Energy storage node scheduling optimization method, system and equipment and storage medium

Abstract

The invention provides an energy storage node scheduling optimization method, system and device and a storage medium, and relates to the technical field of distributed energy storage systems.The energy storage node scheduling optimization method comprises the steps that the current charge state of each battery in a virtual energy storage node of a user side and the total power allocated in the day-ahead stage are obtained; determining a power feasible region of each battery according to the state of charge as a total power distribution requirement; decomposing the total power into a plurality of sub-tasks, wherein each sub-task corresponds to the power distribution of one battery; solving a power distribution scheme by optimizing the objective function and combining the power feasible region; judging whether the scheme meets a total power distribution requirement or not, if not, determining a power difference value, and iteratively updating and optimizing the target function according to the power difference value; and solving the subtasks again by using the updated optimization objective function until a final power distribution scheme meeting requirements is obtained. According to the invention, the power distribution effect of the energy storage nodes is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed energy storage systems, and in particular to an energy storage node scheduling optimization method, system, device and storage medium. Background Art

[0002] Distributed photovoltaic and wind power penetration rates in distribution networks have exceeded 50%. To ensure grid security, a large number of user-side batteries are aggregated into virtual energy storage nodes, necessitating unified scheduling of these virtual energy storage nodes. Existing technologies typically employ a two-level optimization architecture: day-ahead and intraday. During the day-ahead phase, charge and discharge schedules are generated for each virtual energy storage node. During the intraday phase, secondary power decomposition is performed based on the local battery state of charge, enabling rapid balancing and absorption of a high proportion of renewable power.

[0003] In related technologies, the total power for the day is typically allocated to each battery in a single, proportional manner, based on SOC or lifetime weights. The SOC boundaries of each battery are then checked individually. When the SOC boundary is crossed, the power of that battery is reset to zero and re-proportioned. However, when a large number of batteries simultaneously approach the SOC boundary, a repeated cycle of crossing the boundary, resetting to zero, and re-proportioning occurs, rapidly slicing the overall feasible power domain. The remaining batteries are forced to increase their power sharply, triggering another crossing of the boundary, creating a positive feedback-driven feasible domain collapse, leading to intra-day command mismatch and additional lifespan loss. Summary of the Invention

[0004] The problem solved by the present invention is how to improve the power distribution effect on energy storage nodes.

[0005] To solve the above problems, the present invention provides a method, system, device and storage medium for energy storage node scheduling optimization.

[0006] In a first aspect, the present invention provides a method for optimizing energy storage node scheduling, comprising: Obtain the current state of charge of each battery in the user-side virtual energy storage node and the total power allocated in the day-ahead phase; determining a power feasible range of each battery according to the current state of charge of each battery, and using the power feasible range as the allocation requirement of the total power; Decomposing the total power into a plurality of subtasks, wherein each subtask corresponds to a power allocation task of the battery; By optimizing the objective function and combining the power feasible region to solve each of the subtasks, a power allocation scheme for the virtual energy storage node is obtained; Determining whether the power allocation scheme meets the allocation requirement of the total power; determining a power difference value according to the power allocation scheme and the total power, if the power allocation scheme does not satisfy the allocation requirement; iteratively updating the optimization objective function according to the power difference value; re-solving the sub-tasks by the updated optimization objective function to obtain a final power allocation scheme.

[0007] Optionally, the determining a power feasible region of each battery according to the current state of charge of each battery comprises: obtaining an SOC-power boundary mapping table of each battery; obtaining a maximum charging power and a maximum discharging power of the battery by interpolation in the SOC-power boundary mapping table according to the current state of charge; obtaining an upper limit and a lower limit of a life-sensitive power of the battery according to a preset battery aging coefficient, in combination with the maximum charging power and the maximum discharging power; determining the power feasible region of the battery according to the upper limit and the lower limit.

[0008] Optionally, the decomposing the total power into a plurality of sub-tasks comprises: obtaining a proportional weight of the power feasible region of each battery and a total power feasible region of the virtual energy storage node; dividing the total power according to the proportional weight to obtain a sub-task corresponding to each battery; when the power value corresponding to the sub-task of any battery is zero, then skipping the battery and redistributing the proportional weight of the battery to the batteries whose sub-task power values are not zero, and recalculating the power value of each sub-task.

[0009] Optionally, the solving each sub-task by an optimization objective function in combination with the power feasible region to obtain a power allocation scheme of the virtual energy storage node comprises: taking the power value of each sub-task as a decision variable, taking the upper limit and the lower limit of the life-sensitive power of the battery as a constraint boundary, and establishing the optimization objective function according to the decision variable and the constraint boundary; performing convex optimization solving according to the optimization objective function to obtain a decision variable of each battery; collecting the decision variable of each battery to obtain the power allocation scheme of the virtual energy storage node.

[0010] Optionally, the judging whether the power allocation scheme satisfies the allocation requirement of the total power comprises: obtaining a difference between a sum of the decision variables of all the sub-tasks and the total power; judging whether the power allocation scheme satisfies the allocation requirement of the total power according to the difference; wherein, if an absolute value of the difference is less than a preset threshold, it is determined that the power allocation scheme satisfies the allocation requirement; if the absolute value of the difference is greater than or equal to the preset threshold, it is determined that the power allocation scheme does not satisfy the allocation requirement.

[0011] Optionally, the iterative updating of the optimization objective function according to the power difference includes: generating an adjustable relaxation variable according to the power difference; embedding the relaxation variable in the optimization objective function in the form of a Lagrange multiplier to construct an adaptive penalty function; adjusting the Lagrange multiplier in the adaptive penalty function through an adaptive updating rule to monotonically converge the power difference; when the power difference converges, it is determined that the updating of the Lagrange multiplier is completed, and the optimization objective function is reconstructed according to the updated Lagrange multiplier.

[0012] Optionally, the re-solving of the sub-tasks through the updated optimization objective function to obtain a final power allocation scheme includes: re-solving the sub-tasks through the updated optimization objective function to obtain an adjusted power allocation scheme; if the power allocation scheme does not satisfy the allocation requirement, re-obtaining the power difference corresponding to the adjusted power allocation scheme, and iteratively updating the optimization objective function according to the power interpolation until the power allocation scheme satisfies the allocation requirement, and taking the power allocation scheme solved by the optimization objective function in the last iteration as the final power allocation scheme of the virtual energy storage node.

[0013] In a second aspect, the present application provides an energy storage node scheduling optimization system, comprising: a data acquisition unit configured to obtain a current state of charge of each battery in a virtual energy storage node on a user side and a total power allocated in a day-ahead stage; a power feasible region generation unit configured to determine a power feasible region of each battery according to the current state of charge of each battery, and take the power feasible region as an allocation requirement of the total power; a task decomposition unit configured to decompose the total power into a plurality of sub-tasks, wherein each sub-task corresponds to a power allocation task of one battery; An optimization solving unit is configured to solve each of the subtasks by optimizing an objective function in combination with the power feasible region, to obtain a power allocation scheme of the virtual energy storage node; A judging unit is configured to judge whether the power allocation scheme meets the allocation requirement of the total power; A difference calculating unit is configured to, if the power allocation scheme does not meet the allocation requirement, determine a power difference according to the power allocation scheme and the total power; An iterative updating unit is configured to iteratively update the objective function according to the power difference; A scheme outputting unit is configured to re-solve the subtasks by using the updated objective function, to obtain a final power allocation scheme.

[0014] In a third aspect, the present application provides an electronic device, comprising a memory and a processor; The memory is configured to store a computer program; The processor is configured to, when executing the computer program, implement the energy storage node scheduling optimization method as described above.

[0015] In a fourth aspect, the present application provides a computer readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the energy storage node scheduling optimization method as described above is implemented.

[0016] The energy storage node scheduling optimization method, system, device and storage medium of the present application determine the power feasible region of each battery according to its current state of charge (SOC) before power distribution, which is used as a requirement for total power distribution, providing a precise constraint boundary for the power distribution of each battery, thereby ensuring that the power of each battery is within its allowed range when the power is distributed, avoiding the problem of inaccurate power distribution caused by simple proportional allocation without considering the actual state of the battery, thereby improving the accuracy of power distribution. After the total power is decomposed into multiple sub-tasks, each sub-task is solved by optimizing the objective function in combination with the power feasible region, and the objective function is further optimized to ensure the rational distribution of power among batteries, so that the total power can be accurately distributed to each battery according to the actual capacity and constraint conditions of each battery, further improving the accuracy of power distribution. The determination of the power feasible region sets a safety boundary for the power distribution of each battery, ensuring that the battery operates within the allowed power range, avoiding the problem of SOC out-of-bounds caused by overcharging or over-discharging; thereby helping to stabilize the feasible region of each battery, preventing the problem of feasible region collapse caused by battery out-of-bounds, thereby reducing the additional loss of battery life. And when the initial power distribution scheme does not meet the total power requirement, the objective function is iteratively updated according to the power difference, which can timely adjust the power distribution strategy, avoiding the repeated zeroing and redistribution operation caused by battery out-of-bounds in traditional methods, further stabilizing the feasible region and reducing the additional loss of battery life.

[0017] The present application considers the power feasible region of each battery and solves and iteratively updates the power distribution by optimizing the objective function, so that the total power can be accurately distributed to each battery. The power distribution of each battery is strictly limited within its power feasible region, thereby avoiding the over-distribution or under-distribution situation that may occur in the prior art, improving the accuracy of power distribution. The optimization objective function of the present application iteratively updates according to the power difference, so that the power distribution scheme can dynamically adapt to the fluctuation of high proportion of renewable power in the power grid. In the face of rapid changes in distributed photovoltaic, wind power and other renewable energy output, through the dynamic adjustment of the optimization objective function, the sub-tasks can be solved again in time, the power distribution of each battery in the virtual energy storage node can be quickly adjusted, the rapid balance and consumption of renewable power can be realized, and the stability and reliability of the power grid are improved. Through the combination of power feasible region constraint and dynamic adjustment of the optimization objective function, the present application can flexibly cope with power fluctuations in the power grid while ensuring the safe operation of the battery. This combination enhances the adaptability of the entire system to high proportion of renewable energy access, enabling the virtual energy storage node to better perform in complex power grid environments and improve the overall performance of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1A flow chart of the energy storage node scheduling optimization method of the embodiment of the present application; Figure 2 A structural block diagram of the energy storage node scheduling optimization system of the embodiment of the present application; Figure 3 A structural schematic diagram of the electronic device of the embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings. Although some embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms, and should not be interpreted as being limited to the embodiments described herein, on the contrary, these embodiments are provided to make the present application more thorough and complete. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes, and are not intended to limit the scope of protection of the present application.

[0020] It should be understood that each step described in the method embodiments of the present application can be executed in different order and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the execution of the steps shown. The scope of the present application is not limited in this respect.

[0021] The term "comprising" and variations thereof as used herein are open-ended, that is "including but not limited to"; the term "based on" is "based, at least in part, on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". Related definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in the present application are only used to distinguish different devices, modules or units, and are not intended to limit the order or interdependence of the functions performed by these devices, modules or units.

[0022] It should be noted that the modification of "one" or "multiple" mentioned in the present application is illustrative and not limiting, and those skilled in the art should understand that, unless otherwise explicitly indicated in the context, it should be understood as "one or more".

[0023] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.) and signals involved in the present application are authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation portal for user to choose authorization or refusal.

[0024] In combination Figure 1 As shown in the embodiments of the present application, a method for scheduling optimization of energy storage nodes is provided, comprising: Obtaining the current state of charge of each battery in the virtual energy storage node on the user side and the total power allocated in the day-ahead stage.

[0025] Specifically, the current state of charge data can be collected in real time by means of the power monitoring devices installed at each battery, which can accurately measure the proportion of the stored power of the battery to its total capacity, and transmit the data to the scheduling system through the communication module. The total power information allocated in the day-ahead stage is retrieved from the system database, which stores the total power allocation value formulated in the previous stage through complex algorithms combined with factors such as power grid load prediction and power generation plan, providing basic data support for subsequent power allocation, ensuring that the scheduling system knows the existing battery resources and the total amount of tasks, so that the operator can master the initial operating state of the energy storage node in real time, laying a foundation for the next step of accurate power allocation.

[0026] In the preferred embodiments of the present application, the power monitoring device can be a resistance current sensor, which uses the voltage drop generated across a sampling resistor when current flows through it to calculate the current size. Its working principle is based on Ohm's law V=IR, with simple structure, low cost and high measurement accuracy (generally accuracy up to ±0.5%), wide measurement range (from a few milliamperes to thousands of amperes), fast response speed (up to microseconds), and can accurately monitor the charging and discharging current of the battery in real time, thereby indirectly reflecting the change of the battery power. Or a Hall current sensor, based on the Hall effect principle, when current passes through a conductor, a Hall voltage will be generated in the direction perpendicular to the current and the magnetic field, and the sensor measures the current by detecting the Hall voltage. The Hall current sensor has the advantages of high precision (up to ±0.1%), good linearity, fast response speed (nanosecond level), wide bandwidth (up to hundreds of kilohertz), etc., and can measure the current without direct contact with the measured circuit, realizing electrical isolation, ensuring the safety and reliability of the measurement, and being suitable for battery power monitoring under complex conditions such as large current and high voltage.

[0027] The power monitoring chip includes coulomb counter (power meter) chips, such as the commonly used BQ20 series chips, which are specially used for battery power management. Using coulomb counting method, the charging and discharging current of the battery is monitored in real time, and the remaining power of the battery is accurately calculated, so as to determine the remaining power of the battery. Or impedance method power monitoring chip, which uses the relationship between the internal resistance of the battery and the power to estimate the state of charge of the battery. By measuring the AC impedance spectrum of the battery, the change of its impedance characteristics is analyzed, and then the remaining power of the battery is calculated.

[0028] The battery management system (BMS) is a distributed BMS composed of multiple sub-battery management systems, each of which is responsible for managing a single battery or a small number of batteries, and each subsystem interacts with each other through a communication network for data exchange and collaborative work. It can more accurately monitor and manage the power state of each battery, and realize fine management of the battery. Each subsystem has independent power monitoring and control functions, so even if a subsystem fails, it will not affect the normal work of other subsystems, improving the reliability and redundancy of the system.

[0029] According to the current state of charge of each battery, the power feasible region of each battery is determined, and the power feasible region is taken as the allocation requirement of the total power.

[0030] Specifically, according to the current state of charge of each battery, the battery manufacturer's battery characteristic parameter table is consulted, which records the upper and lower limits of the allowed charging and discharging power of the battery at different states of charge in detail. For example, for a lithium battery, when its state of charge is 80%, the upper limit of the charging power is 5kW, and the upper limit of the discharging power is -3kW (negative sign indicating discharging). Based on this, the power feasible region of each battery, i.e. the power range, is determined. The power feasible regions of all batteries are combined to form the constraint condition that the total power allocation must satisfy, i.e. the allocation requirement. This operation can ensure that each battery participates in power allocation within its safe performance range, effectively avoiding performance degradation or even damage due to excessive charging and discharging, prolonging the service life of the battery, and providing clear boundary conditions for subsequent power optimization allocation, ensuring the safe and stable operation of the energy storage system.

[0031] The total power is decomposed into multiple sub-tasks, wherein each sub-task corresponds to a power allocation task of one of the batteries.

[0032] Specifically, according to factors such as the physical location, connection method or rated capacity of the battery pack, the total power is reasonably split into small power corresponding to each battery, i.e. sub-task power. For example, if the batteries are connected in parallel and have the same capacity, the total power can be allocated to each battery in equal proportion; if the capacities are different, the power can be allocated in proportion. Each sub-task focuses only on the power allocation of the corresponding battery, and clearly defines the specific power share that each battery should bear, so that the complex total power allocation problem can be simplified, making it easier to solve the optimal power for each battery individually in the subsequent step, improving the calculation efficiency and solution accuracy, and providing a method basis for realizing fine scheduling.

[0033] For example, if the battery pack is composed of 4 batteries in parallel, and the rated capacity of each battery is the same, the total power is 20kW, then the corresponding sub-task power of each battery is 5kW; if the rated capacity of each battery is different, assuming it is 10kWh, 15kWh, 10kWh, 20kWh respectively, the total power is still 20kW, then according to the capacity proportion distribution, the sub-task power of each battery is about 3.64kW, 5.45kW, 3.64kW, 7.27kW respectively. This decomposition method can fully utilize the capacity characteristics of each battery, so that each battery undertakes the corresponding power task within its ability range, avoids the situation that part of the battery is overloaded while the other battery is idle, improves the overall utilization efficiency of the energy storage system, lays a foundation for subsequent accurate power allocation, and ensures the reasonable allocation of energy storage system resources.

[0034] By optimizing the objective function, each of the sub-tasks is solved in combination with the power feasible region to obtain the power allocation scheme of the virtual energy storage node.

[0035] Specifically, an optimization objective function is constructed to minimize the total loss of the energy storage system, including the electric energy conversion loss in the battery charging and discharging process and the line transmission loss. The power feasible region constraint condition is introduced into the optimization objective function by using the Lagrange multiplier method to form a Lagrange function. The partial derivative of the Lagrange function is taken with respect to the battery power and is set to zero to obtain the optimal power allocation value of each battery. For example, the optimal power of the four batteries is calculated to be 4.5kW, 5.8kW, 4.2kW and 5.5kW (assuming the total power is 20kW). This process excavates the optimal operating state of the energy storage system under the safety constraint through mathematical optimization means, realizes the efficient operation of the energy storage node, reduces the system operation cost, improves the overall performance of the power system, improves the space utilization efficiency, and ensures the economic and stable operation of the system.

[0036] In a preferred embodiment of the present application, specifically, the battery charging and discharging loss can be represented as the product of the internal resistance of each battery and the square of the charging and discharging current, multiplied by the charging and discharging time, The formula is wherein is the internal resistance of the battery , and is the charging and discharging current of the battery . The line transmission loss can be represented as the product of the line resistance and the square of the transmission current, multiplied by the transmission time, and the formula is wherein is the line resistance, is the line transmission current.

[0037] The power feasible region constraint condition is introduced into the optimization objective function by using the Lagrange multiplier method to form a Lagrange function.

[0038] Specifically, the following processes are included: (1) Set the power feasible region constraint condition, i.e., the power allocation value of each battery must satisfy its power upper and lower limits, i.e., wherein and are the power lower and upper limits of the battery , respectively.

[0039] (2) Construct the Lagrange function, the formula is:

[0040] wherein, is the number of batteries, is the total power, is the Lagrange multiplier related to the total power constraint, and are the Lagrange multipliers related to the power lower and upper limit constraints of the battery , respectively.

[0041] (3) Take the partial derivative of each variable (such as , , , ) and set it equal to zero to form a system of equations.

[0042] (4) Solve the above system of equations to obtain the optimal power allocation value of each battery and the corresponding Lagrange multipliers , , , thereby determining the optimal power allocation scheme that satisfies the power feasible region constraint.

[0043] In this way, the optimization objective function can comprehensively consider the various losses of the energy storage system and find the optimal solution under the condition of satisfying the power allocation constraint, effectively reducing the total loss of the energy storage system and improving the operating efficiency and economy of the system.

[0044] determining whether the power allocation scheme satisfies the allocation requirement of the total power.

[0045] Specifically, the obtained power allocation values of each battery are summarized and compared with the total power in numerical value, and it is checked whether the difference between the two is within the set error allowable range, such as ±1%. At the same time, it is checked again whether the power allocated to each battery falls within the previously determined power feasible region, to ensure that there is no over-limit situation.

[0046] For example, if the target total power is 20 kW, the actual total power allocated is 19.8 kW, which is within the allowable error range; and each battery power allocation value does not exceed the upper and lower limits of its power feasible region. This judgment process can timely find deviations and violations in the power allocation scheme, prevent problems such as inaccurate total power allocation or unreasonable individual battery power allocation that affect the normal operation of the energy storage system, and ensure the feasibility and accuracy of the scheduling scheme, which is a key link to realize stable and reliable energy storage scheduling.

[0047] If the power allocation scheme does not meet the allocation requirements, a power difference value is determined according to the power allocation scheme and the total power.

[0048] Specifically, the difference between the actual total power allocated and the target total power is accurately calculated, that is, the target total power is subtracted from the total power after the current scheme is summarized, and the absolute value is the power difference value. That is, the power difference value = target total power - actual total power allocated. For example, if the target total power is 20 kW and the actual total power allocated is 19 kW, the power difference value is 1 kW. This difference value clearly shows the deviation between the current scheme and the expected target, and provides a quantitative basis for subsequent adjustment. This difference value clearly shows the deviation between the current scheme and the expected target, and provides a quantitative basis for subsequent adjustment, so that measures can be taken to make up for the gap, so that the system can continuously improve in the direction of meeting the requirements, and ensure the accuracy and effectiveness of energy storage scheduling.

[0049] The optimization objective function is iteratively updated according to the power difference value.

[0050] Specifically, according to the size and direction of the power difference value, the related parameters in the optimization objective function are modified according to the adjustment strategy, such as adjusting the weight coefficient, correcting the target value, etc. If the power difference value is positive, the loss weight coefficient can be appropriately increased to prompt the system to increase power allocation; if it is negative, the adjustment is reversed. Then the optimization algorithm is used to iteratively solve the system again to search for the optimal solution in the direction of reducing the difference value. In this way, the optimization objective function is dynamically adjusted, so that the subsequent scheme is more in line with the actual demand, and the self-correction ability of the system to deal with deviations is continuously enhanced, and the flexibility and adaptability of energy storage scheduling are improved.

[0051] The subtasks are re-solved by the updated optimization objective function to obtain a final power allocation scheme.

[0052] Specifically, by optimizing the algorithm, the updated optimization objective function is combined with the power feasible region constraint, and the optimal power distribution is recalculated for each battery sub-task. With the adjustment of the objective function, the algorithm will re-search for a better solution for the corresponding battery power distribution, and finally obtain a power distribution scheme that meets the total power distribution requirement and is optimized, ensuring that through continuous iteration and optimization, the final output scheme is not only safe and feasible, but also optimal, achieving efficient and stable scheduling of the energy storage node, fully tapping the value of the energy storage system in the power system, such as improving power quality and promoting renewable energy consumption, and providing strong support for the reliable operation of the entire power system.

[0053] In a preferred embodiment of the present application, an optimization objective function is first constructed, specifically including: Battery charging and discharging loss formula: wherein, is the internal resistance of the battery , is the charging and discharging current of the battery Line transmission loss formula: wherein, is the line resistance, is the line transmission current.

[0054] Then the optimization objective function is: , wherein, is the number of batteries, is the penalty coefficient, is the power difference.

[0055] The Lagrange function is constructed, and the power feasible region constraint condition is: wherein, and are the lower and upper limits of the power of the battery ,

[0056] Lagrange function formula: wherein, is the total power, is the Lagrange multiplier related to the total power constraint, and are the Lagrange multipliers related to the lower and upper limits of the power of the battery ,

[0057] The partial derivative formula of is: .

[0058] The particle swarm algorithm steps for updating the optimization objective function include: (1) Velocity update formula: ; wherein, is a particle velocity of the particle at the i-th iteration, is an inertia weight, , is a learning factor, , is a random number, is a particle individual extreme value position of the particle global extreme value position, is a particle position (power allocation value) of the particle at the i-th iteration.

[0059] (2) Position update formula: ; wherein, denotes the position of the particle at the i-th iteration, denotes the position of the particle in the optimization process, i.e., the current power allocation scheme, the position of each particle represents a set of possible power allocation values, corresponding to the power allocation of each battery. denotes the velocity of the particle at the i-th iteration, the velocity determines the change amount of the position of the particle in the next iteration. denotes the new position of the particle at the i-th iteration, the new position of the particle in the next iteration, i.e., the new power allocation scheme; by updating the position, the particle explores a better solution in the search space. In this embodiment, the optimization objective function is closely related to the Lagrange function, and the optimization objective function aims to minimize the total loss of the energy storage system, including battery charging and discharging loss and line transmission loss. In order to handle the constraint conditions (such as the power feasible region of each battery and the total power requirement) of power allocation, a Lagrange function is constructed to combine the optimization objective function and the constraint conditions; by introducing the Lagrange multiplier, the Lagrange function converts the constrained optimization problem into an unconstrained problem, so that the optimization process can consider the objective function and the constraint conditions at the same time. In the solving process, the Lagrange multiplier is adjusted to balance the target and the constraint, and finally the minimization of the optimization objective function is realized under the premise of meeting all the constraints. This combination provides an effective optimization framework for power scheduling of the energy storage system, ensuring the feasibility and optimality of the power allocation scheme.

[0060] In this embodiment, the optimization objective function is closely related to the Lagrange function, and the optimization objective function aims to minimize the total loss of the energy storage system, including battery charging and discharging loss and line transmission loss. In order to handle the constraint conditions (such as the power feasible region of each battery and the total power requirement) of power allocation, a Lagrange function is constructed to combine the optimization objective function and the constraint conditions; by introducing the Lagrange multiplier, the Lagrange function converts the constrained optimization problem into an unconstrained problem, so that the optimization process can consider the objective function and the constraint conditions at the same time. In the solving process, the Lagrange multiplier is adjusted to balance the target and the constraint, and finally the minimization of the optimization objective function is realized under the premise of meeting all the constraints. This combination provides an effective optimization framework for power scheduling of the energy storage system, ensuring the feasibility and optimality of the power allocation scheme.

[0061] ​​The energy storage node scheduling optimization method of the application determines the power feasible region of each battery according to its current state of charge (SOC) before power distribution, which is used as a requirement for total power distribution and provides accurate constraint boundaries for power distribution of each battery, thereby ensuring that the power of each battery is within its allowed range when the power is distributed, avoiding the problem of inaccurate power distribution caused by simple proportional allocation without considering the actual state of the battery, thereby improving the accuracy of power distribution. After the total power is decomposed into multiple sub-tasks, each sub-task is solved by optimizing the objective function in combination with the power feasible region, and the objective function is further optimized to ensure reasonable power distribution among batteries, so that the total power can be accurately distributed to each battery according to the actual capacity and constraint conditions of each battery, further improving the accuracy of power distribution. The determination of the power feasible region sets a safety boundary for the power distribution of each battery, ensuring that the battery works within the allowed power range, avoiding the problem of SOC out-of-bounds caused by overcharging or over-discharging of the battery; thereby helping to stabilize the feasible region of each battery, preventing the problem of feasible region collapse caused by battery out-of-bounds, thereby reducing the additional loss of battery life. And when the initial power distribution scheme does not meet the total power requirement, the objective function is iteratively updated according to the power difference, which can timely adjust the power distribution strategy, avoiding the repeated zeroing and redistribution operation caused by battery out-of-bounds in the traditional method, further stabilizing the feasible region and reducing the additional loss of battery life.

[0062] The application considers the power feasible region of each battery and solves and iteratively updates the power distribution by optimizing the objective function, so that the total power can be accurately distributed to each battery. The power distribution of each battery is strictly limited within its power feasible region, thereby avoiding the over-distribution or under-distribution that may occur in the prior art, improving the accuracy of power distribution.

[0063] The optimization objective function of the application iteratively updates according to the power difference, so that the power distribution scheme can dynamically adapt to the fluctuation of high proportion of renewable power in the power grid. In the face of rapid changes in distributed photovoltaic, wind power and other renewable energy output, through the dynamic adjustment of the optimization objective function, the sub-tasks can be solved again in time, and the power distribution of each battery in the virtual energy storage node can be quickly adjusted, realizing the rapid balance and consumption of renewable power output, and improving the stability and reliability of the power grid. Through the combination of power feasible region constraint and dynamic adjustment of the optimization objective function, the application can flexibly cope with power fluctuations in the power grid while ensuring the safe operation of the battery. This combination enhances the adaptability of the entire system to high proportion of renewable energy access, enabling the virtual energy storage node to better perform in complex power grid environments and improve the overall performance of the power grid.

[0064] Optionally, the determining the power feasible region of each battery according to the current state of charge of each battery comprises: obtaining an SOC-power boundary mapping table of each battery; interpolating in the SOC-power boundary mapping table according to the current state of charge to obtain the maximum charging power and the maximum discharging power of the battery; obtaining the upper limit and the lower limit of the life-sensitive power of the battery according to a preset battery aging coefficient, in combination with the maximum charging power and the maximum discharging power; determining the power feasible region of the battery according to the upper limit and the lower limit.

[0065] Specifically, an SOC-power boundary mapping table of each battery is obtained, and the SOC-power boundary mapping table (maximum allowed charging power and discharging power mapping table) of each battery is obtained from technical documents or laboratory test data provided by a battery manufacturer. The mapping table records in detail the maximum allowed charging power and discharging power of the battery at different SOC (state of charge).

[0066] Exemplarily, as shown in Table 1, the mapping table can include the following data points: Table 1 Maximum allowed charging power and discharging power mapping table

[0067] These data points are obtained by charging and discharging tests on the battery under different SOC conditions, and reflect the safe power boundary of the battery under different SOC.

[0068] Suppose the current state of charge is 75%, and the two adjacent SOC points (for example, 70% and 80%) in the SOC-power boundary mapping table need to be found, and then linear interpolation is performed to calculate the maximum charging power and the maximum discharging power.

[0069] For the maximum charging power: ; Substituting the data can obtain:

[0070] For the maximum discharging power: ; Substituting the data can obtain: ; wherein, Pmax, current represents the maximum charging power corresponding to the current SOC; Pmax, 70 represents the maximum charging power when the SOC is 70%, which is a known data point and is used for interpolation calculation; represents the current SOC of the battery, is an input condition for determining the current charging power; represents the SOC of 70%, is a specific SOC value among the known data points; represents the SOC of 80%, is a specific SOC value among another known data point; represents the maximum charging power when the SOC is 80%, is also a known data point for interpolation calculation; represents the maximum discharging power corresponding to the current SOC, represents the maximum discharging power when the SOC is 70%, is a known data point for interpolation calculation; represents the maximum discharging power when the SOC is 80%, is also a known data point.

[0071] preset battery aging coefficient , usually determined according to the service life of the battery, the number of charge and discharge cycles, and the manufacturer's recommendations, such as .

[0072] upper limit of life-sensitive power: ; lower limit of life-sensitive power: ; According to the upper and lower limits of the calculated life-sensitive power, the power feasible region is determined as: , that is: ; In this optional embodiment, through the SOC-power boundary mapping table and the interpolation method, the maximum charging and discharging power of the battery at the current SOC can be accurately obtained, ensuring that the power distribution is within the safe working range of the battery. By introducing the battery aging coefficient, the power constraint is adjusted to a more conservative range, reducing the impact of deep charging and discharging on the battery life and prolonging the service life of the battery. Providing an accurate power feasible region for the dispatching optimization of the energy storage system ensures that the optimization algorithm runs under reasonable constraint conditions, improves the feasibility and reliability of the dispatching scheme. Avoiding frequent operation of the battery at the limit power, reducing battery failures caused by overcharging or overdischarging, and improving the overall stability of the energy storage system. By updating the power feasible region of the battery in real time, the performance of the battery changes over time, maintaining the effectiveness and adaptability of the dispatching strategy.

[0073] Optionally, the total power is divided into a plurality of sub-tasks, comprising: obtaining the proportional weight of the power feasible region of each battery and the total power feasible region of the virtual energy storage node; dividing the total power according to the proportional weight to obtain the corresponding sub-task of each battery; When the power value corresponding to the subtask of any of the batteries is zero, the battery is skipped and the proportional weight of the battery is redistributed to the remaining batteries whose subtask power values are not zero, and the power value of each subtask is recalculated.

[0074] Specifically, the proportional weight of the power feasible region of each battery in the total power feasible region is obtained, the power feasible region reflects the safe power range of the battery in the current state, and the total power feasible region is the maximum range of the power feasible regions of all batteries; the proportion of the power feasible region of each battery in the total power feasible region is calculated to determine the weight of each battery in the total power allocation, which provides a basis for subsequent power division. Next, the total power is divided according to the proportional weight to obtain the subtask corresponding to each battery. By allocating the total power according to the weight of each battery, it is ensured that the power task undertaken by each battery matches its capability. Specifically, the total power is multiplied by the proportional weight of each battery to obtain the subtask power value of the battery, thereby realizing the reasonable allocation of the total power to each battery. However, in the actual allocation process, there may be a situation that the power value corresponding to the subtask of some battery is zero, at which time the power allocation scheme needs to be adjusted to ensure that all batteries can effectively participate in power allocation.

[0075] Specifically, when it is found that the subtask power of a certain battery is zero, the battery is first skipped and its power contribution is no longer considered. Then, the proportional weight of the battery is redistributed to the remaining batteries whose subtask power values are not zero, and this step requires recalculating the proportional weight of the remaining batteries, i.e., normalizing the original weight of the remaining batteries to ensure that the total weight is still 1.

[0076] Finally, according to the recalculated proportional weight, the total power is again allocated to the effective batteries to obtain the updated subtask power values of each battery, thereby ensuring that each battery undertakes appropriate power tasks, avoiding resource waste and improving the overall power utilization efficiency of the system.

[0077] In an optional embodiment of the present application, the proportional weight of the power feasible region of each battery in the total power feasible region is calculated; the total power feasible region is the sum of the power feasible regions of all batteries.

[0078] For example, the total power feasible region is , and the power feasible region of each battery is , then the proportional weight can be represented as: ; According to the proportional weight, the total power is divided to obtain the subtask corresponding to each battery, and the total power is divided according to the proportional weight Assigned to each battery. The subtask power corresponding to each battery for: ; When the power value corresponding to the subtask of any battery is zero, the battery is skipped and the proportional weight of the battery is redistributed to the remaining batteries whose subtask power values ​​are not zero, and the power value of each subtask is recalculated.

[0079] If the subtask power of a battery If it is zero, skip the battery and set its proportional weight Reassign the batteries whose power is not zero to other subtasks. For example, suppose the proportion weight of the skipped battery is , the remaining The initial proportional weight of the batteries is , then the proportion weight after redistribution for: , j = 1, 2,..., n ; Then recalculate the power value of each subtask according to the new proportional weight: ; In this optional embodiment, the proportional weight is first obtained, and the power feasible range of each battery is calculated based on the current state of charge (SOC) and power limit of the battery. ; Calculate the sum of the power feasible regions of all batteries to obtain the total power feasible region ; For each battery, calculate its proportional weight The total power is then divided according to the proportional weight of each battery. , the total power Assign to each battery and get the subtask power of each battery .

[0080] Finally, to handle the case of zero power value, check the subtask power of each battery ; If the subtask power of a battery is zero, skip the battery and redistribute its proportional weight to other batteries; calculate the new proportional weights of the remaining batteries and recalculate the power value of each subtask.

[0081] In this optional embodiment, by calculating the proportional weights, the total power can be reasonably allocated according to the power feasible domain of each battery, ensuring that each battery operates within its safe range. When the subtask power of a battery is zero, the proportional weight of the battery can be flexibly reallocated to other batteries to avoid unreasonable or wasteful power allocation. Through reasonable power allocation, the total power capacity of the battery pack is maximized, improving the overall efficiency of the energy storage system.

[0082] Optionally, the optimizing the objective function and solving each of the subtasks in combination with the power feasible region to obtain a power allocation solution for the virtual energy storage node includes: Taking the power value of each subtask as a decision variable, taking the upper limit and lower limit of the life-sensitive power of the battery as constraint boundaries, and establishing the optimization objective function based on the decision variables and the constraint boundaries; Performing convex optimization according to the optimization objective function to obtain a decision variable for each battery; The decision variables of each battery are aggregated to obtain a power allocation plan for the virtual energy storage node.

[0083] Specifically, the power value of each subtask is used as a decision variable. Each subtask corresponds to the power allocation of a battery. This power value is an adjustable and optimized variable used to determine the specific power contribution of each battery within the total power allocation. The upper and lower limits of the battery's lifespan-sensitive power are then used as constraint boundaries. This ensures that the power allocation of each battery remains within its safe and allowable range, preventing overcharging and discharging from adversely affecting battery life.

[0084] For example, if the upper limit of the life-sensitive power of a battery is , the lower limit is , then during the optimization process, the power value of the battery Must meet Then, the optimization objective function is established based on the decision variables and the constraint boundaries. The optimization objective function aims to minimize the total loss of the energy storage system, including the power conversion loss and line transmission loss during the battery charging and discharging process.

[0085] Its mathematical expression is: ; in, It's a battery The internal resistance, It's a battery The charge and discharge current, is the line resistance, is the line transmission current, is the penalty coefficient, is the power difference, is the number of batteries.

[0086] Next, a convex optimization solution is performed based on the optimization objective function to obtain the decision variables of each battery. Convex optimization is an effective mathematical optimization method suitable for solving optimization problems with convex properties.

[0087] In an embodiment of the present application, a convex optimization algorithm (such as the interior point method, etc.) is used to find the power value of each battery that minimizes the optimization objective function under the constraint boundary conditions. Finally, the power allocation scheme of the virtual energy storage node is obtained by collecting the decision variables of each battery. The optimal power values of all batteries are summarized to form a complete power allocation scheme, which is the optimal power allocation result of the virtual energy storage node under the current conditions.

[0088] In this optional embodiment, by taking the subtask power value as the decision variable and combining the constraint boundary of the battery life sensitive power, each battery can be accurately allocated with appropriate power, so that the total power is reasonably distributed among the batteries. Taking the upper and lower limits of the battery life sensitive power as the constraint conditions can avoid the battery working in an unsafe power range, which helps to reduce the aging and damage of the battery and prolong the service life of the battery. Optimizing with the goal of minimizing the total loss of the energy storage system can effectively reduce the battery charging and discharging loss and the line transmission loss, and improve the overall efficiency of the energy storage system. Ensuring that each battery operates within its safe operating range can avoid battery failure caused by overloading or overdischarging, thereby enhancing the stability of the energy storage system. Through convex optimization solution, a global optimal solution can be guaranteed to ensure the optimality of the power allocation scheme and improve the performance and reliability of the system.

[0089] Optionally, the judging whether the power allocation scheme meets the distribution requirement of the total power comprises: obtaining the difference between the sum of the decision variables of all the subtasks and the total power; judging whether the power allocation scheme meets the distribution requirement of the total power according to the difference; wherein, if the absolute value of the difference is less than a preset threshold, it is determined that the power allocation scheme meets the distribution requirement; if the absolute value of the difference is greater than or equal to the preset threshold, it is determined that the power allocation scheme does not meet the distribution requirement.

[0090] Specifically, the difference between the sum of the decision variables of all the subtasks and the total power is obtained. The sum of the decision variables is the total power allocated to all batteries , the total power is given , and the difference calculation formula is .

[0091] Then, according to the difference, it is judged whether the power allocation scheme meets the distribution requirement of the total power, and the absolute value of the difference is compared with a preset threshold . If the absolute value of the difference is less than a preset threshold, i.e. , it is determined that the power allocation scheme meets the allocation requirement, which indicates that the power allocation scheme accurately matches the total power within an acceptable error range. If the absolute value of the difference is greater than or equal to the preset threshold, i.e. , it is determined that the power allocation scheme does not meet the allocation requirement, and further adjustment and optimization of the power allocation scheme are required.

[0092] In this optional embodiment, by calculating the difference and performing threshold judgment, it can be accurately verified whether the power allocation scheme meets the total power requirement, ensuring the accuracy and reliability of power allocation. At the same time, the power scheme that meets the allocation requirement helps the energy storage system to operate efficiently as expected, optimizes energy management, and improves the performance and stability of the entire power system. Moreover, it also avoids energy waste and excessive equipment wear caused by inaccurate power allocation, reduces operating costs, and prolongs the service life of equipment.

[0093] Optionally, the iterative updating of the optimization objective function according to the power difference includes: generating an adjustable slack variable according to the power difference; embedding the slack variable in the optimization objective function in the form of a Lagrange multiplier to construct an adaptive penalty function; adjusting the Lagrange multiplier in the adaptive penalty function through an adaptive updating rule to monotonically converge the power difference; when the power difference converges, determining that the Lagrange multiplier updating is completed, and reconstructing the optimization objective function according to the updated Lagrange multiplier.

[0094] Specifically, an adjustable slack variable is generated according to the power difference. The slack variable is used to adjust the constraint condition in the optimization objective function, and its initial value can be set as the absolute value of the power difference, i.e. The slack variable provides flexibility to the optimization process to avoid difficulty in converging due to strict satisfaction of the constraint condition. Then, the slack variable is embedded in the optimization objective function in the form of a Lagrange multiplier to construct an adaptive penalty function.

[0095] Specifically, a penalty term related to the slack variable is added to the original optimization objective function. The form of the penalty term is usually , where is a Lagrange multiplier which determines the weight of the penalty term in the optimization objective function. The adaptive penalty function constructed in this way can dynamically adjust the punishment degree in the optimization process, so that the optimization direction is more in line with the requirements of total power distribution. Then, the Lagrange multiplier in the adaptive penalty function is adjusted through an adaptive update rule, and the power difference value is monotonically convergent. The adaptive update rule can adjust the Lagrange multiplier based on the change rate of the power difference value. For example, if the absolute value of the power difference value continues to decrease, the Lagrange multiplier is appropriately reduced to reduce the punishment degree and avoid over-optimization; on the contrary, if the absolute value of the power difference value increases, the punishment degree is increased to promote the power difference value to decrease as soon as possible; this adaptive adjustment mechanism can ensure that the power difference value is monotonically convergent in the iteration process and gradually approaches zero. Finally, when the power difference value converges, it is determined that the Lagrange multiplier update is completed, and the optimization objective function is reconstructed according to the updated Lagrange multiplier. When the absolute value of the power difference value is less than a preset convergence threshold, it is considered that the power difference value has converged. At this time, the update of the Lagrange multiplier is stopped, and the optimization objective function is reconstructed using the finally determined Lagrange multiplier, so that this updated optimization objective function is applied in the subsequent power distribution solving to obtain a more accurate and total power distribution requirement satisfying power distribution scheme. In the embodiment of the present application, by introducing the slack variable, the optimization process can achieve a better balance between the constraint condition and the objective function, avoid optimization difficulties caused by strictly meeting the constraints, and improve the adaptability and flexibility of the optimization algorithm. The adaptive update rule can dynamically adjust the Lagrange multiplier according to the change of the power difference value, accelerate the convergence speed of the power difference value, reduce the number of iterations, and improve the optimization efficiency. By continuously adjusting the Lagrange multiplier and reconstructing the optimization objective function, the finally obtained power distribution scheme can more accurately meet the total power distribution requirement, and the accuracy and reliability of the optimization result are improved. The present application ensures the consistency of the power distribution scheme and the total power requirement, which helps to maintain the stable operation of the energy storage system and avoid system fluctuations or failures caused by inaccurate power distribution. At the same time, the adaptive adjustment mechanism avoids the process of manual debugging parameters, reduces the complexity of iterative optimization, and makes the whole optimization process more automated and efficient.

[0096] Optionally, the re-solving of the sub-tasks through the updated optimization objective function to obtain the final power distribution scheme comprises: re-solving the sub-tasks through the updated optimization objective function to obtain the adjusted power distribution scheme; If the power allocation scheme does not meet the allocation requirement, the power difference corresponding to the adjusted power allocation scheme is reacquired, and the optimization objective function is iteratively updated according to the power interpolation, until the power allocation scheme meets the allocation requirement, and the power allocation scheme solved by the optimization objective function in the last iteration is taken as the final power allocation scheme of the virtual energy storage node.

[0097] Specifically, first, the power allocation value of each battery is recalculated according to a new optimization objective function by an optimization algorithm (such as a convex optimization algorithm) to better meet the requirement of total power allocation. The subtasks are solved again using the updated optimization objective function to obtain an adjusted power allocation scheme. Next, it is determined whether the obtained power allocation scheme meets the allocation requirement by calculating the difference between the sum of the power values of all subtasks and the total power. If the absolute value of the difference is less than a preset threshold, it is determined that the current power allocation scheme meets the allocation requirement; otherwise, it is determined that the current power allocation scheme does not meet the allocation requirement. If the power allocation scheme does not meet the allocation requirement, the power difference corresponding to the adjusted power allocation scheme is reacquired. At this time, the power difference under the current power allocation scheme needs to be calculated again to evaluate the degree of deviation from the total power target. Then, the optimization objective function is iteratively updated according to the power difference. This step adjusts the parameters (such as the Lagrange multiplier) in the optimization objective function to enable the optimization objective function to better guide the subsequent optimization process, reduce the power difference, and gradually approach the total power allocation requirement. The above steps are repeated until the power allocation scheme meets the allocation requirement. In each iteration, the optimization objective function is updated and the subtasks are solved again until the obtained power allocation scheme meets the accuracy requirement of total power allocation. Finally, the power allocation scheme solved by the optimization objective function in the last iteration is taken as the final power allocation scheme of the virtual energy storage node; at this time, the power allocation scheme has been optimized in multiple iterations and can accurately meet the total power allocation requirement while considering the life-sensitive power constraints of the batteries, thereby ensuring efficient, stable, and safe operation of the energy storage system.

[0098] In this optional embodiment, the power allocation scheme obtained through iterative updating of the optimization objective function and re-solving of the subtasks can accurately meet the total power allocation requirement, improving the accuracy and reliability of power allocation. At the same time, the power allocation of the energy storage system is ensured to be consistent with the total power target, avoiding system fluctuations caused by inaccurate power allocation and enhancing the stability and reliability of the system. Moreover, the life-sensitive power constraints of the batteries are considered in the optimization process, avoiding excessive charging and discharging and helping to prolong the service life of the batteries and reduce the maintenance cost of the system. Through the iterative optimization process, the optimal power allocation scheme can be efficiently approached, reducing redundant calculations in the optimization process and improving optimization efficiency.

[0099] In combination Figure 2 As shown in the drawings, the application also provides a storage node scheduling optimization system, comprising: A data acquisition unit is configured to acquire the current state of charge of each battery in a virtual storage node on the user side and the total power allocated in the day-ahead stage; A power feasible region generation unit is configured to determine the power feasible region of each battery according to the current state of charge of each battery, and take the power feasible region as the allocation requirement of the total power; A task decomposition unit is configured to decompose the total power into a plurality of sub-tasks, wherein each sub-task corresponds to a power allocation task of one battery; An optimization solving unit is configured to solve each sub-task by optimizing an objective function in combination with the power feasible region, to obtain a power allocation scheme of the virtual storage node; A judgment unit is configured to judge whether the power allocation scheme meets the allocation requirement of the total power; A difference calculation unit is configured to determine a power difference according to the power allocation scheme and the total power, if the power allocation scheme does not meet the allocation requirement; An iterative update unit is configured to iteratively update the optimization objective function according to the power difference; A scheme output unit is configured to re-solve the sub-tasks by the updated optimization objective function, to obtain a final power allocation scheme.

[0100] The storage node scheduling optimization system of the application has the same advantages as the above-mentioned storage node scheduling optimization method compared with the prior art, and thus will not be described here.

[0101] In combination Figure 3 As shown in the drawings, the application also provides an electronic device comprising a memory and a processor; The memory is configured to store a computer program; The processor is configured to implement the above-mentioned storage node scheduling optimization method when executing the computer program.

[0102] The electronic device of the application has the same advantages as the above-mentioned storage node scheduling optimization method compared with the prior art, and thus will not be described here.

[0103] The application also provides a computer readable storage medium, wherein the storage medium stores a computer program, and the computer program is executed by a processor to implement the above-mentioned storage node scheduling optimization method.

[0104] The computer readable storage medium of the present application has the same advantages as the above-mentioned energy storage node scheduling optimization method compared with the prior art, which will not be repeated here.

[0105] Although the present application is disclosed as above, the protection scope of the present application is not limited to this. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application, and these changes and modifications will fall within the protection scope of the present application.

Claims

1. A method for optimizing energy storage node scheduling, characterized in that: include: Obtain the current state of charge of each battery in the user-side virtual energy storage node and the total power allocated in the day-ahead phase; determining a power feasible range of each battery according to the current state of charge of each battery, and using the power feasible range as the allocation requirement of the total power; Decomposing the total power into a plurality of subtasks, wherein each subtask corresponds to a power allocation task of the battery; By optimizing the objective function and combining the power feasible region to solve each of the subtasks, a power allocation scheme for the virtual energy storage node is obtained; Determining whether the power allocation scheme meets the allocation requirement of the total power; If the power allocation scheme does not meet the allocation requirement, determining a power difference according to the power allocation scheme and the total power; Iteratively updating the optimization objective function according to the power difference; The subtasks are re-solved using the updated optimization objective function to obtain a final power allocation solution.

2. The energy storage node scheduling optimization method according to claim 1, characterized in that: The determining, according to the current state of charge of each battery, a power feasible range of each battery includes: Obtaining an SOC-power boundary mapping table for each of the batteries; interpolating the current state of charge in the SOC-power boundary mapping table to obtain the maximum charging power and the maximum discharging power of the battery; Obtaining an upper limit and a lower limit of the life-sensitive power of the battery according to a preset battery aging coefficient and in combination with the maximum charging power and the maximum discharging power; The power feasible range of the battery is determined according to the upper limit and the lower limit.

3. The energy storage node scheduling optimization method according to claim 2, characterized in that: Decomposing the total power into a plurality of subtasks includes: Obtaining a proportional weight of the power feasible region of each battery to the total power feasible region of the virtual energy storage node; Dividing the total power according to the proportional weights to obtain subtasks corresponding to each battery; When the power value corresponding to the subtask of any battery is zero, the battery is skipped, and the proportional weight of the battery is redistributed to the remaining batteries whose subtask power values ​​are not zero, and the power value of each subtask is recalculated.

4. The energy storage node scheduling optimization method according to claim 2, characterized in that: The step of optimizing the objective function and solving each of the subtasks in combination with the power feasible region to obtain a power allocation scheme for the virtual energy storage node includes: Taking the power value of each subtask as a decision variable, taking the upper limit and lower limit of the life-sensitive power of the battery as constraint boundaries, and establishing the optimization objective function based on the decision variables and the constraint boundaries; Performing convex optimization according to the optimization objective function to obtain a decision variable for each battery; The decision variables of each battery are aggregated to obtain a power allocation plan for the virtual energy storage node.

5. The energy storage node scheduling optimization method according to claim 4, characterized in that: The determining whether the power allocation scheme meets the allocation requirement of the total power includes: Obtaining a difference between the sum of the decision variables of all the subtasks and the total power; Determining, based on the difference, whether the power allocation scheme meets the allocation requirement of the total power; If the absolute value of the difference is less than a preset threshold, it is determined that the power allocation scheme meets the allocation requirement; If the absolute value of the difference is greater than or equal to the preset threshold, it is determined that the power allocation scheme does not meet the allocation requirement.

6. The energy storage node scheduling optimization method according to claim 1, characterized in that: The iteratively updating the optimization objective function according to the power difference includes: generating an adjustable slack variable according to the power difference; Embedding the slack variables into the optimization objective function in the form of Lagrange multipliers to construct an adaptive penalty function; Adjusting the Lagrange multiplier in the adaptive penalty function by an adaptive update rule to achieve monotonically convergence on the power difference; When the power difference converges, it is determined that the update of the Lagrangian multiplier is completed, and the optimization objective function is reconstructed according to the updated Lagrangian multiplier.

7. The energy storage node scheduling optimization method according to claim 1, characterized in that: The subtasks are re-solved using the updated optimization objective function to obtain a final power allocation solution, including: Re-solving the subtasks using the updated optimization objective function to obtain the adjusted power allocation solution; If the power allocation scheme does not meet the allocation requirements, the power difference corresponding to the adjusted power allocation scheme is re-obtained, and the optimization objective function is iteratively updated according to the power interpolation until the power allocation scheme meets the allocation requirements, and the power allocation scheme solved by the optimization objective function in the last iteration is used as the final power allocation scheme of the virtual energy storage node.

8. An energy storage node scheduling optimization system, characterized in that: include: A data acquisition unit is used to obtain the current state of charge of each battery in the virtual energy storage node on the user side and the total power allocated in the day-ahead phase; a power feasible region generating unit, configured to determine a power feasible region of each battery according to the current state of charge of each battery, and use the power feasible region as the allocation requirement of the total power; a task decomposition unit, configured to decompose the total power into a plurality of subtasks, wherein each subtask corresponds to a power allocation task of the battery; An optimization solving unit, configured to solve each of the subtasks in combination with the power feasible region by optimizing the objective function to obtain a power allocation solution for the virtual energy storage node; A judging unit, configured to judge whether the power allocation scheme satisfies the allocation requirement of the total power; a difference calculation unit, configured to determine a power difference according to the power allocation scheme and the total power if the power allocation scheme does not meet the allocation requirement; an iterative updating unit, configured to iteratively update the optimization objective function according to the power difference; The solution output unit is used to re-solve the subtasks using the updated optimization objective function to obtain a final power allocation solution.

9. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is configured to implement the energy storage node scheduling optimization method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by the processor, the energy storage node scheduling optimization method according to any one of claims 1 to 7 is implemented.

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