Energy storage multi-benefit collaborative optimization method based on network construction control parameter discrete self-adaptation

Abstract

The application discloses a multi-revenue collaborative optimization method for energy storage based on network construction control parameter discrete self-adaptation, which comprises the following steps: discretizing converter virtual synchronous generator control parameters into three safety modes, establishing dynamic power reservation boundaries corresponding to each mode, establishing a target function with the maximization of total profit in the whole scheduling period as the target, taking into account the three revenues of energy arbitrage, secondary frequency modulation and network support, and subtracting the battery life loss penalty cost, solving by a mixed integer quadratic programming solver, outputting the optimal power instruction sequence and the optimal network construction mode sequence, and the converter switching the corresponding parameter set according to the instruction and executing the disturbance-free switching algorithm. The application quantifies the capacity allocation relationship through mode mutual exclusion constraints and dynamic safety boundary constraints, introduces a frequency modulation high-frequency fluctuation conversion coefficient and a state of charge deviation penalty weight to accurately quantify the battery aging cost, realizes the dynamic optimal allocation of energy storage capacity among multiple markets, and significantly improves the comprehensive economic benefit of the energy storage power station.

Description

Technical Field

[0001] This invention relates to the field of novel power systems and energy storage control, specifically to a method and system for multi-benefit collaborative optimization of energy storage based on discrete adaptive grid control parameters. Background Technology

[0002] To achieve the "dual carbon" goal, the penetration rate of new energy sources such as wind power and photovoltaics in new power systems is constantly increasing. However, the widespread integration of traditional new energy equipment has led to a decrease in the rotational inertia and short-circuit capacity of the power system, seriously affecting the stable operation of the power grid. Grid-based energy storage, with its advantages such as simulating the characteristics of synchronous generators and actively providing virtual inertia and voltage support, has become key to solving the above problems. Scientific scheduling and control of grid-based energy storage in the electricity market is the foundation for fully realizing its grid support capabilities and economic value.

[0003] Researchers have studied the participation of energy storage power stations in the power market dispatch and established relevant economic optimization models. However, existing prediction and dispatch models still do not fully consider all factors. In actual operation, the comprehensive economics of grid-based energy storage is affected by multiple factors. Existing methods typically set the virtual synchronous generator control parameters of the converter to fixed values, resulting in a disconnect between the underlying grid control parameters and the top-level economic dispatch. This makes it impossible to dynamically adjust the control mode according to market price signals and grid conditions, and also makes it difficult to flexibly allocate capacity while ensuring hardware safety boundaries. At the same time, energy storage systems need to simultaneously meet multiple tasks such as energy arbitrage, secondary frequency regulation, and grid support. However, existing models lack dynamic power reserve boundaries for each mode, making it impossible to quantify and constrain the capacity contention between arbitrage operation and grid reserve, which can easily exceed hardware current limits. In addition, grid-based energy storage needs to frequently respond to power when providing active inertia and primary frequency regulation support. This high-frequency fluctuation accelerates the nonlinear aging of batteries. Existing economic optimization models generally ignore or oversimplify battery aging costs and lack refined penalties for high-frequency frequency regulation actions and deviations from the optimal state of charge range, leading to distorted life-cycle benefit assessments. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a multi-benefit collaborative optimization method for energy storage based on discrete adaptive network control parameters, thus solving the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention provides a multi-benefit collaborative optimization method for energy storage based on discrete adaptive network control parameters, comprising the following steps:

[0006] Step 1: Real-time synchronous acquisition of the power system grid connection point operation status and multi-dimensional electricity market price signals; simultaneously, the battery charge status and health status are collected through the battery management system;

[0007] Step 2: Based on the grid connection point operation status obtained in Step 1, the control parameters of the converter virtual synchronous generator are discretized into three safety modes, and dynamic power reserve boundaries are established for each mode to ensure that the hardware current limit is not exceeded when providing grid support.

[0008] Step 3: Based on the three safety modes and their dynamic power reserve boundaries established in Step 2, and combined with the battery state of charge, health status and multi-dimensional market price signals collected in Step 1, establish an objective function with the goal of maximizing total profit over the entire scheduling cycle, taking into account the triple benefits of energy arbitrage, secondary frequency regulation and grid support, and subtracting the penalty cost of battery life loss.

[0009] Step 4: Based on the objective function and its constraints constructed in Step 3, solve the objective function in the energy management system and output the optimal power reference command sequence and the optimal network configuration mode sequence;

[0010] Step 5: Based on the optimal network configuration sequence obtained in Step 4, commands are issued through the industrial protocol. The converter switches the corresponding parameter set according to the commands and executes a disturbance-free switching algorithm to maintain the continuity of the internal state.

[0011] Furthermore, the dynamic power reserve boundary in step two refers to the ultimate support power that the converter must reserve under the three safety modes. The formula for calculating the ultimate support power is as follows:

[0012]

[0013] In the formula: The virtual inertia time constant; This is the droop damping coefficient. The rated apparent capacity of the energy storage converter; The rated frequency of the power grid; and These are the maximum allowable rate of frequency change and the maximum frequency deviation, respectively, according to power grid standards.

[0014] Furthermore, the three security modes in step two include:

[0015] Mode 1 is the energy arbitrage priority mode: setting a smaller virtual inertia. and droop damping The corresponding reserved support power boundary for the network structure is Maximize the release of battery capacity to participate in spot market arbitrage;

[0016] Mode 2 is the balanced response mode: setting the standard virtual inertia. and droop damping The corresponding reserved support power boundary is ;

[0017] Mode 3 is a strong network support mode: setting high virtual inertia. and large droop damping The corresponding reserved support power boundary is It is used when the power grid frequency fluctuates drastically or the compensation unit price is extremely high.

[0018] Furthermore, in step two, the dynamic power reservation boundary must satisfy both the mode mutual exclusion constraint and the dynamic safety boundary constraint of the hardware limit, as calculated in the following formulas:

[0019]

[0020]

[0021] In the formula: For a moment Select the Decision variables for each model; Planned discharge power for participation in energy market arbitrage; For the planned charging power; For the declared secondary frequency regulation capacity; For the first The maximum power limit for network response that is forcibly reserved by the underlying controller in this mode; This represents the maximum power allowed under the current battery condition.

[0022] Furthermore, the objective function in step three is as follows:

[0023]

[0024] In the formula: For arbitrage profits; For secondary frequency modulation revenue; To provide compensation for network support; The cost of penalizing battery degradation.

[0025] Furthermore, the energy arbitrage profit in the spot market during step three... :

[0026]

[0027] in, For discharge power, For charging efficiency, λ e (t) The spot market electricity price in time period t; P dis (t) Discharge power in time period t, P ch (t) Charging power in time period t;

[0028] Secondary frequency modulation revenue A linear prediction is made by combining the frequency modulation capacity price and the revenue from mileage usage:

[0029]

[0030] in, For frequency modulation capacity price, The price is for mileage compensation. This is the probability coefficient for invoking the frequency modulation command; For energy storage frequency regulation performance indicators; (t) The frequency modulation capacity declared by the aggregate in time period t;

[0031] Network support compensation benefits :

[0032]

[0033] in, To provide the market with the unit price of active inertia and primary frequency regulation support capacity for grid-type energy storage; k (t) The activation status variable of the k-th frequency modulation support level in time period t; P gk The rated power capacity corresponding to the kth frequency modulation support level;

[0034] Battery life degradation penalty cost :

[0035]

[0036] In the formula: The depreciation unit price is based on the energy throughput per unit of health status; This is the lifetime reduction factor for frequency modulation high-frequency fluctuations; Penalty weights for deviations in battery state of charge; For optimal health maintenance level; P dis (t) Discharge power in time period t; P ch (t) Charging power in time period t; (t) represents the frequency modulation capacity declared by the polymer in time period t, and SOC(t) represents the state of charge of the battery in time period t.

[0037] Furthermore, the constraints in step four include the introduction of continuity and extremum constraints of the charged state, as well as the linearization of absolute values ​​using the Big M method for mutual exclusion constraints. The relevant calculation formulas are as follows:

[0038]

[0039]

[0040]

[0041]

[0042] Where: SOC(t−1) is the state of charge at the end of the (t−1)th time interval, and P dis (t) Discharge power in time period t, η dis Discharge efficiency, P ch (t) Charging power in time period t, η ch Charging efficiency; Δt is the duration of each time period. Rated energy storage capacity; SOC min State of charge limit; SOC max Upper limit of state of charge Boolean state variables introduced to prevent simultaneous charging and discharging; It is a sufficiently large positive real constant.

[0043] Furthermore, in step four, a mixed-integer quadratic programming solver is used to perform rolling optimization on the objective function to obtain the optimal power command sequence and the optimal network configuration sequence.

[0044] Furthermore, the instruction execution logic in step five is as follows: after the converter receives the mode switching code, a hardware interrupt is triggered, the pre-stored parameter set is called, and a state variable smooth initialization algorithm is used to achieve a smooth switching, avoiding current surges caused by the switching moment.

[0045] Furthermore, the disturbance-free switching algorithm specifically involves: smooth initialization of state variables to maintain the phase continuity of the internal virtual power angle and virtual potential at the switching moment, thereby avoiding current surges caused by parameter mutations.

[0046] This invention provides a multi-benefit collaborative optimization method for energy storage based on discrete adaptive network control parameters. Compared with existing technologies, it has the following advantages:

[0047] 1. This invention discretizes the virtual inertia time constant and droop damping coefficient of the converter's virtual synchronous generator into three safety modes: energy arbitrage priority mode, balanced response mode, and strong grid support mode. It also establishes dynamic power reserve boundary calculation formulas for each mode. By introducing mode decision variables and mutual exclusion constraints, the top-level optimization model can dynamically select the optimal control mode based on spot electricity prices, frequency regulation prices, and grid compensation unit prices. This avoids the economic losses caused by the fixed underlying parameters in traditional methods, while ensuring that the converter does not exceed the hardware current limit during mode switching, thus solving the problem of the disconnect between the underlying grid control parameters and the top-level economic dispatch.

[0048] 2. This invention clarifies the capacity allocation relationship among three revenue modes—energy arbitrage, secondary frequency regulation, and grid support—by constructing mutual exclusion constraints and dynamic safety boundary constraints for hardware limits. This solves the problem of insufficient quantitative constraints in existing models regarding capacity preemption conflicts between arbitrage operation and grid backup, ensuring that the energy storage system operates within the safety boundary at all times. Simultaneously, this invention introduces a battery life degradation penalty cost term into the objective function, including a frequency regulation high-frequency fluctuation lifetime reduction factor and a weighted penalty for state of charge deviation. This accurately quantifies the damage to battery life caused by high-frequency response and deviations from the optimal maintenance level of state of charge, overcoming the distortion in revenue assessment caused by neglecting or simplifying aging costs in existing models.

[0049] 3. This invention utilizes a mixed-integer quadratic programming solver to perform rolling optimization of the objective function, outputting the optimal power command sequence and the optimal grid configuration sequence. Combined with a disturbance-free switching algorithm, it maintains the phase continuity of the virtual power angle and virtual potential inside the converter, realizing the real-time optimal allocation of energy storage capacity among multiple markets such as spot arbitrage, secondary frequency regulation, and grid support, significantly improving the comprehensive economic benefits and asset lifespan of energy storage power stations. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0051] Figure 1 The flowchart of the energy storage multi-benefit collaborative optimization method based on discrete adaptive network control parameters of the present invention is shown. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] To address the technical problems in the background section, the following multi-benefit collaborative optimization method for energy storage based on discrete adaptive network control parameters is proposed: Figure 1 As shown, it includes the following steps:

[0054] Step 1: Real-time synchronous acquisition of the operating status of the power system grid connection point and multi-dimensional electricity market price signals; simultaneously, the battery state of charge (SOC) and state of health (SOH) are collected through the battery management system (BMS); among which, the operating status includes the grid connection point frequency change rate, frequency deviation, and voltage deviation; the market signals include day-ahead / real-time spot electricity price, secondary frequency regulation capacity compensation price and mileage compensation price, as well as the unit price of grid support ancillary services compensation.

[0055] Step Two: Based on the grid connection point operating status obtained in Step One, the control parameters of the virtual synchronous generator of the converter are discretized into three safety modes, and dynamic power reserve boundaries are established for each mode to ensure that the hardware current limit is not exceeded when providing grid support. Specifically: To ensure the stability of the underlying converter hardware during parameter adjustment, the core control parameters of the virtual synchronous generator of the grid-connected energy storage converter are discretized into three safety control modes. Among them, the underlying virtual inertia time constant... With droop damping coefficient Reserve power to support the hardware's maximum capacity The mapping relationship satisfies the following physical equation:

[0056]

[0057] In the formula: The rated apparent capacity of the energy storage converter; The rated frequency of the power grid; and These are the maximum allowable rate of frequency change and the maximum frequency deviation, respectively, according to power grid standards.

[0058] Mode 1 is the energy arbitrage priority mode: setting a smaller virtual inertia. and droop damping The corresponding reserved support power boundary for the network structure is To maximize the release of battery capacity for arbitrage in the spot market.

[0059] Mode 2 is the balanced response mode: setting the standard virtual inertia. and droop damping The corresponding reserved support power boundary is .

[0060] Mode 3 is a strong network support mode: setting high virtual inertia. and large droop damping The corresponding reserved support power boundary is This is used when the grid frequency fluctuates drastically or the compensation unit price is extremely high. For example, in Mode 1, H1=2s and D1=10; in Mode 2, H2=5s and D2=30; and in Mode 3, H3=8s and D3=50. The above values ​​can be adjusted according to the actual energy storage converter parameters and grid standards.

[0061] set up For a moment Select the The decision variables for each pattern must satisfy the pattern mutual exclusion constraint:

[0062]

[0063] The system's real-time output power must meet dynamic safety boundary constraints based on hardware limits:

[0064]

[0065] In the formula: Planned discharge power for participation in energy market arbitrage; For the planned charging power, as a shared energy storage operator, the charging power includes the power purchased directly from the main grid; For the declared secondary frequency regulation capacity; This is the maximum power limit for network response that is forcibly reserved for the underlying controller in the current mode.

[0066] Step 3: Based on the three safety modes and their dynamic power reservation boundaries established in Step 2, and combined with the battery state of charge, health status, and multi-dimensional market price signals collected in Step 1, establish an objective function aimed at maximizing total profit over the entire scheduling cycle. This function takes into account the triple benefits of energy arbitrage, secondary frequency regulation, and grid support, while subtracting the penalty cost of battery life loss. Specifically: Construct a function based on the scheduling cycle... The comprehensive objective function aimed at maximizing the total net profit of the internal power plant The objective function encompasses the superposition of returns from multiple markets and introduces a battery degradation penalty model:

[0067]

[0068] The formulas for calculating the revenue and cost of the sub-items are as follows:

[0069] ① Arbitrage profits in the spot market :

[0070]

[0071] in, and This refers to the charge / discharge efficiency.

[0072] ② Secondary frequency modulation revenue A linear prediction is made by combining the frequency modulation capacity price and the revenue from mileage usage:

[0073]

[0074] in, and These are frequency modulation capacity and mileage compensation price, respectively. This is the probability coefficient for invoking the frequency modulation command; This refers to the frequency regulation performance indicators of energy storage.

[0075] ③ Network support compensation benefits :

[0076]

[0077] in, The unit price for the active inertia and primary frequency regulation support capacity of grid-type energy storage.

[0078] ④ Battery life degradation penalty cost :

[0079]

[0080] In the formula: The depreciation cost per unit of energy throughput is based on the State of Energy (SOH) assessment. This is the lifetime reduction factor for frequency modulation high-frequency fluctuations; Penalty weight for SOC deviation; This represents the optimal level of health maintenance.

[0081] Step 4: Based on the objective function and its constraints constructed in Step 3, solve the objective function in the energy management system and output the optimal power reference command sequence and the optimal network configuration mode sequence; specifically: define the system operation constraints.

[0082] Introducing continuity of charged state and extreme value constraints:

[0083]

[0084]

[0085] In the formula: This refers to the rated capacity of the energy storage.

[0086] To prevent non-physical results such as simultaneous charging and discharging from occurring in operations research solutions, Boolean variables are introduced. Linearization constraints for absolute values ​​are applied using the Big M method:

[0087]

[0088]

[0089] A mixed-integer quadratic programming solver is used to perform rolling optimization on the above objective function to obtain the optimal power command sequence. , and optimal network configuration sequence Where M is a sufficiently large positive real constant, for example, M = 2 * max(P) rated ), where P rated This is the rated power of the energy storage converter.

[0090] Step 5: Based on the optimal grid configuration sequence obtained in Step 4, commands are issued via the industrial protocol. The converter switches the corresponding parameter set according to the commands and executes a bumpless switching algorithm to maintain internal state continuity. Specifically: The local energy management system issues commands via the industrial protocol. After receiving the mode switching command, the grid-type energy storage converter triggers a hardware interrupt through the command parser and retrieves the corresponding parameter set from memory. During the switching process, the underlying controller executes a bumpless switching algorithm, maintaining the phase continuity of the internal virtual power angle and virtual potential through smooth initialization of state variables, avoiding current surges caused by parameter abrupt changes. After the switching is completed, the PCS's internal limiting logic automatically locks the reserved capacity. This achieves a closed loop between the upper-layer multi-benefit objectives and the lower-layer hardware physical response.

[0091] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this document.

[0092] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0093] In the embodiments provided herein, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, apparatuses, or units, or they may be electrical, mechanical, or other forms of connection.

[0094] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments described herein, depending on actual needs.

[0095] Furthermore, the functional units in the various embodiments of this document can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0096] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this paper, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this paper. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0097] This document uses specific embodiments to illustrate the principles and implementation methods of this document. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of this document. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this document. Therefore, the content of this specification should not be construed as a limitation of this document.

Claims

1. A multi-benefit collaborative optimization method for energy storage based on discrete adaptive network control parameters, characterized in that, Includes the following steps: Step 1: Real-time synchronous acquisition of the power system grid connection point operation status and multi-dimensional electricity market price signals; simultaneously, the battery charge status and health status are collected through the battery management system; Step 2: Based on the grid connection point operation status obtained in Step 1, the control parameters of the converter virtual synchronous generator are discretized into three safety modes, and dynamic power reserve boundaries are established for each mode to ensure that the hardware current limit is not exceeded when providing grid support. Step 3: Based on the three safety modes and their dynamic power reserve boundaries established in Step 2, and combined with the battery state of charge, health status and multi-dimensional market price signals collected in Step 1, establish an objective function with the goal of maximizing total profit over the entire scheduling cycle, taking into account the triple benefits of energy arbitrage, secondary frequency regulation and grid support, and subtracting the penalty cost of battery life loss. Step 4: Based on the objective function and its constraints constructed in Step 3, solve the objective function in the energy management system and output the optimal power reference command sequence and the optimal network configuration mode sequence; Step 5: Based on the optimal network configuration sequence obtained in Step 4, commands are issued through the industrial protocol. The converter switches the corresponding parameter set according to the commands and executes a disturbance-free switching algorithm to maintain the continuity of the internal state.

2. The energy storage multi-benefit collaborative optimization method with discrete adaptive network control parameters according to claim 1, characterized in that, The dynamic power reserve boundary in step two refers to the ultimate support power that the converter must reserve under the three safety modes. The formula for calculating the ultimate support power is as follows: In the formula: The virtual inertia time constant; This is the droop damping coefficient. The rated apparent capacity of the energy storage converter; The rated frequency of the power grid; and These are the maximum allowable rate of frequency change and the maximum frequency deviation, respectively, according to power grid standards.

3. The energy storage multi-benefit collaborative optimization method with discrete adaptive network control parameters according to claim 2, characterized in that, The three security modes in step two include: Mode 1 is the energy arbitrage priority mode: setting a smaller virtual inertia. and droop damping The corresponding reserved support power boundary for the network structure is Maximize the release of battery capacity to participate in spot market arbitrage; Mode 2 is the balanced response mode: setting the standard virtual inertia. and droop damping The corresponding reserved support power boundary is ; Mode 3 is a strong network support mode: setting high virtual inertia. and large droop damping The corresponding reserved support power boundary is It is used when the power grid frequency fluctuates drastically or the compensation unit price is extremely high.

4. The energy storage multi-benefit collaborative optimization method with discrete adaptive network control parameters according to claim 3, characterized in that, In step two, the dynamic power reservation boundary must satisfy the dynamic safety boundary constraints of mode mutual exclusion constraints and hardware limits, and the calculation formulas are as follows: In the formula: For a moment Select the Decision variables for each model; Planned discharge power for participation in energy market arbitrage; For the planned charging power; For the declared secondary frequency regulation capacity; For the first The maximum power limit for network response that is forcibly reserved by the underlying controller in this mode; This represents the maximum power allowed under the current battery condition.

5. The energy storage multi-benefit collaborative optimization method with discrete adaptive network control parameters according to claim 1, characterized in that, The objective function in step three is as follows: In the formula: For arbitrage profits; For secondary frequency modulation revenue; To provide compensation for network support; The cost of penalizing battery degradation.

6. The energy storage multi-benefit collaborative optimization method with discrete adaptive network control parameters according to claim 5, characterized in that, Step three involves arbitrage profits in the spot market. :in, For discharge power, For charging efficiency, λ e (t) The spot market electricity price in time period t; P dis (t) Discharge power in time period t, P ch (t) Charging power in time period t; secondary frequency modulation revenue A linear prediction is made by combining the frequency modulation capacity price and the revenue from mileage usage: in, For frequency modulation capacity price, The price is for mileage compensation. This is the probability coefficient for invoking the frequency modulation command; For energy storage frequency regulation performance indicators; (t) The frequency modulation capacity declared by the aggregate in time period t; Network support compensation benefits : in, To provide the market with the unit price of active inertia and primary frequency regulation support capacity for grid-type energy storage; k (t) The activation status variable of the k-th frequency modulation support level in time period t; P gk Rated power capacity corresponding to the kth frequency modulation support level; battery life degradation penalty cost : In the formula: The depreciation unit price is based on the energy throughput per unit of health status; This is the lifetime reduction factor for frequency modulation high-frequency fluctuations; Penalty weights for deviations in battery state of charge; For optimal health maintenance level; P dis (t) Discharge power in time period t; P ch (t) Charging power in time period t; The frequency modulation capacity declared by the polymer in time period t, and SOC(t) is the state of charge of the battery in time period t.

7. The energy storage multi-benefit collaborative optimization method with discrete adaptive network control parameters according to claim 1, characterized in that, The constraints in step four include the introduction of continuity and extremum constraints of the charged state, as well as the linearization of absolute values ​​using the Big M method for mutual exclusion constraints. The relevant calculation formulas are as follows: Where: SOC(t−1) is the state of charge at the end of the (t−1)th time interval, and P dis (t) Discharge power in time period t, η dis Discharge efficiency, P ch (t) Charging power in time period t, η ch Charging efficiency; The duration of each time interval Δt, This refers to the rated capacity of the energy storage. SOC min State of charge limit; SOC max Upper limit of state of charge Boolean state variables introduced to prevent simultaneous charging and discharging; It is a sufficiently large positive real constant.

8. The energy storage multi-benefit collaborative optimization method based on discrete adaptive network control parameters according to claim 1, characterized in that, In step four, a mixed-integer quadratic programming solver is used to perform rolling optimization on the objective function to obtain the optimal power command sequence and the optimal network configuration sequence.

9. The energy storage multi-benefit collaborative optimization method with discrete adaptive network control parameters according to claim 1, characterized in that, The instruction execution logic in step five is as follows: after the converter receives the mode switching code, a hardware interrupt is triggered, the pre-stored parameter set is called, and a smooth initialization algorithm for state variables is used to achieve a smooth switching, avoiding current surges caused by the switching moment.

10. The energy storage multi-benefit collaborative optimization method based on discrete adaptive network control parameters according to claim 9, characterized in that, The disturbance-free switching algorithm is as follows: by smooth initialization of state variables, the internal virtual power angle and virtual potential phase are kept continuous at the switching time to avoid current surges caused by parameter abrupt changes.

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