A coordinated control method and system for a multi-energy storage DC distribution network

Abstract

The present invention relates to a coordinated control method and system for a multi-energy storage DC distribution network. According to the bus voltages of the respective energy units, a voltage compensation amount is obtained by using a voltage prediction model. According to the output current of the DC-DC converter in the energy storage unit and the state of charge information of each storage battery, a current compensation amount is obtained by using a current model. According to the voltage compensation amount and the current compensation amount, a duty cycle is obtained by using a duty cycle adjustment model, and a switching signal is obtained through PWM modulation to control the bus voltage and output power of the energy storage unit. The present invention adopts an optimized compensation method based on droop control, and uses model predictive control to solve for the voltage compensation amount and the current compensation amount. On the basis of reasonably distributing power, the use of flexible and variable prediction coefficients effectively solves problems such as unstable controller performance under the consensus algorithm, and has a good promoting effect on the safe and stable operation of the DC distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution control, and particularly to a coordinated control method and system for a multi-energy storage DC distribution network based on model predictive control. Background Art

[0002] As one of the main ways to achieve carbon emission reduction, renewable energy power generation technologies mainly based on wind power and photovoltaic power have received extensive attention. Compared with traditional AC distribution networks whose power supply reliability is greatly affected by the grid connection of distributed generation systems, DC distribution networks have become increasingly important today due to their low-loss characteristics in the context of the growing demand for DC loads.

[0003] In a DC distribution network with multiple energy storage units, due to the existence of line impedance, droop control cannot take into account both the reasonable distribution of the output power of each unit and the reduction of the steady-state deviation of the bus voltage. Secondary control usually includes a voltage control link and a power distribution link, which can solve such problems. According to different communication methods, it can be divided into centralized, decentralized, and distributed control. Distributed secondary control combines the advantages of centralized and decentralized control and can perform local control through information interaction between adjacent units. It is a widely used cooperative control method.

[0004] Currently, the most widely used in distributed secondary control is the consensus algorithm. Yang Qiufan et al. proposed an optimized compensation method based on the consensus algorithm in "Distributed Control Method for Multiple Photovoltaic-Energy Storage Units in DC Microgrid Based on Consensus Algorithm" published in Proceedings of the Chinese Society for Electrical Engineering, 2020, 40(12): 3919-3928. By calculating the vertical intercept compensation amount corresponding to the voltage and power links, the droop curve is adjusted twice, and on the basis of ensuring the rationality of the power distribution of the photovoltaic-energy storage units, the problem that the initial value of the controller and communication delay in the voltage control link affect the control effect is solved. However, a large number of PI controllers are used in the control, and the transient characteristics of the system need to be improved. Moreover, when the system topology changes, the performance of the controller still needs further research. Li Demin et al. proposed a secondary control method for voltage and frequency based on model predictive control in "Secondary Regulation Strategy for Islanded Microgrid Based on Model Predictive Control" published in Automation of Electric Power Systems, 2019, 43(10): 60-67. However, its centralized communication structure has high requirements for system communication.

[0005] Therefore, it is necessary to propose a new solution to solve the problems such as poor transient characteristics of the voltage control strategy under the traditional consensus algorithm and control errors in some working conditions. Summary of the Invention

[0006] The object of the present invention is to provide a coordinated control method and system for a multi-energy storage DC distribution network based on model predictive control. By means of the rolling optimization of the prediction model and the minimization of the objective function, the optimal voltage and power compensation amounts are obtained, solving problems such as poor transient characteristics of the voltage control strategy under the traditional consensus algorithm and control errors in some working conditions, which is beneficial to the safe and stable operation of the DC distribution network.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] A coordinated control method for a multi-energy storage DC distribution network, the control method comprising:

[0009] Obtain the bus voltages of each energy unit, the output currents of the DC-DC converters in the energy storage units, and the state of charge information of each battery;

[0010] According to the bus voltages of each energy unit, use the voltage prediction model to obtain the voltage compensation amount;

[0011] According to the output currents of the DC-DC converters in the energy storage units and the state of charge information of each battery, use the current model to obtain the current compensation amount;

[0012] According to the voltage compensation amount and the current compensation amount, use the duty cycle adjustment model to obtain the duty cycle, and obtain the switching signal through PWM modulation to control the bus voltage and output power of the energy storage unit.

[0013] Optionally, the voltage prediction model is:

[0014]

[0015]

[0016] Wherein, v dci (k) is the bus voltage of each energy unit, and v dci (k + 1) is the predicted value of the bus voltage at the next moment; Δv si (k) is the voltage compensation amount output by the secondary voltage control link; η i Prediction coefficient; N is the number of energy units in the microgrid system; a ij Is the communication coefficient. When there is a direct communication channel between the i-th energy unit and the j-th energy unit, a ij = 1, otherwise 0;

[0017] J v (k) is the constraint function of the voltage prediction model, and w 1 Is the voltage weight coefficient, v refis the bus voltage reference value, and the minimization of the objective function corresponds to the voltage compensation amount.

[0018] Optionally, the current prediction model is:

[0019]

[0020]

[0021] where i dci (k) is the output current of the DC-DC converter in the energy storage unit, and i dci (k + 1) is the predicted value of the output current of the energy storage unit at the next moment, and Δi si (k) is the current compensation amount, and λ i is the current prediction coefficient; SOC i (k) is the state of charge information of each battery and the state of charge information of each battery, and SOC i (k + 1) is the state of charge information of the battery at the next moment and the state of charge information of each battery;

[0022] J p (k) is the constraint function of the current prediction model, and w 2 is the current weight coefficient.

[0023] Optionally, both the voltage weight coefficient and the current weight coefficient are 0.1.

[0024] Optionally, the duty cycle adjustment model is:

[0025]

[0026] where k di is the droop coefficient, and i di (k) is the output current of the primary control, and i ref * (k) is the current reference value output by the voltage outer loop control.

[0027] A coordinated control system for a multi-energy storage DC distribution network, the control system includes:

[0028] A data acquisition module for acquiring the bus voltage of each energy unit, the output current of the DC-DC converter in the energy storage unit, the state of charge information of each battery and the state of charge information of each battery;

[0029] A voltage prediction module for obtaining a voltage compensation amount by using a voltage prediction model according to the bus voltage of each energy unit;

[0030] The current prediction module is used to obtain a current compensation amount by using a current model according to the output current of the DC-DC converter in the energy storage unit and the state of charge information of each battery and the state of charge information of each battery;

[0031] The duty ratio adjustment module is used to obtain a duty ratio by using a duty ratio adjustment model according to the voltage compensation amount and the current compensation amount, and obtain a switching signal through PWM modulation to control the bus voltage and output power of the energy storage unit.

[0032] Optionally, the voltage prediction model is:

[0033]

[0034]

[0035] where v dci (k) is the bus voltage of each energy unit, and v dci (k + 1) is the predicted value of the bus voltage at the next moment; Δv si (k) is the voltage compensation amount output by the secondary voltage control link; η i is the prediction coefficient; N is the number of energy units in the microgrid system; a ij is the communication coefficient. When there is a direct communication channel between the i-th energy unit and the j-th energy unit, a ij = 1, otherwise it is 0;

[0036] J v (k) is the constraint function of the voltage prediction model, and w 1 is the voltage weight coefficient, v ref is the bus voltage reference value, and the minimization of the objective function corresponds to the voltage compensation amount.

[0037] Optionally, the current prediction model is:

[0038]

[0039]

[0040] where i dci (k) is the output current of the DC-DC converter in the energy storage unit, and i dci (k + 1) is the predicted value of the output current of the energy storage unit at the next moment, and Δi si (k) is the current compensation amount, and λ i is the current prediction coefficient; SOC i (k) is the state of charge information of each battery and the state of charge information of each battery, and SOC i (k + 1) is the state of charge information of each battery and the state of charge information of each battery at the next moment;

[0041] J p (k) is the constraint function of the current prediction model, w 2 is the current weight coefficient.

[0042] Optionally, both the voltage weight coefficient and the current weight coefficient are 0.1.

[0043] Optionally, the duty ratio adjustment model is:

[0044]

[0045] where k di is the droop coefficient, i di (k) is the primary control output current, i ref * (k) is the current reference value output by the voltage outer loop control.

[0046] According to the specific embodiments provided by the present invention, the following technical effects are disclosed:

[0047] The present invention adopts distributed secondary control in the energy storage side DC-DC converter to optimize voltage regulation and power distribution. Among them, both voltage and power secondary control adopt model predictive control to solve the voltage compensation amount and current compensation amount. By setting a certain objective function, the voltage and current compensation amounts are obtained. On the basis of reasonable power distribution, the flexible and variable prediction coefficients are used to effectively solve problems such as unstable controller performance under the consensus algorithm, which has a good promoting effect on the safe and stable operation of the DC distribution network, thereby realizing the reasonable distribution of the power of each battery on the basis of reducing the steady-state deviation of the bus voltage. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0049] Figure 1 is the flowchart of the multi-energy storage DC distribution network coordinated control method provided by the present invention;

[0050] Figure 2 is the structural schematic diagram of the multi-energy storage DC distribution network coordinated control system provided by the present invention;

[0051] Figure 3 is the structural schematic diagram of the DC distribution network system of the present invention;

[0052] Figure 4Control schematic diagram of the energy storage unit DC-DC converter of the present invention. Specific embodiments

[0053] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0054] The terms "first", "second", "third", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence. It should be understood that such described objects can be interchanged under appropriate circumstances. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion.

[0055] In this application, the accompanying drawings discussed below and the various embodiments used to describe the principles disclosed in the present invention are for illustration only and should not be construed as limiting the scope of the disclosure of the present invention. Those skilled in the art will understand that the principles of the present invention can be implemented in any appropriately arranged system.

[0056] The terms used in the specification of the present invention are only used to describe specific embodiments and do not intend to show the concept of the present invention. It should be understood that terms such as "including", "having", and "containing" are intended to indicate the possibility of the presence of the features, numbers, steps, actions, or combinations thereof disclosed in the specification of the present invention, and do not intend to exclude the possibility of the presence or addition of one or more other features, numbers, steps, actions, or combinations thereof. The same reference numerals in the drawings refer to the same parts.

[0057] The purpose of the present invention is to provide a method capable of realizing the resolution measurement of medium-precision and high-precision fiber optic gyroscopes.

[0058] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0059] Figure 3The structural schematic diagram of the DC distribution network system of the present invention is shown, which includes a large power grid unit and multiple energy units; the energy units include distributed generation units, energy storage units and load units; the large power grid unit forms a chain network with each energy unit through a Voltage Source Converter (VSC); the distributed generation units, energy storage units and load units included in the energy units are respectively connected to the corresponding DC bus through corresponding voltage source converters or DC-DC converters (DC-DC Converter); the large power grid unit, distributed generation units, energy storage units and load units included in the DC distribution network system all include control systems, measuring elements and converters; the input ends of the control systems of the large power grid unit, distributed generation units, energy storage units and load units included in the DC distribution network system are respectively connected to the output ends of the corresponding measuring elements, and their output ends are connected to the input ends of the corresponding converters; the DC distribution network system also includes DC measuring elements and AC measuring elements, and the DC measuring elements include DC bus voltage sensors and current sensors on the distributed generation units, energy storage units, large power grid units and load units, as well as voltage sensors and current sensors on the distributed power side, energy storage element side, AC power grid side and load side of the distributed generation units, energy storage units, large power grid units and load units; the energy storage unit includes energy storage elements; information exchange is carried out between adjacent systems through communication lines.

[0060] Figure 1 The flow chart of the coordinated control method for the multi-energy storage DC distribution network of the present invention is shown, which specifically includes four steps of S101 - S104.

[0061] S101: Obtain the bus voltages of each energy unit, the output current of the DC-DC converter in the energy storage unit, the state of charge information of each battery and the state of charge information of each battery.

[0062] S102: According to the bus voltages of each energy unit, use the voltage prediction model to obtain the voltage compensation amount;

[0063] S103: For the output current of the DC-DC converter in the energy storage unit and the state of charge information of each battery and the state of charge information of each battery, use the current model to obtain the current compensation amount.

[0064] S104: According to the voltage compensation amount and the current compensation amount, use the duty ratio adjustment model to obtain the duty ratio, and obtain the switching signal through PWM modulation to control the bus voltage and output power of the energy storage unit.

[0065] In the specific implementation process, the voltage prediction model of S102 is:

[0066]

[0067] Among them, vdci (k) is the bus voltage of each energy unit, v dci (k + 1) is the predicted value of the bus voltage at the next moment; Δv si (k) is the voltage compensation amount output by the secondary voltage control link; η i Prediction coefficient; N is the number of energy units in the microgrid system; a ij is the communication coefficient. When there is a direct communication channel between the i-th energy unit and the j-th energy unit, a ij = 1, otherwise it is 0.

[0068] Based on the collected voltage information, the voltage controller obtains the predicted value of the bus voltage at the next moment through the prediction model, substitutes it into the objective function for optimization, and takes the optimal solution back into the prediction model to obtain the secondary voltage compensation amount.

[0069] In order to improve the system voltage operation level, the average value of the predicted values of the bus voltages in the distribution network should tend to be near the voltage reference value. Therefore, the objective function of the current prediction model is designed as:

[0070]

[0071] Among them, J v (k) is the constraint function of the voltage prediction model, w 1 is the voltage weight coefficient, v ref is the bus voltage reference value, and the minimization of the objective function corresponds to the voltage compensation amount.

[0072] Use the optimizer to iteratively solve the objective function to obtain the prediction coefficient that minimizes the objective function, and then the corresponding voltage compensation amount can be obtained.

[0073] The traditional secondary voltage control method based on the consensus algorithm needs to calculate the average voltage observation value of each unit through a voltage observer, and input the difference between the average observation value and the reference value into the PI controller to obtain the voltage compensation amount. However, the method of the present invention substitutes the predicted value into the objective function for optimization, outputs the prediction coefficient through the optimizer, and substitutes it back into the prediction model to obtain the voltage compensation amount. And a large number of PI controllers based on feedback regulation are used in the consensus algorithm, and its transient characteristics are poor. The method of the present invention solves this problem well through the online correction and rolling optimization of the prediction coefficient. At the same time, the consensus algorithm needs to calculate through an intermediate variable (average voltage observation value), and the accuracy of the intermediate variable determines the accuracy of the output quantity. The model of the method of the present invention is relatively simple, and does not require an intermediate variable, only needs to optimize and solve the predicted value, which improves the stability of the system.

[0074] In the specific implementation process, to avoid the short - board effect caused by the early retirement of individual energy storages due to the differences in droop coefficients and state of charge in a multi - energy - storage DC distribution network, the SOC value is introduced into the current compensation amount prediction model and the objective function. The current prediction model of S103 is designed as follows:

[0075]

[0076] Where, i dci (k) is the output current of the DC - DC converter in the energy storage unit, i dci (k + 1) is the predicted value of the output current of the energy storage unit at the next moment, Δi si (k) is the current compensation amount, λ i is the current prediction coefficient; SOC i (k) is the state - of - charge information of each battery and the state - of - charge information of each battery, SOC i (k + 1) is the state - of - charge information of the battery at the next moment and the state - of - charge information of each battery.

[0077]

[0078] J p (k) is the constraint function of the current prediction model, w 2 is the current weight coefficient.

[0079] Preferably, both the voltage weight coefficient and the current weight coefficient are 0.1.

[0080] Under the constraint of the constraint function of the current prediction model, the optimizer iteratively solves to obtain the optimal current compensation amount that makes the output currents of each energy storage be proportionally distributed according to the battery SOC.

[0081] In the power distribution link of the current prediction model of the present invention, the current compensation method is used. By establishing a prediction model and an objective function related to SOC, the optimal current compensation amount is obtained. For the vertical intercept compensation method based on the translated droop curve under the consensus algorithm, it defines a state variable related to the actual remaining capacity of the energy storage, and ensures that the SOCs of each energy storage tend to be consistent by making the state variables of each unit tend to be consistent. There are essential differences between the two. And compared with the SOC control strategy under the consensus algorithm, this method also does not require a large number of PI controllers, so it is not affected by the initial value of the integrator and has better transient characteristics.

[0082] The optimal voltage compensation amount and the optimal current compensation amount are added to the duty - cycle adjustment model, that is, the droop control is improved. Figure 4The control schematic diagram of the DC-DC converter of the energy storage unit of the present invention is shown. The inner loop adopts PI control to make the actual current of the battery track the current reference value. The duty cycle is calculated according to the voltage average equation within one switching period, and the switching signal is obtained through PWM modulation and sent to the switching tube to control the bus voltage and output power of the energy storage unit. The duty cycle adjustment model is as follows:

[0083]

[0084] Among them, k di is the droop coefficient, i di (k) is the output current of the primary control, and i ref * (k) is the current reference value output by the voltage outer loop control.

[0085] Figure 2 The system corresponding to the coordinated control method of the multi-energy storage DC distribution network is shown. The control system includes: a data acquisition module 201, a voltage prediction module 202, a current prediction module 203, and a duty cycle adjustment module 204.

[0086] The data acquisition module 201 is used to acquire the bus voltage of each energy unit, the output current of the DC-DC converter in the energy storage unit, the state of charge information of each battery, and the state of charge information of each battery.

[0087] The voltage prediction module 202 is used to obtain the voltage compensation amount according to the bus voltage of each energy unit by using the voltage prediction model.

[0088] The current prediction module 203 is used to obtain the current compensation amount according to the output current of the DC-DC converter in the energy storage unit and the state of charge information of each battery and the state of charge information of each battery by using the current model.

[0089] The duty cycle adjustment module 204 is used to obtain the duty cycle according to the voltage compensation amount and the current compensation amount by using the duty cycle adjustment model, and the switching signal is obtained through PWM modulation to control the bus voltage and output power of the energy storage unit.

[0090] In the specific implementation process, the voltage prediction model is as follows:

[0091]

[0092]

[0093] Among them, v dci (k) is the bus voltage of each energy unit, v dci (k + 1) is the predicted value of the bus voltage at the next moment; Δv si (k) is the voltage compensation amount output by the secondary voltage control link; η iPrediction coefficient; N is the number of energy units in the microgrid system; a ij is the communication coefficient. When there is a direct communication channel between the i-th energy unit and the j-th energy unit, a ij = 1; otherwise, it is 0.

[0094] J v (k) is the constraint function of the voltage prediction model, w 1 is the voltage weight coefficient, v ref is the bus voltage reference value, and the minimization of the objective function corresponds to the voltage compensation amount.

[0095] In the specific implementation process, the current prediction model is:

[0096]

[0097]

[0098] where, i dci (k) is the output current of the DC-DC converter in the energy storage unit, i dci (k + 1) is the predicted value of the output current of the energy storage unit at the next moment, Δi si (k) is the current compensation amount, λ i is the current prediction coefficient; SOC i (k) is the state of charge information of each battery and the state of charge information of each battery, SOC i (k + 1) is the state of charge information of the battery at the next moment and the state of charge information of each battery;

[0099] J p (k) is the constraint function of the current prediction model, w 2 is the current weight coefficient.

[0100] In the specific implementation process, both the voltage weight coefficient and the current weight coefficient are 0.1.

[0101] In the specific implementation process, the duty cycle adjustment model is:

[0102]

[0103] where, k di is the droop coefficient, i di (k) is the output current of the primary control, i ref * (k) is the current reference value output by the voltage outer loop control.

[0104] According to the specific embodiments provided by the present invention, the following technical effects of the present invention are disclosed:

[0105] The present invention adopts distributed secondary control in the energy storage side DC-DC converter to optimize voltage regulation and power distribution. In the secondary control of voltage and power, model predictive control is used to solve the voltage compensation amount and current compensation amount. By setting a certain objective function, the voltage and current compensation amounts are obtained. On the basis of reasonably distributing power, the use of flexible and variable prediction coefficients effectively solves problems such as unstable controller performance under the consensus algorithm, which has a good promoting effect on the safe and stable operation of the DC distribution network, thereby realizing the reasonable distribution of the power of each battery on the basis of reducing the steady-state deviation of the bus voltage.

[0106] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other.

[0107] Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A coordinated control method for a multi-energy storage DC distribution network, characterized in that, the control method includes: acquiring the bus voltage of each energy unit, the output current of the DC-DC converter in the energy storage unit, and the state of charge information of each battery; obtaining a voltage compensation amount by using a voltage prediction model according to the bus voltage of each energy unit; the voltage prediction model is: where vdci(k) is the bus voltage of each energy unit, vdci(k + 1) is the predicted value of the bus voltage at the next moment; Δvsi(k) is the voltage compensation amount output by the secondary voltage control link; n is the prediction coefficient; N is the number of energy units in the microgrid system; aij is the communication coefficient, when there is a direct communication channel between the i-th energy unit and the j-th energy unit, aij = 1, otherwise it is 0; J v (k) is the constraint function of the voltage prediction model, w 1 is the voltage weight coefficient, v ref is the bus voltage reference value, and the minimization of the objective function corresponds to the voltage compensation amount; obtaining a current compensation amount by using a current prediction model according to the output current of the DC-DC converter in the energy storage unit and the state of charge information of each battery; the current prediction model is: where i dci (k) is the output current of the DC-DC converter in the energy storage unit, and i dci (k + 1) is the predicted value of the output current of the energy storage unit at the next moment, and Δi si (k) is the current compensation amount, and λ is the current prediction coefficient; SOC i (k) is the state of charge information of each battery, and SOC i (k + 1) is the state of charge information of the battery at the next moment and the state of charge information of each battery; J p (k) is the constraint function of the current prediction model, w 2 is the current weight coefficient; obtaining a duty cycle by using a duty cycle adjustment model according to the voltage compensation amount and the current compensation amount, and obtaining a switching signal through PWM modulation to control the bus voltage and output power of the energy storage unit; the duty cycle adjustment model is: Among them, k di is the sag coefficient, and i di (k) is the primary control output current, and i ref * (k) is the current reference value output by the voltage outer loop control.

2. The coordinated control method for a multi-energy storage DC distribution network according to claim 1, characterized in that, both the voltage weight coefficient and the current weight coefficient are 0.

1.

3. A coordinated control system for a multi-energy storage DC distribution network, characterized in that, the control system includes: a data acquisition module for acquiring the bus voltage of each energy unit, the output current of the DC-DC converter in the energy storage unit, and the state of charge information of each battery; a voltage prediction module for obtaining a voltage compensation amount by using a voltage prediction model according to the bus voltage of each energy unit; the voltage prediction model is: where vdci(k) is the bus voltage of each energy unit, vdci(k + 1) is the predicted value of the bus voltage at the next moment; Δvsi(k) is the voltage compensation amount output by the secondary voltage control link; n is the prediction coefficient; N is the number of energy units in the microgrid system; aij is the communication coefficient, when there is a direct communication channel between the i-th energy unit and the j-th energy unit, aij = 1, otherwise it is 0; J v (k) is the constraint function of the voltage prediction model, w 1 is the voltage weight coefficient, v ref is the bus voltage reference value, and the minimization of the objective function corresponds to the voltage compensation amount; a current prediction module for obtaining a current compensation amount by using a current prediction model according to the output current of the DC-DC converter in the energy storage unit and the state of charge information of each battery; the current prediction model is: where i dci (k) is the output current of the DC-DC converter in the energy storage unit, and i dci (k + 1) is the predicted value of the output current of the energy storage unit at the next moment, and Δi si (k) is the current compensation amount, and λ is the current prediction coefficient; SOC i (k) is the state of charge information of each battery, and SOC i (k + 1) is the state of charge information of the battery at the next moment and the state of charge information of each battery; J p (k) is the constraint function of the current prediction model, w 2 is the current weight coefficient; a duty cycle adjustment module for obtaining a duty cycle by using a duty cycle adjustment model according to the voltage compensation amount and the current compensation amount, and obtaining a switching signal through PWM modulation to control the bus voltage and output power of the energy storage unit; the duty cycle adjustment model is: Among them, k di is the sag coefficient, and i di (k) is the primary control output current, and i ref * (k) is the current reference value output by the voltage outer loop control.

4. The coordinated control system for a multi-energy storage DC distribution network according to claim 3, characterized in that, both the voltage weight coefficient and the current weight coefficient are 0.1.

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