the latest moment in the second target time period is earlier than the earliest moment in the third target time period, the duration of the second target time period is greater than the duration of the third target time period, and the third target time period is a real-time time period.

[0068] In this scheme, multiple time scales can be obtained, so that we can start from multiple time scales and subsequently consider the scheduling optimization of different time periods separately. The division of time scales in this scheme is more refined and the error with the actual situation is smaller.

[0069] Specifically, the optimization of the first operating scenario in this solution can also be called day-ahead optimization, the second operating scenario can also be called intraday optimization, and the third operating scenario can also be called real-time rolling optimization.

[0070] For example, the following optimizations for three scenarios are given below:

[0071] Day-ahead optimization (optimization of the first operating scenario): input multiple wind and solar load forecast scenario data for the target system in the 24 time periods a day before, create and solve the day-ahead optimization mathematical model of the distribution network containing multiple power routers, and obtain the distribution network dispatch plan (i.e., target operation information) corresponding to multiple different scenarios, including the day-ahead power interaction plan between the power router and the distribution network, the day-ahead charging and discharging power plan of the energy storage device, and the day-ahead power interaction plan between the power routers.

[0072] Intraday optimization (optimization of the second operation scenario):

[0073] (1) Generate multiple wind and solar load forecast scenario data for 96 time periods within a day, calculate the scenario similarity with multiple forecast scenarios of the day before, and obtain the forecast scenario of the day before that has the highest similarity with each forecast scenario within the day;

[0074] (2) Taking into account the battery loss caused by the frequent charging and discharging of the energy storage device, the energy storage charging and discharging scheduling plan obtained a few days ago is used as the input of intraday optimization. An intraday optimization mathematical model is created. The scheduling scale is 15 minutes, and the optimization solution is performed every 1 hour to obtain the distribution network scheduling plan (i.e., target operation information) for each forecast scenario within the day, including the intraday power interaction plan between the power router and the distribution network, the intraday charging and discharging power plan of the energy storage device, and the intraday power interaction plan between the power routers.

[0075] Real-time rolling optimization (optimization of the third running scenario):

[0076] (1) Generate real-time wind and solar load forecast data for 288 time periods, calculate the scene similarity between the data and multiple forecast scenes within the day, and obtain the forecast scene with the highest scene similarity;

[0077] (2) The scheduling plan under the intraday prediction scenario with the highest similarity to the scenario (including the intraday power interaction plan between the power router and the distribution network, the intraday charging and discharging power plan of the energy storage device, and the intraday power interaction plan between the power routers) is used as a reference for real-time optimization. A real-time optimization mathematical model is created with the goal of minimizing the deviation between the real-time scheduling plan and the intraday scheduling plan. The scheduling scale is 5 minutes, and the optimization solution is performed every 15 minutes. Finally, the real-time distribution network scheduling plan (i.e., target operation information) within 288 time periods in a day is obtained.

[0078] The final optimization results are compared with the original scheduling results to verify the effectiveness of the proposed method.

[0079] Specifically, for day-ahead optimization, we must first generate multiple wind, photovoltaic and load forecast scenarios. Wind power, photovoltaic power and load are highly random. This solution models and analyzes them based on multi-scenario technology. The specific model is as follows: In the formula, is the power forecast value of the day-ahead scenario s in period t; is the typical value of day-ahead power in period t obtained by clustering historical power data; j is the day-ahead forecast error threshold percentage; R j,tis a random number that obeys a certain distribution; γ is a random distribution correction factor; j can be wind power, photovoltaic power or load. That is, the typical output scenario is obtained by clustering historical data, and a day-ahead prediction error is considered in the typical output scenario. The sum of the typical output value and the prediction error value is taken as the final day-ahead wind and photovoltaic load output value.

[0080] Specifically, for intraday optimization, the method of generating scenarios is the same as that of day-ahead optimization, and will not be described in detail here.

[0081] Specifically, for real-time optimization, the method of generating scenarios is the same as that of intraday optimization, which will not be described here.

[0082] In the specific implementation process, when the above-mentioned operation scenario is the above-mentioned first operation scenario, the above-mentioned initial operation information is optimized according to the target optimization algorithm to obtain the target operation information, which can be achieved through the following steps: obtaining the first constraint information, wherein the above-mentioned first constraint information includes one or more of the constraint information of node power balance, the constraint information of the above-mentioned power router, the constraint information of branch flow, the constraint information of node voltage, the constraint information of branch current, the constraint information of branch capacity, the constraint information of engine power, and the constraint information of energy storage device; using the above-mentioned first constraint information as a constraint, optimizing the first initial operation information according to the above-mentioned target optimization algorithm to obtain the first target operation information, wherein the above-mentioned first initial operation information is the above-mentioned initial operation information corresponding to the above-mentioned first operation scenario.

[0083] In this scheme, by using the first constraint as a restriction condition for optimal scheduling in the distribution network, the parameters of each device in the distribution network, such as current and voltage, etc., can be limited to avoid system overload, short circuit or other faults, which helps to further improve the efficiency of optimal scheduling.

[0084] Specifically, the typical output scenario is obtained by clustering the historical data of wind and solar loads, and then the wind and solar load data of the previous 24 periods are generated as input data. The voltage margin is used as an indicator to reflect the flexibility of the distribution network to create the objective function, that is, the higher the optimized voltage value, the higher the ability to resist fluctuations caused by the outside world. Considering the flow constraints (including voltage constraints, current constraints, power balance constraints, etc.) and energy storage constraints of the entire distribution network after the power router is connected to different nodes, the final optimization scheduling mathematical model is created using MATLAB+YALMIP, including decision variables (voltage of each node, branch current, branch power, energy storage charging and discharging power, transmission power of the interconnection line between the power routers, and exchange power between the power router and the access node), objective function and constraints. Finally, the cplex solver is called to solve the day-ahead optimization scheduling model to obtain the optimal scheduling plan for the day (that is, on the basis of improving the flexibility of the distribution network, the energy storage charging and discharging power plan, the interconnection line transmission power plan between the power routers, and the exchange power plan between the power router and the access node). The time scale of day-ahead optimization is longer than that of intraday and real-time. Considering that the frequency of alternating charging and discharging of energy storage devices increases as the time scale becomes shorter, it will cause damage to the batteries of the energy storage devices. Therefore, the day-ahead energy storage scheduling plan will be used as the input for intraday optimization in the future.

[0085] Specifically, the first constraint information is used as a constraint, and the first initial operation information is optimized according to the target optimization algorithm to obtain the first target operation information. This may be to construct a day-ahead optimization scheduling model, wherein the day-ahead optimization scheduling model is obtained by training the model with a target optimization algorithm using multiple sets of training data, and each set of training data in the multiple sets of training data includes historical first initial operation information, historical first constraint information, historical first initial operation information, and historical first target operation information corresponding to the historical first constraint information obtained within a historical time period; the first initial operation information and the first constraint information are input into the day-ahead optimization scheduling model to obtain the first target operation information corresponding to the first initial operation information and the first constraint information.

[0086] Specifically, the target algorithm may be a MATLAB algorithm, a YALMIP algorithm, a CPLEX algorithm, a MATLAB algorithm and a YALMIP algorithm, a MATLAB algorithm and a CPLEX algorithm, a YALMIP algorithm and a CPLEX algorithm, or a combination of the MATLAB algorithm, the YALMIP algorithm and the CPLEX algorithm.

[0087] Use Matlab to write mathematical modeling and optimization algorithms for scheduling problems. This can include defining the variables, constraints, and objective functions of the scheduling problem, and using functions in Matlab's optimization toolbox to implement the algorithm. Next, use the Yalmip toolbox to convert the mathematical modeling in Matlab into the form of a convex optimization problem. Yalmip is a Matlab toolbox for setting up and solving convex optimization problems. It can help convert non-convex optimization problems into convex optimization problems so that they can be solved more easily using convex optimization solvers such as Cplex. Then, use the Cplex algorithm to solve the convex optimization problem after Yalmip conversion. Cplex is a powerful convex optimization solver that can be used to solve large-scale linear programming, integer programming, and mixed integer programming problems. Finally, feed the solution results of Cplex back to Matlab, analyze and post-process the results through Matlab algorithms, and obtain the optimal scheduling strategy.

[0088] MATLAB algorithm is an algorithm that uses the Matlab programming language and toolbox to implement a specific computing task. Matlab algorithms can include various types of algorithms such as numerical computing, statistical analysis, signal processing, image processing, machine learning, etc. Matlab algorithms are usually written and tested using Matlab and can be run in the Matlab environment.

[0089] The YALMIP algorithm is a toolbox for mathematical modeling and optimization, used to solve complex optimization problems. It is based on the MATLAB environment and provides a simple and powerful language that can be used to describe and solve various optimization problems, including linear programming, quadratic programming, nonlinear programming, semidefinite programming, etc. The YALMIP algorithm also provides a rich set of optimization algorithms and solvers that can automatically select the solution method that best suits the problem, and provides a wealth of visualization and analysis tools to help users understand and debug optimization problems. The design goal of the ALMIP algorithm is to provide a simple and flexible tool that enables users to quickly set up and solve a variety of complex optimization problems.

[0090] The CPLEX algorithm is an algorithm used to solve mathematical optimization problems such as linear programming, integer programming, and mixed integer programming. It helps users optimize decisions, improve efficiency, and reduce costs. The CPLEX algorithm uses a variety of optimization techniques, including linear programming, integer programming, quadratic programming, mixed integer programming, etc. It can handle complex practical problems and give optimal solutions or near-optimal solutions in a relatively short time.

[0091] Specifically, the constraint information of the above node power balance is:

[0092]

[0093]

[0094] In the above formula, P ij,(t) and Q ij,(t) Respectively represent the active power and reactive power at the head end of branch ij at time t; I ij,(t) represents the current on branch ij at time t; R ij and X ij Represent the resistance and reactance of branch ij respectively; P g,j(t) and Q g,j(t) Respectively represent the active and reactive power provided by the generator to node j at time t; P eer,j(t) and Q eer,j(t) They represent the active power and reactive power provided to the power router by the distribution network node j at time t. A positive value indicates that the distribution network provides power to the power router, and a negative value indicates that the power router feeds back power to the grid. f(j) and z(j) represent the upstream node and downstream node of node j, respectively. The principle of the above formula is the power balance constraint of each node, that is, the power flowing into the node is equal to the power flowing out of the node.

[0095] After linearizing the above node power balance constraint information, the following information is obtained:

[0096]

[0097]

[0098] The principle of the above formula is to take into account the existence of the square of the current in the formula, linearize it, and replace the square of the current with another symbol.

[0099] Specifically, the constraint information of the above power router is:

[0100] P eer,j(t) +P DG,j(t) +P eer_exchange,j(t) =P jload(t) +P ESS,j(t) ;

[0101] Q eer,j(t) +Q wg,j(t) =Q jload(t) ;

[0102]

[0103] In the above formula, P DG,j(t) represents the active power provided by the distributed generation to node j at time t; P jload(t) and Q jload(t) Respectively represent the active and reactive loads of node j at time t; P ESS,j(t)represents the charging and discharging power of the energy storage device on node j at time t, with charging as positive and discharging as negative; P eer_exchange,j(t) represents the power exchanged between the node j power router and other power routers through the tie line at time t, with the input being positive and the output being negative; Q wg,j(t) Represents the reactive power provided by the capacitor branch in the energy router of node j at time t. eer_buy(t) 、sign eer_sell(t) and sign eer_static(t) Both are 0-1 variables, representing the sign of the grid giving active power to the energy router, the sign of the energy router feeding back active power to the grid, and the static sign. When 1, it means the grid gives power to the energy router, the energy router feeds back active power to the grid, and the static state (i.e., the interactive power between the two parties is 0). eer_exchange_ru(t) 、sign eer_exchange_chu(t) and sign eer_exchange_static(t) They respectively represent the sign that a node power router receives power from other power routers, the sign that it provides power to other power routers, and the static sign.

[0104] Energy storage devices in distribution networks generally refer to devices used to store electrical energy and release it when needed. Common energy storage devices include batteries, supercapacitors, energy storage inverters, etc.

[0105] The principle of the above formula is that for the node to which the power router is connected, the inflow and outflow power of each port of the power router remain equal.

[0106] Specifically, the constraint information of the branch flow is:

[0107] (U j(t) ) 2 -(U i(t) ) 2 =(R ij 2 +X ij 2 )I ij,(t) 2 -2(R ij P ij,(t) +X ij Q ij,(t) ); In the above formula, U i(t) and U j(t) Represent the voltages of nodes i and j at time t, respectively. Nodes i and j are the first and last nodes of branch ij. After linearization, we get In the above formula, Represents the square of the current value of branch ij at time t; Represents the square of the voltage value of node i at time t.

[0108] The principle of the above formula is that there is a constraint relationship between the voltage between the head node and the terminal node of each branch and the flowing power and current on the branch.

[0109] Specifically, the constraint information of the node voltage is: i,min ≤U i(t) ≤U i,max In the above formula, U i,min and U i,max Represent the lowest and highest voltages allowed at node i respectively. After linearization, we get

[0110] The principle of the above formula is that the voltage value of each node has an upper and lower limit.

[0111] Specifically, the constraint information of the branch current is: 0≤I ij,(t) ≤I ij,max ; In the above formula, I ij,(t) represents the current of branch ij at time t; I ij,max Represents the maximum current value allowed by branch ij. After linearization, we get

[0112] The principle of the above formula is that there is an upper and lower limit for the current in each branch.

[0113] Specifically, the constraint information of the branch capacity is as follows:

[0114]

[0115] The principle of the above formula is that the power transmitted on the branch cannot exceed the upper limit. Considering the existence of square terms in active and reactive transmission power, it is linearized and converted into a second-order cone constraint.

[0116] Specifically, the constraint information of the engine power is: g,min ≤P g,i(t) ≤P g,max ;Q g,min ≤Q g,i(t) ≤Q g,max ; In the above formula, P g,i(t) , Q g,i(t) Respectively represent the active power and reactive power generated by the generator on node i at time t; P g,max , P g,min They represent the maximum and minimum active power provided by the generator respectively; Q g,max , Q g,min They represent the maximum and minimum reactive power provided by the generator respectively.

[0117] The principle of the above formula is that there are upper and lower limits on the generator output power.

[0118] Specifically, the constraint information of the above energy storage device is:

[0119]

[0120] In the above formula, and Respectively represent the charging and discharging power of the energy storage device at time t; and Respectively represent the signs of the energy storage device charging and discharging at time t, and taking 1 means that the energy storage device is charging and discharging at time t; SOC ESS,j(t) represents the charge state of the energy storage device at time t; E bat0 and E bat Respectively represent the initial remaining capacity and rated capacity of the energy storage device; SOC min and SOC max They represent the minimum and maximum state of charge allowed by the energy storage device respectively.

[0121] The principle of the above formula is that the state of the energy storage device at each moment can only be one of charging, discharging and static, and the charging power and discharging power cannot exceed the upper limit, and the charge state (i.e. the remaining power value of the energy storage) at each moment is between the upper and lower limits.

[0122] In some embodiments, when the above-mentioned operation scenario is the above-mentioned second operation scenario, the above-mentioned initial operation information is optimized according to the target optimization algorithm to obtain the target operation information, which can be specifically implemented through the following steps: obtaining the second constraint information, wherein the above-mentioned second constraint information is the constraint information of the charge state; using the above-mentioned first constraint information and the above-mentioned second constraint information as constraints, optimizing the second initial operation information according to the above-mentioned target optimization algorithm to obtain the second target operation information, wherein the above-mentioned second initial operation information is the above-mentioned initial operation information corresponding to the above-mentioned second operation scenario; when the above-mentioned operation scenario is the above-mentioned third operation scenario, optimizing the above-mentioned initial operation information according to the target optimization algorithm to obtain the target operation information, which can be specifically implemented through the following steps: using the above-mentioned first constraint information and the above-mentioned second constraint information as constraints, optimizing the third initial operation information according to the above-mentioned target optimization algorithm to obtain the third target operation information, wherein the above-mentioned third initial operation information is the above-mentioned initial operation information corresponding to the above-mentioned third operation scenario.

[0123] In this scheme, by using the first constraint condition and the second constraint condition as the constraint conditions for optimal scheduling in the distribution network, the parameters of each device in the distribution network, such as current and voltage, etc., can be limited to avoid system overload, short circuit or other faults, which helps to further improve the efficiency of optimal scheduling. In addition, the constraint of charge state is added to reduce the damage to the battery caused by frequent charging and discharging of the energy storage device.

[0124] Specifically, considering the battery aging caused by the frequent charge and discharge of the energy storage device and better tracking the day-ahead energy storage scheduling plan, the state of charge of the energy storage device at the end of each time period in the day-ahead optimization scheduling result is used as the input of the intra-day optimization, that is, the second constraint information is added. The second constraint information can be:

[0125]

[0126] In the formula, and They are the state of charge at the end of each hour for intraday and day-ahead dispatching respectively; and They are the charging marks of the energy storage device within the day and the day before; and They are the discharge signs of the energy storage device within the day and the day before.

[0127] The principle of the above formula is to use the day-ahead energy storage scheduling plan (the state of charge at the end of each time period and the charge and discharge marks of each time period) as the input for intraday optimization to reduce the damage to the battery caused by the frequent charge and discharge alternation of the energy storage device.

[0128] In the intra-day constraints, the scheduling time scale is 15 minutes, and the optimal scheduling solution is performed every 1 hour.

[0129] Specifically, the first constraint information and the second constraint information are used as constraints, and the second initial operation information is optimized according to the target optimization algorithm to obtain the second target operation information. This may be to construct an intraday optimization scheduling model, wherein the intraday optimization scheduling model is obtained by training the target optimization algorithm using multiple sets of training data, and each set of the multiple sets of training data includes historical second initial operation information, historical first constraint information, historical second constraint information obtained within a historical time period, and historical second target operation information corresponding to the historical second initial operation information, historical first constraint information, and historical second constraint information; the second initial operation information, the first constraint information, and the second constraint information are input into the intraday optimization scheduling model to obtain the second target operation information corresponding to the second initial operation information, the first constraint information, and the second constraint information.

[0130] Specifically, the first constraint information and the second constraint information are used as constraints, and the third initial operation information is optimized according to the target optimization algorithm to obtain the third target operation information. This can be to construct a real-time optimization scheduling model, wherein the real-time optimization scheduling model is obtained by training the target optimization algorithm using multiple sets of training data, and each set of training data in the multiple sets of training data includes historical third initial operation information, historical first constraint information, historical second constraint information obtained within a historical time period, and historical third target operation information corresponding to the historical third initial operation information, historical first constraint information, and historical second constraint information; the third initial operation information, the first constraint information, and the second constraint information are input into the real-time optimization scheduling model to obtain the third target operation information corresponding to the third initial operation information, the first constraint information, and the second constraint information.

[0131] In some embodiments, when the above-mentioned operation scenario is the above-mentioned second operation scenario, before optimizing the above-mentioned initial operation information according to the target optimization algorithm to obtain the target operation information, the above-mentioned method further includes the following steps: calculating the similarity between the above-mentioned second operation scenario and all the above-mentioned first operation scenarios to obtain multiple first similarities; extracting the first target operation information of the above-mentioned first operation scenario with the highest first similarity as reference information in the optimization process of the above-mentioned second operation scenario; when the above-mentioned operation scenario is the above-mentioned third operation scenario, before optimizing the above-mentioned initial operation information according to the target optimization algorithm to obtain the target operation information, the above-mentioned method further includes the following steps: calculating the similarity between the above-mentioned third operation scenario and all the above-mentioned second operation scenarios to obtain multiple second similarities; extracting the second target operation information of the above-mentioned second operation scenario with the highest second similarity as reference information in the optimization process of the above-mentioned third operation scenario.

[0132] In this solution, scene similarity is introduced, and the first target operation information corresponding to the first operation scene can be used as reference information in the optimization process of the second operation scene, and the second target operation information corresponding to the second operation scene can be used as reference information in the optimization process of the third operation scene. After multiple calculations of scene similarity, the accuracy of the optimized scheduling of the second operation scene and the third operation scene can be guaranteed to be high.

[0133] Specifically, when the above-mentioned operating scenario is the above-mentioned second operating scenario, a scenario similarity index can be selected to measure the similarity between the intraday wind and solar load prediction scenario and the day-ahead scenario, and the scheduling result under the day-ahead scenario with the highest similarity is selected as the input for intraday optimization.

[0134] Taking wind power as an example, the wind power under a certain forecast scenario within the day and the wind power under each scenario the day before are normalized, then the similarity between the wind power under a certain forecast scenario within the day and the wind power under the scenario the day before is for

[0135]

[0136] In the above formula, N is the length of the wind power sequence in the control time domain, which is 96 periods here; They are the intraday wind and solar power forecast values ​​and the day-ahead wind and solar power forecast values ​​under scenario s. The scene similarity calculation method for photovoltaic and load is the same as the formula.

[0137] Based on the similarity between the wind and solar load data obtained during the day and the wind and solar load data of each scene the day before, the scene similarity is: In the formula, and They are the PV power similarity and load power similarity under intraday and day-ahead scenario s, respectively.

[0138] According to the definition of scenario similarity, the scenario similarity between each scenario within the day and each scenario the day before is calculated respectively, and the scheduling result under the scenario the day before with the highest scenario similarity is selected as the input for intraday optimization.

[0139] The scene similarity is divided into the similarity of wind power, photovoltaics, and load. The similarity of each category is the difference between the intraday output and the day-ahead output, that is, the degree of output proximity. The smaller the difference, the more similar the scene is. The product of the three similarities is taken as the final scene similarity between the intraday wind-solar-load scene and the day-ahead wind-solar-load scene.

[0140] For the optimization of the second operating scenario, the main steps are the same as the day-ahead scenario. The scenario generated within the day is used to calculate the similarity with the day-ahead scenario, and the energy storage dispatch plan under the day-ahead scenario with the highest similarity is used as the intraday input. On the one hand, the loss of the energy storage device is taken into account, and on the other hand, the scenario similarity is used to reduce the deviation of the dispatch plan caused by the large source-load prediction error. Also, the voltage margin is used as the objective function, and the dispatch plan for 96 time periods a day is obtained from the 15-minute time scale. Compared with the day-ahead optimization, the intraday optimization starts from a shorter time scale and still takes the overall flexibility of the power grid as the goal to obtain a more refined dispatch plan.

[0141] Specifically, when the above-mentioned operation scenario is the above-mentioned third operation scenario, the calculation method in the intraday optimization is the same, and the scheduling result under the intraday prediction scenario with the highest scenario similarity is used as the reference value for real-time rolling optimization. The constraints are consistent with the intraday optimization. In real-time optimization, a day is divided into 288 time periods, the scheduling time scale is 5 minutes, and the optimization scheduling solution is performed every 15 minutes.

[0142] The existing multi-time scale optimization is only for the optimization of the distribution network, and does not consider the optimization scheduling of the entire multi-power router system after the multi-power router is connected. Secondly, the existing ones mainly consider the economic efficiency of the power grid and ignore the overall flexibility of the power grid. Real-time optimization once again uses the scene similarity on the basis of intraday optimization, further reducing the deviation of the scheduling plan caused by source-load errors, and obtaining the final scheduling plan at a shorter time scale by correcting the intraday scheduling plan.

[0143] The main innovation of this solution lies in how to use multi-scenario technology and multi-time scale to improve the overall flexibility of the distribution network containing multiple power routers. The entire framework and process are the key points.

[0144] In the specific implementation process, before optimizing the above initial operation information according to the target optimization algorithm to obtain the target operation information, the above method also includes the following steps: obtaining the target function, wherein the above target function is a function in the optimization process according to the target optimization algorithm, and the above target function is:

[0145]

[0146] f represents the above objective function, T represents the total optimization time period, I represents the total number of nodes in the above distribution network, and U i,t represents the voltage of the ith node at time t, U cr,i represents the critical stable voltage of the i-th node.

[0147] In this scheme, the voltage margin can be used as the flexibility of the distribution network as a whole. When the optimized voltage exceeds the critical stable voltage and the larger it is, the stronger the ability to resist fluctuations.

[0148] Specifically, the maximum sum of the voltage margins of the load nodes can be used as the objective function. The voltage safety margin reflects the safety performance of the distribution network, that is, the anti-disturbance ability of the node itself. From the above expression, it can be seen that when the node voltage is greater than the critical stable voltage and the larger it is, the larger the voltage safety margin is, indicating that the node has a stronger ability to resist power fluctuations, the safer the node, and the higher the overall node flexibility of the distribution network. The objective function uses the voltage margin to characterize the overall flexibility of the distribution network. When the optimized voltage exceeds the critical stable voltage and the larger it is, it indicates that the ability to resist fluctuations is stronger.

[0149] The critical stable voltage of a node refers to the value when the voltage at a node in the power system reaches the critical stable state. In the power system, the voltage stability of the node has an important impact on the safe operation and stability of the system. When the voltage of the node reaches the critical stable state, the system can maintain stable operation without voltage collapse or overvoltage.

[0150] For example, if the critical stable voltage of a node is 230kV, then when the system runs to a certain state, the system can maintain stable operation when the voltage of the node reaches 230kV. If the voltage of the node is lower than 230kV, the system may experience voltage collapse; if the voltage of the node exceeds 230kV, the system may experience overvoltage.

[0151] Specifically, for both day-ahead optimization and intraday optimization, you can use:

[0152] as the objective function.

[0153] Specifically, for real-time optimization, the intraday scheduling plan can be corrected in real time by taking the minimum deviation between the real-time optimization scheduling result and the intraday scheduling result as the objective function, so as to generate a final more refined scheduling plan. The specific objective function can be changed to:

[0154]

[0155] The principle of the above formula is to correct the intraday dispatch plan on the basis of ensuring that the overall flexibility of the power grid is improved. In a shorter time scale, based on real-time wind and solar load data, the intraday dispatch plan with the highest scenario similarity is corrected, so that the adjustable resources can track more accurate intraday optimization phase results in real time, thereby reducing the unevenness of the adjustable resource output.

[0156] In order to ensure the accuracy of the data, before obtaining the operating scenario of the distribution network, the above method also includes the following steps: obtaining initial power, wherein the above initial power is one or more of the initial power generated by wind power output, the initial power generated by photovoltaic output, and the initial power generated by load output in the above distribution network; obtaining an error function, wherein the above error function is a function obtained by normal distribution and / or a function obtained by beta distribution; and using the above error function to correct the above initial power to obtain the target power.

[0157] In this scheme, in order to reduce the power error in the system and further improve the stability and reliability of the distribution network, the error function can help the distribution network monitor the power deviation in real time and make corrections in time to ensure that the power distribution and power supply quality in the distribution network are in the best state.

[0158] Specifically, in the above scheme, the photovoltaic and load power prediction errors generally follow the normal distribution, while the wind power prediction error is more suitable to be characterized by the Beta distribution. The corresponding probability distribution is as follows:

[0159] The first error function:

[0160] Second error function:

[0161] μ and σ are the expectation and variance of the normal distribution, respectively; α and β are the shape and scale of the Beta distribution, respectively; N d is the normalization factor, that is, the photovoltaic and load power forecasts are corrected by the first error function, and the wind power forecast error is corrected by the second error function. The forecast errors of wind power, photovoltaic and load at different times are characterized by different distribution functions. Photovoltaic and load use normal distribution to generate forecast error values ​​at different times, and wind power uses Beta distribution.

[0162] On the basis of the above, Monte Carlo sampling technology can also be used to generate multiple scenarios, and the scenarios can be reduced based on the synchronous back substitution method to obtain the final day-ahead wind and solar load prediction scenario.

[0163] As the core equipment in the new distribution network, the power router integrates information technology and power electronic conversion technology. As an energy hub, the power router can provide plug-and-play interfaces for various renewable energy power generation equipment, energy storage equipment and power consumption equipment. At the same time, it controls the energy flow inside the power router and the energy exchange with other power routers or power grids to achieve the comprehensive utilization of "source-grid-load-storage". By controlling the distribution and flow of electric energy in the distribution network, the power router can effectively realize the consumption of renewable energy such as wind and light in the distribution network and optimize the control of power flow on the basis of ensuring the energy balance of the distribution network. It is of great significance to the stable operation and flexibility improvement of the distribution network.

[0164] At present, the research work on energy dispatching in the connection of multiple energy routers to the distribution network mainly focuses on the optimal dispatching of multiple energy router systems considering the system operation cost. On the one hand, it starts from a single time scale based on the forecast data of source and load, and optimizes the dispatching with the goal of minimizing the system operation cost; on the other hand, it adopts a two-layer dispatching strategy, firstly, it optimizes the energy dispatching of a single energy router layer, and then performs global optimization dispatching between energy routers in the distribution network layer. However, these methods ignore the deviation between the dispatching plan and the actual plan caused by the source and load forecasting results and the dispatching under a single time scale, and ignore the positive role of energy routers as energy hubs in improving the overall flexibility of the distribution network.

[0165] In order to make full use of the rapid and flexible regulation of various flexible resources by the power routers connected to the distribution network, the flexibility of the distribution network can be improved and the operation can be optimized. This scheme proposes a multi-time scale optimization scheduling strategy for the distribution network with power routers that integrates multi-scenario analysis for improving the flexibility of the distribution network. Starting from different time scales, day-ahead optimization, intraday optimization and real-time rolling optimization are considered respectively. On this basis, multi-scenario analysis is further introduced to consider the optimization scheduling results under multiple wind and solar load forecasting scenarios. In the day-ahead optimization, the distribution network is optimized and scheduled respectively under multiple wind and solar load forecasting scenarios. In the intraday optimization, the concept of scenario similarity is introduced. The scenario similarity of multiple intraday wind and solar load forecasting scenarios and day-ahead wind and solar load forecasting scenarios is calculated respectively. The optimization scheduling results under the day-ahead scenario with the highest scenario similarity are used as the input of the intraday optimization scheduling to further optimize the intraday distribution network. In the real-time rolling optimization, the scenario similarity of real-time wind and solar load data and multiple intraday wind and solar load forecasting scenarios is calculated. The optimization scheduling results under the intraday scenario with the highest similarity are used as the reference value for real-time optimization, and the intraday scheduling plan is further corrected in real time. By taking advantage of the rapid regulation of power routers, and under the influence of multi-scenario analysis technology and multiple time scales, the deviation of the dispatch plan caused by wind and solar load forecast errors will be minimized to the greatest extent, and the absorption rate of new energy and the overall flexibility of the distribution network will be effectively improved.

[0166] This scheme proposes an optimal scheduling method under multiple time scales that integrates multi-scenario analysis for improving the flexibility of distribution networks. On the one hand, it introduces multi-scenario analysis technology, and considers the optimal scheduling under multiple source and load prediction scenarios on the day-ahead and day-ahead, respectively, reducing the deviation of the scheduling plan caused by the source and load prediction error. On the other hand, starting from multiple time scales, it considers the day-ahead optimization, intraday optimization and real-time rolling optimization respectively. As the time scale continues to decrease, the obtained optimized scheduling plan will become more and more accurate. In addition, scenario similarity is introduced in the intraday optimization and real-time rolling optimization stages, and the scenario similarity is calculated between the intraday source and load prediction scenario and the day-ahead source and load prediction scenario, as well as between the real-time source and load data and the intraday source and load prediction scenario. The scheduling plans under the day-ahead prediction scenario and the intraday prediction scenario with the highest scenario similarity are used as the input of the intraday optimization and the reference value of the real-time optimization, respectively. The accuracy of the scheduling plan after the final real-time optimization is guaranteed under multiple scenario similarity calculations. Compared with traditional methods, the method proposed in this scheme is closer to the actual situation on the one hand, and the obtained scheduling plan is more precise in time scale and has a smaller error with the actual situation. On the other hand, it makes full use of the ability of the power router to quickly regulate flexible resources, which has a good optimization effect on the flow and flexibility of the distribution network, and is of great significance to the safe and stable operation of the distribution network.

[0167] This solution is based on the model of multiple power routers and uses a hierarchical scheduling strategy with the goal of minimizing operating costs to perform hierarchical optimization scheduling. First, energy optimization scheduling of a single power router is performed at the power router layer, and then global optimization scheduling is performed between power routers at the distribution network layer. This can effectively improve the utilization rate of distributed energy and reduce operating costs.

[0168] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the distribution network optimization scheduling method of the present application will be described in detail below in combination with specific embodiments.

[0169] This embodiment relates to a specific distribution network optimization scheduling method, such as Figure 3 As shown, the following steps are included:

[0170] Determine the optimization system, input the system structure parameters, use multi-scenario technology to generate multiple wind and solar load prediction scenarios for the 24 time periods before the day, use voltage margin as the objective function and 1h (hour) as the scheduling time scale to create a day-ahead optimization model (day-ahead optimization scheduling model), solve the day-ahead optimization model from the whole day, and obtain the day-ahead optimized scheduling plan. Take the scheduling plan under the day-ahead scenario with the highest scenario similarity as input, create an intraday optimization scheduling model with voltage margin as the objective function, use 15min as the scheduling time scale, and solve once every 1h to determine whether the scheduling plan solution for the whole day is completed. If completed, obtain the intraday optimized scheduling plan. If not, solve again.

[0171] For intraday optimization, similar to the day-ahead, multi-scenario technology is used to generate multiple wind-solar-load prediction scenarios for 96 time periods within the day, and the scene similarity calculations are performed on the multiple prediction scenarios within the day and the day-ahead scenarios.

[0172] Generate real-time wind and solar load forecast data for 28 time periods, calculate the scene similarity between the real-time data and multiple prediction scenes within the day, take the scheduling plan under the intraday scenario with the highest scene similarity as the reference value for real-time optimization, and modify it in real time. Create a real-time optimization scheduling model with the minimum deviation between the real-time scheduling plan and the intraday scheduling plan as the objective function. Use 5 minutes as the scheduling time scale and solve it every 15 minutes to determine whether the scheduling plan for the whole day has been solved. If it has been completed, implement the scheduling plan after real-time optimization. If not, solve it again.

[0173] In order to prove the effectiveness of this solution, this section uses a 33-node standard system as a case for simulation verification. The system contains 33 nodes and 32 segmented branches. The power routers are connected to nodes 15, 21, and 30 respectively. The power routers are connected through tie lines. The specific structure is as follows: Figure 4 shown.

[0174] The optimization results today:

[0175] Firstly, we use multi-scenario technology to generate multiple wind and solar load forecast scenarios based on historical power. From a full-day perspective, we use voltage margin as the objective function to optimize the scheduling of the distribution system containing multiple power routers, and obtain the scheduling plan of various devices in the distribution network in the next day. The specific results are as follows: Figure 5 , Figure 6 , Figure 7 , Figure 8 and Fig. 9 As shown, Figure 5 and Figure 6 It shows that the multi-scenario technology is used to provide multi-wind and solar load prediction scenario data for 24 time periods in the previous day. Figure 7 and Figure 8 The comparison results of the average voltage of the distribution network nodes after dispatching and the average voltage of the whole network at each moment with the original state under the scenario of wind and solar load forecasting on a certain day ahead are given respectively. It can be seen that the grid voltage after dispatching on the day ahead has been greatly improved compared with the original state as a whole. Fig. 9 In order to plan the charging and discharging power of each energy storage device in the next day after the day-ahead scheduling, the charge state of the energy storage device at the end of each time period is used as the input for intra-day optimization to reduce the frequent charging and discharging alternation of the energy storage device.

[0176] Intraday optimization results:

[0177] Fig.10 and Fig.11 Given the data of multiple wind and solar load prediction scenarios within a day, it can be seen that with the refinement of the time scale, the error between the data in different scenarios and the historical data is gradually decreasing. Table 1 is the result of the similarity calculation between the intraday scenario and the day-ahead scenario. According to the scene similarity calculation results in Table 1, the state of charge of the energy storage at the end of each time period under the day-ahead scenario with the highest scene similarity is selected as the input for the corresponding intraday scenario.

[0178] Table 1

[0179] Scene 1 Scene 2 Scene 3 Scene 4 Intraday scene 1 0.8939 0.8981 0.9002 0.8923 Intraday scene 2 0.8913 0.8929 0.8957 0.8939 Intraday scene 3 0.8886 0.8926 0.8985 0.8912 Intraday scene 4 0.8959 0.8993 0.8968 0.8955

[0180] According to the scenario similarity calculation results in Table 1, the state of charge of the energy storage at the end of each time period in the day-ahead scenario with the highest scenario similarity is selected as the input for the corresponding intraday scenario. In the intraday optimization, 15 minutes is used as the scheduling time scale, and the optimization is solved every 1 hour, and finally the optimized scheduling results under different intraday scenarios will be obtained. Fig.12 , Fig.13 The voltage status of the distribution network after optimized dispatch in a certain scenario within a day is given. Fig.14 The charging and discharging power plan of energy storage after intraday scheduling is given.

[0181] Real-time optimization results:

[0182] Table 2 shows the results of the similarity calculation between the real-time scenario and the intraday scenario. In the real-time optimization, based on the results of the scenario similarity calculation between the real-time prediction data and the intraday scenario, the optimized scheduling results (including the energy storage charging and discharging power results, the power results of the power grid exchange, and the power results of the interconnection lines between the power routers) under the intraday scenario with the highest scenario similarity are used as the reference value for real-time optimization. The objective function is to minimize the deviation between the real-time optimized scheduling results and the day-ahead optimized scheduling plan to obtain the final scheduling plan. With 5 minutes as the scheduling time scale, the optimization solution is performed every 15 minutes, and the real-time optimization solution result is finally obtained. Fig.15 , Fig.16 , Fig.17 and Fig.18 The comparison of grid voltage before and after real-time dispatch is given from the perspectives of the whole day, different time and different nodes. After real-time optimization dispatch, the minimum voltage (per unit value) of the grid changed from 0.9261 to 0.9423, and the average minimum voltage at each time changed from 0.9486 to 0.9605. As the voltage margin increases, the node flexibility of the distribution network also increases. In addition, since the power router, as an energy hub, can quickly and effectively regulate various flexible resources, the net load of the system is reduced, the power margin of the branch transmission is increased, the grid flexibility of the system is improved, and the ability to cope with load power fluctuations is improved.

[0183] Table 2

[0184] Intraday scene 1 Intraday scene 2 Intraday scene 3 Intraday scene 4 Real-time prediction scenarios 0.9655 0.9658 0.9652 0.9662

[0185] Fig.19 , Fig. 20 and Fig.21 The comparison of the optimization scheduling results under three different time scales is given, namely the comparison of energy storage charging and discharging power scheduling results, the comparison of power grid exchange power scheduling results, and the comparison of interconnection line power scheduling results between power routers. It can be seen that as the time scale continues to decrease, various scheduling plans become more refined.

[0186] In general, the use of multi-scenario technology to track the day-ahead optimization through intraday optimization and to correct the intraday optimization through real-time optimization can, on the one hand, obtain a scheduling plan that is closer to the actual situation and more refined; on the other hand, with the power router's rapid and flexible regulation of various flexible resources, the overall flexibility of the distribution network has been greatly improved.

[0187] This scheme is based on the core equipment in the new distribution network, the power router. The power router can be used as an energy hub to provide plug-and-play interfaces for various renewable energy power generation equipment, energy storage equipment and power consumption equipment. At the same time, it controls the energy flow inside the power router and the energy exchange with other power routers or power grids to achieve the comprehensive utilization of "source-grid-load-storage". With the goal of improving the flexibility of the distribution network, a multi-time scale optimization scheduling strategy for the distribution network containing the power router is proposed, which integrates multi-scenario analysis. Compared with the traditional optimization scheduling method, this scheme considers multiple scenarios of the day-ahead and intraday wind and solar load forecast results, and introduces the concept of scenario similarity. The scenario with the highest scenario similarity is selected as the input or reference value for the next stage of optimization, which greatly reduces the deviation between the final scheduling plan and the actual situation caused by the source load forecast error; on the other hand, starting from multiple time scales, the day-ahead, intraday and real-time optimization are considered respectively. After reducing the time scale and adjusting and correcting the scheduling plan multiple times, the final real-time optimized scheduling plan is more refined and closer to the actual situation. The comparative verification shows that this method has certain significance for improving the flexibility of the distribution network and safe and stable operation.

[0188] The embodiment of the present application also provides an optimized dispatching device for a distribution network. It should be noted that the optimized dispatching device for a distribution network in the embodiment of the present application can be used to execute the optimized dispatching method for a distribution network provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0189] The following is an introduction to the optimization and scheduling device for the distribution network provided in the embodiments of the present application.

[0190] Fig. 22 1 is a structural block diagram of an optimized dispatching device for a distribution network according to an embodiment of the present application. Fig. 22 As shown, the device comprises:

[0191] A first acquisition unit 10 is used to acquire an operation scenario of the distribution network, wherein the operation scenario is one or more of a scenario of wind power output generating power, a scenario of photovoltaic output generating power, and a scenario of load output generating power in the distribution network within a target duration;

[0192] The second acquisition unit 20 is used to acquire the initial operation information of the operation scenario, wherein the initial operation information at least includes one or more of the voltage of the power router, the current of the power router, the charge and discharge power of the power router, the transmission power of the power router, and the exchange power of the power router;

[0193] The first processing unit 30 is used to optimize the initial operation information according to a target optimization algorithm to obtain target operation information, wherein the target optimization algorithm includes one or more of a MATLAB algorithm, a YALMIP algorithm, and a CPLEX algorithm;

[0194] The second processing unit 40 is used to optimize the scheduling of the distribution network by using the target operation information, at least use the voltage margin to determine the flexibility of the distribution network after optimization, and continue to optimize the scheduling of the distribution network when the flexibility of the distribution network does not meet the preset flexibility conditions until the flexibility of the distribution network meets the preset flexibility conditions, wherein the voltage margin is positively correlated with the flexibility of the distribution network, and the voltage margin is the voltage fluctuation range of the distribution network.

[0195] Through this embodiment, a target optimization algorithm is used to optimize the power router during the operation of the distribution network. The target optimization algorithm can optimize the load distribution of the power grid, and the power router can control the distribution and flow of electric energy in the distribution network. Therefore, this solution can make full use of the ability of the power router to quickly regulate flexible resources. The voltage margin can reflect the anti-disturbance ability of the distribution network itself. For example, the larger the voltage margin, the stronger the ability to resist power fluctuations, the safer the node, and the higher the overall node flexibility of the distribution network. Therefore, this solution can improve the efficiency of power optimization scheduling of the distribution network, thereby improving the overall flexibility of the distribution network.

[0196] During the specific implementation process, the first acquisition unit includes a first acquisition module, a second acquisition module and a third acquisition module. The first acquisition module is used to acquire a first operating scenario, wherein the first operating scenario is the operating scenario of a first target time period, and the first target time period is a historical time period; the second acquisition module is used to acquire a second operating scenario, wherein the second operating scenario is the operating scenario of a second target time period, the latest moment in the first target time period is earlier than the earliest moment in the second target time period, the duration of the first target time period is greater than the duration of the second target time period, and the second target time period is the current time period; the third acquisition module is used to acquire a third operating scenario, wherein the third operating scenario is the operating scenario of a third target time period, the latest moment in the second target time period is earlier than the earliest moment in the third target time period, the duration of the second target time period is greater than the duration of the third target time period, and the third target time period is a real-time time period.

[0197] In this scheme, multiple time scales can be obtained, so that we can start from multiple time scales and subsequently consider the scheduling optimization of different time periods separately. The division of time scales in this scheme is more refined and the error with the actual situation is smaller.

[0198] During the specific implementation process, when the above-mentioned operating scenario is the above-mentioned first operating scenario, the first processing unit includes a fourth acquisition module and a first processing module, and the fourth acquisition module is used to obtain first constraint information, wherein the above-mentioned first constraint information includes one or more of the constraint information of node power balance, the constraint information of the above-mentioned power router, the constraint information of branch flow, the constraint information of node voltage, the constraint information of branch current, the constraint information of branch capacity, the constraint information of engine power, and the constraint information of energy storage device; the first processing module is used to use the above-mentioned first constraint information as a constraint, and optimize the first initial operating information according to the above-mentioned target optimization algorithm to obtain the first target operating information, wherein the above-mentioned first initial operating information is the above-mentioned initial operating information corresponding to the above-mentioned first operating scenario.

[0199] In this scheme, by using the first constraint as a restriction condition for optimal scheduling in the distribution network, the parameters of each device in the distribution network, such as current and voltage, etc., can be limited to avoid system overload, short circuit or other faults, which helps to further improve the efficiency of optimal scheduling.

[0200] In some embodiments, when the above-mentioned operating scenario is the above-mentioned second operating scenario, the first processing unit includes a fifth acquisition module and a second processing module, and the fifth acquisition module is used to acquire the second constraint information, wherein the above-mentioned second constraint information is the constraint information of the charge state; the second processing module is used to use the above-mentioned first constraint information and the above-mentioned second constraint information as constraints, optimize the second initial operating information according to the above-mentioned target optimization algorithm, and obtain the second target operating information, wherein the above-mentioned second initial operating information is the above-mentioned initial operating information corresponding to the above-mentioned second operating scenario; when the above-mentioned operating scenario is the above-mentioned third operating scenario, the first processing unit includes a third processing module, and the third processing module is used to use the above-mentioned first constraint information and the above-mentioned second constraint information as constraints, optimize the third initial operating information according to the above-mentioned target optimization algorithm, and obtain the third target operating information, wherein the above-mentioned third initial operating information is the above-mentioned initial operating information corresponding to the above-mentioned third operating scenario.

[0201] In this scheme, by using the first constraint condition and the second constraint condition as the constraint conditions for optimal scheduling in the distribution network, the parameters of each device in the distribution network, such as current and voltage, etc., can be limited to avoid system overload, short circuit or other faults, which helps to further improve the efficiency of optimal scheduling. In addition, the constraint of charge state is added to reduce the damage to the battery caused by frequent charging and discharging of the energy storage device.

[0202] In some embodiments, the device further includes a first calculation unit, a first extraction unit, a second calculation unit, and a second extraction unit. The first calculation unit is used for, when the operation scenario is the second operation scenario, before optimizing the initial operation information according to the target optimization algorithm to obtain the target operation information, calculating the similarity between the second operation scenario and all the first operation scenarios to obtain multiple first similarities; the first extraction unit is used for extracting the first target operation information of the first operation scenario with the highest first similarity as reference information in the optimization process of the second operation scenario; the second calculation unit is used for, when the operation scenario is the third operation scenario, before optimizing the initial operation information according to the target optimization algorithm to obtain the target operation information, calculating the similarity between the third operation scenario and all the second operation scenarios to obtain multiple second similarities; the second extraction unit is used for extracting the second target operation information of the second operation scenario with the highest second similarity as reference information in the optimization process of the third operation scenario.

[0203] In this solution, scene similarity is introduced, and the first target operation information corresponding to the first operation scene can be used as reference information in the optimization process of the second operation scene, and the second target operation information corresponding to the second operation scene can be used as reference information in the optimization process of the third operation scene. After multiple calculations of scene similarity, the accuracy of the optimized scheduling of the second operation scene and the third operation scene can be guaranteed to be high.

[0204] In the specific implementation process, before optimizing the above initial operation information according to the target optimization algorithm to obtain the target operation information, the above method also includes the following steps: obtaining the target function, wherein the above target function is a function in the optimization process according to the target optimization algorithm, and the above target function is:

[0205]

[0206] f represents the above objective function, T represents the total optimization time period, I represents the total number of nodes in the above distribution network, and U i,t represents the voltage of the ith node at time t, U cr,i represents the critical stable voltage of the i-th node.

[0207] In this scheme, the voltage margin can be used as the flexibility of the distribution network as a whole. When the optimized voltage exceeds the critical stable voltage and the larger it is, the stronger the ability to resist fluctuations.

[0208] In order to ensure the accuracy of the data, the above-mentioned device also includes a fourth acquisition unit, a fifth acquisition unit and a correction unit. The fourth acquisition unit is used to obtain the initial power before obtaining the operation scenario of the distribution network, wherein the above-mentioned initial power is one or more of the initial power generated by wind power output, the initial power generated by photovoltaic output, and the initial power generated by load output in the above-mentioned distribution network; the fifth acquisition unit is used to obtain the error function, wherein the above-mentioned error function is a function obtained by normal distribution and / or a function obtained by beta distribution; the correction unit is used to correct the above-mentioned initial power using the above-mentioned error function to obtain the target power.

[0209] In this scheme, in order to reduce the power error in the system and further improve the stability and reliability of the distribution network, the error function can help the distribution network monitor the power deviation in real time and make corrections in time to ensure that the power distribution and power supply quality in the distribution network are in the best state.

[0210] The above-mentioned distribution network optimization dispatching device includes a processor and a memory, and the above-mentioned first acquisition unit, second acquisition unit, first processing unit, second processing unit, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in the form of any combination.

[0211] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the problem of low efficiency of power optimization scheduling of the distribution network in the prior art, resulting in poor overall flexibility of the distribution network, can be solved by adjusting kernel parameters.

[0212] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0213] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the distribution network optimization scheduling method.

[0214] An embodiment of the present invention provides a processor, and the processor is used to run a program, wherein the program executes the optimization scheduling method of the distribution network when running.

[0215] An embodiment of the present invention provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, at least steps of a method for optimizing scheduling of a distribution network are implemented.

[0216] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0217] A computer program product includes a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the distribution network optimization scheduling method in each embodiment of the present application are implemented.

[0218] The present application also provides a distribution network optimization system, comprising one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of the above-mentioned distribution network optimization scheduling methods.

[0219] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0220] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0221] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0222] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0223] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0224] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0225] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0226] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0227] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0228] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0229] 1) The distribution network optimization scheduling method of the present application adopts a target optimization algorithm to optimize the power router during the operation of the distribution network. The target optimization algorithm can optimize the load distribution of the power grid, and the power router can control the distribution and flow of electric energy in the distribution network. Therefore, this scheme can make full use of the ability of the power router to quickly regulate flexible resources. The voltage margin can reflect the distribution network's own anti-disturbance ability. For example, the larger the voltage margin, the stronger the ability to resist power fluctuations, the safer the node, and the higher the overall node flexibility of the distribution network. Therefore, this scheme can improve the efficiency of power optimization scheduling of the distribution network, thereby improving the overall flexibility of the distribution network.

[0230] 2) The distribution network optimization and scheduling device of the present application adopts a target optimization algorithm to optimize the power router during the operation of the distribution network. The target optimization algorithm can optimize the load distribution of the power grid, and the power router can control the distribution and flow of electric energy in the distribution network. Therefore, this solution can make full use of the ability of the power router to quickly regulate flexible resources. The voltage margin can reflect the distribution network's own anti-disturbance ability. For example, the larger the voltage margin, the stronger the ability to resist power fluctuations, the safer the node, and the higher the overall node flexibility of the distribution network. Therefore, this solution can improve the efficiency of power optimization scheduling of the distribution network, thereby improving the overall flexibility of the distribution network.

[0231] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing the dispatching of a distribution network, characterized in that: include: Obtaining an operation scenario of the distribution network, wherein the operation scenario is one or more of a scenario of wind power output generating power, a scenario of photovoltaic power output generating power, and a scenario of load output generating power in the distribution network within a target duration; Acquire initial operation information of the operation scenario, wherein the initial operation information includes at least one or more of the voltage of the power router, the current of the power router, the charge and discharge power of the power router, the transmission power of the power router, and the exchange power of the power router; Optimize the initial operation information according to a target optimization algorithm to obtain target operation information, wherein the target optimization algorithm includes one or more of a matlab algorithm, a yalmip algorithm, and a cplex algorithm; The distribution network is optimized and scheduled using the target operation information, and the flexibility of the distribution network after the optimized scheduling is determined using at least the voltage margin. When the flexibility of the distribution network does not meet the preset flexibility conditions, the distribution network is continuously optimized and scheduled until the flexibility of the distribution network meets the preset flexibility conditions, wherein the voltage margin is positively correlated with the flexibility of the distribution network, and the voltage margin is the voltage fluctuation range of the distribution network.

2. The method according to claim 1, characterized in that Obtain the operation scenario of the distribution network, including: Acquire a first operation scenario, wherein the first operation scenario is the operation scenario of a first target time period, and the first target time period is a historical time period; Acquire a second operation scenario, wherein the second operation scenario is the operation scenario of a second target time period, the latest moment in the first target time period is earlier than the earliest moment in the second target time period, the duration of the first target time period is longer than the duration of the second target time period, and the second target time period is the current time period; Obtain a third operating scenario, wherein the third operating scenario is the operating scenario of a third target time period, the latest moment in the second target time period is earlier than the earliest moment in the third target time period, the duration of the second target time period is greater than the duration of the third target time period, and the third target time period is a real-time time period.

3. The method according to claim 2, characterized in that When the operation scenario is the first operation scenario, optimizing the initial operation information according to a target optimization algorithm to obtain target operation information includes: Acquire first constraint information, wherein the first constraint information includes one or more of constraint information of node power balance, constraint information of the power router, constraint information of branch flow, constraint information of node voltage, constraint information of branch current, constraint information of branch capacity, constraint information of engine power, and constraint information of energy storage device; The first constraint information is used as a constraint, and the first initial operation information is optimized according to the target optimization algorithm to obtain first target operation information, wherein the first initial operation information is the initial operation information corresponding to the first operation scenario.

4. The method according to claim 3, characterized in that When the operation scenario is the second operation scenario, optimizing the initial operation information according to a target optimization algorithm to obtain target operation information includes: Acquire second constraint information, wherein the second constraint information is constraint information of the state of charge; The first constraint information and the second constraint information are used as constraints, and the second initial operation information is optimized according to the target optimization algorithm to obtain second target operation information, wherein the second initial operation information is the initial operation information corresponding to the second operation scenario; when the operation scenario is the third operation scenario, the initial operation information is optimized according to the target optimization algorithm to obtain target operation information, including: The first constraint information and the second constraint information are used as constraints, and the third initial operation information is optimized according to the target optimization algorithm to obtain third target operation information, wherein the third initial operation information is the initial operation information corresponding to the third operation scenario.

5. The method according to claim 2, characterized in that: When the operation scenario is the second operation scenario, before optimizing the initial operation information according to the target optimization algorithm to obtain the target operation information, the method further includes: Calculating similarities between the second operation scenario and all the first operation scenarios to obtain multiple first similarities; extracting the first target operation information of the first operation scenario with the highest first similarity as reference information in an optimization process of the second operation scenario; When the operation scenario is the third operation scenario, before optimizing the initial operation information according to the target optimization algorithm to obtain the target operation information, the method further includes: Calculating similarities between the third operation scenario and all the second operation scenarios to obtain multiple second similarities; The second target operation information of the second operation scenario with the highest second similarity is extracted as reference information in the optimization process of the third operation scenario.

6. The method according to any one of claims 1 to 5, characterized in that Before optimizing the initial operation information according to the target optimization algorithm to obtain the target operation information, the method further includes: Obtain an objective function, wherein the objective function is a function in the optimization process according to the target optimization algorithm, and the objective function is: f represents the objective function, T represents the total optimization time period, I represents the total number of nodes in the distribution network, and U i,t represents the voltage of the ith node at time t, U cr,i represents the critical stable voltage of the i-th node.

7. The method according to any one of claims 1 to 5, characterized in that Before obtaining the operation scenario of the distribution network, the method further includes: Acquire initial power, wherein the initial power is one or more of initial power generated by wind power output, initial power generated by photovoltaic output, and initial power generated by load output in the distribution network; Obtaining an error function, wherein the error function is a function obtained through a normal distribution and / or a function obtained through a beta distribution; The initial power is corrected using the error function to obtain the target power.

8. An optimization dispatching device for a distribution network, characterized in that: include: A first acquisition unit is used to acquire an operation scenario of the distribution network, wherein the operation scenario is one or more of a scenario of wind power output generating power, a scenario of photovoltaic output generating power, and a scenario of load output generating power in the distribution network within a target duration; A second acquisition unit, configured to acquire initial operation information of the operation scenario, wherein the initial operation information includes at least one or more of the voltage of the power router, the current of the power router, the charge and discharge power of the power router, the transmission power of the power router, and the exchange power of the power router; A first processing unit is used to optimize the initial operation information according to a target optimization algorithm to obtain target operation information, wherein the target optimization algorithm includes one or more of a MATLAB algorithm, a YALMIP algorithm, and a CPLEX algorithm; The second processing unit is used to optimize the scheduling of the distribution network by using the target operation information, at least use the voltage margin to determine the flexibility of the distribution network after the optimization scheduling, and continue to optimize the scheduling of the distribution network when the flexibility of the distribution network does not meet the preset flexibility conditions until the flexibility of the distribution network meets the preset flexibility conditions, wherein the voltage margin is positively correlated with the flexibility of the distribution network, and the voltage margin is the voltage fluctuation range of the distribution network.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for optimizing the dispatching of a distribution network as claimed in any one of claims 1 to 7 are implemented.

10. A distribution network optimization system, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing the optimized dispatching method of the distribution network described in any one of claims 1 to 7.

Images