Distributed energy storage scheduling method and device based on health state perception, terminal equipment and storage medium

Abstract

The invention discloses a distributed energy storage scheduling method and device based on health state perception, terminal equipment and a storage medium, and relates to the field of energy storage system scheduling, and the method comprises the steps: obtaining performance parameters, real-time operation state parameters, energy storage parameters and energy storage cost characteristic parameters of each energy storage battery; determining a health state index according to the performance parameters and the real-time operation state parameters of the energy storage battery; according to the energy storage energy parameter, the energy storage cost characteristic parameter and the health state index of each energy storage battery, taking the minimum total cost as a target, constructing a centralized optimization model, and constructing a constraint condition; and solving the centralized optimization model through a distributed optimization algorithm to obtain the charge and discharge power and health state of each energy storage battery, and then carrying out charge and discharge control on each energy storage battery. By implementing the invention, the problem that the abrupt attenuation change in the operation of the battery is difficult to capture by depending on offline calibration or regular sampling inspection in the prior art can be solved, and the operation life of the energy storage system is prolonged.

Description

Technical Field

[0001] This invention relates to the field of energy storage system scheduling, and in particular to a distributed energy storage scheduling method, apparatus, terminal equipment, and storage medium based on health status awareness. Background Technology

[0002] As the scale of mobile energy storage aggregation expands, the requirements for scheduling accuracy and health management of energy storage systems have significantly increased. It is necessary to maximize operational benefits through reasonable power allocation, while ensuring the health status (SoH) of each energy storage device to avoid shortening its service life due to excessive wear and tear, thereby balancing short-term economic efficiency and long-term reliability.

[0003] However, the current scheduling and health management of mobile energy storage systems mostly rely on offline calibration or periodic sampling inspections. That is, the battery health status (SoH) is estimated by using a preset battery degradation model, such as an empirical formula based on the number of cycles, or periodic shutdown detection, such as monthly / quarterly capacity testing. This method is difficult to capture the sudden degradation changes of the battery in real time, which may lead to the scheduling strategy overusing degraded batteries, further aggravating the degradation rate and even causing safety hazards. Summary of the Invention

[0004] This invention provides a distributed energy storage scheduling method, device, terminal equipment, and storage medium based on health status awareness, which can solve the problem that existing technologies rely on offline calibration or periodic sampling inspections, making it difficult to capture sudden changes in battery degradation during operation, and improve the operating life of energy storage systems.

[0005] One embodiment of the present invention provides a distributed energy storage scheduling method based on health status awareness, comprising:

[0006] Obtain the performance parameters, real-time operating status parameters, energy storage parameters, and energy storage cost characteristics of each energy storage battery;

[0007] Based on the performance parameters and real-time operating status parameters of the energy storage battery, determine the corresponding health status indicators;

[0008] Based on the energy storage parameters, cost characteristics, and health status indicators of each energy storage battery, a centralized optimization model is constructed with the goal of minimizing the total cost. User-side power constraints, energy storage charge constraints, and charge / discharge power constraints are also constructed.

[0009] Under the constraints of user-side power constraints, energy storage capacity constraints, and charge / discharge power constraints, the centralized optimization model is solved by a distributed optimization algorithm to obtain the charge / discharge power and health status of each energy storage battery.

[0010] The charging and discharging of each energy storage battery is controlled based on its charging and discharging power and health status.

[0011] Furthermore, performance parameters include: initial energy storage capacity, initial internal resistance, decay internal resistance, initial power, and initial efficiency; real-time operating status parameters include: current energy storage capacity, current internal resistance, current power, and current efficiency.

[0012] Based on the performance parameters and real-time operating status parameters of the energy storage battery, the corresponding health status indicators are determined, including:

[0013] Determine the capacity health status based on the current energy storage capacity and the initial energy storage capacity;

[0014] The health status of the internal resistance is determined based on the initial internal resistance, the decay internal resistance, and the current internal resistance.

[0015] Determine the power health status based on the initial internal resistance, decay internal resistance, initial power, and current power;

[0016] Determine the efficiency health status based on the initial internal resistance, decay internal resistance, initial efficiency, and current efficiency;

[0017] The health status index of the energy storage battery is obtained by weighted summation of the capacity health status, internal resistance health status, power health status and efficiency health status.

[0018] Furthermore, real-time operating status parameters also include: current current, current battery temperature, and current ambient temperature;

[0019] The current energy storage capacity is obtained in the following way:

[0020] Obtain the amount of electricity used in the current charge-discharge cycle of the energy storage battery, the energy storage capacity of the previous period, and the cumulative capacity decay value of the previous period;

[0021] The corresponding depth of charge and discharge is calculated based on the amount of electricity used in the current charge and discharge cycle of the energy storage battery and the energy storage capacity in the previous period.

[0022] The current capacity decay value is determined based on the depth of charge and discharge of the energy storage battery, the current current, the current battery temperature, and the current ambient temperature.

[0023] The current capacity decay value is added to the cumulative capacity decay value of the previous period to obtain the cumulative capacity decay value of the current period.

[0024] The current energy storage capacity is obtained by subtracting the cumulative capacity decay value for the current period from the initial energy storage capacity.

[0025] Furthermore, the centralized optimization model includes:

[0026] min(C total )=min(∑ t (Cagg +∑ i C user,i +C loss ));

[0027]

[0028] Among them, C total C represents the total cost. agg C represents the aggregator's cost. user,i C represents the cost to user i. loss γ represents the energy storage loss cost, t represents the time period, and γ represents the energy storage loss cost. s This represents the probability of scenario s occurring. This represents the transaction costs between the aggregator and the power grid. This represents the retail price during time period t. This represents the total energy demand of the i-th energy storage battery during time period t in scenario s. This represents the net measurement price for period t. This represents the base power supplied by the i-th energy storage battery from the system during time period t in scenario s. This represents the operating cost of the i-th energy storage battery during time period t in scenario s. This represents the demand response function of the i-th energy storage battery in scenario s during time period t. This represents the load power of the i-th energy storage battery in scenario s during time period t. This represents the energy storage cost of the i-th energy storage battery. This indicates the initial health status of the energy storage battery. S S represents the health status indicator at the end of the lifespan of an energy storage battery. i,t,s This represents the health status index of the i-th energy storage battery in scenario s during time period t.

[0029] Furthermore, user-side power constraints include:

[0030]

[0031] Energy storage load constraints include:

[0032] E min ≤e i,t,s ≤E i,t,s ;

[0033] Charge and discharge power constraints include:

[0034]

[0035] in, This represents the power of the i-th energy storage battery. This represents the power generation of the photovoltaic distributed power source equipped with the i-th energy storage battery during the time period t in scenario s. This represents the interaction power of the i-th energy storage battery. This represents the charging power of the i-th energy storage battery in scenario s during time period t. E represents the discharge power of the i-th energy storage battery in scenario s during time period t. min Indicates the minimum charge limit for energy storage, e i,t,s E represents the energy storage charge of the i-th energy storage battery in scenario s during time period t. i,t,s P represents the energy storage capacity limit of the i-th energy storage battery in scenario s during time period t. cap This represents the energy storage charging and discharging power limit of the i-th energy storage battery during time period t.

[0036] Furthermore, the centralized optimization model is solved using a distributed optimization algorithm to obtain the charge / discharge power and health status of each energy storage battery, including:

[0037] By introducing auxiliary variables, the centralized optimization model is decomposed into several sub-optimization problems;

[0038] Set the initial values ​​for the auxiliary variables, the initial values ​​for the Lagrange multipliers, and the step size parameter;

[0039] Repeat the distributed solution process to obtain the charging and discharging power and health status of each energy storage battery;

[0040] The distributed solution process includes:

[0041] Based on the current auxiliary variables and the current Lagrange multipliers, each sub-optimization problem is solved independently to obtain the current local variables; where the current auxiliary variables in the first execution of the distributed solution process are the initial values ​​of the auxiliary variables, and the current Lagrange multipliers in the first execution of the distributed solution process are the initial values ​​of the Lagrange multipliers.

[0042] Determine if the deviation between the current local variable and the current auxiliary variable is less than a preset threshold;

[0043] If so, the current local variable is taken as the target result, and the charging and discharging power and health status of each energy storage battery are obtained based on the target result.

[0044] If not, then update the auxiliary variables based on the local variables of all sub-optimization problems to obtain the updated auxiliary variables; update the Lagrange multipliers through gradient descent based on the local variables and auxiliary variables to obtain the updated Lagrange multipliers; use the updated auxiliary variables and updated Lagrange multipliers as the current auxiliary variables and current Lagrange multipliers for the next execution of the distributed solution process.

[0045] Based on the above method embodiments, the present invention provides corresponding device embodiments, including: an energy storage parameter acquisition module, a health index calculation module, an optimization problem construction module, an optimization problem solving module, and an energy storage control module;

[0046] The energy storage parameter acquisition module is used to acquire the performance parameters, real-time operating status parameters, energy storage parameters, and energy storage cost characteristic parameters of each energy storage battery.

[0047] The health indicator calculation module is used to determine the corresponding health status indicators based on the performance parameters and real-time operating status parameters of the energy storage battery.

[0048] The optimization problem construction module is used to construct a centralized optimization model with the goal of minimizing the total cost based on the energy storage parameters, energy storage cost characteristics and health status indicators of each energy storage battery. It also constructs user-side power constraints, energy storage charge constraints and charge / discharge power constraints.

[0049] The optimization problem-solving module is used to solve the centralized optimization model through a distributed optimization algorithm under the constraints of user-side power constraints, energy storage capacity constraints, and charge / discharge power constraints, so as to obtain the charge / discharge power and health status of each energy storage battery.

[0050] The energy storage control module is used to control the charging and discharging of each energy storage battery based on its charging and discharging power and health status.

[0051] Furthermore, performance parameters include: initial energy storage capacity, initial internal resistance, decay internal resistance, initial power, and initial efficiency; real-time operating status parameters include: current energy storage capacity, current internal resistance, current power, and current efficiency.

[0052] The health indicator calculation module includes: a capacity health calculation submodule, an internal resistance health calculation submodule, a power health calculation submodule, an efficiency health calculation submodule, and a health indicator fusion submodule.

[0053] The capacity health calculation submodule is used to determine the capacity health status based on the current energy storage capacity and the initial energy storage capacity.

[0054] The internal resistance health calculation submodule is used to determine the internal resistance health status based on the initial internal resistance, the decayed internal resistance, and the current internal resistance.

[0055] The power health calculation submodule is used to determine the power health status based on the initial internal resistance, decay internal resistance, initial power, and current power.

[0056] The efficiency health calculation submodule is used to determine the efficiency health status based on the initial internal resistance, decay internal resistance, initial efficiency, and current efficiency.

[0057] The health index fusion submodule is used to perform a weighted summation of capacity health status, internal resistance health status, power health status, and efficiency health status to obtain the health status index of the energy storage battery.

[0058] Based on the above method embodiments, the present invention provides a corresponding terminal device embodiment, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the distributed energy storage scheduling method based on health status awareness as described in the present invention.

[0059] Based on the above method embodiments, the present invention provides a corresponding computer-readable storage medium embodiment, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to execute the steps of the distributed energy storage scheduling method based on health status awareness as described in the present invention.

[0060] Compared with the prior art, the beneficial effects of this embodiment are as follows:

[0061] This invention first acquires the performance parameters, real-time operating status parameters, energy storage parameters, and cost characteristics of each energy storage battery. Based on the performance parameters and real-time operating status parameters, corresponding health status indicators are determined, realizing dynamic health status assessment based on real-time data. This replaces traditional offline calibration or periodic sampling inspections and can reflect changes in battery performance in a timely manner. Next, real-time health status indicators are incorporated into the construction of the centralized optimization model, and user-side power constraints, energy storage capacity constraints, and charge / discharge power constraints are established. Then, under the constraints of user-side power constraints, energy storage capacity constraints, and charge / discharge power constraints, the centralized optimization model is solved using a distributed optimization algorithm to obtain the charge / discharge power and health status of each energy storage battery, ensuring an immediate response to changes in battery status. Based on the charge / discharge power and health status of each energy storage battery, charge / discharge control is performed, allowing the control strategy to be adjusted in real time according to battery degradation.

[0062] In summary, this invention dynamically assesses the health status of energy storage batteries by using battery parameters and integrates the health status into a centralized optimization model. This allows the control strategy to be adjusted in real time according to the battery's degradation status, thereby solving the problem in existing technologies that rely on offline calibration or periodic sampling inspections and are unable to capture sudden degradation changes during battery operation, thus improving the lifespan of the energy storage system. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating a distributed energy storage scheduling method based on health status awareness, provided in an embodiment of the present invention.

[0064] Figure 2This is a flowchart illustrating the solution process of a distributed optimization algorithm provided in an embodiment of the present invention;

[0065] Figure 3 This is a schematic diagram of the structure of a distributed energy storage scheduling device based on health status awareness provided in an embodiment of the present invention. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and 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.

[0067] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.

[0068] like Figure 1 As shown, in order to address the problem that existing technologies, which rely on offline calibration or periodic sampling, struggle to capture sudden changes in battery degradation during operation, an embodiment of the present invention provides a distributed energy storage scheduling method based on health status awareness. This method includes at least the following steps:

[0069] Step S1: Obtain the performance parameters, real-time operating status parameters, energy storage parameters, and energy storage cost characteristics of each energy storage battery;

[0070] For step S1, this invention takes a mobile energy storage vehicle with mobility and bidirectional charging and discharging capabilities as an example. The mobile energy storage vehicle interacts with the power distribution network through a V2G interface or fast charging pile to realize flexible injection and recovery of electrical energy. Its embedded health status acquisition system can report key state variables such as battery state of charge (SOC), state of health (SoH), cycle count, temperature, and voltage in real time, constituting the core data source for health status perception.

[0071] In this invention, the aforementioned performance parameters include: initial energy storage capacity E0, initial internal resistance Z0, and decay internal resistance Z. m Initial power P0 and initial efficiency η0;

[0072] The above real-time operating status parameters include: current energy storage capacity E t Current internal resistance Z t Current power P t Current efficiency η t Current current I i (t), Current battery temperature T i (t) and the current ambient temperature Tamb (t);

[0073] The aforementioned energy storage parameters include: the total energy demand of the i-th energy storage battery in scenario s during time period t. The base power supplied by the i-th energy storage battery from the system during time period t in scenario s. Demand response coefficient of the i-th energy storage battery The predicted load power d of the i-th energy storage battery during time period t i,t,s The power of the i-th energy storage battery itself The power generation of the photovoltaic distributed power source equipped with the i-th energy storage battery in the scenario of time period t s

[0074] The aforementioned energy storage cost characteristics parameters include: the retail price during time period t. Net measurement price for period t The probability γ of scenario s occurring s The operating cost of the i-th energy storage battery in scenario s during time period t. The secondary cost coefficient of the interaction between the i-th energy storage battery and the power grid The cost coefficient of the first interaction between the i-th energy storage battery and the grid. Energy storage cost of the i-th energy storage battery Energy storage minimum charge limit E min The energy storage capacity limit E of the i-th energy storage battery in the scenario of time period t s. i,t,s The energy storage charge / discharge power limit P of the i-th energy storage battery during time period t. cap ;

[0075] In a preferred embodiment, the current energy storage capacity is obtained in the following way:

[0076] Obtain the amount of electricity used in the current charge-discharge cycle of the energy storage battery, the energy storage capacity of the previous period, and the cumulative capacity decay value of the previous period;

[0077] The corresponding depth of charge and discharge is calculated based on the amount of electricity used in the current charge and discharge cycle of the energy storage battery and the energy storage capacity in the previous period.

[0078] The current capacity decay value is determined based on the depth of charge and discharge of the energy storage battery, the current current, the current battery temperature, and the current ambient temperature.

[0079] The current capacity decay value is added to the cumulative capacity decay value of the previous period to obtain the cumulative capacity decay value of the current period.

[0080] The current energy storage capacity is obtained by subtracting the cumulative capacity decay value for the current period from the initial energy storage capacity.

[0081] In one embodiment of the present invention, firstly, the amount of electricity E used in the current charge-discharge cycle of the energy storage battery is obtained. used Energy storage capacity E in the previous period t-1 The cumulative capacity decay value ∑ΔQ(t-1) from the previous period. Using the amount of electricity used in the current charge / discharge cycle and the energy storage capacity from the previous period, the corresponding depth of charge / discharge (DoD) is calculated using the following formula:

[0082]

[0083] Where DoD represents the depth of charge / discharge, E used E represents the amount of electricity used in the current charge / discharge cycle. t-1 This indicates the energy storage capacity in the previous period.

[0084] It should be noted that the depth of charge and discharge (DoD) directly reflects the intensity of energy storage during a single charge and discharge cycle. That is, the larger the DoD, the higher the degree of deep charge and discharge of the stored energy in a single cycle, and the more significant the damage to the internal structure of the battery, such as electrode material aging and electrolyte decomposition, which in turn accelerates capacity decay.

[0085] Considering that the depth of charge / discharge, current current, current battery temperature, and current ambient temperature all affect the capacity decay of the energy storage battery, the capacity decay of the battery in time period t is calculated using the following formula:

[0086] ΔQ i (t)=α1·DOD i (t)+α2·I i (t)+α3·(T i (t)-T amb (t));

[0087] Where, ΔQ i (t) represents the capacity decay of the i-th energy storage battery in time period t, DOD i (t) represents the depth of discharge of the i-th energy storage battery in time period t, I i (t) represents the current of the i-th energy storage battery in time period t, where T i (t) represents the current battery temperature of the i-th energy storage battery in time period t, where T amb (t) represents the current ambient temperature of the i-th energy storage battery during time period t, and α1, α2 and α3 represent empirical coefficients.

[0088] The current capacity decay value is added to the cumulative capacity decay value of the previous period to obtain the cumulative capacity decay value of the current period. Finally, the current energy storage capacity is obtained by subtracting the cumulative capacity decay value of the current period from the initial energy storage capacity.

[0089] E t=E0-∑ΔQ(t);

[0090] Among them, E t E0 represents the current energy storage capacity, E0 represents the initial energy storage capacity, and ∑ΔQ(t) represents the cumulative capacity decay value in the t-th time period.

[0091] This invention comprehensively considers the impact of battery usage and environmental factors on capacity, ensuring the accuracy of current energy storage capacity data. If battery degradation is ignored, subsequent health status indicators will fail to reflect the differentiated impact of various operating conditions on capacity, resulting in overly coarse assessments of health status indicators. This makes it impossible to accurately grasp the battery's performance change trends, further affecting the accuracy of cost calculations.

[0092] Step S2: Determine the corresponding health status indicators based on the performance parameters and real-time operating status parameters of the energy storage battery;

[0093] In a preferred embodiment, the corresponding health status indicators are determined based on the performance parameters and real-time operating status parameters of the energy storage battery, including:

[0094] Determine the capacity health status based on the current energy storage capacity and the initial energy storage capacity;

[0095] The health status of the internal resistance is determined based on the initial internal resistance, the decay internal resistance, and the current internal resistance.

[0096] Determine the power health status based on the initial internal resistance, decay internal resistance, initial power, and current power;

[0097] Determine the efficiency health status based on the initial internal resistance, decay internal resistance, initial efficiency, and current efficiency;

[0098] The health status index of the energy storage battery is obtained by weighted summation of the capacity health status, internal resistance health status, power health status and efficiency health status.

[0099] For step S2, the capacity health status is calculated based on the ratio of the current energy storage capacity to the initial energy storage capacity, using the following formula:

[0100]

[0101] Among them, S cap (t) represents the capacity health status during time period t.

[0102] It should be noted that since capacity is the most direct physical definition of battery health status, capacity health status can be directly used as an indicator of the health status of energy storage batteries.

[0103] However, in order to more accurately and comprehensively reflect the health status of energy storage batteries, dynamic corrections are made by combining measurements of internal resistance, power, and efficiency.

[0104] Specifically, SoH (State of Health) and internal resistance are closely related. The battery's internal resistance changes with the state of health. When SoH (State of Health) changes, the internal resistance of the battery changes. t When the internal resistance (Z) decreases, the internal resistance (Z) decreases. t It will move from the initial value Z (Z0) to the end-of-life value (Z0). m The relationship between SoH and internal resistance increases incrementally, specifically described by the following formula:

[0105]

[0106] Among them, Z t Z represents the internal resistance of the energy storage battery during time period t. m Z represents the decay internal resistance of the energy storage battery, and Z0 represents the initial internal resistance of the energy storage battery. This indicates the initial health status of the energy storage battery. S S represents the health status indicator at the end of the lifespan of an energy storage battery. t This represents the health status index for time period t.

[0107] Based on the above correlation, the representation of the internal resistance health state is obtained by reverse calculation using the formula. Substituting the initial internal resistance, the attenuated internal resistance, and the current internal resistance, the internal resistance health state is calculated:

[0108]

[0109] Among them, S res (t) represents the internal resistance health status during time period t.

[0110] Similarly, due to the internal resistance (Z) t The power (P) increases as SoH decreases. t The internal resistance (SRH) decreases as the soH (SoH) decreases. A decrease in SRH leads to capacity decay and increased internal resistance, thus weakening the energy storage's ability to output power. At this point, power is directly related to internal resistance, specifically described by the following formula, which indirectly reflects the connection with SRH:

[0111]

[0112] Among them, P t Pt represents the power at time t, and P0 represents the initial power of the energy storage battery.

[0113] Based on the above correlation, the power health state is represented by the formula. Substituting the initial internal resistance, decay internal resistance, initial power, and current power, the power health state is calculated:

[0114]

[0115] Among them, S pow (t) represents the power health status during time period t.

[0116] Similarly, due to the internal resistance (Z) t The efficiency (η) increases as SoH decreases. t The energy loss during charging and discharging (I) decreases as the SoH decreases. A decrease in SoH leads to an increase in internal resistance, increasing the energy loss during charging and discharging. 2 As Z increases, the energy conversion efficiency of energy storage decreases. At this point, efficiency is directly related to internal resistance, which is described by the following formula, thus indirectly reflecting the connection with SoH:

[0117]

[0118] Where, η t Let ηt represent the efficiency at time t, and η0 represent the initial efficiency of the energy storage battery.

[0119] Based on the above correlation, the representation of the efficiency health state is obtained by reverse calculation using the formula. Substituting the initial internal resistance, decay internal resistance, initial efficiency, and current efficiency, the efficiency health state is calculated as follows:

[0120]

[0121] Among them, S eff (t) represents the efficiency and health status during time period t.

[0122] Finally, the capacity health status, internal resistance health status, power health status, and efficiency health status are weighted and summed to eliminate the error of back-calculation based on a single parameter, resulting in the health status index of the energy storage battery. The specific formula is as follows:

[0123] S final (t)=w cap ·S cap (t)+w res ·S res (t)+w pow ·S pow (t)+w eff ·S eff (t);

[0124] Among them, S final (t) represents the health status index of the energy storage battery, w cap w res w pow and w eff These represent the capacity weight, internal resistance weight, power weight, and efficiency weight, respectively.

[0125] It should be noted that in actual operation, since capacity is the most direct physical definition of SoH, the degradation of battery capacity directly reflects the decline in its energy storage capability. Therefore, in the weight allocation, w cap Choose relatively large values, such as 0.6 to 0.8; internal resistance, power, and efficiency also have a significant impact on battery health, but relative to capacity, they are more of a supplementary indicator of battery condition from different perspectives. res Take a value of 0.1 to 0.2, w pow and w eff We take 0.05 to 0.1 as an auxiliary correction term, and all weights satisfy w cap +w res +w pow +w eff =1.

[0126] Step S3: Based on the energy storage parameters, cost characteristics, and health status indicators of each energy storage battery, construct a centralized optimization model with the goal of minimizing the total cost, and construct user-side power constraints, energy storage capacity constraints, and charge / discharge power constraints.

[0127] For step S3, the centralized optimization model is a mathematical model used to integrate the energy storage parameters, cost characteristics, and health status indicators of each energy storage battery, thereby optimizing the related costs and operation of the energy storage system. The objective is to minimize the total cost, while simultaneously constructing user-side power constraints, energy storage capacity constraints, and charge / discharge power constraints. Through the centralized optimization model, energy storage resources can be rationally allocated at the overall level, minimizing the total operating cost of the energy storage system and improving its economic efficiency and effectiveness while meeting various operational constraints.

[0128] Preferred, centralized optimization models include:

[0129] min C total =min(∑ t (C agg +∑ i C user,i +C loss ));

[0130]

[0131] Among them, C total C represents the total cost. agg C represents the aggregator's cost. user,i C represents the cost to user i. loss γ represents the energy storage loss cost, t represents the time period, and γ represents the energy storage loss cost. s This represents the probability of scenario s occurring. This represents the transaction costs between the aggregator and the power grid. This represents the retail price during time period t. This represents the total energy demand of the i-th energy storage battery during time period t in scenario s. This represents the net measurement price for period t. This represents the base power supplied by the i-th energy storage battery from the system during time period t in scenario s. This represents the operating cost of the i-th energy storage battery during time period t in scenario s. This represents the demand response function of the i-th energy storage battery in scenario s during time period t. This represents the load power of the i-th energy storage battery in scenario s during time period t. This represents the energy storage cost of the i-th energy storage battery. This indicates the initial health status of the energy storage battery. S S represents the health status indicator at the end of the lifespan of an energy storage battery. i,t,s This represents the health status index of the i-th energy storage battery in scenario s during time period t.

[0132] Specifically, the aggregator cost C agg It consists of three parts. The first part is the cost for aggregators to trade electricity at real-time prices, and the corresponding formula is: The second item is the retail revenue from selling electricity to users, and the third item is the cost of purchasing additional power from users.

[0133] In the first item, This refers to the process by which aggregators analyze the load and power consumption of each user in the cloud to obtain the overall net load and net power. This represents the total energy requirement of the battery polymer during time period t in scenario s. This represents the base power supplied by the battery polymer to the system during time period t in scenario s. This represents the energy price during time period t.

[0134] Furthermore, different scenarios s have different occurrence probabilities γ. s Scenario 's' can be peak electricity consumption periods on weekdays, when user load demand is high and grid electricity prices may also be high; or it can be off-peak electricity consumption periods on weekends, when user load demand is low and electricity prices are relatively cheap. Aggregators need to comprehensively consider the possibilities of various scenarios when conducting cost analysis and making decisions.

[0135] Regarding user costs C user,iThe main costs come from electricity trading with aggregators and the user's own operating costs. The user's costs consist of four parts: first, the cost of purchasing electricity from aggregators at retail prices due to the total energy demand of the energy storage vehicle; second, the revenue from selling the energy storage vehicle's net power to aggregators at a net metering price; third, the energy storage vehicle's own operating costs, as shown in the formula:

[0136]

[0137] The fourth part is the demand response function, and the corresponding formula is:

[0138]

[0139] in, This represents the secondary cost coefficient for the interaction between the i-th energy storage battery and the power grid. This represents the cost coefficient for the first interaction between the i-th energy storage battery and the power grid. This represents the interaction power of the i-th energy storage battery in scenario s during time period t. Let d represent the demand response coefficient of the i-th energy storage battery. i,t,s This represents the predicted load power of the i-th energy storage battery during time period t.

[0140] Regarding energy storage loss cost C loss When the energy storage SoH is the initial value At this time, C loss When SoH is 0, no cost is incurred. When SoH reaches its terminal value SoH, C... loss Reaching the maximum value This means that the energy storage system incurs all costs when it reaches the end of its life cycle.

[0141] Preferably, the user-side power constraint includes:

[0142]

[0143] Energy storage load constraints include:

[0144] E min ≤e i,t,s ≤E i,t,s ;

[0145] Charge and discharge power constraints include:

[0146]

[0147] in, This represents the power of the i-th energy storage battery. This represents the power generation of the photovoltaic distributed power source equipped with the i-th energy storage battery during the time period t in scenario s. This represents the interaction power of the i-th energy storage battery. This represents the charging power of the i-th energy storage battery in scenario s during time period t. E represents the discharge power of the i-th energy storage battery in scenario s during time period t. min Indicates the minimum charge limit for energy storage, e i,t,s E represents the energy storage charge of the i-th energy storage battery in scenario s during time period t. i,t,s P represents the energy storage capacity limit of the i-th energy storage battery in scenario s during time period t. cap This represents the energy storage charging and discharging power limit of the i-th energy storage battery during time period t.

[0148] Step S4: Under the constraints of user-side power constraints, energy storage capacity constraints, and charge / discharge power constraints, the centralized optimization model is solved using a distributed optimization algorithm to obtain the charge / discharge power and health status of each energy storage battery.

[0149] In a preferred embodiment, a distributed optimization algorithm is used to solve the centralized optimization model to obtain the charge / discharge power and health status of each energy storage battery, including:

[0150] By introducing auxiliary variables, the centralized optimization model is decomposed into several sub-optimization problems;

[0151] Set the initial values ​​for the auxiliary variables, the initial values ​​for the Lagrange multipliers, and the step size parameter;

[0152] Repeat the distributed solution process to obtain the charging and discharging power and health status of each energy storage battery;

[0153] The distributed solution process includes:

[0154] Based on the current auxiliary variables and the current Lagrange multipliers, each sub-optimization problem is solved independently to obtain the current local variables; where the current auxiliary variables in the first execution of the distributed solution process are the initial values ​​of the auxiliary variables, and the current Lagrange multipliers in the first execution of the distributed solution process are the initial values ​​of the Lagrange multipliers.

[0155] Determine if the deviation between the current local variable and the current auxiliary variable is less than a preset threshold;

[0156] If so, the current local variable is taken as the target result, and the charging and discharging power and health status of each energy storage battery are obtained based on the target result.

[0157] If not, then update the auxiliary variables based on the local variables of all sub-optimization problems to obtain the updated auxiliary variables; update the Lagrange multipliers through gradient descent based on the local variables and auxiliary variables to obtain the updated Lagrange multipliers; use the updated auxiliary variables and updated Lagrange multipliers as the current auxiliary variables and current Lagrange multipliers for the next execution of the distributed solution process.

[0158] For step S4, as Figure 2 As shown, this invention uses a distributed optimization algorithm to solve the centralized optimization model, decomposing the centralized optimization problem into multiple sub-optimization problems, which are then assigned to each mobile energy storage vehicle for processing, in order to obtain the charging and discharging power and health status of each energy storage battery.

[0159] The core idea of ​​distributed algorithms is to decompose the original problem into multiple subproblems by introducing auxiliary variables, with each subproblem corresponding to a mobile energy storage vehicle. For example, assuming there are N mobile energy storage vehicles in the system, this would decompose into N sub-optimization problems. Then, the entire system is iteratively updated to reach its optimal state. In the overall objective function, since the costs of aggregators and users are summed, their transaction costs can be eliminated from the objective function. The original global cost objective function can be equivalently expressed as:

[0160]

[0161] Right now:

[0162]

[0163]

[0164] Further introducing auxiliary variables, for each user i, the corresponding convex optimization problem f i Using constant matrix A, constant vector B, and local variable x i Establish constraints, where matrix A represents the linear constraints coupling all local variables, and X... i It is a local variable x i The convex set it belongs to.

[0165]

[0166] Obey constraints:

[0167] A[x1,x2,…,x n ] = B;

[0168] x i ∈X i ;

[0169] Introducing auxiliary variable z iThe problem is then rephrased in a way that avoids sharing detailed user information, thus protecting user privacy and autonomy. It also transforms the problem into finding the minimum value under specific constraints. In this case, the problem becomes defined under constraints A[z1,z2,…,z…]. n ] = B, z i ∈X i , z i -x i Find the value when the sum is 0. In the form of.

[0170] In addition, the Lagrange multiplier λ is introduced. i Relax the constraints, add the relaxation term to the original objective function, and then define the Lagrangian function L:

[0171]

[0172] Where, L(x) i ,z i ,λ i ) represents the Lagrange function, and N represents the number of sub-optimization problems.

[0173] Next, initialization is performed, setting the auxiliary variable z. i Lagrange multiplier λ i The initial value, and the step size α.

[0174] Then, the distributed solution process begins. Specifically, based on the current auxiliary variables and Lagrange multipliers, each mobile energy storage vehicle (or user) independently solves its own sub-optimization problem to obtain the current local variable x. i .

[0175] Then check the current local variable x i and the current auxiliary variable z i Check if the deviation is less than a preset threshold; if the deviation is less than the threshold, then change the current local variable x. i As the target result, the charging and discharging power and health status of each energy storage battery are obtained.

[0176] If the deviation is not less than the threshold, collect the local variables x of all sub-optimization problems. i Update auxiliary variable z i This yields the updated auxiliary variable; then, based on the local variable x... i and auxiliary variable z i The Lagrange multipliers are iteratively updated using the gradient descent method, and the update formula is as follows:

[0177] λ k+1 =λ k +α i ·(z i-x i );

[0178] Where, α i λ represents the step size. k Let z be the Lagrange multiplier in the k-th iteration. i -x i This represents the constraint bias of the current iteration. Gradient descent adjusts the multipliers using this bias to gradually make z... i ≈x i To satisfy the original constraints.

[0179] Finally, the updated auxiliary variables and updated Lagrange multipliers are used as the current auxiliary variables and current Lagrange multipliers in the next distributed solution process. The steps of solving subproblems, judging biases, and updating auxiliary variables and multipliers are repeated until z... i With x i If the deviation is less than the threshold, then x is obtained. i This represents the optimal result of the distributed algorithm, which in turn determines the charging and discharging power and health status of each energy storage battery.

[0180] To more clearly demonstrate the practical application of this invention, the following is a specific example of power dispatching using a mobile energy storage vehicle in an industrial park:

[0181] There are three mobile energy storage vehicles (numbered 1, 2, and 3) in an industrial park. The aggregator is located in the center of the park (coordinates (5,5)) and is responsible for coordinating the energy exchange between the mobile energy storage vehicles and the power grid, as well as between the mobile energy storage vehicles themselves. The optimal time period is 14:00-14:05 (5 minutes) on weekdays. At this time, the power grid load in the park is high, and the aggregator needs to dispatch the mobile energy storage vehicles to discharge and supplement the load, or receive power from the power grid to charge the energy storage.

[0182] The rated energy storage capacity of a single mobile energy storage vehicle is E0 = 100kWh, and the maximum charging and discharging power is P. cap =50kW, State of Charge (SOC) safe range SOC min =20%, SOC max =90%, avoiding full charging and discharging which would accelerate battery degradation; no distributed generators (only used as energy storage carriers), no fixed loads (the energy exchange partners are aggregators and other mobile energy storage vehicles).

[0183] mobile energy storage vehicle x i =[P i,cha ,P i,dis ,P i,ES SOC i ,L i,x ,L i,y ] T , where L i,x L represents the x-axis position coordinate of the i-th mobile energy storage vehicle.i,y This represents the y-axis position coordinate of the i-th mobile energy storage vehicle, used to reflect the impact of mobility characteristics on energy exchange; for example, mobile energy storage vehicles that are close to each other have lower energy exchange losses. i,ES SOC represents the power of the i-th mobile energy storage vehicle when exchanging energy with other objects. i This represents the state of charge of the i-th mobile energy storage vehicle, reflecting its current energy level.

[0184] In matrix A, there exists a "location-power loss" correlation element. If the distance between mobile energy storage vehicles i and j exceeds 5km, the energy exchange power loss coefficient increases; convex set X i Add position constraint L i,x ∈[0,10]、L i,y ∈[0,10], here a 10km×10km park is set as the optimization area, and the charging and discharging power constraint is adjusted to 0≤P i,cha ≤50kW, 0≤P i,dis ≤50kW, energy exchange power constraint -20kW≤P i,ES ≤20kW, to avoid excessive participation of a single mobile energy storage vehicle in the exchange process, which could lead to insufficient range.

[0185] Mobile energy storage vehicle 1: Location (2,3), SOC = 30% (30kWh), currently no charging or discharging behavior;

[0186] Mobile energy storage vehicle 2: Location (7,4), SOC = 70% (70kWh), currently no charging or discharging behavior;

[0187] Mobile energy storage vehicle 3: Location (5,8), SOC = 50% (50kWh), currently no charging or discharging activity;

[0188] Introducing auxiliary variable z i =[z i,P ,z i,loss ] T , where z i,P For the net power of mobile energy storage vehicle i, z i,loss To account for energy exchange losses, initial values ​​for the Lagrange multipliers are set. The grid purchase price is $0.3 / kWh, the loss cost factor is $0.02 / kWh, and the step size is α. i =0.03; Iteration termination condition

[0189] Net power z i,P The formula for calculation is:

[0190] z i,P =P i,dis -P i,cha -P i,ES ;

[0191] Energy exchange loss z i,loss The formula for calculation is:

[0192]

[0193] Among them, L i,x L represents the x-axis position coordinate of the i-th mobile energy storage vehicle. j,x L represents the x-axis position coordinate of the j-th mobile energy storage vehicle. i,y L represents the y-axis position coordinate of the i-th mobile energy storage vehicle. j,y This represents the y-axis position coordinate of the j-th mobile energy storage vehicle.

[0194] It should be noted that energy exchange loss is closely related to the distance between mobile energy storage vehicles. The greater the distance, the greater the energy loss during the exchange process, with a loss coefficient of 0.01kWh / km.

[0195] Matrix A is 3×6 in dimension, corresponding to 3 mobile energy storage vehicles, each with 6 decision variables. The elements reflect the "power-location" constraint, such as the distance between mobile energy storage vehicle 1 and the aggregator. 3.6km, energy exchange loss coefficient 0.036, corresponding to P in matrix A 1,ES The element is 0.036, and its irrelevant elements are 0. Matrix A is as follows:

[0196]

[0197] B = [0.5 0.5 0.5] T ;

[0198] Where B = [0.5 0.5 0.5] T This indicates that the maximum allowable energy loss for each mobile energy storage vehicle within 5 minutes is 0.5 kWh.

[0199] When the iteration number k = 0, The mobile energy storage vehicle has a fixed initial location and does not move during the optimization period; only charging, discharging, and energy exchange are scheduled.

[0200] Based on the objective function, considering the objective function of the mobile energy storage vehicle, namely charging cost + discharging revenue - loss cost, the local optimal solution for the 0th iteration of mobile energy storage vehicle 1 is obtained as follows:

[0201]

[0202] This means that it does not charge, but discharges 8kW to the park load; receives 5kW of power from energy storage vehicle 2; its state of charge (SOC) rises to 31.2% due to the combined effect of discharging and charging; and its position remains at (2,3). Similarly, solve the local subproblems of mobile energy storage vehicles 2 and 3:

[0203]

[0204] The aggregator uses minimizing the total loss and power deviation of the three mobile energy storage vehicles as the objective function, and obtains the updated values ​​of the auxiliary variables by solving the problem.

[0205]

[0206] Subsequently, by updating the Lagrange multipliers and achieving iterative convergence, the following calculations can be performed:

[0207]

[0208] Repeat the above steps until the termination condition is met when k=6, and the final decision variables are obtained as follows:

[0209] This indicates that the mobile energy storage vehicle 1 discharges 9kW and receives 6kW of electrical energy, with a state of charge of 32.1%.

[0210] This indicates that the mobile energy storage vehicle 2 discharges 13kW and receives 6kW of electrical energy, with a state of charge of 67.8%.

[0211] This indicates that the mobile energy storage vehicle 3 is charging at 11kW and has a state of charge of 52.8%.

[0212] At this time, the three mobile energy storage vehicles discharged a total of 22kW, supplementing the load gap in the park; charged a total of 11kW, storing grid energy; and the energy exchange loss was ≤0.5kWh, meeting the aggregator's optimization target, and no mobile energy storage vehicle's SOC exceeded the safe range.

[0213] Compared with traditional centralized optimization methods, this invention adopts a dual decomposition algorithm based on auxiliary variables to break down the global problem into several local subproblems. The vehicle only exchanges a small number of Lagrange multipliers with the cloud, and sensitive data such as the original load curve and location trajectory remain locally. This greatly reduces the computational burden on the cloud and improves the system's computational efficiency and response speed. At the same time, since each sub-optimization problem can be expanded independently and flexibly, the system's scalability is enhanced. Moreover, it avoids the impact of a single point of failure on the whole under a centralized architecture, further improving the reliability of system operation.

[0214] Step S5: Control the charging and discharging of each energy storage battery according to its charging and discharging power and health status.

[0215] For step S5, the distributed optimization algorithm is solved through step S4 to obtain the charging and discharging power of each energy storage battery and (P). i,cha ,P i,dis ) and health status (SOC) iAfter that, first determine whether the health status is stable within the preset safety range. If the health status is stable within the preset safety range, then directly control the charging and discharging of the energy storage battery according to the calculated charging and discharging power, so that the battery stores or releases energy at the optimized power. If the health status exceeds the preset safety range, the charging and discharging power needs to be adjusted first, for example, when the SOC... i When the SOC approaches the upper limit of the range, the charging power is reduced or even stopped. i When the battery approaches the lower limit of the range, the discharge power is reduced or even stopped. After adjustment, the charge and discharge control is executed again to ensure that the energy storage battery operates in a safe and healthy state.

[0216] To verify the effectiveness of the centralized optimization model of this invention, a cloud energy storage model without energy storage health sensing and a local management model without energy storage health sensing were established. By simulating and analyzing the operation of these models under different scenarios, their economic benefits and energy storage losses were compared to verify the effectiveness of the proposed cloud energy storage model based on the energy storage health sensing model.

[0217] In cloud energy storage models without energy storage health awareness, aggregators still make decisions based on the global cost optimum. However, since the health status of energy storage and the cost of energy storage losses are not considered, the objective function is:

[0218]

[0219] Subject to constraints:

[0220]

[0221] The objective function of the local management model without energy storage health awareness is to minimize the cost of a single energy storage vehicle, without considering energy storage loss costs, and without energy sharing among users. The specific expression of the objective function is as follows:

[0222]

[0223] Since the objective function does not include energy storage loss costs and there is no energy exchange between energy storage vehicles, the cloud energy storage model without energy storage health awareness has no SoH constraint, only power upper and lower limits and energy storage capacity limits. Therefore, the local management model without energy storage health awareness only has local power constraints and follows the following constraints:

[0224]

[0225] Finally, comparing the total costs of the three models, if the global cost C of the model of this invention is... total At the same time, the cost is less than that of a cloud energy storage model without energy storage health sensing. total1 Cost C of local management model total2 Ctotal <C total1 And C total1 <C total2 This demonstrates that the model of the present invention is superior in terms of global cost.

[0226] Secondly, the unit power dispatch cost is calculated, which is the total operating cost divided by the total charging and discharging power during the dispatch period. The lower the unit power dispatch cost of the model in this invention, the stronger the economic efficiency of power dispatch.

[0227] Calculate the average electricity cost per user, i.e., ∑ i C user,i Dividing by the total number of users and comparing the user costs of the three models, if the average electricity cost per user of the present invention is lower, it can be verified that the present invention improves the economic efficiency of the user side.

[0228] like Figure 3 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided;

[0229] An embodiment of the present invention provides a distributed energy storage scheduling device based on health status awareness, comprising: an energy storage parameter acquisition module, a health index calculation module, an optimization problem construction module, an optimization problem solving module, and an energy storage control module;

[0230] The energy storage parameter acquisition module is used to acquire the performance parameters, real-time operating status parameters, energy storage parameters, and energy storage cost characteristic parameters of each energy storage battery.

[0231] The health indicator calculation module is used to determine the corresponding health status indicators based on the performance parameters and real-time operating status parameters of the energy storage battery.

[0232] The optimization problem construction module is used to construct a centralized optimization model with the goal of minimizing the total cost based on the energy storage parameters, energy storage cost characteristics and health status indicators of each energy storage battery. It also constructs user-side power constraints, energy storage charge constraints and charge / discharge power constraints.

[0233] The optimization problem-solving module is used to solve the centralized optimization model through a distributed optimization algorithm under the constraints of user-side power constraints, energy storage capacity constraints, and charge / discharge power constraints, so as to obtain the charge / discharge power and health status of each energy storage battery.

[0234] The energy storage control module is used to control the charging and discharging of each energy storage battery based on its charging and discharging power and health status.

[0235] In a preferred embodiment, the performance parameters include: initial energy storage capacity, initial internal resistance, decay internal resistance, initial power, and initial efficiency; the real-time operating status parameters include: current energy storage capacity, current internal resistance, current power, and current efficiency.

[0236] The health indicator calculation module includes: a capacity health calculation submodule, an internal resistance health calculation submodule, a power health calculation submodule, an efficiency health calculation submodule, and a health indicator fusion submodule.

[0237] The capacity health calculation submodule is used to determine the capacity health status based on the current energy storage capacity and the initial energy storage capacity.

[0238] The internal resistance health calculation submodule is used to determine the internal resistance health status based on the initial internal resistance, the decayed internal resistance, and the current internal resistance.

[0239] The power health calculation submodule is used to determine the power health status based on the initial internal resistance, decay internal resistance, initial power, and current power.

[0240] The efficiency health calculation submodule is used to determine the efficiency health status based on the initial internal resistance, decay internal resistance, initial efficiency, and current efficiency.

[0241] The health index fusion submodule is used to perform a weighted summation of capacity health status, internal resistance health status, power health status, and efficiency health status to obtain the health status index of the energy storage battery.

[0242] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the distributed energy storage scheduling method based on health status awareness provided by any of the above-described method embodiments of the present invention.

[0243] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0244] Based on the above embodiments of the distributed energy storage scheduling method based on health status awareness, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the distributed energy storage scheduling method based on health status awareness of any embodiment of the present invention.

[0245] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0246] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0247] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting various parts of the terminal device via various interfaces and lines.

[0248] Based on the above-described method embodiments, another embodiment is provided: another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the distributed energy storage scheduling method based on health status awareness as described in any of the above-described method embodiments of the present invention.

[0249] The modules / units integrated into the distributed energy storage scheduling device / terminal equipment based on health status awareness, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0250] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A distributed energy storage scheduling method based on health status awareness, characterized in that, include: Obtain the performance parameters, real-time operating status parameters, energy storage parameters, and energy storage cost characteristics of each energy storage battery; Based on the performance parameters and real-time operating status parameters of the energy storage battery, determine the corresponding health status indicators; Based on the energy storage parameters, cost characteristics, and health status indicators of each energy storage battery, a centralized optimization model is constructed with the goal of minimizing the total cost. User-side power constraints, energy storage charge constraints, and charge / discharge power constraints are also constructed. Under the constraints of user-side power constraints, energy storage capacity constraints, and charge / discharge power constraints, the centralized optimization model is solved by a distributed optimization algorithm to obtain the charge / discharge power and health status of each energy storage battery. The charging and discharging of each energy storage battery is controlled based on its charging and discharging power and health status.

2. The distributed energy storage scheduling method based on health status awareness according to claim 1, characterized in that, The performance parameters include: initial energy storage capacity, initial internal resistance, decay internal resistance, initial power, and initial efficiency; the real-time operating status parameters include: current energy storage capacity, current internal resistance, current power, and current efficiency. The process of determining corresponding health status indicators based on the performance parameters and real-time operating status parameters of the energy storage battery includes: Determine the capacity health status based on the current energy storage capacity and the initial energy storage capacity; The health status of the internal resistance is determined based on the initial internal resistance, the decay internal resistance, and the current internal resistance. Determine the power health status based on the initial internal resistance, decay internal resistance, initial power, and current power; Determine the efficiency health status based on the initial internal resistance, decay internal resistance, initial efficiency, and current efficiency; The health status index of the energy storage battery is obtained by weighted summation of the capacity health status, internal resistance health status, power health status and efficiency health status.

3. The distributed energy storage scheduling method based on health status awareness according to claim 2, characterized in that, The real-time operating status parameters also include: current current, current battery temperature, and current ambient temperature; The current energy storage capacity is obtained in the following way: Obtain the amount of electricity used in the current charge-discharge cycle of the energy storage battery, the energy storage capacity of the previous period, and the cumulative capacity decay value of the previous period; The corresponding depth of charge and discharge is calculated based on the amount of electricity used in the current charge and discharge cycle of the energy storage battery and the energy storage capacity in the previous period. The current capacity decay value is determined based on the depth of charge and discharge of the energy storage battery, the current current, the current battery temperature, and the current ambient temperature. The current capacity decay value is added to the cumulative capacity decay value of the previous period to obtain the cumulative capacity decay value of the current period. The current energy storage capacity is obtained by subtracting the cumulative capacity decay value for the current period from the initial energy storage capacity.

4. The distributed energy storage scheduling method based on health status awareness according to claim 1, characterized in that, The centralized optimization model includes: min(C total )=min(∑ t (C agg +∑ i C user,i +C loss )); Among them, C total C represents the total cost. agg C represents the aggregator's cost. user,i C represents the cost to user i. loss γ represents the energy storage loss cost, t represents the time period, and γ represents the energy storage loss cost. s This represents the probability of scenario s occurring. This represents the transaction costs between the aggregator and the power grid. This represents the retail price during time period t. This represents the total energy demand of the i-th energy storage battery during time period t in scenario s. This represents the net measurement price for period t. This represents the base power supplied by the i-th energy storage battery from the system during time period t in scenario s. This represents the operating cost of the i-th energy storage battery during time period t in scenario s. This represents the demand response function of the i-th energy storage battery in scenario s during time period t. This represents the load power of the i-th energy storage battery in scenario s during time period t. This represents the energy storage cost of the i-th energy storage battery. This indicates the initial health status of the energy storage battery. S S represents the health status indicator at the end of the lifespan of an energy storage battery. i,t,s This represents the health status index of the i-th energy storage battery in scenario s during time period t.

5. The distributed energy storage scheduling method based on health status awareness according to claim 4, characterized in that, The user-side power constraints include: The energy storage charge constraint includes: AND min ≤e i,t,s ≤E i,t,s ; The charging and discharging power constraint includes: in, This represents the power of the i-th energy storage battery. This represents the power generation of the photovoltaic distributed power source equipped with the i-th energy storage battery during the time period t in scenario s. This represents the interaction power of the i-th energy storage battery. This represents the charging power of the i-th energy storage battery in scenario s during time period t. E represents the discharge power of the i-th energy storage battery in scenario s during time period t. min Indicates the minimum charge limit for energy storage, e i,t,s E represents the energy storage charge of the i-th energy storage battery in scenario s during time period t. i,t,s P represents the energy storage capacity limit of the i-th energy storage battery in scenario s during time period t. cap This represents the energy storage charging and discharging power limit of the i-th energy storage battery during time period t.

6. The distributed energy storage scheduling method based on health status awareness according to claim 5, characterized in that, The centralized optimization model is solved using a distributed optimization algorithm to obtain the charge / discharge power and health status of each energy storage battery, including: By introducing auxiliary variables, the centralized optimization model is decomposed into several sub-optimization problems; Set the initial values ​​for the auxiliary variables, the initial values ​​for the Lagrange multipliers, and the step size parameter; Repeat the distributed solution process to obtain the charging and discharging power and health status of each energy storage battery; The distributed solution process includes: Based on the current auxiliary variables and the current Lagrange multipliers, each sub-optimization problem is solved independently to obtain the current local variables; where the current auxiliary variables in the first execution of the distributed solution process are the initial values ​​of the auxiliary variables, and the current Lagrange multipliers in the first execution of the distributed solution process are the initial values ​​of the Lagrange multipliers. Determine if the deviation between the current local variable and the current auxiliary variable is less than a preset threshold; If so, the current local variable is taken as the target result, and the charging and discharging power and health status of each energy storage battery are obtained based on the target result. If not, then update the auxiliary variables based on the local variables of all sub-optimization problems to obtain the updated auxiliary variables; update the Lagrange multipliers through gradient descent based on the local variables and auxiliary variables to obtain the updated Lagrange multipliers; use the updated auxiliary variables and updated Lagrange multipliers as the current auxiliary variables and current Lagrange multipliers for the next execution of the distributed solution process.

7. A distributed energy storage scheduling device based on health status awareness, characterized in that, include: The system includes an energy storage parameter acquisition module, a health indicator calculation module, an optimization problem construction module, an optimization problem solving module, and an energy storage control module. The energy storage parameter acquisition module is used to acquire the performance parameters, real-time operating status parameters, energy storage parameters and energy storage cost characteristic parameters of each energy storage battery; The health indicator calculation module is used to determine the corresponding health status indicators based on the performance parameters and real-time operating status parameters of the energy storage battery. The optimization problem construction module is used to construct a centralized optimization model with the goal of minimizing the total cost based on the energy storage parameters, energy storage cost characteristics, and health status indicators of each energy storage battery, and to construct user-side power constraints, energy storage charge constraints, and charge / discharge power constraints. The optimization problem solving module is used to solve the centralized optimization model using a distributed optimization algorithm under the constraints of user-side power constraints, energy storage load constraints, and charge / discharge power constraints, so as to obtain the charge / discharge power and health status of each energy storage battery. The energy storage control module is used to control the charging and discharging of each energy storage battery according to its charging and discharging power and health status.

8. The distributed energy storage scheduling device based on health status awareness according to claim 7, characterized in that, The performance parameters include: initial energy storage capacity, initial internal resistance, decay internal resistance, initial power, and initial efficiency; the real-time operating status parameters include: current energy storage capacity, current internal resistance, current power, and current efficiency. The health indicator calculation module includes: a capacity health calculation submodule, an internal resistance health calculation submodule, a power health calculation submodule, an efficiency health calculation submodule, and a health indicator fusion submodule. The capacity health calculation submodule is used to determine the capacity health status based on the current energy storage capacity and the initial energy storage capacity. The internal resistance health calculation submodule is used to determine the internal resistance health status based on the initial internal resistance, the decayed internal resistance, and the current internal resistance. The power health calculation submodule is used to determine the power health status based on the initial internal resistance, the decay internal resistance, the initial power, and the current power. The efficiency health calculation submodule is used to determine the efficiency health status based on the initial internal resistance, the decay internal resistance, the initial efficiency, and the current efficiency. The health index fusion submodule is used to perform a weighted summation of the capacity health status, internal resistance health status, power health status, and efficiency health status to obtain the health status index of the energy storage battery.

9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the health-aware distributed energy storage scheduling method as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the distributed energy storage scheduling method based on health status awareness as described in any one of claims 1-6.

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