Power distribution network day-ahead scheduling operation method, system and device considering photovoltaic power error and medium

Abstract

The invention belongs to the technical field of new energy power generation system control, and discloses a power distribution network day-ahead scheduling operation method, system and device considering photovoltaic power errors and a medium. The method comprises the following steps: constructing a target function of day-ahead dispatching operation of the power distribution network based on network loss, total income and total cost of the power distribution network; obtaining system parameters in the power distribution network, wherein the system parameters in the power distribution network comprise photovoltaic prediction data, load data and day-ahead electricity price data; obtaining a preset day-ahead scheduling constraint condition; and solving the target function by taking each system parameter in the power distribution network as an input value and combining with the day-ahead scheduling constraint condition until an optimal solution of the target function is obtained and a corresponding day-ahead scheduling instruction of the power distribution network is output, and completing day-ahead scheduling operation of the power distribution network based on the day-ahead scheduling instruction of the power distribution network. According to the day-ahead dispatching operation method of the power distribution network considering the photovoltaic power error, the dispatching operation process is optimized, and the overall performance and economic benefits of a power system are remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of renewable energy power generation system control, and specifically relates to a method, system, equipment and medium for day-ahead dispatching operation of a distribution network taking photovoltaic power errors 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 device 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 operation requirements, the operation of the interconnection switch enables the grid connection method to be flexibly adjusted to meet the system requirements. However, the photovoltaic power prediction error brings uncertainty to the system dispatching 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 day-ahead scheduling operation method, system, equipment and medium that considers photovoltaic power errors and meets one or more of the above-mentioned requirements, so as to effectively regulate system resources and thereby achieve the economic operation goals of the distribution network system in the day-ahead stage.

[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 day-ahead dispatching and operating a distribution network taking into account photovoltaic power errors, comprising the steps of: S1, constructing an objective function for day-ahead dispatching and operating the distribution network based on network losses, total revenue and total cost of the distribution network; S2, obtaining various system parameters in the distribution network, wherein the various system parameters in the distribution network include photovoltaic prediction data, load data and day-ahead electricity price data; S3, obtaining preset day-ahead dispatching constraints; S4, using the various system parameters in the distribution network as input values ​​and solving the objective function in combination with the day-ahead dispatching constraints, until the optimal solution of the objective function is obtained and the corresponding day-ahead dispatching instruction of the distribution network is output, and the day-ahead dispatching and operating of the distribution network is completed based on the day-ahead dispatching instruction of the distribution network.

[0007] As a preferred solution, the expression of the objective function includes: , , where represents the network loss of the distribution network in the day-ahead phase, represents the total revenue of the distribution network in the day-ahead phase, It represents the total cost of the distribution network in the day-ahead phase.

[0008] As a preferred solution, the step S2 of obtaining various system parameters in the distribution network includes the following steps: The normal distribution is used to construct a photovoltaic output mathematical model for the photovoltaic system in the distribution network, and its expression is: , where Indicates time The predicted photovoltaic power generation power, P(t) represents the The photovoltaic power generation power, represents the error term; 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 , , where 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. 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: , 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 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 day-ahead dispatch 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 day-ahead dispatch 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 day-ahead dispatching 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.

[0012] As a preferred solution, the day-ahead dispatch 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 day-ahead dispatching and operating system taking into account photovoltaic power errors, which is used to implement the distribution network day-ahead dispatching and operating 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 day-ahead scheduling operation 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 day-ahead dispatching and operation 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: First, the present invention scientifically constructs the objective function of the day-ahead dispatching operation of the distribution network, which comprehensively considers factors such as network loss, total revenue and total cost, and provides a clear direction and evaluation benchmark for subsequent dispatching operation. Subsequently, the key system parameters in the distribution network are obtained from all aspects and angles, covering photovoltaic prediction data, load data, day-ahead electricity price data and preset dispatching constraints, laying a solid data foundation for solving the objective function. Using these system parameters and constraints, the present invention deeply solves the objective function until the optimal solution is obtained. The day-ahead dispatching instruction of the distribution network corresponding to the optimal solution is intended to ensure the economic operation of the distribution network system in the day-ahead stage. Furthermore, based on this dispatching instruction, the system resources (day-ahead power purchase, energy storage dispatching plan, topological structure, controllable load, etc.) can be flexibly adjusted to effectively deal with the uncertainty caused by photovoltaic power prediction errors. In summary, the day-ahead dispatching operation method of the distribution network considering photovoltaic power errors proposed in the present invention not only optimizes the dispatching operation process, but also significantly improves the overall performance and economic benefits of the power system.

[0017] 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

[0018] 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.

[0019] Figure 1 It is a flow chart of the method for day-ahead dispatching and operation of a distribution network according to an embodiment of the present invention.

[0020] Figure 2 This is a system topology diagram described in Example 5 of the present invention.

[0021] Figure 3 This is the photovoltaic predicted power output described in Example 5 of the present invention.

[0022] Figure 4 It is a comparison chart between the predicted photovoltaic power output and the actual output according to the fifth embodiment of the present invention.

[0023] Figure 5 It is the amount of electricity purchased from the superior power grid within a day as described in the fifth embodiment of the present invention.

[0024] Figure 6 This is the energy storage output situation described in Example 5 of the present invention.

[0025] Figure 7 This is the energy storage SOC described in the fifth embodiment of the present invention.

[0026] Figure 8 It is the active load of the node described in Embodiment 5 of the present invention.

[0027] Fig. 9 It is the node reactive load described in Embodiment 5 of the present invention.

[0028] Fig.10 It is the node voltage described in the fifth embodiment of the present invention.

[0029] Fig.11 It is the controllable load before optimization described in Embodiment 5 of the present invention.

[0030] Fig.12 It is the optimized controllable load described in the fifth embodiment of the present invention.

[0031] Fig.13 This is the load distribution before and after the system optimization described in Example 5 of the present invention.

[0032] Fig.14 is a structural diagram of the electronic device provided in an embodiment of the present invention.

[0033] Figure Number: 1400. Electronic equipment; 1401, processor; 1402, communication bus; 1403, user interface; 1404, network interface; 1405, memory. DETAILED DESCRIPTION

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] The distribution network day-ahead dispatching and operation method described in the embodiments of this specification is applied to power management and optimization processes. In these scenarios, the application of the distribution network day-ahead dispatching and operation method is intended to make the balance of power supply and demand more accurate and efficient, thereby reducing the power grid operation cost and improving the power supply reliability and stability.

[0039] Embodiment 1: like Figure 1As shown, this embodiment provides a method for day-ahead dispatching and operation of a distribution network taking into account photovoltaic power errors, including the steps of: S1, constructing an objective function for day-ahead dispatching and operation of a distribution network based on network losses, total revenue and total cost of the distribution network; S2, obtaining various system parameters in the distribution network, wherein the various system parameters in the distribution network include photovoltaic forecast data, load data and day-ahead electricity price data; S3, obtaining preset day-ahead dispatching constraints; S4, using various system parameters in the distribution network as input values ​​and solving the objective function in combination with the day-ahead dispatching constraints, until the optimal solution of the objective function is obtained and the corresponding day-ahead dispatching instructions of the distribution network are output, and the day-ahead dispatching and operation of the distribution network is completed based on the day-ahead dispatching instructions of the distribution network. More specifically, the day-ahead dispatching instructions of the distribution network at least include the power purchase instructions of the upper power grid, the energy storage dispatching instructions, the instructions for changing the topology of the distribution network, and the day-ahead controllable load instructions. Based on these instructions, the day-ahead dispatching and operation of the distribution network can be completed, ensuring that the power grid achieves maximum economic benefits under the premise of meeting safe and stable operation.

[0040] 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 day-ahead dispatching plan, affect the system's power flow distribution, and change the system's node voltage accordingly. Therefore, this embodiment creatively considers the photovoltaic power error and proposes a corresponding day-ahead dispatching operation method for the distribution network. This 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 operates safely and stably while maximizing economic benefits.

[0041] Specifically, this embodiment provides a preferred implementation, and the expression of the objective function includes: , , where represents the network loss of the distribution network in the day-ahead phase, represents the total revenue of the distribution network in the day-ahead phase, Represents the total cost of the distribution network in the day-ahead stage. It is understandable that network loss cost has a significant impact on the economic efficiency of power grid company operations among many operating indicators. Therefore, this embodiment takes 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.

[0042] Specifically, this embodiment provides a preferred implementation of step S2, wherein the obtaining of each system parameter in the distribution network comprises the steps of: using normal distribution to construct a photovoltaic output mathematical model for the photovoltaic system in the distribution network, and the expression thereof is: , where Indicates time The predicted photovoltaic power generation power, P(t) represents the The photovoltaic power generation power, represents the error term; a battery mathematical model is constructed for the energy storage system in the distribution network. The battery mathematical model expressions when the energy storage battery is charging and discharging are respectively , , where 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. 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 , 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 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.

[0043] 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 or emergencies, 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.

[0044] Specifically, this embodiment provides a preferred implementation method, wherein the day-ahead scheduling 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.

[0045] Specifically, this embodiment provides a preferred implementation method, wherein the day-ahead dispatch 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.

[0046] Specifically, this embodiment provides a preferred implementation method, assuming that the distribution network is a radial type; the day-ahead dispatch 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 intoi +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.

[0047] Specifically, this embodiment provides a preferred implementation method, wherein the day-ahead dispatch constraint condition further 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.

[0048] Embodiment 2: This embodiment provides a distribution network day-ahead dispatching and operating system taking into account photovoltaic power error, which is used to implement the distribution network day-ahead dispatching and operating method as described in the first embodiment.

[0049] Embodiment three: like Fig.14As 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.

[0050] The communication bus can be used to realize the connection and communication among the above-mentioned components.

[0051] The user interface may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0052] The network interface may include but is not limited to a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0053] 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.

[0054] 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 a day-ahead scheduling operation application. The processor may be used to call the day-ahead scheduling operation application stored in the memory and execute the day-ahead scheduling operation steps mentioned in the above-mentioned embodiments.

[0055] 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.

[0056] 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)).

[0057] 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.

[0058] Embodiment five: In order to verify the effectiveness of the distribution network day-ahead dispatching operation method considering photovoltaic power error described in this specification, this embodiment is based on Figure 2The actual application scenario of the day-ahead dispatching operation method of the distribution network shown in the figure is unfolded. This scenario adopts the IEEE 33-node system, which is designed as a closed loop to improve power supply reliability, but remains open-loop in actual operation to limit short-circuit fault current and control the scope of fault spread. Overall, the system presents a radial network structure, that is, starting from the power supply point, power is supplied to each load node through branches at all levels. The system contains 33 nodes, some of which are load nodes, and the rest are transmission nodes or contact nodes. The number of branches is slightly more than the number of nodes, forming a necessary loop structure for easy optimization and reconstruction. The system is equipped with one or more power supply points to supply power to the entire network. The load nodes are connected to loads of different sizes, simulating the power demand of the actual distribution network.

[0059] In this embodiment, photovoltaic power (capacity of 1.5MW) is connected to node 17, energy storage battery (capacity of 1WM) is connected to node 15, and shiftable loads are connected to nodes 4, 15, and 30, and loads that can be reduced are connected to nodes 10 and 26. The scheduling time is set to 1 day, divided into 96 periods, each period of 15 minutes.

[0060] Depend on Figure 3 It can be seen that photovoltaic output is affected by environmental factors such as light intensity and temperature, and is intermittent and volatile. The access of energy storage systems (such as battery energy storage) enables the distribution network to have energy time-shifting characteristics, which can store energy when photovoltaic output is excessive and release energy when photovoltaic output is insufficient. The access of controllable loads increases the flexibility of the distribution network, and by adjusting the power consumption plan of the load, the supply and demand relationship can be better balanced.

[0061] Figure 4 The comparison between the photovoltaic power output predicted by the LSTM prediction algorithm and the actual output is shown. There is a significant deviation between the two. This deviation will affect the power balance of the system, change the scheduling plan a day ago, affect the power flow distribution of the system, and change the node voltage of the system. Figure 5 As shown in Figure 2, due to the change in photovoltaic output, the original power balance has also changed.

[0062] Figure 6-Figure 7 The output of energy storage and its energy storage in the day-ahead are demonstrated. By applying the day-ahead dispatching operation method of the distribution network described in the present invention, in the day-ahead stage, the system power balance is maintained through energy storage dispatching instructions, the system variables are updated under the optimal goal, and the latest dispatching instructions are given. Figure 8-Figure 10The node active load, reactive load and node voltage are displayed. Changes in active and reactive loads will directly affect the node voltage. An increase in active load will increase the current, which will cause a voltage drop; an increase in reactive load will cause a greater voltage drop during the transmission of reactive power. The distribution and capacity of power sources have an important impact on node voltage. The closer the power source point and the larger the capacity, the stronger the support for the node voltage. In addition, the network topology of the distribution network (such as branch impedance, node connection method, etc.) will affect the voltage distribution. For example, long-distance transmission will cause a large voltage drop.

[0063] In order to further verify the effectiveness of the method, a comparative test is also set up in this embodiment. The controllable load before and after optimization is as follows: Figure 11-13 As shown. The control group did not consider the photovoltaic power prediction error, while the experimental group fully considered the impact of the photovoltaic power prediction error. The control group used sine function mathematical simulation of photovoltaic output to ensure that the photovoltaic output was 60%, and used it as the photovoltaic input of the day-ahead scheduling model for scheduling, and the output time was 1 day, and the time interval was 15 minutes. The experimental group first generated multiple photovoltaic output curves with power prediction accuracy of 80%, 85%, 90%, and 95%, respectively. Each prediction accuracy collected 20 different photovoltaic output curves to describe its volatility and uncertainty. And it was used as the photovoltaic input of the day-ahead scheduling model for scheduling, and the output time was 1 day, and the time interval was 15 minutes. The day-ahead topological structure scheduling results of the experimental group are shown in Table 1. The topological structure of the distribution network (such as the connection method of nodes and branches) directly affects the network loss. A reasonable topological structure can reduce losses and improve energy efficiency. In addition, the topological structure also affects the reliability of power supply. Closed-loop design can improve reliability, but open-loop operation can limit the scope of faults. The network loss is selected as the value indicator, and the dispatch results under different prediction errors are compared. The relationship between prediction error and network loss is analyzed as shown in Table 2. By constructing six tiered power prediction accuracy conditions, the economic efficiency of distribution network dispatch operation under different power prediction accuracy is deeply studied. The following conclusions can be drawn from the measurement results: First, through the comparison of working conditions, it is known that the day-ahead dispatch by simulating photovoltaic output through sine function without power prediction cannot achieve the minimum network loss cost. Second, the actual network loss and network loss cost of the distribution network decrease with the improvement of prediction accuracy. When the prediction accuracy increases by 3% from 80%, the system network loss will be reduced by more than 14%. Third, the economic efficiency of distribution network operation under lower prediction accuracy is lower than that without prediction. When the prediction accuracy is higher than the zero limit, considering the prediction result is conducive to improving the economic efficiency of the distribution network. The prediction accuracy zero limit is 87%. At the same time, the difference between the expected network loss and the actual network loss of the distribution network decreases with the improvement of prediction accuracy.

[0064] Table 1

[0065] Table 2

[0066] Based on the above, this embodiment verifies the effectiveness of the distribution network day-ahead dispatching operation method considering photovoltaic power error in this specification.

[0067] 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.

[0068] 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.

[0069] 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 method for day-ahead dispatching of a distribution network considering photovoltaic power error, characterized in that: Includes steps: S1. Based on the network loss, total revenue and total cost of the distribution network, the objective function of the day-ahead dispatching operation of the distribution network is constructed; S2. Acquire various system parameters in the distribution network, wherein the various system parameters in the distribution network include photovoltaic prediction data, load data and day-ahead electricity price data; S3. Obtaining preset day-ahead scheduling constraints; S4. Taking each system parameter in the distribution network as input value and solving the objective function in combination with the day-ahead dispatch constraint condition, until the optimal solution of the objective function is obtained and the corresponding day-ahead dispatch instruction of the distribution network is output, and the day-ahead dispatch operation of the distribution network is completed based on the day-ahead dispatch instruction of the distribution network.

2. A method for day-ahead dispatching of a distribution network considering photovoltaic power error according to claim 1, characterized in that: The expression of the objective function includes: ， ， In the formula, represents the network loss of the distribution network in the day-ahead phase, represents the total revenue of the distribution network in the day-ahead phase, It represents the total cost of the distribution network in the day-ahead phase.

3. A method for day-ahead dispatching of a distribution network considering photovoltaic power error according to claim 2, characterized in that: Step S2 of obtaining various system parameters in the distribution network includes the following steps: The normal distribution is used to construct a photovoltaic output mathematical model for the photovoltaic system in the distribution network, and its expression is: ， In the formula, Indicates time The predicted photovoltaic power generation power, P(t) represents the The photovoltaic power generation power, represents the error term; 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 day-ahead dispatching of a distribution network taking into account photovoltaic power error according to claim 3, characterized in that: The day-ahead dispatch 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 day-ahead dispatching of a distribution network taking into account photovoltaic power error according to claim 4, characterized in that: The day-ahead dispatch constraint condition also includes energy storage constraints set for the energy storage system 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 day-ahead dispatching of a distribution network taking into account photovoltaic power error according to claim 5, characterized in that: Assume that the distribution network is of radial type; The day-ahead dispatch constraints also include power flow constraints of distribution network branches, 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 day-ahead dispatching of a distribution network taking into account photovoltaic power error according to claim 6, characterized in that: The day-ahead dispatch constraint condition also includes a network reconfiguration constraint 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 day-ahead dispatching and operation system for a distribution network taking into account photovoltaic power error, characterized in that: Used to implement the distribution network day-ahead dispatching operation 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 day-ahead dispatching operation method of the 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 day-ahead dispatching operation method of the distribution network as described in any one of claims 1 to 7 is implemented.

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