Direct current micro-grid multi-energy storage unit distributed control method and system, and medium

Abstract

This invention discloses a distributed control method, system, and medium for multiple energy storage units in a DC microgrid. The method includes: for the multiple energy storage converters, primary control employs droop control; this droop control is an improved adaptive droop control obtained by adding a speed regulation factor and a power constraint factor for state-of-charge balancing to the droop coefficient; for the droop control of the primary control, secondary control employs a PI consensus algorithm. This invention can rapidly balance the state of charge of the energy storage units, maintain the bus voltage within the rated range, and exhibit strong robustness to load fluctuations, communication delays, and network faults in the DC microgrid.

Description

Technical Field

[0001] This invention relates to the field of power system operation and control technology, and in particular to a distributed control method, system and medium for multiple energy storage units in a DC microgrid. Background Technology

[0002] With the increasing penetration rate of distributed generation such as photovoltaics and wind power, distributed DC energy storage systems, as a new type of energy system, are widely used in microgrids to stabilize DC bus voltage and power balance due to their high energy utilization efficiency and good adaptability to renewable energy. Simultaneously, to meet the capacity demands of DC microgrids, multiple energy storage units are typically connected in parallel to the DC common bus via bidirectional DC / DC converters. However, due to manufacturing processes that may result in different characteristics among the energy storage units, even if each unit has the same nominal parameters, the state of charge (SOC) among the units may be unbalanced during charging / discharging. This can lead to overcharging / over-discharging of some energy storage units and power imbalance in the system. Although multiple energy storage units may operate at different power levels to achieve SOC balance, after SOC balance is achieved, all energy storage units will be in a similar SOC. At this point, multiple energy storage units with the same SOC will decrease their health status at the same rate, thus helping to improve the operating efficiency of multiple energy storage units in the microgrid. Therefore, SOC balance control of multiple energy storage units is necessary.

[0003] However, the droop control currently used for multiple energy storage units is difficult to quickly achieve a balanced state of charge of multiple energy storage units and is prone to causing bus voltage deviation. Summary of the Invention

[0004] This invention provides a distributed control method, system, and medium for multiple energy storage units in a DC microgrid, to solve the problems of difficulty in balancing the state of charge of multiple energy storage units and the tendency to cause DC bus voltage deviation in existing DC microgrid control methods.

[0005] Firstly, a distributed control method for multiple energy storage units in a DC microgrid is provided, comprising the following steps:

[0006] For multi-energy storage converters, the primary control adopts droop control; this droop control is an improved adaptive droop control obtained by adding a speed regulation factor and a power constraint factor for state-of-charge balancing to the droop coefficient.

[0007] For the droop control in the primary control, the secondary control uses the PI consensus algorithm.

[0008] Furthermore, the energy storage unit of the DC microgrid uses a Buck-Boost converter for voltage boosting. Its primary control adopts droop control, and its secondary control adopts a PI consensus algorithm. Finally, the energy storage converter drive signal is obtained through voltage and current dual PI loop control.

[0009] Furthermore, the improved adaptive droop coefficient droop control is expressed as follows:

[0010] ;

[0011] ;

[0012] ;

[0013] In the formula, U refi U is the reference value for the output voltage of the i-th energy storage converter. dcref This is the reference value for bus voltage. Let R0 be the improved SOC droop factor of the i-th energy storage converter, and C be the initial droop factor. i Let C be the rated capacity of the i-th energy storage unit. max I represents the maximum rated capacity of all energy storage units. oi Let I be the output current of the i-th energy storage converter, and n be the speed regulation factor for state-of-charge balance. oi When I > 0, the i-th energy storage unit is in a discharging state. oi When <0, the i-th energy storage unit is in a charging state. The real-time state of charge (SOC) of the i-th energy storage unit and the average SOC of all energy storage units are given. avg The difference, k i Let α be the power constraint factor, and α be the adjustment parameter, where 0 < α < 1.

[0014] Furthermore, the droop control expression after correction of the bus voltage compensation obtained by the PI consensus algorithm is as follows:

[0015] ;

[0016] In the formula, U refi U is the reference value for the output voltage of the i-th energy storage converter. dcref This is a reference value for the bus voltage. The voltage drop is caused by the droop control of the i-th energy storage converter itself. This is the bus voltage compensation amount for the i-th energy storage converter;

[0017] To achieve bus voltage stability, the objective function is set as follows:

[0018] ;

[0019] In the formula, The bus voltage deviation loss is denoted by N, and N is the total number of energy storage converters.

[0020] Furthermore, the dynamic equation of the PI consensus algorithm is:

[0021] ;

[0022] In the formula, ω is the coefficient of the optimization function, and a ij and b ij These are the proportional and integral coefficients for the PI consensus algorithm, respectively, N. i It is the collection of all energy storage units; For the integration variable of the PI consensus algorithm; The gradient of the objective function; , They represent respectively to and Find the derivative.

[0023] Furthermore, the dynamic equation of the PI consensus algorithm considering communication delay is:

[0024] ;

[0025] In the formula, τ is the time delay constant, t represents time t; ω is the coefficient of the optimization function, a ij and b ij These are the proportional and integral coefficients for the PI consensus algorithm, respectively, N. i It is the collection of all energy storage units; For the integration variable of the PI consensus algorithm; The gradient of the objective function; , They represent respectively to and Find the derivative.

[0026] Furthermore, the distributed control method for multiple energy storage units in the DC microgrid also includes:

[0027] For primary and secondary control, a small-signal model is established to analyze the impact of various parameters in primary and secondary control on the stability of DC microgrid, thereby determining the ideal selection range of various parameters in primary and secondary control.

[0028] Secondly, a distributed control system for multiple energy storage units in a DC microgrid is provided, including:

[0029] A primary control module is used to perform droop-based primary control for multiple energy storage converters; the droop control is an improved adaptive droop control obtained by adding a speed regulation factor and a power constraint factor for state-of-charge balancing to the droop coefficient.

[0030] The secondary control module is used to execute secondary control based on the PI consensus algorithm for the droop control of the primary control.

[0031] Thirdly, a distributed control system for multiple energy storage units in a DC microgrid is provided, including:

[0032] A memory on which computer programs are stored;

[0033] A processor is used to load and execute the computer program to implement the distributed control method for multiple energy storage units in a DC microgrid as described above.

[0034] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the distributed control method for multiple energy storage units in a DC microgrid as described above.

[0035] This invention proposes a distributed control method, system, and medium for multiple energy storage units in a DC microgrid, which has the following beneficial effects: In a DC microgrid with multiple energy storage units, under the action of droop control with an improved adaptive droop coefficient in primary control, the state of charge among multiple energy storage units can be quickly balanced. Furthermore, while ensuring that the output power of each energy storage unit is proportionally distributed according to its rated capacity, the maximum output power of the energy storage units is constrained within a safe operating range. Under the action of the PI consensus algorithm in secondary control, the bus voltage control can be transformed into an optimization problem, thereby eliminating the bus voltage drop caused by droop control and maintaining the DC bus voltage near its rated value. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of the distributed control method for multiple energy storage units in a DC microgrid provided in an embodiment of the present invention;

[0038] Figure 2This is a schematic diagram of distributed control of multiple energy storage units in a DC microgrid provided in an embodiment of the present invention; wherein (a) is a schematic diagram of the DC microgrid topology; and (b) is a block diagram of distributed control of multiple energy storage units in a DC microgrid.

[0039] Figure 3 This is a schematic diagram of the improved energy storage output current ratio and state of charge difference characteristic curve provided by the embodiment of the present invention; wherein (a) is the energy storage output current ratio and ∆SOC characteristic curve corresponding to different n values ​​when the α value is fixed in stage 1; (b) is the energy storage output current ratio and ∆SOC characteristic curve corresponding to different α values ​​when the n value is fixed in stage 2.

[0040] Figure 4 This is a schematic diagram of conventional droop control in the discharge state of energy storage provided by an embodiment of the present invention, wherein (a) is the SOC in the conventional droop control discharge state; (b) is the output current in the conventional droop control discharge state; (c) is the output power in the conventional droop control discharge state; (d) is the voltage drop caused by droop control in the conventional droop control discharge state; (e) is the voltage compensation amount in the conventional droop control discharge state; and (f) is the bus voltage in the conventional droop control discharge state.

[0041] Figure 5 This is a schematic diagram of droop control with improved adaptive droop coefficient in the discharge state provided by an embodiment of the present invention, wherein (a) is the SOC in the discharge state with improved adaptive droop coefficient; (b) is the output current in the discharge state with improved adaptive droop coefficient; (c) is the output power in the discharge state with improved adaptive droop coefficient; (d) is the voltage drop caused by droop control in the discharge state with improved adaptive droop coefficient; (e) is the voltage compensation amount in the discharge state with improved adaptive droop coefficient; and (f) is the bus voltage in the discharge state with improved adaptive droop coefficient.

[0042] Figure 6 This is a schematic diagram of conventional droop control in the charging state of energy storage provided by an embodiment of the present invention, wherein (a) is the SOC in the conventional droop control charging state; (b) is the output current in the conventional droop control charging state; (c) is the output power in the conventional droop control charging state; (d) is the voltage drop caused by droop control in the conventional droop control charging state; (e) is the voltage compensation amount in the conventional droop control charging state; and (f) is the bus voltage in the conventional droop control charging state.

[0043] Figure 7This is a schematic diagram of droop control with improved adaptive droop coefficient in the charging state provided by an embodiment of the present invention, wherein (a) is the SOC in the charging state with improved adaptive droop coefficient; (b) is the output current in the charging state with improved adaptive droop coefficient; (c) is the output power in the charging state with improved adaptive droop coefficient; (d) is the voltage drop caused by droop control in the charging state with improved adaptive droop coefficient; (e) is the voltage compensation amount in the charging state with improved adaptive droop coefficient; and (f) is the bus voltage in the charging state with improved adaptive droop coefficient.

[0044] Figure 8 This is a schematic diagram of the dynamic characteristics of a system with communication delay provided in an embodiment of the present invention, wherein (a) is the SOC under the condition of communication delay; (b) is the output power under the condition of communication delay; (c) is the voltage drop caused by droop control under the condition of communication delay; and (d) is the bus voltage under the condition of communication delay.

[0045] Figure 9 This is a schematic diagram of the communication topology when one communication line is faulty, provided by an embodiment of the present invention;

[0046] Figure 10 This is a schematic diagram of the dynamic characteristics of a system when one communication line is faulted, provided by an embodiment of the present invention. (a) is the SOC when one communication line is faulted; (b) is the output power when one communication line is faulted; (c) is the voltage drop caused by droop control when one communication line is faulted; and (d) is the bus voltage when one communication line is faulted.

[0047] Figure 11 This is a schematic diagram of the communication topology when two communication lines are faulty, provided in an embodiment of the present invention;

[0048] Figure 12 This is a schematic diagram of the dynamic characteristics of a system when there are two communication line failures, provided by an embodiment of the present invention. (a) is the SOC when there are two communication line failures; (b) is the output power when there are two communication line failures; (c) is the voltage drop caused by droop control when there are two communication line failures; and (d) is the bus voltage when there are two communication line failures.

[0049] Figure 13 This is a schematic diagram of the global characteristic value distribution of a DC microgrid provided in an embodiment of the present invention;

[0050] Figure 14This is a schematic diagram illustrating the influence of different parameter values ​​on the key characteristic values ​​of the system provided in this embodiment of the invention, wherein (a) shows the influence of different n values ​​on the key characteristic values ​​of the system (n=5-100); (b) shows the influence of different a values ​​on the key characteristic values ​​of the system. ij Impact on key system eigenvalues ​​(a) ij =5-100); (c) are different b ij Impact on key system eigenvalues ​​(b) ij =5-100);

[0051] Figure 15 This is a schematic diagram of the trajectory of changes in key feature values ​​of the system under communication delay provided in an embodiment of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0053] like Figure 1 As shown, this embodiment of the invention provides a distributed control method for multiple energy storage units in a DC microgrid, comprising the following steps:

[0054] S1: For multi-energy storage converters, the primary control adopts droop control; this droop control is an improved adaptive droop control obtained by adding a speed regulation factor and a power constraint factor for state-of-charge balancing to the droop coefficient.

[0055] Figure 2 Figure (a) shows a schematic diagram of a DC microgrid topology. The DC microgrid mainly includes photovoltaic (PV) power generation units, DC loads, energy storage units (ESU), and a communication network unit. The energy storage units and PV power generation units are connected in parallel to the DC bus via DC / DC converters. The PV power generation units use a boost converter for voltage increase and employ maximum power point tracking (MPPT) control. The distributed control block diagram of the multi-energy storage unit DC microgrid is shown below. Figure 2 As shown in (b), the energy storage unit uses a Buck-Boost converter for voltage boosting. The primary control of the energy storage unit employs droop control, while the secondary control uses a control strategy based on a PI consensus algorithm. Finally, the drive signal for the energy storage converter is obtained through voltage and current dual PI loop control. The voltage and current dual PI loop control is represented as follows:

[0056] ;

[0057] ;

[0058] In the formula, U ref U is the reference value for the output voltage of the energy storage converter. DC For the output voltage of the energy storage converter, i LBref i is the reference value for the output current of the energy storage unit. LB The actual output current of the energy storage unit is given by K, where D is the duty cycle (i.e., the drive signal of the energy storage converter), and K is the output current of the energy storage unit. Pv and K Iv These are the proportional and integral coefficients of the voltage PI loop controller, respectively; K Pc and K Ic These are the proportional and integral coefficients of the current PI loop controller, respectively. These are complex frequency domain variables used to transform time-domain functions to the frequency domain.

[0059] Figure 2 As shown in (b), V Bi Let r be the output voltage of the i-th energy storage unit. b L is the impedance of the DC / DC converter. LB C is the filter inductor for the DC / DC converter. B For the DC / DC converter filter capacitor, R li For line impedance, I oi Let i be the output current of the i-th DC / DC converter. LBi Let a be the output current of the i-th energy storage unit. ij b is the scaling factor for the PI consensus algorithm. ij U represents the integral coefficient of the PI consensus algorithm. dcref U is the reference value for bus voltage (i.e., the rated value of bus voltage). ref The output voltage reference value of the energy storage converter, U DCi Let μ be the output voltage of the i-th DC / DC converter. i This represents the bus voltage compensation for the i-th energy storage unit. The bus voltage deviation of the i-th energy storage unit. In this embodiment, the main simulation parameters are shown in Table 1, which includes three energy storage units.

[0060] ;

[0061] Specifically, the improved adaptive droop coefficient droop control can achieve state-of-charge balance among multiple energy storage units, and while ensuring that the output power of each energy storage unit is distributed proportionally to its rated capacity, it also constrains the maximum output power of the energy storage unit within a safe operating range, as shown below:

[0062] ;

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] In the formula, U refi U is the reference value for the output voltage of the i-th energy storage converter. dcref This is the reference value for bus voltage. Let R0 be the improved SOC droop factor of the i-th energy storage converter, and C be the initial droop factor. i Let C be the rated capacity of the i-th energy storage unit. max I represents the maximum rated capacity of all energy storage units. oi Let I be the output current of the i-th energy storage unit after Buck-Boost conversion, and n be the speed regulation factor for state-of-charge balancing. oi When I > 0, the i-th energy storage unit is in a discharging state. oi When <0, the i-th energy storage unit is in a charging state. The real-time state of charge of the i-th energy storage unit Compared with the average state of charge (SOC) of all energy storage units avg The difference, where N is the number of energy storage units, k i Let α be the power constraint factor, and α be the adjustment parameter, where 0 < α < 1.

[0068] According to the power constraint factor k i From the expression, we can see that its function is divided into two stages. Stage 1: When When the difference is large, k i It acts as a constraint, effectively preventing the ratio between the output currents of energy storage units from increasing rapidly, thereby avoiding over-discharge in some energy storage units. Stage 2: When When k approaches 0, i ≈1 / α,k i It amplifies the SOC differences between various energy storage units, thereby accelerating the SOC balancing process.

[0069] During phase 1 of SOC equalization, the power constraint factor k i The effect of α can be ignored, and the output current ratio of the energy storage unit can be approximated as:

[0070] ;

[0071] Stage 2: From 0 < α < 1, we can obtain k i >1, therefore, the power constraint factor k iThis amplifies the output current ratio, causing energy storage units with a larger SOC to discharge more and those with a smaller SOC to discharge less. At this point, k... i The acceleration range is:

[0072] ;

[0073] In stage 1, with a fixed α value, the ratio of energy storage output current to different n values ​​is... Characteristic curves as follows Figure 3 In stage 2, with a fixed value of n, the ratio of energy storage output current to different values ​​of α is... Characteristic curves as follows Figure 3 (b)

[0074] Depend on Figure 3 From (a) and (b), we can see that in Under the same conditions, the ratio of energy storage output current is directly proportional to the speed regulation factor n of state-of-charge equilibrium and inversely proportional to the regulation parameter α. When n is too large or α is too small, its characteristic curve approximates a sign function characteristic, which can lead to system oscillations and reduce the quality of system output power. Therefore, it is necessary to select appropriate parameters to ensure the stability of the DC microgrid system.

[0075] S2: For the droop control of the primary control, the secondary control adopts the PI consensus algorithm.

[0076] To address the bus voltage deviation caused by the improved droop control in primary control, the voltage drop caused by the droop control itself... Represented as:

[0077] ;

[0078] To achieve bus voltage tracking of the bus voltage reference value, a bus voltage compensation amount is defined. The droop control expression after correction of the bus voltage compensation amount obtained through the PI consensus algorithm is as follows:

[0079] ;

[0080] In the formula, μ i This is the bus voltage compensation amount.

[0081] When the output power of each energy storage unit is distributed proportionally to its rated capacity, the following relationship holds:

[0082] ;

[0083] To achieve balanced state of charge of each energy storage unit and stable bus voltage, the following objective function is proposed:

[0084] ;

[0085] In the formula, The bus voltage deviation loss is denoted by N, and N is the total number of energy storage converters.

[0086] Assumption 1: f1, f2, ..., f N Let be continuously differentiable convex functions, and let their gradients be... For local Lipschitz, .

[0087] Assumption 2: Communication topology diagram G N They are connected and able to communicate with each other.

[0088] ;

[0089] Under assumptions 1 and 2, when the optimization objective reaches the optimal solution, i.e. At that time, it can be obtained That is, the bus voltage returns to the rated voltage. At this time, with As state variables, based on a consensus algorithm, each energy storage unit can achieve precise power allocation.

[0090] The dynamic equation of the PI consensus algorithm is:

[0091] ;

[0092] In the formula, ω is the coefficient of the optimization function, and a ij and b ij These are the proportional and integral coefficients for the PI consensus algorithm, respectively, N. i It is the collection of all energy storage units; For the integration variable of the PI consensus algorithm; The gradient of the objective function; , They represent respectively to and Find the derivative.

[0093] To prove that quadratic control can achieve the optimal solution, the dynamic equation matrix form of the consensus algorithm is as follows:

[0094] ;

[0095] In the formula, L A and L B The consistency weight coefficients a and b are respectively. ij and b ij The corresponding Laplace matrix, , , .

[0096] Lemma 1: If the communication topology is connected, the Laplace matrix L has an eigenvalue of 0 and a corresponding eigenvector of 1. N L1 N =0 N .

[0097] When the system is stable, we can obtain:

[0098] ;

[0099] ;

[0100] For a connected and symmetric communication topology, the Laplace matrix L A and L B It is symmetrical, as can be seen from Lemma 1. , Therefore, the above expression is multiplied by 1 on the left. N We can obtain:

[0101] ;

[0102] The above equation shows that when the system is stable, the PI consensus algorithm for secondary control can reach the optimal solution. As t→∞, it satisfies... This indicates that in steady state, the compensation amount of voltage optimization control is equal and does not affect the power distribution effect determined by droop control. Since the bus voltage of the multiple energy storage units in steady state can be expressed as... ,therefore This indicates that, under steady-state conditions, the bus voltage of a DC microgrid can track the bus voltage reference value, i.e. .

[0103] Figure 4 and Figure 5 The simulation results are presented for the energy storage system operating in discharge mode under traditional droop control and improved adaptive droop coefficient droop control, respectively. Figure 4 (a) and Figure 5 From (a), it can be seen that the state of charge of the energy storage unit using the improved adaptive droop coefficient droop control did not exhibit bifurcation after reaching equilibrium, demonstrating superior equilibration performance compared to traditional droop control. Furthermore, this control maintains a consistent state of charge even during load switching. Figure 4 (b), (c) and Figure 5A comparison of (b) and (c) shows that with traditional droop control, when the states of charge (SCC) of the energy storage units differ significantly, the output power of some units exceeds twice their stable output power. However, the improved adaptive droop control consistently limits the output power of each energy storage unit within a safe range during the balancing process. After the system's SCC reaches equilibrium in approximately 82 seconds, the output power of each energy storage unit exhibits a trend of distribution according to its rated capacity, i.e., 2:3:4. Furthermore, under conditions of SCC equilibrium, even if the load changes, the output power of each energy storage unit can still be proportionally distributed according to its rated capacity. Figure 4 (d) and Figure 5 As can be seen from (d), after the secondary control is implemented, the energy storage units of each energy storage unit... The ability to quickly converge to a uniform state demonstrates that this voltage optimization control can achieve precise power sharing; Figure 4 (e) and Figure 5 In the middle (e), the feedback compensation amount in the voltage optimization controller is shown. It can be seen that when using droop control with an improved adaptive droop coefficient, regardless of the feedback compensation amount or... During the equilibrium process of the state of charge, its changing trend is more stable, from Figure 4 (f) and Figure 5 As can be seen from (f), the control method of the present invention can maintain the bus voltage near the rated value with an error of only ±0.2V.

[0104] Figure 6 and Figure 7 Simulation results are presented for the energy storage system operating in charging mode under traditional droop control and droop control with improved adaptive droop coefficient, respectively. Under the condition of droop control with improved adaptive droop coefficient, the dynamic performance of the energy storage unit operating in charging mode is still better than that of traditional droop control. Overall, after introducing droop control with improved adaptive droop coefficient, the control system exhibits good dynamic characteristics and strong robustness, responding quickly to changes in power distribution. Under the proposed control strategy, all modified droop coefficients change according to the design direction, ensuring that the output power of each energy storage unit is distributed proportionally to its rated capacity. This allows the SOC of each energy storage unit to reach equilibrium within a certain time, and enables rapid adjustment of the system bus voltage to restore it to its rated value.

[0105] To account for potential communication delays in the system, the dynamic equation of the PI consensus algorithm is rewritten as follows:

[0106] ;

[0107] In the formula, τ is the time delay constant, t represents time t; ω is the coefficient of the optimization function, a ij and bij These are the proportional and integral coefficients for the PI consensus algorithm, respectively, N. i It is the collection of all energy storage units; For the integration variable of the PI consensus algorithm; The gradient of the objective function; , They represent respectively to and Find the derivative.

[0108] Applying the Laplace transform to the above equation yields:

[0109] ;

[0110] ;

[0111] In the formula, and For vectors and Initial value at t=0; , , L A (s), L B (s), These are the frequency domain representations of the corresponding matrices. , , Additional items resulting from communication delays, For example, its expression is:

[0112] ;

[0113] In the formula, τ ij This refers to the communication delay time.

[0114] Considering the case where the integrator's initial value is not zero, we can obtain the final value theorem. The steady-state value is:

[0115] ;

[0116] As can be seen from the above formula, regardless of whether the integrator has an initial value or there is a transmission delay in the communication network, as long as the optimal controller can reach a stable state, the optimal solution can still be achieved, that is, the average bus voltage can be restored to the rated value, which has high robustness.

[0117] Figure 8 The simulation results are presented with a 100ms communication delay in the system, and the energy storage system is operating in discharge mode. By comparison... Figure 8 (a) and Figure 5As shown in (a), even a significant time delay does not affect the equilibrium of the state of charge. Figure 8 From (b) and (c), we can conclude that communication delays do not affect the consistency trend of each energy storage unit. This control ensures that each energy storage unit can proportionally allocate its output power according to its rated capacity. Figure 8 The bus voltage shown in (d) demonstrates that when the PI consensus algorithm of the secondary control is activated, the system can respond quickly and achieve precise regulation of the bus voltage. In practical microgrids, communication delays are typically less than the set 100 milliseconds. Therefore, the proposed control method ensures that the system can meet the requirements of state-of-charge balance and precise bus voltage regulation even in the presence of communication delays.

[0118] To verify the impact of communication failures on the system's stability equilibrium and the effect of voltage optimization control on the bus voltage regulation capability, the system operated in loop communication mode for the first 90 seconds. A communication failure occurred at t=90s, as follows: Figure 9 As shown. Figure 10 This demonstrates the dynamic characteristics of the system when a communication line fault exists between nodes 2 and 3. For example... Figure 10 As shown, even if a communication line fails, a directed spanning tree still exists in the communication link, allowing information to be transmitted from each node. Therefore, the system can still achieve a stable state, and the failure of one communication line will not affect its normal and stable operation.

[0119] Figure 11 The system experienced simultaneous failures in lines 1-3 and 2-3 at t=90s. Figure 12 The system's dynamic characteristics are demonstrated when both communication lines fail. Because energy storage 3 is isolated, the system cannot form a directed spanning tree to obtain the state information of each node. For example... Figure 12 As shown in (a), the consistency of the state of charge is achieved only between energy storage 1 and energy storage 2. Figure 12 Figure (b) shows that the output power of energy storage 3 deviates from the original proportional allocation based on the rated capacity maintained by energy storage 1 and energy storage 2. Similarly, Figure 12 (c) and (d) show the voltage drop of energy storage 3. It is no longer consistent with Energy Storage 1 and Energy Storage 2. However, during network failures, the bus voltage of each energy storage unit remains within the rated operating range.

[0120] Therefore, when multiple communication lines fail, the entire communication system may be divided into several parts, preventing the generation of a directed spanning tree. In this case, only when the energy storage in each part can achieve a state of charge balance and distribute power according to their respective rated capacities can the communication system function properly.

[0121] In some preferred embodiments, the distributed control method for multiple energy storage units in a DC microgrid further includes:

[0122] For primary and secondary control, small-signal models are established to analyze the impact of various parameters in primary and secondary control on the stability of the DC microgrid, thereby determining the ideal selection range of each parameter in primary and secondary control. The ideal selection range of each parameter is determined based on the results of small-signal analysis, and is determined by analyzing the stability of the DC microgrid under different values. The ideal selection range refers to the range of values ​​that ensure the stability of the DC microgrid.

[0123] The small-signal model is represented as follows:

[0124] ;

[0125] ;

[0126] In the formula, This represents the derivative with respect to X, where A is a 7×7 matrix and represents the coefficients of each small-signal parameter. , , , , , , These represent the small signal forms of the corresponding signals. The modeling of each small signal is as follows.

[0127] The small-signal model of the dynamic equation for the state of charge of the energy storage unit is as follows:

[0128] ;

[0129] , ;

[0130] δ i =SOC i -SOC avg I o =[I o1 I o2 … I oN ] T ;

[0131] In the formula, , Indicates to Find the derivative.

[0132] The improved droop coefficient at the equilibrium point (I) oi =I o0 δ i =δ i0Linearization at point ) yields:

[0133] ;

[0134] ;

[0135] , ;

[0136] In the formula, This represents the droop coefficient parameter value at the equilibrium point;

[0137] The small-signal equation for the droop control equation is:

[0138] ;

[0139] , , ;

[0140] The output current and bus voltage of multiple energy storage units in a DC microgrid can be expressed by the network admittance matrix Y:

[0141] ;

[0142] ;

[0143] U ref =[U ref1 U ref2 … U refN ] T ;

[0144] In the formula, R loadi R is the load corresponding to the i-th energy storage unit. lij Let be the line impedance between the i-th energy storage unit and the j-th energy storage unit.

[0145] The small-signal dynamic equation obtained from the dynamic equation of the PI consensus algorithm is:

[0146] ;

[0147] In the formula, It is an identity matrix.

[0148] The dynamic equations and small-signal model of the DC / DC converter with multiple energy storage units in a DC microgrid are as follows:

[0149] ;

[0150] ;

[0151] , ;

[0152] ;

[0153] ;

[0154] In the formula, i LBi For the output current of the i-th energy storage unit, i LBrefi Let C be the reference value for the output current of the i-th energy storage unit. B L is the filter capacitor vector of the DC / DC converter. LB Let C be the filter inductor vector of the DC / DC converter, and C B =[C B1 C B2 …C Bn ], L LB =[L LB1 L LB2 … L LBn ];D P and D I Let D be the degree matrix of the consistency weight coefficients aij and bij, respectively, and D P = diag(D B0 ), D I = diag(I LB0 ).

[0155] The small-signal model is established by defining the speed regulation factor n for droop control in the primary control and the proportional coefficient a for the PI consensus algorithm in the secondary control. ij and integral coefficient b ij The selection of parameters provided a basis, and the impact of different levels of communication delay on system stability in the PI consensus algorithm was analyzed.

[0156] The small-signal model of DC microgrids has the following advantages:

[0157] Multi-timescale dynamic decoupling analysis: By integrating the state of charge information of each energy storage unit, the proportional and integral terms of the PI consensus algorithm, the bus voltage compensation, the bus voltage deviation, the converter output current deviation, and the converter turn-on signal, a complete small-signal model of distributed control of multiple energy storage units in a DC microgrid is constructed.

[0158] Optimize balancing speed and bus voltage recovery speed: Find the optimal values ​​of the proposed control parameters and balance the balancing speed, bus voltage recovery speed and the stability of the distributed control system of DC microgrid multi-energy storage unit;

[0159] Systematic parameter optimization: quantitatively analyze the impact of parameter changes on the distributed control system of multiple energy storage units in DC microgrids; ensuring stability: prove that the balancing process does not disrupt system stability.

[0160] Figure 13 The figure shows the global eigenvalue distribution of the DC microgrid. As can be seen from the figure, all eigenvalues ​​of the system lie in the left half-plane, indicating that the system is stable and reliable. However, since some eigenvalues ​​are far from the imaginary axis... Figure 14 and Figure 15 Only those eigenvalues ​​close to the imaginary axis (i.e., key eigenvalues, corresponding to those in the figure) are shown. , ... The trajectory of the device was used to analyze the impact of key parameters in more detail, including the state-of-charge equilibrium rate factor n and the consistency scaling factor a. ij Consistency integral coefficient b ij And communication delays. During this analysis, all energy storage units were in discharge mode.

[0161] Figure 14 Tables (a) to (c) show the conditions when n and a ij and b ij The trajectory of the system's eigenvalues ​​as they vary within the range of 5 to 100. Figure 14 As can be seen from (a), as n increases, the imaginary part of the eigenvalues ​​gradually increases, which will increase the oscillation frequency of the system. From Figure 14 As can be seen in (b), with a ij As a value increases, the system's eigenvalues ​​tend to shift to the left, thus enhancing the system's stability. However, a ij Excessively large values ​​may cause high-frequency oscillations in the system. Figure 14 As can be seen from (c), b ij This parameter primarily affects the system's oscillation frequency.

[0162] Figure 15 The trajectories of the system's eigenvalues ​​are shown within a communication delay range of 5 to 100 milliseconds. It can be seen that as the delay time increases, the real part of the eigenvalues ​​increases, while the imaginary part remains relatively unchanged. This indicates that the communication delay primarily affects the system's stability, rather than its oscillation frequency. The results show that the system remains stable under various conditions.

[0163] This invention also provides a distributed control system for multiple energy storage units in a DC microgrid, comprising:

[0164] A primary control module is used to perform droop-based primary control for multiple energy storage converters; the droop control is an improved adaptive droop control obtained by adding a speed regulation factor and a power constraint factor for state-of-charge balancing to the droop coefficient.

[0165] The secondary control module is used to execute secondary control based on the PI consensus algorithm for the droop control of the primary control.

[0166] It should be understood that the functional unit modules in the various embodiments of the present invention can be concentrated in one processing unit, or each unit module can exist physically separately, or two or more unit modules can be integrated into one unit module, and can be implemented in hardware or software.

[0167] This invention also provides a distributed control system for multiple energy storage units in a DC microgrid, comprising:

[0168] A memory on which computer programs are stored;

[0169] A processor is used to load and execute the computer program to implement the distributed control method for multiple energy storage units in a DC microgrid as described above.

[0170] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the distributed control method for multiple energy storage units in a DC microgrid as described above.

[0171] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0172] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0173] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0174] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0175] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0176] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A distributed control method for multiple energy storage units in a DC microgrid, characterized in that, Includes the following steps: For multi-energy storage converters, the primary control adopts droop control; this droop control is an improved adaptive droop control obtained by adding a speed regulation factor and a power constraint factor for state-of-charge balancing to the droop coefficient. For the droop control in the primary control, the secondary control employs a PI consensus algorithm; The improved adaptive droop coefficient droop control is expressed as follows: ； ； ； In the formula, U refi U is the reference value for the output voltage of the i-th energy storage converter. dcref This is the reference value for bus voltage. Let R0 be the improved droop coefficient of the i-th energy storage converter, and C be the initial droop coefficient. i Let C be the rated capacity of the i-th energy storage unit. max I represents the maximum rated capacity of all energy storage units. oi Let be the output current of the i-th energy storage converter, and n be the speed regulation factor for state-of-charge balance. The real-time state of charge (SOC) of the i-th energy storage unit and the average SOC of all energy storage units are given. avg The difference, k i Let be the power constraint factor, and α be the adjustment parameter, where 0 < α < 1; According to the power constraint factor k i The expression shows that its function is divided into two stages: Stage 1: when When the difference is large, k i This acts as a constraint, effectively preventing a rapid increase in the ratio between the output currents of the energy storage units, thereby avoiding over-discharge in some energy storage units; Stage 2: When When k approaches 0, i ≈1 / α,k i It amplifies the SOC differences between various energy storage units, thereby accelerating the SOC balancing process.

2. The distributed control method for multiple energy storage units in a DC microgrid according to claim 1, characterized in that, The energy storage unit of the DC microgrid uses a Buck-Boost converter for voltage boosting. Its primary control adopts droop control, and its secondary control adopts a PI consensus algorithm. Finally, the energy storage converter drive signal is obtained through voltage and current dual PI loop control.

3. The distributed control method for multiple energy storage units in a DC microgrid according to claim 1, characterized in that, The droop control expression after correction of the bus voltage compensation obtained by the PI consensus algorithm is as follows: ； In the formula, U refi U is the reference value for the output voltage of the i-th energy storage converter. dcref This is a reference value for the bus voltage. The voltage drop is caused by the droop control of the i-th energy storage converter itself. This is the bus voltage compensation amount for the i-th energy storage converter; To achieve bus voltage stability, the objective function is set as follows: ； In the formula, The bus voltage deviation loss is denoted by N, and N is the total number of energy storage converters.

4. The distributed control method for multiple energy storage units in a DC microgrid according to claim 3, characterized in that, The dynamic equation of the PI consensus algorithm is: ； In the formula, ω is the coefficient of the optimization function, and a ij and b ij These are the proportional and integral coefficients for the PI consensus algorithm, respectively, N. i It is the collection of all energy storage units; For the integration variable of the PI consensus algorithm; The gradient of the objective function; , They represent respectively to and Find the derivative.

5. The distributed control method for multiple energy storage units in a DC microgrid according to claim 3, characterized in that, The dynamic equation for the PI consensus algorithm considering communication delay is: ； In the formula, τ is the time delay constant, t represents time t; ω is the coefficient of the optimization function, a ij and b ij These are the proportional and integral coefficients for the PI consensus algorithm, respectively, N. i It is the collection of all energy storage units; For the integration variable of the PI consensus algorithm; The gradient of the objective function; , They represent respectively to and Find the derivative.

6. The distributed control method for multiple energy storage units in a DC microgrid according to any one of claims 1 to 5, characterized in that, Also includes: For primary and secondary control, a small-signal model is established to analyze the impact of various parameters in primary and secondary control on the stability of DC microgrid, thereby determining the ideal selection range of various parameters in primary and secondary control.

7. A distributed control system for multiple energy storage units in a DC microgrid, characterized in that, For implementing the distributed control method for multiple energy storage units in a DC microgrid as described in any one of claims 1 to 6, the system comprises: A primary control module is used to perform droop-based primary control for multiple energy storage converters; the droop control is an improved adaptive droop control obtained by adding a speed regulation factor and a power constraint factor for state-of-charge balancing to the droop coefficient. The secondary control module is used to execute secondary control based on the PI consensus algorithm for the droop control of the primary control.

8. A distributed control system for multiple energy storage units in a DC microgrid, characterized in that, include: A memory on which computer programs are stored; A processor is configured to load and execute the computer program to implement the distributed control method for multiple energy storage units in a DC microgrid as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the distributed control method for multiple energy storage units in a DC microgrid as described in any one of claims 1 to 6.

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