Power distribution network energy storage scheduling method and device, electronic equipment and storage medium

Abstract

The invention discloses a power distribution network energy storage scheduling method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining new energy output data of new energy facilities in a power distribution network; determining a new energy actual power interval of the power distribution network based on the new energy output data; constructing a day-ahead scheduling objective function of the power distribution network based on the new energy actual power interval; based on the new energy actual power interval and the day-ahead scheduling objective function, an intra-day scheduling objective function of the power distribution network is constructed, and the intra-day scheduling objective function is used for representing an objective function constructed based on the main network electricity purchasing cost and the voltage deviation of the power distribution network under intra-day stage scheduling; and determining a power distribution network energy storage scheduling instruction based on the intra-day scheduling objective function, the power distribution network energy storage scheduling instruction being used for performing energy storage scheduling on the power distribution network under intra-day stage scheduling. According to the invention, the technical problem of low accuracy and efficiency of dispatching the energy storage of the power distribution network in the related art is solved.

Description

Technical Field

[0001] The present invention relates to the field of distribution network dispatching, and in particular to a method, device, electronic equipment and storage medium for dispatching energy storage in a distribution network. Background Art

[0002] New renewable energy sources such as solar energy and wind energy have the advantages of being clean, low-carbon, abundant in resources, widely distributed, and having zero carbon emissions and zero marginal costs in the power generation process. However, due to the volatility and uncertainty of the output of new energy sources, the large-scale access of new energy sources will also increase the difficulty of safe and stable operation of the distribution network. The increase of various new loads will further aggravate the problem of imbalance between power supply and demand, which also puts higher requirements on the flexibility of the power system.

[0003] At present, the dispatching methods of distribution network energy storage in related technologies are usually applicable to traditional distribution networks that do not include new energy facilities. With the large-scale access of new energy facilities, the uncertainty and volatility on the source side of the power system continue to increase, and the demand for flexibility in peak load regulation and voltage regulation continues to increase, resulting in low accuracy and efficiency in the dispatching of distribution network energy storage in related technologies.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a method, device, electronic device and storage medium for scheduling energy storage in a distribution network, so as to at least solve the technical problem of low accuracy and efficiency in scheduling energy storage in a distribution network in the related art.

[0006] According to one aspect of an embodiment of the present application, a method for dispatching energy storage in a distribution network is provided, comprising: obtaining new energy output data of new energy facilities in the distribution network; determining the new energy actual power range of the distribution network based on the new energy output data; constructing a day-ahead dispatching target function of the distribution network based on the new energy actual power range, wherein the day-ahead dispatching target function is used to represent a target function constructed based on the operating cost and voltage deviation of the distribution network under day-ahead stage dispatching; constructing an intraday dispatching target function of the distribution network based on the new energy actual power range and the day-ahead dispatching target function, wherein the intraday dispatching target function is used to represent a target function constructed based on the main grid electricity purchase cost and voltage deviation under day-ahead stage dispatching of the distribution network; determining a distribution network energy storage dispatching instruction based on the intraday dispatching target function, wherein the distribution network energy storage dispatching instruction is used to perform energy storage dispatching on the distribution network under day-ahead stage dispatching.

[0007] Optionally, determining the actual power range of new energy in the distribution network based on the new energy output data includes: obtaining actual output data and predicted output data in the new energy output data; determining prediction error data of the distribution network based on the actual output parameter data and the predicted output data; determining the actual power range of new energy based on the actual output data, the predicted output data and the prediction error data.

[0008] Optionally, based on the actual output data, the predicted output data and the predicted error data, the actual power range of the new energy is determined, including: dividing the predicted output data into different interval ranges; determining the probability density function of the predicted error data in different interval ranges; and determining the actual power range of the new energy based on the probability density function, the actual output data and the predicted output data.

[0009] Optionally, based on the probability density function, actual output data and predicted output data, determining the actual power range of the new energy includes: determining the prediction error confidence interval of the prediction error data based on the probability density function; determining the actual power range of the new energy based on the prediction error confidence interval, actual output data and predicted output data.

[0010] Optionally, based on the actual power range of new energy and the day-ahead dispatch objective function, an intraday dispatch objective function of the distribution network is constructed, including: obtaining a day-ahead constraint set under the day-ahead stage dispatch of the distribution network, wherein the day-ahead constraint set is used to represent a constraint set constructed based on the distribution network operation constraints and grid equipment constraints of the distribution network; based on the day-ahead constraint set, the actual power range of new energy and the day-ahead dispatch objective function, an intraday dispatch objective function of the distribution network is constructed.

[0011] Optionally, obtaining a day-ahead constraint set for the distribution network under the day-ahead stage scheduling includes: obtaining distribution network flow constraints, distribution network safe operation constraints, new energy output constraints and distribution facility constraints under the day-ahead stage scheduling of the distribution network, wherein the distribution facility constraints include at least one of the following: transformer operation constraints, circuit breaker operation constraints, soft switch constraints, energy storage system constraints and electric vehicle charging and discharging constraints; constructing a day-ahead constraint set based on the distribution network flow constraints, distribution network safe operation constraints, new energy output constraints and distribution facility constraints.

[0012] According to another aspect of an embodiment of the present invention, there is also provided a dispatching device for energy storage in a distribution network, comprising: an acquisition module for acquiring new energy output data of new energy facilities in the distribution network; a first determination module for determining the new energy actual power range of the distribution network based on the new energy output data; a first construction module for constructing a day-ahead dispatching target function of the distribution network based on the new energy actual power range, wherein the day-ahead dispatching target function is used to represent a target function constructed based on the operating cost and voltage deviation of the distribution network under the day-ahead stage dispatching; a second construction module for constructing an intraday dispatching target function of the distribution network based on the new energy actual power range and the day-ahead dispatching target function, wherein the intraday dispatching target function is used to represent a target function constructed based on the main network power purchase cost and voltage deviation under the intraday stage dispatching of the distribution network; a second determination module for determining a distribution network energy storage dispatching instruction based on the intraday dispatching target function, wherein the distribution network energy storage dispatching instruction is used to perform energy storage dispatching on the distribution network under the intraday stage dispatching.

[0013] According to another aspect of an embodiment of the present invention, there is further provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the method in each embodiment of the present invention is executed when the program is running.

[0014] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0015] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0016] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0017] According to another aspect of the embodiments of the present invention, a computer program is further provided. When the computer program is executed by a processor, the methods in the embodiments of the present invention are implemented.

[0018] In an embodiment of the present invention, a method for dispatching energy storage in a distribution network is provided, including: obtaining new energy output data of new energy facilities in the distribution network; determining the new energy actual power interval of the distribution network based on the new energy output data; constructing a day-ahead dispatching target function of the distribution network based on the new energy actual power interval, wherein the day-ahead dispatching target function is used to represent the target function constructed based on the operating cost and voltage deviation of the distribution network under the day-ahead stage dispatching; constructing an intraday dispatching target function of the distribution network based on the new energy actual power interval and the day-ahead dispatching target function, wherein the intraday dispatching target function is used to represent the target function constructed based on the main network power purchase cost and voltage deviation under the intraday stage dispatching of the distribution network; determining the distribution network energy storage dispatching instruction based on the intraday dispatching target function, wherein the distribution network energy storage dispatching instruction is used to perform energy storage dispatching on the distribution network under the intraday stage dispatching. It is easy to notice that the present application predicts the power output range of new energy facilities by obtaining new energy output data in the distribution network and determining the new energy actual power interval, thereby providing data support for subsequent dispatching decisions. Constructing the day-ahead dispatch objective function can help optimize the operating cost and voltage deviation of the distribution network in the day-ahead stage. Constructing the intraday dispatch objective function based on the actual power range of new energy and the day-ahead dispatch objective function. By representing the objective function of the main grid's electricity purchase cost and voltage deviation, the energy storage dispatch strategy can be optimized, the efficiency and flexibility of intraday dispatch can be improved, and the energy storage dispatch of the distribution network can be carried out in the intraday stage to realize intelligent dispatch decision-making, which helps to better regulate the fluctuation of new energy and load demand and improve the operating efficiency and reliability of the system. This solves the technical problems of low accuracy and efficiency in dispatching energy storage in distribution networks in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0020] Figure 1 is a flow chart of a method for dispatching energy storage in a distribution network according to an embodiment of the present invention;

[0021] Figure 2 is a schematic diagram of an optional dispatching process of distribution network energy storage according to an embodiment of the present invention;

[0022] Figure 3 is a schematic diagram of a dispatching device for energy storage in a distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] According to an embodiment of the present invention, an embodiment of a method for dispatching energy storage in a distribution network is provided. It can be explained that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] Figure 1 is a flow chart of a method for dispatching energy storage in a distribution network according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0027] Step S102, obtaining new energy output data of new energy facilities in the distribution network.

[0028] In an optional embodiment, the new energy output data of the new energy facilities in the distribution network can be obtained. Specifically, first, sensors or monitoring equipment can be installed in the new energy facilities in the distribution network to monitor and collect the output data of the new energy facilities in real time. The sensors may include speed sensors of wind turbines, radiation sensors of solar panels, etc., which are used to record the actual power generation of new energy facilities in real time. The collected new energy output data can be transmitted to the data center or dispatching center through a wireless network or a wired network, which can ensure the real-time and accuracy of the data and provide reliable data support for subsequent dispatching decisions. In the data center or dispatching center, the collected new energy output data can be processed and analyzed, which may include data cleaning, data storage, data mining and other processes to ensure the integrity and availability of the data. In the above process, by monitoring and collecting the output data of the new energy facilities, the power generation of the new energy facilities can be obtained in time to ensure the accuracy and timeliness of the dispatching decision. Accurately obtaining new energy output data can provide reliable data support for energy storage dispatching decisions, help optimize dispatching strategies, and improve the operating efficiency and stability of the system.

[0029] Step S104, determining the actual power range of the new energy in the distribution network based on the new energy output data.

[0030] In an optional embodiment, the actual power interval of the new energy of the distribution network can be determined based on the new energy output data. Specifically, the collected new energy output data can be preprocessed first, including data cleaning, removal of outliers, data smoothing and other operations to ensure the quality and reliability of the data. Then, the preprocessed new energy output data can be used to determine the actual power interval of the new energy facilities in the distribution network through statistical analysis or mathematical modeling methods, which can include calculating the average power, peak power, valley power and other parameters, and determining the upper and lower limits of the power interval according to the fluctuation of historical data. The determined new energy actual power interval is verified and calibrated, and the accuracy of the determined power interval can be ensured by comparing with the actual power generation or cross-validating with other data sources, and the new energy actual power interval can be regularly updated and adjusted, and dynamically adjusted according to the actual operation conditions to adapt to the changes in the new energy output and the actual needs of the distribution network. In the above process, by determining the actual power interval of the new energy of the distribution network, the power output of the new energy facilities can be more accurately predicted and controlled, and the accuracy and stability of the dispatch can be improved. By dynamically updating and adjusting the power range, the dispatch strategy can be flexibly adjusted according to the real-time situation, the operating efficiency of the distribution network can be optimized, and the flexibility and reliability of the system can be improved.

[0031] Step S106, constructing a day-ahead dispatch objective function of the distribution network based on the actual power range of new energy sources.

[0032] Among them, the day-ahead dispatch objective function is used to represent the objective function constructed based on the operating cost and voltage deviation of the distribution network under the day-ahead stage dispatch.

[0033] In an optional embodiment, the day-ahead dispatch objective function of the distribution network can be constructed based on the actual power interval of the new energy. Specifically, first, the optimization target of the day-ahead dispatch can be determined according to the actual situation and demand of the distribution network, which can be to minimize the operating cost and reduce the voltage deviation. The day-ahead dispatch optimization target can be set and adjusted according to the specific situation of the distribution network. Then, the day-ahead dispatch objective function can be constructed based on the actual power interval of the new energy and the determined dispatch target. The day-ahead objective function may include: a cost function related to the operating cost, and a constraint function related to the voltage deviation. These functions may include power generation cost, power purchase cost, energy storage cost, etc. according to the specific situation. When constructing the objective function, various constraints may be considered, such as supply and demand balance constraints, equipment operation restriction constraints, voltage stability constraints, etc. In the above process, by constructing the day-ahead dispatch objective function, the operating cost and voltage deviation of the distribution network can be optimized to improve the economy and stability of the system. The objective function constructed based on the actual power interval can be adjusted according to the actual situation of the new energy facilities, so that the dispatch strategy is more flexible and adaptable to changes in different situations.

[0034] Step S108, constructing the intraday dispatch objective function of the distribution network based on the actual power range of the new energy and the day-ahead dispatch objective function.

[0035] Among them, the intraday dispatch objective function is used to represent the objective function constructed based on the main grid power purchase cost and voltage deviation under the intraday dispatch of the distribution network.

[0036] In an optional embodiment, the intraday dispatch objective function of the distribution network can be constructed based on the actual power interval of new energy and the day-ahead dispatch objective function. Specifically, first, the optimization objective of intraday dispatch can be determined according to the actual situation and demand of the distribution network in the intraday stage, which can be to minimize the main network electricity purchase cost and reduce the voltage deviation. The intraday optimization objective can be set and adjusted according to the specific situation of the distribution network. The intraday dispatch objective function of the distribution network can be constructed in combination with the actual power interval of new energy and the day-ahead dispatch objective function. The intraday dispatch objective function can include the constraints of the main network electricity purchase cost and voltage deviation, while considering the actual power output of the new energy facilities. When constructing the intraday dispatch objective function, the dispatch strategy of the energy storage equipment can be considered to determine the charging and discharging power range of the energy storage to achieve the regulation of the fluctuation of new energy and the satisfaction of the load demand. And a suitable optimization algorithm can be selected to solve the constructed intraday dispatch objective function to obtain the optimal intraday dispatch scheme. Through the above process, the construction of the intraday dispatch objective function can achieve the coordination of day-ahead dispatch and intraday dispatch, making the system operation more efficient and stable. The intraday dispatch objective function constructed based on the actual power range and the day-ahead dispatch objective function can flexibly adjust the dispatch strategy according to the real-time situation, cope with the fluctuation of new energy and load changes, and improve the adaptability and flexibility of the system.

[0037] Step S110, determining the distribution network energy storage dispatching instruction based on the intraday dispatching objective function.

[0038] Among them, the distribution network energy storage dispatching instruction is used to perform energy storage dispatching on the distribution network under intraday stage dispatching.

[0039] In an optional embodiment, the energy storage dispatching instruction of the distribution network can be determined based on the intraday dispatching objective function. Specifically, according to the construction of the intraday dispatching objective function, the energy storage dispatching target of the distribution network in the intraday stage is determined by comprehensively considering factors such as the actual power range of new energy, the main network power purchase cost and voltage deviation. The intraday dispatching target may include optimizing the energy storage charging and discharging strategy to minimize the intraday operating cost and stabilize the voltage. Then, based on the optimization result of the intraday dispatching objective function, combined with the characteristics and operation restrictions of the energy storage device, the energy storage dispatching strategy can be determined, including determining the charging and discharging power, charging and discharging period, charging and discharging amount and other parameters of the energy storage. According to the determined energy storage dispatching strategy, a specific energy storage dispatching instruction can be generated and issued to the energy storage device in the distribution network. The distribution network energy storage dispatching instruction may include specific charging and discharging power setting values, time schedules, etc., which are used to guide the operation of the energy storage device in the intraday stage. In the process of executing the energy storage dispatching, the operation status and system status of the energy storage device can be monitored in real time, and the energy storage dispatching strategy can be adjusted and optimized according to the actual situation to ensure the stability and economy of the system operation. In the above process, by optimizing the energy storage dispatching instructions, the best operation strategy of the energy storage equipment can be achieved, the fluctuation of new energy and load demand can be effectively adjusted, the system operation cost can be reduced, and the dispatching efficiency can be improved. The reasonable formulation of energy storage dispatching instructions can help balance the load demand and power supply capacity of the distribution network and improve the power supply quality and stability of the power grid.

[0040] In an embodiment of the present invention, a method for dispatching energy storage in a distribution network is provided, including: obtaining new energy output data of new energy facilities in the distribution network; determining the new energy actual power interval of the distribution network based on the new energy output data; constructing a day-ahead dispatching target function of the distribution network based on the new energy actual power interval, wherein the day-ahead dispatching target function is used to represent the target function constructed based on the operating cost and voltage deviation of the distribution network under the day-ahead stage dispatching; constructing an intraday dispatching target function of the distribution network based on the new energy actual power interval and the day-ahead dispatching target function, wherein the intraday dispatching target function is used to represent the target function constructed based on the main network power purchase cost and voltage deviation under the intraday stage dispatching of the distribution network; determining the distribution network energy storage dispatching instruction based on the intraday dispatching target function, wherein the distribution network energy storage dispatching instruction is used to perform energy storage dispatching on the distribution network under the intraday stage dispatching. It is easy to notice that the present application predicts the power output range of new energy facilities by obtaining new energy output data in the distribution network and determining the new energy actual power interval, thereby providing data support for subsequent dispatching decisions. Constructing the day-ahead dispatch objective function can help optimize the operating cost and voltage deviation of the distribution network in the day-ahead stage. Constructing the intraday dispatch objective function based on the actual power range of new energy and the day-ahead dispatch objective function. By representing the objective function of the main grid's electricity purchase cost and voltage deviation, the energy storage dispatch strategy can be optimized, the efficiency and flexibility of intraday dispatch can be improved, and the energy storage dispatch of the distribution network can be carried out in the intraday stage to realize intelligent dispatch decision-making, which helps to better regulate the fluctuation of new energy and load demand and improve the operating efficiency and reliability of the system. This solves the technical problems of low accuracy and efficiency in dispatching energy storage in distribution networks in related technologies.

[0041] Optionally, determining the actual power range of new energy in the distribution network based on the new energy output data includes: obtaining actual output data and predicted output data in the new energy output data; determining prediction error data of the distribution network based on the actual output parameter data and the predicted output data; determining the actual power range of new energy based on the actual output data, the predicted output data and the prediction error data.

[0042] In an optional embodiment, the actual power interval of the new energy of the distribution network can be determined based on the new energy output data. Specifically, the actual output data and the predicted output data can be obtained from the new energy facilities first. The actual output data can refer to the power output data actually generated by the new energy facilities, and the predicted output data can refer to the predicted output data for a period of time in the future obtained based on historical data and prediction models. By comparing the actual output data and the predicted output data, the prediction error data can be calculated. The prediction error data can represent the deviation between the predicted output and the actual output, and can be used to evaluate the accuracy of the prediction. Based on the actual output data, the predicted output data and the prediction error data, the actual power interval of the new energy of the distribution network can be determined, which can be carried out by statistical analysis or mathematical modeling methods, combined with the actual data and the error situation, to determine the power output range of the new energy facilities in the future time period. In the above process, based on the comprehensive analysis of the actual output data, the predicted output data and the predicted error data, a more reasonable new energy scheduling strategy can be formulated to effectively respond to new energy fluctuations and improve the operating efficiency of the system. Determining the actual power interval of new energy helps to prevent and reduce the impact of uncertainty on system stability, so that the distribution network can better respond to new energy fluctuations and load changes, and improve power supply reliability and stability.

[0043] Optionally, based on the actual output data, the predicted output data and the predicted error data, the actual power range of the new energy is determined, including: dividing the predicted output data into different interval ranges; determining the probability density function of the predicted error data in different interval ranges; and determining the actual power range of the new energy based on the probability density function, the actual output data and the predicted output data.

[0044] In an optional embodiment, the actual power interval of the new energy can be determined based on the actual output data, the predicted output data and the predicted error data. Specifically, the predicted output data can be divided into different intervals according to its numerical value and fluctuation, and can be determined by statistical methods or mathematical modeling techniques to determine the interval ranges of different power levels. Then, for each predicted power interval, the probability density function of the predicted error data in the interval range can be determined according to the error of the actual output data and the predicted output data, and the probability density function model of the error in different power intervals can be established by a probability statistical analysis method. Finally, the actual power interval of the new energy can be determined by combining the probability density functions of the actual output data, the predicted output data and the predicted error data. By comprehensively considering the situation and probability distribution of the prediction error, the possible power output range of the new energy facility in the future time period can be determined. In the above process, by determining the actual power interval of the new energy, uncertainty and risk can be better evaluated, the risk of system scheduling can be reduced, and the stability and reliability of system operation can be improved. Based on the analysis of the probability density function, more information and basis can be provided for scheduling decisions, making the scheduling strategy more flexible and intelligent, and improving the operating efficiency and economy of the system.

[0045] Optionally, based on the probability density function, actual output data and predicted output data, determining the actual power range of the new energy includes: determining the prediction error confidence interval of the prediction error data based on the probability density function; determining the actual power range of the new energy based on the prediction error confidence interval, actual output data and predicted output data.

[0046] In an optional embodiment, the probability density function can be used to determine the confidence interval of the prediction error data at different confidence levels. The confidence interval of the prediction error is calculated by statistical methods and probability distribution analysis, that is, the range of the prediction error data at a certain confidence level. Then, the predicted output data can be combined with the prediction error data in the confidence interval, and the actual power interval of the new energy can be determined according to the relationship between the actual output data and the predicted output data. According to the error range in the confidence interval, combined with the actual data, the possible power output range of the new energy facility in the future time period is determined. In the above process, based on the analysis of the probability density function and the confidence interval, the range and credibility of the prediction error can be more accurately evaluated, the accuracy and reliability of the prediction can be improved, and the impact of the error can be reduced. The error range in the confidence interval is taken into account when determining the actual power interval of the new energy, which helps to reduce risks and uncertainties, reduce the risks of system scheduling, and improve the stability and reliability of system operation. By comprehensively considering the probability density function, confidence interval and actual data, more accurate information and basis can be provided for scheduling decisions, making the scheduling strategy more flexible and intelligent, and improving the operating efficiency and economy of the system.

[0047] Optionally, based on the actual power range of new energy and the day-ahead dispatch objective function, an intraday dispatch objective function of the distribution network is constructed, including: obtaining a day-ahead constraint set under the day-ahead stage dispatch of the distribution network, wherein the day-ahead constraint set is used to represent a constraint set constructed based on the distribution network operation constraints and grid equipment constraints of the distribution network; based on the day-ahead constraint set, the actual power range of new energy and the day-ahead dispatch objective function, an intraday dispatch objective function of the distribution network is constructed.

[0048] In an optional embodiment, first, a set of day-ahead constraints under the day-ahead stage scheduling of the distribution network can be obtained. The day-ahead constraint set can include the operation constraints of the distribution network, such as load balance, voltage stability, etc., and grid equipment constraints, such as transformer capacity, line capacity, etc. Then, the intraday scheduling objective function of the distribution network can be constructed based on the day-ahead constraint set, the actual power interval of new energy, and the day-ahead scheduling objective function. The intraday scheduling objective function is used to optimize the intraday scheduling scheme under the day-ahead constraint conditions to minimize the main network power purchase cost and reduce the voltage deviation. When constructing the intraday scheduling objective function, the restriction of the actual power interval of new energy can be considered, that is, the power output of the new energy facility should be within the determined actual power interval to ensure the reliability and stability of the new energy. Finally, when constructing the intraday scheduling objective function, the day-ahead constraint conditions, the actual power interval of new energy, and the day-ahead scheduling objective function can be comprehensively considered to realize the scheduling scheme in the intraday stage, which can be realized by mathematical modeling and optimization algorithms. In the above process, the optimized intraday scheduling objective function can better achieve load balance and grid stability, ensure that the grid equipment operates within a suitable range, and improve the operation efficiency of the grid. By considering the actual power range of new energy, the intraday scheduling objective function can better schedule new energy facilities, increase the consumption ratio of new energy, reduce dependence on traditional energy, and promote the use of clean energy.

[0049] Optionally, obtaining a day-ahead constraint set for the distribution network under the day-ahead stage scheduling includes: obtaining distribution network flow constraints, distribution network safe operation constraints, new energy output constraints and distribution facility constraints under the day-ahead stage scheduling of the distribution network, wherein the distribution facility constraints include at least one of the following: transformer operation constraints, circuit breaker operation constraints, soft switch constraints, energy storage system constraints and electric vehicle charging and discharging constraints; constructing a day-ahead constraint set based on the distribution network flow constraints, distribution network safe operation constraints, new energy output constraints and distribution facility constraints.

[0050] In an optional embodiment, during the dispatching process of the energy storage of the distribution network, a set of day-ahead constraints of the distribution network under the day-ahead stage dispatching can be obtained, which can include constraints in multiple aspects, including distribution network flow constraints, safe operation constraints, new energy output constraints and distribution facility constraints. Specifically, the distribution network flow constraints can refer to the need for the flow distribution between nodes in the power grid to be within a certain range under the day-ahead stage dispatching, and the power balance, voltage stability and other constraints of each node can be determined through flow calculation and analysis. The distribution network safe operation constraints can include ensuring the rated operating state of the power grid equipment, preventing abnormal situations such as overload and short circuit, and considering factors such as the stability and reliability of the power grid to ensure the safe operation of the power grid. The new energy output constraints can refer to determining the power output range of new energy in the day-ahead stage according to the actual output of new energy facilities, and can consider the characteristics of new energy volatility and reliability. The distribution facility constraints can include operating constraints of equipment such as transformers, circuit breakers, soft switches, energy storage systems and electric vehicles, and can include aspects such as transformer capacity, circuit breaker status, soft switch control, energy storage system charging and discharging rules, and electric vehicle charging and discharging scheduling. Finally, the day-ahead constraint set can be constructed based on the power flow constraints of the distribution network, safe operation constraints, new energy output constraints, and distribution facility constraints. The day-ahead constraint set contains the constraints that need to be followed under the day-ahead stage scheduling to ensure the safety, stability, and efficiency of the power grid operation. In the above process, by obtaining and considering various constraints, the safety and stability of the power grid can be improved, and the occurrence of problems such as equipment overload and power flow out of control can be avoided. By considering various constraints, the operation plan of the power grid can be optimized, the power supply efficiency of the power grid can be improved, and the operation cost can be reduced.

[0051] The technical solution proposed in this application is described below in combination with an optional embodiment. This application proposes a two-stage scheduling method for energy storage in a distribution network considering intelligent soft switches and electric vehicles. As a flexible resource for a power system, energy storage can achieve decoupling between power generation and power consumption, and provide flexibility regulation and stable support for system operation. Energy storage technology is developing in the direction of rapid response, large capacity and high power, and responds to the challenges brought by the large-scale access of new energy to the power system with different specifications. As a new type of flexible switch device, the intelligent soft switch (Soft Open Point, referred to as SOP) is generally installed in the position of the traditional contact switch, following the design and application principle of "closed-loop design, open-loop operation", and has the characteristics of flexible control mode and rapid response. The access of SOP can make the traditional distribution network operate in a flexible closed-loop state, thereby improving the flexibility and power supply reliability of the distribution network. SOP can replace the traditional contact switch to realize the radial operation or closed-loop operation of the distribution network, and has great potential in improving the level of new energy consumption and balancing the feeder load.

[0052] In this application, in order to quantitatively analyze the flexibility requirements of the system after the access of new energy, the uncertainty of the output of new energy is modeled. According to the output data of new energy, including actual output data and predicted output data, the conditional probability model of predicted output and prediction error is analyzed. Then, in the day-ahead optimization scheduling stage, based on the day-ahead load and the predicted output information of new energy, the control strategy of discrete equipment is determined with the optimization goal of minimizing voltage deviation and main network electricity purchase cost, and the action of discrete equipment is transmitted to the day. Among them, discrete equipment mainly includes stepless voltage regulator (On Load TapChanger, referred to as OLTC) and circuit breaker (Circuit Breakers, referred to as CBs), etc. Their switching speed and daily switching times are limited, which are not suitable for short-term rolling optimization, so the scheduling plan can be determined on the day; at the same time, according to the conditional probability model of new energy predicted output and prediction error, the maximum and minimum values ​​of the new energy output within the day are determined, and the reference range of energy storage charging and discharging power in each period is determined according to the boundary of new energy output within the day. Finally, in the intraday rolling optimization stage, the system performs optimization scheduling at unit time interval Δt. The process starts from the initial time t1, based on the future MΔt, where M is a constant and its value is equal to the load and new energy output information of the intraday optimization time / unit time interval, and the comprehensive operating cost of the period is used as the goal for optimization. This process confirms the action plan of intraday continuous equipment, such as SOP and energy storage system (ESS), but only executes the output of the working status of each continuous equipment at the first moment each time, and then as time goes by, the time window moves backward Δt each time, and the above process is repeated. The design of rolling optimization takes into account the randomness of load and new energy output, and can respond to system changes more accurately than the day-ahead optimization stage.

[0053] In an electric power system containing new energy sources, its uncertainty mainly comes from the following three aspects: uncertainty on the source side from renewable energy, uncertainty on the grid side caused by line failures, and uncertainty on the load side from loads. Among them, the probability of line failures is relatively small and can be excluded from the consideration of this application. The load has a strong regularity and its uncertainty is relatively small. The uncertainty on the source side is mainly manifested as prediction error, so this application can only consider the uncertainty caused by the prediction error of new energy. The prediction error of new energy changes with the change of its predicted output. This application uses the historical data of new energy output, divides the predicted output of new energy into segments according to size, and fits the prediction error probability distribution in each segment through non-parametric kernel density estimation to obtain a conditional probability model of the prediction error and predicted output of new energy.

[0054] The prediction error of new energy at time t, that is, the prediction error data, is calculated as follows:

[0055] e RE =P RE,a -P RE,f ;

[0056] Among them, e RE is the prediction error of new energy, that is, the prediction error data; P RE,a and P RE,f They are the actual output and predicted output of new energy, that is, actual output data and predicted output data.

[0057] In order to facilitate calculation, the new energy prediction error is normalized as follows:

[0058]

[0059] Among them, e RE,n is the per unit value of the new energy prediction error, P RE,n The installed capacity of new energy.

[0060] The predicted output of new energy is divided into n intervals from large to small, and the conditional probability distribution of the prediction error in the i-th interval is recorded as f i (e RE,n |P RE,f,n ), the probability density function obtained by nonparametric kernel density estimation, that is, the probability density function of the prediction error data in different intervals, can be expressed as:

[0061]

[0062] Where N is the total number of samples in the i-th interval; h is the bandwidth, K is the kernel function, represents the jth sample in the ith interval. Since the Gaussian kernel function has a better smoothing effect, the Gaussian kernel function can be used for non-parametric kernel density estimation.

[0063] After obtaining the prediction error conditional probability distribution of each interval through nonparametric kernel density estimation, the cumulative probability distribution function F of each interval can be further obtained. i (e RE,n |P RE,f,n ), and then we can get the confidence interval of the new energy prediction error under the confidence level γ, that is, the prediction error confidence interval Right now:

[0064]

[0065] Therefore, when the new energy forecast output When it is known, the actual output range of new energy under the confidence level γ can be obtained, that is, the actual power range of new energy, which can be expressed as:

[0066]

[0067] Next, a long-time day-ahead optimization model can be constructed. The day-ahead optimization dispatching stage minimizes the distribution network operation cost, including the main network power purchase cost and discrete equipment switching cost, and takes the voltage deviation as the objective function, and considers the power flow constraints, distribution network security constraints, adjustable resource active and reactive constraints, and voltage stability constraints. A long-time day-ahead optimization dispatching model is established, and the time interval can be 1 hour. The specific optimization strategy is as follows: Considering the action characteristics of discrete and continuous equipment, the OLTC, CBs gears and the SOP of continuous equipment, ESS and EV power are used as decision variables to obtain the action plan for the next day. At the same time, the maximum and minimum values ​​of the actual output of new energy are optimized respectively, and the output ESS power is used as the reference value of the intra-day ESS power.

[0068] Taking minimizing the distribution network operation cost and voltage deviation as the objective function, that is, the day-ahead dispatch objective function, it can be expressed as:

[0069] minf=f1+f2+f3;

[0070]

[0071]

[0072] Among them, f1, f2 and f3 represent the main grid power purchase cost, voltage deviation and discrete equipment scheduling cost respectively; P t and p t They represent the main grid purchased power and time-of-use electricity price at time t respectively; C v is the voltage deviation penalty coefficient; and v re They represent the voltage of node i in time period t and the square of the reference voltage respectively; C tap and C cap are the switching costs of OLTC and CBs respectively; K t,ij represents the OLTC position of line ij in time period t, N t,i represents the CBs gear position of node i in time period t.

[0073] The constraints in the day-ahead optimization dispatch stage mainly include constraints related to distribution network operation and grid equipment, which can be specifically as follows:

[0074] The power flow constraint of the distribution network, that is, the power flow constraint condition of the distribution network, is described by the power flow equation (DistFlow) in this application. The power flow equation (DistFlow) can be expressed as follows:

[0075]

[0076] in, and They represent the active power and reactive power passing through line ij in time period t respectively; P i t and are the total active load and reactive load of node i in period t respectively; r ij and x ij are the resistance and reactance of line ij; is the square of the current in line ij; represents the photovoltaic power output and wind power output during time period t; and They represent the active power and reactive power of SOP at node i respectively; and They represent the active power and reactive power of the load at node i respectively; and They represent the active power and reactive power of energy storage charging and discharging respectively.

[0077] The distribution network safe operation constraints, that is, the distribution network safe operation constraints can be expressed as follows:

[0078]

[0079] in: v and Represent the square of the lower and upper limits of the node voltage respectively; and i represent the squares of the upper and lower limits of the branch current respectively.

[0080] The on-load voltage regulator (OLTC) operation constraints, that is, the transformer operation constraints can be expressed as follows:

[0081]

[0082] in, represents the transformation ratio of OLTC in period t; is the gear adjustment amount of OLTC in period t; Indicates the change in OLTC voltage ratio per unit gear; The maximum number of OLTC operations allowed in one day; This is the maximum adjustable gear position of OLTC.

[0083] The circuit breaker (CBs) operation constraints, that is, the circuit breaker operation constraints can be expressed as:

[0084]

[0085] in, represents the reactive power generated by CBs at node i in time period t; represents the CBs gear position at node i during time period t, Indicates the change in reactive power of CBs per unit gear; The maximum number of actions allowed for CBs in one day; This is the maximum adjustable gear for CBs.

[0086] The operation constraints of smart soft switch (SOP), also known as soft switch constraints, can be expressed as:

[0087]

[0088] Among them, P i SOP and represents the active power and reactive power emitted by SOP at node i; P i SOP,L represents the active power loss at node i; represents the loss coefficient of SOP at node i.

[0089] The energy storage system (ESS) operation constraints, that is, the energy storage system constraints, can be expressed as:

[0090]

[0091] in, and are 0-1 variables representing the energy storage charging state and discharging state at node i in time period t, respectively; and Respectively represent the maximum active power and reactive power of energy storage charging and discharging; represents the energy storage capacity at node i in time period t; η ch,i and η dch,i They represent the charging efficiency and discharging efficiency of energy storage respectively; E i and Indicates the upper and lower limits of energy storage capacity.

[0092] The electric vehicle (EV) charging and discharging constraints, that is, the electric vehicle charging and discharging constraints, can be expressed as:

[0093]

[0094] in, and They represent the charging power and discharging power of electric vehicle e during time period t respectively; and They represent the maximum value of the charging and discharging power of the electric vehicle e respectively; and are 0-1 variables representing the charging and discharging status of the electric vehicle e; S e,t and C eThey represent the power and capacity of the electric vehicle e in time period t respectively; η c and η d They represent the charging and discharging efficiency of electric vehicles respectively; and are the maximum and minimum values ​​of the electric vehicle’s e-electricity respectively; Indicates the power of the electric vehicle e when it is connected to the charging pile. is the expected capacity of the electric vehicle e when it leaves the charging station.

[0095] The power of an electric vehicle charging station is expressed as follows:

[0096]

[0097] The new energy output constraint, that is, the new energy output constraint condition, can be expressed as:

[0098] 0≤P t RE ≤P t RE,max ;

[0099] Among them, P t RE is the amount of new energy consumed in period t; P t RE,max is the output of renewable energy in time period t.

[0100] Next, according to the uncertainty quantification method of renewable energy output, the maximum and minimum values ​​of the actual power of renewable energy within the day are determined. The predicted value of renewable energy output is changed to the maximum and minimum value of the actual power of renewable energy, and the long-term optimization is performed on the day before. In this process, the action plan of discrete equipment is the same as when the renewable energy output is the predicted value. At this time, the energy storage charging and discharging power under the three renewable energy output conditions can be obtained, which is recorded as and

[0101] In the intraday optimization scheduling stage, the constraint condition of energy storage charging and discharging active power is modified as follows:

[0102]

[0103] Considering that energy storage charging is equivalent to an increase in load power, which will cause an increase in node voltage deviation, when only optimizing part of the time period, energy storage always tends to charge at a smaller power. Similarly, the power of energy storage discharge will also be larger. Based on the above analysis, we can only limit the minimum charging power and maximum discharging power of energy storage, and modify the above formula as follows:

[0104]

[0105] It is worth noting that this process only provides a reference range for the energy storage daily charging and discharging plan to avoid wasting energy storage capacity due to too low charging power or too high discharging power.

[0106] In this application, the scheduling cycle of the intraday rolling optimization model can be 15 minutes, and the solution time of the rolling optimization is 4 hours, and the optimization is performed under the condition that the charging and discharging power of discrete devices and electric vehicles is determined. The objective function is to minimize the main network power purchase cost and voltage deviation, and the decision variables are the active power and reactive power of the SOP in each period, the active power and reactive power of the ESS, etc. The objective function of the intraday optimization stage can be the same as the day-ahead scheduling objective function, and the constraints can include distribution network flow constraints, distribution network safe operation constraints, SOP operation constraints, ESS operation constraints, and new energy output constraints.

[0107] This application determines the upper and lower limits of the prediction error through conditional probability analysis of historical new energy output data. This application uses the method of non-parametric kernel density estimation to perform conditional probability fitting of the prediction error using the predicted output of new energy, and determines the maximum and minimum values ​​of the actual output of new energy based on this. This application limits the energy storage power during the intraday optimization process, thereby improving the utilization rate of the energy storage capacity. After obtaining the maximum and minimum values ​​of the actual output of new energy a few days ago, the maximum, minimum and predicted output of the actual output of new energy are used as the new energy output for long-term optimization scheduling a few days ago to determine the value range of the intraday energy storage power. This avoids the problem of a decrease in the utilization rate of the energy storage capacity due to the excessively low energy storage power during the intraday optimization process.

[0108] Figure 2 is a schematic diagram of an optional distribution network energy storage scheduling process according to an embodiment of the present invention, such as Figure 2 As shown, the uncertainty interval when the predicted output of new energy is known can be determined based on the predicted output of new energy and the actual output of new energy. In the day-ahead scheduling, the user load power consumption can be predicted in the short term, and the new energy power generation power can be predicted in the short term based on the uncertainty interval when the predicted output of new energy is known, and the upper and lower limits of the actual power of new energy can be obtained, and then the day-ahead scheduling optimization model can be obtained. Based on the day-ahead scheduling optimization model, the gear position of the transformer / circuit breaker in each time period can be obtained, and the day-ahead scheduling instruction can be issued. Based on the day-ahead scheduling optimization model, the reference value of the daily energy storage charging and discharging power can also be obtained; in the intraday scheduling, the ultra-short-term prediction of the power generation power of the new energy power source and the ultra-short-term prediction of the user load power consumption can be made, and the intraday rolling optimization model can be obtained, and then the output plan of the new energy unit, the energy storage power, and the soft switching power of the intelligent system can be obtained, and the rolling optimization instruction can be issued.

[0109] According to another aspect of the embodiments of the present application, a distribution network energy storage scheduling device is also provided, which can execute the distribution network energy storage scheduling method of the above embodiment. The specific implementation method and preferred application scenario are the same as the above embodiment and will not be repeated here.

[0110] Figure 3 is a schematic diagram of a dispatching device for energy storage in a distribution network according to an embodiment of the present application, such as Figure 3 As shown, the apparatus includes the following: an acquisition module 302 , a first determination module 304 , a first construction module 306 , a second construction module 308 and a second determination model 310 .

[0111] Among them, the acquisition module is used to obtain the new energy output data of the new energy facilities in the distribution network; the first determination module is used to determine the new energy actual power range of the distribution network based on the new energy output data; the first construction module is used to construct the day-ahead dispatching target function of the distribution network based on the new energy actual power range, wherein the day-ahead dispatching target function is used to represent the target function constructed based on the operating cost and voltage deviation of the distribution network under the day-ahead stage dispatch; the second construction module is used to construct the intraday dispatching target function of the distribution network based on the new energy actual power range and the day-ahead dispatching target function, wherein the intraday dispatching target function is used to represent the target function constructed based on the main network power purchase cost and voltage deviation under the intraday stage dispatch of the distribution network; the second determination module is used to determine the distribution network energy storage dispatching instruction based on the intraday dispatching target function, wherein the distribution network energy storage dispatching instruction is used to perform energy storage dispatch on the distribution network under the intraday stage dispatch.

[0112] Among them, the first determination module is also used to obtain actual output data and predicted output data in the new energy output data; determine the prediction error data of the distribution network based on the actual output parameter data and the predicted output data; determine the actual power range of the new energy based on the actual output data, the predicted output data and the prediction error data.

[0113] Among them, the first determination module is also used to divide the predicted output data into different intervals; determine the probability density function of the predicted error data in different intervals; and determine the actual power range of the new energy based on the probability density function, the actual output data and the predicted output data.

[0114] Among them, the first determination module is also used to determine the prediction error confidence interval of the prediction error data based on the probability density function; based on the prediction error confidence interval, the actual output data and the predicted output data, determine the actual power range of the new energy.

[0115] Among them, the second construction module is also used to obtain the day-ahead constraint condition set under the day-ahead stage scheduling of the distribution network, wherein the day-ahead constraint condition set is used to represent the constraint condition set constructed based on the distribution network operation constraints and power grid equipment constraints of the distribution network; based on the day-ahead constraint condition set, the actual power range of new energy and the day-ahead scheduling objective function, the intraday scheduling objective function of the distribution network is constructed.

[0116] Among them, the second construction module is also used to obtain the distribution network flow constraints, distribution network safety operation constraints, new energy output constraints and distribution facility constraints under the distribution network scheduling in the day-ahead stage, wherein the distribution facility constraints include at least one of the following: transformer operation constraints, circuit breaker operation constraints, soft switch constraints, energy storage system constraints and electric vehicle charging and discharging constraints; based on the distribution network flow constraints, distribution network safety operation constraints, new energy output constraints and distribution facility constraints, a day-ahead constraint set is constructed.

[0117] An embodiment of the present application further provides an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention when running.

[0118] The above-mentioned memory may refer to a device inside a computer for storing data and programs, and may include memory, hard disk, etc., wherein the memory may be used to temporarily store running programs and data, the hard disk may be used to store programs and data for a long time, and the memory may be used to enable the computer to read and write data, and execute programs; the above-mentioned processor may be responsible for executing instructions in computer programs and performing data processing, and may be responsible for controlling and executing various operations, including arithmetic operations, logical operations, data transmission, etc.

[0119] An embodiment of the present application further provides a computer-readable storage medium, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0120] The above-mentioned computer storage medium may refer to a medium in a computer memory used to store certain discontinuous physical quantities. Computer storage media mainly include semiconductors, magnetic cores, magnetic drums, magnetic tapes, laser disks, etc.; the stored program included in the computer-readable storage medium may be a set of instructions that can be recognized and executed by a computer, running on an electronic computer, and is an information tool that meets certain needs of people.

[0121] An embodiment of the present application further provides a computer program product, including a computer program, which implements the methods in various embodiments of the present invention when executed by a processor.

[0122] The above-mentioned computer program product may refer to a software program that has been written, tested and released and can be run on a computer or other device. The computer program product may include an application, an operating system, tool software, etc., which is used to implement specific functions or solve specific problems.

[0123] An embodiment of the present application further provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0124] The above-mentioned non-volatile computer-readable storage medium may refer to a medium for storing data. The non-volatile computer-readable storage medium can keep the data from being lost when the power is off, and can be used to store long-term data, such as operating systems, applications, and user files. The non-volatile storage medium may include hard disk drives, solid-state drives, optical disks, and flash memory storage devices, etc.

[0125] The embodiments of the present application further provide a computer program, which implements the methods in the above-mentioned embodiments of the present invention when executed by a processor.

[0126] The above-mentioned computer program may refer to a collection of instructions used to tell a computer to perform a specific task or operation. A computer program may be written by a programmer using a specific programming language and may include algorithms, data structures, logic, and control flows. A computer program may be used for a variety of purposes, including application software, operating systems, and the like.

[0127] In the above embodiments of the present invention, 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.

[0128] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0129] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0130] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0131] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0132] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for dispatching energy storage in a distribution network, characterized in that: include: Obtain new energy output data of new energy facilities in the distribution network; Determine the actual power range of the new energy of the distribution network based on the new energy output data; Constructing a day-ahead dispatch objective function of the distribution network based on the actual power interval of the new energy, wherein the day-ahead dispatch objective function is used to represent an objective function constructed based on the operating cost and voltage deviation of the distribution network under the day-ahead stage dispatch; Based on the new energy actual power range and the day-ahead dispatch objective function, construct the intraday dispatch objective function of the distribution network, wherein the intraday dispatch objective function is used to represent the objective function constructed based on the main network power purchase cost and the voltage deviation under the intraday stage dispatch of the distribution network; A distribution network energy storage dispatching instruction is determined based on the intra-day dispatching objective function, wherein the distribution network energy storage dispatching instruction is used to perform energy storage dispatch on the distribution network under the intra-day stage dispatching.

2. The method for dispatching energy storage in a distribution network according to claim 1, characterized in that: Determining the actual power range of the new energy of the distribution network based on the new energy output data includes: Acquiring actual output data and predicted output data from the new energy output data; Determining prediction error data of the power distribution network based on the actual output parameter data and the predicted output data; The actual power range of the new energy source is determined based on the actual output data, the predicted output data and the prediction error data.

3. The method for dispatching energy storage in a distribution network according to claim 2, characterized in that: Determining the actual power range of the new energy source based on the actual output data, the predicted output data and the prediction error data includes: Dividing the predicted output data into different interval ranges; Determine the probability density function of the prediction error data in the different interval ranges; Based on the probability density function, the actual output data and the predicted output data, the actual power range of the new energy is determined.

4. The method for dispatching energy storage in a distribution network according to claim 3, characterized in that: Determining the actual power range of the new energy source based on the probability density function, the actual output data, and the predicted output data includes: determining a prediction error confidence interval for the prediction error data based on the probability density function; Based on the prediction error confidence interval, the actual output data and the predicted output data, the actual power interval of the new energy is determined.

5. The method for dispatching energy storage in a distribution network according to claim 1, characterized in that: Based on the new energy actual power range and the day-ahead dispatch objective function, constructing the day-ahead dispatch objective function of the distribution network, including: Acquire a day-ahead constraint condition set of the distribution network under the day-ahead stage scheduling, wherein the day-ahead constraint condition set is used to represent a constraint condition set constructed based on distribution network operation constraints and power grid equipment constraints of the distribution network; Based on the day-ahead constraint set, the new energy actual power range and the day-ahead scheduling objective function, an intraday scheduling objective function of the distribution network is constructed.

6. The method for dispatching energy storage in a distribution network according to claim 5, characterized in that: Obtaining a set of day-ahead constraint conditions for the distribution network under the day-ahead stage scheduling, including: Obtaining the distribution network flow constraints, distribution network safety operation constraints, new energy output constraints and distribution facility constraints of the distribution network under the day-ahead stage dispatch, wherein the distribution facility constraints include at least one of the following: transformer operation constraints, circuit breaker operation constraints, soft switch constraints, energy storage system constraints and electric vehicle charging and discharging constraints; The day-ahead constraint set is constructed based on the distribution network power flow constraint, the distribution network safe operation constraint, the new energy output constraint and the distribution facility constraint.

7. A dispatching device for energy storage in a distribution network, characterized in that: include: An acquisition module is used to acquire new energy output data of new energy facilities in the distribution network; A first determination module, configured to determine the actual power range of the new energy of the distribution network based on the new energy output data; A first construction module is used to construct a day-ahead dispatch objective function of the distribution network based on the actual power interval of the new energy source, wherein the day-ahead dispatch objective function is used to represent an objective function constructed based on the operating cost and voltage deviation of the distribution network under the day-ahead stage dispatch; The second construction module is used to construct an intraday dispatch objective function of the distribution network based on the actual power range of the new energy and the day-ahead dispatch objective function, wherein the intraday dispatch objective function is used to represent an objective function constructed based on the main network power purchase cost and the voltage deviation under the intraday stage dispatch of the distribution network; The second determination module is used to determine the distribution network energy storage scheduling instruction based on the intra-day scheduling objective function, wherein the distribution network energy storage scheduling instruction is used to perform energy storage scheduling on the distribution network under the intra-day stage scheduling.

8. An electronic device, characterized in that: include: A memory storing an executable program; A processor is used to run the program, wherein when the program is running, the method for dispatching energy storage in a distribution network as described in any one of claims 1 to 6 is executed.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the storage medium is located is controlled to execute the distribution network energy storage scheduling method according to any one of claims 1 to 6.

10. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, implements the method for dispatching energy storage in a distribution network according to any one of claims 1 to 6.

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