Power distribution network intra-day scheduling correction method, system and equipment considering photovoltaic power error, and medium

Abstract

The invention belongs to the technical field of new energy power generation system control, and discloses a photovoltaic power error-considered power distribution network intra-day scheduling correction method, system and device and a medium. The method comprises the following steps: acquiring a preset intra-day initial scheduling instruction of the power distribution network, and controlling the power distribution network to start intra-day initial scheduling based on the instruction; and obtaining a preset correction period parameter, and after the intra-day initial scheduling of the power distribution network is started, executing a period rolling optimization step on the intra-day initial scheduling instruction of the power distribution network based on the correction period parameter. According to the method, the flexibility, the accuracy, the economic benefit, the safety and the stability of intra-day dispatching of the power distribution network are remarkably improved by introducing the periodic rolling optimization step, comprehensively considering the economic index, enhancing the adaptability of photovoltaic power prediction, presetting the correction constraint condition and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of renewable energy power generation system control, and specifically relates to a distribution network intraday dispatch correction method, system, equipment and medium taking photovoltaic power error into consideration. Background Art

[0002] Energy storage systems can store excess energy during peak production periods and release it during off-peak periods, effectively balancing the volatility of renewable energy and significantly improving the stability of the power system. Its introduction has brought flexibility to the power system, allowing it to more easily cope with challenges such as load changes and insufficient energy supply. By implementing a reasonable energy storage scheduling strategy, the energy storage system can be maximized and the scheduling flexibility of the power system can be further enhanced. In addition, the energy storage system can also reduce the peak-shaving and valley-filling pressure of the power system, that is, storing excess electricity during peak periods and releasing it during off-peak periods, thereby smoothing the load curve and improving the economy of the power system.

[0003] As the load of the power system continues to rise, traditional load management methods can no longer meet the demand. Controllable load scheduling, with its intelligence and flexibility, has become a better load management method and can effectively improve system efficiency. It adjusts the load in different time periods to achieve more efficient use of energy and reduce energy waste. At the same time, controllable load scheduling can be flexibly adjusted according to the real-time energy supply and demand situation, effectively responding to the challenge of imbalanced energy supply and demand and ensuring stable system operation.

[0004] In addition, the distribution network interconnection switch plays a vital role as a key equipment of the power system. It can divide a large distribution network into multiple sections to achieve more flexible control. This segmented operation is particularly important during system maintenance, fault isolation or load adjustment. During the dispatching process, by controlling the state of the interconnection switch and adjusting the network structure, different load requirements can be met and the operation of the power system can be optimized. When the power system structure needs to be adjusted according to the operating requirements, the operation of the interconnection switch enables the grid connection method to be flexibly adjusted to meet the system requirements. However, there are deviations in the photovoltaic power prediction, which causes power imbalance in the distribution network, which will affect the consumption of new energy and the distribution of distribution network power flow. The photovoltaic power prediction error brings uncertainty to the system dispatch and affects the economic operation. Summary of the invention

[0005] Based on the above-mentioned shortcomings and deficiencies in the prior art, one of the objects of the present invention is to solve at least one or more of the above-mentioned problems in the prior art. In other words, one of the objects of the present invention is to provide a distribution network intraday scheduling correction method, system, equipment and medium that considers photovoltaic power errors and meets one or more of the aforementioned needs, so as to achieve the purpose of improving the reliability and economy of the power system.

[0006] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a method for correcting distribution network intraday scheduling taking into account photovoltaic power errors, comprising the following steps: S1, obtaining a preset distribution network intraday initial scheduling instruction and controlling the distribution network to start intraday initial scheduling based on the instruction; S2, obtaining a preset correction cycle parameter, and when the distribution network starts initial scheduling within the day, performing a cycle rolling optimization step on the distribution network intraday initial scheduling instruction based on the correction cycle parameter, the cycle rolling optimization step comprising: S21, constructing an objective function for the intraday correction of the distribution network based on the network loss, total revenue and total cost of the distribution network; S22, obtaining each of the distribution network within the cycle System parameters, the system parameters in the distribution network include load data and electricity price data; S23, obtaining the day-ahead photovoltaic prediction curve and using the normrnd function to generate its normal distribution, so as to obtain the intraday photovoltaic prediction data of the distribution network within the period; S24, obtaining the preset intraday correction constraint conditions; S25, using the system parameters in the distribution network and the intraday photovoltaic prediction data as input values ​​and solving the objective function in combination with the intraday correction constraint conditions, until the optimal solution of the objective function is obtained and the corresponding cycle correction instruction is output, and the intraday scheduling correction of the distribution network is completed based on the cycle correction instruction.

[0007] As a preferred solution, the expression of the objective function includes: , , where It represents the network loss corrected within the day of the distribution network, represents the total benefit of the distribution network correction within a day, Represents the total cost of intra-day correction of the distribution network.

[0008] As a preferred solution, the step S22 of obtaining the system parameters in the distribution network during the period includes the steps of: constructing a battery mathematical model for the energy storage system in the distribution network, and the battery mathematical model expressions when the energy storage battery is charging and discharging are respectively: , , where Indicates time Its previous moment The time interval express The power of the energy storage battery at all times, Indicates time The power of the energy storage battery, Indicates the self-discharge coefficient of the energy storage battery, Indicates the actual charging power of the energy storage battery. Indicates the charging efficiency of the energy storage battery, Indicates the actual discharge power of the energy storage battery. Represents the discharge efficiency of the energy storage battery; a controllable charge mathematical model is constructed for the controllable load system in the distribution network, and its expression is: , where Indicates time The total response power, N represents the total number of users who can participate in demand response, Indicates time user The response status includes increasing load, reducing load and not participating in dispatch. Indicates time user The response power of the load system in the distribution network is constructed by building a load mathematical model, and its expression is , where Indicates time The load value, represents the preset load changing function over time, represents the error term.

[0009] As a preferred solution, the intraday correction constraint condition includes a system power balance constraint set for the system power of the distribution network; the expression of the system power balance constraint is: , where Indicates time Distribution network exchange power, Represents photovoltaic output power, Indicates time The load value in the distribution network is Indicates time Distribution network charging power, Indicates time Distribution network discharge power, Indicates the exchange power of the controllable load.

[0010] As a preferred solution, the intraday correction constraint condition also includes an energy storage constraint set for the energy storage system in the distribution network; the expression of the energy storage constraint includes , , , , , , where Indicates time Distribution network exchange power, Indicates the maximum value of the exchange power of the distribution network, represents the minimum value of the exchange power of the distribution network, Indicates the maximum value of the distribution network discharge power, is the maximum value of the charging power of the distribution network, and They represent the upper and lower limits of the state of charge of the energy storage at time period t, Indicates that the energy storage battery is in a discharging state. Indicates that the energy storage battery is in charging state. Indicates that the energy storage battery is in a static state. and They respectively represent the initial and final charge states of energy storage.

[0011] As a preferred solution, the distribution network is assumed to be radial; the intraday correction constraint condition also includes the power flow constraint of the distribution network branch flow, the distribution network node voltage constraint and the distribution network branch transmission power constraint; the power flow constraint equation is , where Indicates a node number in the distribution network. Indicates the flow into i +1 active power of the node, Indicates the flow into i +1 node reactive power, Indicates i The node voltage of the +1 node, and Respectively represent nodes and The resistance and reactance of the branch between Representation Node The magnitude of the voltage at and Respectively represent slave nodes Flow to Node The active power and reactive power of and They represent the active power and reactive power supplied to the node load respectively, and They represent the active power and reactive power generated by the distributed generation output respectively; the expression of the distribution network node voltage constraint is: , where Indicates time node The voltage value, and Respectively represent nodes The upper and lower limits of the voltage value; the expression of the transmission power constraint of the distribution network branch is: , where Indicates the transmission power of a line in the distribution network, Indicates the maximum transmission power of a line in the distribution network.

[0012] As a preferred solution, the intraday correction constraint condition also includes a network reconstruction constraint set for the distribution network structure; the expression of the network reconstruction constraint is: , where is a 0-1 variable, Indicates that the branch is disconnected. Indicates that the branch is closed. n b and n s They represent the total number of distribution network nodes and the number of distribution network root nodes respectively.

[0013] In a second aspect, the present invention provides a distribution network intraday dispatch correction system taking into account photovoltaic power errors, which is used to implement the distribution network intraday dispatch correction method as described in the first aspect.

[0014] In a third aspect, the present invention provides an electronic device, wherein the computer device includes a memory, a processor, and a computer program, and when the computer program is executed by the processor, the distribution network intraday scheduling correction method as described in the first aspect is implemented.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for correcting intraday dispatch of a distribution network as described in the first aspect is implemented.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. Improve dispatch flexibility and accuracy: Based on the day-ahead dispatch instructions, by introducing preset correction cycle parameters in the intraday stage and performing rolling optimization steps in each cycle, the present invention can timely adjust the dispatch instructions of the distribution network according to the actual operating conditions. This can not only more flexibly deal with uncertain factors such as intraday load changes and photovoltaic output fluctuations, but also significantly improve the accuracy and reliability of dispatch.

[0017] 2. Optimize economic benefits: When constructing the objective function of the distribution network intraday correction, multiple economic indicators such as network loss, total revenue and total cost are comprehensively considered. By solving this objective function, the present invention can maximize economic benefits and reduce operating costs while ensuring the safe operation of the power grid.

[0018] 3. Enhance the adaptability of photovoltaic power forecasting: Use the normrnd function to generate the normal distribution of the day-ahead photovoltaic forecast curve to simulate the uncertainty of intraday photovoltaic forecast data. This method not only improves the accuracy of photovoltaic power forecasting, but also enhances the adaptability of the forecasting model to actual operating conditions, making the scheduling correction more in line with actual needs.

[0019] 4. Improve the safety and stability of power grid operation: Through the preset intraday correction constraints, the present invention can ensure the safety and stability of power grid operation while optimizing dispatch instructions. These constraints may include voltage fluctuation range, current limit, power balance, etc., which helps to avoid overload, voltage instability and other problems in the power grid during dispatch.

[0020] 5. Realize intelligent and automated dispatching: The entire intraday dispatching correction process is automatically executed based on preset parameters and algorithms without manual intervention. This not only improves dispatching efficiency, but also realizes the intelligent and automated dispatching of power grids, providing strong support for building smart grids.

[0021] In summary, the present invention significantly improves the flexibility, accuracy, economic benefit, safety and stability of daily scheduling of distribution networks by introducing periodic rolling optimization steps, comprehensively considering economic indicators, enhancing the adaptability of photovoltaic power prediction, and presetting correction constraints.

[0022] Further or more detailed beneficial effects will be described in detail in conjunction with specific examples in the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 It is a schematic diagram of the principle of periodic rolling optimization described in an embodiment of the present invention.

[0025] Figure 2 It is a structural diagram of the electronic device described in Example 3 of the present invention.

[0026] Figure 3 It is a structural diagram of the IEEE 33-node system described in Embodiment 5 of the present invention.

[0027] Figure 4 Schematic diagram of node voltages of an IEEE 33-node system according to a fifth embodiment of the present invention.

[0028] Figure 5 Schematic diagram of line current of the IEEE 33-node system described in Embodiment 5 of the present invention.

[0029] Figure 6 It is a schematic diagram of the active power of the IEEE 33-node system line described in the fifth embodiment of the present invention.

[0030] Figure 7 It is a schematic diagram of the reactive power of the IEEE 33-node system line described in the fifth embodiment of the present invention.

[0031] Figure 8 It is a schematic diagram of the line purchased power of the IEEE 33-node system described in the fifth embodiment of the present invention.

[0032] Fig. 9 It is a schematic diagram of the daily power of the IEEE 33-node system line energy storage described in Example 5 of the present invention.

[0033] Fig.10 It is a schematic diagram of the IEEE 33-node system energy storage SOC described in Example 5 of the present invention.

[0034] Figure Number: 300. Electronic equipment; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0035] 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.

[0036] In the following description, multiple embodiments of the present invention are provided, and different embodiments may be replaced or combined, so the present invention may also be considered to include all possible combinations of the same and / or different embodiments described. Therefore, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present invention should also be considered to include embodiments containing one or more of A, B, C, and D, all other possible combinations, even though the embodiment may not be clearly described in the following text.

[0037] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present invention. Various processes or components may be appropriately omitted, substituted or added to each example. For example, the described method may be performed in an order different from the described order, and various steps may be added, omitted or combined. In addition, the features described in some examples may be combined in other examples.

[0038] In order to facilitate a better understanding of the embodiments of the present invention, before explaining the specific implementation modes of the present invention in detail, its application scenarios are first described.

[0039] The distribution network intraday dispatch correction method described in the embodiments of this specification is applied to the distribution network intraday operation optimization process. In these scenarios, the application of the distribution network intraday dispatch correction method is intended to make the balance of electricity supply and demand more accurate and efficient, thereby reducing the operating costs of the power grid and improving the reliability and stability of power supply.

[0040] The following is a brief explanation of the periodic rolling optimization and normrnd function involved in multiple embodiments of this specification: Cycle rolling optimization refers to dynamically optimizing the control strategy in each sampling cycle (or control cycle) according to the current state of the system and the predicted future state, following the preset finite time domain objective function. This process is not completed offline at one time, but is continuously carried out online as the sampling time continues to advance, to ensure that the control strategy can adapt to changes in the system state in real time.

[0041] The normrnd function is a function in MATLAB that is used to generate random numbers that follow a normal distribution. The core parameters of this function include the mean and standard deviation, where the mean determines the center position of the normal distribution, and the standard deviation reflects the degree of dispersion or width of the normal distribution. In the practice of intraday dispatch correction of distribution networks, the normrnd function can play an important role. It can be used to simulate the impact of various random factors on the system, such as random fluctuations in load demand and uncertainty in renewable energy output. By generating random numbers that conform to the normal distribution, we can simulate these random factors more realistically, thereby providing a more accurate reference for dispatch correction.

[0042] Embodiment 1: The present embodiment provides a method for correcting distribution network intraday scheduling taking into account photovoltaic power errors, comprising the following steps: S1, obtaining a preset distribution network intraday initial scheduling instruction and controlling the distribution network to start intraday initial scheduling based on the instruction; S2, obtaining a preset correction cycle parameter, and when the distribution network starts initial scheduling within the day, performing a cycle rolling optimization step on the distribution network intraday initial scheduling instruction based on the correction cycle parameter, the cycle rolling optimization step comprising: S21, constructing an objective function for the intraday correction of the distribution network based on the network loss, total revenue and total cost of the distribution network; S22, obtaining the system parameters of each system in the distribution network within the cycle Parameters, the system parameters in the distribution network include load data and electricity price data; S23, obtain the day-ahead photovoltaic prediction curve and use the normrnd function to generate its normal distribution, so as to obtain the intraday photovoltaic prediction data of the distribution network within the period; S24, obtain the preset intraday correction constraint conditions; S25, use the system parameters in the distribution network and the intraday photovoltaic prediction data as input values ​​and solve the objective function in combination with the intraday correction constraint conditions until the optimal solution of the objective function is obtained and the corresponding cycle correction instructions are output, and the distribution network intraday scheduling correction is completed based on the cycle correction instructions. More specifically, the cycle correction instructions include at least the upper power grid power purchase electricity instructions, energy storage scheduling instructions, distribution network topology change instructions, and intraday controllable load instructions. Based on these instructions, the day-ahead scheduling operation of the distribution network can be completed to ensure that the power grid achieves maximum economic benefits under the premise of meeting safe and stable operation.

[0043] It is particularly important to note that the initial daily dispatching instructions for the distribution network can be obtained in at least the following ways: by analyzing the historical operating data of the distribution network, combined with current load demand, renewable energy forecasts, etc., and generated using advanced forecasting models (such as time series analysis, machine learning models, etc.); by the superior power grid based on the operating conditions and needs of the overall power grid, issuing them to the distribution network through the dispatching system; the dispatching personnel of the distribution network may manually formulate the initial daily dispatching instructions based on their own experience and judgment, combined with information such as the current power grid operating conditions and weather forecasts.

[0044] More specifically, the day-ahead photovoltaic prediction curve can at least be obtained by constructing a photovoltaic output mathematical model for the photovoltaic system in the distribution network using a normal distribution, and the photovoltaic output mathematical model expression is: , where Indicates time The predicted photovoltaic power generation power, P(t) represents the The photovoltaic power generation power, represents the error term.

[0045] It is understandable that the actual output of photovoltaic power often deviates from the predicted value, and this deviation will affect the power balance of the system, change the scheduling plan of the day before, affect the power flow distribution of the system, and change the node voltage of the system. Therefore, this embodiment creatively considers the photovoltaic power error and proposes a corresponding distribution network intraday scheduling correction method. Figure 1 As shown, the correction of the initial dispatching instruction within the day in this embodiment is not performed offline once, but is based on the preset correction cycle parameters, and is repeatedly performed online as the sampling moment advances, so it is called cycle rolling optimization. Although cycle rolling optimization may not be able to obtain an absolute global optimal solution, it can quickly respond to various complex situations that occur in the control process by continuously optimizing the deviation at each sampling moment. Unlike traditional global optimization, the optimization performance index of cycle rolling optimization at each moment only covers a limited time period from that moment to the future. As time goes by, this optimization time period will also move forward accordingly to achieve continuous online optimization. At each moment, the system generates a set of future control actions, but only executes the control actions at the current moment. At the next moment, the system will re-perform predictive optimization, generate new control actions, and also only execute the current control actions. This process embodies the principle of feedback correction, making predictive control predictive. Through rolling optimization and feedback correction, the method can better adapt to the actual system and show stronger robustness. This innovative method not only optimizes the dispatching operation process, but also significantly improves the overall performance and economic benefits of the power system, ensuring that the power grid can operate safely and stably while maximizing economic benefits.

[0046] Specifically, this embodiment provides a preferred implementation, and the expression of the objective function includes: , , where It represents the network loss corrected within the day of the distribution network, represents the total benefit of the distribution network correction within a day, It represents the total cost of the daily correction of the distribution network. It is understandable that the network loss cost has a significant impact on the economic efficiency of the operation of the power grid company among many operating indicators. Therefore, this embodiment takes the system network loss as a core value indicator for in-depth discussion, aiming to further improve the reliability and economy of the power system by accurately analyzing the inherent relationship between power prediction accuracy and value indicators.

[0047] Specifically, this embodiment provides a preferred implementation of step S22, wherein the obtaining of each system parameter in the distribution network within the period includes the steps of: constructing a battery mathematical model for the energy storage system in the distribution network, wherein the battery mathematical model expressions when the energy storage battery is charged and when it is discharged are respectively: , , where Indicates time Its previous moment The time interval express The power of the energy storage battery at all times, Indicates time The power of the energy storage battery, Indicates the self-discharge coefficient of the energy storage battery, Indicates the actual charging power of the energy storage battery. Indicates the charging efficiency of the energy storage battery, Indicates the actual discharge power of the energy storage battery. Represents the discharge efficiency of the energy storage battery; a controllable charge mathematical model is constructed for the controllable load system in the distribution network, and its expression is: , where Indicates time The total response power, N represents the total number of users who can participate in demand response, Indicates time user The response status includes increasing load, reducing load and not participating in dispatch. Indicates time user The response power of the load system in the distribution network is constructed by building a load mathematical model, and its expression is , where Indicates time The load value, represents the preset load changing function over time, represents the error term.

[0048] It is understandable that controllable loads play an important role in the distribution network. First, dynamic adjustment. Controllable loads can be dynamically adjusted according to system requirements, thereby reducing power consumption during peak load periods and increasing power consumption during low load periods, thereby improving the overall flexibility of the power system. Second, optimizing resource allocation. By reasonably controlling loads, grid resources can be used more effectively, unnecessary waste can be reduced, and energy efficiency can be improved. Third, fault response. When a distribution network fails, controllable loads can respond quickly, reducing or cutting off the power supply of some non-critical loads to ensure the power supply of critical loads and improve power supply reliability. Fourth, island operation. Under certain circumstances, controllable loads can be combined with distributed power sources to form island operation, thereby maintaining power supply in local areas when the main grid fails and reducing the scope and time of power outages. Fifth, intelligent dispatching. Controllable loads are an important part of smart grids. Through integration with smart grid systems, more accurate power dispatching and load management can be achieved. Sixth, demand-side response. Controllable loads can actively participate in demand-side response, adjust power consumption behavior according to market price signals or system instructions, and promote the healthy development of the power market. Seventh, cost saving. By reasonably controlling the load, the peak-to-valley difference of the power grid can be reduced, the operating cost of the power grid can be reduced, and the electricity bill expenditure can also be saved for users. Eighth, energy conservation and emission reduction. The optimized scheduling of controllable loads helps to reduce the power loss and environmental pollution of the power grid and promote the sustainable development of the power industry. Ninth, emergency guarantee. In emergency situations, such as power grid failures caused by natural disasters, the rapid response and flexible adjustment capabilities of controllable loads are of great significance for ensuring the power supply of important facilities. In summary, the role of controllable loads in the distribution network is multifaceted, including improving system flexibility, improving power supply reliability, promoting the development of smart grids, optimizing economic efficiency, and responding to emergencies. These roles together constitute the indispensable and important position of controllable loads in the power system. With the continuous development of active distribution network technology and advanced measurement systems, the proportion of flexible loads that can participate in grid dispatching in power systems is increasing. From the perspective of user autonomous response characteristics, flexible loads can be divided into three categories: the first is transferable loads, where the total power consumption remains unchanged during the cycle, but the power consumption characteristics and power consumption time are flexible, such as electric vehicle replacement stations, ice storage, energy storage, and partial loads of industrial and commercial users; the second is shiftable loads, which are constrained by the production process and can only be shifted at different times, such as large industrial users; the third is reducible loads, which can reduce power consumption at specific times, such as temperature control equipment such as air conditioners. This embodiment can achieve accurate description and quantitative analysis of flexible loads by constructing a controllable charge mathematical model for the controllable load system in the distribution network. This not only helps to have a deeper understanding of the operating characteristics and response mechanism of flexible loads, but also provides a solid theoretical basis for subsequent optimization and dispatching strategies. By combining actual distribution network operation data and user needs, the economic, reliable, and green operation of the power system can be achieved.

[0049] Specifically, this embodiment provides a preferred implementation method, wherein the intraday correction constraint condition includes a system power balance constraint set for the system power of the distribution network; the expression of the system power balance constraint is: , where Indicates time Distribution network exchange power, Represents photovoltaic output power, Indicates time The load value in the distribution network is Indicates time Distribution network charging power, Indicates time Distribution network discharge power, Indicates the exchange power of the controllable load.

[0050] Specifically, this embodiment provides a preferred implementation method, wherein the intra-day correction constraint condition also includes an energy storage constraint set for the energy storage system in the distribution network; the expression of the energy storage constraint includes , , , , , , where Indicates time Distribution network exchange power, Indicates the maximum value of the exchange power of the distribution network, represents the minimum value of the exchange power of the distribution network, Indicates the maximum value of the distribution network discharge power, is the maximum value of the charging power of the distribution network, and They represent the upper and lower limits of the state of charge of the energy storage at time period t, Indicates that the energy storage battery is in a discharging state. Indicates that the energy storage battery is in charging state. Indicates that the energy storage battery is in a static state. and They respectively represent the initial and final charge states of energy storage.

[0051] Specifically, this embodiment provides a preferred implementation method, assuming that the distribution network is a radial type; the intraday correction constraint condition also includes the power flow constraint of the distribution network branch power flow, the distribution network node voltage constraint and the distribution network branch transmission power constraint; the power flow constraint equation is , where Indicates a node number in the distribution network. Indicates the flow into i +1 active power of the node, Indicates the flow into i +1 node reactive power, Indicates i The node voltage of the +1 node, and Respectively represent nodes and The resistance and reactance of the branch between Representation Node The magnitude of the voltage at and Respectively represent slave nodes Flow to Node The active power and reactive power of and They represent the active power and reactive power supplied to the node load respectively, and They represent the active power and reactive power generated by the distributed generation output respectively; the expression of the distribution network node voltage constraint is: , where Indicates time node The voltage value, and Respectively represent nodes The upper and lower limits of the voltage value; the expression of the transmission power constraint of the distribution network branch is: , where Indicates the transmission power of a line in the distribution network, Indicates the maximum transmission power of a line in the distribution network.

[0052] Specifically, this embodiment provides a preferred implementation method, wherein the intraday correction constraint condition also includes a network reconstruction constraint set for the distribution network structure; the expression of the network reconstruction constraint is: , where is a 0-1 variable, Indicates that the branch is disconnected. Indicates that the branch is closed. n b and n s They represent the total number of distribution network nodes and the number of distribution network root nodes respectively.

[0053] Embodiment 2: This embodiment provides a distribution network intraday dispatch correction system taking into account photovoltaic power errors, which is used to implement the distribution network intraday dispatch correction method as described in the first embodiment.

[0054] Embodiment three: like Figure 2As shown, this embodiment provides an electronic device, which may include: at least one processor, at least one network interface, a user interface, a memory, and at least one communication bus.

[0055] The communication bus can be used to realize the connection and communication among the above-mentioned components.

[0056] The user interface may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0057] The network interface may include but is not limited to a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0058] Among them, the processor may include one or more processing cores. The processor uses various interfaces and lines to connect the various parts of the entire electronic device, and executes various functions of the electronic device and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Optionally, the processor can be implemented in at least one hardware form of DSP, FPGA, and PLA. The processor can integrate one or a combination of CPU, GPU, modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display; the modem is used to handle wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor, but may be implemented separately through a chip.

[0059] Among them, the memory may include RAM or ROM. Optionally, the memory includes a non-transitory computer-readable medium. The memory can be used to store instructions, programs, codes, code sets or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory may also be at least one storage device located away from the aforementioned processor. The memory as a computer storage medium may include an operating system, a network communication module, a user interface module, and an intraday scheduling correction application. The processor may be used to call the intraday scheduling correction application stored in the memory and execute the steps of the distribution network intraday scheduling correction method mentioned in the above-mentioned embodiment.

[0060] Embodiment 4: This embodiment provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is executed on a computer or a processor, the computer or the processor executes the above-mentioned Figure 1 One or more steps in the illustrated embodiment. If the components of the electronic device described above are implemented in the form of software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0061] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of this specification is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state drive (SSD)).

[0062] A person of ordinary skill in the art can understand that all or part of the processes in the method of the first embodiment can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes. In the absence of conflict, the technical features in this embodiment and the implementation scheme can be combined arbitrarily.

[0063] Embodiment five: In order to verify the effectiveness of the distribution network intraday dispatch correction method considering photovoltaic power error described in this specification, this embodiment is based on the actual application scenario of the distribution network intraday dispatch correction method. Figure 3The IEEE 33-bus system shown in the figure has node voltage, line current, line active power, line reactive power, line purchased power, line energy storage day power and energy storage SOC as shown in the figure. Figure 4-Figure 10 As shown, the objective function shown is to minimize the network loss. Compared with the dispatch result based only on the initial dispatch instruction of the distribution network during the day, after the cycle rolling optimization, the actual network loss of the distribution network during the day is reduced by 32% on average. More specifically, after 96 cycles of rolling optimization, the actual network loss of the distribution network during the day is reduced by 5.6% on average.

[0064] Based on the above, this embodiment verifies the effectiveness of the method, system, device and medium for correcting intraday scheduling of distribution networks taking into account photovoltaic power errors in this specification.

[0065] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0066] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0067] The above is only an exemplary embodiment of the present invention and cannot be used to limit the scope of the present invention. That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. After considering the specification and practicing the disclosure here, it will be easy for those skilled in the art to think of the implementation scheme of the present invention. The present invention is intended to cover any modification, use or adaptation of the present invention, which follows the general principles of the present invention and includes common knowledge or customary technical means in the art that are not recorded in the present invention. The description and examples are only regarded as exemplary, and the scope and spirit of the present invention are defined by the claims.

Claims

1. A distribution network intraday dispatch correction method considering photovoltaic power error, characterized in that: Includes steps: S1. Obtaining a preset distribution network daily initial dispatching instruction and based on this, controlling the distribution network to start the daily initial dispatching; S2. Obtain preset correction cycle parameters. When the distribution network starts initial dispatching within a day, perform a cycle rolling optimization step on the initial dispatching instruction of the distribution network within a day based on the correction cycle parameters. The cycle rolling optimization step includes: S21. Construct the objective function of the intraday correction of the distribution network based on the network loss, total revenue and total cost of the distribution network; S22, obtaining various system parameters in the distribution network within the period, wherein the various system parameters in the distribution network include load data and electricity price data; S23, obtaining the day-ahead photovoltaic prediction curve and using the normrnd function to generate its normal distribution, so as to obtain the intraday photovoltaic prediction data of the distribution network within the period; S24, obtaining preset intraday correction constraint conditions; S25. Taking the system parameters in the distribution network and the intraday photovoltaic forecast data as input values ​​and solving the objective function in combination with the intraday correction constraint conditions, until the optimal solution of the objective function is obtained and the corresponding periodic correction instructions are output, and the intraday dispatch correction of the distribution network is completed based on the periodic correction instructions.

2. According to claim 1, a distribution network intraday scheduling correction method considering photovoltaic power error is characterized in that: The expression of the objective function includes: ， ， In the formula, It represents the network loss corrected within the day of the distribution network, represents the total benefit of the daily correction of the distribution network, Represents the total cost of intra-day correction of the distribution network.

3. A method for correcting distribution network intraday scheduling considering photovoltaic power error according to claim 2, characterized in that: Step S22 of obtaining various system parameters in the distribution network within the cycle includes the following steps: A battery mathematical model is constructed for the energy storage system in the distribution network. The expressions of the battery mathematical model when the energy storage battery is charging and discharging are respectively: ， ， In the formula, Indicates time Compared with the previous moment The time interval express The power of the energy storage battery at all times, Indicates time The power of the energy storage battery, Indicates the self-discharge coefficient of the energy storage battery, Indicates the actual charging power of the energy storage battery. Indicates the charging efficiency of the energy storage battery, Indicates the actual discharge power of the energy storage battery. Indicates the discharge efficiency of the energy storage battery; A controllable charge mathematical model is constructed for the controllable load system in the distribution network, and its expression is: ， In the formula, Indicates time The total response power, N represents the total number of users who can participate in demand response, Indicates time user The response status includes increasing load, reducing load and not participating in dispatch. Indicates time user The response power; A load mathematical model is constructed for the load system in the distribution network, and its expression is: ， In the formula, Indicates time The load value, represents the preset load changing function over time, represents the error term.

4. A method for correcting distribution network intraday scheduling considering photovoltaic power error according to claim 3, characterized in that: The intraday correction constraint condition includes a system power balance constraint set for the system power of the distribution network; The expression of the system power balance constraint is: ， In the formula, Indicates time Distribution network exchange power, Represents photovoltaic output power, Indicates time The load value in the distribution network is Indicates time Distribution network charging power, Indicates time Distribution network discharge power, Indicates the exchange power of the controllable load.

5. A method for correcting distribution network intraday scheduling considering photovoltaic power error according to claim 4, characterized in that: The intraday correction constraint conditions also include energy storage constraints set for energy storage systems in the distribution network; The expression of the energy storage constraint includes ， ， ， ， ， ， In the formula, Indicates time Distribution network exchange power, Indicates the maximum value of the exchange power of the distribution network, represents the minimum value of the exchange power of the distribution network, Indicates the maximum value of the distribution network discharge power, is the maximum value of the charging power of the distribution network, and They represent the upper and lower limits of the state of charge of the energy storage at time period t, Indicates that the energy storage battery is in a discharging state. Indicates that the energy storage battery is in charging state. Indicates that the energy storage battery is in a static state. and They respectively represent the initial and final charge states of energy storage.

6. A method for correcting distribution network intraday scheduling considering photovoltaic power error according to claim 5, characterized in that: Assume that the distribution network is of radial type; The intraday correction constraints also include power flow constraints of distribution network branch power flow, distribution network node voltage constraints and distribution network branch transmission power constraints; The power flow constraint equation is ， In the formula, Indicates a node number in the distribution network. Indicates the flow into i +1 active power of the node, Indicates the flow into i +1 node reactive power, Indicates i The node voltage of the +1 node, and Respectively represent nodes and The resistance and reactance of the branch between Representation Node The magnitude of the voltage at and Respectively represent slave nodes Flow to Node The active power and reactive power of and They represent the active power and reactive power supplied to the node load respectively, and They represent the active power and reactive power generated by the distributed generation output respectively; The expression of the distribution network node voltage constraint is: ， In the formula, Indicates time node The voltage value, and Respectively represent nodes The upper and lower limits of the voltage value; The expression of the transmission power constraint of the distribution network branch is: ， In the formula, Indicates the transmission power of a line in the distribution network, Indicates the maximum transmission power of a line in the distribution network.

7. A method for correcting distribution network intraday scheduling considering photovoltaic power error according to claim 6, characterized in that: The intraday correction constraint conditions also include network reconstruction constraints set for the distribution network structure; The expression of the network reconstruction constraint is: ， In the formula, is a 0-1 variable, Indicates that the branch is disconnected. Indicates that the branch is closed. n b and n s They represent the total number of distribution network nodes and the number of distribution network root nodes respectively.

8. A distribution network intraday dispatch correction system considering photovoltaic power error, characterized in that: Used to implement the distribution network intraday dispatch correction method as described in any one of claims 1 to 7.

9. A computer device, comprising a memory, a processor and a computer program, characterized in that: When the computer program is executed by a processor, the method for correcting intraday dispatch of a distribution network as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for correcting intraday dispatch of a distribution network as described in any one of claims 1 to 7 is implemented.

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