Energy storage system distributed economic dispatching method, system and equipment considering inertia support stress and life loss, and medium

Abstract

The invention discloses an energy storage system distributed economic dispatching method, system, equipment and medium considering inertia support stress and life loss, and belongs to the technical field of power system operation and control, and the method comprises the steps: collecting the real-time state data of a distributed energy storage unit, and constructing an instantaneous operation cost function of the energy storage unit; establishing a double-layer control architecture of the energy storage unit, and executing iterative updating based on a distributed consistency algorithm; and according to the converged incremental cost, combining an instantaneous operation cost function and utilizing physical constraints, controlling the output of the energy storage unit. According to the method, the algorithm automatically tends to call the energy storage unit with high health degree and large capacity to bear an inertia task, and overload of an old battery is avoided; a consistency algorithm is adopted, a central node is not needed, and a single-point communication fault does not affect the overall operation of the system; through a potential energy field penalty term, it is ensured that the electric quantity of the energy storage unit is maintained in an ideal interval during long-term operation, and the risk of electric quantity exhaustion caused by excessive pursuit of economical efficiency is avoided.

Description

Technical Field

[0001] This invention relates to the field of power system operation and control technology, specifically to a distributed economic dispatch method, system, equipment, and medium for energy storage systems that considers inertia support stress and lifespan loss. Background Technology

[0002] As the proportion of new energy sources such as wind power and photovoltaics in the power system continues to increase, these new energy units are typically connected to the grid via power electronic interfaces. Lacking the rotating inertia of traditional synchronous generators, this leads to a decrease in the grid's ability to withstand disturbances and poses a serious challenge to frequency stability. Electrochemical energy storage, due to its rapid power response capability, has become a key resource providing virtual inertia and frequency support.

[0003] In existing technologies, research on energy storage participation in the electricity market or economic dispatch mainly focuses on centralized optimization methods. For example, existing literature proposes a method based on the full life-cycle equivalent full-cycle theory, using rainflow counting to calculate lifetime losses, and performing unified clearing calculations in a trading center through mixed-integer linear programming. However, this existing technology has the following significant drawbacks when applied to large-scale distributed energy storage clusters: The computing architecture is limited, centralized solutions rely on a central controller, resulting in a heavy communication burden, the risk of single point of failure, and difficulty in adapting to the plug-and-play requirements of massive distributed energy storage resources.

[0004] The model has insufficient applicability. The traditional rainflow counting method is nonlinear and nondifferentiable, and cannot be directly applied to distributed consensus algorithms that require gradient information, making it difficult to achieve decentralized collaborative optimization.

[0005] Ignoring instantaneous stress costs, existing economic models typically only consider the impact of charge and discharge depth on lifespan, while neglecting the irreversible damage to the internal electrochemical structure of the battery caused by short-term high-rate current surges during inertia support.

[0006] The ability to maintain the state of charge is weak, and there is a lack of dynamic potential energy constraint on the state of charge. After providing inertial support, the energy storage is prone to depletion or overcharging, resulting in the loss of subsequent regulation capabilities.

[0007] Therefore, there is an urgent need for a method that can balance distributed computing efficiency, quantify battery inertia response stress cost, and maintain long-term stability. A healthy approach to economic regulation. Summary of the Invention

[0008] In view of the above-mentioned problems, the present invention is proposed.

[0009] Therefore, the technical problem to be solved by this invention is: how to achieve the minimization of total system operating cost and effective extension of battery life in a distributed network without a central controller, while providing inertia support through multiple energy storage nodes.

[0010] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a distributed economic dispatch method for energy storage systems considering inertia support stress and lifespan loss, comprising, Collect real-time status data of distributed energy storage units and construct the instantaneous operating cost function of the energy storage units; establish a two-layer control architecture for the energy storage units and perform iterative updates based on a distributed consensus algorithm; control the output of the energy storage units based on the converged incremental cost, combined with the instantaneous operating cost function and physical constraints.

[0011] As a preferred embodiment of the distributed economic dispatch method for energy storage systems that considers inertia support stress and lifespan loss as described in this invention, the real-time status data of the distributed energy storage units collected includes state of charge, battery health, and system frequency change rate.

[0012] As a preferred embodiment of the distributed economic dispatch method for energy storage systems that considers inertia support stress and lifetime loss as described in this invention, the instantaneous operating cost function of the energy storage unit is constructed by constructing a strongly convex quadratic function, which includes a quadratic term reflecting basic operating losses, a linear term reflecting regulation service revenue, and a potential energy term reflecting the penalty for deviation from the state of charge. The coefficient of the quadratic term is defined as the dynamic inertia stress coefficient. When the frequency change rate is large, the cost factor automatically increases, limiting the high-rate output of severely aged or small-capacity batteries.

[0013] As a preferred embodiment of the distributed economic dispatch method for an energy storage system considering inertia support stress and lifespan loss as described in this invention, the dual-layer control architecture includes a physical layer fast channel and an economic layer slow channel. Real-time response of physical layer fast channel to frequency change output inertia power based on droop control; Economic layer slow channel optimization power benchmark based on consensus algorithm.

[0014] As a preferred embodiment of the distributed economic dispatch method for energy storage systems that considers inertia support stress and lifespan loss as described in this invention, the distributed consensus algorithm includes a supply-demand balance feedback mechanism in its iteration rules. Each energy storage unit exchanges information only with its neighboring nodes in the communication topology. The exchanged data includes incremental costs and estimates of the global power imbalance. During the scheduling cycle, [the following is performed / conducted]. In the next iteration, the state variables are defined, including the incremental cost. Power imbalance estimator ; The iteration rule is to exchange information and receive information from neighboring nodes. of and Update incremental costs: in, To converge the step size, For supply and demand feedback gain, Let $i$ be the incremental cost of the $i$-th node. For the set of neighboring nodes, The communication weight between nodes i and j The global power imbalance is estimated locally. Update power imbalance: in, Let be the local load of the i-th node. Let be the output power of the i-th energy storage unit in the k-th iteration; ensure that the total power generation of the system tracks the total load. By using a discrete consensus protocol to iteratively update its incremental cost, the incremental cost of all energy storage units in the network tends to be consistent.

[0015] As a preferred embodiment of the distributed economic dispatch method for energy storage systems that considers inertia support stress and lifespan loss as described in this invention, the method for controlling the output of the energy storage unit includes calculating the optimal power and constraint projection based on the converged incremental cost, combined with the instantaneous operating cost function. The physical constraints include physical constraints processed by projection operators, based on the current... Values ​​and preset cutoff Threshold, dynamically calculates the maximum allowable charging power at the current moment. and maximum discharge power ; According to the incremental rate criterion Calculate the unconstrained power: in This is the calculated unconstrained power reference value; The coefficient for the linear term reflecting the adjustment of service revenue; This is the dynamic inertia stress coefficient, whose value is dynamically adjusted with the rate of change of the system frequency. Applying projection operators to handle KKT constraints: in, and It is based on the current And the physical boundary of the dynamic calculation of the maximum magnification limit.

[0016] The preferred technical solution in the embodiments of the present invention has the following advantages: by dynamically calculating the power boundary and applying the projection operator, the energy storage unit can be ensured to output power economically and efficiently within physical constraints, while adapting to real-time frequency changes to reduce lifespan loss.

[0017] As a preferred embodiment of the distributed economic dispatch method for energy storage systems that considers inertia support stress and lifespan loss as described in this invention, the control of energy storage unit output further includes: if the calculated optimal power reference value exceeds the current range, it is forcibly truncated to the boundary value, implicitly satisfying the complementary relaxation in the KKT condition. When the calculated unconstrained optimal power fall into Within the interval, according to the complementary relaxation condition, the corresponding Lagrange multiplier is zero, and the output power is the calculated value at this time. When the calculated value exceeds the boundary, the corresponding constraints take effect, the Lagrange multipliers become non-zero, and according to the qualitative requirements in the KKT conditions, the optimal solution lies on the boundary. The truncation operation ensures that the final solution strictly satisfies the necessary conditions of KKT for constrained optimization problems; After convergence As a baseline instruction, the droop control component of the physical layer is superimposed and sent to the PCS for execution. Based on the updated incremental cost, the optimal power reference value is calculated by combining the inverse function of the derivative of the instantaneous operating cost model.

[0018] The beneficial effects of the preferred technical solution in the embodiments of the present invention are as follows: by implicitly satisfying the KKT conditions through the truncation operation, the optimality and feasibility of the optimized solution are guaranteed, and the droop control is combined to achieve rapid frequency stabilization and coordination of distributed economic scheduling.

[0019] Another objective of this invention is to provide a distributed economic dispatch system for energy storage systems that takes into account inertia support stress and lifespan loss.

[0020] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a distributed economic dispatch system for energy storage system considering inertia support stress and life loss, comprising: an energy storage converter PCS, a local controller connected to each PCS, and a communication network connecting each local controller; The local controller is configured to collect real-time status data of the distributed energy storage unit, construct the instantaneous operating cost function of the energy storage unit, establish a two-layer control architecture for the energy storage unit, perform iterative updates based on a distributed consensus algorithm, and control the output of the energy storage unit based on the converged incremental cost, combined with the instantaneous operating cost function and physical constraints. The active power output of the PCS is adjusted based on the calculation results.

[0021] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of a distributed economic dispatch method for an energy storage system considering inertia support stress and lifespan loss.

[0022] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of a distributed economic dispatch method for an energy storage system that considers inertia support stress and lifespan loss.

[0023] The beneficial effects of this invention are: It protects battery life by innovatively introducing a dynamic stress coefficient coupled with the rate of frequency change. This automatically increases the "economic cost" of high-rate discharge when the grid frequency fluctuates drastically, causing the algorithm to automatically prioritize energy storage units with high health and large capacity to handle inertia tasks, thus avoiding overloading older batteries. It also offers decentralization and robustness by using a consensus algorithm instead of centralized optimization. No central node is required, and single-point communication failures do not affect the overall system operation, providing good scalability. Finally, it balances economy and safety through… The potential energy field penalty term ensures that the energy storage unit maintains its power within the ideal range during long-term operation, avoiding the risk of power depletion due to excessive pursuit of economic efficiency. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Fig. 1 The above is a general flowchart of a distributed economic dispatch method for energy storage systems that considers inertia support stress and lifespan loss, provided as an embodiment of the present invention.

[0026] Fig. 2 This is a logical block diagram of a two-layer control architecture for a distributed economic dispatch method for energy storage systems that considers inertia support stress and lifespan loss, as provided in an embodiment of the present invention.

[0027] Fig. 3 This is a schematic diagram of the dynamic inertia stress coefficient versus frequency rate of change in a distributed economic dispatch method for an energy storage system that considers inertia support stress and lifespan loss, provided as an embodiment of the present invention.

[0028] Fig. 4 This is a schematic diagram of neighbor node information interaction in a distributed consensus algorithm for a distributed economic dispatch method of an energy storage system that considers inertia support stress and lifespan loss, provided as an embodiment of the present invention. Detailed Implementation

[0029] 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. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0030] Example 1, referring to Figs. 1-4 This is one embodiment of the present invention, which provides a distributed economic dispatch method for energy storage systems that considers inertia support stress and lifetime loss, including: S100: Collect real-time status data of distributed energy storage units and construct the instantaneous operating cost function of the energy storage units; S200: Establish a two-layer control architecture for the energy storage unit and perform iterative updates based on a distributed consensus algorithm; S300: Based on the converged incremental cost, combined with the instantaneous operating cost function, and utilizing physical constraints, control the output of the energy storage unit. It should be noted that traditional energy storage economic dispatch methods typically separate the rapid grid inertia support from the slow economic power allocation, leading to a conflict between their objectives. Their dispatch models employ static cost functions and fixed power constraints, failing to detect the "inertia stress" that exacerbates battery aging due to providing instantaneous frequency support, and also failing to flexibly adjust output boundaries based on the real-time health status of the batteries. This often results in insufficient economic efficiency in dispatching and may cause irreversible lifespan damage to energy storage devices during periods of high disturbance.

[0031] Therefore, to address the aforementioned problems, through steps S100-S300, this invention constructs a dynamically coupled real-time optimization model of "stress-cost," designs a fast-slow coordinated two-layer distributed control architecture, and introduces a constraint processing mechanism based on real-time status, thus unifying inertia support and economic dispatch objectives. This method enables the energy storage system to automatically weigh support benefits against lifetime loss costs when responding to grid disturbances, dynamically suppress high-stress output, and allocate power according to the actual health status of each unit. This significantly improves the overall economic efficiency and lifespan of energy storage assets while ensuring grid frequency stability.

[0032] Example 2, refer to Figs. 1-4 This is one embodiment of the present invention, which provides a distributed economic dispatch method for energy storage systems that considers inertia support stress and lifetime loss, including: In this embodiment of the invention, step S100 involves collecting real-time status data of the distributed energy storage unit and constructing an instantaneous operating cost function for the energy storage unit, including the following steps S101-S102: In an embodiment of the present invention, S101, collecting real-time status data of the distributed energy storage units includes collecting real-time data of each energy storage unit. Real-time acquisition of its own state of charge Battery health Rated capacity and the system frequency detected locally and rate of change of frequency .

[0033] In an alternative implementation, real-time acquisition is replaced with periodic sampling, where each energy storage unit acquires its own state of charge, battery health, rated capacity, system frequency, and rate of change of frequency at fixed time intervals, and keeps this data unchanged within a cycle.

[0034] S102. Construct the instantaneous operating cost function of the energy storage unit, that is, construct a strongly convex quadratic function, which includes a quadratic term reflecting the basic operating loss, a linear term reflecting the regulation service revenue, and a potential energy term reflecting the penalty for deviation from the state of charge. In an embodiment of the present invention, the coefficient of the quadratic term is defined as the dynamic inertia stress coefficient; Specifically, define energy storage units Cost function as follows: in, For output power, The coefficient of the first-order term related to energy prices, for Maintain the penalty coefficient. It is usually set to 0.5. The dynamic inertia stress coefficient is calculated using the following formula: In the formula, These are baseline aging parameters, which are related to the battery's internal resistance. The preset stress gain constant, It is a non-linear exponent.

[0035] When the power grid is disturbed Enlargement, leading to The sudden increase in power leads to a sharp rise in the unit power cost of the battery, thus suppressing excessive instantaneous power output in economic dispatch, unless the battery... Excellent (large denominator).

[0036] In one alternative implementation, the quadratic term is a piecewise linear penalty term, which sets an ideal center point for the state of charge. When the actual value deviates from this center point, the cost increases linearly with the deviation distance, rather than increasing by the square of the distance. However, this implementation is less effective at suppressing extreme deviations.

[0037] In another alternative implementation, the quadratic term is to eliminate the dynamic potential energy term that is related to the real-time state of charge, and to add a fixed operating cost coefficient to the cost function. This coefficient is preset according to the long-term average state of charge or type of the energy storage unit and is not adjusted with changes in the real-time state of charge. However, this implementation cannot dynamically guide the energy storage unit to maintain a state of charge that is conducive to extending its lifespan.

[0038] In this embodiment of the invention, a two-layer control architecture for the energy storage unit is established in S200, and iterative updates are performed based on a distributed consensus algorithm, including the following steps S201-S202: S201, the layer control architecture includes a physical layer fast channel and an economic layer slow channel; Real-time response of physical layer fast channel to frequency change output inertia power based on droop control; Economic layer slow channel optimization power benchmark based on consensus algorithm.

[0039] S202, The iterative rules of the distributed consensus algorithm include a supply and demand balance feedback mechanism; Each energy storage unit exchanges information only with its neighboring nodes in the communication topology. The exchanged data includes incremental costs and estimates of the global power imbalance. During the scheduling cycle, [the following is performed / conducted]. In the next iteration, the state variables are defined, including the incremental cost. Power imbalance estimator ; The iteration rule is to exchange information and receive information from neighboring nodes. of and Update incremental costs: in, To converge the step size, For supply and demand feedback gain, Let $i$ be the incremental cost of the $i$-th node. For the set of neighboring nodes, The communication weight between nodes i and j The global power imbalance is estimated locally. Update power imbalance: in, Let be the local load of the i-th node. Let be the output power of the i-th energy storage unit in the k-th iteration; ensure that the total power generation of the system tracks the total load. By using a discrete consensus protocol to iteratively update its incremental cost, the incremental cost of all energy storage units in the network tends to be consistent.

[0040] In an embodiment of the present invention, step S300 controls the output of the energy storage unit based on the converged incremental cost, combined with the instantaneous operating cost function, and utilizing physical constraints, including the following steps S301-S302: S301. In each iteration of the distributed consensus algorithm, the calculated power reference value may exceed the physical limits of the energy storage unit (i.e., the upper and lower limits of charging and discharging power). To ensure the physical feasibility of the scheduling results and satisfy mathematical optimality, this invention introduces a projection operator based on KKT (Karush-Kuhn-Tucker) conditions. The projection operator is introduced to handle the upper and lower power limit constraints, and dynamically adjusts the power boundary to satisfy the KKT conditions based on the current SoC predicting the state at the next moment; finally, the corrected power command is sent to the underlying controller.

[0041] Based on the converged incremental cost, combined with the instantaneous operating cost function, the optimal power and constraint projection are calculated. The physical constraints include physical constraints processed by projection operators, based on the current... Values ​​and preset cutoff Threshold, dynamically calculates the maximum allowable charging power at the current moment. and maximum discharge power ; According to the incremental rate criterion Calculate the unconstrained power: The denominator includes the real-time inertia stress coefficient, where This is the calculated unconstrained power reference value; The coefficient for the linear term reflecting the adjustment of service revenue; This is the dynamic inertia stress coefficient, whose value is dynamically adjusted with the rate of change of the system frequency. Applying projection operators to handle KKT constraints: in, and It is based on the current And the physical boundary of the dynamic calculation of the maximum magnification limit.

[0042] S302. If the calculated optimal power reference value exceeds the current range, it is forcibly truncated to the boundary value, implicitly satisfying the complementary relaxation in the KKT conditions. When the calculated unconstrained optimal power fall into Within the interval, according to the complementary relaxation condition, the corresponding Lagrange multiplier is zero, and the output power is the calculated value at this time. When the calculated value exceeds the boundary, the corresponding constraints take effect, the Lagrange multipliers become non-zero, and according to the qualitative requirements in the KKT conditions, the optimal solution lies on the boundary. The truncation operation ensures that the final solution strictly satisfies the necessary conditions of KKT for constrained optimization problems; After convergence As a baseline instruction, the droop control component of the physical layer is superimposed and sent to the PCS for execution. Based on the updated incremental cost, the optimal power reference value is calculated by combining the inverse function of the derivative of the instantaneous operating cost model.

[0043] Example 3 is an embodiment of the present invention. The above is an illustrative scheme of a distributed economic dispatch method for energy storage systems considering inertia support stress and lifespan loss. It should be noted that the technical solution of a distributed economic dispatch system for energy storage systems considering inertia support stress and lifespan loss belongs to the same concept as the above-described distributed economic dispatch method for energy storage systems considering inertia support stress and lifespan loss. Details not described in detail in the technical solution of the distributed economic dispatch system for energy storage systems considering inertia support stress and lifespan loss in this embodiment can be found in the description of the above-described distributed economic dispatch method for energy storage systems considering inertia support stress and lifespan loss.

[0044] This embodiment provides a distributed economic dispatch system for energy storage systems that considers inertia support stress and lifespan loss, including: an energy storage converter PCS, a local controller connected to each PCS, and a communication network connecting each local controller. The local controller is configured to collect real-time status data of the distributed energy storage unit, construct the instantaneous operating cost function of the energy storage unit, establish a two-layer control architecture for the energy storage unit, perform iterative updates based on a distributed consensus algorithm, and control the output of the energy storage unit based on the converged incremental cost, combined with the instantaneous operating cost function and physical constraints. The active power output of the PCS is adjusted based on the calculation results.

[0045] This embodiment also provides an electronic device applicable to a distributed economic dispatch method for an energy storage system considering inertia support stress and lifespan loss, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the distributed economic dispatch method for an energy storage system considering inertia support stress and lifespan loss as proposed in the above embodiment.

[0046] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a distributed economic dispatch method for energy storage systems that considers inertia support stress and lifespan loss as proposed in the above embodiment.

[0047] The storage medium proposed in this embodiment and the method for implementing a distributed economic dispatch of an energy storage system that considers inertia support stress and lifespan loss proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0048] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0049] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A distributed economic dispatch method for energy storage systems considering inertia support stress and lifespan loss, characterized in that: include, Collect real-time status data of distributed energy storage units and construct the instantaneous operating cost function of the energy storage units; A two-layer control architecture for energy storage units is established, and iterative updates are performed based on a distributed consensus algorithm; Based on the converged incremental cost, combined with the instantaneous operating cost function, and utilizing physical constraints, the output of the energy storage unit is controlled.

2. The distributed economic dispatch method for energy storage systems considering inertia support stress and lifespan loss as described in claim 1, characterized in that: The real-time status data collected from the distributed energy storage unit includes state of charge, battery health, and system frequency change rate.

3. The distributed economic dispatch method for energy storage systems considering inertia support stress and lifespan loss as described in claim 2, characterized in that: The instantaneous operating cost function for constructing the energy storage unit includes constructing a strongly convex quadratic function, which includes a quadratic term reflecting basic operating losses, a linear term reflecting regulation service revenue, and a potential energy term reflecting the penalty for deviation from the state of charge. The coefficient of the quadratic term is defined as the dynamic inertia stress coefficient. When the frequency change rate is large, the cost factor automatically increases, limiting the high-rate output of severely aged or small-capacity batteries.

4. The distributed economic dispatch method for energy storage systems considering inertia support stress and lifespan loss as described in claim 3, characterized in that: The dual-layer control architecture includes a physical layer fast channel and an economic layer slow channel; Real-time response of physical layer fast channel to frequency change output inertia power based on droop control; Economic layer slow channel optimization power benchmark based on consensus algorithm.

5. A distributed economic dispatch method for energy storage systems considering inertia support stress and lifespan loss as described in claim 4, characterized in that: The distributed consensus algorithm includes an iterative rule that incorporates a supply-demand balance feedback mechanism. Each energy storage unit exchanges information only with its neighboring nodes in the communication topology. The exchanged data includes incremental costs and estimates of the global power imbalance. During the scheduling cycle, [the following is performed / conducted]. In the next iteration, the state variables are defined, including the incremental cost. Power imbalance estimator ; The supply and demand balance feedback mechanism of the iterative rules involves information exchange and receiving feedback from neighboring nodes. of and Update incremental costs: in, To converge the step size, For supply and demand feedback gain, Let $i$ be the incremental cost of the $i$-th node. For the set of neighboring nodes, The communication weight between nodes i and j The global power imbalance is estimated locally. Update power imbalance: in, Let be the local load of the i-th node. Let be the output power of the i-th energy storage unit in the k-th iteration; ensure that the total power generation of the system tracks the total load. By using a discrete consensus protocol to iteratively update its incremental cost, the incremental cost of all energy storage units in the network tends to be consistent.

6. The distributed economic dispatch method for energy storage systems considering inertia support stress and lifespan loss as described in claim 5, characterized in that: The control of the energy storage unit output includes calculating the optimal power and constraint projection based on the converged incremental cost, combined with the instantaneous operating cost function. The physical constraints include physical constraints processed by projection operators, based on the current... Values ​​and preset cutoff Threshold, dynamically calculates the maximum allowable charging power at the current moment. and maximum discharge power ; According to the incremental rate criterion Calculate the unconstrained power: in This is the calculated unconstrained power reference value; The coefficient for the linear term reflecting the adjustment of service revenue; This is the dynamic inertia stress coefficient, whose value is dynamically adjusted with the rate of change of the system frequency. Applying projection operators to handle KKT constraints: in, and It is based on the current And the physical boundary of the dynamic calculation of the maximum magnification limit.

7. A distributed economic dispatch method for energy storage systems considering inertia support stress and lifespan loss as described in claim 6, characterized in that: The control of the energy storage unit output also includes, if the calculated optimal power reference value exceeds the current range, forcibly truncate it to the boundary value, implicitly satisfying the complementary relaxation in the KKT conditions; When the calculated unconstrained optimal power fall into Within the interval, according to the complementary relaxation condition, the corresponding Lagrange multiplier is zero, and the output power is the calculated value at this time. When the calculated value exceeds the boundary, the corresponding constraints take effect, the Lagrange multipliers become non-zero, and according to the qualitative requirements in the KKT conditions, the optimal solution lies on the boundary. The truncation operation ensures that the final solution strictly satisfies the necessary conditions of KKT for constrained optimization problems; After convergence As a baseline instruction, the droop control component of the physical layer is superimposed and sent to the PCS for execution. Based on the updated incremental cost, the optimal power reference value is calculated by combining the inverse function of the derivative of the instantaneous operating cost model.

8. A distributed economic dispatch system for energy storage systems considering inertia support stress and lifespan loss, employing the distributed economic dispatch method for energy storage systems considering inertia support stress and lifespan loss as described in any one of claims 1 to 7, characterized in that, include: Energy storage converter PCS, local controller connected to each PCS, and communication network connecting each local controller; The local controller is configured to collect real-time status data of the distributed energy storage unit, construct the instantaneous operating cost function of the energy storage unit, establish a two-layer control architecture for the energy storage unit, perform iterative updates based on a distributed consensus algorithm, and control the output of the energy storage unit based on the converged incremental cost, combined with the instantaneous operating cost function and physical constraints. The active power output of the PCS is adjusted based on the calculation results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of any one of claims 1 to 7: a distributed economic dispatch method for an energy storage system that considers inertia support stress and lifespan loss.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of any one of claims 1 to 7 of the distributed economic dispatch method for an energy storage system that considers inertia support stress and lifespan loss.

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