A DC microgrid adaptive droop control method, system, device and medium

Abstract

The present invention discloses a DC microgrid adaptive droop control method, device and storage medium. The method includes: obtaining an expression of voltage-current droop characteristics according to a circuit model, analyzing the expression, and adding a droop coefficient correction amount and / or a reference voltage compensation amount in the circuit as key parameters; deriving an efficiency evaluation function according to the circuit model, and constructing an efficiency objective function according to the efficiency evaluation function; according to the efficiency objective output function, using a swarm intelligence optimization algorithm to perform an optimization calculation on the key parameters of the droop control to obtain the optimal key parameters, and performing stability control on the circuit model according to the optimal key parameters. The present invention increases key parameters and adaptively adjusts the key parameters of the droop control strategy by using a swarm intelligence optimization algorithm, reduces the efficiency loss of the system, enables the system to always operate in the optimal efficiency state, and thus improves the overall performance of the system. The present invention can be widely applied to the field of DC microgrids.

Description

Technical Field

[0001] The present invention relates to the field of DC microgrids, and in particular, to a DC microgrid adaptive droop control method, device, and storage medium. Background Art

[0002] DC microgrids have attracted much attention due to their excellent reliability, scalability, and efficiency. Compared with AC microgrids, DC microgrids have many advantages. They can effectively integrate distributed power sources with inherently DC characteristics such as photovoltaic, energy storage, and fuel cells, without considering complex issues such as phase, frequency, and reactive power, making the control process simpler. Currently, DC microgrids have become the most promising solution for the efficient and reliable integration of renewable energy, battery energy storage systems, and loads. Droop control has been widely used in DC microgrids because of its simple implementation, no need for complex communication and control systems, and the characteristic of automatically and accurately allocating loads.

[0003] However, traditional DC microgrid droop control strategies usually have the following limitations: (1) Uneven power distribution problem. One of the core objectives of droop control in a DC microgrid is to achieve reasonable power distribution. However, when the line impedances (mainly resistances) are mismatched, droop control may not be able to achieve precise power sharing. This is because droop control usually relies on line impedances to achieve power distribution, and impedance mismatches will lead to uneven power distribution. (2) Difficulty in controlling the bus voltage. If the slope of droop control is selected too large, it will result in a large voltage deviation. If the slope is selected too small, it may lead to a decrease in power distribution ability. In addition, droop control itself has an inherent voltage droop characteristic, which will cause the output voltage to continuously deviate from the rated voltage when the load increases, also increasing the difficulty of bus voltage control. (3) Current sharing under light loads problem. Traditional droop control achieves current sharing by adjusting the droop coefficient. However, the selection of the droop coefficient is not always ideal, especially under light load conditions. When the load is light, the output current of the converter is small, and the current sharing effect of droop control may be greatly affected. (4) Low conversion efficiency problem. The droop control strategy is essentially a type of differential control, which means it cannot achieve precise voltage and current control. The problems of uneven power sharing, difficulty in controlling the bus voltage, and current sharing under light loads caused by this difference will increase the efficiency loss of the system, making the system unable to operate at the most efficient point. Summary of the Invention

[0004] To solve at least one of the technical problems existing in the prior art to a certain extent, an object of the present invention is to provide a DC microgrid adaptive droop control method, device, and storage medium based on swarm intelligence optimization.

[0005] The first technical solution adopted by the present invention is:

[0006] A DC microgrid adaptive droop control method includes the following steps:

[0007] Obtain the voltage-current droop characteristic expression according to the circuit model, analyze the expression, and add a droop coefficient correction amount and / or a reference voltage compensation amount to the circuit as key parameters;

[0008] Derive an efficiency evaluation function according to the circuit model and construct an efficiency objective function based on the efficiency evaluation function;

[0009] According to the efficiency objective output function, use a swarm intelligence optimization algorithm to optimize the key parameters of the droop control, obtain the optimal key parameters, and perform stability control on the circuit model according to the optimal key parameters.

[0010] Further, the obtaining the voltage-current droop characteristic expression according to the circuit model, analyzing the expression, and adding a droop coefficient correction amount and / or a reference voltage compensation amount to the circuit as key parameters includes:

[0011] Obtain the parallel equivalent circuit of the dual PCS converters, and according to the loop voltage equation, the voltage-current droop characteristic expression of the DC microgrid equivalent circuit is:

[0012]

[0013] In the formula, U ref is the bus voltage given value, U bus is the bus voltage feedback value, K is the droop coefficient, r1 is the equivalent line impedance of PCS1 and the equivalent line impedance of PCS2, i1 is the bus current of PCS1, and i2 is the bus current of PCS2;

[0014] According to formula (1), while ensuring the bus voltage control accuracy, to achieve power sharing, the condition that must be satisfied is:

[0015] K + r1 = K + r2 (2)

[0016] Since there will be a certain difference between the line impedances r1 and r2, equation (2) does not hold. Therefore, a droop coefficient correction amount ΔK1 and ΔK2 are added to both sides of the equation to make equation (2) hold, and at this time, power sharing is achieved;

[0017] Extended to the multi-converter control structure, the formula is:

[0018] U ref = U bus +(K + ΔK i + r i )i i (3)

[0019] By adjusting the droop coefficient correction amount ΔK of each converter i and making the coefficients before the current terms in formula (3) equal, the current can be evenly divided;

[0020] Due to the existence of the droop coefficient and line impedance, it will cause U ref >U bus , so a reference voltage compensation amount is required, and the magnitude of the reference voltage compensation amount satisfies:

[0021] ΔU i =(K + ΔK i + r i )i i (4)

[0022] Generalized to the multi-converter control structure, the formula is:

[0023] U ref = U bus +(K + ΔK i + r i )i i +ΔU i (5)

[0024] Take the droop coefficient correction amount ΔK i and the reference voltage compensation amount ΔU i as the key parameters of this circuit.

[0025] Furthermore, the efficiency evaluation function is derived according to the circuit model, and the efficiency objective function is constructed based on the efficiency evaluation function, including:

[0026] The expression for obtaining the real-time charging conversion efficiency of the PCS is:

[0027]

[0028] Where:

[0029] P AC = I AC U AC cosθ

[0030] P DC = I DC U DC

[0031] U DC = U bus +(K + ΔK i + r i )i i +ΔU i

[0032] The expression of the efficiency objective function is:

[0033] F = η max -η atc

[0034] Where η max represents the optimal target operating efficiency of the PCS, and η atc represents the current actual operating efficiency of the PCS; P DC is the DC-side power of the PCS, and P AC is the active power on the AC side of the PCS, and I DC is the bus current of the PCS, and U DC is the bus voltage of the PCS; I AC is the AC-side current of the PCS, and U AC is the AC-side voltage of the PCS, and θ is the phase angle difference between the AC-side voltage and current of the PCS.

[0035] Furthermore, according to the efficiency target output function, a swarm intelligence optimization algorithm is used to optimize the key parameters of the droop control to obtain the optimal key parameters, and the stability control of the circuit model is performed according to the optimal key parameters, including:

[0036] For the key parameters, an initial population is generated using a preset strategy;

[0037] The positions and velocities of the individuals in the population are updated using a preset algorithm;

[0038] The efficiency target function is used as the fitness function to evaluate the fitness of the position of each individual to guide the search direction of the individual;

[0039] Judge whether the convergence condition is reached. If it is reached, output the final optimization result; if not, return to execute the step of updating the positions and velocities of the individuals in the population.

[0040] Furthermore, the initial population is generated using a strategy based on prior knowledge;

[0041] The preset algorithm is the particle swarm optimization algorithm;

[0042] The judgment of whether the iteration condition is reached includes:

[0043] The residual change judgment method is used to judge whether the convergence condition is reached.

[0044] Furthermore, the DC microgrid adaptive droop control method further includes the following steps:

[0045] Calculate the current power and the operating duration of each PCS module, and dynamically adjust the number of operating PCS modules according to the load condition.

[0046] Further, calculating the current power and the running duration of each PCS module, and dynamically adjusting the number of running PCS modules according to the load condition, includes:

[0047] Calculating the current power magnitude, and determining the number of PCS modules N to be turned on according to the power magnitude; the power here refers to the total charge and discharge power of all PCS modules on the DC bus, and the number of redundant PCS modules can be determined according to the total charge and discharge power requirement on the DC bus and the maximum running power of the PCS module;

[0048] Calculating the cumulative duration of the current PCS module, and selecting the label i of the PCS module to be turned on;

[0049] Turning on the currently selected PCS module;

[0050] Among them, in the case of light load, some PCS modules are turned off, and only the necessary number is retained for operation to reduce the power consumption of the entire system.

[0051] The second technical solution adopted by the present invention is:

[0052] A DC microgrid adaptive droop control system includes:

[0053] A key parameter selection module, configured to obtain a voltage-current droop characteristic expression according to a circuit model, analyze the expression, and add a droop coefficient correction amount and / or a reference voltage compensation amount to the circuit as key parameters;

[0054] An objective function construction module, configured to derive an efficiency evaluation function according to the circuit model and construct an efficiency objective function according to the efficiency evaluation function;

[0055] A key parameter optimization module, configured to perform an optimization calculation on the key parameters of the droop control using a swarm intelligence optimization algorithm according to the efficiency target output function, obtain the optimal key parameters, and perform stability control on the circuit model according to the optimal key parameters.

[0056] The third technical solution adopted by the present invention is:

[0057] A DC microgrid adaptive droop control device includes:

[0058] At least one processor;

[0059] At least one memory for storing at least one program;

[0060] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0061] The fourth technical solution adopted by the present invention is:

[0062] A computer-readable storage medium stores a program executable by a processor, and the program executable by the processor is used to execute the method as described above when executed by the processor.

[0063] The beneficial effects of the present invention are as follows: by increasing the droop coefficient correction amount, more accurate power sharing is achieved, thereby optimizing the operation performance of the DC microgrid; by adding a reference voltage compensation amount, a part of the voltage is increased to compensate for the losses of the virtual impedance and the output line impedance. In addition, the key parameters of the droop control strategy are adaptively adjusted through the swarm intelligence optimization algorithm, reducing the efficiency loss of the system, enabling the system to always operate in the optimal efficiency state, and thus improving the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following introduces the related technical solution drawings in the embodiments of the present invention or the prior art. It should be understood that the drawings introduced below are only for conveniently and clearly expressing some embodiments of the technical solutions in the present invention, and those skilled in the art can also obtain other drawings based on these drawings without creative efforts.

[0065] Figure 1 is the equivalent circuit diagram of the parallel connection of two PCS converters in the embodiment of the present invention;

[0066] Figure 2 is the flow schematic diagram of intelligent scheduling optimization in the embodiment of the present invention;

[0067] Figure 3 is the construction schematic diagram of the efficiency objective function in the embodiment of the present invention;

[0068] Figure 4 is the step flow chart of a DC microgrid adaptive droop control method in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0069] The embodiments of the present invention are described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation of the present invention. For the step numbers in the following embodiments, they are only set for the convenience of explanation and illustration, and no limitation is imposed on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0070] In the description of the present invention, it should be understood that for the orientation description, such as the orientation or positional relationship indicated by up, down, front, back, left, right, etc., it is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention.

[0071] In the description of the present invention, the meaning of several is one or more, the meaning of multiple is two or more. Understanding greater than, less than, exceeding, etc. does not include the present number, and understanding above, below, within, etc. includes the present number. If the first and second are described only for the purpose of distinguishing technical features, they cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence relationship of the indicated technical features. In addition, "and / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0072] In the description of the present invention, unless otherwise clearly defined, words such as setting, installing, connecting, etc. should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above words in the present invention in combination with the specific content of the technical solution.

[0073] Term Explanation:

[0074] PCS module: Bidirectional AC / DC power conversion module.

[0075] As Figure 4 shown, this embodiment provides a DC microgrid adaptive droop control method, including the following steps:

[0076] S1. Obtain the voltage-current droop characteristic expression according to the circuit model, analyze the expression, and add a droop coefficient correction amount and a reference voltage compensation amount to the circuit as key parameters.

[0077] According to the current circuit model, calculate the voltage-current droop characteristic expression, generalize it to a multi-converter control structure, add corresponding correction amounts or compensation amounts, and find the key parameters.

[0078] S2. Calculate the current power and the running duration of each PCS module, and dynamically adjust the running number of PCS modules according to the load condition.

[0079] According to the current circuit model, calculate the current power and the operating duration of each PCS module, and dynamically adjust the number of operating PCSs according to the load conditions. When the load is light, turn off some PCSs and only keep the necessary number running to reduce the power consumption of the entire system.

[0080] S3. Derive an efficiency evaluation function based on the circuit model, and construct an efficiency objective function according to the efficiency evaluation function.

[0081] According to the current circuit model and the physical definition of efficiency, derive an efficiency evaluation function, and construct the desired efficiency objective function according to the derived efficiency evaluation function.

[0082] S4. According to the efficiency target output function, use the swarm intelligence optimization algorithm to perform optimization calculations on the key parameters of droop control to obtain the optimal key parameters, and perform stability control on the circuit model according to the optimal key parameters.

[0083] According to the current key parameters and the target output function, use the swarm intelligence optimization algorithm to perform optimization calculations on the key parameters of droop control to obtain the optimal key parameters, and perform stability control on the model with the optimal key parameters.

[0084] Generally speaking, the method of this embodiment solves the following technical problems:

[0085] 1) The power distribution problem. One of the core objectives of droop control in a DC microgrid is to achieve reasonable power distribution. However, when the line impedance (mainly resistance) is mismatched, droop control may not be able to achieve precise power sharing. This is because droop control usually relies on line impedance to achieve power distribution, and impedance mismatch will lead to uneven power distribution. To address this problem, increase the droop coefficient correction amount ΔK i to achieve power sharing.

[0086] 2) There is a problem with the difficulty of controlling the bus voltage. If the slope of droop control is selected too large, it will result in a large voltage deviation. If the slope is selected too small, it may lead to a decrease in power distribution ability. In addition, droop control itself has an inherent voltage droop characteristic, which will cause the output voltage to continuously deviate from the rated voltage when the load increases, and also increases the difficulty of controlling the bus voltage. To address this problem, add a reference voltage compensation amount to increase part of the voltage to compensate for the losses of the virtual impedance and the output line impedance r i so that the actual bus voltage increases.

[0087] 3) Light load current sharing problem. Traditional droop control achieves current sharing by adjusting the droop coefficient. However, the selection of the droop coefficient is not always ideal, especially under light load conditions. When the load is light, the output current of the converter is small, and the current sharing effect of droop control may be greatly affected. To address this issue, an intelligent scheduling optimization algorithm is run to dynamically adjust the number of operating PCS modules according to the load situation. Under light load, some PCS modules can be turned off, and only the necessary number is retained to operate, so as to reduce the power consumption of the entire system.

[0088] 4) Low conversion efficiency problem. The droop control strategy is essentially a type of differential control, which means it cannot achieve precise voltage and current control. Problems such as uneven power sharing caused by this difference, difficulty in bus voltage control, and current sharing under light load will increase the efficiency loss of the system, making the system unable to operate at the most efficient point. To address this issue, an efficiency objective function is constructed, and a swarm intelligence optimization algorithm is used to obtain the optimal key parameters to achieve efficiency optimization.

[0089] The above methods are explained in detail below with reference to the accompanying drawings.

[0090] (1) Finding key parameters according to the model

[0091] See Figure 1 , Figure 1 is the equivalent circuit of two parallel PCS converters. From the Figure 1 equivalent circuit, according to the loop voltage equation, the voltage-current droop characteristic expression of the DC microgrid equivalent circuit is obtained as:

[0092]

[0093] According to Equation (1), while ensuring the accuracy of bus voltage control, to achieve power sharing (satisfying i1 = i2), the condition that must be met is:

[0094] K + r1 = K + r2 (2)

[0095] Since there will be a certain difference between the line impedances r1 and r2, Equation (2) does not hold, Equation (1) does not satisfy i1 = i2, and U ref > U bus . This shows that the traditional droop control method cannot fully meet the requirements of equal output power sharing of each converter and cannot meet the zero-error control of the bus voltage.

[0096] Since there will be a certain difference between the line impedances r1 and r2, Equation (2) does not hold. If a suitable droop coefficient correction amount ΔK1 and ΔK2 are added to both sides of the equation to make the equation in Equation (2) hold, then the power is evenly shared at this time.

[0097] Extended to the multi-converter control structure, the formula is:

[0098] U ref = U bus +(K + ΔK i + r i )i i (3)

[0099] By adjusting the droop coefficient correction amount ΔK i of each converter to make the coefficients in front of the current terms in formula (3) equal, the current can be evenly divided;

[0100] However, due to the existence of the droop coefficient and line impedance, it will cause U ref > U bus . Therefore, a reference voltage compensation amount is required, and its magnitude should satisfy:

[0101] ΔU i = (K + ΔK i + r i )i i (4)

[0102] Making the original reference voltage increase, and using the increased part of the voltage to compensate for the losses of the virtual impedance and the output line impedance r i , so as to increase the actual bus voltage.

[0103] Generalized to the multi-converter control structure, the formula is:

[0104] U ref = U bus +(K + ΔK i + r i )i i + ΔU i (5)

[0105] Taking the droop coefficient correction amount ΔK i and the reference voltage compensation amount ΔU i as the key parameters of this circuit.

[0106] (2) Intelligent scheduling optimization

[0107] In the pcs multi-machine parallel DC microgrid, dynamically adjust the number of operating PCS modules according to the load conditions. When the load is light, some PCS modules can be turned off, and only the necessary number is retained to run, so as to reduce the power consumption of the entire system and ensure that the system is always in the best working state.

[0108] Most of the existing advanced pcs systems are built-in with a timing module to track the running time, status changes and other key times of the system. A small number of PCS modules may need to implement the timing function through external devices or software. If there is indeed a timing module in the PCS module, it usually has the following functions and characteristics:

[0109] 1) Clock source: The timing module contains a stable clock source, which may be a crystal oscillator or other device capable of generating an accurate time reference. This clock source provides the basis for time measurement in the timing module.

[0110] 2) Timing function: The timing module can record the total running time of the system, as well as the duration of possible specific times or operations. It can provide timing functions in time units such as seconds, minutes, and hours, and can be accumulated or reset as needed.

[0111] 3) Data storage: The timing module usually contains registers or memories for storing timing data. These data can include the accumulated running time, the last start time, the last shutdown time, etc. These data can be read by other parts of the system or external devices when needed.

[0112] 4) Trigger and interrupt: The timing module may support time-based trigger and interrupt functions. For example, when a preset time threshold is reached, it can generate an interrupt signal to notify the system to perform specific operations and tasks.

[0113] 5) Interface and communication: The timing module usually interacts with other parts of the PCS, so appropriate interfaces and communication mechanisms are required. This may include links to other control units, sensors, or actuators, as well as communication with external systems through bus or network interfaces.

[0114] If there is no timing module in the PCS module, add a timing module to the PCS module in the following way:

[0115] 1) Requirement analysis: Clearly define the functional requirements of the timing module, such as the required timing accuracy, range, and whether interrupt triggering is needed. Clearly define the interaction method between the timing module and other parts of the PCS.

[0116] 2) Hardware selection: Select a suitable timing hardware, which can be an independent timer module or a microcontroller or FPGA with timing functions. Ensure that the selected hardware is compatible with the interface of the PCS, for example, it can communicate with the PCS through interfaces such as serial port, I2C, SPI, etc.

[0117] 3) Hardware integration: Link the timing hardware to the appropriate interface of the PCS. As needed, it may also be necessary to provide power and ground connections for the timing hardware.

[0118] 4) Software development: Write or modify the software of the PCS to communicate with and control the timing hardware. Implement the timing logic, including initializing the timer, starting and stopping the timing, reading the timing value, etc. If necessary, add interrupt handling logic to perform specific operations when the timing reaches a specific value.

[0119] 5) Testing and verification: After adding the timing module, conduct a comprehensive test on the entire system to ensure the accuracy and stability of the timing function. Check whether the interaction between the timing module and other parts of the pcs is normal and whether it meets the expected functional requirements.

[0120] 6) Documentation: Record the process of modifying and adding the timing module, including the hardware used, software code, test results, etc. Update the system's technical documentation and user manual to reflect these changes.

[0121] See Figure 2 , and the key steps of its intelligent scheduling optimization are as follows:

[0122] (a) Calculate the current power magnitude to confirm the number of pcs units N turned on;

[0123] (b) Calculate the cumulative duration of the current pcs to confirm the enabled label i;

[0124] (c) Turn on the currently selected pcs.

[0125] (3) Construct the efficiency objective function

[0126] See Figure 3 , and the construction process of the efficiency objective function is as follows:

[0127] First, obtain the expression for efficiency:

[0128]

[0129] Where:

[0130] P DC = I DC U DC

[0131] P AC = I AC U AC cosθ (7)

[0132] And:

[0133] U DC = U bus + (K + ΔK i + r i )i i + ΔU i (8)

[0134] The expression for the efficiency objective function is:

[0135] F = η max - η atc (9)

[0136] (4) Use the swarm intelligence optimization algorithm to obtain the optimal key parameters

[0137] According to the key parameters and the target output function, which is composed of the output parameters of the droop control and is used to characterize the output characteristics of the droop control. Based on the key parameters and the target function, use the swarm intelligence optimization algorithm to optimize the key parameters of the droop control, obtain the optimal key parameters, and use the optimal key parameters to control the stability of the model.

[0138] The swarm intelligence optimization algorithm is a class of optimization algorithms based on swarm intelligence and cooperative behavior, mainly including the following steps:

[0139] 1) Initialize the population: At the beginning of the algorithm, a population containing multiple individuals is initialized, and each individual represents a potential solution in the solution space. These initial solutions can be generated randomly or based on some prior knowledge or specific strategies. A good initial population affects the process of the evolutionary algorithm to find the global optimum. Since the droop coefficient K i and the bus voltage deviation U i have a certain value range in a fixed circuit, this embodiment selects to generate an initial population based on prior knowledge or specific strategies.

[0140] 2) Update the position and velocity of individuals: In the population, each individual has its own position and velocity. The update of the position and velocity of individuals is one of the core steps of the algorithm, which determines the way and efficiency of the algorithm to search the solution space. Currently, common algorithms include the particle swarm optimization algorithm (PSO), ant colony optimization algorithm (ACO), genetic algorithm (GA), etc., according to the design objectives and problem characteristics of the algorithm. Specifically, this embodiment selects the PSO algorithm. The update formulas for position and velocity are generally as follows:

[0141] Velocity update:

[0142] V i = w*v[i]+c1*rand()*(pBest[i]-pos[i])+c2*rand()*(gBest-pos[i]) (10)

[0143] Position update:

[0144] pos[i]= pos[i]+v[i] (11)

[0145] where w is the inertia weight, c1 and c2 are learning factors, rand() is a random number generation function, pBest is the historical optimal position, and gBest is the global optimal position in the population.

[0146] 3) Adaptive evaluation: The fitness of each individual's position is evaluated, that is, the quality of the solution corresponding to this position is calculated. Generally, the adaptive evaluation mainly includes steps such as defining the fitness function, calculating the fitness value, comparing and sorting, and guiding the search process. First, according to the engineering scenario problem, the efficiency objective function in formula (9) is defined as the fitness function; secondly, according to the defined fitness function, the fitness value of each individual in the population is calculated; then, according to the calculated fitness value, the individuals in the population are compared and sorted; finally, combined with the results of the adaptive evaluation, the search direction of other individuals is guided to find a solution with higher quality.

[0147] 4) Convergence judgment: Convergence judgment is mainly used to determine whether the algorithm has found a sufficiently good solution or whether it has reached a state where no further optimization is possible. Common convergence judgment methods include iteration number judgment, objective function value change judgment, solution diversity judgment, residual change judgment, etc. Among them, the iteration number judgment is to set a maximum number of iterations, and when the iteration number of the algorithm reaches this threshold, it is judged whether the algorithm converges. Although this method is simple and intuitive, it may fall into a local optimum. The objective function value change judgment refers to defining an objective function and observing the change of the objective function during the iteration process. If the change of the objective function value is less than the set threshold, it can be considered that the algorithm has converged. This method requires choosing an appropriate threshold. Too small a threshold may cause the algorithm to converge prematurely, and too large a threshold may make the algorithm unable to correctly judge the convergence state. The solution diversity judgment is to observe the distribution of solutions in the population. If the solution diversity gradually decreases, that is, most of the data is concentrated in a certain local area, it means that the algorithm has converged to a local optimal solution. This method requires calculating the diversity measure of solutions, such as variance, entropy, etc., and the computational complexity is relatively high. The residual change judgment is to observe the change of the residual for the algorithm that iteratively solves a specific problem. This method is very effective when solving a specific problem. The droop control of the DC microgrid is a specific engineering scenario problem. Therefore, in this embodiment, the residual change judgment method is selected to judge whether convergence occurs.

[0148] In summary, compared with the prior art, the method of the present invention has at least the following advantages and beneficial effects:

[0149] 1) The mismatch of line impedance is an inherent problem in traditional droop control strategies, which directly affects the balanced distribution of power. Therefore, in order to overcome this limitation problem, the present invention realizes more accurate power sharing by increasing the droop coefficient correction amount ΔL i to optimize the operation performance of the DC microgrid.

[0150] 2) When the power load undergoes a sudden change, the deficiency of traditional droop control in terms of bus voltage regulation becomes particularly evident. Since the system cannot respond promptly to the load change, the bus voltage often easily exceeds the rated range, thereby affecting the stability of the entire microgrid and the power quality. To address this issue, the present invention increases a reference voltage compensation amount to raise a part of the voltage to compensate for the losses of the virtual impedance and the output line impedance. In this way, even when the power load undergoes a sudden change, the system can respond promptly and maintain the stability of the bus voltage by adjusting the reference voltage compensation amount, thereby ensuring the stable operation of the microgrid and high-quality power supply.

[0151] 3) The current sharing problem under light load conditions is also a major challenge faced by the droop control strategy. When the load is light, the output current of the converter is small, which may significantly affect the current sharing effect of the droop control. To improve this problem, the present invention proposes to run an intelligent scheduling optimization algorithm to dynamically adjust the number of PCS modules in operation according to the load conditions. When the load is light, some PCS modules can be turned off, and only the necessary number is retained for operation to reduce the power consumption of the entire system.

[0152] 4) Due to the above problems of power uneven distribution, difficult bus voltage control, and current sharing under light load, the efficiency loss of the system is often large, resulting in the system being unable to operate at the most efficient point. This not only reduces the economy of the system but may also affect the long-term stable operation of the microgrid. Therefore, on the basis of proposing the above solutions, the present invention proposes a DC microgrid adaptive droop control method based on swarm intelligence efficiency optimization, which adaptively adjusts the key parameters of the droop control strategy through a swarm intelligence optimization algorithm, reduces the efficiency loss of the system, and enables the system to always operate at the most efficient state, thereby improving the overall performance of the system.

[0153] This embodiment also provides a DC microgrid adaptive droop control system, including:

[0154] A key parameter selection module, configured to obtain an expression of the voltage-current droop characteristic according to the circuit model, analyze the expression, and add a droop coefficient correction amount and / or a reference voltage compensation amount to the circuit as key parameters;

[0155] An objective function construction module, configured to derive an efficiency evaluation function according to the circuit model and construct an efficiency objective function according to the efficiency evaluation function;

[0156] A key parameter optimization module, configured to perform an optimization calculation on the key parameters of the droop control using a swarm intelligence optimization algorithm according to the efficiency target output function, obtain the optimal key parameters, and perform stability control on the circuit model according to the optimal key parameters.

[0157] A DC microgrid adaptive droop control system according to this embodiment can execute a DC microgrid adaptive droop control method provided by an embodiment of the method of the present invention, can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0158] This embodiment also provides a DC microgrid adaptive droop control device, including:

[0159] At least one processor;

[0160] At least one memory for storing at least one program;

[0161] When the at least one program is executed by the at least one processor, the at least one processor is caused to implement Figure 4 The method shown.

[0162] A DC microgrid adaptive droop control device according to this embodiment can execute a DC microgrid adaptive droop control method provided by an embodiment of the method of the present invention, can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0163] This application embodiment also discloses a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes Figure 4 The method shown.

[0164] This embodiment also provides a storage medium storing instructions or a program that can execute a DC microgrid adaptive droop control method provided by an embodiment of the method of the present invention. When the instructions or the program are run, any combination of implementation steps of the method embodiment can be executed, and the corresponding functions and beneficial effects of the method are possessed.

[0165] In some alternative embodiments, the functions / operations mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, two consecutive blocks shown can actually be executed substantially simultaneously or the blocks can sometimes be executed in the reverse order. In addition, the embodiments presented and described in the flowcharts of the present invention are provided by way of example for the purpose of providing a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated, where the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0166] In addition, although the present invention has been described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the described functions and / or features may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It should also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. Rather, given the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the modules will be understood within the ordinary skills of an engineer. Thus, those skilled in the art can implement the present invention as set forth in the claims without undue experimentation. It should also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0167] If the described function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0168] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0169] More specific examples (nonexhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0170] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0171] In the foregoing description of the present specification, the descriptions referring to the terms "one embodiment / example", "another embodiment / example", or "certain embodiments / examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0172] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

[0173] The above has specifically described the preferred embodiments of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A DC microgrid adaptive droop control method, characterized in that: The following steps are involved: The voltage and current droop characteristic expression is obtained according to the circuit model, the expression is analyzed, and the droop coefficient correction amount and the reference voltage compensation amount are added to the circuit as key parameters; The efficiency evaluation function is derived according to the circuit model, and the efficiency objective function is constructed according to the efficiency evaluation function; According to the efficiency objective function, a swarm intelligence optimization algorithm is used to perform optimization calculation on key parameters of droop control to obtain optimal key parameters, and stability control is performed on the circuit model according to the optimal key parameters; The voltage and current droop characteristic expression is obtained according to the circuit model, the expression is analyzed, and the droop coefficient correction amount and the reference voltage compensation amount are added to the circuit as key parameters, including: The parallel equivalent circuit of the dual PCS converters is obtained, and the voltage and current droop characteristic expression of the DC microgrid equivalent circuit is obtained according to the loop voltage equation: Where U ref is the bus voltage given value, U bus is the bus voltage feedback value, K is the droop coefficient, r1 is the equivalent line impedance of PCS1, r2 is the equivalent line impedance of PCS2, i1 is the bus current of PCS1, and i2 is the bus current of PCS2. According to formula (1), in order to achieve power sharing while ensuring the bus voltage control accuracy, the following conditions must be met: K+r1=K+r2 (2) Since there is a certain difference between the line impedances r1 and r2, equation (2) does not hold. Therefore, a droop coefficient correction value ΔK1 and ΔK2 are added to both sides of the equation to make equation (2) equal. At this time, the power is evenly distributed. Extended to a multi-converter control structure, the formula is: IN ref =U bus +(K+ΔK i +r i )and i (3) By adjusting the droop coefficient correction value ΔK of each converter i By adjusting the size of , we can make the coefficients before the current terms in formula (3) equal, so that the current can be evenly distributed; Due to the existence of droop coefficient and line impedance, U ref >U bus , so a reference voltage compensation is required, and the size of the reference voltage compensation satisfies: ΔU i =(K+ΔK i +r i )and i (4) Extended to the multi-converter control structure, the formula is: IN ref =U bus +(K+ΔK i +r i )and i +ΔU i (5) The droop coefficient correction value ΔK i And the reference voltage compensation ΔU i As the key parameter of this circuit; The step of deriving the efficiency evaluation function according to the circuit model and constructing the efficiency target function according to the efficiency evaluation function includes: The expression for obtaining the PCS real-time charging conversion efficiency is: in: P AC =I AC U AC cosθ P DC =I DC IN DC IN DC =U bus +(K+ΔK i +r i )and i +ΔU i The expression of the efficiency objective function is: F=η max -or atc Where η max represents the PCS optimal target operating efficiency, η atc Indicates the actual operating efficiency of PCS; P DC is the PCS DC side power, P AC is the active power on the PCS AC side, I DC is the PCS bus current, U DC is the PCS bus voltage; I AC is the current on the PCS AC side, U AC is the voltage on the PCS AC side, and θ is the phase angle difference between the voltage and current on the PCS AC side.

2. A DC microgrid adaptive droop control method according to claim 1, characterized in that: According to the efficiency objective function, a swarm intelligence optimization algorithm is used to perform optimal calculation on key parameters of droop control to obtain optimal key parameters, and stability control is performed on the circuit model according to the optimal key parameters, including: For key parameters, a preset strategy is used to generate an initial population; Use a preset algorithm to update the position and speed of individuals in the population; The efficiency objective function is used as the fitness function, and the fitness of each individual position is evaluated to guide the individual's search direction; Determine whether the convergence condition is met. If so, output the final optimization result; if not, return to execute the step of updating the individual positions and velocities in the population.

3. A DC microgrid adaptive droop control method according to claim 2, characterized in that: The initial population is generated using a strategy based on prior knowledge; The preset algorithm is a particle swarm optimization algorithm; The determining whether the convergence condition is met includes: The residual change judgment method is used to determine whether the convergence conditions are met.

4. A DC microgrid adaptive droop control method according to claim 1, characterized in that: The DC microgrid adaptive droop control method further comprises the following steps: Calculate the current power and the operating time of each PCS module, and dynamically adjust the number of operating PCS modules according to the load conditions.

5. A DC microgrid adaptive droop control method according to claim 4, characterized in that: The calculation of the current power and the operating time of each PCS module, and the dynamic adjustment of the operating number of the PCS module according to the load condition, includes: Calculate the current power and determine the number N of PCS modules to be turned on based on the power; Calculate the current PCS module cumulative duration and select the number i to start the PCS module; Turn on the currently selected PCS module; Among them, when the load is light, some PCS modules are turned off and only the necessary number are kept running to reduce the power consumption of the entire system.

6. A DC microgrid adaptive droop control system, applied to a DC microgrid adaptive droop control method according to any one of claims 1 to 5, characterized in that: include: A key parameter selection module is used to obtain the voltage and current droop characteristic expression according to the circuit model, analyze the expression, and add the droop coefficient correction amount and the reference voltage compensation amount in the circuit as key parameters; An objective function construction module is used to derive an efficiency evaluation function according to a circuit model, and to construct an efficiency objective function according to the efficiency evaluation function; The key parameter optimization module is used to calculate the key parameters of droop control according to the efficiency objective function using a swarm intelligence optimization algorithm to obtain the optimal key parameters, and perform stability control on the circuit model according to the optimal key parameters.

7. A DC microgrid adaptive droop control device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to perform the method according to any one of claims 1 to 5 when executed by the processor.

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