An energy storage node scheduling optimization method, system, device and storage medium

Abstract

The application provides a kind of energy storage node scheduling optimization method, system, equipment and storage medium, it is related to distributed energy storage system technical field, and energy storage node scheduling optimization method includes: obtaining the current state of charge of each battery in the virtual energy storage node of user side and total power allocated in day-ahead stage;Determine the power feasible region of each battery according to state of charge, as total power allocation requirement;Total power is decomposed into multiple sub-tasks, and each sub-task corresponds to the power allocation of one battery;Solve power allocation scheme by optimizing objective function and combining power feasible region;Determine whether the scheme meets the total power allocation requirement, if not, determine the power difference and update the optimization objective function according to it;Solve sub-tasks again with updated optimization objective function until the final power allocation scheme that meets the requirement is obtained.The application improves the power allocation effect of energy storage node.

Description

Technical Field

[0001] This invention relates to the field of distributed energy storage system technology, and more specifically, to an energy storage node scheduling optimization method, system, device, and storage medium. Background Technology

[0002] The penetration rate of distributed photovoltaic and wind power in the power distribution network has exceeded half. To ensure grid security, a large number of user-side batteries are aggregated into virtual energy storage nodes. Therefore, it is necessary to uniformly schedule these virtual energy storage nodes. Existing technologies typically adopt a two-level optimization architecture of day-ahead and intraday. In the day-ahead stage, a charge and discharge plan curve is generated for each virtual energy storage node; in the intraday stage, a secondary power decomposition is performed based on the local battery state of charge, thereby achieving rapid balancing and absorption of high proportion of renewable energy output.

[0003] In related technologies, the total power of the day-ahead is typically allocated to each battery proportionally based on State of Charge (SOC) or lifespan weight. Then, the SOC boundary of each battery is checked individually. When the SOC boundary is exceeded, the power of that battery is hard reset to zero and re-allocated proportionally. However, when a large number of batteries approach the SOC boundary simultaneously, the cycle of exceeding the boundary, resetting to zero, and re-allocating will repeatedly occur, rapidly cutting off the overall feasible power domain. The remaining batteries are forced to increase their power sharply, triggering the boundary exceedance again, forming a positive feedback loop that causes the feasible domain to collapse, resulting in intraday command mismatch and additional lifespan loss. Summary of the Invention

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

[0005] To address the above problems, this invention provides a method, system, device, and storage medium for optimizing energy storage node scheduling.

[0006] In a first aspect, the present invention provides a method for optimizing the scheduling of energy storage nodes, comprising:

[0007] Obtain the current state of charge and the total power allocated in the day-ahead phase for each battery in the virtual energy storage node on the user side;

[0008] Based on the current state of charge of each battery, determine the power feasible region of each battery, and use the power feasible region as the allocation requirement for the total power;

[0009] The total power is decomposed into multiple sub-tasks, wherein each sub-task corresponds to a power allocation task for the battery;

[0010] By optimizing the objective function and solving each subtask in conjunction with the power feasible region, the power allocation scheme of the virtual energy storage node is obtained.

[0011] Determine whether the power allocation scheme meets the allocation requirements of the total power;

[0012] If the power allocation scheme does not meet the allocation requirements, then the power difference is determined based on the power allocation scheme and the total power.

[0013] The optimization objective function is iteratively updated based on the power difference;

[0014] The subtasks are re-solved using the updated optimization objective function to obtain the final power allocation scheme.

[0015] Optionally, determining the power feasible region of each battery based on its current state of charge includes:

[0016] Obtain the SOC-power boundary mapping table for each of the batteries;

[0017] The maximum charging power and maximum discharging power of the battery are obtained by interpolating the current state of charge in the SOC-power boundary mapping table.

[0018] Based on the preset battery aging coefficient, combined with the maximum charging power and the maximum discharging power, the upper and lower limits of the battery's lifespan-sensitive power are obtained.

[0019] The power feasible range of the battery is determined based on the upper limit and the lower limit.

[0020] Optionally, the step of decomposing the total power into multiple sub-tasks includes:

[0021] Obtain the proportional weight of the power feasible region of each battery to the total power feasible region of the virtual energy storage node;

[0022] The total power is divided according to the proportional weight to obtain the sub-task corresponding to each battery;

[0023] 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 batteries whose power values ​​of the remaining subtasks are not zero, and the power value of each subtask is recalculated.

[0024] Optionally, the step of optimizing the objective function and solving each subtask in conjunction with the power feasible region to obtain the power allocation scheme for the virtual energy storage node includes:

[0025] The power value of each subtask is used as a decision variable, the upper and lower limits of the battery's lifetime-sensitive power are used as constraint boundaries, and the optimization objective function is established based on the decision variables and the constraint boundaries.

[0026] Convex optimization is performed based on the objective function to obtain the decision variables for each battery.

[0027] The power allocation scheme of the virtual energy storage node is obtained by aggregating the decision variables of each battery.

[0028] Optionally, determining whether the power allocation scheme meets the allocation requirements of the total power includes:

[0029] Obtain the difference between the sum of the decision variables of all the subtasks and the total power;

[0030] Based on the difference, determine whether the power allocation scheme meets the allocation requirements of the total power;

[0031] If the absolute value of the difference is less than a preset threshold, then the power allocation scheme is determined to meet the allocation requirements.

[0032] If the absolute value of the difference is greater than or equal to the preset threshold, then the power allocation scheme is determined to not meet the allocation requirements.

[0033] Optionally, the iterative update of the optimization objective function based on the power difference includes:

[0034] An adjustable relaxation variable is generated based on the power difference;

[0035] The slack variables are embedded into the optimization objective function in the form of Lagrange multipliers to construct an adaptive penalty function;

[0036] The Lagrange multipliers in the adaptive penalty function are adjusted by an adaptive update rule, and the power difference converges monotonically.

[0037] Once the power difference converges, the Lagrange multiplier update is completed, and the optimization objective function is reconstructed based on the updated Lagrange multiplier.

[0038] Optionally, the step of resolving the subtask using the updated optimization objective function to obtain the final power allocation scheme includes:

[0039] The subtasks are solved again by resolving the updated optimization objective function to obtain the adjusted power allocation scheme;

[0040] If the power allocation scheme does not meet the allocation requirements, the power difference corresponding to the adjusted power allocation scheme is re-acquired, and the optimization objective function is iteratively updated according to the power interpolation until the power allocation scheme meets the allocation requirements. The power allocation scheme obtained by solving the optimization objective function in the last iteration is taken as the final power allocation scheme of the virtual energy storage node.

[0041] In a second aspect, the present invention provides an energy storage node scheduling optimization system, comprising:

[0042] The data acquisition unit is used to obtain the current state of charge and the total power allocated in the day-ahead phase for each battery in the virtual energy storage node on the user side.

[0043] A power feasible region generation unit is used to determine the power feasible region of each battery based on the current state of charge of each battery, and to use the power feasible region as the allocation requirement of the total power;

[0044] The task decomposition unit is used to decompose the total power into multiple sub-tasks, wherein each sub-task corresponds to a power allocation task of the battery;

[0045] An optimization and solution unit is used to solve each of the sub-tasks by optimizing the objective function and combining it with the power feasible region to obtain the power allocation scheme of the virtual energy storage node;

[0046] A judgment unit is used to determine whether the power allocation scheme meets the allocation requirements of the total power;

[0047] The difference calculation unit is used to determine the power difference based on the power allocation scheme and the total power if the power allocation scheme does not meet the allocation requirements.

[0048] An iterative update unit is used to iteratively update the optimization objective function based on the power difference;

[0049] The scheme output unit is used to re-solve the sub-task using the updated optimization objective function to obtain the final power allocation scheme.

[0050] Thirdly, the present invention provides an electronic device, including a memory and a processor;

[0051] The memory is used to store computer programs;

[0052] The processor is configured to implement the energy storage node scheduling optimization method as described above when executing the computer program.

[0053] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the energy storage node scheduling optimization method as described above.

[0054] The energy storage node scheduling optimization method, system, device, and storage medium of this invention, before power allocation, determines the feasible power region of each battery based on its current state of charge (SOC). This feasible region serves as the requirement for total power allocation, providing precise constraint boundaries for the power allocation of each battery. This ensures that the power of each battery is within its allowable range during power allocation, avoiding the inaccurate power allocation caused by simple proportional allocation without considering the actual state of the batteries, thus improving the accuracy of power allocation. After decomposing the total power into multiple sub-tasks, each sub-task is solved by optimizing the objective function in conjunction with the feasible power region. Optimizing the objective function further ensures the reasonable allocation of power among batteries, enabling the total power to be accurately allocated to each battery according to its actual capacity and constraints, further improving the accuracy of power allocation. Determining the feasible power region sets a safety boundary for power allocation to each battery, ensuring that the battery operates within the allowable power range and avoiding SOC exceeding the limit due to overcharging or over-discharging. This helps stabilize the feasible region of each battery, preventing feasible region collapse caused by battery exceeding the limit, thereby reducing additional battery life loss. Furthermore, when the initial power allocation scheme does not meet the total power requirement, the optimization objective function is iteratively updated based on the power difference. This process can adjust the power allocation strategy in a timely manner, avoiding the repeated zeroing and re-allocation operations caused by battery exceeding the limit in traditional methods, further stabilizing the feasible region and reducing additional battery life loss.

[0055] This invention, by considering the power feasible region of each battery and solving and iteratively updating the power allocation through an optimized objective function, ensures that the total power is accurately allocated to each battery. This strictly limits the power allocation of each battery to its power feasible region, thus avoiding over-allocation or under-allocation that may occur in existing technologies and improving the accuracy of power allocation. The optimized objective function of this invention, through iterative updates based on power differences, allows the power allocation scheme to dynamically adapt to fluctuations in the high proportion of renewable energy output in the power grid. When facing rapid changes in the output of distributed photovoltaic, wind power, and other renewable energy sources, the dynamic adjustment of the optimized objective function allows for timely re-solving of sub-tasks and rapid adjustment of the power allocation of each battery in the virtual energy storage node, achieving rapid balancing and absorption of renewable energy output and improving the stability and reliability of the power grid. By combining the constraints of the power feasible region with the dynamic adjustment of the optimized objective function, this invention can flexibly respond to power fluctuations in the power grid while ensuring the safe operation of the batteries. This combination enhances the adaptability of the entire system to the integration of high proportions of renewable energy, enabling the virtual energy storage node to function better in complex power grid environments and improving the overall performance of the power grid. Attached Figure Description

[0056] Figure 1 This is a flowchart of the energy storage node scheduling optimization method according to an embodiment of the present invention;

[0057] Figure 2 This is a structural block diagram of the energy storage node scheduling optimization system according to an embodiment of the present invention;

[0058] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0059] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0060] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0061] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based 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"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0062] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0063] 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 used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties. The collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0064] Combination Figure 1 As shown, this embodiment of the invention provides a method for optimizing energy storage node scheduling, including:

[0065] Obtain the current state of charge and the total power allocated to each battery in the virtual energy storage node on the user side during the day-ahead phase.

[0066] Specifically, real-time state-of-charge (SOC) data can be collected using power monitoring devices installed at each battery. These devices can accurately measure the proportion of stored charge to total capacity and transmit the data to the dispatch system via a communication module. The total power allocation information for the day-ahead phase is retrieved from the system database. This database stores the total power allocation values ​​determined in the early stages using complex algorithms combined with grid load forecasts, generation plans, and other factors. This provides fundamental data support for subsequent power allocation, ensuring the dispatch system is aware of existing battery resources and the total workload. This allows operators to monitor the initial operating status of energy storage nodes in real time, laying the foundation for precise power allocation in the next step.

[0067] In a preferred embodiment of the present invention, the power monitoring device can be a resistive current sensor, which utilizes the voltage drop generated across a sampling resistor when current flows through it, and calculates the current magnitude by measuring this voltage drop. Its working principle is based on Ohm's law V=IR, and it features a simple structure, low cost, and high measurement accuracy (typically ±0.5%), a wide measurement range (from a few milliamps to several thousand amps), and fast response speed (down to the microsecond level). It can monitor the charging and discharging current of the battery in real time and accurately, thereby indirectly reflecting changes in battery power. Alternatively, a Hall current sensor can be used, based on the Hall effect principle. When current flows through a conductor, a Hall voltage is generated in a direction perpendicular to both the current and the magnetic field. The sensor measures the current by detecting the Hall voltage. Hall current sensors have advantages such as high accuracy (up to ±0.1%), good linearity, fast response speed (nanosecond level), and wide bandwidth (up to hundreds of kilohertz). They can measure current without direct contact with the circuit under test, achieving electrical isolation and ensuring the safety and reliability of the measurement. They are suitable for battery power monitoring under complex operating conditions such as high current and high voltage.

[0068] Battery power monitoring chips include coulomb counters (fuel gauges), such as the common BQ20 series chips, specifically designed for battery power management. Using coulomb measurement, they accurately calculate the amount of charge and discharge by monitoring the battery's charging and discharging current in real time, thus determining the remaining battery capacity. Alternatively, impedance-based power monitoring chips estimate the battery's state of charge (SOC) by utilizing the relationship between the battery's internal resistance and its charge level. By measuring the battery's AC impedance spectrum and analyzing changes in its impedance characteristics, the remaining battery capacity can be calculated.

[0069] A Battery Management System (BMS) is a distributed BMS, composed of multiple sub-BMS systems. Each subsystem manages a single battery or a few batteries, and the subsystems interact and collaborate via a communication network. It enables more precise monitoring and management of the state of charge of each battery, achieving refined battery management. Each subsystem has independent charge monitoring and control functions; even if one subsystem fails, it will not affect the normal operation of other subsystems, improving system reliability and redundancy.

[0070] Based on the current state of charge of each battery, a power feasible region for each battery is determined, and the power feasible region is used as the allocation requirement for the total power.

[0071] Specifically, based on the current state of charge (SOC) of each battery, the battery characteristic parameter table provided by the battery manufacturer is consulted. This parameter table details the upper and lower limits of the allowable charge and discharge power for the battery at different SOC states. For example, for a lithium battery, when its SOC is 80%, the upper limit of charging power is 5kW, and the upper limit of discharging power is -3kW (the negative sign indicates discharging). Based on this, the feasible power domain for each battery is defined, i.e., the range of power values. The feasible power domains of all batteries are combined to form the constraints that the total power allocation must meet, i.e., the allocation requirements. This operation ensures that each battery participates in power allocation within its safe performance range, effectively avoiding battery performance degradation or even damage due to overcharging and discharging, extending battery life, and providing clear boundary conditions for subsequent power optimization allocation, ensuring the safe and stable operation of the energy storage system.

[0072] The total power is decomposed into multiple sub-tasks, where each sub-task corresponds to a power allocation task for one battery.

[0073] Specifically, based on factors such as the physical location, connection method, or rated capacity of the battery pack, the total power is rationally divided into smaller power portions 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 distributed proportionally to each battery; if the capacities are different, the power is distributed proportionally. Each sub-task focuses only on the power allocation of its corresponding battery, clearly defining the specific power share that each battery should bear. This simplifies the complex and massive problem of total power allocation, facilitating subsequent targeted calculation of the optimal power for each battery individually, improving computational efficiency and solution accuracy, and providing a methodological foundation for achieving fine-grained scheduling.

[0074] For example, if a battery pack consists of four batteries connected in parallel with the same rated capacity and a total power of 20kW, then the sub-task power of each battery is 5kW. If the rated capacities of the batteries are different, say 10kWh, 15kWh, 10kWh, and 20kWh respectively, but the total power is still 20kW, then according to the capacity ratio, the sub-task power of each battery is approximately 3.64kW, 5.45kW, 3.64kW, and 7.27kW respectively. This decomposition method can fully utilize the capacity characteristics of each battery, allowing each battery to undertake its corresponding power task within its capacity range. This avoids situations where some batteries are overloaded while others are idle, improving the overall utilization efficiency of the energy storage system and laying the foundation for subsequent precise power allocation, ensuring the rational allocation of energy storage system resources.

[0075] By optimizing the objective function and solving each subtask in conjunction with the power feasible region, the power allocation scheme of the virtual energy storage node is obtained.

[0076] Specifically, an optimization objective function is constructed to minimize the total losses of the energy storage system, including energy conversion losses during battery charging and discharging, as well as line transmission losses. The power feasible region constraint is introduced into the optimization objective function using the Lagrange multiplier method, forming a Lagrange function. Using battery power as a variable, the partial derivative of the Lagrange function is calculated and set equal to zero to obtain the optimal power allocation value for each battery. For example, the calculated optimal powers for the four batteries are 4.5kW, 5.8kW, 4.2kW, and 5.5kW (assuming a total power of 20kW). This process uses mathematical optimization techniques to uncover the optimal operating state of the energy storage system under safety constraints, achieving efficient operation of energy storage nodes, reducing system operating costs, improving the overall performance of the power system, enhancing space utilization efficiency, and ensuring the economical and stable operation of the system.

[0077] In a preferred embodiment of the present invention, specifically, the battery charging and discharging loss can be expressed 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.

[0078] The formula is ,in It is a battery internal resistance, It is a battery The charging and discharging current. Line transmission loss can be expressed as the product of the line resistance and the square of the transmission current, multiplied by the transmission time, as shown in the formula: ,in It is the line resistance. It is the current transmitted through the line.

[0079] The power feasible region constraint is introduced into the optimization objective function using the Lagrange multiplier method, thus forming the Lagrange function.

[0080] Specifically, the process includes the following steps:

[0081] (1) Set power feasible region constraints, i.e. power allocation value for each battery. Its power upper and lower limits must be met, i.e. ,in and These are batteries The lower and upper limits of power.

[0082] (2) Construct the Lagrange function, the formula is:

[0083]

[0084] in, It refers to the number of batteries. It is the total power. These are Lagrange multipliers related to the total power constraint. and These are related to batteries Lagrange multipliers related to the lower and upper limits of power constraints.

[0085] (3) For each variable (e.g.) , , , Find the partial derivatives and set them equal to zero to form a system of equations.

[0086] (4) Solve the above system of equations to obtain the optimal power allocation value for each battery. and the corresponding Lagrange multipliers , , This allows us to determine the optimal power allocation scheme that satisfies the power feasible region constraint.

[0087] In this way, the objective function can comprehensively consider various losses of the energy storage system and find the optimal solution under the condition of satisfying power allocation constraints, effectively reducing the total loss of the energy storage system and improving the system's operating efficiency and economy.

[0088] Determine whether the power allocation scheme meets the allocation requirements of the total power.

[0089] Specifically, the obtained power allocation values ​​for each battery are summarized and compared with the total power to check 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 range to ensure that there are no cases of exceeding the limit.

[0090] For example, if the target total power is 20kW and the actual allocated total power is 19.8kW, it is within the allowable error range; and the power allocation value of each battery does not exceed the upper and lower limits of its feasible power domain. This judgment process can promptly detect deviations and violations in the power allocation scheme, preventing problems such as inaccurate total power allocation or unreasonable power allocation of individual batteries that affect the normal operation of the energy storage system. It ensures the feasibility and accuracy of the scheduling scheme and is a key link in achieving stable and reliable energy storage scheduling.

[0091] If the power allocation scheme does not meet the allocation requirements, then the power difference is determined based on the power allocation scheme and the total power.

[0092] Specifically, the difference between the actual allocated total power and the target total power is accurately calculated. This is done by subtracting the total power after the current scheme is summarized from the target total power, and the absolute value is the power difference. That is, power difference = target total power - actual allocated total power. For example, if the target total power is 20kW and the actual allocated total power is 19kW, then the power difference is 1kW. This difference clarifies the degree of deviation between the current scheme and the expected target, providing a quantitative basis for subsequent adjustments. This allows for targeted measures to bridge the gap, enabling the system to continuously improve towards meeting requirements and ensuring the accuracy and effectiveness of energy storage dispatch.

[0093] The optimization objective function is iteratively updated based on the power difference.

[0094] Specifically, based on the magnitude and direction of the power difference, the relevant parameters in the optimization objective function are modified according to the adjustment strategy, such as adjusting the weighting coefficients and correcting the target value. If the power difference is positive, the loss weighting coefficient can be appropriately increased to encourage the system to increase power allocation; if it is negative, the adjustment is reversed. Then, the optimization algorithm is used iteratively to solve the problem again, allowing the system to search for the optimal solution again in the direction of reducing the difference. In this way, the optimization objective function is dynamically adjusted to make subsequent schemes more in line with actual needs, continuously enhance the system's self-correction ability to cope with deviations, and improve the flexibility and adaptability of energy storage scheduling.

[0095] The subtasks are re-solved using the updated optimization objective function to obtain the final power allocation scheme.

[0096] Specifically, by optimizing the algorithm, the updated objective function is combined with the power feasible region constraint to recalculate the optimal power allocation for each battery subtask. As the objective function is adjusted, the algorithm will re-search for a better solution for the corresponding battery power allocation, ultimately obtaining an optimized power allocation scheme that meets the total power allocation requirements. This ensures that through continuous iterative optimization, the final output scheme not only achieves safety and feasibility but also reaches the optimal state, realizing efficient and stable scheduling of energy storage nodes. This fully leverages the value of energy storage systems in the power system, such as improving power quality and promoting the consumption of renewable energy, providing strong support for the reliable operation of the entire power system.

[0097] In a preferred embodiment of the present invention, the objective function for optimization is first constructed, specifically including:

[0098] Battery charging and discharging loss formula: ,in, It is a battery internal resistance, It is a battery The charging and discharging current. Line transmission loss formula: ,in, It is the line resistance. It is the current transmitted through the line.

[0099] Then optimize the objective function: ,

[0100] in, It refers to the number of batteries. It is the penalty coefficient. It is the power difference.

[0101] Construct the Lagrangian function, with the power feasible region constraint as follows: ,in, and These are batteries The lower and upper limits of power.

[0102] Lagrange function formula: ,in, It is the total power. These are Lagrange multipliers related to the total power constraint. and These are related to batteries Lagrange multipliers related to the lower and upper limits of power constraints.

[0103] right Find the partial derivative formula: .

[0104] The specific steps of updating and optimizing the objective function in the particle swarm optimization algorithm include:

[0105] (1) Velocity update formula:

[0106] ;

[0107] in, It is a particle In the Speed ​​at the next iteration It is inertial weight. , It is a learning factor. , It is a random number. It is a particle The individual extreme value position, It is the location of the global extremum. It is a particle In the Position (power allocation value) at the next iteration.

[0108] (2) Position update formula:

[0109] ;

[0110] in, Represents particles In the The position at the next iteration indicates the particle's position during the optimization process. The current position, i.e. the current power allocation scheme, represents a set of possible power allocation values ​​for each particle, corresponding to the power allocation of each battery. Represents particles In the The velocity at the next iteration determines the amount of change in the particle's position in the next iteration. Represents particles In the The new position at the next iteration is for the particle. In the next iteration, the particle will move to a new position, i.e., a new power allocation scheme; by updating its position, the particle explores a better solution in the search space.

[0111] In this embodiment, the objective function is closely related to the Lagrangian function. The objective function aims to minimize the total losses of the energy storage system, including battery charging and discharging losses and line transmission losses. To address the constraints of power allocation (such as the feasible power domain of each battery and the total power requirement), a Lagrangian function is constructed, combining the objective function with the constraints. By introducing Lagrange multipliers, the Lagrangian function transforms the constrained optimization problem into an unconstrained one, allowing the optimization process to consider both the objective function and the constraints simultaneously. During the solution process, the Lagrange multipliers are adjusted to balance the objective and constraints, ultimately minimizing the objective function while satisfying all constraints. This combination provides an effective optimization framework for the power scheduling of the energy storage system, ensuring the feasibility and optimality of the power allocation scheme.

[0112] The energy storage node scheduling optimization method of this invention determines the feasible power region of each battery based on its current state of charge (SOC) before power allocation. This feasible region serves as the requirement for total power allocation, providing a precise constraint boundary for the power allocation of each battery. This ensures that the power of each battery is within its allowable range during power allocation, avoiding the inaccurate power allocation caused by simple proportional allocation without considering the actual state of the batteries, thus improving the accuracy of power allocation. After decomposing the total power into multiple sub-tasks, each sub-task is solved by optimizing the objective function in conjunction with the feasible power region. Optimizing the objective function further ensures the reasonable allocation of power among batteries, enabling the total power to be accurately allocated to each battery according to its actual capacity and constraints, further improving the accuracy of power allocation. Determining the feasible power region sets a safety boundary for power allocation to each battery, ensuring that the battery operates within the allowable power range and avoiding SOC exceeding the limit due to overcharging or over-discharging. This helps stabilize the feasible region of each battery, preventing feasible region collapse caused by battery exceeding the limit, thereby reducing additional battery life loss. Furthermore, when the initial power allocation scheme does not meet the total power requirement, the optimization objective function is iteratively updated based on the power difference. This process can adjust the power allocation strategy in a timely manner, avoiding the repeated zeroing and re-allocation operations caused by battery exceeding the limit in traditional methods, further stabilizing the feasible region and reducing additional battery life loss.

[0113] This invention considers the feasible power domain of each battery and solves and iteratively updates the power allocation by optimizing the objective function, enabling the total power to be accurately allocated to each battery. This strictly limits the power allocation of each battery to its feasible power domain, thus avoiding over-allocation or under-allocation that may occur in existing technologies and improving the accuracy of power allocation.

[0114] The objective function of this invention is iteratively updated based on power differences, enabling the power allocation scheme to dynamically adapt to fluctuations in the high proportion of renewable energy output in the power grid. When facing rapid changes in the output of distributed photovoltaic and wind power, the dynamic adjustment of the objective function allows for timely resolving of sub-tasks and rapid adjustment of the power allocation among the batteries in the virtual energy storage node, achieving rapid balancing and absorption of renewable energy output and improving the stability and reliability of the power grid. By combining the constraints of the power feasible region with the dynamic adjustment of the objective function, this invention can flexibly respond to power fluctuations in the power grid while ensuring the safe operation of the batteries. This combination enhances the adaptability of the entire system to the integration of high proportions of renewable energy, enabling the virtual energy storage node to function better in complex power grid environments and improving the overall performance of the power grid.

[0115] Optionally, determining the power feasible region of each battery based on its current state of charge includes:

[0116] Obtain the SOC-power boundary mapping table for each of the batteries;

[0117] The maximum charging power and maximum discharging power of the battery are obtained by interpolating the current state of charge in the SOC-power boundary mapping table.

[0118] Based on the preset battery aging coefficient, combined with the maximum charging power and the maximum discharging power, the upper and lower limits of the battery's lifespan-sensitive power are obtained.

[0119] The power feasible range of the battery is determined based on the upper limit and the lower limit.

[0120] Specifically, a SOC-power boundary mapping table is obtained for each battery. This table (maximum allowable charging power and discharging power mapping table) is retrieved from technical documents provided by the battery manufacturer or from laboratory test data. This mapping table records in detail the maximum allowable charging power and discharging power of the battery at different SOCs (states of charge).

[0121] For example, as shown in Table 1, the mapping table may include the following data points:

[0122] Table 1 Mapping table of maximum allowable charging power and discharging power

[0123]

[0124] These data points were obtained by charging and discharging the battery under different SOC conditions, reflecting the safe power boundary of the battery under different SOC conditions.

[0125] Assuming the current state of charge is 75%, we need to find two adjacent SOC points (e.g., 70% and 80%) in the SOC-power boundary mapping table, and then perform linear interpolation to calculate the maximum charging power and maximum discharging power.

[0126] For maximum charging power:

[0127] ;

[0128] Substituting the data, we get:

[0129]

[0130] For maximum discharge power:

[0131] ;

[0132] Substituting the data, we get:

[0133] ;

[0134] in, This indicates the maximum charging power corresponding to the current SOC; This represents the maximum charging power when the SOC is 70%. These are known data points used for interpolation calculations. The current SOC of the battery is the input condition used to determine the current charging power. 70% represents a specific SOC value among known data points; This represents 80% of the SOC, which is a specific SOC value in another known data point; This represents the maximum charging power when the SOC is 80%, and it is also a known data point used for interpolation calculation. This indicates the maximum discharge power corresponding to the current SOC. This represents the maximum discharge power at a SOC of 70%, and is a known data point used for interpolation calculations. This represents the maximum discharge power at a SOC of 80%, which is also a known data point.

[0135] Preset battery aging factor The specific value is usually determined based on the battery's lifespan, the number of charge-discharge cycles, and the manufacturer's recommendations, such as... .

[0136] Lifetime-sensitive power limit:

[0137] ;

[0138] Lifetime-sensitive power limit:

[0139] ;

[0140] Based on the calculated upper and lower limits of lifetime-sensitive power, the feasible power region is determined as follows:

[0141] ,Right now: ;

[0142] In this optional embodiment, the maximum charge and discharge power of the battery at the current SOC can be accurately obtained through a SOC-power boundary mapping table and interpolation methods, ensuring that power allocation is within the battery's safe operating range. By introducing a battery aging factor, the power constraint is adjusted to a more conservative range, reducing the impact of deep charge and discharge on battery life and extending battery lifespan. This provides an accurate power feasible region for energy storage system scheduling optimization, ensuring that the optimization algorithm operates under reasonable constraints, improving the feasibility and reliability of the scheduling scheme. It avoids frequent operation of the battery at its maximum power, reducing battery failures caused by overcharging or over-discharging, and improving the overall stability of the energy storage system. By updating the battery's power feasible region in real time, it adapts to changes in battery performance over time, maintaining the effectiveness and adaptability of the scheduling strategy.

[0143] Optionally, the step of decomposing the total power into multiple sub-tasks includes:

[0144] Obtain the proportional weight of the power feasible region of each battery to the total power feasible region of the virtual energy storage node;

[0145] The total power is divided according to the proportional weight to obtain the sub-task corresponding to each battery;

[0146] 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 batteries whose power values ​​of the remaining subtasks are not zero, and the power value of each subtask is recalculated.

[0147] Specifically, the proportional weight of the power feasible domain of each battery to the total power feasible domain of the virtual energy storage node is obtained. The power feasible domain reflects the safe power range of the battery in the current state, while the total power feasible domain is the maximum range of the power feasible domains of all batteries. The proportion of each battery's power feasible domain to the total power feasible domain is calculated to determine the weight of each battery in the total power allocation, providing a basis for subsequent power partitioning. Next, the total power is partitioned according to the proportional weight to obtain the sub-tasks corresponding to each battery. By allocating the total power according to the weights of each battery, it is ensured that the power task undertaken by each battery matches its capacity. Specifically, the total power is multiplied by the proportional weight of each battery to obtain the sub-task power value of that battery, thereby achieving a reasonable allocation of the total power to each battery. However, in the actual allocation process, there may be cases where the power value corresponding to the sub-task of some batteries is zero. In this case, the power allocation scheme needs to be adjusted to ensure that all batteries can effectively participate in the power allocation.

[0148] Specifically, when a battery's subtask power is found to be zero, it is first skipped and its power contribution is no longer considered. Then, the proportional weight of that battery is redistributed to the remaining batteries whose subtask power values ​​are not zero. This step requires recalculating the proportional weights of the remaining batteries, that is, normalizing the original weights of the remaining batteries to ensure that the sum of the weights is still 1.

[0149] Finally, based on the recalculated proportional weights, the total power is redistributed to these effective batteries, resulting in updated subtask power values ​​for each battery. This ensures that each battery undertakes an appropriate power task, avoids resource waste, and improves the overall power utilization efficiency of the system.

[0150] In an optional embodiment of the present invention, 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.

[0151] For example, the feasible region of total power is The feasible power range for each battery is Then proportional weight It can be represented as:

[0152] ;

[0153] The total power is divided according to the proportional weights to obtain sub-tasks corresponding to each battery, and the total power is then divided according to the proportional weights. Power is allocated to each battery. The subtask power corresponding to each battery... for:

[0154] ;

[0155] 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 batteries whose power values ​​of the remaining subtasks are not zero, and the power value of each subtask is recalculated.

[0156] If the subtask power of a certain battery If the value is zero, skip the battery and adjust its proportional weight. Reassign batteries that have non-zero power to other subtasks. For example, assume the weight of the skipped batteries is... The remaining The initial proportional weight of each battery is Then the redistributed proportional weights for:

[0157] , j=1,2,...,n ;

[0158] Then, the power values ​​for each subtask are recalculated based on the new proportional weights:

[0159] ;

[0160] In this optional embodiment, the proportional weights are first obtained, and the power feasible region for each battery is calculated based on the battery's current state of charge (SOC) and power limitations. 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. Then the total power is divided according to the proportional weight of each battery. , to the total power Assign power to each battery to obtain the subtask power for each battery. .

[0161] Finally, for the zero power value case, check the sub-task power of each battery. If the subtask power of a battery is zero, skip that battery and reassign its proportional weight to other batteries; calculate the new proportional weight of the remaining batteries and recalculate the power value of each subtask.

[0162] In this optional embodiment, the total power can be reasonably allocated according to the power feasible domain of each battery through the calculation of proportional weights, ensuring that each battery operates within its safe range. When the sub-task power of a battery is zero, the proportional weight of that battery can be flexibly redistributed to other batteries, avoiding unreasonable power allocation or waste. Through reasonable power allocation, the total power capacity of the battery pack is maximized, improving the overall efficiency of the energy storage system.

[0163] Optionally, the step of optimizing the objective function and solving each subtask in conjunction with the power feasible region to obtain the power allocation scheme for the virtual energy storage node includes:

[0164] The power value of each subtask is used as a decision variable, the upper and lower limits of the battery's lifetime-sensitive power are used as constraint boundaries, and the optimization objective function is established based on the decision variables and the constraint boundaries.

[0165] Convex optimization is performed based on the objective function to obtain the decision variables for each battery.

[0166] The power allocation scheme of the virtual energy storage node is obtained by aggregating the decision variables of each battery.

[0167] Specifically, the power value of each sub-task is used as a decision variable, with each sub-task corresponding to the power allocation of one battery. This power value is an adjustable and optimizable variable used to determine the specific power level of each battery in the total power allocation. Then, the upper and lower limits of the battery's lifespan-sensitive power are used as constraint boundaries. This is to ensure that the power allocation of each battery is within its safe and permissible range, avoiding the adverse effects of overcharging and discharging on battery life.

[0168] For example, if the upper limit of the lifespan-sensitive power of a certain battery is... The lower limit is During the optimization process, the power value of the battery will be... Must meet Next, based on the decision variables and the constraint boundaries, the optimization objective function is established. The optimization objective function aims to minimize the total losses of the energy storage system, including energy conversion losses during battery charging and discharging and line transmission losses.

[0169] Its mathematical expression is:

[0170] ;

[0171] in, It is a battery internal resistance, It is a battery The charging and discharging current, It is the line resistance. It is the current transmitted through the line. It is the penalty coefficient. It is the power difference. It refers to the number of batteries.

[0172] Next, convex optimization is performed based on the objective function to obtain the decision variables for each battery. Convex optimization is an effective mathematical optimization method suitable for solving optimization problems with convex properties.

[0173] In one embodiment of the present invention, a convex optimization algorithm (such as the interior point method) is used to find the optimization objective function under the constraint boundary conditions. Minimum power value per battery Finally, the decision variables of each battery are aggregated to obtain the power allocation scheme for the virtual energy storage node. The optimal power values ​​of all batteries are then summarized to form a complete power allocation scheme, which represents the optimal power allocation result for the virtual energy storage node under the current conditions.

[0174] In this optional embodiment, by using the sub-task power value as a decision variable and combining it with the constraint boundary of battery life-sensitive power, appropriate power can be accurately allocated to each battery, ensuring a reasonable distribution of total power among them. Using the upper and lower limits of battery life-sensitive power as constraints prevents batteries from operating within unsafe power ranges, helping to reduce battery aging and damage, and extending battery life. Optimization aimed at minimizing the total loss of the energy storage system effectively reduces battery charging and discharging losses and line transmission losses, improving the overall efficiency of the energy storage system. Ensuring each battery operates within its safe operating range avoids battery failures caused by overload or over-discharge, thereby enhancing the stability of the energy storage system. Convex optimization ensures a globally optimal solution, guaranteeing the optimality of the power allocation scheme and improving system performance and reliability.

[0175] Optionally, determining whether the power allocation scheme meets the allocation requirements of the total power includes:

[0176] Obtain the difference between the sum of the decision variables of all the subtasks and the total power;

[0177] Based on the difference, determine whether the power allocation scheme meets the allocation requirements of the total power;

[0178] If the absolute value of the difference is less than a preset threshold, then the power allocation scheme is determined to meet the allocation requirements.

[0179] If the absolute value of the difference is greater than or equal to the preset threshold, then the power allocation scheme is determined to not meet the allocation requirements.

[0180] Specifically, the difference between the sum of the decision variables for 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. The formula for calculating the difference is: .

[0181] Next, based on the difference, it is determined whether the power allocation scheme meets the allocation requirements of the total power, and the absolute value of the difference is compared with a preset threshold. :

[0182] If the absolute value of the difference is less than the preset threshold, that is If the power allocation scheme meets the allocation requirements, it 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. If the power allocation scheme does not meet the allocation requirements, further adjustments and optimizations to the power allocation scheme are needed.

[0183] In this optional embodiment, by calculating the difference and performing a threshold judgment, it is possible to accurately verify whether the power allocation scheme meets the total power requirements, ensuring the accuracy and reliability of power allocation. Simultaneously, a power scheme that meets the allocation requirements helps the energy storage system operate efficiently as expected, optimizes energy management, and improves the performance and stability of the entire power system. Furthermore, it avoids energy waste and excessive equipment wear caused by inaccurate power allocation, reducing operating costs and extending equipment lifespan.

[0184] Optionally, the iterative update of the optimization objective function based on the power difference includes:

[0185] An adjustable relaxation variable is generated based on the power difference;

[0186] The slack variables are embedded into the optimization objective function in the form of Lagrange multipliers to construct an adaptive penalty function;

[0187] The Lagrange multipliers in the adaptive penalty function are adjusted by an adaptive update rule, and the power difference converges monotonically.

[0188] Once the power difference converges, the Lagrange multiplier update is completed, and the optimization objective function is reconstructed based on the updated Lagrange multiplier.

[0189] Specifically, adjustable relaxation variables are generated based on the power difference. These relaxation variables are used to adjust the constraints in the objective function. Their initial values ​​can be set to the absolute value of the power difference, i.e. Slack variables are introduced to provide flexibility to the optimization process and prevent convergence difficulties caused by strictly satisfying constraints. These slack variables are then embedded into the objective function as Lagrange multipliers to construct an adaptive penalty function.

[0190] Specifically, this involves adding a penalty term related to the slack variables to the original objective function. The penalty term typically takes the form of... ,in The Lagrange multipliers determine the weight of the penalty term in the objective function. This constructed adaptive penalty function dynamically adjusts the penalty intensity during the optimization process, making the optimization direction more aligned with the total power allocation requirements. Then, the Lagrange multipliers in the adaptive penalty function are adjusted using an adaptive update rule, achieving monotonically convergence to the power difference. The adaptive update rule can be based on the rate of change of the power difference to adjust the Lagrange multipliers. For example, if the absolute value of the power difference continuously decreases, the Lagrange multipliers are appropriately reduced. To reduce the penalty and avoid over-optimization; conversely, if the absolute value of the power difference increases, then increase... To mitigate the power difference, a stronger penalty is applied, prompting the power difference to decrease as quickly as possible. This adaptive adjustment mechanism ensures that the power difference monotonically converges during the iteration process, gradually approaching zero. Finally, once the power difference converges, the Lagrange multiplier update is considered complete, and the optimization objective function is reconstructed based on the updated Lagrange multipliers. When the absolute value of the power difference is less than a preset convergence threshold, the power difference is considered to have converged. At this point, the Lagrange multiplier update is stopped, and the optimization objective function is reconstructed using the final determined Lagrange multipliers. This updated optimization objective function is then applied in subsequent power allocation solutions to obtain a more accurate power allocation scheme that meets the total power allocation requirements.

[0191] In this embodiment of the invention, by introducing slack variables, the optimization process can achieve a better balance between constraints and the objective function, avoiding optimization difficulties caused by strictly satisfying constraints, and improving the adaptability and flexibility of the optimization algorithm. The adaptive update rule can dynamically adjust the Lagrange multipliers according to changes in the power difference, accelerating the convergence speed of the power difference, reducing the number of iterations, and improving optimization efficiency. By continuously adjusting the Lagrange multipliers and reconstructing the optimization objective function, the final power allocation scheme can more accurately meet the total power allocation requirements, improving the accuracy and reliability of the optimization results. This invention ensures the consistency between the power allocation scheme and the total power requirements, helping to maintain the stable operation of the energy storage system and avoiding system fluctuations or failures caused by inaccurate power allocation. Simultaneously, the adaptive adjustment mechanism avoids the process of manually adjusting parameters, reducing the complexity of iterative optimization and making the entire optimization process more automated and efficient.

[0192] Optionally, the step of resolving the subtask using the updated optimization objective function to obtain the final power allocation scheme includes:

[0193] The subtasks are solved again by resolving the updated optimization objective function to obtain the adjusted power allocation scheme;

[0194] If the power allocation scheme does not meet the allocation requirements, the power difference corresponding to the adjusted power allocation scheme is re-acquired, and the optimization objective function is iteratively updated according to the power interpolation until the power allocation scheme meets the allocation requirements. The power allocation scheme obtained by solving the optimization objective function in the last iteration is taken as the final power allocation scheme of the virtual energy storage node.

[0195] Specifically, firstly, using an optimization algorithm (e.g., a convex optimization algorithm), the power allocation value for each battery is recalculated based on a new optimization objective function to better meet the overall power allocation requirements. The sub-tasks are then resolved using the updated optimization objective function to obtain an adjusted power allocation scheme. Next, the difference between the sum of the power values ​​of all sub-tasks and the total power is calculated to determine whether the obtained power allocation scheme meets the allocation requirements. If the absolute value of the difference is less than a preset threshold, the current power allocation scheme is considered to meet the allocation requirements; otherwise, it is considered not to. If the power allocation scheme does not meet the allocation requirements, the power difference corresponding to the adjusted power allocation scheme is re-obtained. At this point, the power difference under the current power allocation scheme needs to be recalculated to assess the degree of deviation from the overall power target. Then, the optimization objective function is iteratively updated based on the power difference. This step adjusts the parameters (e.g., Lagrange multipliers) in the optimization objective function to better guide the subsequent optimization process, reduce the power difference, and gradually approach the overall power allocation requirements. The above steps are repeated until the power allocation scheme meets the allocation requirements. In each iteration, the objective function is continuously updated and optimized, and the subtasks are resolved until the resulting power allocation scheme meets the accuracy requirements of the total power allocation. Finally, the power allocation scheme obtained from the final iteration is used as the final power allocation scheme for the virtual energy storage nodes. At this point, the power allocation scheme has been continuously optimized through multiple iterations, accurately meeting the total power allocation requirements, while also considering factors such as battery life-sensitive power constraints, ensuring the efficient, stable, and safe operation of the energy storage system.

[0196] In this optional embodiment, by iteratively updating and optimizing the objective function and resolving the subtasks, the final power allocation scheme accurately meets the total power allocation requirements, improving the accuracy and reliability of power allocation. Simultaneously, it ensures that the power allocation of the energy storage system is consistent with the total power target, avoiding system fluctuations caused by inaccurate power allocation and enhancing system stability and reliability. Furthermore, the optimization process considers the battery's lifespan-sensitive power constraints, avoiding overcharging and discharging, which helps extend battery life and reduce system maintenance costs. Through iterative optimization, the optimal power allocation scheme can be efficiently approximated, reducing redundant calculations during the optimization process and improving optimization efficiency.

[0197] Combination Figure 2 As shown, the present invention also provides an energy storage node scheduling optimization system, comprising:

[0198] The data acquisition unit is used to obtain the current state of charge and the total power allocated in the day-ahead phase for each battery in the virtual energy storage node on the user side.

[0199] A power feasible region generation unit is used to determine the power feasible region of each battery based on the current state of charge of each battery, and to use the power feasible region as the allocation requirement of the total power;

[0200] The task decomposition unit is used to decompose the total power into multiple sub-tasks, wherein each sub-task corresponds to a power allocation task of the battery;

[0201] An optimization and solution unit is used to solve each of the sub-tasks by optimizing the objective function and combining it with the power feasible region to obtain the power allocation scheme of the virtual energy storage node;

[0202] A judgment unit is used to determine whether the power allocation scheme meets the allocation requirements of the total power;

[0203] The difference calculation unit is used to determine the power difference based on the power allocation scheme and the total power if the power allocation scheme does not meet the allocation requirements.

[0204] An iterative update unit is used to iteratively update the optimization objective function based on the power difference;

[0205] The scheme output unit is used to re-solve the sub-task using the updated optimization objective function to obtain the final power allocation scheme.

[0206] The energy storage node scheduling optimization system of the present invention has the same advantages over the prior art as the energy storage node scheduling optimization method described above, and will not be repeated here.

[0207] Combination Figure 3 As shown, the present invention also provides an electronic device, including a memory and a processor;

[0208] The memory is used to store computer programs;

[0209] The processor is configured to implement the energy storage node scheduling optimization method as described above when executing the computer program.

[0210] The electronic device of the present invention has the same advantages over the prior art as the energy storage node scheduling optimization method described above, and will not be repeated here.

[0211] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the energy storage node scheduling optimization method described above.

[0212] The computer-readable storage medium of the present invention has the same advantages over the prior art as the energy storage node scheduling optimization method described above, and will not be repeated here.

[0213] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A method for scheduling optimization of energy storage nodes, characterized in that, The method comprises the following steps: obtaining the current state of charge of each battery in a virtual energy storage node on the user side and the total power allocated in the day-ahead stage; determining the power feasible region of each battery according to the current state of charge of each battery; decomposing the total power into multiple sub-tasks, wherein each sub-task corresponds to a power allocation task of one battery; solving each sub-task by optimizing an objective function in combination with the power feasible region to obtain a power allocation scheme of the virtual energy storage node; judging whether the power allocation scheme meets the allocation requirement of the total power; wherein the allocation requirement is met if the difference between the sum of the power allocation values of all batteries and the total power is within a preset threshold range; if the power allocation scheme does not meet the allocation requirement, determining a power difference value according to the power allocation scheme and the total power; iteratively updating the objective function according to the power difference value; re-solving the sub-tasks by using the updated objective function to obtain a final power allocation scheme.

2. The energy storage node dispatch optimization method of claim 1, wherein, The method of determining the power feasible region of each battery according to the current state of charge of each battery comprises the following steps: obtaining an SOC-power boundary mapping table of each battery; obtaining the maximum charging power and the maximum discharging power of the battery by interpolation in the SOC-power boundary mapping table according to the current state of charge; 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.

3. The energy storage node dispatch optimization method of claim 2, wherein, The method of decomposing the total power into multiple sub-tasks comprises the following steps: 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 a sub-task corresponding to each battery; when the power value corresponding to the sub-task of any battery is zero, the battery is skipped, and the proportional weight of the battery is redistributed to the batteries whose sub-task power values are not zero, and the power values of each sub-task are recalculated.

4. The energy storage node dispatch optimization method of claim 2, wherein, The method of solving each sub-task by optimizing an objective function in combination with the power feasible region to obtain a power allocation scheme of the virtual energy storage node comprises the following steps: 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 objective function according to the decision variable and the constraint boundary; performing convex optimization solving according to the objective function to obtain the decision variable of each battery; collecting the decision variable of each battery to obtain the power allocation scheme of the virtual energy storage node.

5. The energy storage node dispatch optimization method of claim 4, wherein, The method of judging whether the power allocation scheme meets the allocation requirement of the total power comprises the following steps: obtaining the difference between the sum of the decision variables of all sub-tasks and the total power; judging whether the power allocation scheme meets the allocation requirement of the total power according to the difference. If the absolute value of the difference value 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 value 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 dispatch optimization method of claim 1, wherein, The iterative updating of the optimization objective function according to the power difference value comprises: generating an adjustable relaxation variable according to the power difference value; 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 value; when the power difference value 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.

7. The energy storage node dispatch optimization method of claim 1, wherein, The re-solving of the sub-tasks through the updated optimization objective function to obtain a final power allocation scheme comprises: 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 meet the allocation requirement, re-acquiring the power difference value corresponding to the adjusted power allocation scheme, and iteratively updating the optimization objective function according to the power difference value until the power allocation scheme meets 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.

8. An energy storage node dispatch optimization system, comprising: comprise: a data acquisition unit configured to acquire 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; 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 configured to solve each sub-task through an optimization objective function in combination with the power feasible region to obtain a power allocation scheme of the virtual energy storage node; a judgment unit configured to judge whether the power allocation scheme meets an allocation requirement of the total power; wherein the allocation requirement is met if a difference value between a sum of power allocation values of all batteries and the total power is within a preset threshold range; a difference value calculation unit configured to, if the power allocation scheme does not meet the allocation requirement, determine a power difference value according to the power allocation scheme and the total power; an iterative updating unit configured to iteratively update the optimization objective function according to the power difference value; a scheme output unit configured to re-solve the sub-tasks through the updated optimization objective function to obtain a final power allocation scheme.

9. An electronic device, comprising: comprise 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 according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium has stored thereon a computer program which, when executed by a processor, implements the energy storage node scheduling optimization method according to any one of claims 1-7.

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