A Distributed Collaborative Source-Grid-Load-Storage Cluster Joint Optimization Method and Device

Abstract

This invention relates to the field of energy storage technology, and discloses a distributed collaborative method and apparatus for clustered joint optimization of energy storage, grid, and load. The method includes: determining the current active power demand and reactive power demand; acquiring the internal resistance, rate of change of internal resistance with temperature, and heat capacity of the energy storage unit; determining the positive feedback strength kernel based on the internal resistance, rate of change of internal resistance with temperature, and heat capacity, and forming an allocation criterion by minimizing the maximum value of the positive feedback strength kernel; generating a current command to satisfy the active power demand according to the allocation criterion; and generating a reactive current command to satisfy the reactive power demand according to the allocation criterion. This invention can extend the overall operating life of the energy storage cluster, and significantly improve the system's economy and reliability while meeting active and reactive power demands and apparent power safety constraints.

Description

Technical Field

[0001] This invention relates to the field of source-grid-load-storage technology, and more specifically, to a method and apparatus for clustered joint optimization of source-grid-load-storage based on distributed collaboration. Background Technology

[0002] With the rapid development of new energy power generation and energy storage systems, large-scale battery energy storage is increasingly being applied in the coordinated optimization of power generation, grid, load, and storage. Parallel battery modules or energy storage clusters, operating in a clustered manner, can quickly respond to tasks such as frequency regulation, peak shaving and valley filling, and voltage support. However, in practical engineering, different battery cells often exhibit different internal resistance and capacity levels due to differences in manufacturing processes, usage history, and heat dissipation environments. In parallel systems, this difference directly determines the bias in current distribution, thus affecting the stability and lifespan of the system.

[0003] However, in parallel systems, cells with low internal resistance are more likely to carry more current. This current generates significant heat as it passes through these cells, further increasing their temperature. The increased temperature then reduces the cell's resistance, allowing it to carry even more current in subsequent cycles. Simultaneously, energy storage systems often operate under conditions of frequent shallow cycles and narrow state of charge when regulating frequency or smoothing renewable energy output. This operational characteristic accelerates the imbalance in current distribution. These operating mechanisms lead to a gradual widening of the degradation rate among different battery cells, resulting in performance inconsistencies within the system.

[0004] During long-term operation, the interaction between uneven current distribution and temperature effects causes an abnormal phenomenon where healthy units fail first. Units that were initially performing well reach their lifespan prematurely due to continuously bearing excessive electrical and thermal stress, while weaker units passively share less current and delay degradation, ultimately reversing the overall lifespan ranking. This not only weakens the overall lifespan of the energy storage cluster but also reduces the system's reliability and economy, becoming a key technical problem that urgently needs to be solved in the current optimization of energy source-grid-load-storage systems. Summary of the Invention

[0005] This invention provides a distributed collaborative source-grid-load-storage clustered joint optimization method and apparatus, which solves the technical problems mentioned in the background art.

[0006] In a first aspect, the present invention provides a distributed collaborative source-grid-load-storage clustered joint optimization method, including:

[0007] Determine the active power demand and reactive power demand at the current moment;

[0008] Obtain the internal resistance, rate of change of internal resistance with temperature, and heat capacity of the energy storage unit;

[0009] The positive feedback intensity kernel is determined based on the internal resistance, the rate of change of internal resistance with temperature, and the heat capacity, and the allocation criterion is formed by minimizing the maximum value of the positive feedback intensity kernel.

[0010] Current commands are generated to meet the active power demand according to the allocation criteria.

[0011] Reactive current commands are generated to meet reactive power requirements according to allocation criteria.

[0012] Furthermore, the internal resistance, rate of change of internal resistance with temperature, and heat capacity of the energy storage unit are obtained, including:

[0013] The current of energy storage unit i is collected at fixed time intervals. Terminal voltage and unit temperature ;

[0014] The process of obtaining the internal resistance of energy storage unit i is as follows:

[0015] Internal resistance was calculated using least-squares linear fitting. , ;

[0016] in, Indicates the number of fitted samples. This represents the average current of the fitted sample. This represents the average terminal voltage of the fitted sample;

[0017] The process of obtaining the rate of change of internal resistance of energy storage unit i with respect to temperature is as follows:

[0018] Current moment The corresponding internal resistance and unit temperature are combined to obtain: ;

[0019] The rate of change of internal resistance with temperature was calculated using least-squares linear fitting. ,as follows:

[0020]

[0021] in, This represents the average cell temperature of the fitted sample. This represents the average internal resistance of the fitted sample;

[0022] The process of obtaining the heat capacity of energy storage unit i is as follows:

[0023] At any moment up to the current moment Between these steps, the current to energy storage unit i is increased to calculate its heat capacity, as follows:

[0024]

[0025]

[0026] in, This represents the heat capacity of energy storage unit i. Indicates heat generation power. Indicates a fixed time interval.

[0027] Furthermore, the positive feedback strength kernel is determined based on internal resistance, the rate of change of internal resistance with temperature, and heat capacity, including:

[0028]

[0029] in, Represents the kernel coefficient of energy storage unit i;

[0030] According to the current Calculate the energy mapping function, including:

[0031]

[0032] The product of the energy mapping function and the kernel coefficients is used as the positive feedback strength kernel. .

[0033] Furthermore, the allocation criterion is formed by minimizing the maximum value of the positive feedback strength kernel, including:

[0034] The allocation criteria expressed in terms of the infinite norm include: ;

[0035] in, Represents the allocation criterion function. This represents the positive feedback strength kernel of the i-th energy storage unit. This represents the intensity kernel vector formed by the positive feedback intensity kernels of N energy storage units. It represents the infinite norm.

[0036] Furthermore, current commands are generated to meet active power demand according to allocation criteria, including:

[0037] The current increment and power increment of energy storage unit i are measured simultaneously; where the current increment and power increment represent the values ​​at the current moment, respectively. With time The difference in current and the difference in power;

[0038] The ratio of power increment to current increment is used as the active power conversion factor for energy storage unit i.

[0039] The product of the kernel coefficient, internal resistance, and fixed time interval of energy storage unit i is used as the first parameter. ;

[0040] The active power conversion factor is used as the second parameter. .

[0041] Furthermore, generating current commands to meet active power demand according to allocation criteria also includes:

[0042] The first optimization model is constructed based on the first and second parameters, as follows:

[0043]

[0044] in, This represents the active power demand, where N represents the number of energy storage units. Indicates auxiliary variables;

[0045] Obtaining the closed-form solution of the first optimization model includes:

[0046]

[0047] in, This represents the i-th component of the optimal current vector obtained by solving the optimization model;

[0048] And Current command as energy storage unit i .

[0049] Furthermore, reactive current commands are generated to meet reactive power demand according to allocation criteria, including:

[0050] The reactive current increment and reactive power increment of energy storage unit i are measured simultaneously; where the reactive current increment and reactive power increment represent the current moment. With time The difference between reactive current and reactive power;

[0051] The ratio of reactive power increment to reactive current increment is used as the reactive power conversion factor. ;

[0052] Read the AC terminal voltage of energy storage unit i and the corresponding apparent power limit To construct apparent power constraints, including:

[0053]

[0054] in, This represents the reactive current to be solved on the AC side of energy storage unit i.

[0055] Furthermore, generating reactive current commands according to the allocation criteria to meet reactive power demand also includes:

[0056] At any moment up to the current moment Between these steps, the current to energy storage unit i is increased, and the AC / DC equivalent coefficient is calculated. , ;in, This represents the perturbation difference operator. This represents the DC-side current of the i-th energy storage unit. This represents the active current component on the AC side of the i-th energy storage unit;

[0057] The product of the AC-DC equivalent coefficient and the first parameter is used as the AC-side kernel constant. ;

[0058] The second optimization model is constructed based on the AC side kernel constant and reactive power conversion coefficient, as follows:

[0059]

[0060] in, Indicates reactive power demand. Indicates an upper bound variable;

[0061] If the apparent power constraint of energy storage unit i is not triggered, the reactive current to be solved is calculated, including:

[0062]

[0063] And Current command as energy storage unit i ;

[0064] If the apparent power constraint of energy storage unit i is triggered, calculate the cutoff value for the reactive current to be solved. Determining the remaining reactive power demand based on the cutoff value The remaining reactive power demand is repeatedly solved on the untriggered set to finally obtain the reactive current command. Among them, the remaining reactive power demand The calculation formula is as follows:

[0065]

[0066] in, This represents the set of energy storage units that trigger apparent power constraints.

[0067] Secondly, a distributed collaborative source-grid-load-storage clustered joint optimization device, applied in any of the aforementioned distributed collaborative source-grid-load-storage clustered joint optimization methods, includes:

[0068] The data acquisition module is used to determine the active power demand and reactive power demand at the current moment.

[0069] The data acquisition module is used to acquire the internal resistance, the rate of change of internal resistance with temperature, and the heat capacity of the energy storage unit.

[0070] The allocation criterion module is used to determine the positive feedback strength kernel based on the internal resistance, the rate of change of internal resistance with temperature, and the heat capacity, and to form the allocation criterion by minimizing the maximum value of the positive feedback strength kernel.

[0071] The active current module is used to generate current commands according to the allocation criteria to meet the active power demand.

[0072] The reactive current module is used to generate reactive current commands to meet reactive power requirements according to allocation criteria.

[0073] The beneficial effects of this invention include: by constructing a positive feedback strength core based on internal resistance, temperature sensitivity, and heat capacity, a unified measure is taken of the tendency of energy storage units to change admittance due to temperature rise under current load; and further, a distribution criterion is constructed. This prevents healthy units from continuously bearing overload due to low resistance, thus avoiding accelerated degradation and achieving balanced load distribution and synchronized degradation among units. The ultimate effect is to reliably extend the overall operational life of the energy storage cluster, while significantly improving system economy and reliability while meeting active and reactive power requirements and apparent power safety constraints. Attached Figure Description

[0074] Figure 1 This is a flowchart of the method of the present invention;

[0075] Figure 2 This is a device module diagram of the present invention. Detailed Implementation

[0076] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0077] Example 1:

[0078] like Figure 1 As shown, the distributed collaborative source-grid-load-storage clustered joint optimization method includes:

[0079] Determine the active power demand and reactive power demand at the current moment;

[0080] Obtain the internal resistance, rate of change of internal resistance with temperature, and heat capacity of the energy storage unit;

[0081] The positive feedback intensity kernel is determined based on the internal resistance, the rate of change of internal resistance with temperature, and the heat capacity, and the allocation criterion is formed by minimizing the maximum value of the positive feedback intensity kernel.

[0082] Current commands are generated to meet the active power demand according to the allocation criteria.

[0083] Reactive current commands are generated to meet reactive power requirements according to allocation criteria.

[0084] It should be noted that the active power demand and reactive power demand are obtained based on a prediction model. For example, by using an LSTM neural network model, the time-series active power demand and reactive power demand are obtained as training data, thereby obtaining an LSTM neural network model that can predict active power demand and reactive power demand.

[0085] It's important to note that thermal resistance shunting refers to the uneven current distribution among parallel units in an energy storage cluster due to differences in resistance. This unevenness is further amplified by temperature changes, creating a dynamic coupling process: low-resistance units naturally receive more current, generating more heat and causing their temperature to rise. If the resistance decreases with increasing temperature, the resistance of that energy storage unit further decreases, resulting in it receiving more current in the next cycle. This forms a positive feedback loop: increased current → increased heat generation → increased temperature → decreased resistance → even greater current. This positive feedback loop can cause initially healthy energy storage units to age prematurely due to continuous overload.

[0086] In one embodiment of the present invention, obtaining the internal resistance, the rate of change of internal resistance with temperature, and the heat capacity of the energy storage unit includes:

[0087] The current of energy storage unit i is collected at fixed time intervals. Terminal voltage and unit temperature ;

[0088] The process of obtaining the internal resistance of energy storage unit i is as follows:

[0089] Internal resistance was calculated using least-squares linear fitting. , ;

[0090] in, Indicates the number of fitted samples. This represents the average current of the fitted sample. This represents the average terminal voltage of the fitted sample;

[0091] In detail, based on a linear circuit model, where the terminal voltage and current satisfy a linear relationship, where is the open-circuit voltage and is the internal resistance, the least squares fitting method minimizes the fitting error and improves the accuracy of resistance estimation by calculating the sum of the products of the deviations between current and voltage for each set of sample data and then dividing by the sum of the squares of the current deviations. The selection of the number of fitting samples needs to balance computational efficiency and accuracy. Average current and average terminal voltage are used as intermediate quantities to eliminate the influence of DC bias in the data, making the fitting results closer to the true resistance characteristics.

[0092] The process of obtaining the rate of change of internal resistance of energy storage unit i with respect to temperature is as follows:

[0093] The current moment The corresponding internal resistance and unit temperature are combined to obtain: ;

[0094] The rate of change of internal resistance with temperature was calculated using least-squares linear fitting. ,as follows:

[0095]

[0096] in, This represents the average cell temperature of the fitted sample. This represents the average internal resistance of the fitted sample;

[0097] In detail, within a local temperature range, resistance and temperature approximately satisfy a linear relationship, where denoted as and is the intercept, representing the rate of change of resistance with respect to temperature. During the fitting process, the trend of resistance change with temperature can be quantified by calculating the sum of the products of temperature deviation and resistance deviation, divided by the sum of the squares of the temperature deviations. Average cell temperature and average internal resistance are used to eliminate data fluctuations, making the calculation of the rate of change more accurate.

[0098] The process of obtaining the heat capacity of energy storage unit i is as follows:

[0099] At any moment up to the current moment Between these steps, the current to energy storage unit i is increased to calculate its heat capacity, as follows:

[0100]

[0101]

[0102] in, This represents the heat capacity of energy storage unit i. Indicates heat generation power. Indicates a fixed time interval.

[0103] In detail, the current is increased to enhance the thermal signal, making temperature changes more significant, so as to accurately calculate the heat capacity.

[0104] In detail, at any time up to the current moment The current is increased intermittently to induce a measurable heat change in the unit, from which the heat capacity is calculated. The calculation of heat generation power is based on Joule's law, which states that the power generated by current flowing through a resistor is proportional to the square of the current and the resistance. The formula for calculating heat capacity originates from the principle of thermal equilibrium, where the ratio of heat generation power to the rate of temperature change reflects the unit's ability to absorb heat. Fixed time intervals ensure a consistent time base for calculating the rate of temperature change, avoiding errors in heat capacity due to different time scales.

[0105] It should be noted that the rate of change of internal resistance with temperature is used to reflect the trend of internal resistance of the energy storage unit as a function of temperature, and to reflect the mapping relationship between temperature change and resistance change.

[0106] In one embodiment of the present invention, determining the positive feedback strength kernel based on internal resistance, the rate of change of internal resistance with temperature, and heat capacity includes:

[0107]

[0108] in, Represents the kernel coefficient of energy storage unit i;

[0109] The detailed formula for calculating the kernel coefficient is as follows: By integrating the internal resistance of the energy storage unit, the rate of change of internal resistance with temperature, and heat capacity, a potential for positive feedback is formed. The rate of change of internal resistance with temperature represents the effect of temperature on resistance: when... When the resistance is less than 0, an increase in temperature leads to a decrease in resistance, which is the core driving factor of thermal resistance shunt positive feedback. The negative sign is introduced in the formula to... When the value is less than 0, the kernel coefficient is positive, thus representing a positive feedback trend.

[0110] In detail, internal resistance is the basis for heat generation by current; the greater the resistance, the more heat is generated under the same current. Heat capacity reflects the ease with which the temperature changes after the unit absorbs heat; the smaller the heat capacity, the greater the temperature rise under the same amount of heat, and the easier it is to amplify positive feedback. Internal resistance, the rate of change of internal resistance with temperature, and heat capacity are coupled to make the core coefficient a quantitative indicator of the admittance amplification capability under unit heat injection.

[0111] According to the current Calculate the energy mapping function, including:

[0112]

[0113] In detail, the Joule heat generated by an electric current passing through a resistor follows the Joule heat formula, where, It is the heat output per unit time, multiplied by a fixed time interval. back, Used to reflect the current time The total heat generated by the internal current. Since the core of positive feedback is the coupling of current, heat capacity, and internal resistance, the effect of temperature change on resistance cannot be correlated solely through current or power. However, after being converted into energy, it can be correlated with the kernel coefficient. The electrical energy mapping function is used to convert current into heat energy.

[0114] The product of the energy mapping function and the kernel coefficients is used as the positive feedback strength kernel. .

[0115] In detail, the positive feedback strength kernel is used to quantify the real-time strength of the positive feedback.

[0116] When the unit is in Temperature range less than 0 (most operating scenarios). For positive, Since the value is non-negative (heat generation from current is always positive), the positive feedback strength kernel is positive. The larger the positive feedback strength kernel, the stronger the positive feedback cycle of the energy storage unit under the current current: temperature rise due to heat generation → resistance decrease → current shunt increase. Greater than or equal to 0 (resistance remains unchanged or increases with increasing temperature). If the value is non-positive, the positive feedback strength kernel is also non-positive, indicating that the positive feedback is weakened or there is no significant positive feedback.

[0117] In one embodiment of the present invention, the allocation criterion is formed by minimizing the maximum value of the positive feedback intensity kernel, including:

[0118] The allocation criteria expressed in terms of the infinite norm include: ;

[0119] in, Represents the allocation criterion function. This represents the positive feedback strength kernel of the i-th energy storage unit. This represents the intensity kernel vector formed by the positive feedback intensity kernels of N energy storage units. It represents the infinite norm.

[0120] In detail, the allocation criterion is formed by minimizing the maximum value of the positive feedback strength kernel, thereby constructing an index for quantifying the positive feedback strength, and optimizing the current allocation with this index as the target. Its mathematical expression is: Positive feedback strength kernel From kernel coefficients Mapping function with electrical energy It is obtained by multiplication. The kernel coefficient integrates the resistance temperature sensitivity, inherent resistance, and heat capacity characteristics of the unit, reflecting the inherent positive feedback potential of the unit; the electric energy mapping function relates the magnitude of the current to the action time, quantifying the degree of thermal disturbance of the current working condition to the unit. The combination of the kernel coefficient and the electric energy mapping function can describe the strength of the positive feedback cycle corresponding to the heat generation by the current → resistance change → increased shunt.

[0121] Specifically, the intensity kernel vector is composed of the positive feedback intensity kernels of all energy storage units, including the positive feedback characteristics of each unit under the current current. The role of the infinity norm is that the positive feedback of thermal resistance shunt has a non-linear amplification characteristic, that is, the larger the positive feedback intensity kernel of a certain energy storage unit, the more obvious the resistance reduction, the more shunt, and the stronger the positive feedback cycle formed under the same current increment. Since the harm of positive feedback is mainly determined by the most sensitive unit, that is, if the positive feedback of a certain energy storage unit is too strong, it will enter the vicious cycle of thermal resistance shunt first, and ultimately lead to the premature failure of the energy storage unit. Therefore, taking the maximum value as the optimization target can directly control the most dangerous link. The allocation criterion is used to determine the most significant positive feedback intensity among all energy storage units under the current current distribution. By optimizing to make minimized, so as to make the strongest positive feedback as weak as possible, thus balancing the burden of each energy storage unit and avoiding the accelerated degradation of a single energy storage unit due to too strong positive feedback.

[0122] Specifically, the allocation criterion provides a clear goal for current distribution: under the premise of meeting the total power demand, adjust the current of each unit to minimize the allocation criterion. That is, when the positive feedback intensity kernel of a certain energy storage unit is large, by reducing its current, the electric energy mapping function is reduced (because the electric energy mapping function is proportional to the square of the current), so as to reduce the positive feedback intensity kernel; for the energy storage unit with a small positive feedback intensity kernel, the current can be appropriately increased to ensure that the total power demand is met. Finally, the positive feedback intensity kernels of all energy storage units tend to be balanced, and the maximum value is minimized.

[0123] In an embodiment of the present invention, generating a current command according to the allocation criterion to meet the active power demand includes:

[0124] Simultaneously measuring the current increment and power increment of energy storage unit i; where the current increment and power increment respectively represent the current difference and power difference at the current moment and the moment ;

[0125] Specifically, by simultaneously measuring the current increment and power increment, the mapping relationship between current and power is established by capturing the change of electrical parameters within a fixed time.

[0126] Taking the ratio of the power increment to the current increment as the active conversion coefficient of energy storage unit i;

[0127] In detail, the active power conversion factor is used to quantify the power change caused by a unit change in current, reflecting the current-to-power conversion efficiency of the energy storage unit under current operating conditions. For example, when the current increases by 1 ampere, if the power increase is 50 watts, the active power conversion factor is 50, indicating that the energy storage unit has a strong current-to-power driving capability.

[0128] The product of the kernel coefficient, internal resistance, and fixed time interval of energy storage unit i is used as the first parameter. ;

[0129] In detail, the formula for calculating the first parameter is the product of the kernel coefficient, the internal resistance, and the fixed time interval.

[0130] The first parameter combines the inherent characteristics of the energy storage unit (resistance, resistance temperature sensitivity, heat capacity) with the time factor to obtain a comprehensive scale that reflects the admittance change under unit energy action, thereby providing an energy dimension benchmark for subsequent current allocation and ensuring the comparability of the characteristics of different energy storage units.

[0131] The active power conversion factor is used as the second parameter. .

[0132] In detail, the second parameter, the active power conversion factor, is related to current and power, and provides a conversion from current command to power output.

[0133] In one embodiment of the present invention, generating a current command according to the allocation criteria to meet the active power demand further includes:

[0134] The first optimization model is constructed based on the first and second parameters, as follows:

[0135]

[0136] in, This represents the active power demand, where N represents the number of energy storage units. Indicates auxiliary variables;

[0137] In detail, the core of the first optimization model is to "minimize the maximum value of positive feedback intensity" while meeting the total active power demand and balancing the positive feedback trend of each energy storage unit.

[0138] The objective function is , represents minimizing the auxiliary variable ζ, where ζ reflects the maximum allowable value of the positive feedback strength of all energy storage units.

[0139] :in As the first parameter, Let be the current flowing through energy storage unit i. This constraint ensures the positive feedback strength of each energy storage unit under the current (as determined by...). The quantization must not exceed ζ to ensure that no unit falls into a vicious cycle of thermal resistance shunting due to excessive positive feedback.

[0140] :in For the second parameter, This represents the total active power demand. This constraint ensures that the sum of the active power outputs of all energy storage units meets the system requirements, reflecting the combination of optimization objectives and actual power balance.

[0141] In detail, N represents the number of energy storage units, and its value is determined by the cluster size; It integrates the unit's resistance temperature sensitivity, inherent resistance, and thermal capacity characteristics. It relates to the conversion relationship between current and active power. and Together, we ensure that the first optimization model not only includes the inherent characteristics of the unit, but also incorporates the operating conditions.

[0142] Obtaining the closed-form solution of the first optimization model includes:

[0143]

[0144] in, This represents the i-th component of the optimal current vector obtained by solving the optimization model;

[0145] The detailed formula for calculating the closed-form solution is as follows: The derivation logic is as follows:

[0146] 1. In an ideal situation without hard constraints on equipment (such as current limits or temperature limits), the optimal solution must satisfy that the positive feedback strength of each unit is equal, i.e. ( (This is the optimal auxiliary variable). Due to the objective function's characteristic of minimizing the maximum value, the maximum value can only be minimized when the positive feedback strength of all energy storage units reaches the same level.

[0147] 2. Power balance constraint: Substitute the total power constraint , can be obtained Solve after organizing. ,get .

[0148] 3. Substitution That is, to obtain the optimal current of each unit. This ensures that the optimal current of energy storage unit i guarantees both a balanced positive feedback strength and meets the total power demand.

[0149] And Current command as energy storage unit i .

[0150] In detail, the optimal current components Directly used as the current command for energy storage unit i .

[0151] Based on power requirements and inherent characteristics of the unit and The joint decision reflects both the system's demand for active power and the unit's ability to suppress positive feedback, ensuring that the instructions match the unit's state.

[0152] Consistency with the objective of suppressing positive feedback: Current command Size and Inversely proportional, that is The larger the value, the stronger the positive feedback potential of the energy storage unit and the smaller the current command. This reflects the optimization goal of suppressing strong positive feedback units and avoiding the exacerbation of positive feedback loops due to excessive current in such energy storage units.

[0153] In one embodiment of the present invention, generating a reactive current command according to the allocation criteria to meet the reactive power demand includes:

[0154] The reactive current increment and reactive power increment of energy storage unit i are measured simultaneously; where the reactive current increment and reactive power increment represent the current moment. With time The difference between reactive current and reactive power;

[0155] The ratio of reactive power increment to reactive current increment is used as the reactive power conversion factor. ;

[0156] In detail, the reactive power conversion factor is the ratio of the reactive power increment to the reactive current increment. It reflects the reactive power increment corresponding to a unit increase in reactive current and is used to convert reactive current into reactive power. In AC circuits, the relationship between reactive power and reactive current can be approximated as follows: reactive power is directly proportional to reactive current. The reactive power conversion factor can eliminate interference such as voltage fluctuations and obtain a linearized conversion relationship.

[0157] Read the AC terminal voltage of energy storage unit i and the corresponding apparent power limit To construct apparent power constraints, including:

[0158]

[0159] in, This represents the reactive current to be solved on the AC side of energy storage unit i.

[0160] In detail, the apparent power limit is a rated parameter of the equipment, reflecting the maximum capacity limit of the energy storage unit.

[0161] In detail, the apparent power generated by the combined active and reactive currents must not exceed the upper limit of apparent power to avoid equipment overload due to excessive total current. The square term reflects the orthogonal relationship between active and reactive currents, conforming to the physical laws of apparent power in AC circuits.

[0162] In one embodiment of the present invention, generating reactive current commands according to allocation criteria to meet reactive power requirements further includes:

[0163] At any moment up to the current moment Between these steps, the current to energy storage unit i is increased, and the AC / DC equivalent coefficient is calculated. , ;in, This represents the perturbation difference operator. This represents the DC-side current of the i-th energy storage unit. This represents the active current component on the AC side of the i-th energy storage unit;

[0164] In detail, in order to equate the impact of AC-side reactive current on the system's positive feedback to a measurement system consistent with that of the active side, it is necessary to calculate the AC-DC equivalent coefficient. It is achieved through perturbation difference: at adjacent time points (from time point...) up to the current moment After applying a boosting perturbation to the current of energy storage unit i, the ratio of the change in the square of the DC current to the change in the sum of the squares of the AC current is quantified by the following formula: Among them, among them, For perturbation difference operators, For energy storage unit i, the DC side current is... This refers to the active component current on the AC side. The AC-DC equivalent coefficient is used to reflect the equivalent influence of changes in AC-side current on positive feedback-related characteristics such as DC-side heating.

[0165] It should be noted that the process of obtaining the perturbation difference operator is as follows:

[0166] Select a reference time Simultaneously collect three types of current parameters from energy storage unit i:

[0167] DC side current: ;

[0168] AC active current: ;

[0169] AC side reactive current: .

[0170] At any moment up to the current moment Inside, an upward disturbance is applied to the AC side current of energy storage unit i, for example:

[0171] Only increase the active current: make from Increase to ;

[0172] Only increase reactive current: make from Increase to ;

[0173] Simultaneously boost both active and reactive currents: causing them to flow from... Increase to , .

[0174] At the present moment The three types of current parameters of energy storage unit i are collected again synchronously:

[0175] DC side current: ;

[0176] AC active current: ;

[0177] AC side reactive current: .

[0178] The perturbation difference operator is used to quantify the magnitude of changes in physical quantities before and after the perturbation, and requires calculation of:

[0179] 1. Change in the square of the DC current:

[0180] Positive feedback is directly related to the "square term of current (corresponding to positive feedback driving factors such as power and heat generation)," therefore, we should focus on the changes in the square of the DC-side current. ;

[0181] 2. Changes in the sum of squares of active and reactive currents on the AC side:

[0182] The total current effect on the AC side is determined by both active and reactive currents (in accordance with the physical laws of apparent current), therefore, we should focus on the change in the sum of the squares of active and reactive currents on the AC side: ;

[0183] The disturbance differential operator Δ quantifies the impact of AC-side current variation on DC-side positive feedback correlation characteristics into AC-DC equivalent coefficients by calculating the ratio of the changes. The formula is: .

[0184] The product of the AC-DC equivalent coefficient and the first parameter is used as the AC-side kernel constant. ;

[0185] In detail, the AC-DC equivalent coefficient is multiplied by the first parameter to obtain the AC-side kernel constant: the AC-side kernel constant represents the positive feedback potential coefficient after integrating AC and DC characteristics, so that the influence of reactive current on positive feedback can be quantified in a kernel form consistent with active current, ensuring that reactive power allocation can follow the allocation criterion of minimizing the maximum value of positive feedback intensity.

[0186] The second optimization model is constructed based on the AC side kernel constant and reactive power conversion coefficient, as follows:

[0187]

[0188] in, Indicates reactive power demand. Indicates an upper bound variable;

[0189] In detail, based on the AC-DC equivalent coefficient and reactive power conversion coefficient, a second optimization model is constructed with the objective of minimizing the maximum positive feedback strength:

[0190]

[0191] Wherein, objective function middle, The upper bound variable represents the maximum allowable value of the positive feedback strength on the AC side of all energy storage units; constraint By combining the fixed influence of the active current command with the variable influence of the reactive current to be determined, the contribution of the total AC current to the positive feedback is quantified, requiring that the positive feedback strength of all units does not exceed [a certain value]. ;constraint To ensure total reactive power demand The needs are met.

[0192] If the apparent power constraint of energy storage unit i is not triggered, the reactive current to be solved is calculated, including:

[0193]

[0194] And Current command as energy storage unit i ;

[0195] In detail, if the apparent power constraint of energy storage unit i (in AC terminal voltage, If the apparent power limit is not triggered (i.e., the optimal solution will not cause equipment overload), then the optimal solution satisfies the condition that the positive feedback strength on the AC side of all units is equal (the optimality condition of minimizing the maximum value), that is:

[0196] in, This is the optimal upper bound;

[0197] Combined with total reactive power constraint By combining the results, we can obtain:

[0198]

[0199] At this time, It is directly used as the reactive current command for energy storage unit i, which not only meets the total reactive power demand, but also ensures that the positive feedback intensity on the AC side of each energy storage unit is balanced and minimized.

[0200] In detail, if the apparent power constraint of a certain energy storage unit i is triggered (i.e. The reactive current needs to be cut off, and the cutoff value needs to be calculated. (Usually, the apparent power constraint equation is assumed to hold, i.e.) (To ensure that the limits are not exceeded).

[0201] Subsequently, the set of energy storage units that trigger apparent power constraints is defined. ), calculate the total reactive power provided by these units through the cutoff value. And deduct it from the total reactive power demand to obtain the remaining reactive power demand: .

[0202] If the apparent power constraint of energy storage unit i is triggered, calculate the cutoff value for the reactive current to be solved. Determining the remaining reactive power demand based on the cutoff value The remaining reactive power demand is repeatedly solved on the untriggered set to finally obtain the reactive current command. Among them, the remaining reactive power demand The calculation formula is as follows:

[0203]

[0204] in, This represents the set of energy storage units that trigger apparent power constraints.

[0205] In detail, in the set of cells where constraints are not triggered (non- On the part) For the new reactive power demand, repeat the solution process of the second optimization model described above until all units satisfy the apparent power constraint, and finally obtain the reactive current command of each energy storage unit. (The element that triggers the constraint uses a cutoff value) (For those not triggered, the original values ​​are re-evaluated).

[0206] It should be noted that the cutoff value The acquisition process is as follows:

[0207] When the apparent power constraint of energy storage unit i is triggered (i.e. ,in AC terminal voltage, This is an active current command. The original reactive current, When the apparent power limit is set, the cutoff value needs to be solved by making the apparent power constraint equation hold to ensure that the device does not exceed the limit.

[0208] The equation for apparent power constraint is as follows: ;

[0209] Solve the transformation of this equation :

[0210] Divide both sides by : ;

[0211] Rearrange terms and take the positive square root (current is non-negative): ;

[0212] Cutoff value It is the maximum reactive current that the unit can provide under apparent power constraints, ensuring that the equipment does not overload.

[0213] Example 2:

[0214] like Figure 2 As shown, the distributed collaborative source-grid-load-storage clustered joint optimization device, applied in any of the distributed collaborative source-grid-load-storage clustered joint optimization methods described above, includes:

[0215] The data acquisition module is used to determine the active power demand and reactive power demand at the current moment.

[0216] The data acquisition module is used to acquire the internal resistance, the rate of change of internal resistance with temperature, and the heat capacity of the energy storage unit;

[0217] The allocation criterion module is used to determine the positive feedback strength kernel based on the internal resistance, the rate of change of internal resistance with temperature, and the heat capacity, and to form the allocation criterion by minimizing the maximum value of the positive feedback strength kernel.

[0218] The active current module is used to generate current commands according to the allocation criteria to meet the active power demand.

[0219] The reactive current module is used to generate reactive current commands to meet reactive power requirements according to allocation criteria.

[0220] The embodiments of this example have been described above. However, this example is not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of this example, and all of them are within the protection scope of this example.

Claims

1. A distributed collaborative source-grid-load-storage clustered joint optimization method, characterized in that, include: Determine the active power demand and reactive power demand at the current moment; Obtain the internal resistance, rate of change of internal resistance with temperature, and heat capacity of the energy storage unit, including: The current of energy storage unit i is collected at fixed time intervals. Terminal voltage and unit temperature ; The process of obtaining the internal resistance of energy storage unit i is as follows: Internal resistance was calculated using least-squares linear fitting. , ; in, Indicates the number of fitted samples. This represents the average current of the fitted sample. This represents the average terminal voltage of the fitted sample; The process of obtaining the rate of change of internal resistance of energy storage unit i with respect to temperature is as follows: Current moment The corresponding internal resistance and unit temperature are combined to obtain: ; The rate of change of internal resistance with temperature was calculated using least-squares linear fitting. ,as follows: ； in, This represents the average cell temperature of the fitted sample. This represents the average internal resistance of the fitted sample; The process of obtaining the heat capacity of energy storage unit i is as follows: At any moment up to the current moment Between these steps, the current to energy storage unit i is increased to calculate its heat capacity, as follows: ； ； in, This represents the heat capacity of energy storage unit i. Indicates heat generation power. Indicates a fixed time interval; The positive feedback intensity kernel is determined based on the internal resistance, the rate of change of internal resistance with temperature, and the heat capacity, and the allocation criterion is formed by minimizing the maximum value of the positive feedback intensity kernel. The positive feedback strength kernel is calculated as follows: ； in, Represents the kernel coefficient of energy storage unit i; According to the current Calculate the energy mapping function, including: ； The product of the energy mapping function and the kernel coefficients is used as the positive feedback strength kernel. ; The allocation criteria include: The allocation criteria expressed in terms of the infinite norm include: ; in, Represents the allocation criterion function. This represents the positive feedback strength kernel of the i-th energy storage unit. This represents the intensity kernel vector formed by the positive feedback intensity kernels of N energy storage units. Represents the infinite norm; Current commands are generated to meet the active power demand according to the allocation criteria. Reactive current commands are generated to meet reactive power requirements according to allocation criteria.

2. The distributed collaborative source-grid-load-storage clustered joint optimization method according to claim 1, characterized in that, Current commands are generated to meet active power demand according to allocation criteria, including: The current increment and power increment of energy storage unit i are measured simultaneously; where the current increment and power increment represent the values ​​at the current moment, respectively. With time The difference in current and the difference in power; The ratio of power increment to current increment is used as the active power conversion factor for energy storage unit i. The product of the kernel coefficient, internal resistance, and fixed time interval of energy storage unit i is used as the first parameter. ; The active power conversion factor is used as the second parameter. .

3. The distributed collaborative source-grid-load-storage clustered joint optimization method according to claim 2, characterized in that, The current command generated according to the allocation criteria to meet the active power demand also includes: The first optimization model is constructed based on the first and second parameters, as follows: ； in, This represents the active power demand, where N represents the number of energy storage units. Indicates auxiliary variables; Obtaining the closed-form solution of the first optimization model includes: ； in, This represents the i-th component of the optimal current vector obtained by solving the optimization model; And Current command as energy storage unit i .

4. The distributed collaborative source-grid-load-storage clustered joint optimization method according to claim 3, characterized in that, Reactive current commands are generated to meet reactive power demand according to allocation criteria, including: The reactive current increment and reactive power increment of energy storage unit i are measured simultaneously; where the reactive current increment and reactive power increment represent the current moment. With time The difference between reactive current and reactive power; The ratio of reactive power increment to reactive current increment is used as the reactive power conversion factor. ; Read the AC terminal voltage of energy storage unit i and the corresponding apparent power limit To construct apparent power constraints, including: ； in, This represents the reactive current to be solved on the AC side of energy storage unit i.

5. The distributed collaborative source-grid-load-storage clustered joint optimization method according to claim 4, characterized in that, The reactive current command generated according to the allocation criteria to meet the reactive power demand also includes: At any moment up to the current moment Between these steps, the current to energy storage unit i is increased, and the AC / DC equivalent coefficient is calculated. , ;in, This represents the perturbation difference operator. This represents the DC-side current of the i-th energy storage unit. This represents the active current component on the AC side of the i-th energy storage unit; The product of the AC-DC equivalent coefficient and the first parameter is used as the AC-side kernel constant. ; The second optimization model is constructed based on the AC side kernel constant and reactive power conversion coefficient, as follows: ； in, Indicates reactive power demand. Indicates an upper bound variable; If the apparent power constraint of energy storage unit i is not triggered, the reactive current to be solved is calculated, including: ； And Current command as energy storage unit i ; If the apparent power constraint of energy storage unit i is triggered, calculate the cutoff value for the reactive current to be solved. Determining the remaining reactive power demand based on the cutoff value The remaining reactive power demand is repeatedly solved on the untriggered set to finally obtain the reactive current command. Among them, the remaining reactive power demand The calculation formula is as follows: ； in, This represents the set of energy storage units that trigger apparent power constraints.

6. A distributed collaborative source-grid-load-storage clustered joint optimization device, applied in the distributed collaborative source-grid-load-storage clustered joint optimization method according to any one of claims 1 to 5, characterized in that, include: The data acquisition module is used to determine the active power demand and reactive power demand at the current moment. The data acquisition module is used to acquire the internal resistance, the rate of change of internal resistance with temperature, and the heat capacity of the energy storage unit. The allocation criterion module is used to determine the positive feedback strength kernel based on the internal resistance, the rate of change of internal resistance with temperature, and the heat capacity, and to form the allocation criterion by minimizing the maximum value of the positive feedback strength kernel. The active current module is used to generate current commands according to the allocation criteria to meet the active power demand. The reactive current module is used to generate reactive current commands to meet reactive power requirements according to allocation criteria.

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